ORCID Profile
0000-0002-4431-2861
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Veterinary Parasitology | Medicinal and Biomolecular Chemistry | Biologically Active Molecules | Veterinary Pharmacology
Expanding Knowledge in the Chemical Sciences | Expanding Knowledge in the Agricultural and Veterinary Sciences | Veterinary Pharmaceutical Treatments (e.g. Antibiotics) |
Publisher: Cold Spring Harbor Laboratory
Date: 16-03-2022
DOI: 10.1101/2022.03.14.484309
Abstract: Nitrogen (N) and phosphorus (P) are two essential plant macronutrients that can limit plant growth by different mechanisms. We aimed to shed light on how soybean respond to low nitrogen (LN), low phosphorus (LP) and their combined deficiency (LNP). Generally, these conditions triggered changes in gene expression of the same processes, including cell wall organization, defense response, response to oxidative stress, and photosynthesis, however, response was different in each condition. A typical primary response to LN and LP was detected also in soybean, i.e., the enhanced uptake of N and P, respectively, by upregulation of genes for the corresponding transporters. The regulation of genes involved in cell wall organization showed that in LP roots tended to produce more casparian strip, in LN more secondary wall biosynthesis occurred, and in LNP reduction in expression of genes involved in secondary wall production accompanied by cell wall loosening was observed. Flavonoid biosynthesis also showed distinct pattern of regulation in different conditions: more anthocyanin production in LP, and more isoflavonoid production in LN and LNP, which we confirmed also on the metabolite level. Interestingly, in soybean the nutrient deficiencies reduced defense response by lowering expression of genes involved in defense response, suggesting a role of N and P nutrition in plant disease resistance. In conclusion, we provide detailed information on how LN, LP, and LNP affect different processes in soybean roots on the molecular and physiological levels.
Publisher: Public Library of Science (PLoS)
Date: 05-11-2015
Publisher: Frontiers Media SA
Date: 30-06-2014
Publisher: Springer Science and Business Media LLC
Date: 11-02-2015
DOI: 10.1007/S11259-015-9629-2
Abstract: Bovine leukemia virus (BLV) is the causative agent of enzootic bovine leukosis (EBL). BLV can interact with telomerase and inhibits telomere shortening, contributing in leukemogenesis and tumour induction. The role of telomerase in BLV-induced lymphosarcoma and aging has been extensively studied. To date, the interaction of both BLV and aging on telomerase mis-regulation have, however, not been investigated. In the present study, telomerase activity in BLV positive and negative cows was compared over a wide range of ages (11-85 months). Lymphocyte counts were also measured in both BLV positive and negative groups. Telomerase activity was detected in all BLV infected animals with persistent lymphocytosis (PL), especially in older in iduals. This study revealed that the cells undergo the natural telomerase shortening even in the presence of an existing viral infection. We also show that viral infection, especially during the PL phase of the disease, increases telomerase activity. A statistically significant interaction between age and viral infection was observed for telomere shortening during BLV infection. Older animals with BLV infection, especially those with persistent lymphocytosis or visible tumors, exhibited a sharp increase in telomerase activity. This study demonstrates that there is a significant interaction between BLV infection and telomerase up-regulation and lymphocytosis.
Publisher: Public Library of Science (PLoS)
Date: 10-08-2011
Publisher: Springer Science and Business Media LLC
Date: 25-06-2019
DOI: 10.1038/S41598-019-45661-7
Abstract: Native chickens are endangered genetic resources that are kept by farmers for different purposes. Native chickens distributed in a wide range of altitudes, have developed adaptive mechanisms to deal with hypoxia. For the first time, we report variants associated with high-altitude adaptation in Iranian native chickens by whole genome sequencing of lowland and highland chickens. We found that these adaptive variants are involved in DNA repair, organs development, immune response and histone binding. Amazingly, signature selection analysis demonstrated that differential variants are adaptive in response to hypoxia and are not due to other evolutionary pressures. Cellular component analysis of variants showed that mitochondrion is the most important organelle for hypoxia adaptation. A total of 50 variants was detected in mtDNA for highland and lowland chickens. High-altitude associated with variant discovery highlighted the importance of COX3 , a gene involved in cell respiration, in hypoxia adaptation. The results of study suggest that MIR6644-2 is involved in hypoxia and high-altitude adaptations by regulation of embryo development. Finally, 3877 novel SNVs including the mtDNA ones, were submitted to EBI (PRJEB24944). Whole-genome sequencing and variant discovery of native chickens provided novel insights about adaptation mechanisms and highlights the importance of valuable genomic variants in chickens.
Publisher: Public Library of Science (PLoS)
Date: 11-12-2012
Publisher: Cambridge University Press (CUP)
Date: 20-07-2015
DOI: 10.1017/S0022029915000321
Abstract: Developing a reliable mastitis challenge infection model is required to test new intramammary antimicrobial preparations, other novel bovine mastitis treatments, and study mastitis pathogenesis. Three treatment groups of Holstein Friesian cows in active lactation were administered two doses (10 4 and 10 6 cfu/quarter) on a single occasion with one of the three Streptococcus uberis strains (BFR6019, MFF1283 and SA002) suspended in 5 ml of sterile PBS, administered via intramammary inoculation immediately after milking. All quarters that were challenged with S. uberis strains MLF1283 and BFR6019 showed clinical signs of mastitis on day 1 and 2 after the challenge. Strain SA002 had a lower rate of inducing clinical mastitis which was detected later than day 3 after the challenge. We successfully developed a rapid and reliable model for inducing experimental S. uberis mastitis with 100% success rate in cows in active lactation. On the basis of the correlation results between strains, RAPD fingerprinting results, clinical findings, and a 100% success rate of mastitis induction for low and high doses S. uberis strains MLF1283 and BFR6019, strain virulence seems to be a more important effect than challenge dose in induction of clinical mastitis following experimental challenge.
Publisher: Maad Rayan Publishing Company
Date: 30-06-2017
Publisher: Wiley
Date: 25-04-2019
DOI: 10.1111/AVJ.12799
Abstract: Koalas in the Mount Lofty Ranges, South Australia, have a high prevalence of oxalate nephrosis, or calcium oxalate kidney crystals. Gastrointestinal tract oxalate-degrading bacteria, particularly Oxalobacter formigenes, have been identified in other animal species and humans, and their absence or low abundance is postulated to increase the risk of renal oxalate diseases. This study aimed to identify oxalate-degrading bacteria in the gastrointestinal tract of koalas and determine their association with oxalate nephrosis. Caecal and faecal s les were collected at necropsy from 22 Mount Lofty Ranges koalas that had been euthanased on welfare grounds, with 8 koalas found to have oxalate nephrosis by renal histopathology. S les were analysed by PCR for the oxc gene, which encodes oxalyl-CoA decarboxylase, and also by Illumina sequencing of the V3-V4 region of the bacterial 16S rRNA gene. The oxc gene was detected in 100% of koala s les, regardless of oxalate nephrosis status. Oxalobacter formigenes was detected in all but one faecal s le, with no difference in abundance between koalas affected and unaffected by oxalate nephrosis. Other species of known oxalate-degrading bacteria were infrequently detected. This is the first study to identify Oxalobacter and other oxalate-degrading bacterial species in koalas, but an association with oxalate nephrosis and absence or low abundance of Oxalobacter was not found. This suggests other mechanisms underlie the risk of oxalate nephrosis in koalas.
