Transcriptome analysis of the potential molecular mechanism of enteritis in intestine and hepatopancreas of Litopenaeus vannamei.

The prawn, Litopenaeus vannamei (L. vannamei), is the most widely farmed species in the world but the incidence of enteritis in L. vannamei has increased in recent years. However, the pathogenesis of enteritis remains unclear. In this study, high-throughput sequencing was used to analyze the hepatopancreatic and intestinal transcriptome of healthy and enteritis-affected individuals from the same pond. In total, 1209 and 1608 differently-expressed genes (DEGs) were detected in the hepatopancreatic and intestinal transcriptomes, respectively. Significantly changed genes were enriched in the intestinal immune network for IgA Production, Lysosomes, Sphingolipid Metabolism and the Peroxisome Signaling Pathway. Expression of the integrin α4ß7 gene was significantly increased in the intestine of L. vannamei with enteritis, while expression of 38 DEGs associated with the lysosome was significantly down-regulated. Furthermore, the expression of sphingolipid metabolism-related enzymes and superoxide dismutase (SOD) genes was also significantly decreased, indicating that abnormal autoimmune function, weak intestinal resistance to external pathogenic microbial invasion, and self-healing ability were important factors associated with enteritis in L. vannamei. In addition, the expression of trypsin and pancreatic lipase was decreased in the hepatopancreas of L. vannamei with enteritis. This study provided new insights into the possible molecular pathogenesis of enteritis in L. vannamei.

Deconvolution of bulk blood eQTL effects into immune cell subpopulations.

BACKGROUND: Expression quantitative trait loci (eQTL) studies are used to interpret the function of disease-associated genetic risk factors. To date, most eQTL analyses have been conducted in bulk tissues, such as whole blood and tissue biopsies, which are likely to mask the cell type-context of the eQTL regulatory effects. Although this context can be investigated by generating transcriptional profiles from purified cell subpopulations, current methods to do this are labor-intensive and expensive. We introduce a new method, Decon2, as a framework for estimating cell proportions using expression profiles from bulk blood samples (Decon-cell) followed by deconvolution of cell type eQTLs (Decon-eQTL). RESULTS: The estimated cell proportions from Decon-cell agree with experimental measurements across cohorts (R ≥ 0.77). Using Decon-cell, we could predict the proportions of 34 circulating cell types for 3194 samples from a population-based cohort. Next, we identified 16,362 whole-blood eQTLs and deconvoluted cell type interaction (CTi) eQTLs using the predicted cell proportions from Decon-cell. CTi eQTLs show excellent allelic directional concordance with eQTL (≥ 96-100%) and chromatin mark QTL (≥87-92%) studies that used either purified cell subpopulations or single-cell RNA-seq, outperforming the conventional interaction effect. CONCLUSIONS: Decon2 provides a method to detect cell type interaction effects from bulk blood eQTLs that is useful for pinpointing the most relevant cell type for a given complex disease. Decon2 is available as an R package and Java application (https://github.com/molgenis/systemsgenetics/tree/master/Decon2) and as a web tool (www.molgenis.org/deconvolution).