Multi-Omics Analysis of Key microRNA-mRNA Metabolic Regulatory Networks in Skeletal Muscle of Obese Rabbits.
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ABSTRACT:
SUBMITTER: Li Y
PROVIDER: S-EPMC8072691 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:MicroRNAs (miRNAs) play a key role in mediating the action of insulin on cell growth and the development of diabetes. However, few studies have been conducted to provide a comprehensive overview of the miRNA-mediated signaling network in response to glucose in pancreatic beta cells. In our study, we established a computational framework integrating multi-omics profiles analyses, including RNA sequencing (RNA-seq) and small RNA sequencing (sRNA-seq) data analysis, inverse expression pattern analysis, public data integration, and miRNA targets prediction to illustrate the miRNA-mediated regulatory network at different glucose concentrations in INS-1 pancreatic beta cells (INS-1), which display important characteristics of the pancreatic beta cells. RESULTS:We applied our computational framework to the expression profiles of miRNA/mRNA of INS-1, at different glucose concentrations. A total of 1437 differentially expressed genes (DEGs) and 153 differentially expressed miRNAs (DEmiRs) were identified from multi-omics profiles. In particular, 121 DEmiRs putatively regulated a total of 237 DEGs involved in glucose metabolism, fatty acid oxidation, ion channels, exocytosis, homeostasis, and insulin gene regulation. Moreover, Argonaute 2 immunoprecipitation sequencing, qRT-PCR, and luciferase assay identified Crem, Fn1, and Stc1 are direct targets of miR-146b and elucidated that miR-146b acted as a potential regulator and promising target to understand the insulin signaling network. CONCLUSIONS:In this study, the integration of experimentally verified data with system biology framework extracts the miRNA network for exploring potential insulin-associated miRNA and their target genes. The findings offer a potentially significant effect on the understanding of miRNA-mediated insulin signaling network in the development and progression of pancreatic diabetes.
| S-EPMC7496206 | biostudies-literature
Project description:BackgroundTranscriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features.ResultsWe describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for sequence kernel association test analyses of gene expression.ConclusionsOur models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation.
| S-EPMC8056605 | biostudies-literature
Project description:Puberty is a complex physiological event by which animals mature into an adult capable of sexual reproduction. In order to enhance our understanding of the genes and regulatory pathways and networks involved in puberty, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed to achieve puberty (i.e., longissimus dorsi muscle, adipose, and liver). These tissues were collected from pre- and post-pubertal Brangus heifers (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) derived from a population of cattle used to identify quantitative trait loci associated with fertility traits (i.e., age of first observed corpus luteum (ACL), first service conception (FSC), and heifer pregnancy (HPG)). In order to exploit the power of complementary omics analyses, pre- and post-puberty co-expression gene networks were constructed by combining the results from genome-wide association studies (GWAS), RNA-Seq, and bovine transcription factors. Eight tissues among pre-pubertal and post-pubertal Brangus heifers revealed 1,515 differentially expressed and 943 tissue-specific genes within the 17,832 genes confirmed by RNA-Seq analysis. The hypothalamus experienced the most notable up-regulation of genes via puberty (i.e., 204 out of 275 genes). Combining the results of GWAS and RNA-Seq, we identified 25 loci containing a single nucleotide polymorphism (SNP) associated with ACL, FSC, and (or) HPG. Seventeen of these SNP were within a gene and 13 of the genes were expressed in uterus or endometrium. Multi-tissue omics analyses revealed 2,450 co-expressed genes relative to puberty. The pre-pubertal network had 372,861 connections whereas the post-pubertal network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, DACH2, PROP1, SIX6, etc.). Results from these multi-tissue omics analyses improve understanding of the number of genes and their complex interactions for puberty in cattle.