Publisher: Public Library of Science (PLoS)
Date: 10-01-2018
Publisher: IEEE
Date: 11-2010
Publisher: Elsevier BV
Date: 06-2019
Publisher: Springer Science and Business Media LLC
Date: 14-03-2022
DOI: 10.1007/S11033-022-07257-9
Abstract: Splice-disrupt genomic variants are one of the causes of cancer-causing errors in gene expression. Little is known about splice-disrupt genomic variants. Here, pattern of splice-disrupt variants was investigated using 21,842,764 genomic variants in different types of prostate cancer. A particular attention was paid to genomic locations of splice-disrupt variants on target genes. HLA-A in prostate cancer, MSR1 in familial prostate cancer, and EGFR in both castration-resistant prostate cancer and metastatic castration-resistant had the highest allele frequencies of splice-disrupt variations. Some splice-disrupt variants, located on coding sequences of NCOR2 , PTPRC , and CRP , were solely present in the advanced metastatic castration-resistant prostate cancer. High-risk splice-disrupt variants were identified based on computationally calculated Polymorphism Phenotyping (PolyPhen), Sorting Intolerant From Tolerant (SIFT), and Genomic Evolutionary Rate Profiling (GERP) + + scores as well as the recorded clinical significance in dbSNP database of NCBI. Functional annotation of damaging splice-disrupt variants highlighted important cancer-associated functions, including endocrine resistance, lipid metabolic process, steroid metabolic process, regulation of mitotic cell cycle, and regulation of metabolic process. This is the first study that profiles the splice-disrupt genomic variants and their target genes in prostate cancer. Literature mining based variant analysis highlighted the importance of rs1800716 variant, located on the CYP2D6 gene, involved in a range of important functions, such as RNA spicing, drug interaction, death, and urotoxicity. This is the first study that profiles the splice-disrupt genomic variants and their target genes in different types of prostate cancer. Unravelling alternative splicing opens a new avenue towards the establishment of new diagnostic and prognostic markers for prostate cancer progression and metastasis.
Publisher: MDPI AG
Date: 04-02-2021
Abstract: Our knowledge of the evolution and the role of untranslated region (UTR) in SARS-CoV-2 pathogenicity is very limited. Leader sequence, originated from UTR, is found at the 5′ ends of all encoded SARS-CoV-2 transcripts, highlighting its importance. Here, evolution of leader sequence was compared between human pathogenic and non-pathogenic coronaviruses. Then, profiling of microRNAs that can inactivate the key UTR regions of coronaviruses was carried out. A distinguished pattern of evolution in leader sequence of SARS-CoV-2 was found. Mining all available microRNA families against leader sequences of coronaviruses resulted in discovery of 39 microRNAs with a stable thermodynamic binding energy. Notably, SARS-CoV-2 had a lower binding stability against microRNAs. hsa-MIR-5004-3p was the only human microRNA able to target the leader sequence of SARS and to a lesser extent, also SARS-CoV-2. However, its binding stability decreased remarkably in SARS-COV-2. We found some plant microRNAs with low and stable binding energy against SARS-COV-2. Meta-analysis documented a significant (p 0.01) decline in the expression of MIR-5004-3p after SARS-COV-2 infection in trachea, lung biopsy, and bronchial organoids as well as lung-derived Calu-3 and A549 cells. The paucity of the innate human inhibitory microRNAs to bind to leader sequence of SARS-CoV-2 can contribute to its high replication in infected human cells.
Publisher: Public Library of Science (PLoS)
Date: 17-04-2015
Publisher: Armenian Green Publishing Co.
Date: 09-02-2015
DOI: 10.15171/IJB.1045
Publisher: Frontiers Media SA
Date: 12-11-2018
Publisher: Springer Science and Business Media LLC
Date: 31-07-2007
Publisher: Springer Science and Business Media LLC
Date: 2011
Publisher: Public Library of Science (PLoS)
Date: 07-08-2018
Publisher: Springer Science and Business Media LLC
Date: 12-12-2018
DOI: 10.1007/S10495-017-1431-X
Abstract: The original version of this article unfortunately contained a mistake. The affiliation of first author Dr. Ibrahim Alanazi was incorrect.
Publisher: MDPI AG
Date: 28-09-2021
Abstract: Spinal cord injury (SCI) is a debilitating condition within the neural system which is clinically manifested by sensory-motor dysfunction, leading, in some cases, to neural paralysis for the rest of the patient’s life. In the current study, mesenchymal stem cells (MSCs) were isolated from the human amniotic fluid, in order to study their juxtacrine and paracrine activities. Flow cytometry analysis was performed to identify the MSCs. A conditioned medium (CM) was collected to measure the level of BDNF, IL-1β, and IL-6 proteins using the ELISA assay. Following the SCI induction, MSCs and CM were injected into the lesion site, and also CM was infused intraperitoneally in the different groups. Two weeks after SCI induction, the spinal cord s les were examined to evaluate the expression of the doublecortin (DCX) and glial fibrillary acid protein (GFAP) markers using immunofluorescence staining. The MSCs’ phenotype was confirmed upon the expression and un-expression of the related CD markers. Our results show that MSCs increased the expression level of the DCX and decreased the level of the GFAP relative to the injury group (p 0.001). Additionally, the CM promoted the DCX expression rate (p 0.001) and decreased the GFAP expression rate (p 0.01) as compared with the injury group. Noteworthily, the restorative potential of the MSCs was higher than that of the CM (p 0.01). Large-scale meta-analysis of transcriptomic data highlighted PAK5, ST8SIA3, and NRXN1 as positively coexpressed genes with DCX. These genes are involved in neuroactive ligand–receptor interaction. Overall, our data revealed that both therapeutic interventions could promote the regeneration and restoration of the damaged neural tissue by increasing the rate of neuroblasts and decreasing the astrocytes.
Publisher: Springer Science and Business Media LLC
Date: 20-06-2016
DOI: 10.1007/S11033-016-4025-8
Abstract: Diminished ovarian reserve (DOR) is one of the reasons for infertility that not only affects both older and young women. Ovarian reserve assessment can be used as a new prognostic tool for infertility treatment decision making. Here, up- and down-regulated gene expression profiles of granulosa cells were analysed to generate a putative interaction map of the involved genes. In addition, gene ontology (GO) analysis was used to get insight intol the biological processes and molecular functions of involved proteins in DOR. Eleven up-regulated genes and nine down-regulated genes were identified and assessed by constructing interaction networks based on their biological processes. PTGS2, CTGF, LHCGR, CITED, SOCS2, STAR and FSTL3 were the key nodes in the up-regulated networks, while the IGF2, AMH, GREM, and FOXC1 proteins were key in the down-regulated networks. MIRN101-1, MIRN153-1 and MIRN194-1 inhibited the expression of SOCS2, while CSH1 and BMP2 positively regulated IGF1 and IGF2. Ossification, ovarian follicle development, vasculogenesis, sequence-specific DNA binding transcription factor activity, and golgi apparatus are the major differential groups between up-regulated and down-regulated genes in DOR. Meta-analysis of publicly available transcriptomic data highlighted the high coexpression of CTGF, connective tissue growth factor, with the other key regulators of DOR. CTGF is involved in organ senescence and focal adhesion pathway according to GO analysis. These findings provide a comprehensive system biology based insight into the aetiology of DOR through network and gene ontology analyses.
