ORCID Profile
0000-0002-0649-7033
Current Organisation
CSIRO
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Publisher: Springer Science and Business Media LLC
Date: 29-03-2019
DOI: 10.1038/S41598-019-41813-X
Abstract: Improving nutrient utilization efficiency is essential for livestock, given the current scenario of increasing demand for animal protein and sustainable resource use. In this context, understanding the biology of feed efficiency (FE) in beef cattle allows the development of markers for identification and selection of best animals for animal production. Thus, 98 young Nellore bulls were evaluated for FE and at the end of the experiment liver s les from six High Feed Efficient (HFE) and six Low Feed Efficient (LFE) animals were collected for protein extraction, digestion and analysis by HPLC-MS/MS. Data were analyzed for differential abundant proteins (DAPs), protein networks, and functional enrichment. Serum endotoxin was also quantified. We found 42 DAPs and 3 protein networks significantly related to FE. The main pathways associated with FE were: microbial metabolism biosynthesis of fatty acids, amino acids and vitamins glycolysis/gluconeogenesis xenobiotic metabolism and antigen processing and presentation. Serum endotoxins were significantly higher in LFE animals supporting the results. Therefore, the findings presented here confirmed the altered hepatic metabolism and pronounced hepatic inflammation in LFE animals supporting that the increased bacterial load is at least in part responsible for the hepatic lesions and inflammation in LFE animals.
Publisher: Frontiers Media SA
Date: 26-05-2020
Publisher: Oxford University Press (OUP)
Date: 09-11-2019
DOI: 10.1093/JAS/SKZ344
Abstract: The growing concern with the environment is making important for livestock producers to focus on selection for efficiency-related traits, which is a challenge for commercial cattle herds due to the lack of pedigree information. To explore a cost-effective opportunity for genomic evaluations of commercial herds, this study compared the accuracy of bulls’ genomic estimated breeding values (GEBV) using different pooled genotype strategies. We used ten replicates of previously simulated genomic and phenotypic data for one low (t1) and one moderate (t2) heritability trait of 200 sires and 2,200 progeny. Sire’s GEBV were calculated using a univariate mixed model, with a hybrid genomic relationship matrix (h-GRM) relating sires to: 1) 1,100 pools of 2 animals 2) 440 pools of 5 animals 3) 220 pools of 10 animals 4) 110 pools of 20 animals 5) 88 pools of 25 animals 6) 44 pools of 50 animals and 7) 22 pools of 100 animals. Pooling criteria were: at random, grouped sorting by t1, grouped sorting by t2, and grouped sorting by a combination of t1 and t2. The same criteria were used to select 110, 220, 440, and 1,100 in idual genotypes for GEBV calculation to compare GEBV accuracy using the same number of in idual genotypes and pools. Although the best accuracy was achieved for a given trait when pools were grouped based on that same trait (t1: 0.50–0.56, t2: 0.66–0.77), pooling by one trait impacted negatively on the accuracy of GEBV for the other trait (t1: 0.25–0.46, t2: 0.29–0.71). Therefore, the combined measure may be a feasible alternative to use the same pools to calculate GEBVs for both traits (t1: 0.45–0.57, t2: 0.62–0.76). Pools of 10 in iduals were identified as representing a good compromise between loss of accuracy (~10%–15%) and cost savings (~90%) from genotype assays. In addition, we demonstrated that in more than 90% of the simulations, pools present higher sires’ GEBV accuracy than in idual genotypes when the number of genotype assays is limited (i.e., 110 or 220) and animals are assigned to pools based on phenotype. Pools assigned at random presented the poorest results (t1: 0.07–0.45, t2: 0.14–0.70). In conclusion, pooling by phenotype is the best approach to implementing genomic evaluation using commercial herd data, particularly when pools of 10 in iduals are evaluated. While combining phenotypes seems a promising strategy to allow more flexibility to the estimates made using pools, more studies are necessary in this regard.
Publisher: Springer Science and Business Media LLC
Date: 22-11-2019
DOI: 10.1038/S41598-019-53915-7
Abstract: Targeting self-renewal and tumorigenicity has been proposed as a potential strategy against cancer stem cells (CSCs). Epigenetic proteins are key modulators of gene expression and cancer development contributing to regulation and maintenance of self-renewal and tumorigenicity. Here, we have screened a small-molecule epigenetic inhibitor library using 3D in vitro models in order to determine potential epigenetic targets associated with self-renewal and tumorigenicity in Canine Mammary Cancer (CMC) cells. We identified inhibition of BET proteins as a promising strategy to inhibit CMC colonies and tumorspheres formation. Low doses of (+)-JQ1 were able to downregulate important genes associated to self-renewal pathways such as WNT, NOTCH, Hedgehog, PI3K/AKT/mTOR, EGF receptor and FGF receptor in CMC tumorspheres. In addition, we observed downregulation of ZEB2 , a transcription factor important for the maintenance of self-renewal in canine mammary cancer cells. Furthermore, low doses of (+)-JQ1 were not cytotoxic in CMC cells cultured in 2D in vitro models but induced G2/M cell cycle arrest accompanied by upregulation of G2/M checkpoint-associated genes including BTG2 and CCNG2 . Our work indicates the BET inhibition as a new strategy for canine mammary cancers by modulating the self-renewal phenotype in tumorigenic cells such as CSCs.
