Biology subjects

Kadarmideen, H. N.

Publications and source records attributed to Kadarmideen, H. N..

7 recordsLinked to original sources

bioRxiv2021

GEDI: an R package for integration of transcriptomic data from multiple high-throughput platforms

Transcriptomic data is often expensive and difficult to generate in large cohorts in comparison to genomic data and therefore is often important to integrate multiple transcriptomic datasets from both microarray and next generation sequencing (NGS) based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including re-annotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining already existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically re-annotating the data and removing the batch effect. The removal of the batch effect is verified with Principal Component Analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. The datasets included Affymetrix, Agilent and RNA-sequencing data. Furthermore, we compared the GEDI package to already existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration.

bioinformatics↗

bioRxiv2020

Integrative Analysis of Metabolomic and Transcriptomic Profiles Uncovers Biological Mechanism of Feed Efficiency in Pigs

Feed efficiency (FE) is an economically important trait. Thus, reliable predictors would help to reduce the production cost and provide sustainability to the pig industry. We carried out metabolome-transcriptome integration analysis on 40 purebred Duroc and Landrace male uncastrated pigs to identify potential gene-metabolite interactions and explore the molecular mechanism underlying FE. To this end, we applied untargeted metabolomics and RNA-seq approaches to the same animals. After data quality control, we used a linear model approach to integrate the data and find significant differently correlated gene-metabolite pairs separately for the breeds (Duroc and Landrace) and FE groups (low and high FE) followed by a pathway over-representation analysis. We identified 21 and 12 significant gene-metabolite pairs for each group. The valine-leucine-isoleucine biosynthesis/degradation and arginine-proline metabolism pathways were associated with unique metabolites. The unique genes obtained from significant metabolite-gene pairs were associated with sphingolipid catabolism, multicellular organismal process, cGMP, and purine metabolic processes. While some of the genes and metabolites identified were known for their association with FE, others are novel and provide new avenues for further research. Further validation of genes, metabolites, and gene-metabolite interactions in larger cohorts will help us to elucidate the regulatory mechanisms and pathways underlying FE.

systems biology↗

bioRxiv2020

Genetic variations (eQTLs) in muscle transcriptome and mitochondrial genes, and trans-eQTL molecular pathways in feed efficiency from Danish breeding pigs

Feed efficiency (FE) is a key trait in pig production, as it has both high economic and environmental impact. FE is a challenging phenotype to study, as it is complex and affected by many factors, such as metabolism, growth and activity level. Furthermore, testing for FE is expensive, as it requires costly equipment to measure feed intake of individual animals, making FE biomarkers valuable. Therefore, there has been a desire to find single nucleotide polymorphisms (SNPs) as biomarkers, to assist with improved selection and improve our biological understanding of FE. We have done a cis- and trans-eQTL (expressed quantitative trait loci) analysis, in a population of Danbred Durocs (N=11) and Danbred Landrace (N=27) using both a linear and Anova model. We used bootstrapping and enrichment analysis to validate and analyze our detected eQTLs. We identified 15 eQTLs with FDR < 0.01, affecting several genes found in previous studies of commercial pig breeds. Examples include IFI6, PRPF39, TMEM222, CSRNP1,PARK7 and MFF. The bootstrapping results showed statistically significant enrichment of eQTLs with p-value < 0.01 (p-value < 2.2x0-16) in both cis and trans-eQTLs. Based on this, enrichment analysis of top trans-eQTLs revealed high enrichment for gene categories and gene ontologies associated with genomic context and expression regulation. This includes transcription factors (p-value=1.0x10-13), DNA-binding (GO:0003677, p-value=8.9x10-14), DNA-binding transcription factor activity (GO:0003700,) nucleus gene (GO:0005634, p-value<2.2x10-16), positive regulation of expression (GO:0010628), negative regulation of expression (GO:0010629, p-value<2.2x10-16). These results would be useful for future genome assisted breeding of pigs to improve FE, and in the improved understanding of the functional mechanism of trans-eQTLs.

bioinformatics↗

bioRxiv2020

Metabolite genome-wide association study (mGWAS) and gene-metabolite interaction network analysis reveal potential biomarkers for feed efficiency in pigs

Metabolites represent the ultimate response of biological systems, so metabolomics is considered to link the genotypes and phenotypes. Feed efficiency is one of the most important phenotypes in sustainable pig production and is the main breeding goal trait. We utilized metabolic and genomic datasets from a total of 108 pigs from our own previously published studies that involved 59 Duroc and 49 Landrace pigs with data on feed efficiency (residual feed intake or RFI), genotype (PorcineSNP80 BeadChip) data and metabolomic data (45 final metabolite datasets derived from LC-MS system). Utilizing these datasets, our main aim was to identify genetic variants (single-nucleotide polymorphisms or SNPs) that affect 45 different metabolite concentrations in plasma collected at the start and end of the performance testing of pigs categorized as high or low in their feed efficiency (based on RFI values). Genome-wide significant genetic variants could be then used as potential genetic or biomarkers in breeding programs for feed efficiency. The other objective was to reveal the biochemical mechanisms underlying genetic variations for pigs feed efficiency. In order to achieve these objectives, we firstly conducted a metabolite genome-wide association study (mGWAS) based on mixed linear models and found 152 genome-wide significant SNPs (P-value < 1.06E-06) in association with 17 metabolites that included 90 significant SNPs annotated to 52 genes. On chromosome one alone, 51 significant SNPs associated with isovalerylcarnitine and propionylcarnitine were found to be in strong linkage disequilibrium (LD). SNPs in strong LD annotated to FBXL4 and CCNC consisted of two haplotype blocks where three SNPs (ALGA0004000, ALGA0004041 and ALGA0004042) were in the intron regions of FBXL4 and CCNC. The interaction network revealed that CCNC and FBXL4 were linked by the hub gene N6AMT1 that was associated with isovalerylcarnitine and propionylcarnitine. Moreover, three metabolites (i.e., isovalerylcarnitine, propionylcarnitine and pyruvic acid) were clustered in one group based on the low-high RFI pigs. This study performed a comprehensive metabolite-based GWAS analysis for pigs with differences in feed efficiency and provided significant metabolites for which there is a significant genetic variation as well as biological interaction networks. The identified metabolite genetic variants, genes and networks in high versus low feed efficient pigs could be considered as potential genetic or biomarkers for feed efficiency.