Publisher: Wiley
Date: 10-09-2018
DOI: 10.1002/JCP.27330
Abstract: Neurodegenerative diseases are disorders in the central nervous system with consequent progressive neurological symptoms including behavioral and cognitive disabilities. Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, Parkinson’s disease, multiple sclerosis, and schizophrenia are the most important and abundant neurodegenerative diseases that affect different parts of the brain. Detailed studies unveiled the molecular mechanisms and pathways affected in each of these disorders. The role of many genes has been documented in the onset and progression of each disease. Although many system‐level approaches have been used to understand the exact cause of these diseases, there is no comparative analysis in this regard. Despite all differences in the molecular basis of these diseases, overlapping symptoms might indicate the involvement of the similar pathways and processes. Here, we have applied a system biology approach to uncover many aspects of main neurodegenerative diseases using microarray data obtained from 118 cases of postmortem brain s les. Our analysis has identified key genes that might contribute to the status of diseases. We have also compared the involved biological process and pathway between different disease to find possible similar mechanisms that exist in all of them. We also predicted potentially important transcription factors in each disease and predicted the core gene regulatory networks. We have provided a list of possible new key regulators that could be further explored and also discussed the role of these hub genes. The results of this study would be useful to develop new diagnostic strategies and also to find new drug targets.
Publisher: Springer Science and Business Media LLC
Date: 13-05-2016
DOI: 10.1007/S12033-016-9938-X
Abstract: Novel computational systems biology tools such as common targets analysis, common regulators analysis, pathway discovery, and transcriptomic-based hotspot discovery provide new opportunities in understanding of apoptosis molecular mechanisms. In this study, after measuring the global contribution of microRNAs in the course of apoptosis by Affymetrix platform, systems biology tools were utilized to obtain a comprehensive view on the role of microRNAs in apoptosis process. Network analysis and pathway discovery highlighted the crosstalk between transcription factors and microRNAs in apoptosis. Within the transcription factors, PRDM1 showed the highest upregulation during the course of apoptosis, with more than 9-fold expression increase compared to non-apoptotic condition. Within the microRNAs, MIR1208 showed the highest expression in non-apoptotic condition and downregulated by more than 6 fold during apoptosis. Common regulators algorithm showed that TNF receptor is the key upstream regulator with a high number of regulatory interactions with the differentially expressed microRNAs. BCL2 and AKT1 were the key downstream targets of differentially expressed microRNAs. Enrichment analysis of the genomic locations of differentially expressed microRNAs led us to the discovery of chromosome bands which were highly enriched (p < 0.01) with the apoptosis-related microRNAs, such as 13q31.3, 19p13.13, and Xq27.3 This study opens a new avenue in understanding regulatory mechanisms and downstream functions in the course of apoptosis as well as distinguishing genomic-enriched hotspots for apoptosis process.
Publisher: Cold Spring Harbor Laboratory
Date: 12-03-2018
Publisher: Springer Science and Business Media LLC
Date: 16-11-2016
Publisher: Genetics and Molecular Research
Date: 2012
Publisher: Elsevier BV
Date: 04-2018
Publisher: MDPI AG
Date: 23-12-2021
DOI: 10.3390/ANI12010029
Abstract: Mastitis, a disease with high incidence worldwide, is the most prevalent and costly disease in the dairy industry. Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the leading agents causing acute severe infection with clinical signs. E. Coli, environmental mastitis pathogens, are the primary etiological agents of bovine mastitis in well-managed dairy farms. Response to E. Coli infection has a complex pattern affected by genetic and environmental parameters. On the other hand, the efficacy of antibiotics and/or anti-inflammatory treatment in E. coli mastitis is still a topic of scientific debate, and studies on the treatment of clinical cases show conflicting results. Unraveling the bio-signature of mastitis in dairy cattle can open new avenues for drug repurposing. In the current research, a novel, semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration, was used to potentially identify novel therapeutic avenues for the treatment of E. coli mastitis. Online data repositories relevant to known diseases, drugs, and gene targets, along with other specialized biological information for E. coli mastitis, including critical genes with robust bio-signatures, drugs, and related disorders, were used as input data for analysis with the Heter-LP algorithm. Our research identified novel drugs such as Glibenclamide, Ipratropium, Salbutamol, and Carbidopa as possible therapeutics that could be used against E. coli mastitis. Predicted relationships can be used by pharmaceutical scientists or veterinarians to find commercially efficacious medicines or a combination of two or more active compounds to treat this infectious disease.
Publisher: Maad Rayan Publishing Company
Date: 24-10-2019
Abstract: Cancer has long been considered as a heterogeneous population of uncontrolled proliferation ofdifferent transformed cell types. The recent findings concerning tumorigeneses have highlightedthe fact that tumors can progress through tight relationships among tumor cells, cellular, andnon-cellular components which are present within tumor tissues. In recent years, studies haveshown that mesenchymal stem cells (MSCs) are essential components of non-tumor cells withinthe tumor tissues that can strongly affect tumor development. Several forms of MSCs have beenidentified within tumor stroma. Naïve (innate) mesenchymal stem cells (N-MSCs) derived fromdifferent sources are mostly recruited into the tumor stroma. N-MSCs exert dual and ergenteffects on tumor growth through different conditions and factors such as toll-like receptorpriming (TLR-priming), which is the primary underlying causes of opposite effects. Moreover,MSCs also have the contrary effects by various molecular mechanisms relying on direct cellto-cell connections and indirect communications through the autocrine, paracrine routes, andtumor microenvironment (TME).Overall, cell-based therapies will hold great promise to provide novel anticancer treatments.However, the application of intact MSCs in cancer treatment can theoretically cause adverseclinical outcomes. It is essential that to extensively analysis the effective factors and conditionsin which underlying mechanisms are adopted by MSCs when encounter with cancer.The aim is to review the cellular and molecular mechanisms underlying the dual effects ofMSCs followed by the importance of polarization of MSCs through priming of TLRs. br /
Publisher: American Association for Cancer Research (AACR)
Date: 14-05-2018
DOI: 10.1158/1078-0432.CCR-17-1199
Abstract: Purpose: Consensus is lacking regarding the androgen receptor (AR) as a prognostic marker in breast cancer. The objectives of this study were to comprehensively review the literature on AR prognostication and determine optimal criteria for AR as an independent predictor of breast cancer survival. Experimental Design: AR positivity was assessed by immunostaining in two clinically validated primary breast cancer cohorts [training cohort, n = 219 validation cohort, n = 418 77% and 79% estrogen receptor alpha (ERα) positive, respectively]. The optimal AR cut-point was determined by ROC analysis in the training cohort and applied to both cohorts. Results: AR was an independent prognostic marker of breast cancer outcome in 22 of 46 (48%) previous studies that performed multivariate analyses. Most studies used cut-points of 1% or 10% nuclear positivity. Herein, neither 1% nor 10% cut-points were robustly prognostic. ROC analysis revealed that a higher AR cut-point (78% positivity) provided optimal sensitivity and specificity to predict breast cancer survival in the training (HR, 0.41 P = 0.015) and validation (HR, 0.50 P = 0.014) cohorts. Tenfold cross-validation confirmed the robustness of this AR cut-point. Patients with ERα-positive tumors and AR positivity ≥78% had the best survival in both cohorts (P & 0.0001). Among the combined ERα-positive cases, those with comparable or higher levels of AR (AR:ERα-positivity ratio & .87) had the best outcomes (P & 0.0001). Conclusions: This study defines an optimal AR cut-point to reliably predict breast cancer survival. Testing this cut-point in prospective cohorts is warranted for implementation of AR as a prognostic factor in the clinical management of breast cancer. Clin Cancer Res 24(10) 2328–41. ©2018 AACR.