Publisher: MDPI AG
Date: 07-02-2022
Abstract: Mast cell tumors (MCTs) are common neoplasms in dogs, and treatments for these diseases include surgery, polychemotherapy and targeted therapy with tyrosine kinase inhibitors. This study aimed to evaluate the response and the adverse events of treatment with imatinib mesylate (IM) compared to conventional therapy using vinblastine and prednisolone (VP) in canine cutaneous MCTs. Twenty-four dogs were included in the study 13 animals were treated with IM and 11 with VP. Tumor tissue s les were submitted for histological diagnosis, grading and KIT immunostaining. The response to treatment was assessed by tomographic measurements according to VCOG criteria. Adverse events were classified according to VCOG-CTCAE criteria. The IM and VP groups had dogs with similar breeds, gender, ages, MCT localization, WHO stages and lymph node metastasis profiles. Most MCTs were grade 2/low and had KIT- patterns 2 and 3. The objective response rate (ORR) was significantly higher (30.79%) in the IM group then in VP group (9.09%). Adverse events (AE) in IM group were all grade 1, significantly different from VP. In conclusion, IM presented better ORR and less severe adverse events when compared to VP, representing a suitable option for the treatment of low-grade canine MCTs.
Publisher: MDPI AG
Date: 27-06-2020
Abstract: Most of the milk produced by sheep is used for the production of high-quality cheese. Consequently, traits related to milk coagulation properties and cheese yield are economically important to the Spanish dairy industry. The present study aims to identify candidate genes and their regulators related to 14 milk and cheese-making traits and to develop a low-density panel of markers that could be used to predict an in idual’s genetic potential for cheese-making efficiency. In this study, we performed a combination of the classical genome-wide association study (GWAS) with a stepwise regression method and a pleiotropy analysis to determine the best combination of the variants located within the confidence intervals of the potential candidate genes that may explain the greatest genetic variance for milk and cheese-making traits. Two gene networks related to milk and cheese-making traits were created using the genomic relationship matrices built through a stepwise multiple regression approach. Several co-associated genes in these networks are involved in biological processes previously found to be associated with milk synthesis and cheese-making efficiency. The methodology applied in this study enabled the selection of a co-association network comprised of 374 variants located in the surrounding of genes showing a potential influence on milk synthesis and cheese-making efficiency.
Publisher: Elsevier BV
Date: 12-2014
Publisher: Cold Spring Harbor Laboratory
Date: 17-01-2020
DOI: 10.1101/2020.01.17.910216
Abstract: Genome-wide gene expression is routinely used as a tool to gain a systems-level understanding of complex, biological processes. Numerical approaches that have been used to highlight influential genes include abundance, differential expression, differential variation, network connectivity and differential connectivity. Network connectivity tends to be built on a small subset of extremely high co-expression signals that are deemed significant, but this overlooks the vast majority of pairwise signals. Here, we aimed to assess a complementary strategy, namely whether the entire shape of the distribution of genome-wide co-expression values contains a meaningful biological signal that has hitherto remained hidden from view. We have developed a computational pipeline to assign one of 8 distributions (including normal, skewed, bimodal, kurtotic, inverted) to every gene. We then used a hypergeometric enrichment process to determine if particular genes (regulators versus non-regulators) and properties (differentially expressed or not) tend to be associated with particular distributions greater than would be expected by chance. Examination of several distinct data sets spanning 4 species indicates that there is indeed an additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches. High-throughput technologies, such as RNA-Seq, enables access to a vast amount of data. Here, we describe a new approach to interrogate these data and extract further information to help researchers to understand complex phenotypes. Our method is based on gene-level co-expression distributions which were compared to eight possible template shapes to group genes with similar behaviours. The method was tested using five different datasets and the consistency of the results indicate it can be used as a complementary strategy to analyse transcriptomic data.
