Jean Yang

Phyllodes tumours of the breast: A clinicopathological analysis of 65 cases from a single institution

Publisher: Elsevier BV

Date: 06-2009

DOI: 10.1016/J.BREAST.2009.03.001

Abstract: The aim of this study was to document the clinical and pathological features of a large single institutional series of ethnically erse patients with phyllodes tumours (PTs), and to determine which characteristics were predictive of outcome. Sixty five PTs were analysed 34 were benign, 23 borderline and eight malignant (34 low grade and 31 high grade PTs on a two tiered grading system). Nine patients (15%) had local recurrences. A greater percentage of higher grade tumours recurred and women of Asian origin had a higher recurrence rate compared to the non-Asian patients. The 5 year disease-free survival was 81% and time to recurrence was significantly lower in the high grade group. No metastases or deaths from disease were recorded. The mean age at diagnosis significantly increased with tumour grade. The mean tumour volume also significantly increased with grade. Tumour grade was the only parameter related significantly to outcome.

Increased DNA microarray hybridization specificity using sscDNA targets

Publisher: Springer Science and Business Media LLC

Date: 22-04-2005

DOI: 10.1186/1471-2164-6-57

Abstract: The most widely used lification method for microarray analysis of gene expression uses T7 RNA polymerase-driven in vitro transcription (IVT) to produce complementary RNA (cRNA) that can be hybridized to arrays. However, multiple rounds of lification are required when assaying very small amounts of starting RNA. Moreover, certain cRNA-DNA mismatches are more stable than the analogous cDNA-DNA mismatches and this might increase non-specific hybridization. We sought to determine whether a recently developed linear isothermal lification method (ribo-SPIA) that produces single stranded cDNA would offer advantages over traditional IVT-based methods for microarray-based analyses of transcript expression. A single round of ribo-SPIA lification produced sufficient sscDNA for hybridizations when as little as 5 ng of starting total RNA was used. Comparisons of probe set signal intensities obtained from replicate lifications showed consistently high correlations (r = 0.99). We compared gene expression in two different human RNA s les using ribo-SPIA. Compared with one round IVT, ribo-SPIA had a larger dynamic range and correlated better with quantitative PCR results even though we used 1000-fold less starting RNA. The improved dynamic range was associated with decreases in hybridization to mismatch control probes. The use of lified sscDNA may offer substantial advantages over IVT-based lification methods, especially when very limited amounts of starting RNA are available. The use of sscDNA targets instead of cRNA targets appears to improve hybridization specificity.

A prediction model for viability at the end of the first trimester after a single early pregnancy evaluation

Publisher: Wiley

Date: 02-2013

DOI: 10.1111/AJO.12046

Abstract: The aim was to develop a new model to predict the outcome at the end of the 1st trimester after a single visit to the early pregnancy unit (EPU). Prospective observational study in the EPU at Nepean Hospital, between November 2006 and February 2009. Data were collected from all women in the 1st trimester of their pregnancy who had a live intrauterine pregnancy (IUP) at the 1st transvaginal ultrasound scan (TVS). 29 historical, clinical and ultrasound end points were recorded. Women were followed until the final diagnosis was established at the end of the 1st trimester: viability or nonviability. A multinomial logistic regression model was developed. The performance of this model was evaluated using receiver operating characteristic (ROC) curves. Data from 416 pregnancies were included: 92.1% were live beyond the 1st trimester, and 7.9% had miscarried. The most useful prognostic variables for developing the logistic regression model were gestational age by dates, vaginal (PV) bleeding, PV clots, gestational age by TVS, consistency with menstrual dates, mean gestational sac (GS) size, mean yolk sac (YS) size and number of previous caesarean sections. Used retrospectively on 416 women based on 25 imputations, the model gave an AUC of 0.88. Based on cross-validation, the independent predictive power obtained an AUC of 0.78. We have developed a new model to predict the outcome of the 1st trimester in women with live IUP at the 1st scan.

Gene expression profiles of human inner cell mass cells and embryonic stem cells

Publisher: Elsevier BV

Date: 07-2009

DOI: 10.1016/J.DIFF.2009.03.004

Abstract: Human embryonic stem cell (hESC) lines are derived from the inner cell mass (ICM) of preimplantation human blastocysts obtained on days 5-6 following fertilization. Based on their derivation, they were once thought to be the equivalent of the ICM. Recently, however, studies in mice reported the derivation of mouse embryonic stem cell lines from the epiblast these epiblast lines bear significant resemblance to human embryonic stem cell lines in terms of culture, differentiation potential and gene expression. In this study, we compared gene expression in human ICM cells isolated from the blastocyst and embryonic stem cells. We demonstrate that expression profiles of ICM clusters from single embryos and hESC populations were highly reproducible. Moreover, comparison of global gene expression between in idual ICM clusters and human embryonic stem cells indicated that these two cell types are significantly different in regards to gene expression, with fewer than one half of all genes expressed in both cell types. Genes of the isolated human inner cell mass that are upregulated and downregulated are involved in numerous cellular pathways and processes a subset of these genes may impart unique characteristics to hESCs such as proliferative and self-renewal properties.

Prediction modeling—part 2: using machine learning strategies to improve transplantation outcomes

Publisher: Elsevier BV

Date: 04-2021

DOI: 10.1016/J.KINT.2020.08.026

CD103+ tumor-resident CD8+ T cell numbers underlie improved patient survival in oropharyngeal squamous cell carcinoma

Publisher: BMJ

Date: 06-2020

DOI: 10.1136/JITC-2019-000452

Abstract: Human Papillomavirus (HPV) associated oropharyngeal squamous cell carcinoma (OPSCC) is one of the fastest growing cancers in the Western world. When compared to OPSCCs induced by smoking or alcohol, patients with HPV+ OPSCC, have better survival and the mechanisms remain unclear. The Cancer Genome Atlas (TCGA) database was examined for genes associated with tissue-resident CD8+ T cells. Multiplex immunohistochemistry (IHC) staining was performed on tumor specimen taken from 35 HPV+ and 27 HPV- OPSCC patients. TCGA database revealed that the expression of genes encoding CD103 and CD69 were significantly higher in HPV+ head and neck SCCs (HNSCC) than in HPV- HNSCC. Higher expression levels of these two genes were also associated with better overall survival. IHC staining showed that the proportion of CD103+ tumor-resident CD8+ T cells were significantly higher in HPV+ OPSCCs when compared to HPV- OPSCC. This higher level was also associated with both lower risk of loco-regional failure, and better overall survival. Importantly, patients with HPV- OPSCC who had comparable levels of CD103+ tumor-resident CD8+ T cells to those with HPV+ OPSCC demonstrated similar survival as those with HPV+OPSCC. Our results show that CD103+ tumor-resident CD8+ T cells are critical for protective immunity in both types of OPSCCs. Our data further suggest that the enhanced local protective immunity provided by tumor-resident T cell responses is the underlying factor driving favorable clinical outcomes in HPV+ OPSCCs over HPV- OPSCCs.

Scope+: An open source generalizable architecture for single-cell atlases at sample and cell levels

Publisher: Cold Spring Harbor Laboratory

Date: 05-12-2022

DOI: 10.1101/2022.12.03.518997

Abstract: Recent advancements in the use of single-cell technologies in large cohort studies enable the investigation of cellular response and mechanisms associated with disease outcome, including COVID-19. Several efforts have been made using single-cell RNA-sequencing to better understand the immune response to COVID-19 virus infection. Nonetheless, it is often difficult to compare or integrate data from multiple data sets due to challenges in data normalisation, metadata harmonisation, and having a common interface to quickly query and access this vast amount of data. Here we present Covidscope ( covidsc.d24h.hk/ ), a well-curated open web resource that currently contains single-cell gene expression data and associated metadata of almost 5 million blood and immune cells extracted from almost 1,000 COVID-19 patients across 20 studies around the world. Our collection contains the integrated data with harmonised metadata and multi-level cell type annotations. By combining NoSQL and optimised index, our Covidscope achieves rapid subsetting of high-dimensional gene expression data based on both data set level, donor-level (e.g., age and sex of patients) and cell-level (e.g., expression of specific gene markers) metadata, enabling multiple efficient downstream single-cell meta-analysis.

ZRANB2 localizes to supraspliceosomes and influences the alternative splicing of multiple genes in the transcriptome

Publisher: Springer Science and Business Media LLC

Date: 11-05-2013

DOI: 10.1007/S11033-013-2637-9

Abstract: Alternative splicing is a major source of protein ersity in humans. The human splicing factor zinc finger, Ran-binding domain containing protein 2 (ZRANB2) is a splicing protein whose specific endogenous targets are unknown. Its upregulation in grade III ovarian serous papillary carcinoma could suggest a role in some cancers. To determine whether ZRANB2 is part of the supraspliceosome, nuclear supernatants from human embryonic kidney 293 cells were prepared and then fractioned on a glycerol gradient, followed by Western blotting. The same was done after treatment with a tyrosine kinase to induce phosphorylation. This showed for the first time that ZRANB2 is part of the supraspliceosome, and that phosphorylation affects its subcellular location. Studies were then performed to understand the splicing targets of ZRANB2 at the whole-transcriptome level. HeLa cells were transfected with a vector containing ZRANB2 or with a vector-only control. RNA was extracted, converted to cDNA and hybridized to Affymetrix GeneChip(®) Human Exon 1.0 ST Arrays. At the FDR ≤1.3 significance level we found that ZRANB2 influenced the alternative splicing of primary transcripts of CENTB1, WDR78, C10orf18, CABP4, SMARCC2, SPATA13, OR4C6, ZNF263, CAPN10, SALL1, ST18 and ZP2. Several of these have been implicated in tumor development. In conclusion ZRANB2 is part of the supraspliceosome and causes differential splicing of numerous primary transcripts, some of which might have a role in cancer.

Evaluating stably expressed genes in single cells

Publisher: Oxford University Press (OUP)

Date: 09-2019

DOI: 10.1093/GIGASCIENCE/GIZ106

Abstract: Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level is intrinsically stochastic and noisy. Yet, on the cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how can their expression stability be assessed? We have previously proposed a computational framework for ranking expression stability of genes in single cells for scRNA-seq data normalization and integration. In this study, we perform detailed evaluation and characterization of SEGs derived from this framework. Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets and the "Mouse Atlas" dataset are reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across erse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in in idual cells. SEGs identified in this study have immediate utility both for understanding variation and stability of single-cell transcriptomes and for practical applications such as scRNA-seq data normalization. Our framework for calculating gene stability index, "scSEGIndex," is incorporated into the scMerge Bioconductor R package (sydneybiox.github.io/scMerge/reference/scSEGIndex.html) and can be used for identifying genes with stable expression in scRNA-seq datasets.

Supplementary Data from Circulating Cytokines Predict Immune-Related Toxicity in Melanoma Patients Receiving Anti-PD-1–Based Immunotherapy

Publisher: American Association for Cancer Research (AACR)

Date: 31-03-2023

DOI: 10.1158/1078-0432.22471476.V1

Abstract: Supplementary Table 1: Number of plasma s les included in the study Supplementary Table 2: Association of toxicity with treatment response Supplementary Table 3: Differentially expressed cytokines in patients with severe irAEs compared to those with no-severe irAEs at PRE in Cohort 2 Supplementary Table 4: Differentially expressed cytokines in patients with severe irAEs compared to those with no-severe irAEs at EDT in Cohort 2 Supplementary Table 5: Univariate analysis of cytokine expression and association with overall survival Supplementary Table 6: Univariate analysis of cytokine expression and association with RECIST response Supplementary Figure 1: Distribution of relative fluorescence intensity units of 65 circulating cytokines in plasma collected from 98 melanoma patients (cohorts 1 and 2) prior to therapy initiation. Supplementary Figure 2: Hierarchical clustering of cytokine expression profiles in cohorts 1 and 2.

Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information

Publisher: Wiley

Date: 24-07-2015

DOI: 10.1002/IJC.29047

Abstract: In patients with metastatic melanoma, the identification and validation of accurate prognostic biomarkers will assist rational treatment planning. Studies based on "-omics" technologies have focussed on a single high-throughput data type such as gene or microRNA transcripts. Occasionally, these features have been evaluated in conjunction with limited clinico-pathologic data. With the increased availability of multiple data types, there is a pressing need to tease apart which of these sources contain the most valuable prognostic information. We evaluated and integrated several data types derived from the same tumor specimens in AJCC stage III melanoma patients-gene, protein, and microRNA expression as well as clinical, pathologic and mutation information-to determine their relative impact on prognosis. We used classification frameworks based on pre-validation and bootstrap multiple imputation to compare the prognostic power of each data source, both in idually as well as integratively. We found that the prognostic utility of clinico-pathologic information was not out-performed by any of the various "-omics" platforms. Rather, a combination of clinico-pathologic variables and mRNA expression data performed best. Furthermore, a patient-based classification analysis revealed that the prognostic accuracy of various data types was not the same for different patients. This indicates that ongoing development in the in idualized evaluation of melanoma patients must take account of the value of both traditional and novel "-omics" measurements.

Hyperactivation of the insulin signaling pathway improves intracellular proteostasis by coordinately up-regulating the proteostatic machinery in adipocytes

Publisher: Elsevier BV

Date: 12-2016

DOI: 10.1074/JBC.M116.741140

Supplementary Data from Distinct Molecular Profiles and Immunotherapy Treatment Outcomes of V600E and V600K <i>BRAF</i>-Mutant Melanoma

Publisher: American Association for Cancer Research (AACR)

Date: 31-03-2023

DOI: 10.1158/1078-0432.22467960.V1

Abstract: Supplementary Tables 1-2, and Figures 1-5.

Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development

Publisher: Springer Science and Business Media LLC

Date: 12-2017

DOI: 10.1186/S12864-017-4342-X

Fluid intake and all-cause mortality, cardiovascular mortality and kidney function: a population-based longitudinal cohort study

Publisher: Oxford University Press (OUP)

Date: 06-01-2014

DOI: 10.1093/NDT/GFT507

Abstract: Drinking eight glasses of fluid or water each day is widely believed to improve health, but evidence is sparse and conflicting. We aimed to investigate the association between fluid consumption and long-term mortality and kidney function. We conducted a longitudinal analysis within a prospective, population-based cohort study of 3858 men and women aged 49 years or older residing in Australia. Daily fluid intake from food and beverages not including water was measured using a food frequency questionnaire. We did multivariable adjusted Cox proportional hazard models for all-cause and cardiovascular mortality and a boot-strapping procedure for estimated glomerular filtration rate (eGFR). Upper and lower quartiles of daily fluid intake corresponded to >3 L and <2 L, respectively. During a median follow-up of 13.1 years (total 43 093 years at risk), 1127 deaths (26.1 per 1000 years at risk) including 580 cardiovascular deaths (13.5 per 1000 years at risk) occurred. Daily fluid intake (per 250 mL increase) was not associated with all-cause [adjusted hazard ratio (HR) 0.99 (95% CI 0.98-1.01)] or cardiovascular mortality [HR 0.98 (95% CI 0.95-1.01)]. Overall, eGFR reduced by 2.2 mL/min per 1.73 m(2) (SD 10.9) in the 1207 (31%) participants who had repeat creatinine measurements and this was not associated with fluid intake [adjusted regression coefficient 0.06 mL/min/1.73 m(2) per 250 mL increase (95% CI -0.03 to 0.14)]. Fluid intake from food and beverages excluding water is not associated with improved kidney function or reduced mortality.

NEMoE: A nutrition aware regularized mixture of experts model addressing diet-cohort heterogeneity of gut microbiota in Parkinson’s disease

Publisher: Cold Spring Harbor Laboratory

Date: 11-11-2021

DOI: 10.1101/2021.11.10.21266194

Abstract: The microbiome plays a fundamental role in human health and diet is one of the strongest modulators of the gut microbiome. However, interactions between microbiota and host health are complex and erse. Understanding the interplay between diet, the microbiome and health state could enable the design of personalized intervention strategies and improve the health and wellbeing of affected in iduals. A common approach to this is to ide the study population into smaller cohorts based on dietary preferences in the hope of identifying specific microbial signatures. However, classification of patients based solely on diet is unlikely to reflect the microbiome-host health relationship or the taxonomic microbiome makeup. To this end, we present a novel approach, the N utrition- E cotype M ixture o f E xperts (NEMoE) model, for establishing associations between gut microbiota and health state that accounts for diet-specific cohort variability using a regularized mixture of experts model framework with an integrated parameter sharing strategy to ensure data driven diet-cohort identification consistency across taxonomic levels. The success of our approach was demonstrated through a series of simulation studies, in which NEMoE showed robustness with regard to parameter selection and varying degrees of data heterogeneity. Further application to real-world microbiome data from a Parkinson’s disease cohort revealed that NEMoE is capable of not only improving predictive performance for Parkinson’s Disease but also for identifying diet-specific microbiome markers of disease. Our results indicate that NEMoE can be used to uncover diet-specific relationships between nutritional-ecotype and patient health and to contextualize precision nutrition for different diseases.

Overcoming cohort heterogeneity for the prediction of subclinical cardiovascular disease risk

Publisher: Elsevier BV

Date: 05-2023

DOI: 10.1016/J.ISCI.2023.106633

Gene expression allelic imbalance in ovine brown adipose tissue impacts energy homeostasis

Publisher: Public Library of Science (PLoS)

Date: 30-06-2017

DOI: 10.1371/JOURNAL.PONE.0180378

Identification, Review, and Systematic Cross-Validation of microRNA Prognostic Signatures in Metastatic Melanoma

Publisher: Elsevier BV

Date: 2016

DOI: 10.1038/JID.2015.355

Thinking process templates for constructing data stories with SCDNEY

Publisher: F1000 Research Ltd

Date: 10-03-2023

DOI: 10.12688/F1000RESEARCH.130623.1

Abstract: Background : Globally, scientists now have the ability to generate a vast amount of high throughput biomedical data that carry critical information for important clinical and public health applications. This data revolution in biology is now creating a plethora of new single-cell datasets. Concurrently, there have been significant methodological advances in single-cell research. Integrating these two resources, creating tailor-made, efficient, and purpose-specific data analysis approaches can assist in accelerating scientific discovery. Methods: We developed a series of living workshops for building data stories, using Single-cell data integrative analysis (scdney). scdney is a wrapper package with a collection of single-cell analysis R packages incorporating data integration, cell type annotation, higher order testing and more. Results: Here, we illustrate two specific workshops. The first workshop examines how to characterise the identity and/or state of cells and the relationship between them, known as phenotyping. The second workshop focuses on extracting higher-order features from cells to predict disease progression. Conclusions: Through these workshops, we not only showcase current solutions, but also highlight critical thinking points. In particular, we highlight the Thinking Process Template that provides a structured framework for the decision-making process behind such single-cell analyses. Furthermore, our workshop will incorporate dynamic contributions from the community in a collaborative learning approach, thus the term ‘living’.

Reduced p16 and Increased Cyclin D1 and pRb Expression Are Correlated With Progression in Cutaneous Melanocytic Tumors

Publisher: SAGE Publications

Date: 10-08-2009

DOI: 10.1177/1066896909336177

Abstract: The immunohistochemical expression of cell cycle proteins p16, cyclin D1, and pRb was assessed in 112 benign and malignant melanocytic tumors and correlated with tumor progression, prognosis, and outcome. Comparing benign and malignant tumors, there were significant differences in the median score for all 3 proteins, with decreased p16 ( P = .000001), increased cyclin D1 ( P = .01), and increased pRb in melanomas ( P = .01). There was a progressive loss of expression of p16 with progression from benign naevi to primary melanomas and to metastases. p16 was significantly decreased in primary tumors from melanoma patients who developed recurrent disease ( P = .0000013). Cyclin D1 and pRb showed a progressive increase in expression from benign to malignant tumors but with relative decreases in the more advanced tumors (thick primaries and metastatic melanomas). Alterations in cell cycle proteins involved in G1/S transition are implicated in melanocytic tumor progression and have a potential role in diagnosis and prognostication.

P16 and pRb immunohistochemical expression increases with increasing tumour grade in mammary phyllodes tumours

Publisher: Wiley

Date: 06-2010

DOI: 10.1111/J.1365-2559.2010.03562.X

Abstract: Control of cell cycling and proliferation is critical to the development of neoplasia and may play a role in the pathogenesis of phyllodes tumours (PTs). This study aimed to evaluate the immunohistochemical expression of certain proteins from the G(1)/S transition of the cell cycle in a cohort of PTs, to determine their role in tumour pathogenesis and to identify any associations with patient outcome. Sixty-five PTs (34 benign, 23 borderline and eight malignant) diagnosed at a single institution between 1990 and 2006 were analysed. Immunohistochemistry for p16, pRb, cyclin D1 and Ki67 was performed. Expression of the following markers increased significantly with tumour grade: stromal nuclear and cytoplasmic p16 (P = 0.01 and 0.002, respectively), stromal and epithelial pRb (P = 0.000,000,06 and 0.004, respectively), and stromal and epithelial Ki67 (P = 0.03 and 0.04, respectively). Epithelial pRb scores of 7 (range 0-7) were significantly associated with reduced disease-free survival (DFS) compared with scores of <7 (P = 0.0009). No relationship was found between cyclin D1 expression in either the epithelium or the stroma, and grade or DFS. The results suggest that alterations at the G(1)/S transition of the cell cycle play an important role in the progression of PTs.

Novel subdomains of the mouse olfactory bulb defined by molecular heterogeneity in the nascent external plexiform and glomerular layers

Publisher: Springer Science and Business Media LLC

Date: 16-05-2007

DOI: 10.1186/1471-213X-7-48

Abstract: In the mouse olfactory system, the role of the olfactory bulb in guiding olfactory sensory neuron (OSN) axons to their targets is poorly understood. What cell types within the bulb are necessary for targeting is unknown. What genes are important for this process is also unknown. Although projection neurons are not required, other cell-types within the external plexiform and glomerular layers also form synapses with OSNs. We hypothesized that these cells are important for targeting, and express spatially differentially expressed guidance cues that act to guide OSN axons within the bulb. We used laser microdissection and microarray analysis to find genes that are differentially expressed along the dorsal-ventral, medial-lateral, and anterior-posterior axes of the bulb. The expression patterns of these genes ide the bulb into previously unrecognized subdomains. Interestingly, some genes are expressed in both the medial and lateral bulb, showing for the first time the existence of symmetric expression along this axis. We use a regeneration paradigm to show that several of these genes are altered in expression in response to deafferentation, consistent with the interpretation that they are expressed in cells that interact with OSNs. We demonstrate that the nascent external plexiform and glomerular layers of the bulb can be ided into multiple domains based on the expression of these genes, several of which are known to function in axon guidance, synaptogenesis, and angiogenesis. These genes represent candidate guidance cues that may act to guide OSN axons within the bulb during targeting.

scFeatures: multi-view representations of single-cell and spatial data for disease outcome prediction

Publisher: Oxford University Press (OUP)

Date: 30-08-2022

DOI: 10.1093/BIOINFORMATICS/BTAC590

Abstract: With the recent surge of large-cohort scale single cell research, it is of critical importance that analytical methods can fully utilize the comprehensive characterization of cellular systems that single cell technologies produce to provide insights into s les from in iduals. Currently, there is little consensus on the best ways to compress information from the complex data structures of these technologies to summary statistics that represent each s le (e.g. in iduals). Here, we present scFeatures, an approach that creates interpretable cellular and molecular representations of single-cell and spatial data at the s le level. We demonstrate that summarizing a broad collection of features at the s le level is both important for understanding underlying disease mechanisms in different experimental studies and for accurately classifying disease status of in iduals. scFeatures is publicly available as an R package at github.com/SydneyBioX/scFeatures. All data used in this study are publicly available with accession ID reported in the Section 2. Supplementary data are available at Bioinformatics online.

Prediction of alternatively skipped exons and splicing enhancers from exon junction arrays

Publisher: Springer Science and Business Media LLC

Date: 2008

DOI: 10.1186/1471-2164-9-551

Hepatic metallothionein expression in chronic hepatitis C virus infection is IFNL3 genotype-dependent

Publisher: Springer Science and Business Media LLC

Date: 16-01-2014

DOI: 10.1038/GENE.2013.66

Abstract: The IFNL3 genotype predicts the clearance of hepatitis C virus (HCV), spontaneously and with interferon (IFN)-based therapy. The responder genotype is associated with lower expression of interferon stimulated genes (ISGs) in liver biopsies from chronic hepatitis C patients. However, ISGs represent many interacting molecular pathways, and we hypothesised that the IFNL3 genotype may produce a characteristic pattern of ISG expression explaining the effect of genotype on viral clearance. For the first time, we identified an association between a cluster of ISGs, the metallothioneins (MTs) and IFNL3 genotype. Importantly, MTs were significantly upregulated (in contrast to most other ISGs) in HCV-infected liver biopsies of rs8099917 responders. An association between lower fibrosis scores and higher MT levels was demonstrated underlying clinical relevance of this association. As expected, overall ISGs were significantly downregulated in biopsies from subjects with the IFNL3 rs8099917 responder genotype (P=2.38 × 10(-7)). Peripheral blood analysis revealed paradoxical and not previously described findings with upregulation of ISGs seen in the responder genotype (P=1.00 × 10(-4)). The higher MT expression in responders may contribute to their improved viral clearance and MT-inducing agents may be useful adjuncts to therapy for HCV. Upregulation of immune cell ISGs in responders may also contribute to the IFNL3 genotype effect.

