ScGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks.
Ontology highlight
ABSTRACT:
SUBMITTER: Jin T
PROVIDER: S-EPMC8161957 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Objectives: Uncovering the genetic basis of COVID-19 may shed insight into its pathogenesis and help to improve treatment measures. We aimed to investigate the host genetic variants associated with COVID-19. Methods: The summary result of a COVID-19 GWAS (9,373 hospitalized COVID-19 cases and 1,197,256 controls) was obtained from the COVID-19 Host Genetic Initiative GWAS meta-analyses. We tested colocalization of the GWAS signals of COVID-19 with expression and methylation quantitative traits loci (eQTL and mQTL, respectively) using the summary data-based Mendelian randomization (SMR) analysis. Four eQTL and two mQTL datasets were utilized in the SMR analysis, including CAGE blood eQTL data (n = 2,765), GTEx v7 blood (n = 338) and lung (n = 278) eQTL data, Geuvadis lymphoblastoid cells eQTL data, LBC-BSGS blood mQTL data (n = 1,980), and Hannon blood mQTL summary data (n = 1,175). We conducted a transcriptome-wide association study (TWAS) on COVID-19 with precomputed prediction models of GTEx v8 eQTL in lung and blood using S-PrediXcan. Results: Our SMR analyses identified seven protein-coding genes (TYK2, IFNAR2, OAS1, OAS3, XCR1, CCR5, and MAPT) associated with COVID-19, including two novel risk genes, CCR5 and tau-encoding MAPT. The TWAS revealed four genes for COVID-19 (CXCR6, CCR5, CCR9, and PIGN), including two novel risk genes, CCR5 and PIGN. Conclusion: Our study highlighted the functional relevance of some known genome-wide risk genes of COVID-19 and revealed novel genes contributing to differential outcomes of COVID-19 disease.
| S-EPMC8452849 | 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:BackgroundThe genetic underpinnings of late-onset Alzheimer's disease (LOAD) are yet to be fully elucidated. Although numerous LOAD-associated loci have been discovered, the causal variants and their target genes remain largely unknown. Since the brain is composed of heterogenous cell subtypes, it is imperative to study the brain on a cell subtype specific level to explore the biological processes underlying LOAD.MethodsHere, we present the largest parallel single-nucleus (sn) multi-omics study to simultaneously profile gene expression (snRNA-seq) and chromatin accessibility (snATAC-seq) to date, using nuclei from 12 normal and 12 LOAD brains. We identified cell subtype clusters based on gene expression and chromatin accessibility profiles and characterized cell subtype-specific LOAD-associated differentially expressed genes (DEGs), differentially accessible peaks (DAPs) and cis co-accessibility networks (CCANs).ResultsIntegrative analysis defined disease-relevant CCANs in multiple cell subtypes and discovered LOAD-associated cell subtype-specific candidate cis regulatory elements (cCREs), their candidate target genes, and trans-interacting transcription factors (TFs), some of which, including ELK1, JUN, and SMAD4 in excitatory neurons, were also LOAD-DEGs. Finally, we focused on a subset of cell subtype-specific CCANs that overlap known LOAD-GWAS regions and catalogued putative functional SNPs changing the affinities of TF motifs within LOAD-cCREs linked to LOAD-DEGs, including APOE and MYO1E in a specific subtype of microglia and BIN1 in a subpopulation of oligodendrocytes.ConclusionsTo our knowledge, this study represents the most comprehensive systematic interrogation to date of regulatory networks and the impact of genetic variants on gene dysregulation in LOAD at a cell subtype resolution. Our findings reveal crosstalk between epigenetic, genomic, and transcriptomic determinants of LOAD pathogenesis and define catalogues of candidate genes, cCREs, and variants involved in LOAD genetic etiology and the cell subtypes in which they act to exert their pathogenic effects. Overall, these results suggest that cell subtype-specific cis-trans interactions between regulatory elements and TFs, and the genes dysregulated by these networks contribute to the development of LOAD.