Publisher: Springer Science and Business Media LLC
Date: 07-01-2019
Publisher: MDPI AG
Date: 20-10-2020
Abstract: Genome-wide gene expression analysis are routinely used to gain a systems-level understanding of complex processes, including network connectivity. Network connectivity tends to be built on a small subset of extremely high co-expression signals that are deemed significant, but this overlooks the vast majority of pairwise signals. Here, we developed a computational pipeline to assign to every gene its pair-wise genome-wide co-expression distribution to one of 8 template distributions shapes varying between unimodal, bimodal, skewed, or symmetrical, representing different proportions of positive and negative correlations. We then used a hypergeometric test to determine if specific genes (regulators versus non-regulators) and properties (differentially expressed or not) are associated with a particular distribution shape. We applied our methodology to five publicly available RNA sequencing (RNA-seq) datasets from four organisms in different physiological conditions and tissues. Our results suggest that genes can be assigned consistently to pre-defined distribution shapes, regarding the enrichment of differential expression and regulatory genes, in situations involving contrasting phenotypes, time-series, or physiological baseline data. There is indeed a striking additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches. Our method can be applied to extract further information from transcriptomic data and help uncover the molecular mechanisms involved in the regulation of complex biological process and phenotypes.
Publisher: MDPI AG
Date: 30-07-2020
DOI: 10.20944/PREPRINTS202007.0711.V1
Abstract: Long non-coding RNA (lncRNA) can regulate several aspects of gene expression, being associated with complex phenotypes in humans and livestock species. In taurine beef cattle, recent evidence points to the involvement of lncRNA in feed efficiency (FE), a proxy for increased productivity and sustainability. Here, we hypothesized specific regulatory roles of lncRNA in FE of indicine cattle. Using RNA-Seq data from liver, muscle, hypothalamus, pituitary and adrenal gland from Nellore bulls with ergent FE, we submitted new transcripts to a series of filters to confidently predict lncRNA. Then, we identified lncRNA that were differentially expressed (DE) and/or key regulators of FE. Finally, we explored lncRNA genomic location and interactions with miRNA and mRNA to infer potential function. We were able to identify 126 relevant lncRNA for FE in Bos indicus, some with high homology to previously identified lncRNA in Bos taurus and some possible specific regulators of FE in indicine cattle. Moreover, lncRNA identified here were linked to previously described mechanisms related to FE in hypothalamus-pituitary-adrenal axis and are expected to help elucidate this complex phenotype. This study contributes to expanding the catalogue of lncRNA, particularly in indicine cattle, and identifies candidates for further studies in animal selection and management.
Publisher: Springer Science and Business Media LLC
Date: 12-2015
Publisher: Oxford University Press (OUP)
Date: 19-05-2020
DOI: 10.1093/JAS/SKAA170
Abstract: In this study, we aimed to assess the value of genotyping DNA pools as a strategy to generate accurate and cost-effective genomic estimated breeding values (GEBV) of sires in multi-sire mating systems. In order to do that, we used phenotypic records of 2,436 Australian Angus cattle from 174 sires, including yearling weight (YWT N = 1,589 records), coat score (COAT N = 2,026 records), and Meat Standards Australia marbling score (MARB N = 1,304 records). Phenotypes were adjusted for fixed effects and age at measurement and pools of 2, 5, 10, 15, 20, and 25 animals were explored. Pools were created either by phenotype or at random. When pools were created at random, 10 replicates were examined to provide a measure of s ling variation. The relative accuracy of each pooling strategy was measured by the Pearson correlation coefficient between the sire’s GEBV with pooled progeny and the GEBV using in idually genotyped progeny. Random pools allow the computation of sire GEBV that are, on average, moderately correlated (i.e., r & 0.5 at pool sizes [PS] ≤ 10) with those obtained without pooling. However, for pools assigned at random, the difference between the best and the worst relative accuracy obtained out of the 10 replicates was as high as 0.41 for YWT, 0.36 for COAT, and 0.61 for MARB. This uncertainty associated with the relative accuracy of GEBV makes randomly assigning animals to pools an unreliable approach. In contrast, pooling by phenotype allowed the estimation of sires’ GEBV with a relative accuracy ≥ 0.9 at PS & 10 for all three phenotypes. Moreover, even with larger PS, the lowest relative accuracy obtained was 0.88 (YWT, PS = 20). In agreement with results using simulated data, we conclude that pooling by phenotype is a robust approach to implementing genomic evaluation using commercial herd data, and PS larger than 10 in iduals can be considered.