Nonutilization of Kidneys From Donors After Circulatory Determinant of Death

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 13-05-2022

DOI: 10.1097/TXD.0000000000001331

Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis

Publisher: Springer Science and Business Media LLC

Date: 12-2019

DOI: 10.1186/S12859-019-3179-5

Abstract: Single-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of in idual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex s les or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification. Here, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets to generate clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k -means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metric used. Our results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from edcom/scCCESS

Dynamic adipocyte phosphoproteome reveals that akt directly regulates mTORC2

Publisher: Elsevier BV

Date: 06-2013

DOI: 10.1016/J.CMET.2013.04.010

Whole-genome landscapes of major melanoma subtypes

Publisher: Springer Science and Business Media LLC

Date: 05-2017

DOI: 10.1038/NATURE22071

Abstract: Melanoma of the skin is a common cancer only in Europeans, whereas it arises in internal body surfaces (mucosal sites) and on the hands and feet (acral sites) in people throughout the world. Here we report analysis of whole-genome sequences from cutaneous, acral and mucosal subtypes of melanoma. The heavily mutated landscape of coding and non-coding mutations in cutaneous melanoma resolved novel signatures of mutagenesis attributable to ultraviolet radiation. However, acral and mucosal melanomas were dominated by structural changes and mutation signatures of unknown aetiology, not previously identified in melanoma. The number of genes affected by recurrent mutations disrupting non-coding sequences was similar to that affected by recurrent mutations to coding sequences. Significantly mutated genes included BRAF, CDKN2A, NRAS and TP53 in cutaneous melanoma, BRAF, NRAS and NF1 in acral melanoma and SF3B1 in mucosal melanoma. Mutations affecting the TERT promoter were the most frequent of all however, neither they nor ATRX mutations, which correlate with alternative telomere lengthening, were associated with greater telomere length. Most melanomas had potentially actionable mutations, most in components of the mitogen-activated protein kinase and phosphoinositol kinase pathways. The whole-genome mutation landscape of melanoma reveals erse carcinogenic processes across its subtypes, some unrelated to sun exposure, and extends potential involvement of the non-coding genome in its pathogenesis.

scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets

Publisher: Proceedings of the National Academy of Sciences

Date: 26-04-2019

DOI: 10.1073/PNAS.1820006116

Abstract: Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with in idual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.

Scalable workflow for characterization of cell-cell communication in COVID-19 patients

Publisher: Public Library of Science (PLoS)

Date: 05-10-2022

DOI: 10.1371/JOURNAL.PCBI.1010495

Abstract: COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable and scalable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use this to examine eight public single-cell RNA-seq datasets (six from peripheral blood mononuclear cells, one from bronchoalveolar lavage and one from nasopharyngeal), with a total of 211 in idual s les. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate erse communication patterns across in iduals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients. Overall, this workflow can be generalized and scaled to combine multiple scRNA-seq datasets to uncover cell-cell interactions.

A multi-array multi-SNP genotyping algorithm for affymetrix SNP microarrays

Publisher: Oxford University Press (OUP)

Date: 25-04-2007

DOI: 10.1093/BIOINFORMATICS/BTM131

Abstract: Motivation: Modern strategies for mapping disease loci require efficient genotyping of a large number of known polymorphic sites in the genome. The sensitive and high-throughput nature of hybridization-based DNA microarray technology provides an ideal platform for such an application by interrogating up to hundreds of thousands of single nucleotide polymorphisms (SNPs) in a single assay. Similar to the development of expression arrays, these genotyping arrays pose many data analytic challenges that are often platform specific. Affymetrix SNP arrays, e.g. use multiple sets of short oligonucleotide probes for each known SNP, and require effective statistical methods to combine these probe intensities in order to generate reliable and accurate genotype calls. Results: We developed an integrated multi-SNP, multi-array genotype calling algorithm for Affymetrix SNP arrays, MAMS, that combines single-array multi-SNP (SAMS) and multi-array, single-SNP (MASS) calls to improve the accuracy of genotype calls, without the need for training data or computation-intensive normalization procedures as in other multi-array methods. The algorithm uses res ling techniques and model-based clustering to derive single array based genotype calls, which are subsequently refined by competitive genotype calls based on (MASS) clustering. The res ling scheme caps computation for single-array analysis and hence is readily scalable, important in view of expanding numbers of SNPs per array. The MASS update is designed to improve calls for atypical SNPs, harboring allele-imbalanced binding affinities, that are difficult to genotype without information from other arrays. Using a publicly available data set of HapMap s les from Affymetrix, and independent calls by alternative genotyping methods from the HapMap project, we show that our approach performs competitively to existing methods. Availability: R functions are available upon request from the authors. Contact: yxiao@itsa.ucsf.edu and rufang@biostat.ucsf.edu Supplementary information: Supplementary data are available at Bioinformatics online.

AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 05-2019

DOI: 10.1109/TCYB.2018.2816984

Metabolic signatures in coronary artery disease: Results from the bioHEART-CT study

Publisher: MDPI AG

Date: 22-04-2021

DOI: 10.3390/CELLS10050980

Abstract: Despite effective prevention programs targeting cardiovascular risk factors, coronary artery disease (CAD) remains the leading cause of death. Novel biomarkers are needed for improved risk stratification and primary prevention. To assess for independent associations between plasma metabolites and specific CAD plaque phenotypes we performed liquid chromatography mass-spectrometry on plasma from 1002 patients in the BioHEART-CT study. Four metabolites were examined as candidate biomarkers. Dimethylguanidino valerate (DMGV) was associated with presence and amount of CAD (OR) 1.41 (95% Confidence Interval [CI] 1.12–1.79, p = 0.004), calcified plaque, and obstructive CAD (p 0.05 for both). The association with amount of plaque remained after adjustment for traditional risk factors, ß-coefficient 0.17 (95% CI 0.02–0.32, p = 0.026). Glutamate was associated with the presence of non-calcified plaque, OR 1.48 (95% CI 1.09–2.01, p = 0.011). Phenylalanine was associated with amount of CAD, ß-coefficient 0.33 (95% CI 0.04–0.62, p = 0.025), amount of calcified plaque, (ß-coefficient 0.88, 95% CI 0.23–1.53, p = 0.008), and obstructive CAD, OR 1.84 (95% CI 1.01–3.31, p = 0.046). Trimethylamine N-oxide was negatively associated non-calcified plaque OR 0.72 (95% CI 0.53–0.97, p = 0.029) and the association remained when adjusted for traditional risk factors. In targeted metabolomic analyses including 53 known metabolites and controlling for a 5% false discovery rate, DMGV was strongly associated with the presence of calcified plaque, OR 1.59 (95% CI 1.26–2.01, p = 0.006), obstructive CAD, OR 2.33 (95% CI 1.59–3.43, p = 0.0009), and amount of CAD, ß-coefficient 0.3 (95% CI 0.14–0.45, p = 0.014). In multivariate analyses the lipid and nucleotide metabolic pathways were both associated with the presence of CAD, after adjustment for traditional risk factors. We report novel associations between CAD plaque phenotypes and four metabolites previously associated with CAD. We also identified two metabolic pathways strongly associated with CAD, independent of traditional risk factors. These pathways warrant further investigation at both a biomarker and mechanistic level.

The Gut Microbiome in Parkinson’s Disease: A Longitudinal Study of the Impacts on Disease Progression and the Use of Device-Assisted Therapies

Publisher: Frontiers Media SA

Date: 17-05-2022

DOI: 10.3389/FNAGI.2022.875261

Abstract: Altered gut microbiome (GM) composition has been established in Parkinson’s disease (PD). However, few studies have longitudinally investigated the GM in PD, or the impact of device-assisted therapies. To investigate the temporal stability of GM profiles from PD patients on standard therapies and those initiating device-assisted therapies (DAT) and define multivariate models of disease and progression. We evaluated validated clinical questionnaires and stool s les from 74 PD patients and 74 household controls (HCs) at 0, 6, and 12 months. Faster or slower disease progression was defined from levodopa equivalence dose and motor severity measures. 19 PD patients initiating Deep Brain Stimulation or Levodopa-Carbidopa Intestinal Gel were separately evaluated at 0, 6, and 12 months post-therapy initiation. Persistent underrepresentation of short-chain fatty-acid-producing bacteria, Butyricicoccus, Fusicatenibacter, Lachnospiraceae ND3007 group , and Erysipelotrichaceae UCG-003 , were apparent in PD patients relative to controls. A sustained effect of DAT initiation on GM associations with PD was not observed. PD progression analysis indicated that the genus Barnesiella was underrepresented in faster progressing PD patients at t = 0 and t = 12 months. Two-stage predictive modeling, integrating microbiota abundances and nutritional profiles, improved predictive capacity (change in Area Under the Curve from 0.58 to 0.64) when assessed at Amplicon Sequence Variant taxonomic resolution. We present longitudinal GM studies in PD patients, showing persistently altered GM profiles suggestive of a reduced butyrogenic production potential. DATs exerted variable GM influences across the short and longer-term. We found that specific GM profiles combined with dietary factors improved prediction of disease progression in PD patients.

Nanomedicine in Cancer

Publisher: CRC Press

Date: 25-08-2017

DOI: 10.1201/9781315114361

PD-L1 Negative Status is Associated with Lower Mutation Burden, Differential Expression of Immune-Related Genes, and Worse Survival in Stage III Melanoma

Publisher: American Association for Cancer Research (AACR)

Date: 31-07-2016

DOI: 10.1158/1078-0432.CCR-15-1714

Abstract: Purpose: Understanding why some melanomas test negative for PD-L1 by IHC may have implications for the application of anti-PD-1 therapies in melanoma management. This study sought to determine somatic mutation and gene expression patterns associated with tumor cell PD-L1 expression, or lack thereof, in stage III metastatic melanoma to better define therapeutically relevant patient subgroups. Experimental Design: IHC for PD-L1 was assessed in 52 American Joint Committee on Cancer stage III melanoma lymph node specimens and compared with specimen-matched comprehensive clinicopathologic, genomic, and transcriptomic data. Results: PD-L1–negative status was associated with lower nonsynonymous mutation (NSM) burden (P = 0.017) and worse melanoma-specific survival [HR = 0.28 (0.12–0.66), P = 0.002] in stage III melanoma. Gene set enrichment analysis identified an immune-related gene expression signature in PD-L1–positive tumors. There was a marked increase in cytotoxic T-cell and macrophage-specific genes in PD-L1–positive melanomas. CD8Ahigh gene expression was associated with better melanoma-specific survival [HR = 0.2 (0.05–0.87), P = 0.017] and restricted to PD-L1–positive stage III specimens. NF1 mutations were restricted to PD-L1–positive tumors (P = 0.041). Conclusions: Tumor negative PD-L1 status in stage III melanoma lymph node metastasis is a marker of worse patient survival and is associated with a poor immune response gene signature. Lower NSM levels were associated with PD-L1–negative status suggesting differences in somatic mutation profiles are a determinant of PD-L1–associated antitumor immunity in stage III melanoma. Clin Cancer Res 22(15) 3915–23. ©2016 AACR.

ClassifyR: An R package for performance assessment of classification with applications to transcriptomics

Publisher: Oxford University Press (OUP)

Date: 02-2015

DOI: 10.1093/BIOINFORMATICS/BTV066

Abstract: Although a large collection of classification software packages exist in R, a new generic framework for linking custom classification functions with classification performance measures is needed. A generic classification framework has been designed and implemented as an R package in an object oriented style. Its design places emphasis on parallel processing, reproducibility and extensibility. Finally, a comprehensive set of performance measures are available to ease post-processing. Taken together, these important characteristics enable rapid and reproducible benchmarking of alternative classifiers. Availability and implementation: ClassifyR is implemented in R and can be obtained from the Bioconductor project: ackages/release/bioc/html/ClassifyR.html Contact : dario.strbenac@sydney.edu.au Supplementary information : Supplementary data are available at Bioinformatics online.

Microarrays—Planning Your Experiment

Publisher: Humana Press

Date: 2008

DOI: 10.1007/978-1-60327-148-6_5

Construction of a Human Cell Landscape of COVID-19 Infection at Single-cell Level

Publisher: Aging and Disease

Date: 2021

DOI: 10.14336/AD.2021.0301

Analysis of cDNA microarray images.

Publisher: Oxford University Press (OUP)

Date: 2001

DOI: 10.1093/BIB/2.4.341

Abstract: Microarrays are part of a new class of biotechnologies that allow the monitoring of expression levels for thousands of genes simultaneously. Image analysis is an important aspect of microarray experiments, one that can have a potentially large impact on subsequent analyses, such as clustering or the identification of differentially expressed genes. This paper reviews a number of existing image analysis methods used on cDNA microarray data. In particular, it describes and discusses the different segmentation and background adjustment methods. It was found that in some cases background adjustment can substantially reduce the precision--that is, increase the variability of low-intensity spot values. In contrast, the choice of segmentation procedure seems to have a smaller impact.

Improved moderation for gene-wise variance estimation in RNA-Seq via the exploitation of external information

Publisher: Springer Science and Business Media LLC

Date: 2013

DOI: 10.1186/1471-2164-14-S1-S9

Abstract: The cost of RNA-Seq has been decreasing over the last few years. Despite this, experiments with four or less biological replicates are still quite common. Estimating the variances of gene expression estimates becomes both a challenging and interesting problem in these situations of low replication. However, with the wealth of microarray and other publicly available gene expression data readily accessible on public repositories, these sources of information can be leveraged to make improvements in variance estimation. We have proposed a novel approach called Tshrink+ for inferring differential gene expression through improved modelling of the gene-wise variances. Existing methods share information between genes of similar average expression by shrinking, or moderating, the gene-wise variances to a fitted common variance. We have been able to achieve improved estimation of the common variance by using gene-wise s le variances from external experiments, as well as gene length. Using biological data we show that utilising additional external information can improve the modelling of the common variance and hence the calling of differentially expressed genes. These sources of additional information include gene length and gene-wise s le variances from other RNA-Seq and microarray datasets, of both related and seemingly unrelated tissue types. The results of this are promising, with our differential expression test, Tshrink+, performing favourably when compared to existing methods such as DESeq and edgeR when considering both gene ranking and sensitivity. These improved variance models could easily be implemented in both DESeq and edgeR and highlight the need for a database that offers a profile of gene variances over a range of tissue types and organisms.

Transcriptomic analysis of sea star development through metamorphosis to the highly derived pentameral body plan with a focus on neural transcription factors

Publisher: Oxford University Press (OUP)

Date: 02-2020

DOI: 10.1093/DNARES/DSAA007

Abstract: The Echinodermata is characterized by a secondarily evolved pentameral body plan. While the evolutionary origin of this body plan has been the subject of debate, the molecular mechanisms underlying its development are poorly understood. We assembled a de novo developmental transcriptome from the embryo through metamorphosis in the sea star Parvulastra exigua. We use the asteroid model as it represents the basal-type echinoderm body architecture. Global variation in gene expression distinguished the gastrula profile and showed that metamorphic and juvenile stages were more similar to each other than to the pre-metamorphic stages, pointing to the marked changes that occur during metamorphosis. Differential expression and gene ontology (GO) analyses revealed dynamic changes in gene expression throughout development and the transition to pentamery. Many GO terms enriched during late metamorphosis were related to neurogenesis and signalling. Neural transcription factor genes exhibited clusters with distinct expression patterns. A suite of these genes was up-regulated during metamorphosis (e.g. Pax6, Eya, Hey, NeuroD, FoxD, Mbx, and Otp). In situ hybridization showed expression of neural genes in the CNS and sensory structures. Our results provide a foundation to understand the metamorphic transition in echinoderms and the genes involved in development and evolution of pentamery.

Core functional nodes and sex-specific pathways in human ischaemic and dilated cardiomyopathy.

Publisher: Springer Science and Business Media LLC

Date: 02-06-2020

DOI: 10.1038/S41467-020-16584-Z

Abstract: Poor access to human left ventricular myocardium is a significant limitation in the study of heart failure (HF). Here, we utilise a carefully procured large human heart biobank of cryopreserved left ventricular myocardium to obtain direct molecular insights into ischaemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM), the most common causes of HF worldwide. We perform unbiased, deep proteomic and metabolomic analyses of 51 left ventricular (LV) s les from 44 cryopreserved human ICM and DCM hearts, compared to age-, gender-, and BMI-matched, histopathologically normal, donor controls. We report a dramatic reduction in serum amyloid A1 protein in ICM hearts, perturbed thyroid hormone signalling pathways and significant reductions in oxidoreductase co-factor riboflavin-5-monophosphate and glycolytic intermediate fructose-6-phosphate in both unveil gender-specific changes in HF, including nitric oxide-related arginine metabolism, mitochondrial substrates, and X chromosome-linked protein and metabolite changes and provide an interactive online application as a publicly-available resource.

The impact of device-assisted therapies on the gut microbiome in Parkinson’s disease

Publisher: Springer Science and Business Media LLC

Date: 15-06-2021

DOI: 10.1007/S00415-021-10657-9

Abstract: Microbiome feedbacks are proposed to influence Parkinson's disease (PD) pathophysiology. A number of studies have evaluated the impact of oral medication on the gut microbiome (GM) in PD. However, the influence of PD device-assisted therapies (DATs) on the GM remains to be investigated. To profile acute gut microbial community alterations in response to PD DAT initiation. Clinical data and stool s les were collected from 21 PD patients initiating either deep brain stimulation (DBS) or levodopa-carbidopa intestinal gel (LCIG) and ten spousal healthy control (HC) subjects. 16S licon sequencing of stool DNA enabled comparison of temporal GM stability between groups and with clinical measures, including disease alterations relative to therapy initiation. We assessed GM response to therapy in the PD group by comparing pre-therapy (- 2 and 0 weeks) with post-therapy initiation timepoints (+ 2 and + 4 weeks) and HCs at baseline (0 weeks). Altered GM compositions were noted between the PD and HC groups at various taxonomic levels, including specific differences for DBS (overrepresentation of Clostridium_XlVa, Bilophila, Parabacteroides, Pseudoflavonifractor and underrepresentation of Dorea) and LCIG therapy (overrepresentation of Pseudoflavonifractor, Escherichia/Shigella, and underrepresentation of Gemmiger). Beta ersity changes were also found over the 4 week post-treatment initiation period. We report on initial short-term GM changes in response to the initiation of PD DATs. Prior to the introduction of the DAT, a PD-associated GM was observed. Following initiation of DAT, several DAT-specific changes in GM composition were identified, suggesting DATs can influence the GM in PD.

A benchmark study of simulation methods for single-cell RNA sequencing data

Publisher: Cold Spring Harbor Laboratory

Date: 02-06-2021

DOI: 10.1101/2021.06.01.446157

Abstract: Single-cell RNA-seq (scRNA-seq) data simulation is critical for evaluating computational methods for analysing scRNA-seq data especially when ground truth is experimentally unattainable. The reliability of evaluation depends on the ability of simulation methods to capture properties of experimental data. However, while many scRNA-seq data simulation methods have been proposed, a systematic evaluation of these methods is lacking. We developed a comprehensive evaluation framework, SimBench, including a novel kernel density estimation measure to benchmark 12 simulation methods through 35 scRNA-seq experimental datasets. We evaluated the simulation methods on a panel of data properties, ability to maintain biological signals, scalability and applicability. Our benchmark uncovered performance differences among the methods and highlighted the varying difficulties in simulating data characteristics. Furthermore, we identified several limitations including maintaining heterogeneity of distribution. These results, together with the framework and datasets made publicly available as R packages, will guide simulation methods selection and their future development.

Two-step cross-entropy feature selection for microarrays-power through complementarity

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 07-2011

DOI: 10.1109/TCBB.2011.30

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

Publisher: Cold Spring Harbor Laboratory

Date: 12-07-2021

DOI: 10.1101/2021.07.11.451967

Abstract: Survival analysis is a branch of statistics that deals with both, the tracking of time and of the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic statistical survival models, with prediction accuracy often serving as the sole metric of model performance. Moreover, survival analysis approaches for censored omics data have not been thoroughly investigated. The common approach is to binarise the survival time and perform a classification analysis. Here, we develop a benchmarking framework, SurvBenchmark, that evaluates a erse collection of survival models for both clinical and omics datasets. SurvBenchmark not only focuses on classical approaches such as the Cox model, but it also evaluates state-of-art machine learning survival models. All approaches were assessed using multiple performance metrics, these include model predictability, stability, flexibility and computational issues. Our systematic comparison framework with over 320 comparisons (20 methods over 16 datasets) shows that the performances of survival models vary in practice over real-world datasets and over the choice of the evaluation metric. In particular, we highlight that using multiple performance metrics is critical in providing a balanced assessment of various models. The results in our study will provide practical guidelines for translational scientists and clinicians, as well as define possible areas of investigation in both survival technique and benchmarking strategies. jean.yang@sydney.edu.au

Analysis of gene expression in the developing mouse retina

Publisher: Proceedings of the National Academy of Sciences

Date: 17-04-2003

DOI: 10.1073/PNAS.0831080100

Abstract: In the visual system, differential gene expression underlies development of the anterior–posterior and dorsal–ventral axes. Here we present the results of a microarray screen to identify genes differentially expressed in the developing retina. We assayed gene expression in nasal (anterior), temporal (posterior), dorsal, and ventral embryonic mouse retina. We used a statistical method to estimate gene expression between different retina regions. Genes were clustered according to their expression pattern and were ranked within each cluster. We identified groups of genes expressed in gradients or with restricted patterns of expression as verified by in situ hybridization. A common theme for the identified genes is the differential expression in the dorsal-ventral axis. By analyzing gene expression patterns, we provide insight into the molecular organization of the developing retina.

Combining BET and HDAC inhibitors synergistically induces apoptosis of melanoma and suppresses AKT and YAP signaling

Publisher: Impact Journals, LLC

Date: 05-06-2015

DOI: 10.18632/ONCOTARGET.4242

Investigating higher-order interactions in single-cell data with scHOT

Publisher: Springer Science and Business Media LLC

Date: 13-07-2020

DOI: 10.1038/S41592-020-0885-X

Gene expression profiling of the human maternal-fetal interface reveals dramatic changes between midgestation and term

Publisher: The Endocrine Society

Date: 03-2007

DOI: 10.1210/EN.2006-0683

Abstract: Human placentation entails the remarkable integration of fetal and maternal cells into a single functional unit. In the basal plate region (the maternal-fetal interface) of the placenta, fetal cytotrophoblasts from the placenta invade the uterus and remodel the resident vasculature and avoid maternal immune rejection. Knowing the molecular bases for these unique cell-cell interactions is important for understanding how this specialized region functions during normal pregnancy with implications for tumor biology and transplantation immunology. Therefore, we undertook a global analysis of the gene expression profiles at the maternal-fetal interface. Basal plate biopsy specimens were obtained from 36 placentas (14–40 wk) at the conclusion of normal pregnancies. RNA was isolated, processed, and hybridized to HG-U133A& B Affymetrix GeneChips. Surprisingly, there was little change in gene expression during the 14- to 24-wk interval. In contrast, 418 genes were differentially expressed at term (37–40 wk) as compared with midgestation (14–24 wk). Subsequent analyses using quantitative PCR and immunolocalization approaches validated a portion of these results. Many of the differentially expressed genes are known in other contexts to be involved in differentiation, motility, transcription, immunity, angiogenesis, extracellular matrix dissolution, or lipid metabolism. One sixth were nonannotated or encoded hypothetical proteins. Modeling based on structural homology revealed potential functions for 31 of these proteins. These data provide a reference set for understanding the molecular components of the dialogue taking place between maternal and fetal cells in the basal plate as well as for future comparisons of alterations in this region that occur in obstetric complications.

