Immune gene expression covaries with gut microbiome composition in stickleback

Abstract

Commensal microbial communities have immense effects on their vertebrate hosts, contributing to a number of physiological functions as well as host fitness. In particular, host immunity is strongly linked to microbiota composition through poorly understood bi-directional links. Gene expression may be a potential mediator of these links between microbial communities and host function. However few studies have investigated connections between microbiota composition and expression of host immune genes in complex systems. Here we leverage a large study of laboratory-raised fish from the species Gasterosteus aculeatus (three-spined stickleback) to document correlations between gene expression and microbiome composition. First, we examined correlations between microbiome alpha diversity and gene expression. Our results demonstrate robust positive associations between microbial alpha diversity and expression of host immunity. Next, we examined correlations between host gene expression and abundance of microbial taxa. We identified 15 microbial families that were highly correlated to host gene expression. These families were all tightly correlated to host expression of immune genes and processes, falling into one of three categories: those positively correlated, negatively correlated, and neutrally related to immune processes. Furthermore, we highlight several important immune processes that are commonly associated with abundance of these taxons, including both macrophage and B cell functions. Further functional characterization of microbial taxa will help disentangle the mechanisms of the correlations described here. In sum, our study supports prevailing hypotheses of intimate links between host immunity and gut microbiome composition.

Evolution of a costly immunity to cestode parasites is a pyrrhic victory

“…We then used a binomial GLM to test, separately for each lake, whether nesting status of males (or gonad mass of females) differed depending on whether the fish were infected, or had fibrosis. Additional analyses and detailed methods are described in deLisle and Bolnick, (2021). These collections were approved by the British Columbia Ministry of the Environment (Scientific Fish Collecting Permit NA19-457335) and the University of Connecticut IACUC (protocol A18-008).…”

“…We used the same library preparation and bioinformatic steps to obtain 3’TagSeq data on ### F2 (intercross and reciprocal backcross) fish from the QTL study described above (Fuess et al 2021). We used DESeq2 to test for differences in gene expression between infected versus uninfected fish, and fibrotic versus non-fibrotic fish.…”

“…We used DESeq2 to test for differences in gene expression between infected versus uninfected fish, and fibrotic versus non-fibrotic fish. The bulk of these analyses are reported elsewhere (Fuess et al 2021), here we focus on reporting one specific result from Ingenuity Pathway Analysis related to an upstream regulator of fibrosis.…”

“…To to pinpoint regions of exceptional allele frequency divergence within the broad regions of each QTL and narrow our search for candidate genes, we used whole genome sequence data from pooled population samples (PoolSeq) from Roberts and Gosling Lakes, and an outgroup (a marine anadromous population, Sayward Estuary, S), to calculate scaled population branch statistics (PBS) measures for each locus (i.e., the proportion of total allele frequency change happening in each population). We require candidate genes to fit four lines of evidence: (1) fall within a QTL for a focal trait, (2) be an exceptional target of selection within that trait (both high PBS in R or G, and high allele frequency divergence (Fst), (3) have a known effect on the focal trait, and (4) preferably be differentially expressed in transcriptomic data from pure R and G fish (Lohman et al 2017), and in the F2 hybrids (Fuess et al 2021). Only a few loci met three or four of the criteria.…”

Single cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis

“…This fish has long been a focus of study in behavior, ecology and evolution (Bell and Foster 1994; Colosimo et al 2004; Cresko et al 2004, 2007; Hohenlohe et al 2010; Reid, Bell, and Veeramah 2021; M. D. Shapiro et al 2004), and is now a nascent system for biomedical research (E A Beck et al 2020; Emily A. Beck et al 2021; Fuess et al 2021; Gardell et al 2017; Miller et al 2007; Small et al 2017). Although stickleback have a well assembled genome, it’s 3′ UTR annotations are incomplete which limits scRNAseq’s utility.…”