Organ Level Protein Networks As a Reference for the Host Effects of the Microbiome
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Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press 1 Organ level protein networks as a reference for the host effects of the microbiome 2 3 Robert H. Millsa,b,c,d, Jacob M. Wozniaka,b, Alison Vrbanacc, Anaamika Campeaua,b, Benoit 4 Chassainge,f,g,h, Andrew Gewirtze, Rob Knightc,d, and David J. Gonzaleza,b,d,# 5 6 a Department of Pharmacology, University of California, San Diego, California, USA 7 b Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, 8 California, USA 9 c Department of Pediatrics, and Department of Computer Science and Engineering, University of 10 California, San Diego California, USA 11 d Center for Microbiome Innovation, University of California, San Diego, California, USA 12 e Center for Inflammation, Immunity and Infection, Institute for Biomedical Sciences, Georgia State 13 University, Atlanta, GA, USA 14 f Neuroscience Institute, Georgia State University, Atlanta, GA, USA 15 g INSERM, U1016, Paris, France. 16 h Université de Paris, Paris, France. 17 18 Key words: Microbiota, Tandem Mass Tags, Organ Proteomics, Gnotobiotic Mice, Germ-free Mice, 19 Protein Networks, Proteomics 20 21 # Address Correspondence to: 22 David J. Gonzalez, PhD 23 Department of Pharmacology and Pharmacy 24 University of California, San Diego 25 La Jolla, CA 92093 26 E-mail: [email protected] 27 Phone: 858-822-1218 28 1 Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press 29 Abstract 30 Connections between the microbiome and health are rapidly emerging in a wide range of 31 diseases. However, a detailed mechanistic understanding of how different microbial communities are 32 influencing their hosts is often lacking. One method researchers have used to understand these effects are 33 germ-free mouse models. Differences found within the organ systems within these model organisms may 34 highlight generalizable mechanisms that microbiome dysbioses have throughout the host. Here, we 35 applied multiplexed, quantitative proteomics on the brains, spleens, hearts, small intestines and colons of 36 conventionally raised and germ-free mice, identifying associations to colonization state in over 7,000 37 proteins. Highly ranked associations were constructed into protein-protein interaction networks and 38 visualized onto an interactive 3D mouse model for user-guided exploration. These results act as a 39 resource for microbiome researchers hoping to identify host effects of microbiome colonization on a 40 given organ of interest. Our results include validation of previously reported effects in xenobiotic 41 metabolism, the innate immune system and glutamate-associated proteins while simultaneously providing 42 organism-wide context. We highlight organism-wide differences in mitochondrial proteins including 43 consistent increases in NNT, a mitochondrial protein with essential roles in influencing levels of NADH 44 and NADPH, in all analyzed organs of conventional mice. Our networks also reveal new associations for 45 further exploration including protease responses in the spleen, high-density lipoproteins in the heart, and 46 glutamatergic signaling in the brain. In total, our study provides a resource for microbiome researchers 47 through detailed tables and visualization of the protein-level effects of microbial colonization on several 48 organ systems. 49 50 Introduction 51 The gut microbiome is emerging as a critical component of human health. It has been shown that 52 the microbial communities colonizing our bodies play important roles in the immune development of 53 infants(Milani et al. 2017) and the regulation of the innate immune system(Thaiss et al. 2016). Further, a 2 Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press 54 dysbiosis of the gut microbiome has been correlated with many diseases including Inflammatory Bowel 55 Disease (IBD)(Sartor and Wu 2017), diabetes(Tilg and Moschen 2014), obesity(Bouter et al. 2017), 56 cardiovascular disease(Ahmadmehrabi and Tang 2017) and mental health disorders(Nguyen et al. 2018). 57 Microbial production or modification of metabolites such as bile acids, choline derivatives, vitamins and 58 lipids provide some insight into the underlying host-microbe interactions in these diseases(Nicholson et 59 al. 2012). However, many mechanisms mediating these disease states remain unknown. 