| S-EPMC10546724 | 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:Neuroinflammation and immune dysregulation play a key role in Alzheimer's disease (AD) and are also associated with severe Covid-19 and neurological symptoms. Also, genome-wide association studies found many risk single nucleotide polymorphisms (SNPs) for AD and Covid-19. However, our understanding of underlying gene regulatory mechanisms from risk SNPs to AD, Covid-19 and phenotypes is still limited. To this end, we performed an integrative multi-omics analysis to predict gene regulatory networks for major brain regions from population data in AD. Our networks linked transcription factors (TFs) to TF binding sites (TFBSs) on regulatory elements to target genes. Comparative network analyses revealed cross-region-conserved and region-specific regulatory networks, in which many immunological genes are present. Furthermore, we identified a list of AD-Covid genes using our networks involving known and Covid-19 genes. Our machine learning analysis prioritized 36 AD-Covid candidate genes for predicting Covid severity. Our independent validation analyses found that these genes outperform known genes for classifying Covid-19 severity and AD. Finally, we mapped genome-wide association study SNPs of AD and severe Covid that interrupt TFBSs on our regulatory networks, revealing potential mechanistic insights of those disease risk variants. Our analyses and results are open-source available, providing an AD-Covid functional genomic resource at the brain region level.
| S-EPMC10196687 | biostudies-literature
Project description:BackgroundIn recent years, omics technologies have offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user-friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to provide a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline.ResultsHere, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from 2 to 4 distinct omics data types, including 16S ribosomal RNA (rRNA) gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration, and visualization approaches, enhancing the toolkit for a more insightful analysis of microbiomes. The functionality of these new features is showcased through the use of 4 microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives.ConclusionsgNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, offering novel insights in both host-associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.
| S-EPMC11240238 | biostudies-literature
Project description:The ability of bacteria to respond to environmental change is based on the ability to coordinate, redirect and fine-tune their genetic repertoire as and when required. While we can learn a great deal from reductive analysis of individual pathways and global approaches to gene regulation, a deeper understanding of these complex signaling networks requires the simultaneous consideration of several regulatory layers at the genome scale. To highlight the power of this approach we analyzed the Hfq transcriptional/translational regulatory network in the model bacterium Pseudomonas fluorescens. We first used extensive 'omics' analyses to assess how hfq deletion affects mRNA abundance, mRNA translation and protein abundance. The subsequent, multi-level integration of these datasets allows us to highlight the discrete contributions by Hfq to gene regulation at different levels. The integrative approach to regulatory analysis we describe here has significant potential, for both dissecting individual signaling pathways and understanding the strategies bacteria use to cope with external challenges.
| S-EPMC5627042 | biostudies-literature
Project description:BackgroundGlioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes.ResultsIn this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner.ConclusionsPRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.
| S-EPMC7294636 | 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.
2014-02-28 | GSE55435 | GEO
Project description:Disease comorbidity is a pervasive phenomenon impacting patients' health outcomes, disease management, and clinical decisions. This review presents past, current and future research directions leveraging both phenotypic and molecular information to uncover disease similarity underpinning the biology and etiology of disease comorbidity.We retrieved ~130 publications and retained 59, ranging from 2006 to 2015, that comprise a minimum number of five diseases and at least one type of biomolecule. We surveyed their methods, disease similarity metrics, and calculation of comorbidities in the electronic health records, if present.Among the surveyed studies, 44% generated or validated disease similarity metrics in context of comorbidity, with 60% being published in the last two years. As inputs, 87% of studies utilized intragenic loci and proteins while 13% employed RNA (mRNA, LncRNA or miRNA). Network modeling was predominantly used (35%) followed by statistics (28%) to impute similarity between these biomolecules and diseases. Studies with large numbers of biomolecules and diseases used network models or naïve overlap of disease-molecule associations, while machine learning, statistics, and information retrieval were utilized in smaller and moderate sized studies. Multiscale computations comprising shared function, network topology, and phenotypes were performed exclusively on proteins.This review highlighted the growing methods for identifying the molecular mechanisms underpinning comorbidities that leverage multiscale molecular information and patterns from electronic health records. The survey unveiled that intergenic polymorphisms have been overlooked for similarity imputation compared to their intragenic counterparts, offering new opportunities to bridge the mechanistic and similarity gaps of comorbidity.
| S-EPMC5171562 | biostudies-literature