Publisher: Oxford University Press (OUP)
Date: 17-11-2020
DOI: 10.1093/JAS/SKAA370
Abstract: Sheep milk is mainly intended to manufacture a wide variety of high-quality cheeses. The ovine cheese industry would benefit from an improvement, through genetic selection, of traits related to the milk coagulation properties (MCPs) and cheese yield-related traits, broadly denoted as “cheese-making traits.” Considering that routine measurements of these traits needed for genetic selection are expensive and time-consuming, this study aimed to evaluate the accuracy of a cheese-making phenotype imputation method based on the information from official milk control records combined with the pH of the milk. For this study, we analyzed records of milk production traits, milk composition traits, and measurements of cheese-making traits available from a total of 1,145 dairy ewes of the Spanish Assaf sheep breed. Cheese-making traits included five related to the MCPs and two cheese yield-related traits. The milk and cheese-making phenotypes were adjusted for significant effects based on a general linear model. The adjusted phenotypes were used to define a multiple-phenotype imputation procedure for the cheese-making traits based on multivariate normality and Markov chain Monte Carlo s ling. Five of the seven cheese-making traits considered in this study achieved a prediction accuracy of 0.60 computed as the correlation between the adjusted phenotypes and the imputed phenotypes. Particularly the logarithm of curd-firming time since rennet addition (logK20) (0.68), which has been previously suggested as a potential candidate trait to improve the cheese ability in this breed, and the logarithm of the ratio between the rennet clotting time and the curd firmness at 60 min (logRCT/A60) (0.65), which has been defined by other studies as an indicator trait of milk coagulation efficiency. This study represents a first step toward the possible use of the phenotype imputation of cheese-making traits to develop a practical methodology for the dairy sheep industry to impute cheese-making traits only based on the analysis of a milk s le without the need of pedigree information. This information could be also used in future planning of specific breeding programs considering the importance of the cheese-making efficiency in dairy sheep and highlights the potential of phenotype imputation to leverage s le size on expensive, hard-to-measure phenotypes.
Publisher: CSIRO Publishing
Date: 17-08-2021
DOI: 10.1071/AN21054
Abstract: Context Immune competence is a proxy trait for general disease resistance and is based on combined measures of an animal’s ability to mount both a cell-mediated immune response (Cell-IR) and an antibody-mediated immune response (Ab-IR). On the basis of previously described arithmetic, we combined these measures into a single proxy trait for immune competence, named ImmuneDEX (IDEX). Aims Using a population of 3715 Australian Angus steers (n = 2395) and heifers (n = 1320) with genotypes for 45 364 single-nucleotide polymorphisms, we provide the latest genomic estimates of heritability and genetic correlations for IDEX and the components Cell-IR and Ab-IR immune competence phenotypes. Accuracy and bias of genomic predictions of breeding values are also presented and discussed. Methods Measures of Cell-IR, Ab-IR and IDEX were analysed jointly in a tri-variate genomic restricted maximum-likelihood model that contained the fixed effects of contemporary group with 80 levels, the linear covariates of age at measurement and change in skin thickness at control site, and the random polygenic (genomic estimated breeding value, GEBV) and residual effects. Following Method LR procedures, we estimate accuracy, bias and dispersion of genomic predictions using a cross-validation scheme based on five year-of-birth cohorts. Key results We report genomic restricted maximum-likelihood model estimates of heritability of 0.247 ± 0.040 for Cell-IR, 0.326 ± 0.059 for Ab-IR, 0.275 ± 0.046 for IDEX. While a small positive genetic correlation (rg) was estimated between Cell-IR and Ab-IR (rg = 0.138 ± 0.095), strongly positive estimates were obtained between IDEX and Cell-IR (rg = 0.740 ± 0.044) and between IDEX and Ab-IR (rg = 0.741 ± 0.036). Averaged across the five validation sets, the accuracy of GEBV for Cell-IR, Ab-IR and IDEX was 0.405, 0.443 and 0.411 respectively. Also, some significant bias or dispersion can be expected depending on the cohort used as the validation population. Conclusions Consistent with previous findings, immune competence phenotypes are moderately heritable and accurate GEBV can be generated to allow the selection of cattle with an improved ability to mount a general immune response. Implications Our analyses suggest that ImmuneDEX will provide a tool to underpin long-term genetic strategies aimed at improving the immune competence of Australian Angus cattle in production systems, which, in turn, is expected to reduce the incidence of disease and our reliance on antibiotics to treat disease.
Publisher: MDPI AG
Date: 30-09-2020
Abstract: Pilchard orthomyxovirus (POMV) is an emerging pathogen of concern to the salmon industry in Australia. To explore the molecular events that underpin POMV infection, we challenged Atlantic salmon (Salmo salar) post-smolts in seawater via cohabitation. Tissue s les of the head kidney and liver were collected from moribund and surviving in iduals and analyzed using transcriptome sequencing. Viral loads were higher in the head kidney compared to the liver, yet the liver presented more upregulated genes. Fish infected with POMV showed a strong innate immune response that included the upregulation of pathogen recognition receptors such as RIG-I and Toll-like receptors as well as the induction of interferon-stimulated genes (MX, ISG15). Moribund fish also presented a dramatic induction of pro-inflammatory cytokines, contributing to severe tissue damage and morbidity. An induction of major histocompatibility complex (MHC) class I genes (B2M) and markers of T cell-mediated immunity (CD8-alpha, CD8-beta, Perforin-1, Granzyme-A) was observed in both moribund fish and survivors. In addition, differential connectivity analysis showed that three key regulators (RELA 65, PRDM1, and HLF) related to cell-mediated immunity had significant differences in connectivity in “clinically healthy” versus “clinically affected” or moribund fish. Collectively, our results show that T cell-mediated immunity plays a central role in the response of Atlantic salmon to the infection with POMV.