60 Germ-free (GF) mouse models, wherein a mouse is raised without any exposure to microbes, 61 have been an invaluable tool for assessing causal effects in microbiome research(Bhattarai and Kashyap 62 2016). GF models also provide an opportunity to understand the fundamental effects of microbial 63 colonization at an organismal scale. Systems level analyses of the tissues of GF mice have been 64 performed, but these studies have generally highlighted a select few organ tissues. Protein-level studies 65 have shown varying responses to colonization along different regions of the gastrointestinal (GI) tract 66 (Lichtman et al. 2016), changes in drug metabolizing proteins in livers and kidneys(Kuno et al. 2016), and 67 differences in circulating fatty acids from an analysis of serum and livers(Kindt et al. 2018). They have 68 also shown that microbial colonization alters post-translational modifications including histone 69 acetylation and methylation in liver, colon, and adipose tissue(Krautkramer et al. 2016), as well as lysine 70 acetylation in the gut and liver(Simon et al. 2012). An important transcriptomic study revealed a strong 71 connection between colonization and increased Nnt, a mitochondrial protein that has functions in redox 72 homeostasis and biosynthetic pathways through the generation of NADH and NADP+(Mardinoglu et al. 73 2015). The authors found Nnt transcripts increased in several conventional mouse tissues including 74 sections of the small intestine, colon and liver, which correlated with significant alterations in host amino 75 acid levels and glutathione metabolism(Mardinoglu et al. 2015). Other related studies found transcript 76 differences in the brain(Diaz Heijtz et al. 2011), and further highlighted the GI-dependent transcript 77 effects of microbial colonization in Myd88 deficient mice(Larsson et al. 2012). 3 Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press 78 Here, we sought to further detail the protein effects of microbiota colonization occurring both 79 inside and outside of the GI tract as a reference for microbiome researchers interested in a given host 80 protein, organ system or protein-network. Associations of highly ranked proteins were constructed into 81 protein-protein interaction networks for the brain, spleen, heart, small intestine and colon, as well as a 82 global network. While the brains and gastrointestinal tract of GF mice have been characterized in several 83 studies, other organs such as the heart and spleen may be of interest given the emerging roles of the 84 microbiota in atherosclerosis(Karlsson et al. 2012) and immune development(Chung et al. 2012). We 85 hypothesized that applying methods for improved accuracy in quantitative proteomics (Ting et al. 2011) 86 would further define the influence of the microbiota in each of these organs. With this, we hope to reveal 87 microbiota-induced changes that could underlie disease states. Our results validate a body of literature in 88 the field, provide visualization tools for contextualizing the organism-wide effects of microbial 89 colonization, and identify several new host-microbiota associations for further investigation. 90 91 Results 92 Construction of protein-protein interaction networks 93 To determine the protein-level consequences of microbial colonization, three biological replicates 94 of five different tissues (brain, small intestine, colon, spleen, heart) were analyzed from conventionally- 95 raised or GF mice. Multiplexed proteomic analysis of tissue homogenates resulted in the quantification of 96 7752 proteins overall, of which 4663 were quantified across all samples. These 4663 proteins were used 97 for downstream analysis. A separate pilot study of the brain tissue resulted in the quantification of 6203 98 proteins. 99 On a per-organ basis, we contrasted the protein abundances of tissue collected from conventional 100 mice against tissue from GF mice. We ranked the association of each protein to colonization state by 101 accounting for both significance level and fold-change (Supplemental Table S1). Interaction networks 102 were built in order to identify groups of proteins whose expression was modulated by microbial 4 Downloaded from genome.cshlp.org on October 6, 2021 - Published by Cold Spring Harbor Laboratory Press 103 colonization. We first constructed organ-specific protein interaction networks containing all the proteins 104 with a highly ranked (|π| > 1) association to the colonization state of a given organ (Fig. 1). Functional 105 enrichments within these organ networks were then assessed by gene-set enrichment analysis 106 (Supplemental Table S2, Fig. 2). To identify overlapping mechanisms occurring throughout all organs, 107 we compiled all highly ranked proteins within each organ (|π| > 1) into a single protein network (Fig. 3). 108 Colonization state of the mouse appeared to have larger impacts on organs in direct contact with 109 the gut microbiota, namely the small intestine and colon. The GI organs analyzed had an average of 210 110 proteins