Publisher: Oxford University Press (OUP)
Date: 12-11-2020
DOI: 10.1093/GBE/EVAA231
Abstract: The application of high-throughput genotyping or sequencing data helps us to understand the genomic response to natural and artificial selection. In this study, we scanned the genomes of five indigenous buffalo populations belong to three recognized breeds, adapted to different geographical and agro-ecological zones in Iran, to unravel the extent of genomic ersity and to localize genomic regions and genes underwent past selection. A total of 46 river buffalo whole genomes, from West and East Azerbaijan, Gilan, Mazandaran, and Khuzestan provinces, were resequenced. Our sequencing data reached to a coverage above 99% of the river buffalo reference genome and an average read depth around 9.2× per s le. We identified 20.55 million SNPs, including 63,097 missense, 707 stop-gain, and 159 stop-loss mutations that might have functional consequences. Genomic ersity analyses showed modest structuring among Iranian buffalo populations following frequent gene flow or admixture in the recent past. Evidence of positive selection was investigated using both differentiation (Fst) and fixation (Pi) metrics. Analysis of fixation revealed three genomic regions in all three breeds with aberrant polymorphism contents on BBU2, 20, and 21. Fixation signal on BBU2 overlapped with the OCA2-HERC2 genes, suggestive of adaptation to UV exposure through pigmentation mechanism. Further validation using resequencing data from other five bovine species as well as the Axiom Buffalo Genotyping Array 90K data of river and sw buffaloes indicated that these fixation signals persisted across river and sw buffaloes and extended to taurine cattle, implying an ancient evolutionary event occurred before the speciation of buffalo and taurine cattle. These results contributed to our understanding of major genetic switches that took place during the evolution of modern buffaloes.
Publisher: Springer Science and Business Media LLC
Date: 18-11-2014
DOI: 10.1007/S11033-014-3837-7
Abstract: Diabetes, a disease caused by excessive blood sugar, is caused by the lack of insulin. For commercial production, insulin is made in bacteria or yeast by protein recombinant technology. The focus of this research is evaluating another resource and producing of recombinant insulin protein in as strawberry as this plant has high potential in production of pharmaceutical proteins. Strawberry is a suitable bioreactor for production of recombinant proteins especially edible vaccines. In this research, human pro-insulin gene was cloned in pCAMBIA1304 vector under CaMV35S promoter and NOS terminator. Agrobacterium tumefaciens LBA4404, AGL1, EHA105, EHA101, C58, C58 (pGV2260) and C58 (pGV3101) strains were used for transformation of pro-insulin gene into strawberry cv. Camarosa, Selva, Sarian Hybrid, Pajaro, Paros, Gaviota, Alpine. Additionally, Agrobacterium rhizogenes K599, R1000, A4 and MSU440 strains were utilized for gene transformation into hairy roots. PCR analysis indicated the presence of transformed human pro-insulin gene in the strawberry and hairy roots. Also, its transcription was confirmed using RT-PCR. Furthermore, the analysis of plants, fruits and hairy roots at the level of proteins using dot blot, ELISA, SDS-PAGE and ECL tests re-confirmed the expression of this protein in the transgenic plants as well as hairy roots. Protein purification of human pro-insulin from transgenic tissues was performed using affinity chromatography. Finally, the bioassay of recombinant pro-insulin was performed. The analysis of second generations of transgenic plants (T1) at DNA and protein levels was also performed as a complementary experiment. This study opens a new avenue in molecular farming of human pro-insulin through its mass production in roots and shoots of strawberry.
Publisher: SAGE Publications
Date: 2011
DOI: 10.4137/BBI.S6206
Abstract: Phytoremediation refers to the use of plants for extraction and detoxification of pollutants, providing a new and powerful weapon against a polluted environment. In some plants, such as Thlaspi spp, heavy metal ATPases are involved in overall metal ion homeostasis and hyperaccumulation. P1B-ATPases pump a wide range of cations, especially heavy metals, across membranes against their electrochemical gradients. Determination of the protein characteristics of P1B-ATPases in hyperaccumulator plants provides a new opportuntity for engineering of phytoremediating plants. In this study, using erse weighting and modeling approaches, 2644 protein characteristics of primary, secondary, and tertiary structures of P1B-ATPases in hyperaccumulator and nonhyperaccumulator plants were extracted and compared to identify differences between proteins in hyperaccumulator and nonhyperaccumulator pumps. Although the protein characteristics were variable in their weighting, tree and rule induction models glycine count, frequency of glutamine-valine, and valine-phenylalanine count were the most important attributes highlighted by 10, five, and four models, respectively. In addition, a precise model was built to discriminate P1B-ATPases in different organisms based on their structural protein features. Moreover, reliable models for prediction of the hyperaccumulating activity of unknown P1B-ATPase pumps were developed. Uncovering important structural features of hyperaccumulator pumps in this study has provided the knowledge required for future modification and engineering of these pumps by techniques such as site-directed mutagenesis.
Publisher: Elsevier BV
Date: 11-2019
DOI: 10.1016/J.COMPBIOMED.2019.103456
Abstract: Sub-clinical bovine mastitis decreases milk quality and production. Moreover, sub-clinical mastitis leads to the use of antibiotics with consequent increased risk of the emergence of antibiotic-resistant bacteria. Therefore, early detection of infected cows is of great importance. The Somatic Cell Count (SCC) day-test used for mastitis surveillance, gives data that fluctuate widely between days, creating questions about its reliability and early prediction power. The recent identification of risk parameters of sub-clinical mastitis based on milking parameters by machine learning models is emerging as a promising new tool to enhance early prediction of mastitis occurrence. To develop the optimal approach for early sub-clinical mastitis prediction, we implemented 2 steps: (1) Finding the best statistical models to accurately link patterns of risk factors to sub-clinical mastitis, and (2) Extending this application from the farms tested to new farms (method generalization). Herein, we applied various machine learning-based prediction systems on a big milking dataset to uncover the best predictive models of sub-clinical mastitis. Data from 364,249 milking instances were collected by an electronic automated in-line monitoring system where milk volume, lactose concentration, electrical conductivity (EC), protein concentration, peak flow and milking time for each s le were measured. To provide a platform for the application of the models developed to other farms, the Z transformation approach was employed. Following this, various prediction systems [Deep Learning (DL), Naïve Bayes, Generalized Liner Model, Logistic Regression, Decision Tree, Gradient-Boosted Tree (GBT) and Random Forest] were applied to the non-transformed milking dataset and to a Z-standardized dataset. ROC (Receiver Operating Characteristics Curve), AUC (Area Under The Curve), and high accuracy demonstrated the high sensitivity of GBT and DL in detecting sub-clinical mastitis. GBT was the most accurate model (accuracy of 84.9%) in prediction of sub-clinical bovine mastitis. These data demonstrate how these models could be applied for prediction of sub-clinical mastitis in multiple bovine herds regardless of the size and s ling techniques.