Publisher: Springer Science and Business Media LLC
Date: 06-2016
Publisher: Springer Science and Business Media LLC
Date: 26-09-2021
DOI: 10.1186/S12711-021-00673-8
Abstract: Improving feedlot performance, carcase weight and quality is a primary goal of the beef industry worldwide. Here, we used data from 3408 Australian Angus steers from seven years of birth (YOB) cohorts (2011–2017) with a minimal level of sire linkage and that were genotyped for 45,152 SNPs. Phenotypic records included two feedlot and five carcase traits, namely average daily gain (ADG), average daily dry matter intake (DMI), carcase weight (CWT), carcase eye muscle area (EMA), carcase Meat Standard Australia marbling score (MBL), carcase ossification score (OSS) and carcase subcutaneous rib fat depth (RIB). Using a 7-way cross-validation based on YOB cohorts, we tested the quality of genomic predictions using the linear regression (LR) method compared to the traditional method (Pearson’s correlation between the genomic estimated breeding value (GEBV) and its associated adjusted phenotype ided by the square root of heritability) explored the factors, such as heritability, validation cohort, and phenotype that affect estimates of accuracy, bias, and dispersion calculated with the LR method and suggested a novel interpretation for translating differences in accuracy into phenotypic differences, based on GEBV quartiles (Q1Q4). Heritability (h 2 ) estimates were generally moderate to high (from 0.29 for ADG to 0.53 for CWT). We found a strong correlation (0.73, P- value 0.001) between accuracies using the traditional method and those using the LR method, although the LR method was less affected by random variation within and across years and showed a better ability to discriminate between extreme GEBV quartiles. We confirmed that bias of GEBV was not significantly affected by h 2 , validation cohort or trait. Similarly, validation cohort was not a significant source of variation for any of the GEBV quality metrics. Finally, we observed that the phenotypic differences were larger for higher accuracies. Our estimates of h 2 and GEBV quality metrics suggest a potential for accurate genomic selection of Australian Angus for feedlot performance and carcase traits. In addition, the Q1Q4 measure presented here easily translates into possible gains of genomic selection in terms of phenotypic differences and thus provides a more tangible output for commercial beef cattle producers.
Publisher: Public Library of Science (PLoS)
Date: 19-06-2019
Publisher: Springer Science and Business Media LLC
Date: 28-04-2016
Publisher: Springer Science and Business Media LLC
Date: 21-09-2021
DOI: 10.1186/S13059-021-02489-7
Abstract: Spatiotemporal changes in the chromatin accessibility landscape are essential to cell differentiation, development, health, and disease. The quest of identifying regulatory elements in open chromatin regions across different tissues and developmental stages is led by large international collaborative efforts mostly focusing on model organisms, such as ENCODE. Recently, the Functional Annotation of Animal Genomes (FAANG) has been established to unravel the regulatory elements in non-model organisms, including cattle. Now, we can transition from prediction to validation by experimentally identifying the regulatory elements in tropical indicine cattle. The identification of regulatory elements, their annotation and comparison with the taurine counterpart, holds high promise to link regulatory regions to adaptability traits and improve animal productivity and welfare. We generate open chromatin profiles for liver, muscle, and hypothalamus of indicine cattle through ATAC-seq. Using robust methods for motif discovery, motif enrichment and transcription factor binding sites, we identify potential master regulators of the epigenomic profile in these three tissues, namely HNF4, MEF2, and SOX factors, respectively. Integration with transcriptomic data allows us to confirm some of their target genes. Finally, by comparing our results with Bos taurus data we identify potential indicine-specific open chromatin regions and overlaps with indicine selective sweeps. Our findings provide insights into the identification and analysis of regulatory elements in non-model organisms, the evolution of regulatory elements within two cattle subspecies as well as having an immediate impact on the animal genetics community in particular for a relevant productive species such as tropical cattle.
