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Modeling tissue co-regulation estimates tissue-specific contributions to disease.

ABSTRACT: Integrative analyses of genome-wide association studies and gene expression data have implicated many disease-critical tissues. However, co-regulation of genetic effects on gene expression across tissues impedes distinguishing biologically causal tissues from tagging tissues. In the present study, we introduce tissue co-regulation score regression (TCSC), which disentangles causal tissues from tagging tissues by regressing gene-disease association statistics (from transcriptome-wide association studies) on tissue co-regulation scores, reflecting correlations of predicted gene expression across genes and tissues. We applied TCSC to 78 diseases/traits (average n = 302,000) and gene expression prediction models for 48 GTEx tissues. TCSC identified 21 causal tissue-trait pairs at a 5% false discovery rate (FDR), including well-established findings, biologically plausible new findings (for example, aorta artery and glaucoma) and increased specificity of known tissue-trait associations (for example, subcutaneous adipose, but not visceral adipose, and high-density lipoprotein). TCSC also identified 17 causal tissue-trait covariance pairs at 5% FDR. In conclusion, TCSC is a precise method for distinguishing causal tissues from tagging tissues.

SUBMITTER: Amariuta T

PROVIDER: S-EPMC10904330 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Modeling tissue co-regulation estimates tissue-specific contributions to disease.

Amariuta Tiffany T Siewert-Rocks Katherine K Price Alkes L AL

Nature genetics 20230814 9

Integrative analyses of genome-wide association studies and gene expression data have implicated many disease-critical tissues. However, co-regulation of genetic effects on gene expression across tissues impedes distinguishing biologically causal tissues from tagging tissues. In the present study, we introduce tissue co-regulation score regression (TCSC), which disentangles causal tissues from tagging tissues by regressing gene-disease association statistics (from transcriptome-wide association ...[more]

PMID: 37580597

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Project description:BackgroundCaenorhabditis elegans has traditionally been used as a model for studying nematode biology, but its small size limits the ability for researchers to perform some experiments such as high-throughput tissue-specific gene expression studies. However, the dissection of individual tissues is possible in the parasitic nematode Ascaris suum due to its relatively large size. Here, we take advantage of the recent genome sequencing of Ascaris suum and the ability to physically dissect its separate tissues to produce a wide-scale tissue-specific nematode RNA-seq datasets, including data on three non-reproductive tissues (head, pharynx, and intestine) in both male and female worms, as well as four reproductive tissues (testis, seminal vesicle, ovary, and uterus). We obtained fundamental information about the biology of diverse cell types and potential interactions among tissues within this multicellular organism.Methodology/principal findingsOverexpression and functional enrichment analyses identified many putative biological functions enriched in each tissue studied, including functions which have not been previously studied in detail in nematodes. Putative tissue-specific transcriptional factors and corresponding binding motifs that regulate expression in each tissue were identified, including the intestine-enriched ELT-2 motif/transcription factor previously described in nematode intestines. Constitutively expressed and novel genes were also characterized, with the largest number of novel genes found to be overexpressed in the testis. Finally, a putative acetylcholine-mediated transcriptional network connecting biological activity in the head to the male reproductive system is described using co-expression networks, along with a similar ecdysone-mediated system in the female.Conclusions/significanceThe expression profiles, co-expression networks and co-expression regulation of the 10 tissues studied and the tissue-specific analysis presented here are a valuable resource for studying tissue-specific biological functions in nematodes.

Project description:The global emergence of novel pathogenic viruses presents an important challenge for research, as high biosafety levels are required to process samples. While inactivation of infectious agents facilitates the use of less stringent safety conditions, its effect on other biological entities of interest present in the sample is generally unknown. Here, we analyzed the effect of five inactivation methods (heat, ethanol, formaldehyde, psoralen, and TRIzol) on microbiome composition and diversity in samples collected from four different body sites (gut, nasal, oral, and skin) and compared them against untreated samples from the same tissues. We performed 16S rRNA gene sequencing and estimated abundance and diversity of bacterial taxa present in all samples. Nasal and skin samples were the most affected by inactivation, with ethanol and TRIzol inducing the largest changes in composition, and heat, formaldehyde, TRIzol, and psoralen inducing the largest changes in diversity. Oral and stool microbiomes were more robust to inactivation, with no significant changes in diversity and only moderate changes in composition. Firmicutes was the taxonomic group least affected by inactivation, while Bacteroidetes had a notable enrichment in nasal samples and moderate enrichment in fecal and oral samples. Actinobacteria were more notably depleted in fecal and skin samples, and Proteobacteria exhibited a more variable behavior depending on sample type and inactivation method. Overall, our results demonstrate that inactivation methods can alter the microbiome in a tissue-specific manner and that careful consideration should be given to the choice of method based on the sample type under study. IMPORTANCE Understanding how viral infections impact and are modulated by the microbiome is an important problem in basic research but is also of high clinical relevance under the current pandemic. To facilitate the study of interactions between microbial communities and pathogenic viruses under safe conditions, the infectious agent is generally inactivated prior to processing samples. The effect of this inactivation process in the microbiome is, however, unknown. Further, it is unclear whether biases introduced by inactivation methods are dependent on the sample type under study. Estimating the magnitude and nature of the changes induced by different methods in samples collected from various body sites thus provides important information for current and future studies that require inactivation of pathogenic agents.

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Modeling tissue co-regulation estimates tissue-specific contributions to disease.

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Modeling tissue co-regulation estimates tissue-specific contributions to disease.

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