| S-EPMC4105537 | biostudies-literature
Project description:The mechanisms of chronic pain are complex, and genetic factors play an essential role in the development of chronic pain. Neuropathic pain (NP) and inflammatory pain (IP) are two primary components of chronic pain. Previous studies have uncovered some common biological processes in NP and IP. However, the shared genetic mechanisms remained poorly studied. We utilized multi-omics systematic analyses to investigate the shared genetic mechanisms of NP and IP. First, by integrating several genome-wide association studies (GWASs) with multi-omics data, we revealed the significant overlap of the gene co-expression modules in NP and IP. Further, we uncovered the shared biological pathways, including the previously reported mitochondrial electron transport and ATP metabolism, and stressed the role of genetic factors in chronic pain with neurodegenerative diseases. Second, we identified 24 conservative key drivers (KDs) contributing to NP and IP, containing two well-established pain genes, IL1B and OPRM1, and some novel potential pain genes, such as C5AR1 and SERPINE1. The subnetwork of those KDs highlighted the processes involving the immune system. Finally, gene expression analysis of the KDs in mouse models underlined two of the KDs, SLC6A15 and KCNQ5, with unidirectional regulatory functions in NP and IP. Our study provides strong evidence to support the current understanding of the shared genetic regulatory networks underlying NP and IP and potentially benefit the future common therapeutic avenues for chronic pain.
| S-EPMC9599593 | biostudies-literature
Project description:Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo ( https://github.com/cellmapslab/kimono ). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.
| S-EPMC7990936 | biostudies-literature
Project description:Puberty is a complex physiological event measured by various indicator traits in genetic improvement programs of beef cattle; thus, developing a more complete understanding of the genes and regulatory pathways and networks involved in puberty will provide knowledge to help improve genetic selection strategies. Herein, we characterized the transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to growth and metabolism needed for cattle to achieve puberty (i.e., longissimus dorsi muscle, fat, and liver). These tissues were collected from pre (PRE)- and post (POST)-pubertal Brangus (3/8 Brahman; Bos indicus x 5/8 Angus; Bos taurus) heifers derived from a population of cattle used to identify QTL associated with fertility traits. In order to exploit the power of complementary omics analyses, PRE and POST puberty co-expression gene networks were constructed by combining the results from RNA-Seq, GWAS, and bovine transcription factors. RNA-Seq of 8 tissues among PRE and POST Brangus heifers revealed 1515 differentiallyexpressed and 943 tissue-specific genes within the 17,832 genes confirmed by metrics of RNA-Seq analysis. Combining the results from RNA-Seq and GWAS indentified a total of 25 QTL associated to heifer fertility. The hypothalamus experienced the most notable up-regulation of genes via puberty. Complementary omics procedures revealed 2,450 co-expressed genes across the 8 tissues relative to puberty. The PRE network had 372,861 connections whereas the POST network had 328,357 connections. A sub-network from this process revealed key transcriptional regulators (i.e., PITX2, FOXA1, TSG1D1, DACH2, LHX4, PROP1 and SIX6). Results from multiples sources of omics data will facilitate the design of breeding strategies to improve fertility in Bos indicus-influenced composite cattle. Sixty-one samples from PRE and POST pubertal composite beef heifers were analyzed with RNA-Seq. The transcriptome of five reproductive tissues (i.e. hypothalamus, pituitary gland, ovary, uterus, and endometrium) as well as tissues known to be relevant to metabolism andbody morphometrics needed for cattle to achieve puberty (i.e.,) was characterized. These tissues were collected from pre (PRE)- and post (POST)-pubertal Brangus (3/8 Brahman x 5/8 Angus) heifers derived from a population of cattle used to identify QTL associated with fertility. Total RNA was purified using a Trizol protocol (Invitrogen, Carlsbad, CA). Sequencing libraries were made using TruSeq RNA Sample Preparation kit of Illumina (San Diego, CA).
2014-02-28 | E-GEOD-55435 | biostudies-arrayexpress
Project description:Skeletal muscle degenerates progressively, losing mass (sarcopenia) over time, which leads to reduced physical ability and often results in secondary diseases such as diabetes and obesity. The regulation of gene expression by microRNAs is a key event in muscle development and disease. To understand genome?wide changes in microRNAs and mRNAs during muscle aging, we sequenced microRNAs and mRNAs from mouse gastrocnemius muscles at two different ages (6 and 24 months). Thirty?four microRNAs (15 up?regulated and 19 down?regulated) were differentially expressed with age, including the microRNAs miR?206 and ?434, which were differentially expressed in aged muscle in previous studies. Interestingly, eight microRNAs in a microRNA cluster at the imprinted Dlk1?Dio3 locus on chromosome 12 were coordinately down?regulated. In addition, sixteen novel microRNAs were identified. Integrative analysis of microRNA and mRNA expression revealed that microRNAs may contribute to muscle aging through the positive regulation of transcription, metabolic processes, and kinase activity. Many of the age?related microRNAs have been implicated in human muscular diseases. We suggest that genome?wide microRNA profiling will expand our knowledge of microRNA function in the muscle aging process.