Publisher: Oxford University Press (OUP)
Date: 15-10-2020
DOI: 10.1093/JAS/SKAA337
Abstract: Genomic tools to better define breed composition in agriculturally important species have sparked scientific and commercial industry interest. Knowledge of breed composition can inform multiple scientifically important decisions of industry application including DNA marker-assisted selection, identification of signatures of selection, and inference of product provenance to improve supply chain integrity. Genomic tools are expensive but can be economized by deploying a relatively small number of highly informative single-nucleotide polymorphisms (SNP) scattered evenly across the genome. Using resources from the 1000 Bull Genomes Project we established calibration (more stringent quality criteria N = 1,243 cattle) and validation (less stringent N = 864) data sets representing 17 breeds derived from both taurine and indicine bovine subspecies. Fifteen successively smaller panels (from 500,000 to 50 SNP) were built from those SNP in the calibration data that increasingly satisfied 2 criteria, high differential allele frequencies across the breeds as measured by average Euclidean distance (AED) and high uniformity (even spacing) across the physical genome. Those SNP awarded the highest AED were in or near genes previously identified as important signatures of selection in cattle such as LCORL, NCAPG, KITLG, and PLAG1. For each panel, the genomic breed composition (GBC) of each animal in the validation dataset was estimated using a linear regression model. A systematic exploration of the predictive accuracy of the various sized panels was then undertaken on the validation population using 3 benchmarking approaches: (1) % error (expressed relative to the estimated GBC made from over 1 million SNP), (2) % breed misassignment (expressed relative to each in idual’s breed recorded), and (3) Shannon’s entropy of estimated GBC across the 17 target breeds. Our analyses suggest that a panel of just 250 SNP represents an adequate balance between accuracy and cost—only modest gains in accuracy are made as one increases panel density beyond this point.
Publisher: Springer Science and Business Media LLC
Date: 30-01-2019
DOI: 10.1038/S41598-018-37413-W
Abstract: Melatonin is a pleiotropic anti-cancer molecule that controls cancer growth by multiple mechanisms. RNA-Seq can potentially evaluate therapeutic response and its use in xenograft tumor models can differentiate the changes that occur specifically in tumor cells or in the tumor microenvironment (TME). Melatonin actions were evaluated in a xenograft model of triple-negative breast cancer. Balb/c nude mice bearing MDA-MB-231 tumors were treated with melatonin or vehicle. RNA-Seq was performed on the Illumina HiSeq. 2500 and data were mapped against human and mouse genomes separately to differentiate species-specific expression. Differentially expressed (DE) genes were identified and Weighted Gene Co-expression Network Analysis (WGCNA) was used to detect clusters of highly co-expressed genes. Melatonin treatment reduced tumor growth (p 0.01). 57 DE genes were identified in murine cells, which represented the TME, and were mainly involved in immune response. The WGCNA detected co-expressed genes in tumor cells and TME, which were related to the immune system among other biological processes. The upregulation of two genes (Tnfaip8l2 and Il1f6) by melatonin was validated in the TME, these genes play important roles in the immune system. Taken together, the transcriptomic data suggests that melatonin anti-tumor actions occur through modulation of TME in this xenograft tumor model.
Publisher: Springer Science and Business Media LLC
Date: 09-2017
Publisher: Frontiers Media SA
Date: 22-03-2019
Publisher: Colegio Brasileiro de Reproducao Animal - CBRA
Date: 2017
Publisher: MDPI AG
Date: 25-08-2020
Abstract: Long non-coding RNA (lncRNA) can regulate several aspects of gene expression, being associated with complex phenotypes in humans and livestock species. In taurine beef cattle, recent evidence points to the involvement of lncRNA in feed efficiency (FE), a proxy for increased productivity and sustainability. Here, we hypothesized specific regulatory roles of lncRNA in FE of indicine cattle. Using RNA-Seq data from the liver, muscle, hypothalamus, pituitary gland and adrenal gland from Nellore bulls with ergent FE, we submitted new transcripts to a series of filters to confidently predict lncRNA. Then, we identified lncRNA that were differentially expressed (DE) and/or key regulators of FE. Finally, we explored lncRNA genomic location and interactions with miRNA and mRNA to infer potential function. We were able to identify 126 relevant lncRNA for FE in Bos indicus, some with high homology to previously identified lncRNA in Bos taurus and some possible specific regulators of FE in indicine cattle. Moreover, lncRNA identified here were linked to previously described mechanisms related to FE in hypothalamus-pituitary-adrenal axis and are expected to help elucidate this complex phenotype. This study contributes to expanding the catalogue of lncRNA, particularly in indicine cattle, and identifies candidates for further studies in animal selection and management.
Publisher: Springer International Publishing
Date: 2016
Publisher: Public Library of Science (PLoS)
Date: 26-01-2023
DOI: 10.1371/JOURNAL.PONE.0279398
Abstract: Worldwide, most beef breeding herds are naturally mated. As such, the ability to identify and select fertile bulls is critically important for both productivity and genetic improvement. Here, we collected ten fertility-related phenotypes for 6,063 bulls from six tropically adapted breeds. Phenotypes were comprised of four bull conformation traits and six traits directly related to the quality of the bull’s semen. We also generated high-density DNA genotypes for all the animals. In total, 680,758 single nucleotide polymorphism (SNP) genotypes were analyzed. The genomic correlation of the same trait observed in different breeds was positive for scrotal circumference and sheath score on most breed comparisons, but close to zero for the percentage of normal sperm, suggesting a ergent genetic background for this trait. We confirmed the importance of a breed being present in the reference population to the generation of accurate genomic estimated breeding values (GEBV) in an across-breed validation scenario. Average GEBV accuracies varied from 0.19 to 0.44 when the breed was not included in the reference population. The range improved to 0.28 to 0.59 when the breed was in the reference population. Variants associated with the gene HDAC4, six genes from the spermatogenesis-associated (SPATA) family of proteins, and 29 transcription factors were identified as candidate genes. Collectively these results enable very early in-life selection for bull fertility traits, supporting genetic improvement strategies currently taking place within tropical beef production systems. This study also improves our understanding of the molecular basis of male fertility in mammals.