Erratum: Meta-analysis of genome-wide gene expression differences in onset and maintenance phases of genetic hypertension (Hypertension (2010) 56 (319-324))

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 04-2011

DOI: 10.1161/HYPERTENSIONAHA.111.170191

Detection and classification of peaks in 5' cap RNA sequencing data

Publisher: Springer Science and Business Media LLC

Date: 10-2013

DOI: 10.1186/1471-2164-14-S5-S9

scClassify: sample size estimation and multiscale classification of cells using single and multiple reference

Publisher: EMBO

Date: 06-2020

DOI: 10.15252/MSB.20199389

Re-Fraction: A machine learning approach for deterministic identification of protein homologues and splice variants in large-scale MS-based proteomics

Publisher: American Chemical Society (ACS)

Date: 30-03-2012

DOI: 10.1021/PR300072J

Abstract: A key step in the analysis of mass spectrometry (MS)-based proteomics data is the inference of proteins from identified peptide sequences. Here we describe Re-Fraction, a novel machine learning algorithm that enhances deterministic protein identification. Re-Fraction utilizes several protein physical properties to assign proteins to expected protein fractions that comprise large-scale MS-based proteomics data. This information is then used to appropriately assign peptides to specific proteins. This approach is sensitive, highly specific, and computationally efficient. We provide algorithms and source code for the current version of Re-Fraction, which accepts output tables from the MaxQuant environment. Nevertheless, the principles behind Re-Fraction can be applied to other protein identification pipelines where data are generated from s les fractionated at the protein level. We demonstrate the utility of this approach through reanalysis of data from a previously published study and generate lists of proteins deterministically identified by Re-Fraction that were previously only identified as members of a protein group. We find that this approach is particularly useful in resolving protein groups composed of splice variants and homologues, which are frequently expressed in a cell- or tissue-specific manner and may have important biological consequences.

Transcriptomic Analysis of the Highly Derived Radial Body Plan of a Sea Urchin

Publisher: Oxford University Press (OUP)

Date: 04-2014

DOI: 10.1093/GBE/EVU070

Cepo uncovers cell identity through differential stability

Publisher: Cold Spring Harbor Laboratory

Date: 11-01-2021

DOI: 10.1101/2021.01.10.426138

Abstract: We present Cepo, a method to generate cell-type-specific gene statistics of differentially stable genes from single-cell RNA-sequencing (scRNA-seq) data to define cell identity. Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells, and lineage inference of single cells.

Deep Multimodal Graph-Based Network for Survival Prediction from Highly Multiplexed Images and Patient Variables

Publisher: Cold Spring Harbor Laboratory

Date: 20-07-2022

DOI: 10.1101/2022.07.19.500604

Abstract: The spatial architecture of the tumour microenvironment and phenotypic heterogeneity of tumour cells have been shown to be associated with cancer prognosis and clinical outcomes, including survival. Recent advances in highly multiplexed imaging, including imaging mass cytometry (IMC), capture spatially resolved, high-dimensional maps that quantify dozens of disease-relevant biomarkers at single-cell resolution, that contain potential to inform patient-specific prognosis. However, existing automated methods for predicting survival typically do not leverage spatial phenotype information captured at the single-cell level, and current methods tend to focus on a single modality, such as patient variables (PVs). There is no end-to-end method designed to leverage the rich information in whole IMC images and all marker channels, and aggregate this information with PVs in a complementary manner to predict survival with enhanced accuracy. We introduce a deep multimodal graph-based network (DMGN) that integrates entire IMC images and multiple PVs for end-to-end survival prediction of breast cancer. We propose a multimodal graph-based module that considers relationships between spatial phenotype information in all image regions and all PVs, and scales each region–PV pair based on its relevance to survival. We propose another module to automatically generate embeddings specialised for each PV to enhance multimodal aggregation. We show that our modules are consistently effective at improving survival prediction performance using two public datasets, and that DMGN can be applied to an independent validation dataset across the same antigens but different antibody clones. Our DMGN outperformed state-of-the-art methods at survival prediction.

Trash or Treasure: Rescuing Discard Kidneys

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 19-08-2021

DOI: 10.1097/TP.0000000000003663

QCMAP: An Interactive Web-Tool for Performance Diagnosis and Prediction of LC-MS Systems

Publisher: Wiley

Date: 13-06-2019

DOI: 10.1002/PMIC.201900068

Abstract: The increasing role played by liquid chromatography-mass spectrometry (LC-MS)-based proteomics in biological discovery has led to a growing need for quality control (QC) on the LC-MS systems. While numerous quality control tools have been developed to track the performance of LC-MS systems based on a pre-defined set of performance factors (e.g., mass error, retention time), the precise influence and contribution of the performance factors and their generalization property to different biological s les are not as well characterized. Here, a web-based application (QCMAP) is developed for interactive diagnosis and prediction of the performance of LC-MS systems across different biological s le types. Leveraging on a standardized HeLa cell s le run as QC within a multi-user facility, predictive models are trained on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC-MS systems. It is demonstrated that the learned model can be applied to predict LC-MS system performance for brain s les generated from an independent study. By compiling these predictive models into our web-application, QCMAP allows users to benchmark the performance of their LC-MS systems using their own s les and identify key factors for instrument optimization. QCMAP is freely available from: shiny.maths.usyd.edu.au/QCMAP/.

Transcriptomic analysis of Nodal – and BMP- associated genes during development to the juvenile seastar in Parvulastra exigua (Asterinidae)

Publisher: Elsevier BV

Date: 10-2021

DOI: 10.1016/J.MARGEN.2021.100857

Melanoma Explorer: a web application to allow easy reanalysis of publicly available and clinically annotated melanoma omics data sets

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 06-2019

DOI: 10.1097/CMR.0000000000000533

Disease-Specific Gene Expression Profiling in Multiple Models of Lung Disease

Publisher: American Thoracic Society

Date: 15-02-2008

DOI: 10.1164/RCCM.200702-333OC

Stepwise normalization of two-channel spotted microarrays

Publisher: Walter de Gruyter GmbH

Date: 07-01-2005

DOI: 10.2202/1544-6115.1117

Abstract: Intensities measurements of spotted microarrays embody many undesirable systematic variations. Very commonly, varying amounts and types of such variations are observed in different arrays. Although various normalization methods have been proposed to remove such systematic effects, it has not been well studied how to assess or select the most appropriate method for different arrays and data sets. To address this issue, we present a novel normalization technique, STEPNORM, for data-dependent and adaptive normalization of two-channel spotted microarrays. STEPNORM performs a stepwise interrogation of a range of different normalization models and selects the appropriate method based on formal model selection criteria. In addition, we evaluate the effectiveness of STEPNORM and other commonly used normalization methods utilizing a set of specially constructed splicing arrays.

Quantifying the effect of body mass index, age, and depression severity on 24-h activity patterns in persons with a lifetime history of affective disorders

Publisher: Springer Science and Business Media LLC

Date: 09-09-2016

DOI: 10.1186/S12888-016-1023-2

Metabolite signatures of heart failure, sleep apnoea, their interaction, and outcomes in the community.

Publisher: Wiley

Date: 17-10-2021

DOI: 10.1002/EHF2.13631

Abstract: Sleep apnoea and congestive heart failure (CHF) commonly co‐exist, but their interaction is unclear. Metabolomics may clarify their interaction and relationships to outcome. We assayed 372 circulating metabolites and lipids in 1919 and 1524 participants of the Framingham Heart Study (FHS) (mean age 54 ± 10 years, 53% women) and Women's Health Initiative (WHI) (mean age 67 ± 7 years), respectively. We used linear and Cox regression to relate plasma concentrations of metabolites and lipids to echocardiographic parameters CHF and its subtypes heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF) and sleep indices. Adenine dinucleotide phosphate (ADP) associated with left ventricular (LV) fractional shortening phosphocreatine with LV wall thickness lysosomal storage molecule sphingomyelin 18:2 with LV mass and nicotine metabolite cotinine with time spent with an oxygen saturation less than 90% ( β = 2.3 min, P = 2.3 × 10 −5 ). Pro‐hypertrophic metabolite hydroxyglutarate partly mediated the association between LV wall thickness and HFpEF. Central sleep apnoea was significantly associated with HFpEF ( P = 0.03) but not HFrEF ( P = 0.5). There were three significant metabolite canonical variates, one of which conferred protection from cardiovascular death [hazard ratio = 0.3 (0.11, 0.81), P = 0.02]. Energetic metabolites were associated with cardiac function energy‐ and lipid‐storage metabolites with LV wall thickness and mass plasma levels of nicotine metabolite cotinine were associated with increased time spent with a sleep oxygen saturation less than 90%, a clinically significant marker of outcome, indicating a significant hazard for smokers who have sleep apnoea.

Stability of feature selection algorithms and ensemble feature selection methods in bioinformatics

Publisher: Wiley

Date: 16-12-2014

DOI: 10.1002/9781118617151.CH14

3D reconstruction of spatial expression

Publisher: Springer Science and Business Media LLC

Date: 05-2022

DOI: 10.1038/S41592-022-01476-5

The transcriptional response to oxidative stress is part of, but not sufficient for, insulin resistance in adipocytes

Publisher: Springer Science and Business Media LLC

Date: 29-01-2018

DOI: 10.1038/S41598-018-20104-X

Abstract: Insulin resistance is a major risk factor for metabolic diseases such as Type 2 diabetes. Although the underlying mechanisms of insulin resistance remain elusive, oxidative stress is a unifying driver by which numerous extrinsic signals and cellular stresses trigger insulin resistance. Consequently, we sought to understand the cellular response to oxidative stress and its role in insulin resistance. Using cultured 3T3-L1 adipocytes, we established a model of physiologically-derived oxidative stress by inhibiting the cycling of glutathione and thioredoxin, which induced insulin resistance as measured by impaired insulin-stimulated 2-deoxyglucose uptake. Using time-resolved transcriptomics, we found 2000 genes differentially-expressed over 24 hours, with specific metabolic and signalling pathways enriched at different times. We explored this coordination using a knowledge-based hierarchical-clustering approach to generate a temporal transcriptional cascade and identify key transcription factors responding to oxidative stress. This response shared many similarities with changes observed in distinct insulin resistance models. However, an anti-oxidant reversed insulin resistance phenotypically but not transcriptionally, implying that the transcriptional response to oxidative stress is insufficient for insulin resistance. This suggests that the primary site by which oxidative stress impairs insulin action occurs post-transcriptionally, warranting a multi-level ‘trans-omic’ approach when studying time-resolved responses to cellular perturbations.

Differential gene expression by integrin beta 7+ and beta 7- memory T helper cells.

Publisher: Springer Science and Business Media LLC

Date: 2004

DOI: 10.1186/1471-2172-5-13

scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data

Publisher: Cold Spring Harbor Laboratory

Date: 22-05-2023

DOI: 10.1101/2023.05.18.541381

Abstract: Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mecha-nisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal datasets, we demonstrate scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome dataset we generated from differentiating mouse embryonic stem cells over time, we demonstrate scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

Publisher: Oxford University Press (OUP)

Date: 15-02-2002

DOI: 10.1093/NAR/30.4.E15

Abstract: There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray s le pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.

Transcriptome-wide targets of alternative splicing by RBM4 and possible role in cancer

Publisher: Elsevier BV

Date: 04-2016

DOI: 10.1016/J.YGENO.2016.02.003

Abstract: This study determined transcriptome-wide targets of the splicing factor RBM4 using Affymetrix GeneChip(®) Human Exon 1.0 ST Arrays and HeLa cells treated with RBM4-specific siRNA. This revealed 238 transcripts that were targeted for alternative splicing. Cross-linking and immunoprecipitation experiments identified 945 RBM4 targets in mouse HEK293 cells, 39% of which were ascribed to "alternative splicing" by in silico pathway analysis. Mouse embryonic stem cells transfected with Rbm4 siRNA hairpins exhibited reduced colony numbers and size consistent with involvement of RBM4 in cell proliferation. RBM4 cDNA probing of a cancer cDNA array involving 18 different tumor types from 13 different tissues and matching normal tissue found overexpression of RBM4 mRNA (p<0.01) in cervical, breast, lung, colon, ovarian and rectal cancers. Many RBM4 targets we identified have been implicated in these cancers. In conclusion, our findings reveal transcriptome-wide targets of RBM4 and point to potential cancer-related targets and mechanisms that may involve RBM4.

Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway

Publisher: Oxford University Press (OUP)

Date: 24-10-2014

DOI: 10.1093/BIOINFORMATICS/BTT616

Abstract: Motivation: With the advancement of high-throughput techniques, large-scale profiling of biological systems with multiple experimental perturbations is becoming more prevalent. Pathway analysis incorporates prior biological knowledge to analyze genes roteins in groups in a biological context. However, the hypotheses under investigation are often confined to a 1D space (i.e. up, down, either or mixed regulation). Here, we develop direction pathway analysis (DPA), which can be applied to test hypothesis in a high-dimensional space for identifying pathways that display distinct responses across multiple perturbations. Results: Our DPA approach allows for the identification of pathways that display distinct responses across multiple perturbations. To demonstrate the utility and effectiveness, we evaluated DPA under various simulated scenarios and applied it to study insulin action in adipocytes. A major action of insulin in adipocytes is to regulate the movement of proteins from the interior to the cell surface membrane. Quantitative mass spectrometry-based proteomics was used to study this process on a large-scale. The combined dataset comprises four separate treatments. By applying DPA, we identified that several insulin responsive pathways in the plasma membrane trafficking are only partially dependent on the insulin-regulated kinase Akt. We subsequently validated our findings through targeted analysis of key proteins from these pathways using immunoblotting and live cell microscopy. Our results demonstrate that DPA can be applied to dissect pathway networks testing erse hypotheses and integrating multiple experimental perturbations. Availability and implementation: The R package ‘directPA’ is distributed from CRAN under GNU General Public License (GPL)-3 and can be downloaded from: eb ackages/directPA/index.html Contact: jean.yang@sydney.edu.au Supplementary Information: Supplementary data are available at Bioinformatics online.

The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field

Publisher: Springer Science and Business Media LLC

Date: 26-08-2022

DOI: 10.1038/S41746-022-00640-7

Abstract: Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of in idual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.

Genetic variation of macronutrient tolerance in Drosophila melanogaster

Publisher: Springer Science and Business Media LLC

Date: 28-03-2022

DOI: 10.1038/S41467-022-29183-X

Abstract: Carbohydrates, proteins and lipids are essential nutrients to all animals however, closely related species, populations, and in iduals can display dramatic variation in diet. Here we explore the variation in macronutrient tolerance in Drosophila melanogaster using the Drosophila genetic reference panel, a collection of ~200 strains derived from a single natural population. Our study demonstrates that D. melanogaster , often considered a “dietary generalist”, displays marked genetic variation in survival on different diets, notably on high-sugar diet. Our genetic analysis and functional validation identify several regulators of macronutrient tolerance, including CG10960/GLUT8 , Pkn and Eip75B . We also demonstrate a role for the JNK pathway in sugar tolerance and de novo lipogenesis. Finally, we report a role for tailles s, a conserved orphan nuclear hormone receptor, in regulating sugar metabolism via insulin-like peptide secretion and sugar-responsive CCHamide-2 expression. Our study provides support for the use of nutrigenomics in the development of personalized nutrition.

BcGST-an interactive bias-correction method to identify over-represented gene-sets in boutique arrays

Publisher: Oxford University Press (OUP)

Date: 12-09-2019

DOI: 10.1093/BIOINFORMATICS/BTY783

Abstract: Gene annotation and pathway databases such as Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes are important tools in Gene-Set Test (GST) that describe gene biological functions and associated pathways. GST aims to establish an association relationship between a gene-set of interest and an annotation. Importantly, GST tests for over-representation of genes in an annotation term. One implicit assumption of GST is that the gene expression platform captures the complete or a very large proportion of the genome. However, this assumption is neither satisfied for the increasingly popular boutique array nor the custom designed gene expression profiling platform. Specifically, conventional GST is no longer appropriate due to the gene-set selection bias induced during the construction of these platforms. We propose bcGST, a bias-corrected GST by introducing bias-correction terms in the contingency table needed for calculating the Fisher’s Exact Test. The adjustment method works by estimating the proportion of genes captured on the array with respect to the genome in order to assist filtration of annotation terms that would otherwise be falsely included or excluded. We illustrate the practicality of bcGST and its stability through multiple differential gene expression analyses in melanoma and the Cancer Genome Atlas cancer studies. The bcGST method is made available as a Shiny web application at shiny.maths.usyd.edu.au/bcGST/. Supplementary data are available at Bioinformatics online.

Clinical and pathological associations of the activating RAC1 P29S mutation in primary cutaneous melanoma.

Publisher: Wiley

Date: 04-08-2014

DOI: 10.1111/PCMR.12295

Abstract: Activating mutations in the GTPase RAC1 are a recurrent event in cutaneous melanoma. We investigated the clinical and pathological associations of RAC1(P29S) in a cohort of 814 primary cutaneous melanomas with known BRAF and NRAS mutation status. The RAC1(P29S) mutation had a prevalence of 3.3% and was associated with increased thickness (OR=1.6 P = 0.001), increased mitotic rate (OR=1.3 P = 0.03), ulceration (OR=2.4 P = 0.04), nodular subtype (OR=3.4 P = 0.004), and nodal disease at diagnosis (OR=3.3 P = 0.006). BRAF mutant tumors were also associated with nodal metastases (OR=1.9 P = 0.004), despite being thinner at diagnosis than BRAF WT (median 1.2 mm versus 1.6 mm, P < 0.001). Immunohistochemical analysis of 51 melanomas revealed that 47% were immunoreactive for RAC1. Melanomas were more likely to show RAC1 immunoreactivity if they were BRAF mutant (OR=5.2 P = 0.01). RAC1 may therefore be important in regulating the early migration of BRAF mutant tumors. RAC1 mutations are infrequent in primary melanomas but may accelerate disease progression.

PhosphOrtholog: a web-based tool for cross-species mapping of orthologous protein post-translational modifications

Publisher: Springer Science and Business Media LLC

Date: 19-08-2015

DOI: 10.1186/S12864-015-1820-X

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data

Publisher: Cold Spring Harbor Laboratory

Date: 23-02-2023

DOI: 10.1101/2023.02.22.529627

Abstract: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in in idual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterisation of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy. We propose SnapCCESS for clustering cells by integrating data modalities in multimodal singlecell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterisation of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data. SnapCCESS is implemented as a Python package and is freely available from ulijia/SnapCCESS .

Gene-gene interaction filtering with ensemble of filters

Publisher: Springer Science and Business Media LLC

Date: 15-02-2011

DOI: 10.1186/1471-2105-12-S1-S10

Protein signatures correspond to survival outcomes of AJCC stage III melanoma patients

Publisher: Wiley

Date: 14-08-2014

DOI: 10.1111/PCMR.12290

An easily fabricated three-dimensional threaded lemniscate-shaped micromixer for a wide range of flow rates

Publisher: AIP Publishing

Date: 2017

DOI: 10.1063/1.4974904

Abstract: Mixing fluid s les or reactants is a paramount function in the fields of micro total analysis system (μTAS) and microchemical processing. However, rapid and efficient fluid mixing is difficult to achieve inside microchannels because of the difficulty of diffusive mass transfer in the laminar regime of the typical microfluidic flows. It has been well recorded that the mixing efficiency can be boosted by migrating from two-dimensional (2D) to three-dimensional (3D) geometries. Although several 3D chaotic mixers have been designed, most of them offer a high mixing efficiency only in a very limited range of Reynolds numbers (Re). In this work, we developed a 3D fine-threaded lemniscate-shaped micromixer whose maximum numerical and empirical efficiency is around 97% and 93%, respectively, and maintains its high performance (i.e., & %) over a wide range of 1 & Re & 1000 which meets the requirements of both the μTAS and microchemical process applications. The 3D micromixer was designed based on two distinct mixing strategies, namely, the inducing of chaotic advection by the presence of Dean flow and diffusive mixing through thread-like grooves around the curved body of the mixers. First, a set of numerical simulations was performed to study the physics of the flow and to determine the essential geometrical parameters of the mixers. Second, a simple and cost-effective method was exploited to fabricate the convoluted structure of the micromixers through the removal of a 3D-printed wax structure from a block of cured polydimethylsiloxane. Finally, the fabricated mixers with different threads were tested using a fluorescent microscope demonstrating a good agreement with the results of the numerical simulation. We envisage that the strategy used in this work would expand the scope of the micromixer technology by broadening the range of efficient working flow rate and providing an easy way to the fabrication of 3D convoluted microstructures.

78Gy with Fiducial Marker Image-Guided Radiotherapy in Prostate Cancer: Single Center Analysis of 301 Patients

Publisher: Wiley

Date: 10-11-2017

DOI: 10.1111/AJCO.12637

Abstract: In prostate cancer, fiducial marker image-guided radiotherapy (FMIGRT) allows correction of setup errors and interfraction physiological motion resulting in improved accuracy of target and sparing of at risk organs. We aim to report outcomes and toxicities observed in patients treated with dose escalation to 78Gy with FMIGRT in our center. Retrospective review of consecutive patients with histologically confirmed T1-4N0M0 localized prostate cancer treated with dose escalation to 78Gy with FMIGRT in our center. All patients had 3-D conformal radiotherapy. Duration of androgen deprivation therapy use was tailored to risk group. Toxicity was scored according to CTCAE.v04. Kaplan-Meier analysis was performed for freedom from biochemical failure (FFBF), prostate cancer-specific survival and overall survival. Median follow-up was 48.6 months. Median duration of androgen deprivation therapy was 6 and 23 months in the intermediate- and high-risk group, respectively. FFBF at 5 years was 88.8%. FFBFs when stratified to risk groups were 100% for low risk, 88.9% for low-intermediate risk, 89.9% for high-intermediate risk and 85.4% for high risk, respectively. Acute severe toxicity (grade≥3) rate for both genitourinary (GU) and gastrointestinal (GI) was 1%. Late moderate-to-severe toxicity (grade≥2) rates for GU and GI were 15% and 17%, respectively, with severe (grade≥3) toxicity rate for GU and GI at 2% and 3%, respectively. Dose escalation to 78Gy with FMIGRT in our series achieved good FFBF at 5 years with low acute and late toxicity rates. These results provide a good comparator cohort to our current use of image-guided intensity modulated radiotherapy.

Construction and optimization of multi-platform precision pathways for precision medicine

Publisher: Cold Spring Harbor Laboratory

Date: 24-05-2023

DOI: 10.1101/2023.05.23.541873

Abstract: In the enduring challenge against disease, advancements in medical technology have empowered clinicians with novel diagnostic platforms. Whilst in some cases, a single test may provide a confident diagnosis, often additional tests are required. However, to strike a balance between diagnostic accuracy and cost-effectiveness, one must rigorously construct the clinical pathways. Here, we developed a framework to build multi-platform precision pathways in an automated, unbiased way, recommending the key steps a clinician would take to reach a diagnosis. We achieve this by developing a confidence score, used to simulate a clinical scenario, where at each stage, either a confident diagnosis is made, or another test is performed. Our framework provides a range of tools to interpret, visualize and compare the pathways, improving communication and enabling their evaluation on accuracy and cost, specific to different contexts. This framework will guide the development of novel diagnostic pathways for different diseases, accelerating the implementation of precision medicine into clinical practice.

hRUV: Hierarchical approach to removal of unwanted variation for large-scale metabolomics data

Publisher: Research Square Platform LLC

Date: 04-02-2021

DOI: 10.21203/RS.3.RS-156243/V1

Abstract: Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological s le replicates to quantify variance within and between batches and a novel workflow that uses these replicates to remove unwanted variation in a hierarchical (hRUV) manner. We use this design to produce a dataset of more than 1,000 human plasma s les run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our novel tools not only provide a strategy for large scale data normalization, but also provides guidance on the design strategy for large omics studies.

treekoR: identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data

Publisher: Springer Science and Business Media LLC

Date: 29-11-2021

DOI: 10.1186/S13059-021-02526-5

Abstract: High-throughput single-cell technologies hold the promise of discovering novel cellular relationships with disease. However, analytical workflows constructed for these technologies to associate cell proportions with disease often employ unsupervised clustering techniques that overlook the valuable hierarchical structures that have been used to define cell types. We present treekoR, a framework that empirically recapitulates these structures, facilitating multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data — as failing to do so can lead to missing important biological insights.

Freshly isolated rat alveolar type I cells, type II cells, and cultured type II cells have distinct molecular phenotypes

Publisher: American Physiological Society

Date: 2005

DOI: 10.1152/AJPLUNG.00272.2004

Abstract: We used microarray analysis with Affymetrix rat chips to determine gene expression profiles of freshly isolated rat type I (TI) and TII cells and cultured TII cells. Our goals were 1) to describe molecular phenotypic “fingerprints” of TI and TII cells, 2) to gain insight into possible functional differences between the two cell types through differentially expressed genes, 3) to identify genes that might indicate potential functions of TI cells, since so little is known about this cell type, and 4) to ascertain the similarities and differences in gene expression between cultured TII cells and freshly isolated TI cells. For these experiments, we used preparations of isolated TI and TII cells that contained % cross-contamination. With a false discovery rate of 1%, 601 genes demonstrated over twofold different expression between TI and TII cells. Those genes with very high levels of differential expression may be useful as markers of cell phenotype and in generating novel hypotheses about functions of TI and TII cells. We found similar numbers of differentially expressed genes between freshly isolated TI or TII cells and cultured TII cells (698, 637 genes) and freshly isolated TI and TII cells (601 genes). Tests of sameness/difference including cluster dendrograms and log/log identity plots indicated major differences between the phenotypes of freshly isolated TI cell and cultured type II cell populations. The latter results suggest that experiments with TII cells cultured under these conditions should be interpreted with caution with respect to biological relevance to TI or TII cells.