Publisher: Springer Science and Business Media LLC
Date: 21-11-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Public Library of Science (PLoS)
Date: 21-02-2013
Publisher: Bentham Science Publishers Ltd.
Date: 09-2010
Publisher: Public Library of Science (PLoS)
Date: 08-05-2014
Publisher: Public Library of Science (PLoS)
Date: 30-06-2016
Publisher: Public Library of Science (PLoS)
Date: 15-02-2017
Publisher: Springer Science and Business Media LLC
Date: 06-08-2015
DOI: 10.1007/S11033-015-3916-4
Abstract: Pandemic influenza remains as a substantial threat to humans with a widespread panic worldwide. In contrast, seasonal (non-pandemic) has a mild non-lethal infection each year. The underlying mechanisms governing the detrimental effects of pandemic influenza are yet to be known. Transcriptomic-based network discovery and gene ontology (GO) analysis of host response to pandemic influenza, compared to seasonal influenza, can shed light on the differential mechanisms which pandemic influenza is employed during evolution. Here, using microarray data of infected ferrets with pandemic and seasonal influenza (as a model), we evaluated the possible link between altered genes after pandemic infection with activation of neuronal disorders. To this end, we utilized novel computational biology techniques including differential transcriptome analysis, network construction, GO enrichment, and GO network to investigate the underlying mechanisms of pandemic influenza infection and host interaction. In comparison to seasonal influenza, pandemic influenza differentially altered the expression of 31 genes with direct involvement in activity of central nervous system (CNS). Network topology highlighted the high interactions of IRF1, NKX2-1 and NR5A1 as well as MIR27A, MIR19A, and MIR17. TGFB2, NCOA3 and SP1 were the central transcription factors in the networks. Pandemic influenza remarkably downregulated GPM6A and GTPase. GO network demonstrated the key roles of GPM6A and GTPase in regulation of important functions such as synapse assembly and neuron projection. For the first time, we showed that besides interference with cytokine/chemokine storm and neuraminidase enzyme, H1N1 pandemic influenza is able to directly affect neuronal gene networks. The possibility of application of some key regulators such as GPM6A protein, MIR128, and MIR367 as candidate therapeutic agents is discussed. The presented approach established a new way to unravel unknown pathways in virus-associated CNS dysfunction by utilizing global transcriptomic data, network and GO analysis.
Publisher: Elsevier BV
Date: 03-2016
DOI: 10.1016/J.GENE.2015.12.023
Abstract: Nanog, an important transcription factor in embryonic stem cells (ESC), is the key factor in maintaining pluripotency to establish ESC identity and has the ability to induce embryonic germ layers. Nanog is responsible for self-renewal and pluripotency of stem cells as well as cancer invasiveness, tumor cell proliferation, motility and drug-resistance. Understanding the underlying mechanisms of Nanog evolution and regulation can lead to future advances in treatment of cancers. Recent integration of machine learning models with genetics has provided a powerful tool for knowledge discovery and uncovering evolutionary pathways. Herein, sequences of 47 Nanog genes from various species were extracted and two datasets of features were computationally extracted from these sequences. At the first dataset, 76 nucleotide acid attributes were calculated for each Nanog sequence. The second dataset was prepared based on the 10,480 repeated nucleotide sequences (from 5 to 50bp lengths). Then, various data mining algorithms such as decision tree models were applied on these datasets to find the evolutionary pathways of Nanog ersion. Attribute weighting models were highlighted features such as the frequencies of AA and GC as the most important genomic features in Nanog gene classification and differentiation. Similar findings were obtained by tree induction algorithms. Results from the second database showed that some short sequence strings, such as ACTACT, TCCTGA, CCTGA, GAAGAC, and TATCCC can be effectively used to identify Nanog genes in various species. The outcomes of this study, for the first time, unravels the importance of particular genomic features in Nanog gene evolution paving roads toward better understanding of stem cell development and human targeted disorder therapy.
Publisher: Elsevier BV
Date: 05-2020
DOI: 10.1016/J.JSBMB.2019.105548
Abstract: Medroxyprogesterone acetate (MPA) is a first generation progestin that has been in clinical use for various hormonal conditions in women since the 1960s. Although developed as a progesterone receptor (PR) agonist, MPA also has strong binding affinity for other steroid receptors. This promiscuity confounds the mechanistic action of MPA in target cells that express multiple steroid receptors. This study is the first to assess the relative contribution of progesterone, androgen and glucocorticoid receptors in mediating the transcriptional activity of MPA on endogenous targets in breast cancer cells that endogenously express all three receptors at comparable levels. Gene expression profiling in estrogen receptor positive (ER+) ZR-75-1 breast cancer cells demonstrated that although the MPA-regulated transcriptome strongly overlapped with that of Progesterone (PROG), 5α-dihydrotestosterone (DHT) and Dexamethasone (DEX), it clustered most strongly with that of PROG, suggesting that MPA predominantly acts via the progesterone receptor (PR) rather than androgen receptor (AR) or glucocorticoid receptor (GR). Subsequent experiments manipulating levels of these receptors, either through specific culture conditions or with lentiviral shRNAs targeting in idual receptors, also revealed a stronger contribution of PR compared to AR and GR on the expression of endogenous target genes that are either commonly regulated by all ligands or specifically regulated only by MPA. A predominant contribution of PR to MPA action in ER+ T-47D breast cancer cells was also observed, although a stronger role for AR was evident in T-47D compared to that observed in ZR-75-1 cells. Network analysis of ligand-specific and commonly regulated genes demonstrated that MPA utilises different transcription factors and signalling pathways to inhibit proliferation compared with PROG. This study reaffirms the importance of PR in mediating MPA action in an endogenous breast cancer context where multiple steroid receptors are co-expressed and has potential implications for PR-targeting therapeutic strategies in ER+ breast cancer.