Publisher: Genetics and Molecular Research
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 21-02-2021
DOI: 10.1186/S40168-020-00994-8
Abstract: Analyses of gut microbiome composition in livestock species have shown its potential to contribute to the regulation of complex phenotypes. However, little is known about the host genetic control over the gut microbial communities. In pigs, previous studies are based on classical “single-gene-single-trait” approaches and have evaluated the role of host genome controlling gut prokaryote and eukaryote communities separately. In order to determine the ability of the host genome to control the ersity and composition of microbial communities in healthy pigs, we undertook genome-wide association studies (GWAS) for 39 microbial phenotypes that included 2 ersity indexes, and the relative abundance of 31 bacterial and six commensal protist genera in 390 pigs genotyped for 70 K SNPs. The GWAS results were processed through a 3-step analytical pipeline comprised of (1) association weight matrix (2) regulatory impact factor and (3) partial correlation and information theory. The inferred gene regulatory network comprised 3561 genes (within a 5 kb distance from a relevant SNP– P 0.05) and 738,913 connections (SNP-to-SNP co-associations). Our findings highlight the complexity and polygenic nature of the pig gut microbial ecosystem. Prominent within the network were 5 regulators, PRDM15 , STAT1 , ssc-mir-371 , SOX9 and RUNX2 which gathered 942, 607, 588, 284 and 273 connections, respectively. PRDM15 modulates the transcription of upstream regulators of WNT and MAPK-ERK signaling to safeguard naive pluripotency and regulates the production of Th1- and Th2-type immune response. The signal transducer STAT1 has long been associated with immune processes and was recently identified as a potential regulator of vaccine response to porcine reproductive and respiratory syndrome. The list of regulators was enriched for immune-related pathways, and the list of predicted targets includes candidate genes previously reported as associated with microbiota profile in pigs, mice and human, such as SLIT3 , SLC39A8 , NOS1 , IL1R2 , DAB1 , TOX3 , SPP1 , THSD7B , ELF2 , PIANP , A2ML1 , and IFNAR1 . Moreover, we show the existence of host-genetic variants jointly associated with the relative abundance of butyrate producer bacteria and host performance. Taken together, our results identified regulators, candidate genes, and mechanisms linked with microbiome modulation by the host. They further highlight the value of the proposed analytical pipeline to exploit pleiotropy and the crosstalk between bacteria and protists as significant contributors to host-microbiome interactions and identify genetic markers and candidate genes that can be incorporated in breeding program to improve host-performance and microbial traits.
Publisher: Genetics and Molecular Research
Date: 2019
DOI: 10.4238/GMR18226
Publisher: Frontiers Media SA
Date: 16-02-2021
DOI: 10.3389/FGENE.2021.619857
Abstract: Machine learning (ML) methods have shown promising results in identifying genes when applied to large transcriptome datasets. However, no attempt has been made to compare the performance of combining different ML methods together in the prediction of high feed efficiency (HFE) and low feed efficiency (LFE) animals. In this study, using RNA sequencing data of five tissues (adrenal gland, hypothalamus, liver, skeletal muscle, and pituitary) from nine HFE and nine LFE Nellore bulls, we evaluated the prediction accuracies of five analytical methods in classifying FE animals. These included two conventional methods for differential gene expression (DGE) analysis ( t -test and edgeR) as benchmarks, and three ML methods: Random Forests (RFs), Extreme Gradient Boosting (XGBoost), and combination of both RF and XGBoost (RX). Utility of a subset of candidate genes selected from each method for classification of FE animals was assessed by support vector machine (SVM). Among all methods, the smallest subsets of genes (117) identified by RX outperformed those chosen by t -test, edgeR, RF, or XGBoost in classification accuracy of animals. Gene co-expression network analysis confirmed the interactivity existing among these genes and their relevance within the network related to their prediction ranking based on ML. The results demonstrate a great potential for applying a combination of ML methods to large transcriptome datasets to identify biologically important genes for accurately classifying FE animals.