Impaired Akt phosphorylation in insulin-resistant human muscle is accompanied by selective and heterogeneous downstream defects

Publisher: Springer Science and Business Media LLC

Date: 24-01-2013

DOI: 10.1007/S00125-012-2811-Y

Abstract: Muscle insulin resistance, one of the earliest defects associated with type 2 diabetes, involves changes in the phosphoinositide 3-kinase/Akt network. The relative contribution of obesity vs insulin resistance to perturbations in this pathway is poorly understood. We used phosphospecific antibodies against targets in the Akt signalling network to study insulin action in muscle from lean, overweight/obese and type 2 diabetic in iduals before and during a hyperinsulinaemic-euglycaemic cl . Insulin-stimulated Akt phosphorylation at Thr309 and Ser474 was highly correlated with whole-body insulin sensitivity. In contrast, impaired phosphorylation of Akt substrate of 160 kDa (AS160 also known as TBC1D4) was associated with adiposity, but not insulin sensitivity. Neither insulin sensitivity nor obesity was associated with defective insulin-dependent phosphorylation of forkhead box O (FOXO) transcription factor. In view of the resultant basal hyperinsulinaemia, we predicted that this selective response within the Akt pathway might lead to hyperactivation of those processes that were spared. Indeed, the expression of genes targeted by FOXO was downregulated in insulin-resistant in iduals. These results highlight non-linearity in Akt signalling and suggest that: (1) the pathway from Akt to glucose transport is complex and (2) pathways, particularly FOXO, that are not insulin-resistant, are likely to be hyperactivated in response to hyperinsulinaemia. This facet of Akt signalling may contribute to multiple features of the metabolic syndrome.

Jurisdictional inequalities in deceased donor kidney allocation in Australia

Publisher: Elsevier BV

Date: 07-2021

DOI: 10.1016/J.KINT.2021.04.028

Transcriptional profiles in CD8+ T cells from HIV+ progressors on HAART are characterized by coordinated up-regulation of oxidative phosphorylation enzymes and interferon responses

Publisher: Elsevier BV

Date: 10-2008

DOI: 10.1016/J.VIROL.2008.06.039

Abstract: The functional impairment and numerical decline of CD8+ T cells during HIV infection has a profound effect on disease progression, but only limited microarray studies have used CD8+ T cells. To understand the interactions of HIV and host CD8+ T cells at different disease status, we used the Illumina Human-6 BeadChips to evaluate the transcriptional profile (>48,000 transcripts) in primary CD8+ T cells from HIV+ therapy-naive non-progressors and therapy-experienced progressors. 68 differentially expressed genes were identified, of which 6 have been reported in HIV context, while others are associated with biological functions relevant to HIV pathogenesis. By GSEA, the coordinated up-regulation of oxidative phosphorylation enzymes and interferon responses were detected as fingerprints in HIV progressors on HAART, whereas LTNP displayed a transcriptional signature of coordinated up-regulation of components of MAPK and cytotoxicty pathways. These results will provide biological insights into natural control of HIV versus HIV control under HAART.

PD-L1 expression and immune escape in melanoma resistance to MAPK inhibitors

Publisher: American Association for Cancer Research (AACR)

Date: 12-10-2017

DOI: 10.1158/1078-0432.CCR-16-1688

Abstract: Purpose: To examine the relationship between immune activity, PD-L1 expression, and tumor cell signaling, in metastatic melanomas prior to and during treatment with targeted MAPK inhibitors. Experimental Design: Thirty-eight tumors from 17 patients treated with BRAF inhibitor (n = 12) or combination BRAF/MEK inhibitors (n = 5) with known PD-L1 expression were analyzed. RNA expression arrays were performed on all pretreatment (PRE, n = 17), early during treatment (EDT, n = 8), and progression (PROG, n = 13) biopsies. HLA-A/HLA-DPB1 expression was assessed by IHC. Results: Gene set enrichment analysis (GSEA) of PRE, EDT, and PROG melanomas revealed that transcriptome signatures indicative of immune cell activation were strongly positively correlated with PD-L1 staining. In contrast, MAPK signaling and canonical Wnt/-β-catenin activity was negatively associated with PD-L1 melanoma expression. The expression of PD-L1 and immune activation signatures did not simply reflect the degree or type of immune cell infiltration, and was not sufficient for tumor response to MAPK inhibition. Conclusions: PD-L1 expression correlates with immune cells and immune activity signatures in melanoma, but is not sufficient for tumor response to MAPK inhibition, as many PRE and PROG melanomas displayed both PD-L1 positivity and immune activation signatures. This confirms that immune escape is common in MAPK inhibitor–treated tumors. This has important implications for the selection of second-line immunotherapy because analysis of mechanisms of immune escape will likely be required to identify patients likely to respond to such therapies. Clin Cancer Res 23(20) 6054–61. ©2017 AACR.

Network-based biomarkers enhance classical approaches to prognostic gene expression signatures

Publisher: Springer Science and Business Media LLC

Date: 12-2014

DOI: 10.1186/1752-0509-8-S4-S5

Transcriptomic analysis of Nodal- and BMP-associated genes during juvenile development of the sea urchin Heliocidaris erythrogramma

Publisher: Elsevier BV

Date: 12-2015

DOI: 10.1016/J.MARGEN.2015.05.019

Abstract: Understanding the unusual radial body plan of echinoderms and its relationship to the bilateral plan of other deuterostomes remains a challenge. The molecular processes of embryonic and early larval development in sea urchins are well characterised, but those giving rise to the adult and its radial body remain poorly studied. We used the developmental transcriptome generated for Heliocidaris erythrogramma, a species that forms the juvenile soon after gastrulation, to investigate changes in gene expression underlying radial body development. As coelomogenesis is key to the development of pentamery and juvenile formation on the left side of the larva, we focussed on genes associated with the nodal and BMP2/4 network that pattern this asymmetry. We identified 46 genes associated with this Nodal and BMP2/4 signalling network, and determined their expression profiles from the gastrula, through to rudiment development, metamorphosis and the fully formed juvenile. Genes associated with Nodal signalling shared similar expression profiles, indicating that they may have a regulatory relationship in patterning morphogenesis of the juvenile sea urchin. Similarly, many genes associated with BMP2/4 signalling had similar expression profiles through juvenile development. Further examination of the roles of Nodal- and BMP2/4-associated genes is required to determine function and whether the gene expression profiles seen in H. erythrogramma are due to ongoing activity of gene networks established during early development, or to redeployment of regulatory cassettes to pattern the adult radial body plan.

Investigating higher order interactions in single cell data with scHOT

Publisher: Cold Spring Harbor Laboratory

Date: 14-11-2019

DOI: 10.1101/841593

Abstract: Single-cell RNA-sequencing has transformed our ability to examine cell fate choice. For ex le, in the context of development and differentiation, computational ordering of cells along ‘pseudotime’ enables the expression profiles of in idual genes, including key transcription factors, to be examined at fine scale temporal resolution. However, while cell fate decisions are typically marked by profound changes in expression, many such changes are observed in genes downstream of the initial cell fate decision. By contrast, the genes directly involved in the cell fate decision process are likely to interact in subtle ways, potentially resulting in observed changes in patterns of correlation and variation rather than mean expression prior to cell fate commitment. Herein, we describe a novel approach, scHOT – single cell Higher Order Testing - which provides a flexible and statistically robust framework for identifying changes in higher order interactions among genes. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations as well as accommodate any higher order measurement such as variability or correlation. We demonstrate the utility of scHOT by studying embryonic development of the liver, where we find coordinated changes in higher order interactions of programs related to differentiation and liver function. We also demonstrate its ability to find subtle changes in gene-gene correlation patterns across space using spatially-resolved expression data from the mouse olfactory bulb. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data.

Association of Global Coagulation Profiles With Cardiovascular Risk Factors and Atherosclerosis: A Sex Disaggregated Analysis From the BioHEART‐CT Study

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 19-10-2021

DOI: 10.1161/JAHA.120.020604

Abstract: Although the association between dysregulated coagulation and atherosclerosis is well recognized, in idual assays have been of minimal value in understanding disease susceptibility. Here we investigated the association of global coagulation profiles with coronary artery disease with consideration of sex differences. The study included patients from the BioHEART‐CT (The BioHEART Study: Assessing Patients With Suspected Cardiovascular Disease for New Disease Markers and Risk Factors) biobank who had computed tomography coronary angiograms scored for coronary artery calcium score (CACS) and Gensini score. The cohort included 206 adult patients who were referred for clinically indicated computed tomography coronary angiography and had a median of 2 major cardiac risk factors 50% were women and the average age was 62.6 years (±9.9 years). The overall hemostatic potential (OHP) and calibrated automated thrombography generation assays were performed on platelet‐poor plasma. CACS and Gensini score in men were significantly correlated in bivariate analysis with measures from the OHP assay, and regression models predicting disease severity by CACS or Gensini score were improved by adding the OHP assay variables in men but not in women. The calibrated automated thrombography generation assay demonstrated a more hypercoagulable profile in women than in men. The OHP assay showed hypercoagulable profiles in women with hyperlipidemia and men with obesity. The OHP assay identified hypercoagulable profiles associated with different risk factors for each sex and was associated with CACS and Gensini score severity in men, emphasizing the associations between increased fibrin generation and reduced fibrinolysis with cardiac risk factors and early atherosclerosis. www.anzctr.org.au . Identifier: ACTRN12618001322224.

Targeting activating mutations of EZH2 leads to potent cell growth inhibition in human melanoma by derepression of tumor suppressor genes

Publisher: Impact Journals, LLC

Date: 12-08-2015

DOI: 10.18632/ONCOTARGET.4809

Cross-species gene expression analysis identifies a novel set of genes implicated in human insulin sensitivity

Publisher: Springer Science and Business Media LLC

Date: 12-11-2015

DOI: 10.1038/NPJSBA.2015.10

Proteins from the Wnt pathway are involved in the pathogenesis and progression of mammary phyllodes tumours

Publisher: BMJ

Date: 27-10-2009

DOI: 10.1136/JCP.2009.066977

Abstract: The Wnt pathway is important in cell signalling transduction and is involved in the pathogenesis of multiple tumour types. A comprehensive analysis of the expression of Wnt signalling pathway proteins in mammary phyllodes tumours (PTs) has not been previously performed. To evaluate the immunohistochemical expression of Wnt pathway proteins in a cohort of PTs, to determine their role in tumour pathogenesis and to identify any associations with patient outcome. 65 PTs (34 benign, 23 borderline and 8 malignant) diagnosed at a single institution between 1990 and 2006 were analysed. Immunohistochemical stains were performed on tissue microarrays for beta-catenin, Wnt1, Wnt5a, SFRP4 and E-cadherin. Stroma and epithelium were scored separately. Stromal cytoplasmic Wnt5a and SFRP4 expression showed significant progressive increases in expression with increasing grade (p = 0.002 and p = 0.02 respectively). Epithelial membranous and stromal nuclear beta-catenin, epithelial cytoplasmic Wnt1 and epithelial E-cadherin all also showed increasing expression with increasing tumour grade, however, the differences were not significant. Disease-free survival was significantly decreased (p = 0.0017) with positive epithelial E-cadherin staining. Results suggest that alterations in the Wnt pathway are important in the progression and in the epithelial and stromal interactions in PTs. They have important implications for understanding the pathogenesis of these uncommon but clinically important tumours.

Combining serum microRNA and CA-125 as prognostic indicators of preoperative surgical outcome in women with high-grade serous ovarian cancer

Publisher: Elsevier BV

Date: 2018

DOI: 10.1016/J.YGYNO.2017.11.005

Abstract: The most widely used approach for the clinical management of women with high-grade serous ovarian cancer (HGSOC) is surgery, followed by platinum and taxane based chemotherapy. The degree of macroscopic disease remaining at the conclusion of surgery is a key prognostic factor determining progression free and overall survival. We sought to develop a non-invasive test to assist surgeons to determine the likelihood of achieving complete surgical resection. This knowledge could be used to plan surgical approaches for optimal clinical management. We profiled 170 serum microRNAs (miRNAs) using the Serum/Plasma Focus miRNA PCR panel containing locked nucleic acid (LNA) primers (Exiqon) in women with HGSOC (N=56) and age-matched healthy volunteers (N=30). Additionally, we measured serum CA-125 levels in the same s les. The HGSOC cohort was further classified based on the degree of macroscopic disease at the conclusion of surgery. Stepwise logistic regression was used to identify predictive markers. We identified a combination of miR-375 and CA-125 as the strongest discriminator of healthy versus HGSOC serum, with an area under the curve (AUC) of 0.956. The inclusion of miR-210 increased the AUC to 0.984 however, miR-210 was affected by hemolysis. The combination of miR-34a-5p and CA-125 was the strongest predictor of completeness of surgical resection with an AUC of 0.818. A molecular test incorporating circulating miRNA to predict completeness of surgical resection for women with HGSOC has the potential to contribute to planning for optimal patient management, ultimately improving patient outcome.

Integrative Analysis of Prognostic Biomarkers for Acute Rejection in Kidney Transplant Recipients

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 02-11-2021

DOI: 10.1097/TP.0000000000003516

LC-N2G: a local consistency approach for nutrigenomics data analysis

Publisher: Springer Science and Business Media LLC

Date: 17-11-2020

DOI: 10.1186/S12859-020-03861-3

Abstract: Nutrigenomics aims at understanding the interaction between nutrition and gene information. Due to the complex interactions of nutrients and genes, their relationship exhibits non-linearity. One of the most effective and efficient methods to explore their relationship is the nutritional geometry framework which fits a response surface for the gene expression over two prespecified nutrition variables. However, when the number of nutrients involved is large, it is challenging to find combinations of informative nutrients with respect to a certain gene and to test whether the relationship is stronger than chance. Methods for identifying informative combinations are essential to understanding the relationship between nutrients and genes. We introduce Local Consistency Nutrition to Graphics (LC-N2G), a novel approach for ranking and identifying combinations of nutrients with gene expression. In LC-N2G, we first propose a model-free quantity called Local Consistency statistic to measure whether there is non-random relationship between combinations of nutrients and gene expression measurements based on (1) the similarity between s les in the nutrient space and (2) their difference in gene expression. Then combinations with small LC are selected and a permutation test is performed to evaluate their significance. Finally, the response surfaces are generated for the subset of significant relationships. Evaluation on simulated data and real data shows the LC-N2G can accurately find combinations that are correlated with gene expression. The LC-N2G is practically powerful for identifying the informative nutrition variables correlated with gene expression. Therefore, LC-N2G is important in the area of nutrigenomics for understanding the relationship between nutrition and gene expression information.

scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq

Publisher: Cold Spring Harbor Laboratory

Date: 03-01-2021

DOI: 10.1101/2020.12.31.424916

Abstract: Single-cell multi-omics data continues to grow at an unprecedented pace, and effectively integrating different modalities holds the promise for better characterization of cell identities. Although a number of methods have demonstrated promising results in integrating multiple modalities from the same tissue, the complexity and scale of data compositions typically present in cell atlases still pose a significant challenge for existing methods. Here we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semi-supervised framework and uses a neural network to simultaneously train labeled and unlabeled data, enabling label transfer and joint visualization in an integrative framework. Using multiple atlas data and a biologically varying multi-modal data, we demonstrate scJoint is computationally efficient and consistently achieves significantly higher cell type label accuracy than existing methods while providing meaningful joint visualizations. This suggests scJoint is effective in overcoming the heterogeneity in different modalities towards a more comprehensive understanding of cellular phenotypes.

Endoplasmic reticulum protein 5 attenuates platelet endoplasmic reticulum stress and secretion in a mouse model

Publisher: American Society of Hematology

Date: 26-04-2023

DOI: 10.1182/BLOODADVANCES.2022008457

Abstract: Extracellular protein disulfide isomerases (PDIs), including PDI, endoplasmic reticulum protein 57 (ERp57), ERp72, ERp46, and ERp5, are required for in vivo thrombus formation in mice. Platelets secrete PDIs upon activation, which regulate platelet aggregation. However, platelets secrete only ∼10% of their PDI content extracellularly. The intracellular role of PDIs in platelet function is unknown. Here, we aim to characterize the role of ERp5 (gene Pdia6) using platelet conditional knockout mice, platelet factor 4 (Pf4) Cre+/ERp5floxed (fl)/fl. Pf4Cre+/ERp5fl/fl mice developed mild macrothrombocytopenia. Platelets deficient in ERp5 showed marked dysregulation of their ER, indicated by a twofold upregulation of ER proteins, including PDI, ERp57, ERp72, ERp46, 78 kilodalton glucose-regulated protein (GRP78), and calreticulin. ERp5-deficient platelets showed an enhanced ER stress response to ex vivo and in vivo ER stress inducers, with enhanced phosphorylation of eukaryotic translation initiation factor 2A and inositol-requiring enzyme 1 (IRE1). ERp5 deficiency was associated with increased secretion of PDIs, an enhanced response to thromboxane A2 receptor activation, and increased thrombus formation in vivo. Our results support that ERp5 acts as a negative regulator of ER stress responses in platelets and highlight the importance of a disulfide isomerase in platelet ER homeostasis. The results also indicate a previously unanticipated role of platelet ER stress in platelet secretion and thrombosis. This may have important implications for the therapeutic applications of ER stress inhibitors in thrombosis.

scDC: single cell differential composition analysis

Publisher: Springer Science and Business Media LLC

Date: 12-2019

DOI: 10.1186/S12859-019-3211-9

Abstract: Differences in cell-type composition across subjects and conditions often carry biological significance. Recent advancements in single cell sequencing technologies enable cell-types to be identified at the single cell level, and as a result, cell-type composition of tissues can now be studied in exquisite detail. However, a number of challenges remain with cell-type composition analysis – none of the existing methods can identify cell-type perfectly and variability related to cell s ling exists in any single cell experiment. This necessitates the development of method for estimating uncertainty in cell-type composition. We developed a novel single cell differential composition (scDC) analysis method that performs differential cell-type composition analysis via bootstrap res ling. scDC captures the uncertainty associated with cell-type proportions of each subject via bias-corrected and accelerated bootstrap confidence intervals. We assessed the performance of our method using a number of simulated datasets and synthetic datasets curated from publicly available single cell datasets. In simulated datasets, scDC correctly recovered the true cell-type proportions. In synthetic datasets, the cell-type compositions returned by scDC were highly concordant with reference cell-type compositions from the original data. Since the majority of datasets tested in this study have only 2 to 5 subjects per condition, the addition of confidence intervals enabled better comparisons of compositional differences between subjects and across conditions. scDC is a novel statistical method for performing differential cell-type composition analysis for scRNA-seq data. It uses bootstrap res ling to estimate the standard errors associated with cell-type proportion estimates and performs significance testing through GLM and GLMM models. We have made this method available to the scientific community as part of the scdney package ( S ingle C ell D ata I n t e grative Anal y sis) R package, available from github.com/SydneyBioX/scdney .

Design issues for cDNA microarray experiments

Publisher: Springer Science and Business Media LLC

Date: 08-2002

DOI: 10.1038/NRG863

Normalization for cDNA microarray data

Publisher: SPIE

Date: 04-06-2001

DOI: 10.1117/12.427982

A benchmark study of simulation methods for single-cell RNA sequencing data

Publisher: Springer Science and Business Media LLC

Date: 25-11-2021

DOI: 10.1038/S41467-021-27130-W

Abstract: Single-cell RNA-seq (scRNA-seq) data simulation is critical for evaluating computational methods for analysing scRNA-seq data especially when ground truth is experimentally unattainable. The reliability of evaluation depends on the ability of simulation methods to capture properties of experimental data. However, while many scRNA-seq data simulation methods have been proposed, a systematic evaluation of these methods is lacking. We develop a comprehensive evaluation framework, SimBench, including a kernel density estimation measure to benchmark 12 simulation methods through 35 scRNA-seq experimental datasets. We evaluate the simulation methods on a panel of data properties, ability to maintain biological signals, scalability and applicability. Our benchmark uncovers performance differences among the methods and highlights the varying difficulties in simulating data characteristics. Furthermore, we identify several limitations including maintaining heterogeneity of distribution. These results, together with the framework and datasets made publicly available as R packages, will guide simulation methods selection and their future development.

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

Publisher: Cold Spring Harbor Laboratory

Date: 18-01-2023

DOI: 10.1101/2023.01.18.524481

Abstract: The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarised and incorporated into patient outcome prediction models in several ways, however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integration approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using each single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalisation when using multiple datasets as the model input. This work assesses and compares the performance of three categories of workflow consisting of 350 analytical combinations for outcome prediction using multi-s le, multi-conditions single-cell studies. We observed that using ensemble of feature types performs better than using in idual feature type We found that in the current data, all learning approaches including deep learning exhibit similar predictive performance. When combining multiple datasets as the input, our study found that integrating multiple datasets at the cell level performs similarly to simply concatenating the patient representation without modification.

Benchmarking clustering algorithms on estimating the number of cell types from single-cell RNA-sequencing data

Publisher: Springer Science and Business Media LLC

Date: 08-02-2022

DOI: 10.1186/S13059-022-02622-0

Abstract: A key task in single-cell RNA-seq (scRNA-seq) data analysis is to accurately detect the number of cell types in the s le, which can be critical for downstream analyses such as cell type identification. Various scRNA-seq data clustering algorithms have been specifically designed to automatically estimate the number of cell types through optimising the number of clusters in a dataset. The lack of benchmark studies, however, complicates the choice of the methods. We systematically benchmark a range of popular clustering algorithms on estimating the number of cell types in a variety of settings by s ling from the Tabula Muris data to create scRNA-seq datasets with a varying number of cell types, varying number of cells in each cell type, and different cell type proportions. The large number of datasets enables us to assess the performance of the algorithms, covering four broad categories of approaches, from various aspects using a panel of criteria. We further cross-compared the performance on datasets with high cell numbers using Tabula Muris and Tabula Sapiens data. We identify the strengths and weaknesses of each method on multiple criteria including the deviation of estimation from the true number of cell types, variability of estimation, clustering concordance of cells to their predefined cell types, and running time and peak memory usage. We then summarise these results into a multi-aspect recommendation to the users. The proposed stability-based approach for estimating the number of cell types is implemented in an R package and is freely available from ( github.com/PYangLab/scCCESS ).

Viral integration plays a minor role in the development and prognostication of oral squamous cell carcinoma

Publisher: MDPI AG

Date: 24-10-2022

DOI: 10.3390/CANCERS14215213

Abstract: Viruses are well known drivers of several human malignancies. A causative factor for oral cavity squamous cell carcinoma (OSCC) in patients with limited exposure to traditional risk factors, including tobacco use, is yet to be identified. Our study aimed to comprehensively evaluate the role of viral drivers in OSCC patients with low cumulative exposure to traditional risk factors. Patients under 50 years of age with OSCC, defined using strict anatomic criteria were selected for WGS. The WGS data was interrogated using viral detection tools (Kraken 2 and BLASTN), together examining ,000 viruses. The findings were further verified using tissue microarrays of OSCC s les using both immunohistochemistry and RNA in situ hybridisation (ISH). 28 patients underwent WGS and comprehensive viral profiling. One 49-year-old male patient with OSCC of the hard palate demonstrated HPV35 integration. 657 cases of OSCC were then evaluated for the presence of HPV integration through immunohistochemistry for p16 and HPV RNA ISH. HPV integration was seen in 8 (1.2%) patients, all middle-aged men with predominant floor of mouth involvement. In summary, a wide-ranging interrogation of ,000 viruses using OSCC WGS data showed HPV integration in a minority of male OSCC patients and did not carry any prognostic significance.