Publisher: Elsevier BV
Date: 05-2019
DOI: 10.1016/J.GENE.2019.01.014
Abstract: Influenza has a negative sense, single-stranded, and segmented RNA. In the context of pandemic influenza research, most studies have focused on variations in the surface proteins (Hemagglutinin and Neuraminidase). However, new findings suggest that all internal and external proteins of influenza viruses can contribute in pandemic emergence, pathogenicity and increasing host range. The occurrence of the 2009 influenza pandemic and the availability of many external and internal segments of pandemic and non-pandemic sequences offer a unique opportunity to evaluate the performance of machine learning models in discrimination of pandemic from seasonal sequences using mutation positions in all segments. In this study, we hypothesized that identifying mutation positions in all segments (proteins) encoded by the influenza genome would enable pandemic and seasonal strains to be more reliably distinguished. In a large scale study, we applied a range of data mining techniques to all segments of influenza for rule discovery and discrimination of pandemic from seasonal strains. CBA (classification based on association rule mining), Ripper and Decision tree algorithms were utilized to extract association rules among mutations. CBA outperformed the other models. Our approach could discriminate pandemic sequences from seasonal ones with more than 95% accuracy for PA and NP, 99.33% accuracy for NA and 100% accuracy, precision, specificity and sensitivity (recall) for M1, M2, PB1, NS1, and NS2. The values of precision, specificity, and sensitivity were more than 90% for other segments except PB2. If sequences of all segments of one strain were available, the accuracy of discrimination of pandemic strains was 100%. General rules extracted by rule base classification approaches, such as M1-V147I, NP-N334H, NS1-V112I, and PB1-L364I, were able to detect pandemic sequences with high accuracy. We observed that mutations on internal proteins of influenza can contribute in distinguishing the pandemic viruses, similar to the external ones.
Publisher: Springer Science and Business Media LLC
Date: 08-09-2014
Publisher: Springer Science and Business Media LLC
Date: 10-2011
Publisher: Springer Science and Business Media LLC
Date: 18-01-2021
DOI: 10.1038/S41591-020-01168-7
Abstract: The role of the androgen receptor (AR) in estrogen receptor (ER)-α-positive breast cancer is controversial, constraining implementation of AR-directed therapies. Using a erse, clinically relevant panel of cell-line and patient-derived models, we demonstrate that AR activation, not suppression, exerts potent antitumor activity in multiple disease contexts, including resistance to standard-of-care ER and CDK4/6 inhibitors. Notably, AR agonists combined with standard-of-care agents enhanced therapeutic responses. Mechanistically, agonist activation of AR altered the genomic distribution of ER and essential co-activators (p300, SRC-3), resulting in repression of ER-regulated cell cycle genes and upregulation of AR target genes, including known tumor suppressors. A gene signature of AR activity positively predicted disease survival in multiple clinical ER-positive breast cancer cohorts. These findings provide unambiguous evidence that AR has a tumor suppressor role in ER-positive breast cancer and support AR agonism as the optimal AR-directed treatment strategy, revealing a rational therapeutic opportunity.
Publisher: Public Library of Science (PLoS)
Date: 16-10-2014
Publisher: MDPI AG
Date: 04-11-2022
DOI: 10.3390/NU14214670
Abstract: Further examination of the molecular regulators of long-term calorie restriction (CR), reported to have an anxiolytic effect, may highlight novel therapeutic targets for anxiety disorders. Here, adult male Hooded Wistar rats were exposed to a 25% CR whilst anxiety-like behaviour was assessed at 6-, 12-, and 18-months of age via the elevated plus maze, open field, and acoustic startle tests. Next-generation sequencing was then used to measure transcriptome-wide gene expression in the hypothalamus, amygdala, pituitary, and adrenal glands. Results showed an anxiolytic behavioural profile across early, middle, and late adulthood by CR, with the strongest effects noted at 6-months. Transcriptomic analysis by seven attribute weighting algorithms, including Info Gain Ratio, Rule, Chi Squared, Gini Index, Uncertainty, Relief, and Info Gain, led to the development of a signature of long-term CR, independent of region. Complement C1q A chain (C1qa), an extracellular protein, expression was significantly decreased by CR in most regions examined. Furthermore, text mining highlighted the positive involvement of C1qa in anxiety, depression, neurodegeneration, stress, and ageing, collectively identifying a suitable biomarker candidate for CR. Overall, the current study identified anxiety-related phenotypic changes and a novel transcriptome signature of long-term CR, indicating potential therapeutic targets for anxiety, depression, and neurodegeneration.
Publisher: Springer Science and Business Media LLC
Date: 07-12-2007
Publisher: Wiley
Date: 03-05-2017
DOI: 10.1111/VDE.12379
Abstract: Topical antimicrobial preparations are of utmost importance in treating suspected and confirmed meticillin-resistant Staphylococcus pseudintermedius (MRSP) infections due to the increasing incidence of widespread resistance to systemic antimicrobials. Lasalocid is active against MRSP in vitro and this may become an important topical antimicrobial for the treatment of canine pyoderma. To determine effects of various formulation types on penetration and retention of lasalocid applied to canine skin in vitro. Normal canine skin was collected from the thorax of five dogs that had been euthanized on the basis of health and/or intractable behavioural issues. Solution, lotion and ointment containing 2% lasalocid were applied to ex vivo canine skin. Transdermal penetration was assessed for a 24 h period and retention of lasalocid was assessed at the conclusion of the study. The solution had significantly higher skin retention of lasalocid and proportion of applied dose retained in skin than lotion and ointment (Tukey-Kramer Honest Significant Difference test, P < 0.01). Lasalocid could not be detected in the receptor fluid of any Franz cell at any time point. Lasalocid was not identified in the receptor fluid of any s le, indicating that systemic absorption of the active ingredient in vivo is unlikely. Lasalocid may be useful in the treatment of MRSP infections if in vivo studies support safety and efficacy.
Publisher: Public Library of Science (PLoS)
Date: 04-11-2011
Publisher: Public Library of Science (PLoS)
Date: 11-03-2013
Publisher: Elsevier BV
Date: 11-2014
DOI: 10.1016/J.COMPBIOMED.2014.08.019
Abstract: α-linolenic acid (ALA) is the most frequent omega-3 in plants. The content of ALA is highly variable, ranging from 0 to 1% in rice and corn to >50% in perilla and flax. ALA production is strongly correlated with the enzymatic activity of omega-3 fatty acid desaturase. To unravel the underlying mechanisms of omega-3 ersity, 895 protein features of omega-3 fatty acid desaturase were compared between plants with high and low omega-3. Attribute weighting showed that this enzyme in plants with high omega-3 content has higher amounts of Lys, Lys-Phe, and Pro-Asn but lower Aliphatic index, Gly-His, and Pro-Leu. The Random Forest model with Accuracy criterion when run on the dataset pre-filtered with Info Gain algorithm was the best model in distinguishing high omega-3 content based on the frequency of Lys-Lys in the structure of fatty acid desaturase. Interestingly, the discriminant function algorithm could predict the level of omega-3 only based on the six important selected attributes (out of 895 protein attributes) of fatty acid desaturase with 75% accuracy. We developed "Plant omega3 predictor" to predict the content of α-linolenic acid based on structural features of omega-3 fatty acid desaturase. The software calculates the 6 key structural protein features from imported Fasta sequence of omega-3 fatty acid desaturase or utilizes the imported features and predicts the ALA content using discriminant function formula. This work unravels an underpinning mechanism of omega-3 ersity via discovery of the key protein attributes in the structure of omega-3 desaturase offering a new approach to obtain higher omega-3 content.
