Joint eQTL mapping and inference of gene regulatory network improves power of detecting both cis- and trans-eQTLs

Deciphering the coordinated roles of the host genome, duodenal mucosal genes, and microbiota in regulating complex traits in chickens

BACKGROUND

The complex interactions between host genetics and the gut microbiome are well documented. However, the specific impacts of gene expression patterns and microbial composition on each other remain to be further explored.

RESULTS

Here, we investigated this complex interplay in a sizable population of 705 hens, employing integrative analyses to examine the relationships among the host genome, mucosal gene expression, and gut microbiota. Specific microbial taxa, such as the cecal family Christensenellaceae, which showed a heritability of 0.365, were strongly correlated with host genomic variants. We proposed a novel concept of regulatability ( r b 2 ), which was derived from h2, to quantify the cumulative effects of gene expression on the given phenotypes. The duodenal mucosal transcriptome emerged as a potent influencer of duodenal microbial taxa, with much higher r b 2 values (0.17 ± 0.01, mean ± SE) than h2 values (0.02 ± 0.00). A comparative analysis of chickens and humans revealed similar average microbiability values of genes (0.18 vs. 0.20) and significant differences in average r b 2 values of microbes (0.17 vs. 0.04). Besides, cis ( h cis 2 ) and trans heritability ( h trans 2 ) were estimated to assess the effects of genetic variations inside and outside the cis window of the gene on its expression. Higher h trans 2 values than h cis 2 values and a greater prevalence of trans-regulated genes than cis-regulated genes underscored the significant role of loci outside the cis window in shaping gene expression levels. Furthermore, our exploration of the regulatory effects of duodenal mucosal genes and the microbiota on 18 complex traits enhanced our understanding of the regulatory mechanisms, in which the CHST14 gene and its regulatory relationships with Lactobacillus salivarius jointly facilitated the deposition of abdominal fat by modulating the concentration of bile salt hydrolase, and further triglycerides, total cholesterol, and free fatty acids absorption and metabolism.

CONCLUSIONS

Our findings highlighted a novel concept of r b 2 to quantify the phenotypic variance attributed to gene expression and emphasize the superior role of intestinal mucosal gene expressions over host genomic variations in elucidating host‒microbe interactions for complex traits. This understanding could assist in devising strategies to modulate host-microbe interactions, ultimately improving economic traits in chickens.

A compendium of methods: Searching allele specific expression via RNA sequencing

Diploid mammalian genome has paired alleles for each gene; typically allowing for equal expression of the two alleles within the cell/tissue. However, genetic regulatory elements and epigenetic modifications can disrupt this equality, leading to preferential expression of one allele. Examining high-confidence allele-specific expression (ASE) is vital for understanding genetic variations and their impact on major diseases like cancers and diabetes. ASE analysis not only aids in disease prognosis and diagnosis but also helps to identify regulatory mechanisms operating within the genome. While advances in sequencing technologies have greatly improved our understanding of ASE, challenges remain in estimating it accurately. In this article, we reviewed methods for detecting ASE using both bulk RNASeq and single-cell RNASeq data to provide deeper insights beyond the mere prediction of ASE genes. Fundamentally, ASE detection methods are data-driven and can be classified according to type of data used. Some methods utilize both, DNA genotyping information and RNASeq while others rely solely on RNASeq data. This article offers a comparative analysis of these methods and compilation of repositories providing valuable insights.

eQTLs identify regulatory networks and drivers of variation in the individual response to sepsis

Sepsis is a clinical syndrome of life-threatening organ dysfunction caused by a dysregulated response to infection, for which disease heterogeneity is a major obstacle to developing targeted treatments. We have previously identified gene-expression-based patient subgroups (sepsis response signatures [SRS]) informative for outcome and underlying pathophysiology. Here, we aimed to investigate the role of genetic variation in determining the host transcriptomic response and to delineate regulatory networks underlying SRS. Using genotyping and RNA-sequencing data on 638 adult sepsis patients, we report 16,049 independent expression (eQTLs) and 32 co-expression module (modQTLs) quantitative trait loci in this disease context. We identified significant interactions between SRS and genotype for 1,578 SNP-gene pairs and combined transcription factor (TF) binding site information (SNP2TFBS) and predicted regulon activity (DoRothEA) to identify candidate upstream regulators. Overall, these approaches identified putative mechanistic links between host genetic variation, cell subtypes, and the individual transcriptomic response to infection.

High-throughput approaches to functional characterization of genetic variation in yeast

Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.