Diagonal Discriminant Analysis With Feature Selection for High-Dimensional Data

Publisher: Informa UK Limited

Date: 16-08-2020

DOI: 10.1080/10618600.2019.1637748

The yeast homolog of heme oxygenase-1 affords cellular antioxidant protection via the transcriptional regulation of known antioxidant genes

Publisher: Elsevier BV

Date: 2011

DOI: 10.1074/JBC.M110.187062

Factors Associated With Advanced Colorectal Neoplasia in Patients With CKD

Publisher: Elsevier BV

Date: 04-2022

DOI: 10.1053/J.AJKD.2021.07.011

Abstract: The risk of developing colorectal cancer in patients with chronic kidney disease (CKD) is twice that of the general population, but the factors associated with colorectal cancer are poorly understood. The aim of this study was to identify factors associated with advanced colorectal neoplasia in patients with CKD. Prospective cohort study. Patients with CKD stages 3-5, including those treated with maintenance dialysis or transplantation across 11 sites in Australia, New Zealand, Canada, and Spain, were screened for colorectal neoplasia using a fecal immunochemical test (FIT) as part of the Detecting Bowel Cancer in CKD (DETECT) Study. Baseline characteristics for patients at the time of study enrollment were ascertained, including duration of CKD, comorbidities, and medications. Advanced colorectal neoplasia was identified through a 2-step verification process with colonoscopy following positive FIT and 2-year clinical follow-up for all patients. Potential factors associated with advanced colorectal neoplasia were explored using multivariable logistic regression. Sensitivity analyses were performed using grouped LASSO (least absolute shrinkage and selection operator) logistic regression. Among 1,706 patients who received FIT-based screening-791 with CKD stages 3-5 not receiving kidney replacement therapy (KRT), 418 receiving dialysis, and 497 patients with a functioning kidney transplant-117 patients (6.9%) were detected to have advanced colorectal neoplasia (54 with CKD stages 3-5 without KRT, 34 receiving dialysis, and 29 transplant recipients), including 9 colorectal cancers. The factors found to be associated with advanced colorectal neoplasia included older age (OR per year older, 1.05 [95% CI, 1.03-1.07], P<0.001), male sex (OR, 2.27 [95% CI, 1.45-3.54], P<0.001), azathioprine use (OR, 2.99 [95% CI, 1.40-6.37], P=0.005), and erythropoiesis-stimulating agent use (OR, 1.92 [95% CI, 1.22-3.03], P=0.005). Grouped LASSO logistic regression revealed similar associations between these factors and advanced colorectal neoplasia. Unmeasured confounding factors. Older age, male sex, erythropoiesis-stimulating agents, and azathioprine were found to be significantly associated with advanced colorectal neoplasia in patients with CKD.

Significant association of PD-L1 expression with human papillomavirus positivity and its prognostic impact in oropharyngeal cancer

Publisher: Elsevier BV

Date: 05-2019

DOI: 10.1016/J.ORALONCOLOGY.2019.03.012

Abstract: The programmed death pathway plays a role in persistent human papillomavirus (HPV) infection as well as in resistance to immune elimination during malignant progression. In this study, we examined PD-L1 expression by immunohistochemistry and tumour infiltrating lymphocytes (TIL) in 214 patients with oropharyngeal squamous cell cancer (OPSCC) to assess its clinical significance. HPV-positive OPSCC were significantly more likely to express PD-L1 than HPV-negative OPSCC (85.2% vs 57.1%, p < 0.05). PD-L1 staining was more likely to be associated with TILs in HPV-positive OPSCC (67.9% vs 49.6%, p = 0.01). Relative to those patients with HPV-positive/PD-L1-positive OPSCC, patients with HPV negative/PD-L1 negative OPSCC were 6.4 times more likely to develop a local recurrence, 5.8 times more likely to develop an event and 6.5 times more likely to die. Within the HPV positive cases, PD-L1 expression also significantly impacted on the outcomes with PD-L1 negative cases more likely to develop a locoregional recurrence (HR 4.16), to have an event (HR 2.5) and to die (HR 3.16). Evidence of an interaction between HPV status and PD-L1 expression was found for overall survival (p < 0.005). Our findings suggested that different immune profiles in oropharyngeal cancer by HPV status and the effect of HPV on the outcomes is modified by PD-L1 expression.

A field guide to cultivating computational biology

Publisher: Public Library of Science (PLoS)

Date: 07-10-2021

DOI: 10.1371/JOURNAL.PBIO.3001419

Abstract: Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, in idual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant in idual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional in idual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

A Review of Ensemble Methods in Bioinformatics

Publisher: Bentham Science Publishers Ltd.

Date: 12-2010

DOI: 10.2174/157489310794072508

Characterization of cell-cell communication in COVID-19 patients

Publisher: Cold Spring Harbor Laboratory

Date: 30-12-2020

DOI: 10.1101/2020.12.30.424641

Abstract: COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use it to examine five public single-cell RNA-seq datasets with a total of 85 in idual s les. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate erse communication patterns across in iduals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients.

Supplementary Data from Distinct Molecular Profiles and Immunotherapy Treatment Outcomes of V600E and V600K <i>BRAF</i>-Mutant Melanoma

Publisher: American Association for Cancer Research (AACR)

Date: 31-03-2023

DOI: 10.1158/1078-0432.22467960

Abstract: Supplementary Tables 1-2, and Figures 1-5.

A multi-step classifier addressing cohort heterogeneity improves performance of prognostic biomarkers in three cancer types

Publisher: Impact Journals, LLC

Date: 11-08-2016

DOI: 10.18632/ONCOTARGET.13203

Data from Circulating Cytokines Predict Immune-Related Toxicity in Melanoma Patients Receiving Anti-PD-1–Based Immunotherapy

Publisher: American Association for Cancer Research (AACR)

Date: 31-03-2023

DOI: 10.1158/1078-0432.C.6527907.V1

Abstract: AbstractPurpose: Combination PD-1 and CTLA-4 inhibitor therapy has dramatically improved the survival of patients with advanced melanoma but is also associated with significant immune-related toxicities. This study sought to identify circulating cytokine biomarkers of treatment response and immune-related toxicity. Experimental Design: The expression of 65 cytokines was profiled longitudinally in 98 patients with melanoma treated with PD-1 inhibitors, alone or in combination with anti-CTLA-4, and in an independent validation cohort of 49 patients treated with combination anti-PD-1 and anti-CTLA-4. Cytokine expression was correlated with RECIST response and immune-related toxicity, defined as toxicity that warranted permanent discontinuation of treatment and administration of high-dose steroids. Results: Eleven cytokines were significantly upregulated in patients with severe immune-related toxicities at baseline (PRE) and early during treatment (EDT). The expression of these 11 cytokines was integrated into a single toxicity score, the CYTOX (cytokine toxicity) score, and the predictive utility of this score was confirmed in the discovery and validation cohorts. The AUC for the CYTOX score in the validation cohort was 0.68 at PRE [95% confidence interval (CI), 0.51–0.84 i P /i = 0.037] and 0.70 at EDT (95% CI, 0.55–0.85 i P /i = 0.017) using ROC analysis. Conclusions: The CYTOX score is predictive of severe immune-related toxicity in patients with melanoma treated with combination anti-CTLA-4 and anti-PD-1 immunotherapy. This score, which includes proinflammatory cytokines such as IL1a, IL2, and IFNα2, may help in the early management of severe, potentially life-threatening immune-related toxicity. i See related commentary by Johnson and Balko, p. 1452 /i /

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

Publisher: Springer Science and Business Media LLC

Date: 17-07-2023

DOI: 10.1038/S41467-023-39923-2

Abstract: The recent emergence of multi-s le multi-condition single-cell multi-cohort studies allows researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that in idual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-s le multi-condition single-cell studies. We have generalized scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ in iduals, we demonstrate that scMerge2 enables multi-s le multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

Sex differences in oncogenic mutational processes

Publisher: Springer Science and Business Media LLC

Date: 28-08-2020

DOI: 10.1038/S41467-020-17359-2

Abstract: Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research.

Deep multimodal graph-based network for survival prediction from highly multiplexed images and patient variables

Publisher: Elsevier BV

Date: 03-2023

DOI: 10.1016/J.COMPBIOMED.2023.106576

Benchmarking of analytical combinations for COVID-19 outcome prediction using single-cell RNA sequencing data

Publisher: Oxford University Press (OUP)

Date: 24-04-2023

DOI: 10.1093/BIB/BBAD159

Abstract: The advances of single-cell transcriptomic technologies have led to increasing use of single-cell RNA sequencing (scRNA-seq) data in large-scale patient cohort studies. The resulting high-dimensional data can be summarized and incorporated into patient outcome prediction models in several ways however, there is a pressing need to understand the impact of analytical decisions on such model quality. In this study, we evaluate the impact of analytical choices on model choices, ensemble learning strategies and integrate approaches on patient outcome prediction using five scRNA-seq COVID-19 datasets. First, we examine the difference in performance between using single-view feature space versus multi-view feature space. Next, we survey multiple learning platforms from classical machine learning to modern deep learning methods. Lastly, we compare different integration approaches when combining datasets is necessary. Through benchmarking such analytical combinations, our study highlights the power of ensemble learning, consistency among different learning methods and robustness to dataset normalization when using multiple datasets as the model input.

Characterizing mutation–expression network relationships in multiple cancers

Publisher: Elsevier BV

Date: 08-2016

DOI: 10.1016/J.COMPBIOLCHEM.2016.02.009

Abstract: Data made available through large cancer consortia like The Cancer Genome Atlas make for a rich source of information to be studied across and between cancers. In recent years, network approaches have been applied to such data in uncovering the complex interrelationships between mutational and expression profiles, but lack direct testing for expression changes via mutation. In this pan-cancer study we analyze mutation and gene expression information in an integrative manner by considering the networks generated by testing for differences in expression in direct association with specific mutations. We relate our findings among the 19 cancers examined to identify commonalities and differences as well as their characteristics. Using somatic mutation and gene expression information across 19 cancers, we generated mutation-expression networks per cancer. On evaluation we found that our generated networks were significantly enriched for known cancer-related genes, such as skin cutaneous melanoma (p<0.01 using Network of Cancer Genes 4.0). Our framework identified that while different cancers contained commonly mutated genes, there was little concordance between associated gene expression changes among cancers. Comparison between cancers showed a greater overlap of network nodes for cancers with higher overall non-silent mutation load, compared to those with a lower overall non-silent mutation load. This study offers a framework that explores network information through co-analysis of somatic mutations and gene expression profiles. Our pan-cancer application of this approach suggests that while mutations are frequently common among cancer types, the impact they have on the surrounding networks via gene expression changes varies. Despite this finding, there are some cancers for which mutation-associated network behaviour appears to be similar: suggesting a potential framework for uncovering related cancers for which similar therapeutic strategies may be applicable. Our framework for understanding relationships among cancers has been integrated into an interactive R Shiny application, PAn Cancer Mutation Expression Networks (PACMEN), containing dynamic and static network visualization of the mutation-expression networks. PACMEN also features tools for further examination of network topology characteristics among cancers.

Global identification of the genes and pathways differentially expressed in hypothalamus in early and established neurogenic hypertension

Publisher: American Physiological Society

Date: 06-2011

DOI: 10.1152/PHYSIOLGENOMICS.00009.2011

Abstract: The hypothalamus has an important etiological role in the onset and maintenance of hypertension and stress responses in the Schlager high blood pressure (BP) (BPH/2J) mouse, a genetic model of neurogenic hypertension. Using Affymetrix GeneChip Mouse Gene 1.0 ST Arrays we identified 1,019 hypothalamic genes whose expression differed between 6 wk old BPH/2J and normal BP (BPN/3J) strains, and 466 for 26 wk old mice. Of these, 459 were in 21 mouse BP quantitative trait loci. We validated 46 genes by qPCR. Gene changes that would increase sympathetic outflow at both ages were: Dynll1 encoding dynein light chain LC8-type 1, which physically destabilizes neuronal nitric oxide synthase, decreasing neuronal nitric oxide, and Hcrt encoding hypocretin and Npsr1 encoding neuropeptide S receptor 1, each involved in sympathetic response to stress. At both ages we identified genes for inflammation, such as CC-chemokine ligand 19 ( Ccl19), and oxidative stress. Via reactive oxygen species generation, these could contribute to oxidative damage. Other genes identified could be responding to such perturbations. Atp2b1, the major gene from genome-wide association studies of BP variation, was underexpressed in the early phase. Comparison of profiles of young and adult BPH/2J mice, after adjusting for maturation genes, pointed to the proopiomelanocortin-α gene ( Pomc) and neuropeptide Y gene ( Npy), among others, as potentially causative. The present study has identified a ersity of genes and possible mechanisms involved in hypertension etiology and maintenance in the hypothalamus of BPH/2J mice, highlighting both common and ergent processes in each phase of the condition.

Data from Distinct Molecular Profiles and Immunotherapy Treatment Outcomes of V600E and V600K <i>BRAF</i>-Mutant Melanoma

Publisher: American Association for Cancer Research (AACR)

Date: 31-03-2023

DOI: 10.1158/1078-0432.C.6526800.V1

Abstract: AbstractPurpose: i BRAF /i V600E and V600K melanomas have distinct clinicopathologic features, and V600K appear to be less responsive to i BRAFi /i ± i MEKi /i . We investigated mechanisms for this and explored whether genotype affects response to immunotherapy. Experimental Design: Pretreatment formalin-fixed paraffin-embedded tumors from patients treated with i BRAFi /i ± i MEKi /i underwent gene expression profiling and DNA sequencing. Molecular results were validated using The Cancer Genome Atlas (TCGA) data. An independent cohort of V600E/K patients treated with anti–PD-1 immunotherapy was examined. Results: Baseline tissue and clinical outcome with i BRAFi /i ± i MEKi /i were studied in 93 patients (78 V600E, 15 V600K). V600K patients had numerically less tumor regression (median, −31% vs. −52%, i P /i = 0.154) and shorter progression-free survival (PFS median, 5.7 vs. 7.1 months, i P /i = 0.15) compared with V600E. V600K melanomas had lower expression of the ERK pathway feedback regulator dual-specificity phosphatase 6, confirmed with TCGA data (116 V600E, 17 V600K). Pathway analysis showed V600K had lower expression of ERK and higher expression of PI3K-AKT genes than V600E. Higher mutational load was observed in V600K, with a higher proportion of mutations in i PIK3R1 /i and tumor-suppressor genes. In patients treated with anti–PD-1, V600K ( i n /i = 19) had superior outcomes than V600E ( i n /i = 84), including response rate (53% vs. 29%, i P /i = 0.059), PFS (median, 19 vs. 2.7 months, i P /i = 0.049), and overall survival (20.4 vs. 11.7 months, i P /i = 0.081). Conclusions: i BRAF /i V600K melanomas appear to benefit less from i BRAFi /i ± i MEKi /i than V600E, potentially due to less reliance on ERK pathway activation and greater use of alternative pathways. In contrast, these melanomas have higher mutational load and respond better to immunotherapy. /

Identifying cellular-to-phenotype associations by elucidating hierarchical relationships in high-dimensional cytometry data

Publisher: Cold Spring Harbor Laboratory

Date: 09-07-2021

DOI: 10.1101/2021.07.08.451609

Abstract: High-throughput single cell technologies hold the promise of discovering novel cellular relationships with disease. However, analytical workflows constructed for these technologies to associate cell proportions with disease often employ unsupervised clustering techniques that overlook the valuable hierarchical structures that have been used to define cell types. We present treekoR, a framework that empirically recapitulates these structures, facilitating multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data — as failing to do so can lead to missing important biological insights.

Transcriptional downregulation of MHC class I and melanoma de- differentiation in resistance to PD-1 inhibition

Publisher: Springer Science and Business Media LLC

Date: 20-04-2020

DOI: 10.1038/S41467-020-15726-7

Abstract: Transcriptomic signatures designed to predict melanoma patient responses to PD-1 blockade have been reported but rarely validated. We now show that intra-patient heterogeneity of tumor responses to PD-1 inhibition limit the predictive performance of these signatures. We reasoned that resistance mechanisms will reflect the tumor microenvironment, and thus we examined PD-1 inhibitor resistance relative to T-cell activity in 94 melanoma tumors collected at baseline and at time of PD-1 inhibitor progression. Tumors were analyzed using RNA sequencing and flow cytometry, and validated functionally. These analyses confirm that major histocompatibility complex (MHC) class I downregulation is a hallmark of resistance to PD-1 inhibitors and is associated with the MITF low /AXL high de-differentiated phenotype and cancer-associated fibroblast signatures. We demonstrate that TGFß drives the treatment resistant phenotype (MITF low /AXL high ) and contributes to MHC class I downregulation in melanoma. Combinations of anti-PD-1 with drugs that target the TGFß signaling pathway and/or which reverse melanoma de-differentiation may be effective future therapeutic strategies.

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Publisher: Springer Science and Business Media LLC

Date: 21-09-2020

DOI: 10.1038/S41467-020-18151-Y

Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA s les, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological ergences between two reproducible somatic variant detection efforts.

Integrative analysis identifies co-dependent gene expression regulation of BRG1 and CHD7 at distal regulatory sites in embryonic stem cells

Publisher: Oxford University Press (OUP)

Date: 14-02-2017

DOI: 10.1093/BIOINFORMATICS/BTX092

Abstract: DNA binding proteins such as chromatin remodellers, transcription factors (TFs), histone modifiers and co-factors often bind cooperatively to activate or repress their target genes in a cell type-specific manner. Nonetheless, the precise role of cooperative binding in defining cell-type identity is still largely uncharacterized. Here, we collected and analyzed 214 public datasets representing chromatin immunoprecipitation followed by sequencing (ChIP-Seq) of 104 DNA binding proteins in embryonic stem cell (ESC) lines. We classified their binding sites into those proximal to gene promoters and those in distal regions, and developed a web resource called Proximal And Distal (PAD) clustering to identify their co-localization at these respective regions. Using this extensive dataset, we discovered an extensive co-localization of BRG1 and CHD7 at distal but not proximal regions. The comparison of co-localization sites to those bound by either BRG1 or CHD7 alone showed an enrichment of ESC master TFs binding and active chromatin architecture at co-localization sites. Most notably, our analysis reveals the co-dependency of BRG1 and CHD7 at distal regions on regulating expression of their common target genes in ESC. This work sheds light on cooperative binding of TF binding proteins in regulating gene expression in ESC, and demonstrates the utility of integrative analysis of a manually curated compendium of genome-wide protein binding profiles in our online resource PAD. PAD is freely available at pad.victorchang.edu.au/ and its source code is available via an open source GPL 3.0 license at github.com/VCCRI/PAD/ Supplementary data are available at Bioinformatics online.

A 14-Protein Signature for Rapid Identification of Poor Prognosis Stage III Metastatic Melanoma

Publisher: Wiley

Date: 19-01-2018

DOI: 10.1002/PRCA.201700094

Abstract: To validate differences in protein levels between good and poor prognosis American Joint Committee on Cancer (AJCC) stage III melanoma patients and compile a protein panel to stratify patient risk. Protein extracts from melanoma metastases within lymph nodes in patients with stage III disease with good (n = 16, >4 years survival) and poor survival (n = 14, <2 years survival) were analyzed by selected reaction monitoring (SRM). Diagonal Linear Discriminant Analysis (DLDA) was performed to generate a protein biomarker panel. SRM analysis identified ten proteins that were differentially abundant between good and poor prognosis stage III melanoma patients. The ten differential proteins were combined with 22 proteins identified in our previous work. A panel of 14 proteins was selected by DLDA that was able to accurately classify patients into prognostic groups based on levels of these proteins. The ten differential proteins identified by SRM have biological significance in cancer progression. The final signature of 14 proteins identified by SRM could be used to identify AJCC stage III melanoma patients likely to have poor outcomes who may benefit from adjuvant systemic therapy.

Psychosocial stratification of antenatal indicators to guide population-based programs in perinatal depression.

Publisher: Springer Science and Business Media LLC

Date: 06-04-2021

DOI: 10.1186/S12884-021-03722-8

Abstract: There is increasing awareness that perinatal psychosocial adversity experienced by mothers, children, and their families, may influence health and well-being across the life course. To maximise the impact of population-based interventions for optimising perinatal wellbeing, health services can utilise empirical methods to identify subgroups at highest risk of poor outcomes relative to the overall population. This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. Subgroup differences in antenatal and postnatal depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale. Latent class analysis identified four distinct subgroups within the cohort, who were distinguished empirically on the basis of their native language, current smoking status, previous involvement with Family-and-Community Services (FaCS), history of child abuse, presence of a supportive partner, and a history of intimate partner psychological violence. One group consisted of socially supported ‘local’ women who speak English as their primary language (Group L), another of socially supported ‘migrant’ women who speak a language other than English as their primary language (Group M), another of socially stressed ‘local’ women who speak English as their primary language (Group Ls), and socially stressed ‘migrant’ women who speak a language other than English as their primary language (Group Ms.). Compared to local and not socially stressed residents (L group), the odds of antenatal depression were nearly three times higher for the socially stressed groups (Ls OR: 2.87 95%CI 2.10–3.94) and nearly nine times more in the Ms. group (Ms OR: 8.78, 95%CI 5.13–15.03). Antenatal symptoms of depression were also higher in the not socially stressed migrant group (M OR: 1.70 95%CI 1.47–1.97) compared to non-migrants. In the postnatal period, Group M was 1.5 times more likely, while the Ms. group was over five times more likely to experience suboptimal mental health compared to Group L (OR 1.50, 95%CI 1.22–1.84 and OR 5.28, 95%CI 2.63–10.63, for M and Ms. respectively). The application of empirical subgrouping analysis permits an informed approach to targeted interventions and resource allocation for optimising perinatal maternal wellbeing.

Systematic functional identification of cancer multi-drug resistance genes

Publisher: Springer Science and Business Media LLC

Date: 07-02-2020

DOI: 10.1186/S13059-020-1940-8

Abstract: Drug resistance is a major obstacle in cancer therapy. To elucidate the genetic factors that regulate sensitivity to anti-cancer drugs, we performed CRISPR-Cas9 knockout screens for resistance to a spectrum of drugs. In addition to known drug targets and resistance mechanisms, this study revealed novel insights into drug mechanisms of action, including cellular transporters, drug target effectors, and genes involved in target-relevant pathways. Importantly, we identified ten multi-drug resistance genes, including an uncharacterized gene C1orf115 , which we named Required for Drug-induced Death 1 ( RDD1 ). Loss of RDD1 resulted in resistance to five anti-cancer drugs. Finally, targeting RDD1 leads to chemotherapy resistance in mice and low RDD1 expression is associated with poor prognosis in multiple cancers. Together, we provide a functional landscape of resistance mechanisms to a broad range of chemotherapeutic drugs and highlight RDD1 as a new factor controlling multi-drug resistance. This information can guide personalized therapies or instruct rational drug combinations to minimize acquisition of resistance.

Quantitative Performance Evaluator for Proteomics (QPEP): Web-based Application for Reproducible Evaluation of Proteomics Preprocessing Methods.

Publisher: American Chemical Society (ACS)

Date: 05-06-2017

DOI: 10.1021/ACS.JPROTEOME.6B00882

Abstract: Tandem mass spectrometry is one of the most popular techniques for quantitation of proteomes. There exists a large variety of options in each stage of data preprocessing that impact the bias and variance of the summarized protein-level values. Using a newly released data set satisfying a replicated Latin squares design, a erse set of performance metrics has been developed and implemented in a web-based application, Quantitative Performance Evaluator for Proteomics (QPEP). QPEP has the flexibility to allow users to apply their own method to preprocess this data set and share the results, allowing direct and straightforward comparison of new methodologies. Application of these new metrics to three case studies highlights that (i) the summarization of peptides to proteins is robust to the choice of peptide summary used, (ii) the differences between iTRAQ labels are stronger than the differences between experimental runs, and (iii) the commercial software ProteinPilot performs equivalently well at between-s le normalization to more complicated methods developed by academics. Importantly, finding (ii) underscores the benefits of using the principles of randomization and blocking to avoid the experimental measurements being confounded by technical factors. Data are available via ProteomeXchange with identifier PXD003608.

An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity

Publisher: Springer Science and Business Media LLC

Date: 11-08-2019

DOI: 10.1007/S12021-018-9395-8

Abstract: Analysis and interpretation of functional magnetic resonance imaging (fMRI) has been used to characterise many neuronal diseases, such as schizophrenia, bipolar disorder and Alzheimer's disease. Functional connectivity networks (FCNs) are widely used because they greatly reduce the amount of data that needs to be interpreted and they provide a common network structure that can be directly compared. However, FCNs contain a range of data uncertainties stemming from inherent limitations, e.g. during acquisition, as well as the loss of voxel-level data, and the use of thresholding in data abstraction. Additionally, human uncertainties arise during interpretation due to the complexity in understanding the data. While existing FCN visual analytics tools have begun to mitigate the human ambiguities, reducing the impact of data limitations is an open problem. In this paper, we propose a novel visual analytics framework with three linked, purpose-designed components to evoke deeper interpretation of the fMRI data: (i) an enhanced FCN abstraction (ii) a temporal signal viewer and (iii) the anatomical context. Each component has been specifically designed with novel visual cues and interaction to expose the impact of uncertainties on the data. We augment this with two methods designed for comparing subjects, by using a small multiples and a marker approach. We demonstrate the enhancements enabled by our framework on three case studies of common research scenarios, using clinical schizophrenia data, which highlight the value in interpreting fMRI FCN data with an awareness of the uncertainties. Finally, we discuss our framework in the context of fMRI visual analytics and the extensibility of our approach.