Publisher: MDPI AG
Date: 06-2021
DOI: 10.3390/ANI11061638
Abstract: Subclinical mastitis, an economically challenging disease of dairy cattle, is associated with an increased use of antimicrobials which reduces milk quantity and quality. It is more common than clinical mastitis and far more difficult to detect. Recently, much attention has been paid to the development of machine-learning expert systems for early detection of subclinical mastitis from milking features. However, differences between animals within a farm as well as between farms, particularly across multiple years, are major obstacles to the generalisation of machine learning models. Here, for the first time, we integrated scaling by quartiling with classification based on associations in a multi-year study to deal with farm heterogeneity by discovery of multiple patterns towards mastitis. The data were obtained from one farm comprising Holstein Friesian cows in Ongaonga, New Zealand, using an electronic automated monitoring system. The data collection was repeated annually over 3 consecutive years. Some discovered rules, such as when the milking peak flow is low, electrical conductivity (EC) of milk is low, milk lactose is low, milk fat is high, and milk volume is low, the cow has subclinical mastitis, reached high confidence ( %) in multiple years. On averages, over 3 years, low level of milk lactose and high value of milk EC were part of 93% and 83.8% of all subclinical mastitis detecting rules, offering a reproducible pattern of subclinical mastitis detection. The scaled year-independent combinational rules provide an easy-to-apply and cost-effective machine-learning expert system for early detection of hidden mastitis using milking parameters.
Publisher: Springer Science and Business Media LLC
Date: 21-06-2018
Publisher: Public Library of Science (PLoS)
Date: 13-08-2013
Publisher: Wiley
Date: 03-03-2015
DOI: 10.1111/COBI.12479
Abstract: The high number of failures is one reason why translocation is often not recommended. Considering how behavior changes during translocations may improve translocation success. To derive decision‐tree models for species’ translocation, we used data on the short‐term responses of an endangered Australian skink in 5 simulated translocations with different release conditions. We used 4 different decision‐tree algorithms (decision tree, decision‐tree parallel, decision stump, and random forest) with 4 different criteria (gain ratio, information gain, gini index, and accuracy) to investigate how environmental and behavioral parameters may affect the success of a translocation. We assumed behavioral changes that increased dispersal away from a release site would reduce translocation success. The trees became more complex when we included all behavioral parameters as attributes, but these trees yielded more detailed information about why and how dispersal occurred. According to these complex trees, there were positive associations between some behavioral parameters, such as fight and dispersal, that showed there was a higher chance, for ex le, of dispersal among lizards that fought than among those that did not fight. Decision trees based on parameters related to release conditions were easier to understand and could be used by managers to make translocation decisions under different circumstances. Minimizar el Costo del Fracaso de la Reubicación con Modelos de Árboles de Decisión que Predigan la Respuesta Conductual de la Especie en los Sitios de Reubicación
Publisher: IEEE
Date: 05-2009
Publisher: Elsevier BV
Date: 12-2018
Publisher: Cambridge University Press (CUP)
Date: 05-2018
DOI: 10.1017/S0022029918000249
Abstract: Sub-clinical mastitis (SCM) affects milk composition. In this study, we hypothesise that large-scale mining of milk composition features by pattern recognition models can identify the best predictors of SCM within the milk composition features. To this end, using data mining algorithms, we conducted a large-scale and longitudinal study to evaluate the ability of various milk production parameters as indicators of SCM. SCM is the most prevalent disease of dairy cattle, causing substantial economic loss for the dairy industry. Developing new techniques to diagnose SCM in its early stages improves herd health and is of great importance. Test-day Somatic Cell Count (SCC) is the most common indicator of SCM and the primary mastitis surveillance approach worldwide. However, test-day SCC fluctuates widely between days, causing major concerns for its reliability. Consequently, there would be great benefit to identifying additional efficient indicators from large-scale and longitudinal studies. With this intent, data was collected at every milking (twice per day) for a period of 2 months from a single farm using in-line electronic equipment (346 248 records in total). The following data were analysed: milk volume, protein concentration, lactose concentration, electrical conductivity (EC), milking time and peak flow. Three SCC cut-offs were used to estimate the prevalence of SCM: Australian ≥ 250 000 cells/ml, European ≥200 000 cells/ml and New Zealand ≥ 150 000 cells/ml. At first, 10 different Attribute Weighting Algorithms (AWM) were applied to the data. In the absence of SCC, lactose concentration featured as the most important variable, followed by EC. For the first time, using attribute weighted modelling, we showed that the concentration of lactose in milk can be used as a strong indicator of SCM. The development of machine-learning expert systems using two or more milk variables (such as lactose concentration and EC) may produce a predictive pattern for early SCM detection.
Publisher: Springer Science and Business Media LLC
Date: 05-10-2012
Abstract: Regarding the possible multiple functions of a specific gene, finding the alternative roles of genes is a major challenge. Huge amount of available expression data and the central role of the promoter and its regulatory elements provide unique opportunely to address this issue. The question is that how the expression data and promoter analysis can be applied to uncover the different functions of a gene. A computational approach has been presented here by analysis of promoter regulatory elements, coexpressed gene as well as protein domain and prosite analysis. We applied our approach on Thaumatin like protein (TLP) as ex le. TLP is of group 5 of pathogenesis related proteins which their antifungal role has been proved previously. In contrast, Osmotin like proteins (OLPs) are basic form of TLPs with proved role only in abiotic stresses. We demonstrated the possible outstanding homolouges involving in both biotic and abiotic stresses by analyzing 300 coexpressed genes for each Arabidopsis TLP and OLP in biotic, abiotic, hormone, and light microarray experiments based on mutual ranking. In addition, promoter analysis was employed to detect transcription factor binding sites (TFBs) and their differences between OLPs and TLPs. A specific combination of five TFBs was found in all TLPs presenting the key structure in functional response of TLP to fungal stress. Interestingly, we found the fungal response TFBs in some of salt responsive OLPs, indicating the possible role of OLPs in biotic stresses. Thirteen TFBS were unique for all OLPs and some found in TLPs, proposing the possible role of these TLPs in abiotic stresses. Multivariate analysis showed the possibility of estimating models for distinguishing biotic and abiotic functions of TIPs based on promoter regulatory elements. This is the first report in identifying multiple roles of TLPs and OLPs in biotic and abiotic stresses. This study provides valuable clues for screening and discovering new genes with possible roles in tolerance against both biotic and abiotic stresses. Interestingly, principle component analysis showed that promoter regulatory elements of TLPs and OLPs are more variable than protein properties reinforcing the prominent role of promoter architecture in determining gene function alteration.