genomics↗

bioRxiv2020

GeneDMRs: an R package for Gene-based Differentially Methylated Regions analysis

DNA methylation in gene or promoter or gene body could restrict/promote the gene transcription. Moreover, methylation in the gene regions along with CpG island regions could modulate the transcription to undetectable gene expression levels. Therefore, it is necessary to investigate the methylation levels within the gene, gene body, CpG island regions and their overlapped regions and then identify the gene-based differentially methylated regions (GeneDMRs). Here, R package GeneDMRs aims to facilitate computing gene based methylation rate using next generation sequencing (NGS)-based methylome data. The user-friendly R package GeneDMRs is presented to analyze the methylation levels in each gene/promoter/exon/intron/CpG island/CpG island shore or each overlapped region (e.g., gene-CpG island/promoter-CpG island/exon-CpG island/intron-CpG island/gene-CpG island shore/promoter-CpG island shore/exon-CpG island shore/intron-CpG island shore). Here, we used the public reduced representation bisulfite sequencing (RRBS) data of mouse (GSE62392) for evaluating software and found novel biologically significant results to supplement the previous research. The R package GeneDMRs can facilitate computing gene based methylation rate to interpret complex interplay between methylation levels and gene expression differences or similarities across physiological conditions or disease states.

bioinformatics↗

bioRxiv2020

Genome regulation and gene interaction networks inferred from muscle transcriptome underlying feed efficiency in Pigs

Improvement of feed efficiency (FE) is key for sustainability and cost reduction in pig production. Our aim was to characterize the muscle transcriptomic profiles in Danbred Duroc (Duroc) and Danbred Landrace (Landrace), in relation to FE for identifying potential biomarkers. RNA-seq data was analyzed employing differential gene expression methods, gene-gene interaction and network analysis, including pathway and functional analysis. We compared the results with genome regulation in human exercise data. In the differential expression analysis, 13 genes were differentially expressed, including: MRPS11, MTRF1, TRIM63, MGAT4A, KLH30. Based on a novel gene selection method, the divergent count, we performed pathway enrichment analysis. We found 5 significantly enriched pathways related to feed conversion ratio (FCR). These pathways were mainly mitochondrial, and summarized in the mitochondrial translation elongation (MTR) pathway. In the gene interaction analysis, highlights include the mitochondrial genes: PPIF, MRPL35, NDUFS4and the fat metabolism and obesity genes: AACS, SMPDL3B, CTNNBL1, NDUFS4 and LIMD2. In the network analysis, we identified two modules significantly correlated with FCR. Pathway enrichment of modules identified MTR, electron transport chain and DNA repair as enriched pathways. In the network analysis, the mitochondrial gene group NDUF was a key hub group, showing potential as biomarkers. Comparing with human transcriptomic exercise studies, genes related to exercise displayed enrichment in our FCR related genes. We conclude that mitochondrial activity is a driver for FCR in muscle tissue, and mitochondrial genes could be potential biomarkers for FCR in pigs. We hypothesize that increased FE mimics processes triggered in exercised muscle.

bioinformatics↗

bioRxiv2020

Characterization of the Endometrial Transcriptome in Early Diestrus Influencing Pregnancy Establishment in Dairy Cattle after Transfer of In-Vitro Produced Embryos

Modifications of the endometrial transcriptome at day 7 of the estrus cycle are crucial to maintain gestation after transfer of in-vitro produced (IVP) embryos. The aim of this study was to identify genes, and their related biological mechanisms, in the endometria of recipient lactating dairy cows that become pregnant in the subsequent estrus cycle upon transfer of IVP embryos. Endometrial biopsies were taken from lactating Holstein Friesian cows on day 6-8 of the estrus cycle followed by embryo transfer in the following cycle. Animals were classified retrospectively as pregnant (PR, n=8) or non-pregnant (non-PR, n=11) cows, according to pregnancy status at 26-47 days. Extracted mRNAs from endometrial samples were sequenced with an Illumina platform to determine differentially expressed genes (DEG) between the endometrial transcriptome from PR and non-PR cows. There were 111 DEG (FDR<0.05), which were mainly related to extracellular matrix interaction, histotroph metabolic composition, prostaglandin synthesis, TGF-{beta} signaling as well as inflammation and leukocyte activation. Comparison of these DEG with DEG identified in two public external datasets confirmed the more fertile endometrial molecular profile of PR cows. In conclusion, this study provides insights into the key early endometrial mechanisms for pregnancy establishment, after IVP embryo transfer in dairy cows.

systems biology↗