Data from Distinct Molecular Profiles and Immunotherapy Treatment Outcomes of V600E and V600K <i>BRAF</i>-Mutant Melanoma

Publisher: American Association for Cancer Research (AACR)

Date: 31-03-2023

DOI: 10.1158/1078-0432.C.6526800

Abstract: AbstractPurpose: i BRAF /i V600E and V600K melanomas have distinct clinicopathologic features, and V600K appear to be less responsive to i BRAFi /i ± i MEKi /i . We investigated mechanisms for this and explored whether genotype affects response to immunotherapy. Experimental Design: Pretreatment formalin-fixed paraffin-embedded tumors from patients treated with i BRAFi /i ± i MEKi /i underwent gene expression profiling and DNA sequencing. Molecular results were validated using The Cancer Genome Atlas (TCGA) data. An independent cohort of V600E/K patients treated with anti–PD-1 immunotherapy was examined. Results: Baseline tissue and clinical outcome with i BRAFi /i ± i MEKi /i were studied in 93 patients (78 V600E, 15 V600K). V600K patients had numerically less tumor regression (median, −31% vs. −52%, i P /i = 0.154) and shorter progression-free survival (PFS median, 5.7 vs. 7.1 months, i P /i = 0.15) compared with V600E. V600K melanomas had lower expression of the ERK pathway feedback regulator dual-specificity phosphatase 6, confirmed with TCGA data (116 V600E, 17 V600K). Pathway analysis showed V600K had lower expression of ERK and higher expression of PI3K-AKT genes than V600E. Higher mutational load was observed in V600K, with a higher proportion of mutations in i PIK3R1 /i and tumor-suppressor genes. In patients treated with anti–PD-1, V600K ( i n /i = 19) had superior outcomes than V600E ( i n /i = 84), including response rate (53% vs. 29%, i P /i = 0.059), PFS (median, 19 vs. 2.7 months, i P /i = 0.049), and overall survival (20.4 vs. 11.7 months, i P /i = 0.081). Conclusions: i BRAF /i V600K melanomas appear to benefit less from i BRAFi /i ± i MEKi /i than V600E, potentially due to less reliance on ERK pathway activation and greater use of alternative pathways. In contrast, these melanomas have higher mutational load and respond better to immunotherapy. /

Analysis of the prototypical Staphylococcus aureus multiresistance plasmid pSK1

Publisher: Elsevier BV

Date: 11-2010

DOI: 10.1016/J.PLASMID.2010.06.001

Abstract: The Staphylococcus aureus multiresistance plasmid pSK1 is the prototype of a family of structurally related plasmids that were first identified in epidemic S. aureus strains isolated in Australia during the 1980s and subsequently in Europe. Here we present the complete 28.15kb nucleotide sequence of pSK1 and discuss the genetic content and evolution of the 14kb region that is conserved throughout the pSK1 plasmid family. In addition to the previously characterized plasmid maintenance functions, this backbone region encodes 12 putative gene products, including a lipoprotein, teichoic acid translocation permease, cell wall anchored surface protein and an Fst-like toxin as part of a Type I toxin-antitoxin system. Furthermore, transcriptional profiling has revealed that plasmid carriage most likely has a minimal impact on the host, a factor that may contribute to the ability of pSK1 family plasmids to carry multiple resistance determinants.

Biobanking for discovery of novel cardiovascular biomarkers using imaging-quantified disease burden: protocol for the longitudinal, prospective, BioHEART-CT cohort study

Publisher: BMJ

Date: 09-2019

DOI: 10.1136/BMJOPEN-2018-028649

Abstract: Coronary artery disease (CAD) persists as a major cause of morbidity and mortality worldwide despite intensive identification and treatment of traditional risk factors. Data emerging over the past decade show a quarter of patients have disease in the absence of any known risk factor, and half have only one risk factor. Improvements in quantification and characterisation of coronary atherosclerosis by CT coronary angiography (CTCA) can provide quantitative measures of subclinical atherosclerosis—enhancing the power of unbiased ‘omics’ studies to unravel the missing biology of personal susceptibility, identify new biomarkers for early diagnosis and to suggest new targeted therapeutics. BioHEART-CT is a longitudinal, prospective cohort study, aiming to recruit 5000 adult patients undergoing clinically indicated CTCA. After informed consent, patient data, blood s les and CTCA imaging data are recorded. Follow-up for all patients is conducted 1 month after recruitment, and then annually for the life of the study. CTCA data provide volumetric quantification of total calcified and non-calcified plaque, which will be assessed using established and novel scoring systems. Comprehensive molecular phenotyping will be performed using state-of-the-art genomics, metabolomics, proteomics and immunophenotyping. Complex network and machine learning approaches will be applied to biological and clinical datasets to identify novel pathophysiological pathways and to prioritise new biomarkers. Discovery analysis will be performed in the first 1000 patients of BioHEART-CT, with validation analysis in the following 4000 patients. Outcome data will be used to build improved risk models for CAD. The study protocol has been approved by the human research ethics committee of North Shore Local Health District in Sydney, Australia. All findings will be published in peer-reviewed journals or at scientific conferences. ACTRN12618001322224.

Uncovering cell identity through differential stability with Cepo

Publisher: Springer Science and Business Media LLC

Date: 20-12-2021

DOI: 10.1038/S43588-021-00172-2

Analysis of Post-Liver Transplant Hepatitis C Virus Recurrence Using Serial Cluster of Differentiation Antibody Microarrays

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 09-2015

DOI: 10.1097/TP.0000000000000617

Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data

Publisher: Oxford University Press (OUP)

Date: 24-04-2015

DOI: 10.1093/BIOINFORMATICS/BTV220

Abstract: Motivation: In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult. Results: We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small s le sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature. Availability and implementation: This framework is implemented as an R function, pMim, in the package sydSeq available from -packages. Contact: jean.yang@sydney.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

SurvBenchmark: comprehensive benchmarking study of survival analysis methods using both omics data and clinical data

Publisher: Oxford University Press (OUP)

Date: 2022

DOI: 10.1093/GIGASCIENCE/GIAC071

Abstract: Survival analysis is a branch of statistics that deals with both the tracking of time and the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic statistical survival models, with prediction accuracy often serving as the sole metric of model performance. Moreover, survival analysis approaches for censored omics data have not been thoroughly investigated. The common approach is to binarize the survival time and perform a classification analysis. Here, we develop a benchmarking design, SurvBenchmark, that evaluates a erse collection of survival models for both clinical and omics data sets. SurvBenchmark not only focuses on classical approaches such as the Cox model but also evaluates state-of-the-art machine learning survival models. All approaches were assessed using multiple performance metrics these include model predictability, stability, flexibility, and computational issues. Our systematic comparison design with 320 comparisons (20 methods over 16 data sets) shows that the performances of survival models vary in practice over real-world data sets and over the choice of the evaluation metric. In particular, we highlight that using multiple performance metrics is critical in providing a balanced assessment of various models. The results in our study will provide practical guidelines for translational scientists and clinicians, as well as define possible areas of investigation in both survival technique and benchmarking strategies.

Differential gene expression profiling after conditional müller-cell ablation in a novel transgenic model

Publisher: Association for Research in Vision and Ophthalmology (ARVO)

Date: 27-03-2013

DOI: 10.1167/IOVS.12-11559

Abstract: Müller cells, the principal glial cells in the mammalian retina, play an important role in the maintenance of retinal homeostasis. Recent reports suggest that Müller-cell dysfunction may contribute to the pathogenesis of retinal diseases such as idiopathic macular telangiectasia type 2. In the present study, we used microarray to compare retinae isolated from transgenic mice in which the Müller cells of adult mice retinae can be selectively ablated with control mice. Retinae were isolated 1 week, 1 month, and 3 months after tamoxifen-induced selective Müller-cell ablation and microarray were performed with Affymatrix microarrays. Differentially expressed (DE) genes, temporal trends of DE genes, and pathway analysis were conducted. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the results. Strong upregulation of mRNA of proteins involved in gliosis, apoptosis, and neurotrophism was found 1 week after ablation and their related pathways such as the apoptotic and Jak/Stat pathways were identified. Three months after induced Müller-cell ablation, Müller-cell metabolic pathways and vasculopathy-related pathways such as genes involved in glycolysis and tight junctions were downregulated. qRT-PCR analysis showed consistent expression trends of selected genes. The results were generally consistent with the previous morphologic findings in this model, in which photoreceptor degeneration soon after Müller-cell ablation, accompanied by blood-retinal barrier breakdown and subsequent retinal neovascularization were reported. These results are consistent with a significant contribution of Müller-cell dysfunction on retinal neuronal injury and vascular pathology at the mRNA level.

Genetic variation of macronutrient tolerance in Drosophila melanogaster.

Publisher: Apollo - University of Cambridge Repository

Date: 2022

DOI: 10.17863/CAM.84061

Strategies to enable large-scale proteomics for reproducible research

Publisher: Springer Science and Business Media LLC

Date: 30-07-2020

DOI: 10.1038/S41467-020-17641-3

Abstract: Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight s les containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with ~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.

Molecular Analysis of Gene Expression in the Developing Pontocerebellar Projection System

Publisher: Elsevier BV

Date: 10-2002

DOI: 10.1016/S0896-6273(02)01016-4

Abstract: As an approach toward understanding the molecular mechanisms of neuronal differentiation, we utilized DNA microarrays to elucidate global patterns of gene expression during pontocerebellar development. Through this analysis, we identified groups of genes specific to neuronal precursor cells, associated with axon outgrowth, and regulated in response to contact with synaptic target cells. In the cerebellum, we identified a phase of granule cell differentiation that is independent of interactions with other cerebellar cell types. Analysis of pontine gene expression revealed that distinct programs of gene expression, correlated with axon outgrowth and synapse formation, can be decoupled and are likely influenced by different cells in the cerebellar target environment. Our approach provides insight into the genetic programs underlying the differentiation of specific cell types in the pontocerebellar projection system.

Improving X!Tandem on peptide identification from mass spectrometry by self-boosted percolator

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 09-2012

DOI: 10.1109/TCBB.2012.86

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

Publisher: Oxford University Press (OUP)

Date: 13-01-2022

DOI: 10.1093/NARGAB/LQAC023

Abstract: Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data

Publisher: Oxford University Press (OUP)

Date: 06-2023

DOI: 10.1093/BIOINFORMATICS/BTAD382

Abstract: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in in idual cells. While the increasing availability of multiple data modalities is expected to provide a more accurate clustering and characterization of cells, the development of computational methods that are capable of extracting information embedded across data modalities is still in its infancy. We propose SnapCCESS for clustering cells by integrating data modalities in multimodal single-cell omics data using an unsupervised ensemble deep learning framework. By creating snapshots of embeddings of multimodality using variational autoencoders, SnapCCESS can be coupled with various clustering algorithms for generating consensus clustering of cells. We applied SnapCCESS with several clustering algorithms to various datasets generated from popular multimodal single-cell omics technologies. Our results demonstrate that SnapCCESS is effective and more efficient than conventional ensemble deep learning-based clustering methods and outperforms other state-of-the-art multimodal embedding generation methods in integrating data modalities for clustering cells. The improved clustering of cells from SnapCCESS will pave the way for more accurate characterization of cell identity and types, an essential step for various downstream analyses of multimodal single-cell omics data. SnapCCESS is implemented as a Python package and is freely available from github.com/PYangLab/SnapCCESS under the open-source license of GPL-3. The data used in this study are publicly available (see section ‘Data availability’).

Fast and approximate exhaustive variable selection for generalised linear models with APES

Publisher: Wiley

Date: 12-2019

DOI: 10.1111/ANZS.12276

Computational approaches for direct cell reprogramming: from the bulk omics era to the single cell era

Publisher: Oxford University Press (OUP)

Date: 11-04-2022

DOI: 10.1093/BFGP/ELAC008

Abstract: Recent advances in direct cell reprogramming have made possible the conversion of one cell type to another cell type, offering a potential cell-based treatment to many major diseases. Despite much attention, substantial roadblocks remain including the inefficiency in the proportion of reprogrammed cells of current experiments, and the requirement of a significant amount of time and resources. To this end, several computational algorithms have been developed with the goal of guiding the hypotheses to be experimentally validated. These approaches can be broadly categorized into two main types: transcription factor identification methods which aim to identify candidate transcription factors for a desired cell conversion, and transcription factor perturbation methods which aim to simulate the effect of a transcription factor perturbation on a cell state. The transcription factor perturbation methods can be broken down into Boolean networks, dynamical systems and regression models. We summarize the contributions and limitations of each method and discuss the innovation that single cell technologies are bringing to these approaches and we provide a perspective on the future direction of this field.

Effect of chronic exercise in healthy young male adults: a metabolomic analysis

Publisher: Oxford University Press (OUP)

Date: 04-2020

DOI: 10.1093/CVR/CVAA051

Abstract: To examine the metabolic adaptation to an 80-day exercise intervention in healthy young male adults where lifestyle factors such as diet, sleep, and physical activities are controlled. This study involved cross-sectional analysis before and after an 80-day aerobic and strength exercise intervention in 52 young, adult, male, newly enlisted soldiers in 2015. Plasma metabolomic analyses were performed using liquid chromatography, tandem mass spectrometry. Data analyses were performed between March and August 2019. We analysed changes in metabolomic profiles at the end of an 80-day exercise intervention compared to baseline, and the association of metabolite changes with changes in clinical parameters. Global metabolism was dramatically shifted after the exercise training programme. Fatty acids and ketone body substrates, key fuels used by exercising muscle, were dramatically decreased in plasma in response to increased aerobic fitness. There were highly significant changes across many classes of metabolic substrates including lipids, ketone bodies, arginine metabolites, endocannabinoids, nucleotides, markers of proteolysis, products of fatty acid oxidation, microbiome-derived metabolites, markers of redox stress, and substrates of coagulation. For statistical analyses, a paired t-test was used and Bonferroni-adjusted P-value of & .0004 was considered to be statistically significant. The metabolite dimethylguanidino valeric acid (DMGV) (recently shown to predict lack of metabolic response to exercise) tracked maladaptive metabolic changes to exercise those with increases in DMGV levels had increases in several cardiovascular risk factors changes in DMGV levels were significantly positively correlated with increases in body fat (P = 0.049), total and LDL cholesterol (P = 0.003 and P = 0.007), and systolic blood pressure (P = 0.006). This study was approved by the Departments of Defence and Veterans’ Affairs Human Research Ethics Committee and written informed consent was obtained from each subject. For the first time, the true magnitude and extent of metabolic adaptation to chronic exercise training are revealed in this carefully designed study, which can be leveraged for novel therapeutic strategies in cardiometabolic disease. Extending the recent report of DMGV’s predictive utility in sedentary, overweight in iduals, we found that it is also a useful marker of poor metabolic response to exercise in young, healthy, fit males.

Latin square dataset for evaluating the accuracy of mass spectrometry-based protein identification and quantification

Publisher: IEEE

Date: 12-2013

DOI: 10.1109/BIBM.2013.6732765

scReClassify: post hoc cell type classification of single-cell rNA-seq data

Publisher: Springer Science and Business Media LLC

Date: 12-2019

DOI: 10.1186/S12864-019-6305-X

Abstract: Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. Here, we propose a semi-supervised learning framework, named scReClassify, for ‘post hoc’ cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types. scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from github.com/SydneyBioX/scReClassify

Clonal evolution in liver cancer at single-cell and single-variant resolution

Publisher: Springer Science and Business Media LLC

Date: 02-02-2021

DOI: 10.1186/S13045-021-01036-Y

Abstract: Genetic heterogeneity of tumor is closely related to its clonal evolution, phenotypic ersity and treatment resistance, and such heterogeneity has only been characterized at single-cell sub-chromosomal scale in liver cancer. Here we reconstructed the single-variant resolution clonal evolution in human liver cancer based on single-cell mutational profiles. The results indicated that key genetic events occurred early during tumorigenesis, and an early metastasis followed by independent evolution was observed in primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. By parallel single-cell RNA-Seq, the transcriptomic phenotype of HCC was found to be related with genetic heterogeneity. For the first time we reconstructed the single-cell and single-variant clonal evolution in human liver cancer, and dissection of both genetic and phenotypic heterogeneity will facilitate better understanding of their relationship.

Oral Squamous Cell Carcinoma in Young Patients Show Higher Rates of EGFR Amplification: Implications for Novel Personalized Therapy

Publisher: Frontiers Media SA

Date: 29-11-2021

DOI: 10.3389/FONC.2021.750852

Abstract: There is an increasing worldwide incidence of patients under 50 years of age presenting with oral squamous cell carcinoma (OSCC). The molecular mechanisms driving disease in this emerging cohort remain unclear, limiting impactful treatment options for these patients. To identify common clinically actionable targets in this cohort, we used whole genome and transcriptomic sequencing of OSCC patient s les from 26 in iduals under 50 years of age. These molecular profiles were compared with those of OSCC patients over 50 years of age (n=11) available from TCGA. We show for the first time that a molecular signature comprising of EGFR lification and increased EGFR RNA abundance is specific to the young subset of OSCC patients. Furthermore, through functional assays using patient tumor-derived cell lines, we reveal that this EGFR lification results in increased activity of the EGFR pathway. Using a panel of clinically relevant EGFR inhibitors we determine that an EGFR - lified patient-derived cell line is responsive to EGFR inhibition, suggesting EGFR lification represents a valid therapeutic target in this subset of OSCC patients. In particular, we demonstrate sensitivity to the second-generation EGFR tyrosine kinase inhibitor afatinib, which offers a new and promising therapeutic avenue versus current EGFR-targeting approaches. We propose that testing for EGFR lification could easily be integrated into current diagnostic workflows and such measures could lead to more personalized treatment approaches and improved outcomes for this younger cohort of OSCC patients.

Normalization for cDNA microarray data: A robust composite method addressing single and multiple slide systematic variation

Publisher: Springer New York

Date: 2012

DOI: 10.1007/978-1-4614-1347-9_15

Lipid-mediated antimicrobial resistance: a phantom menace or a new hope?

Publisher: Springer Science and Business Media LLC

Date: 02-2022

DOI: 10.1007/S12551-021-00912-8

Abstract: The proposition of a post-antimicrobial era is all the more realistic with the continued rise of antimicrobial resistance. The development of new antimicrobials is failing to counter the ever-increasing rates of bacterial antimicrobial resistance. This necessitates novel antimicrobials and drug targets. The bacterial cell membrane is an essential and highly conserved cellular component in bacteria and acts as the primary barrier for entry of antimicrobials into the cell. Although previously under-exploited as an antimicrobial target, the bacterial cell membrane is attractive for the development of novel antimicrobials due to its importance in pathogen viability. Bacterial cell membranes are erse assemblies of macromolecules built around a central lipid bilayer core. This lipid bilayer governs the overall membrane biophysical properties and function of its membrane-embedded proteins. This mini-review will outline the mechanisms by which the bacterial membrane causes and controls resistance, with a focus on alterations in the membrane lipid composition, chemical modification of constituent lipids, and the efflux of antimicrobials by membrane-embedded efflux systems. Thorough insight into the interplay between membrane-active antimicrobials and lipid-mediated resistance is needed to enable the rational development of new antimicrobials. In particular, the union of computational approaches and experimental techniques for the development of innovative and efficacious membrane-active antimicrobials is explored.

Comparison of methods for image analysis on cDNA microarray data

Publisher: Informa UK Limited

Date: 03-2002

DOI: 10.1198/106186002317375640

Genes Influencing Circadian Differences in Blood Pressure in Hypertensive Mice

Publisher: Public Library of Science (PLoS)

Date: 26-04-2011

DOI: 10.1371/JOURNAL.PONE.0019203

A Distinctive Alveolar Macrophage Activation State Induced by Cigarette Smoking

Publisher: American Thoracic Society

Date: 12-2005

DOI: 10.1164/RCCM.200505-686OC

Nutritional Intake and Gut Microbiome Composition Predict Parkinson’s Disease

Publisher: Frontiers Media SA

Date: 11-05-2022

DOI: 10.3389/FNAGI.2022.881872

Abstract: Models to predict Parkinson’s disease (PD) incorporating alterations of gut microbiome (GM) composition have been reported with varying success. To assess the utility of GM compositional changes combined with macronutrient intake to develop a predictive model of PD. We performed a cross-sectional analysis of the GM and nutritional intake in 103 PD patients and 81 household controls (HCs). GM composition was determined by 16S licon sequencing of the V3-V4 region of bacterial ribosomal DNA isolated from stool. To determine multivariate disease-discriminant associations, we developed two models using Random Forest and support-vector machine (SVM) methodologies. Using updated taxonomic reference, we identified significant compositional differences in the GM profiles of PD patients in association with a variety of clinical PD characteristics. Six genera were overrepresented and eight underrepresented in PD patients relative to HCs, with the largest difference being overrepresentation of Lactobacillaceae at family taxonomic level. Correlation analyses highlighted multiple associations between clinical characteristics and select taxa, whilst constipation severity, physical activity and pharmacological therapies associated with changes in beta ersity. The random forest model of PD, incorporating taxonomic data at the genus level and carbohydrate contribution to total energy demonstrated the best predictive capacity [Area under the ROC Curve (AUC) of 0.74]. The notable differences in GM ersity and composition when combined with clinical measures and nutritional data enabled the development of a predictive model to identify PD. These findings support the combination of GM and nutritional data as a potentially useful biomarker of PD to improve diagnosis and guide clinical management.

Genome-wide analysis in Drosophila reveals diet-by-gene interactions and uncovers diet-responsive genes

Publisher: Oxford University Press (OUP)

Date: 27-05-2021

DOI: 10.1093/G3JOURNAL/JKAB171

Abstract: Genetic and environmental factors play a major role in metabolic health. However, they do not act in isolation, as a change in an environmental factor such as diet may exert different effects based on an in idual’s genotype. Here, we sought to understand how such gene–diet interactions influenced nutrient storage and utilization, a major determinant of metabolic disease. We subjected 178 inbred strains from the Drosophila genetic reference panel (DGRP) to diets varying in sugar, fat, and protein. We assessed starvation resistance, a holistic phenotype of nutrient storage and utilization that can be robustly measured. Diet influenced the starvation resistance of most strains, but the effect varied markedly between strains such that some displayed better survival on a high carbohydrate diet (HCD) compared to a high-fat diet while others had opposing responses, illustrating a considerable gene × diet interaction. This demonstrates that genetics plays a major role in diet responses. Furthermore, heritability analysis revealed that the greatest genetic variability arose from diets either high in sugar or high in protein. To uncover the genetic variants that contribute to the heterogeneity in starvation resistance, we mapped 566 diet-responsive SNPs in 293 genes, 174 of which have human orthologs. Using whole-body knockdown, we identified two genes that were required for glucose tolerance, storage, and utilization. Strikingly, flies in which the expression of one of these genes, CG4607 a putative homolog of a mammalian glucose transporter, was reduced at the whole-body level, displayed lethality on a HCD. This study provides evidence that there is a strong interplay between diet and genetics in governing survival in response to starvation, a surrogate measure of nutrient storage efficiency and obesity. It is likely that a similar principle applies to higher organisms thus supporting the case for nutrigenomics as an important health strategy.

Nodal and BMP expression during the transition to pentamery in the sea urchin Heliocidaris erythrogramma: insights into patterning the enigmatic echinoderm body plan

Publisher: Springer Science and Business Media LLC

Date: 13-02-2017

DOI: 10.1186/S12861-017-0145-1

scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

Publisher: Cold Spring Harbor Laboratory

Date: 22-01-2022

DOI: 10.1101/2022.01.20.476845

Abstract: Recent advances in single-cell technologies enable scientists to measure molecular data at high-resolutions and hold the promise to substantially improve clinical outcomes through personalised medicine. However, due to a lack of tools specifically designed to represent each s le (e.g. patient) from the collection of cells sequenced, disease outcome prediction on the s le level remains a challenging task. Here, we present scFeatures, a tool that creates interpretable molecular representation of single-cell and spatial data using 17 types of features motivated by current literature. The feature types span across six distinct categories including cell type proportions, cell type specific gene expressions, cell type specific pathway scores, cell type specific cell–cell interaction scores, overall aggregated gene expressions and spatial metrics. By generating molecular representation using scFeatures for single-cell RNA-seq, spatial proteomic and spatial transcriptomic data, we demonstrate that different types of features are important for predicting different disease outcomes in different datasets and the downstream analysis of features uncover novel biological discoveries.

