Organ-level protein networks as a reference for the host effects of the microbiome

Abstract

Connections between the microbiome and health are rapidly emerging in a wide range of diseases. However, a detailed mechanistic understanding of how different microbial communities are influencing their hosts is often lacking. One method researchers have used to understand these effects are germ-free (GF) mouse models. Differences found within the organ systems of these model organisms may highlight generalizable mechanisms that microbiome dysbioses have throughout the host. Here, we applied multiplexed, quantitative proteomics on the brains, spleens, hearts, small intestines, and colons of conventionally raised and GF mice, identifying associations to colonization state in over 7000 proteins. Highly ranked associations were constructed into protein–protein interaction networks and visualized onto an interactive 3D mouse model for user-guided exploration. These results act as a resource for microbiome researchers hoping to identify host effects of microbiome colonization on a given organ of interest. Our results include validation of previously reported effects in xenobiotic metabolism, the innate immune system, and glutamate-associated proteins while simultaneously providing organism-wide context. We highlight organism-wide differences in mitochondrial proteins including consistent increases in NNT, a mitochondrial protein with essential roles in influencing levels of NADH and NADPH, in all analyzed organs of conventional mice. Our networks also reveal new associations for further exploration, including protease responses in the spleen, high-density lipoproteins in the heart, and glutamatergic signaling in the brain. In total, our study provides a resource for microbiome researchers through detailed tables and visualization of the protein-level effects of microbial colonization on several organ systems.

Multi-omics analyses of the ulcerative colitis gut microbiome link Bacteroides vulgatus proteases with disease severity

“…Student’s t-tests comparing protein abundances of samples from mice receiving patient samples H19 and L3 were performed in scipy using unequal variance. T-test P values were then combined with the fold-change differences to rank associated proteins via the Pi score statistic 108 , as previously described 109 . Top proteins associated with the H19 samples were then presented in a barplot using the seaborn python package.…”

Urinary Exosomes Identify Inflammatory Pathways in Vancomycin Associated Acute Kidney Injury

“…The strength of the association between each protein and V-AKI status was ranked using the pi-value metric which combines p -value significance with fold-change [ 12 ]. As previously described [ 46 ] top ranked, significant associations were defined as having a pi-value > |1|. Each protein association to V-AKI status was plotted in a volcano plot to further highlight the location of the top-10 ranked proteins.…”

Key features of the genetic architecture and evolution of host-microbe interactions revealed by high-resolution genetic mapping of the mucosa-associated gut microbiome in hybrid mice

“…several bacterial taxa fulfill the same function within the gut microbiome (Moya and Ferrer, 2016;Tian et al, 2020). Additionally, there is significant overlap of genes within loci identified in the current study and proteins differentially expressed in the intestine of germ-free mice compared to conventionally raised mice (Mills et al, 2020). Finally, by analyzing the functions of the genes closest to significant SNPs, we found that 12 of the 14 significantly enriched KEGG pathways were shown to be related to interactions with bacteria (Fonken et al, 2010;Thaiss et al, 2014;Neumann et al, 2014;Thaiss et al, 2015a;Thaiss et al, 2015b;Castoldi et al, 2015;Erdman and Poutahidis, 2016;Thaiss et al, 2016;Deaver et al, 2018;Wu et al, 2018;Peng et al, 2020;Nagpal et al, 2020;Hollander and Kaunitz, 2020; Suppl.…”

“…Hub genes SNP network and their first neighbors, the hub genes from the 'differentially expressed in GF mice'-network and their respective first neighbors, genes found in both Mills et al (2020) and other mouse QTL studies, closest genes to a SNP found in Mills et al (2020), genes situated in the 20 smallest intervals, six genes in the two intervals with the lowest P values, twenty genes in intervals found in most different taxa, genes situated in the region with most overlap within our study, and finally the…”