Publisher: Elsevier BV
Date: 06-2020
Publisher: Springer Science and Business Media LLC
Date: 28-07-2013
DOI: 10.1007/S10495-013-0887-6
Abstract: A431 cells, derived from epidermoid carcinoma, overexpress the epidermal growth factor receptor (EGFR) and when treated with a high dose of EGF will undergo apoptosis. We exploited microarray and proteomics techniques and network prediction to study the regulatory mechanisms of EGF-induced apoptosis in A431 cells. We observed significant changes in gene expression in 162 genes, approximately evenly split between pro-apoptotic and anti-apoptotic genes and identified 30 proteins from the proteomic data that had either pro or anti-apoptotic annotation. Our correlation analysis of gene expression and proteome modeled a number of distinct sub-networks that are associated with the onset of apoptosis, allowing us to identify specific pathways and components. These include components of the interferon signalling pathway, and down stream components, including cytokines and suppressors of cytokine signalling. A central component of almost all gene expression sub-networks identified was TP53, which is mutated in A431 cells, and was down regulated. This down regulation of TP53 appeared to be correlated with proteomic sub-networks of cytoskeletal or cell adhesion components that might induce apoptosis by triggering cytochrome C release. Of the only three genes also differentially expressed as proteins, only serpinb1 had a known association with apoptosis. We confirmed that up regulation and cleavage of serpinb1 into L-DNAaseII was correlated with the induction of apoptosis. It is unlikely that a single pathway, but more likely a combination of pathways is needed to trigger EGF induced apoptosis in A431cells.
Publisher: Wiley
Date: 31-01-2019
DOI: 10.1002/MC.22975
Abstract: A considerable number of deposited variants has provided new possibilities for knowledge discovery in different types of prostate cancer. Here, we analyzed variants located on 3′UTR, 5′UTR, CDs, Intergenic, and Intronic regions in castration‐resistant prostate cancer (8496 variants), familial prostate cancer (3241 variants), metastatic castration‐resistant prostate cancer (3693 variants), and prostate cancer (16599 variants). Chromosome regions 10p15‐p14 and 2p13 were highly enriched ( P 0.00001) for variants located in 3′UTR, 5′UTR, CDs, intergenic, and intronic regions in castration‐resistant prostate cancer. In contrast, 10p15‐p14, 10q23.3, 12q13.11, 13q12.3, 1q25, and 8p22 regions were enriched ( P 0.001) in familial prostate cancer. In metastatic castration‐resistant prostate cancer, 10p15‐p14, 10q23.3, 11q22‐q23, 14q21.1, and 14q32.13 were highly variant regions ( P 0.001). Chromosome 2 and chromosome 1 hosted many enriched variant regions. AKR1C3 , BRCA1 , BRCA2 , CHGA , CYP19A1 , HOXB13 , KLK3 , and PTEN contained the highest number of 3′UTR, 5′UTR, CDs, Intergenic, and Intronic variants. Network analysis showed that these genes are upstream of important functions including prostate gland development, tumor recurrence, prostate cancer‐specific survival, tumor progression, cancer mortality, long‐term survival, cancer recurrence, angiogenesis, and AR. Interestingly, all of EGFR , JAK2 , NR3C1 , PDZD2 , and SEMA3C genes had single nucleotide polymorphisms (SNP) in castration‐resistant prostate cancer, consistent with high selection pressure on these genes during drug treatment and consequent resistance. High occurrence of variants in 3′UTRs suggests the importance of regulatory variants in different types of prostate cancer an area that has been neglected compared with coding variants. This study provides a comprehensive overview of genomic regions contributing to different types of prostate cancer.
Publisher: Wiley
Date: 23-01-2020
DOI: 10.1111/AVJ.12919
Abstract: In northern Australian koala populations (Queensland and New South Wales), periodontal disease (gingivitis and periodontitis) is common while koala retrovirus subtype A is endogenous, with other subtypes transmitted exogenously. Koala retrovirus has been hypothesised to cause immune suppression and may predispose koalas to diseases caused by concurrent infections. In southern Australia populations (Victoria and South Australia) periodontal disease has not been investigated, and koala retrovirus is presumably exogenously transmitted. This study described oral health in South Australian koalas and investigated if an association between periodontal disease and koala retrovirus exists. Oral health was examined for wild-caught koalas from the Mount Lofty Ranges (n = 75). Koala retrovirus provirus was detected in whole blood using nested PCR and proviral load determined with qPCR. Periodontal disease severity was recorded and used to calculate the Final Oral Health Index (0-normal, 24-severe).Results Periodontal disease was observed in 84% (63/75) of koalas 77% had gingivitis (58/75) and 65% (49/75) had periodontitis. The average Final Oral Health Index was 5.47 (s.d 3.13). Most cases of periodontal disease were associated with the incisors. Koala retrovirus-infected koalas were more likely to present with periodontitis (p = 0.042) and the Final Oral Health Index was negatively correlated with proviral load (ρ = -0.353, p = 0.017). South Australian koalas had a high prevalence of gingivitis and periodontitis. Periodontal disease was more prevalent in the incisors. Exogenous koala retrovirus infection may also facilitate the development of periodontitis by modulation of the immune response to concurrent oral bacterial infections.
Publisher: Elsevier BV
Date: 11-2021
Publisher: Elsevier BV
Date: 06-2018
DOI: 10.1016/J.GENE.2018.03.038
Abstract: Exponentially growing scientific knowledge in scientific publications has resulted in the emergence of a new interdisciplinary science of literature mining. In text mining, the machine reads the published literature and transfers the discovered knowledge to mathematical-like formulas. In an integrative approach in this study, we used text mining in combination with network discovery, pathway analysis, and enrichment analysis of genomic regions for better understanding of biomarkers in lung cancer. Particular attention was paid to non-coding biomarkers. In total, 60 MicroRNA biomarkers were reported for lung cancer, including some prognostic biomarkers. MIR21, MIR155, MALAT1, and MIR31 were the top non-coding RNA biomarkers of lung cancer. Text mining identified 447 proteins which have been studied as biomarkers in lung cancer. EGFR (receptor), TP53 (transcription factor), KRAS, CDKN2A, ENO2, KRT19, RASSF1, GRP (ligand), SHOX2 (transcription factor), and ERBB2 (receptor) were the most studied proteins. Within small molecules, thymosin-a1, oestrogen, and 8-OHdG have received more attention. We found some chromosomal bands, such as 7q32.2, 18q12.1, 6p12, 11p15.5, and 3p21.3 that are highly involved in deriving lung cancer biomarkers.
Publisher: Springer Science and Business Media LLC
Date: 22-03-2018
DOI: 10.1038/S41598-018-23245-1
Abstract: In eukaryotes, different combinations of exons lead to multiple transcripts with various functions in protein level, in a process called alternative splicing (AS). Unfolding the complexity of functional genomics through genome-wide profiling of AS and determining the altered ultimate products provide new insights for better understanding of many biological processes, disease progress as well as drug development programs to target harmful splicing variants. The current available tools of alternative splicing work with raw data and include heavy computation. In particular, there is a shortcoming in tools to discover AS events directly from transcripts. Here, we developed a Windows-based user-friendly tool for identifying AS events from transcripts without the need to any advanced computer skill or database download. Meanwhile, due to online working mode, our application employs the updated SpliceGraphs without the need to any resource updating. First, SpliceGraph forms based on the frequency of active splice sites in pre-mRNA. Then, the presented approach compares query transcript exons to SpliceGraph exons. The tool provides the possibility of statistical analysis of AS events as well as AS visualization compared to SpliceGraph. The developed application works for transcript sets in human and model organisms.
No related organisations have been discovered for Esmaeil Ebrahimie.
Start Date: 12-2023
End Date: 12-2026
Amount: $282,339.00
Funder: Australian Research Council
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