Data from Circulating Cytokines Predict Immune-Related Toxicity in Melanoma Patients Receiving Anti-PD-1–Based Immunotherapy

Publisher: American Association for Cancer Research (AACR)

Date: 31-03-2023

DOI: 10.1158/1078-0432.C.6527907

Abstract: AbstractPurpose: Combination PD-1 and CTLA-4 inhibitor therapy has dramatically improved the survival of patients with advanced melanoma but is also associated with significant immune-related toxicities. This study sought to identify circulating cytokine biomarkers of treatment response and immune-related toxicity. Experimental Design: The expression of 65 cytokines was profiled longitudinally in 98 patients with melanoma treated with PD-1 inhibitors, alone or in combination with anti-CTLA-4, and in an independent validation cohort of 49 patients treated with combination anti-PD-1 and anti-CTLA-4. Cytokine expression was correlated with RECIST response and immune-related toxicity, defined as toxicity that warranted permanent discontinuation of treatment and administration of high-dose steroids. Results: Eleven cytokines were significantly upregulated in patients with severe immune-related toxicities at baseline (PRE) and early during treatment (EDT). The expression of these 11 cytokines was integrated into a single toxicity score, the CYTOX (cytokine toxicity) score, and the predictive utility of this score was confirmed in the discovery and validation cohorts. The AUC for the CYTOX score in the validation cohort was 0.68 at PRE [95% confidence interval (CI), 0.51–0.84 i P /i = 0.037] and 0.70 at EDT (95% CI, 0.55–0.85 i P /i = 0.017) using ROC analysis. Conclusions: The CYTOX score is predictive of severe immune-related toxicity in patients with melanoma treated with combination anti-CTLA-4 and anti-PD-1 immunotherapy. This score, which includes proinflammatory cytokines such as IL1a, IL2, and IFNα2, may help in the early management of severe, potentially life-threatening immune-related toxicity. i See related commentary by Johnson and Balko, p. 1452 /i /

Identifying differentially expressed genes from microarray experiments via statistic synthesis

Publisher: Oxford University Press (OUP)

Date: 28-10-2005

DOI: 10.1093/BIOINFORMATICS/BTI108

Abstract: Motivation: A common objective of microarray experiments is the detection of differential gene expression between s les obtained under different conditions. The task of identifying differentially expressed genes consists of two aspects: ranking and selection. Numerous statistics have been proposed to rank genes in order of evidence for differential expression. However, no one statistic is universally optimal and there is seldom any basis or guidance that can direct toward a particular statistic of choice. Results: Our new approach, which addresses both ranking and selection of differentially expressed genes, integrates differing statistics via a distance synthesis scheme. Using a set of (Affymetrix) spike-in datasets, in which differentially expressed genes are known, we demonstrate that our method compares favorably with the best in idual statistics, while achieving robustness properties lacked by the in idual statistics. We further evaluate performance on one other microarray study. Availability: The approach is implemented in an R package called DEDS, which is available for download from the Bioconductor website (www.bioconductor.org/). Contact: mark@biostat.ucsf.edu

Mitochondrial CoQ deficiency is a common driver of mitochondrial oxidants and insulin resistance

Publisher: eLife Sciences Publications, Ltd

Date: 06-02-2018

DOI: 10.7554/ELIFE.32111

Abstract: Insulin resistance in muscle, adipocytes and liver is a gateway to a number of metabolic diseases. Here, we show a selective deficiency in mitochondrial coenzyme Q (CoQ) in insulin-resistant adipose and muscle tissue. This defect was observed in a range of in vitro insulin resistance models and adipose tissue from insulin-resistant humans and was concomitant with lower expression of mevalonate/CoQ biosynthesis pathway proteins in most models. Pharmacologic or genetic manipulations that decreased mitochondrial CoQ triggered mitochondrial oxidants and insulin resistance while CoQ supplementation in either insulin-resistant cell models or mice restored normal insulin sensitivity. Specifically, lowering of mitochondrial CoQ caused insulin resistance in adipocytes as a result of increased superoxide/hydrogen peroxide production via complex II. These data suggest that mitochondrial CoQ is a proximal driver of mitochondrial oxidants and insulin resistance, and that mechanisms that restore mitochondrial CoQ may be effective therapeutic targets for treating insulin resistance.

Ensemble deep learning in bioinformatics

Publisher: Springer Science and Business Media LLC

Date: 17-08-2020

DOI: 10.1038/S42256-020-0217-Y

scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning

Publisher: Springer Science and Business Media LLC

Date: 20-01-2022

DOI: 10.1038/S41587-021-01161-6

Discriminating lymphomas and reactive lymphadenopathy in lymph node biopsies by gene expression profiling

Publisher: Springer Science and Business Media LLC

Date: 31-03-2011

DOI: 10.1186/1755-8794-4-27

Abstract: Diagnostic accuracy of lymphoma, a heterogeneous cancer, is essential for patient management. Several ancillary tests including immunophenotyping, and sometimes cytogenetics and PCR are required to aid histological diagnosis. In this proof of principle study, gene expression microarray was evaluated as a single platform test in the differential diagnosis of common lymphoma subtypes and reactive lymphadenopathy (RL) in lymph node biopsies. 116 lymph node biopsies diagnosed as RL, classical Hodgkin lymphoma (cHL), diffuse large B cell lymphoma (DLBCL) or follicular lymphoma (FL) were assayed by mRNA microarray. Three supervised classification strategies (global multi-class, local binary-class and global binary-class classifications) using diagonal linear discriminant analysis was performed on training sets of array data and the classification error rates calculated by leave one out cross-validation. The independent error rate was then evaluated by testing the identified gene classifiers on an independent (test) set of array data. The binary classifications provided prediction accuracies, between a subtype of interest and the remaining s les, of 88.5%, 82.8%, 82.8% and 80.0% for FL, cHL, DLBCL, and RL respectively. Identified gene classifiers include LIM domain only-2 ( LMO2 ), Chemokine (C-C motif) ligand 22 ( CCL22 ) and Cyclin-dependent kinase inhibitor-3 ( CDK3 ) specifically for FL, cHL and DLBCL subtypes respectively. This study highlights the ability of gene expression profiling to distinguish lymphoma from reactive conditions and classify the major subtypes of lymphoma in a diagnostic setting. A cost-effective single platform "mini-chip" assay could, in principle, be developed to aid the quick diagnosis of lymph node biopsies with the potential to incorporate other pathological entities into such an assay.

Distinct molecular profiles and immunotherapy treatment outcomes of V600E and V600K BRAF-mutant melanoma

Publisher: American Association for Cancer Research (AACR)

Date: 15-02-2019

DOI: 10.1158/1078-0432.CCR-18-1680

Abstract: BRAF V600E and V600K melanomas have distinct clinicopathologic features, and V600K appear to be less responsive to BRAFi±MEKi. We investigated mechanisms for this and explored whether genotype affects response to immunotherapy. Pretreatment formalin-fixed paraffin-embedded tumors from patients treated with BRAFi±MEKi underwent gene expression profiling and DNA sequencing. Molecular results were validated using The Cancer Genome Atlas (TCGA) data. An independent cohort of V600E/K patients treated with anti–PD-1 immunotherapy was examined. Baseline tissue and clinical outcome with BRAFi±MEKi were studied in 93 patients (78 V600E, 15 V600K). V600K patients had numerically less tumor regression (median, −31% vs. −52%, P = 0.154) and shorter progression-free survival (PFS median, 5.7 vs. 7.1 months, P = 0.15) compared with V600E. V600K melanomas had lower expression of the ERK pathway feedback regulator dual-specificity phosphatase 6, confirmed with TCGA data (116 V600E, 17 V600K). Pathway analysis showed V600K had lower expression of ERK and higher expression of PI3K-AKT genes than V600E. Higher mutational load was observed in V600K, with a higher proportion of mutations in PIK3R1 and tumor-suppressor genes. In patients treated with anti–PD-1, V600K (n = 19) had superior outcomes than V600E (n = 84), including response rate (53% vs. 29%, P = 0.059), PFS (median, 19 vs. 2.7 months, P = 0.049), and overall survival (20.4 vs. 11.7 months, P = 0.081). BRAF V600K melanomas appear to benefit less from BRAFi±MEKi than V600E, potentially due to less reliance on ERK pathway activation and greater use of alternative pathways. In contrast, these melanomas have higher mutational load and respond better to immunotherapy.

Whole genome duplication in oral squamous cell carcinoma in patients younger than 50 years: implications for prognosis and adverse clinicopathological factors

Publisher: Wiley

Date: 24-06-2022

DOI: 10.1002/GCC.23076

Abstract: Oral squamous cell carcinoma (OSCC) in the young ( years), without known carcinogenic risk factors, is on the rise globally. Whole genome duplication (WGD) has been shown to occur at higher rates in cancers without an identifiable carcinogenic agent. We aimed to evaluate the prevalence of WGD in a cohort of OSCC patients under the age of 50 years. Whole genome sequencing (WGS) was performed on 28 OSCC patients from the Sydney Head and Neck Cancer Institute (SHNCI) biobank. An additional nine cases were obtained from The Cancer Genome Atlas (TCGA). WGD was seen in 27 of 37 (73%) cases. Non‐synonymous, somatic TP53 mutations occurred in 25 of 27 (93%) cases of WGD and were predicted to precede WGD in 21 (77%). WGD was significantly associated with larger tumor size ( p = 0.01) and was frequent in patients with recurrences (87%, p = 0.36). Overall survival was significantly worse in those with WGD ( p = 0.05). Our data, based on one of the largest WGS datasets of young patients with OSCC, demonstrates a high frequency of WGD and its association with adverse pathologic characteristics and clinical outcomes. TP53 mutations also preceded WGD, as has been described in other tumors without a clear mutagenic driver.

A hierarchical approach to removal of unwanted variation for large-scale metabolomics data

Publisher: Springer Science and Business Media LLC

Date: 17-08-2021

DOI: 10.1038/S41467-021-25210-5

Abstract: Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological s le replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma s les run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies.

Comparison study of microarray meta-analysis methods

Publisher: Springer Science and Business Media LLC

Date: 03-08-2010

DOI: 10.1186/1471-2105-11-408

Gene Expression Profiling Reveals Renin mRNA Overexpression in Human Hypertensive Kidneys and a Role for MicroRNAs

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 12-2011

DOI: 10.1161/HYPERTENSIONAHA.111.180729

Abstract: The kidney has long been invoked in the etiology of essential hypertension. This could involve alterations in expression of specific genes and microRNAs (miRNAs). The aim of the present study was to identify, at the transcriptome-wide level, mRNAs and miRNAs that were differentially expressed between kidneys of 15 untreated hypertensive and 7 normotensive white male subjects of white European ancestry. By microarray technology we found 14 genes and 11 miRNAs that were differentially expressed in the medulla. We then selected and confirmed by real-time quantitative PCR expression differences for NR4A1 , NR4A2 , NR4A3 , PER1 , and SIK1 mRNAs and for the miRNAs hsa-miR-638 and hsa-let-7c. Luciferase reporter gene experiments in human kidney (HEK293) cells confirmed the predicted binding of hsa-let-7c to the 3′ untranslated region of NR4A2 mRNA. In the renal cortex we found differential expression of 46 genes and 13 miRNAs. We then confirmed expression differences for AIFM1 , AMBP , APOE , CD36 , EFNB1 , NDUFAF1 , PRDX5 , REN , RENBP , SLC13A1 , STX4 , and TNNT2 mRNAs and for miRNAs hsa-miR-21, hsa-miR-126, hsa-miR-181a, hsa-miR-196a, hsa-miR-451, hsa-miR-638, and hsa-miR-663. Functional experiments in HEK293 cells demonstrated that hsa-miR-663 can bind to the REN and APOE 3′ untranslated regions and can regulate REN and APOE mRNA levels, whereas hsa-miR-181a regulated REN and AIFM1 mRNA. Our data demonstrated for the first time that miRNAs can regulate renin expression. The observed downregulation of 2 miRNAs in hypertension could explain the elevation in intrarenal renin mRNA. Renin, CD36, and other mRNAs, as well as miRNAs and associated pathways identified in the present study, provide novel insights into hypertension etiology.

Analysis of a Splice Array Experiment Elucidates Roles of Chromatin Elongation Factor Spt4–5 in Splicing

Publisher: Public Library of Science (PLoS)

Date: 16-09-2005

DOI: 10.1371/JOURNAL.PCBI.0010039

Estimation of data-specific constitutive exons with RNA-Seq data

Publisher: Springer Science and Business Media LLC

Date: 29-01-2013

DOI: 10.1186/1471-2105-14-31

Abstract: RNA-Seq has the potential to answer many erse and interesting questions about the inner workings of cells. Estimating changes in the overall transcription of a gene is not straightforward. Changes in overall gene transcription can easily be confounded with changes in exon usage which alter the lengths of transcripts produced by a gene. Measuring the expression of constitutive exons— exons which are consistently conserved after splicing— offers an unbiased estimation of the overall transcription of a gene. We propose a clustering-based method, exClust, for estimating the exons that are consistently conserved after splicing in a given data set. These are considered as the exons which are “constitutive” in this data. The method utilises information from both annotation and the dataset of interest. The method is implemented in an openly available R function package, sydSeq. When used on two real datasets exClust includes more than three times as many reads as the standard UI method, and improves concordance with qRT-PCR data. When compared to other methods, our method is shown to produce robust estimates of overall gene transcription.

Shark bites and public attitudes: Policy implications from the first before and after shark bite survey

Publisher: Elsevier BV

Date: 03-2013

DOI: 10.1016/J.MARPOL.2012.06.017

eNODAL: an experimentally guided nutriomics data clustering method to unravel complex drug-diet interactions

Publisher: Cold Spring Harbor Laboratory

Date: 04-10-2023

DOI: 10.1101/2023.10.03.560662

Spatial patterns of gene expression in the olfactory bulb

Publisher: Proceedings of the National Academy of Sciences

Date: 10-08-2004

DOI: 10.1073/PNAS.0404872101

Abstract: How olfactory sensory neurons converge on spatially invariant glomeruli in the olfactory bulb is largely unknown. In one model, olfactory sensory neurons interact with spatially restricted guidance cues in the bulb that orient and guide them to their target. Identifying differentially expressed molecules in the olfactory bulb has been extremely difficult, however, hindering a molecular analysis of convergence. Here, we describe several such genes that have been identified in a screen that compiled microarray data to create a three-dimensional model of gene expression within the mouse olfactory bulb. The expression patterns of these identified genes form the basis of a nascent spatial map of differential gene expression in the bulb.

Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data

Publisher: Springer Science and Business Media LLC

Date: 19-10-2023

DOI: 10.1038/S41540-023-00312-6

Health-Related Quality of Life in People Across the Spectrum of CKD

Publisher: Elsevier BV

Date: 12-2020

DOI: 10.1016/J.EKIR.2020.09.028

Integrated single cell data analysis reveals cell specific networks and novel coactivation markers

Publisher: Springer Science and Business Media LLC

Date: 12-2016

DOI: 10.1186/S12918-016-0370-4

Pan-cancer analysis of whole genomes

Publisher: Springer Science and Business Media LLC

Date: 05-02-2020

DOI: 10.1038/S41586-020-1969-6

Abstract: Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale 1–3 . Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution in acral melanoma, for ex le, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter 4 identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation 5,6 analyses timings and patterns of tumour evolution 7 describes the erse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity 8,9 and evaluates a range of more-specialized features of cancer genomes 8,10–18 .

Mutational and transcriptomic landscapes of a rare human prostate basal cell carcinoma

Publisher: Wiley

Date: 02-03-2020

DOI: 10.1002/PROS.23965

Differential distribution improves gene selection stability and has competitive classification performance for patient survival

Publisher: Oxford University Press (OUP)

Date: 17-05-2016

DOI: 10.1093/NAR/GKW444

Challenges for Brain Data Analysis in VR Environments

Publisher: IEEE

Date: 04-2019

DOI: 10.1109/PACIFICVIS.2019.00013

Immunoglobulin E Sensitization to Mammalian Oligosaccharide Galactose-α-1,3 (α-Gal) Is Associated With Noncalcified Plaque, Obstructive Coronary Artery Disease, and ST-Segment–Elevated Myocardial Infa

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 03-2022

DOI: 10.1161/ATVBAHA.121.316878

Abstract: Treating known risk factors for coronary artery disease (CAD) has substantially reduced CAD morbidity and mortality. However, a significant burden of CAD remains unexplained. Immunoglobulin E sensitization to mammalian oligosaccharide galactose-α-1,3-galactose (α-Gal) was recently associated with CAD in a small observational study. We sought to confirm that α-Gal sensitization is associated with CAD burden, in particular noncalcified plaque. Additionally, we sort to assess whether that α-Gal sensitization is associated with ST-segment–elevated myocardial infarction (STEMI) We performed a cross-sectional analysis of participants enrolled in the BioHEART cohort study. We measured α-Gal specific-immunoglobulin E antibodies in serum of 1056 patients referred for CT coronary angiography for suspected CAD and 100 selected patients presenting with STEMI, enriched for patients without standard modifiable risk factors. CT coronary angiograms were assessed using coronary artery calcium scores and segmental plaque scores. α-Gal sensitization was associated with presence of noncalcified plaque (odds ratio, 1.62 [95% CI, 1.04–2.53], P =0.03) and obstructive CAD (odds ratio, 2.05 [95% CI, 1.29-3.25], P =0.002), independent of age, sex, and traditional risk factors. The α-Gal sensitization rate was 12.8-fold higher in patients with STEMI compared with matched healthy controls and 2.2-fold higher in the patients with STEMI compared with matched stable CAD patients (17% versus 1.3%, P =0.01 and 20% versus 9%, P =0.03, respectively). α-Gal sensitization is independently associated with noncalcified plaque burden and obstructive CAD and occurs at higher frequency in patients with STEMI than those with stable or no CAD. These findings may have implications for in iduals exposed to ticks, as well as public health policy. URL: www.anzctr.org.au Unique identifier: ACTRN12618001322224.

Taking Kidneys for Granted? Time to Reflect on the Choices We Make

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 12-2017

DOI: 10.1097/TP.0000000000001850

Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data

Publisher: Cold Spring Harbor Laboratory

Date: 14-06-2023

DOI: 10.1101/2023.06.13.544733

Abstract: Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, including cell-cell interactions, enabling great potential in biological discovery.

Asthma investigators begin to reap the fruits of genomics.

Publisher: Springer Science and Business Media LLC

Date: 2003

DOI: 10.1186/GB-2003-4-11-232

ISL1 regulates peroxisome proliferator-activated receptor γ activation and early adipogenesis via bone morphogenetic protein 4-dependent and -independent mechanisms

Publisher: Informa UK Limited

Date: 10-2014

DOI: 10.1128/MCB.00583-14

NEMoE: a nutrition aware regularized mixture of experts model to identify heterogeneous diet-microbiome-host health interactions

Publisher: Springer Science and Business Media LLC

Date: 15-03-2023

DOI: 10.1186/S40168-023-01475-4

Abstract: Unrevealing the interplay between diet, the microbiome, and the health state could enable the design of personalized intervention strategies and improve the health and well-being of in iduals. A common approach to this is to ide the study population into smaller cohorts based on dietary preferences in the hope of identifying specific microbial signatures. However, classification of patients based solely on diet is unlikely to reflect the microbiome-host health relationship or the taxonomic microbiome makeup. We present a novel approach, the Nutrition-Ecotype Mixture of Experts (NEMoE) model, for establishing associations between gut microbiota and health state that accounts for diet-specific cohort variability using a regularized mixture of experts model framework with an integrated parameter sharing strategy to ensure data-driven diet-cohort identification consistency across taxonomic levels. The success of our approach was demonstrated through a series of simulation studies, in which NEMoE showed robustness with regard to parameter selection and varying degrees of data heterogeneity. Further application to real-world microbiome data from a Parkinson’s disease cohort revealed that NEMoE is capable of not only improving predictive performance for Parkinson’s Disease but also for identifying diet-specific microbial signatures of disease. In summary, NEMoE can be used to uncover diet-specific relationships between nutritional-ecotype and patient health and to contextualize precision nutrition for different diseases.

Statistical Issues in cDNA Microarray Data Analysis

Publisher: Humana Press

Date: 2003

DOI: 10.1385/1-59259-364-X:111

Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2

Publisher: Cold Spring Harbor Laboratory

Date: 08-12-2022

DOI: 10.1101/2022.12.08.519588

Abstract: The recent emergence of multi-s le multi-condition single-cell multi-cohort studies allow researchers to investigate different cell states. The effective integration of multiple large-cohort studies promises biological insights into cells under different conditions that in idual studies cannot provide. Here, we present scMerge2, a scalable algorithm that allows data integration of atlas-scale multi-s le multi-condition single-cell studies. We have generalised scMerge2 to enable the merging of millions of cells from single-cell studies generated by various single-cell technologies. Using a large COVID-19 data collection with over five million cells from 1000+ in iduals, we demonstrate that scMerge2 enables multi-s le multi-condition scRNA-seq data integration from multiple cohorts and reveals signatures derived from cell-type expression that are more accurate in discriminating disease progression. Further, we demonstrate that scMerge2 can remove dataset variability in CyTOF, imaging mass cytometry and CITE-seq experiments, demonstrating its applicability to a broad spectrum of single-cell profiling technologies.

Single-cell and single-variant resolution analysis of clonal evolution in human liver cancer

Publisher: Cold Spring Harbor Laboratory

Date: 02-01-2021

DOI: 10.1101/2020.12.30.424907

Abstract: Genetic heterogeneity of tumor is closely related to clonal evolution, phenotypic ersity and treatment resistance. Such heterogeneity has been characterized in liver cancer at single-cell sub-chromosomal scale, and a more precise single-variant resolution analysis is lacking. Here we employed a strategy to analyze both the single-cell genomic mutations and transcriptomic changes in 5 patients with liver cancer. Target sequencing was done for a total of 480 single cells in a patient-specific manner. DNA copy number status of point mutations was obtained from single-cell mutational profiling. The clonal structures of liver cancers were then uncovered at single-variant resolution, and mutation combinations in single cells enabled reconstruction of their evolutionary history. A common origin but independent evolutionary fate was revealed for primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. The mutational signature suggested early evolutionary process may be related to specific etiology like aristolochic acids. By parallel single-cell RNA-Seq, the transcriptomic phenotype was found to be related with genetic heterogeneity in liver cancer. We reconstructed the single-cell and single-variant resolution clonal evolutionary history of liver cancer, and dissection of both genetic and phenotypic heterogeneity provides knowledge for mechanistic understanding of liver cancer initiation and progression.

Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine

Publisher: Springer Science and Business Media LLC

Date: 04-07-2022

DOI: 10.1038/S41746-022-00618-5

Abstract: In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments. CPOP improves on the traditional prediction framework of using gene-based features by selecting ratio-based features with similar estimated effect sizes. These components gave CPOP the ability to have a stable performance across datasets of similar biology, minimising the effect of technical noise often generated by omics platforms. We present a comprehensive evaluation using melanoma transcriptomics data to demonstrate its potential to be used as a critical part of a clinical screening framework for precision medicine. Additional assessment of generalisation was demonstrated with ovarian cancer and inflammatory bowel disease studies.

Impact of similarity metrics on single-cell RNA-seq data clustering

Publisher: Oxford University Press (OUP)

Date: 22-08-2018

DOI: 10.1093/BIB/BBY076

Abstract: Advances in high-throughput sequencing on single-cell gene expressions [single-cell RNA sequencing (scRNA-seq)] have enabled transcriptome profiling on in idual cells from complex s les. A common goal in scRNA-seq data analysis is to discover and characterise cell types, typically through clustering methods. The quality of the clustering therefore plays a critical role in biological discovery. While numerous clustering algorithms have been proposed for scRNA-seq data, fundamentally they all rely on a similarity metric for categorising in idual cells. Although several studies have compared the performance of various clustering algorithms for scRNA-seq data, currently there is no benchmark of different similarity metrics and their influence on scRNA-seq data clustering. Here, we compared a panel of similarity metrics on clustering a collection of annotated scRNA-seq datasets. Within each dataset, a stratified subs ling procedure was applied and an array of evaluation measures was employed to assess the similarity metrics. This produced a highly reliable and reproducible consensus on their performance assessment. Overall, we found that correlation-based metrics (e.g. Pearson’s correlation) outperformed distance-based metrics (e.g. Euclidean distance). To test if the use of correlation-based metrics can benefit the recently published clustering techniques for scRNA-seq data, we modified a state-of-the-art kernel-based clustering algorithm (SIMLR) using Pearson’s correlation as a similarity measure and found significant performance improvement over Euclidean distance on scRNA-seq data clustering. These findings demonstrate the importance of similarity metrics in clustering scRNA-seq data and highlight Pearson’s correlation as a favourable choice. Further comparison on different scRNA-seq library preparation protocols suggests that they may also affect clustering performance. Finally, the benchmarking framework is available at www.maths.usyd.edu.au/u/SMS/bioinformatics/software.html.