| S-EPMC4153621 | biostudies-literature
Project description:Cancer is a complex disease where cancer cells express epigenetic and transcriptomic mechanisms to promote tumor initiation, progression, and survival. To extract relevant features from the 2019 Cancer Cell Line Encyclopedia (CCLE), a multi-layer nonnegative matrix factorization approach is used. We used relevant feature genes and DNA promoter regions to construct genomic interaction network to study gene-gene and gene-DNA promoter methylation relationships. Here, we identified a set of gene transcripts and methylated DNA promoter regions for different clusters, including one homogeneous lymphoid neoplasms cluster. In this cluster, we found different methylated transcription factors that affect transcriptional activation of EGFR and downstream interactions. Furthermore, the hippo-signaling pathway might not function properly because of DNA hypermethylation and low gene expression of both LATS2 and YAP1. Finally, we could identify a potential dysregulation of the CD28-CD86-CTLA4 axis. Characterizing the interaction of the epigenome and the transcriptome is vital for our understanding of cancer cell line behavior, not only for deepening insights into cancer-related processes but also for future disease treatment and drug development. Here we have identified potential candidates that characterize cancer cell lines, which give insight into the development and progression of cancers.
| S-EPMC8131752 | biostudies-literature
Project description:A major goal in systems biology is a comprehensive description of the entirety of all complex interactions between different types of biomolecules-also referred to as the interactome-and how these interactions give rise to higher, cellular and organism level functions or diseases. Numerous efforts have been undertaken to define such interactomes experimentally, for example yeast-two-hybrid based protein-protein interaction networks or ChIP-seq based protein-DNA interactions for individual proteins. To complement these direct measurements, genome-scale quantitative multi-omics data (transcriptomics, proteomics, metabolomics, etc.) enable researchers to predict novel functional interactions between molecular species. Moreover, these data allow to distinguish relevant functional from non-functional interactions in specific biological contexts. However, integration of multi-omics data is not straight forward due to their heterogeneity. Numerous methods for the inference of interaction networks from homogeneous functional data exist, but with the advent of large-scale paired multi-omics data a new class of methods for inferring comprehensive networks across different molecular species began to emerge. Here we review state-of-the-art techniques for inferring the topology of interaction networks from functional multi-omics data, encompassing graphical models with multiple node types and quantitative-trait-loci (QTL) based approaches. In addition, we will discuss Bayesian aspects of network inference, which allow for leveraging already established biological information such as known protein-protein or protein-DNA interactions, to guide the inference process.
| S-EPMC6582773 | biostudies-literature
Project description:MicroRNAs are small non-coding RNAs that can regulate expressions of their target genes at the post-transcriptional level. In this study, we propose a tri-component strategy that combines the conservation of microRNAs, homology of mRNA coding regions, and conserved microRNA binding sites in the 3' untranslated regions to discover conserved microRNA-mRNA interactions. To validate the performance of our conservation strategy, we collected the experimentally validated microRNA-mRNA interactions from three databases as the golden standard. We found that the proposed strategy can improve the performance of existing target prediction algorithms by approximately 2-4 fold. In addition, we demonstrated that the proposed strategy could efficiently retain highly confident interactions from the intersection results of the existing algorithms and filter out the possible false positive predictions in the union one. Furthermore, this strategy can facilitate our ability to trace the homologues in different species that are targeted by the same miRNA family because it combines these three features to identify the conserved miRNA-mRNA interactions during evolution. Through an extensive application of the proposed conservation strategy to a study of the miR-1/206 regulatory network, we demonstrate that the target mRNA recruiting process could be associated with expansion of miRNA family during its evolution. We also uncovered the functional evolution of the miR-1/206 regulatory network. In this network, the early targeted genes tend to participate in more general and development-related functions. In summary, the conservation strategy is capable of helping to highlight the highly confident miRNA-mRNA interactions and can be further applied to reveal the evolutionary features of miRNA regulatory network and functions.
| S-EPMC4108425 | biostudies-literature