Publisher: Elsevier BV
Date: 12-2019
DOI: 10.1016/J.TVJL.2019.105393
Abstract: Mammary gland tumors are a heterogeneous group of neoplastic diseases. Genetic studies make it possible to determine genetic profiles and identify new molecular markers. The aim of the study was to evaluate the gene expression profile of canine mammary carcinomas and identify potential prognostic markers. Twelve mammary cancer s les from bitches were collected for the evaluation of global gene expression. Microarray assays were performed using commercial kits. Statistical analysis of the microarray was done using moderate t-statistic and adjusted using the Benjamini and Hochberg procedure. Differential connectivity analysis was also performed. Enrichment analyses were conducted using WebGestalt. P-values were calculated using hypergeometric statistics and adjusted using the Benjamini and Hochberg procedure. The HYAL-1 gene was validated using quantitative PCR (qPCR). There were 878 upregulated genes and 821 downregulated genes in the neoplasms studied. Enrichment analysis (in idual analysis) identified the HYAL-1 gene as a potential marker of tumorigenesis and tumor recurrence. Differential connectivity analysis demonstrated 262 differentially connected genes.
Publisher: Cold Spring Harbor Laboratory
Date: 09-06-2021
DOI: 10.1101/2021.06.08.447584
Abstract: The aim of the present work was to identify microbial biomarkers linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs. To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (h 2 proportion of phenotypic variance explained by the host genetics) and microbiability (m 2 proportion of variance explained by the microbial composition) showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that were dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m 2 (0.275) compared to h 2 (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r=0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r=-0.34) and between Megasphaera and serum concentration of haptoglobin (r=0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively. Overall, our findings reveal an important connection between immunity traits and gut microbiota in pigs and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs.
Publisher: Wiley
Date: 27-05-2015
DOI: 10.1111/JBG.12167
Abstract: The aim of this study was to identify candidate genes and genomic regions associated with ultrasound-derived measurements of the rib-eye area (REA), backfat thickness (BFT) and rumpfat thickness (RFT) in Nellore cattle. Data from 640 Nellore steers and young bulls with genotypes for 290 863 single nucleotide polymorphisms (SNPs) were used for genomewide association mapping. Significant SNP associations were explored to find possible candidate genes related to physiological processes. Several of the significant markers detected were mapped onto functional candidate genes including ARFGAP3, CLSTN2 and DPYD for REA OSBPL3 and SUDS3 for BFT and RARRES1 and VEPH1 for RFT. The physiological pathway related to lipid metabolism (CLSTN2, OSBPL3, RARRES1 and VEPH1) was identified. The significant markers within previously reported QTLs reinforce the importance of the genomic regions, and the other loci offer candidate genes that have not been related to carcass traits in previous investigations.
Publisher: Wiley
Date: 06-04-2022
DOI: 10.1111/AGE.13199
Abstract: The beef fatty acid (FA) profile has the potential to impact human health, and displays polygenic and complex features. This study aimed to identify the transcriptomic FA profile in the longissimus thoracis muscle in Nellore beef cattle finished in feedlot. Forty‐four young bulls were s led to assess the beef FA profile by considering 14 phenotypes and including differentially expressed genes (DEG), co‐expressed (COE), and differentially co‐expressed genes (DCO) analyses. All s les ( n = 44) were used for COE analysis, whereas 30 s les with extreme phenotypes for the beef FA profile were used for DEG and DCO. A total of 912 DEG were identified, and the polyunsaturated ( n = 563) and unsaturated ω‐3 ( n = 346) FA sums groups were the most frequently observed. The COE analyses identified three modules, of which the blue module ( n = 1776) was correlated with eight of 14 FA phenotypes. Also, 759 DCO genes were listed, and the oleic acid ( n = 358) and monounsaturated fatty acids sum ( n = 120) were the most frequent. Furthermore, 243 and 13, 319 and seven, and 173 and 12 gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways were enriched respectively for the DEG, COE, and DCO analyses. Combining the results, we highlight the unexplored GIPC2 , ASB5 , and PPP5C genes in cattle. Besides LIPE and INSIG2 genes in COE modules, the ACSL3 , ECI1 , DECR2 , FITM1 , and SDHB genes were signaled in at least two analyses. These findings contribute to understand the genetic mechanisms underlying the beef FA profile in Nellore beef cattle finished in feedlot.
Location: Australia
Start Date: 2017
End Date: 2018
Funder: Fundação de Amparo à Pesquisa do Estado de São Paulo
View Funded ActivityStart Date: 2015
End Date: 2018
Funder: Fundação de Amparo à Pesquisa do Estado de São Paulo
View Funded ActivityStart Date: 2014
End Date: 2014
Funder: Fundação de Amparo à Pesquisa do Estado de São Paulo
View Funded ActivityStart Date: 2016
End Date: 2019
Funder: Fundação de Amparo à Pesquisa do Estado de São Paulo
View Funded ActivityStart Date: 2012
End Date: 2015
Funder: Fundação de Amparo à Pesquisa do Estado de São Paulo
View Funded Activity