Positive unlabeled learning via wrapper-based adaptive sampling

Publisher: No publisher found

Date: 2017

DOI: 10.24963/IJCAI.2017/457

Multi-omic Profiling Reveals Dynamics of the Phased Progression of Pluripotency

Publisher: Elsevier BV

Date: 05-2019

DOI: 10.1016/J.CELS.2019.03.012

Analysis of the whole-exome sequencing of tumor and circulating tumor DNA in metastatic melanoma

Publisher: MDPI AG

Date: 29-11-2019

DOI: 10.3390/CANCERS11121905

Abstract: The use of circulating tumor DNA (ctDNA) to monitor cancer progression and response to therapy has significant potential but there is only limited data on whether this technique can detect the presence of low frequency subclones that may ultimately confer therapy resistance. In this study, we sought to evaluate whether whole-exome sequencing (WES) of ctDNA could accurately profile the mutation landscape of metastatic melanoma. We used WES to identify variants in matched, tumor-derived genomic DNA (gDNA) and plasma-derived ctDNA isolated from a cohort of 10 metastatic cutaneous melanoma patients. WES parameters such as sequencing coverage and total sequencing reads were comparable between gDNA and ctDNA. The mutant allele frequency of common single nucleotide variants was lower in ctDNA, reflecting the lower read depth and minor fraction of ctDNA within the total circulating free DNA pool. There was also variable concordance between gDNA and ctDNA based on the total number and identity of detected variants and this was independent of the tumor biopsy site. Nevertheless, established melanoma driver mutations and several other melanoma-associated mutations were concordant between matched gDNA and ctDNA. This study highlights that WES of ctDNA could capture clinically relevant mutations present in melanoma metastases and that enhanced sequencing sensitivity will be required to identify low frequency mutations.

MicroRNA and mRNA expression profiling in metastatic melanoma reveal associations with BRAF mutation and patient prognosis

Publisher: Wiley

Date: 05-01-2015

DOI: 10.1111/PCMR.12343

Abstract: The role of microRNAs (miRNAs) in melanoma is unclear. We examined global miRNA expression profiles in fresh-frozen metastatic melanomas in relation to clinical outcome and BRAF mutation, with validation in independent cohorts of tumours and sera. We integrated miRNA and mRNA information from the same s les and elucidated networks associated with outcome and mutation. Associations with prognosis were replicated for miR-150-5p, miR-142-3p and miR-142-5p. Co-analysis of miRNA and mRNA uncovered a network associated with poor prognosis (PP) that paradoxically favoured expression of miRNAs opposing tumorigenesis. These miRNAs are likely part of an autoregulatory response to oncogenic drivers, rather than drivers themselves. Robust association of miR-150-5p and the miR-142 duplex with good prognosis and earlier stage metastatic melanoma supports their potential as biomarkers. miRNAs overexpressed in association with PP in an autoregulatory fashion will not be suitable therapeutic targets.

Dissecting asthma using focused transgenic modeling and functional genomics

Publisher: Elsevier BV

Date: 08-2005

DOI: 10.1016/J.JACI.2005.03.024

Abstract: Asthma functional genomics studies are challenging because it is difficult to relate gene expression changes to specific disease mechanisms or pathophysiologic features. Use of simplified model systems might help to address this problem. One such model is the IL-13/Epi (IL-13-overexpressing transgenic mice with STAT6 expression limited to epithelial cells) focused transgenic mouse, which isolates the effects of a single mediator, IL-13, on a single cell type, the airway epithelial cell. These mice develop airway hyperreactivity and mucus overproduction but not airway inflammation. To identify how effects of IL-13 on airway epithelial cells contribute to gene expression changes in murine asthma models and determine whether similar changes are seen in people with asthma. We analyzed gene expression in ovalbumin allergic mice, IL-13-overexpressing mice, and IL-13/Epi mice with microarrays. We analyzed the expression of human orthologues of genes identified in the mouse studies in airway epithelial cells from subjects with asthma and control subjects. In comparison with the other 2 models, IL-13/Epi mice had a remarkably small subset of gene expression changes. Human orthologues of some genes identified as increased in the mouse models were more highly expressed in airway epithelial cells from subjects with asthma than in controls. These included calcium-activated chloride channel 1, 15-lipoxygenase, trefoil factor 2, and intelectin. The combination of focused transgenic models, DNA microarray analyses, and translational studies provides a powerful approach for analyzing the contributions of specific mediators and cell types and for focusing attention on a limited number of genes associated with specific pathophysiologic aspects of asthma.

Meta-analysis of genome-wide gene expression differences in onset and maintenance phases of genetic hypertension

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 08-2010

DOI: 10.1161/HYPERTENSIONAHA.110.155366

Abstract: Gene expression differences accompany both the onset and established phases of hypertension. By an integrated genome-transcriptome approach we performed a meta-analysis of data from 74 microarray experiments available on public databases to identify genes with altered expression in the kidney, adrenal, heart, and artery of spontaneously hypertensive and Lyon hypertensive rats. To identify genes responsible for the onset of hypertension we used a statistical approach that sought to eliminate expression differences that occur during maturation unrelated to hypertension. Based on this adjusted fold-difference statistic, we found 36 genes for which the expression differed between the prehypertensive phase and established hypertension. Genes having possible relevance to hypertension onset included Actn2, Ankrd1, ApoE, Cd36, Csrp3, Me1, Myl3, Nppa, Nppb, Pln, Postn, Spp1 , Slc21a4, Slc22a2, Thbs4 , and Tnni3. In established hypertension 102 genes exhibited altered expression after Bonferroni correction ( P .05). These included Atp5o, Ech1, Fabp3, Gnb3, Ldhb, Myh6, Lpl, Pkkaca, Vegfb, Vcam1 , and reduced nicotinamide-adenine dinucleotide dehydrogenases. Among the genes identified, there was an overrepresentation of gene ontology terms involved in energy production, fatty acid and lipid metabolism, oxidation, and transport. These could contribute to increases in reactive oxygen species. Our meta-analysis has revealed many new genes for which the expression is altered in hypertension, so pointing to novel potential causative, maintenance, and responsive mechanisms and pathways.

OCAP: An open comprehensive analysis pipeline for iTRAQ

Publisher: Oxford University Press (OUP)

Date: 29-03-2012

DOI: 10.1093/BIOINFORMATICS/BTS150

Abstract: Motivation: Mass spectrometry-based iTRAQ protein quantification is a high-throughput assay for determining relative protein expressions and identifying disease biomarkers. Processing and analysis of these large and complex data involves a number of distinct components and it is desirable to have a pipeline to efficiently integrate these together. To date, there are limited public available comprehensive analysis pipelines for iTRAQ data and many of these existing pipelines have limited visualization tools and no convenient interfaces with downstream analyses. We have developed a new open source comprehensive iTRAQ analysis pipeline, OCAP, integrating a wavelet-based preprocessing algorithm which provides better peak picking, a new quantification algorithm and a suite of visualizsation tools. OCAP is mainly developed in C++ and is provided as a standalone version (OCAP_standalone) as well as an R package. The R package (OCAP) provides the necessary interfaces with downstream statistical analysis. Availability: OCAP is freely available and can be downloaded at www.maths.usyd.edu.au/u enghao Contact: penghao.wang@sydney.edu.au

Genome-wide analysis of primary CD4+ and CD8+ T cell transcriptomes shows evidence for a network of enriched pathways associated with HIV disease

Publisher: Springer Science and Business Media LLC

Date: 16-03-2011

DOI: 10.1186/1742-4690-8-18

Abstract: HIV preferentially infects CD4+ T cells, and the functional impairment and numerical decline of CD4+ and CD8+ T cells characterize HIV disease. The numerical decline of CD4+ and CD8+ T cells affects the optimal ratio between the two cell types necessary for immune regulation. Therefore, this work aimed to define the genomic basis of HIV interactions with the cellular transcriptome of both CD4+ and CD8+ T cells. Genome-wide transcriptomes of primary CD4+ and CD8+ T cells from HIV+ patients were analyzed at different stages of HIV disease using Illumina microarray. For each cell subset, pairwise comparisons were performed and differentially expressed (DE) genes were identified (fold change and B-statistic ) followed by quantitative PCR validation. Gene ontology (GO) analysis of DE genes revealed enriched categories of complement activation, actin filament, proteasome core and proton-transporting ATPase complex. By gene set enrichment analysis (GSEA), a network of enriched pathways functionally connected by mitochondria was identified in both T cell subsets as a transcriptional signature of HIV disease progression. These pathways ranged from metabolism and energy production (TCA cycle and OXPHOS) to mitochondria meditated cell apoptosis and cell cycle dysregulation. The most unique and significant feature of our work was that the non-progressing status in HIV+ long-term non-progressors was associated with MAPK, WNT, and AKT pathways contributing to cell survival and anti-viral responses. These data offer new comparative insights into HIV disease progression from the aspect of HIV-host interactions at the transcriptomic level, which will facilitate the understanding of the genetic basis of transcriptomic interaction of HIV in vivo and how HIV subverts the human gene machinery at the in idual cell type level.

Cross-species PTM mapping from phosphoproteomic data

Publisher: Springer New York

Date: 2017

DOI: 10.1007/978-1-4939-6783-4_22

Abstract: Protein post-translational modifications (PTMs) are crucial for signal transduction in cells. In order to understand key cell signaling events, identification of functionally important PTMs, which are more likely to be evolutionarily conserved, is necessary. In recent times, high-throughput mass spectrometry (MS) has made quantitative datasets in erse species readily available, which has led to a growing need for tools to facilitate cross-species comparison of PTM data. Cross-species comparison of PTM sites is difficult since they often lie in structurally disordered protein domains. Current tools that address this can only map known PTMs between species based on previously annotated orthologous phosphosites and do not enable cross-species mapping of newly identified modification sites. Here, we describe an automated web-based tool, PhosphOrtholog, that accurately maps annotated and novel orthologous PTM sites from high-throughput MS-based experimental data obtained from different species without relying on existing PTM databases. Identification of conserved PTMs across species from large-scale experimental data increases our knowledgebase of evolutionarily conserved and functional PTM sites that influence most biological processes. In this Chapter, we illustrate with ex les how to use PhosphOrtholog to map novel PTM sites from cross-species MS-based phosphoproteomics data.

One-Time Fecal Immunochemical Screening for Advanced Colorectal Neoplasia in Patients with CKD (DETECT Study)

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 30-04-2019

DOI: 10.1681/ASN.2018121232

Abstract: Colorectal cancer screening using fecal immunochemical testing (FIT) is recommended for patients with CKD, whose risk of developing and dying from this malignancy is at least 1.5 times higher than that of the sex- and age-matched general population. However, FIT accuracy in this setting is unknown and is likely to be affected by occult gastrointestinal bleeding from dysfunctional platelets and increased bleeding sensitivity to aspirin. In a large, multinational study, the authors found that FIT appears to be an accurate screening test for patients with CKD, but the risk of major complications from work-up colonoscopies (1.5%) is high compared with this risk in the general population. These findings provide useful estimates of harms and test accuracies to inform colorectal cancer screening decisions across the full spectrum of CKD. In patients with CKD, the risk of developing colorectal cancer is high and outcomes are poor. Screening using fecal immunochemical testing (FIT) is effective in reducing mortality from colorectal cancer, but performance characteristics of FIT in CKD are unknown. To determine the detection rates and performance characteristics of FIT for advanced colorectal neoplasia (ACN) in patients with CKD, we used FIT to prospectively screen patients aged 35–74 years with CKD (stages 3–5 CKD, dialysis, and renal transplant) from 11 sites in Australia, New Zealand, Canada, and Spain. All participants received clinical follow-up at 2 years. We used a two-step reference standard approach to estimate disease status. Overall, 369 out of 1706 patients who completed FIT (21.6%) tested positive 323 (87.5%) underwent colonoscopies. A total of 1553 (91.0%) completed follow-up 82 (4.8%) had died and 71 (4.2%) were lost. The detection rate of ACN using FIT was 6.0% (5.6%, 7.4%, and 5.6% for stages 3–5 CKD, dialysis, and transplant). Sensitivity, specificity, and positive and negative predictive values of FIT for ACN were 0.90, 0.83, 0.30, and 0.99, respectively. Of participants who underwent colonoscopy, five (1.5%) experienced major colonoscopy-related complications, including bowel perforation and major bleeding. FIT appears to be an accurate screening test for patients with CKD, such that a negative test may rule out the diagnosis of colorectal cancer within 2 years. However, the risk of major complications from work-up colonoscopy are at least ten-fold higher than in the general population.

PD-L1 expression in tonsillar cancer is associated with human papillomavirus positivity and improved survival: Implications for anti-PD1 clinical trials

Publisher: Impact Journals, LLC

Date: 20-10-2016

DOI: 10.18632/ONCOTARGET.12776

Survival and Quality of Life Impact of a Risk-based Allocation Algorithm for Deceased Donor Kidney Transplantation

Publisher: Ovid Technologies (Wolters Kluwer Health)

Date: 09-2018

DOI: 10.1097/TP.0000000000002144

Tuning of delta-protocadherin adhesion through combinatorial diversity

Publisher: eLife Sciences Publications, Ltd

Date: 14-12-2018

DOI: 10.7554/ELIFE.41050

Abstract: The delta-protocadherins (δ-Pcdhs) play key roles in neural development, and expression studies suggest they are expressed in combination within neurons. The extent of this combinatorial ersity, and how these combinations influence cell adhesion, is poorly understood. We show that in idual mouse olfactory sensory neurons express 0–7 δ-Pcdhs. Despite this apparent combinatorial complexity, K562 cell aggregation assays revealed simple principles that mediate tuning of δ-Pcdh adhesion. Cells can vary the number of δ-Pcdhs expressed, the level of surface expression, and which δ-Pcdhs are expressed, as different members possess distinct apparent adhesive affinities. These principles contrast with those identified previously for the clustered protocadherins (cPcdhs), where the particular combination of cPcdhs expressed does not appear to be a critical factor. Despite these differences, we show δ-Pcdhs can modify cPcdh adhesion. Our studies show how intra- and interfamily interactions can greatly lify the impact of this small subfamily on neuronal function.

Circulating Cytokines Predict Immune-Related Toxicity in Melanoma Patients Receiving Anti-PD-1–Based Immunotherapy

Publisher: American Association for Cancer Research (AACR)

Date: 03-2019

DOI: 10.1158/1078-0432.CCR-18-2795

Abstract: Combination PD-1 and CTLA-4 inhibitor therapy has dramatically improved the survival of patients with advanced melanoma but is also associated with significant immune-related toxicities. This study sought to identify circulating cytokine biomarkers of treatment response and immune-related toxicity. The expression of 65 cytokines was profiled longitudinally in 98 patients with melanoma treated with PD-1 inhibitors, alone or in combination with anti-CTLA-4, and in an independent validation cohort of 49 patients treated with combination anti-PD-1 and anti-CTLA-4. Cytokine expression was correlated with RECIST response and immune-related toxicity, defined as toxicity that warranted permanent discontinuation of treatment and administration of high-dose steroids. Eleven cytokines were significantly upregulated in patients with severe immune-related toxicities at baseline (PRE) and early during treatment (EDT). The expression of these 11 cytokines was integrated into a single toxicity score, the CYTOX (cytokine toxicity) score, and the predictive utility of this score was confirmed in the discovery and validation cohorts. The AUC for the CYTOX score in the validation cohort was 0.68 at PRE [95% confidence interval (CI), 0.51–0.84 P = 0.037] and 0.70 at EDT (95% CI, 0.55–0.85 P = 0.017) using ROC analysis. The CYTOX score is predictive of severe immune-related toxicity in patients with melanoma treated with combination anti-CTLA-4 and anti-PD-1 immunotherapy. This score, which includes proinflammatory cytokines such as IL1a, IL2, and IFNα2, may help in the early management of severe, potentially life-threatening immune-related toxicity. See related commentary by Johnson and Balko, p. 1452

A dynamic wavelet-based algorithm for pre-processing tandem mass spectrometry data

Publisher: Oxford University Press (OUP)

Date: 13-07-2010

DOI: 10.1093/BIOINFORMATICS/BTQ403

Abstract: Motivation: Mass spectrometry (MS)-based proteomics is one of the most commonly used research techniques for identifying and characterizing proteins in biological and medical research. The identification of a protein is the critical first step in elucidating its biological function. Successful protein identification depends on various interrelated factors, including effective analysis of MS data generated in a proteomic experiment. This analysis comprises several stages, often combined in a pipeline or workflow. The first component of the analysis is known as spectra pre-processing. In this component, the raw data generated by the mass spectrometer is processed to eliminate noise and identify the mass-to-charge ratio (m/z) and intensity for the peaks in the spectrum corresponding to the presence of certain peptides or peptide fragments. Since all downstream analyses depend on the pre-processed data, effective pre-processing is critical to protein identification and characterization. There is a critical need for more robust pre-processing algorithms that perform well on tandem mass spectra under a variety of different conditions and can be easily integrated into sophisticated data analysis pipelines for practical wet-lab applications. Result: We have developed a new pre-processing algorithm. Based on wavelet theory, our method uses a dynamic peak model to identify peaks. It is designed to be easily integrated into a complete proteomic analysis workflow. We compared the method with other available algorithms using a reference library of raw MS and tandem MS spectra with known protein composition information. Our pre-processing algorithm results in the identification of significantly more peptides and proteins in the downstream analysis for a given false discovery rate. Availability: Software available at: www.maths.usyd.edu.au/u enghao/index.html Contact: penghao.wang@sydney.edu.au

Utilizing state‐of‐the‐art “omics” technology and bioinformatics to identify new biological mechanisms and biomarkers for coronary artery disease

Publisher: Wiley

Date: 23-07-2018

DOI: 10.1111/MICC.12488

Abstract: Identification of the four standard modifiable cardiovascular risk factors (SMuRFs)-diabetes mellitus, hyperlipidaemia, hypertension, and cigarette smoking-has allowed the development of risk scores. These have been used in conjunction with primary and secondary prevention strategies targeting SMuRFs to reduce the burden of CAD. Recent studies show that up to 25% of ACS patients do not have any SMuRFs. Thus, SMuRFs do not explain the entire burden of CAD. There appears to be variation at the in idual level rendering some in iduals relatively susceptible or resilient to developing atherosclerosis. Important disease pathways remain to be discovered, and there is renewed enthusiasm to discover novel biomarkers, biological mechanisms, and therapeutic targets for atherosclerosis. Two broad approaches are being taken: traditional approaches investigating known candidate pathways and unbiased omics approaches. We review recent progress in the field and discuss opportunities made possible by technological and data science advances. Developments in network analytics and machine learning algorithms used in conjunction with large-scale multi-omic platforms have the potential to uncover biological networks that may not have been identifiable using traditional approaches. These approaches are useful for both biomedical research and precision medicine strategies.

scREMOTE: Using multimodal single cell data to predict regulatory gene relationships and to build a computational cell reprogramming model

Publisher: Cold Spring Harbor Laboratory

Date: 12-10-2021

DOI: 10.1101/2021.10.11.463798

Abstract: Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.

Treatment usage patterns of oral appliances for obstructive sleep apnea over the first 60 days: a cluster analysis

Publisher: American Academy of Sleep Medicine (AASM)

Date: 09-2021

DOI: 10.5664/JCSM.9288

scSTAR reveals hidden heterogeneity with a real-virtual cell pair structure across conditions in single-cell RNA sequencing data

Publisher: Oxford University Press (OUP)

Date: 22-02-2023

DOI: 10.1093/BIB/BBAD062

Abstract: Cell-state transition can reveal additional information from single-cell ribonucleic acid (RNA)-sequencing data in time-resolved biological phenomena. However, most of the current methods are based on the time derivative of the gene expression state, which restricts them to the short-term evolution of cell states. Here, we present single-cell State Transition Across-s les of RNA-seq data (scSTAR), which overcomes this limitation by constructing a paired-cell projection between biological conditions with an arbitrary time span by maximizing the covariance between two feature spaces using partial least square and minimum squared error methods. In mouse ageing data, the response to stress in CD4+ memory T cell subtypes was found to be associated with ageing. A novel Treg subtype characterized by mTORC activation was identified to be associated with antitumour immune suppression, which was confirmed by immunofluorescence microscopy and survival analysis in 11 cancers from The Cancer Genome Atlas Program. On melanoma data, scSTAR improved immunotherapy-response prediction accuracy from 0.8 to 0.96.

Metabolites downstream of predicted loss-of-function variants inform relationship to disease.

Publisher: Elsevier BV

Date: 12-2019

DOI: 10.1016/J.YMGME.2019.10.002

Abstract: A small minority ( 400,000 subjects in UK Biobank - with metabolites. We conducted these analyses in three community-based cohorts: the Framingham Heart Study (FHS) Offspring Cohort, FHS Generation 3, and the KORA F4 cohort. We identified 19 new low-frequency or rare (minor allele frequency (MAF) 5%) pLOF variant-metabolite associations. Rare pLOF variants in the genes BTN3A2, ENPEP, and GEM that have been associated with blood pressure in UK Biobank, were associated with vasoactive metabolites indoxyl sulfate, asymmetric dimethylarginine (ADMA), and with niacinamide, respectively. A common pLOF variant in gene CCHCR1, associated with asthma in UK Biobank, was associated with histamine and niacinamide in FHS Generation 3, both reported to play a role in this disease. Common variants in olfactory receptor gene OX4C11 that associated with blood pressure in UK Biobank were associated with the nicotine metabolite cotinine, suggesting an interaction between altered olfaction, smoking behaviour, and blood pressure. These findings provide biological validity for pLOF variant-disease associations, and point to the effector roles of common metabolites. Such an approach may provide novel disease markers and therapeutic targets.

Supplementary Data from Circulating Cytokines Predict Immune-Related Toxicity in Melanoma Patients Receiving Anti-PD-1–Based Immunotherapy

Publisher: American Association for Cancer Research (AACR)

Date: 31-03-2023

DOI: 10.1158/1078-0432.22471476

Abstract: Supplementary Table 1: Number of plasma s les included in the study Supplementary Table 2: Association of toxicity with treatment response Supplementary Table 3: Differentially expressed cytokines in patients with severe irAEs compared to those with no-severe irAEs at PRE in Cohort 2 Supplementary Table 4: Differentially expressed cytokines in patients with severe irAEs compared to those with no-severe irAEs at EDT in Cohort 2 Supplementary Table 5: Univariate analysis of cytokine expression and association with overall survival Supplementary Table 6: Univariate analysis of cytokine expression and association with RECIST response Supplementary Figure 1: Distribution of relative fluorescence intensity units of 65 circulating cytokines in plasma collected from 98 melanoma patients (cohorts 1 and 2) prior to therapy initiation. Supplementary Figure 2: Hierarchical clustering of cytokine expression profiles in cohorts 1 and 2.

Genome-wide profiling identifies epithelial cell genes associated with asthma and with treatment response to corticosteroids

Publisher: Proceedings of the National Academy of Sciences

Date: 02-10-2007

DOI: 10.1073/PNAS.0707413104

Abstract: Airway inflammation and epithelial remodeling are two key features of asthma. IL-13 and other cytokines produced during T helper type 2 cell-driven allergic inflammation contribute to airway epithelial goblet cell metaplasia and may alter epithelial–mesenchymal signaling, leading to increased subepithelial fibrosis or hyperplasia of smooth muscle. The beneficial effects of corticosteroids in asthma could relate to their ability to directly or indirectly decrease epithelial cell activation by inflammatory cells and cytokines. To identify markers of epithelial cell dysfunction and the effects of corticosteroids on epithelial cells in asthma, we studied airway epithelial cells collected from asthmatic subjects enrolled in a randomized controlled trial of inhaled corticosteroids, from healthy subjects and from smokers (disease control). By using gene expression microarrays, we found that chloride channel, calcium-activated, family member 1 ( CLCA1 ), periostin , and serine peptidase inhibitor, clade B (ovalbumin), member 2 ( serpinB2 ) were up-regulated in asthma but not in smokers. Corticosteroid treatment down-regulated expression of these three genes and markedly up-regulated expression of FK506-binding protein 51 ( FKBP51 ). Whereas high baseline expression of CLCA1 , periostin , and serpinB2 was associated with a good clinical response to corticosteroids, high expression of FKBP51 was associated with a poor response. By using airway epithelial cells in culture, we found that IL-13 increased expression of CLCA1 , periostin , and serpinB2 , an effect that was suppressed by corticosteroids. Corticosteroids also induced expression of FKBP51. Taken together, our findings show that airway epithelial cells in asthma have a distinct activation profile and identify direct and cell-autonomous effects of corticosteroid treatment on airway epithelial cells that relate to treatment responses and can now be the focus of specific mechanistic studies.

University Of California, Berkeley

Location: United States of America

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University Of Sydney

Location: Australia

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Discovery Projects - Grant ID: DP0984267

Start Date: 03-2009

End Date: 12-2012

Amount: $260,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0990067

Start Date: 12-2009

End Date: 12-2012

Amount: $370,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0770395

Start Date: 02-2007

End Date: 12-2010

Amount: $255,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP170100654

Start Date: 05-2017

End Date: 05-2020

Amount: $354,500.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP130100488

Start Date: 06-2013

End Date: 06-2017

Amount: $390,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP120102849

Start Date: 2012

End Date: 05-2015

Amount: $340,000.00

Funder: Australian Research Council

ARC Future Fellowships - Grant ID: FT0991918

Start Date: 02-2010

End Date: 03-2016

Amount: $606,400.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE220100157

Start Date: 04-2022

End Date: 12-2023

Amount: $535,000.00

Funder: Australian Research Council