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1 Interspecies variation in hominid gut microbiota controls host gene regulation 2 Amanda L. Muehlbauer1 ,2, Allison L. Richards 3, Adnan Alazizi3 , Michael Burns4 , Andres 3 Gomez 5, Jonathan B. Clayton6 ,7, Klara Petrzelkova8 ,9,10, Camilla Cascardo 3, Justyna Resztak3 , 4 Xiaoquan Wen 11, Roger Pique-Regi 3,12, Francesca Luca 3,12,*, and Ran Blekhman 1,2,* 5 *Corresponding authors ( [email protected] , [email protected]) 6 1. Department of Genetics, Cell Biology and Development, University of Minnesota, 7 Minneapolis, Minnesota, USA 8 2. Department of Ecology, Evolution and Behavior, University of Minnesota, Minneapolis, 9 Minnesota, USA 10 3. Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 11 48201, USA 12 4. Department of Biology, Loyola University, Chicago, Illinois, 60660, USA 13 5. Department of Animal Science, University of Minnesota, Saint Paul, Minnesota, USA 14 6. Department of Biology, University of Nebraska at Omaha, Omaha, Nebraska, USA 15 7. Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, 16 Nebraska, USA 17 8. The Czech Academy of Sciences, Institute of Vertebrate Biology, Brno, Czech Republic 18 9. Liberec Zoo, Liberec, Czech Republic 19 10. The Czech Academy of Sciences, Institute of Parasitology, Ceske Budejovice, Czech 20 Republic 21 11. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA 22 12. Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan 23 48201, USA

24 Abstract 25 The gut microbiome exhibits extreme compositional variation between hominid hosts. 26 However, it is unclear how this variation impacts host physiology, and whether this effect can be 27 mediated through microbial regulation of host gene expression in interacting epithelial cells. 28 Here, we characterized the transcriptional response of colonic epithelial cells in vitro to live 29 microbial communities extracted from humans, chimpanzees, gorillas, and orangutans. We found 30 most host genes exhibit a conserved response, whereby they respond similarly to the four 31 hominid microbiomes, while some genes respond only to microbiomes from specific host 32 species. Genes that exhibit such a divergent response are associated with relevant intestinal 33 diseases in humans, such as inflammatory bowel disease and Crohn’s disease. Lastly, we found 34 that inflammation-associated microbial species regulate the expression of host genes previously

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35 associated with inflammatory bowel disease, suggesting health-related consequences for 36 species-specific host-microbiome interactions across hominids.

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37 Introduction 38 The microbiome of the primate gastrointestinal tract plays an important role in host 39 physiology and health. Extreme variation in the gut microbiome has been observed between 40 healthy human individuals; this variation is even more pronounced between different species of 41 great apes (Human Microbiome Project Consortium, 2012; Nishida & Ochman, 2019). 42 Microbiome composition is strongly correlated with the species of the host, a pattern known as 43 co-diversification. Within hominids and other nonhuman primates, co-diversification between 44 host and microbial symbionts has led to overall microbiome composition clustering along the 45 expected phylogenetic relationships of the host species, including bacterial, archeal and 46 eukaryotic groups within the gut microbiome (Amato, et al., 2019; Mann et al., 2019; Moeller et 47 al., 2012; Ochman et al., 2010; Raymann et al., 2017). However, reports show that these 48 phylogenetic constraints are flexible, depending on diet and subsistence strategy (Gomez et al., 49 2019) . For example, compared with industrialized human groups, small scale rural or agricultural 50 human populations share a greater number of gut microbiome traits with wild nonhuman 51 primates (Amato, et al., 2019; Gomez et al., 2019).

52 Different hominid species harbor many of the same bacterial phyla in the gastrointestinal 53 tract, but in varying abundances. For example, both the human and chimpanzee guts are 54 primarily colonized by Bacteroidetes and Firmicutes, but the chimpanzee gut also harbors higher 55 abundances of microbial phyla that are relatively rare in humans, including Actinobacteria, 56 Euryarcheaota, Tenericutes, and Verrucomicrobia (Nishida & Ochman, 2019; Ochman et al., 57 2010) . Gorillas, besides also displaying presence of these rare taxa, harbor greater abundances of 58 Chloroflexi, Tenericutes, and Fibrobacteres (Gomez et al., 2015, 2016; Hicks et al., 2018). 59 Although the orangutan microbiome has not been characterized as thoroughly, a previous report 60 has shown that orangutan guts harbor higher diversity in archaeal lineages compared to other 61 great apes, in addition to similar microbial phyla as gorillas and chimpanzees (Delsuc et al., 62 2014; Raymann et al., 2017) . At lower microbial taxonomic levels, very different microbial 63 species are present in human and chimpanzee microbiomes, resulting in greater divergence 64 (Nishida & Ochman, 2019) .

65 Overall gut microbiome composition is shaped by a combination of host genetics, host 66 physiology, and environmental factors. Studies have shown that host genetic variation influences 67 microbiome composition within humans, but has yet to be studied in other hominids (Blekhman 68 et al., 2015; Goodrich et al., 2014) . Among environmental influences, diet has a large impact on 69 the primate gut microbiome (Gomez, et al., 2016; Hicks et al., 2018; Nagpal et al., 2018) . Most 70 non-human great ape species in the wild and in captivity subsist on a primarily plant-based diet 71 of fruit and vegetation that is occasionally supplemented by animal protein, such as meat or 72 insects (Tutin & Fernandez, 1993; Vogel et al., 2015; Watts et al., 2012). In contrast, human diets 73 are usually omnivorous and highly variable depending on cultural influences, agricultural

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74 practices, geographic location, and individual dietary preferences (Lang et al., 2018; Wu et al., 75 2011) . Other environmental factors that can influence microbiome composition between primates 76 include variation in geography, seasonality, and other social behaviors such as grooming 77 (Grieneisen et al., 2019; Tung et al., 2015). In addition, physiological differences between 78 primate species, such as differences in gut morphology and digestive processes, also contribute 79 to differences in microbiome composition (Amato, et al., 2019) . Although a large effort has been 80 made to characterize the factors that influence variation in the microbiome, it is unclear how 81 variation in microbiome composition between great ape species can impact relevant host 82 phenotypes.

83 A likely mechanism by which the microbiome can affect host physiology is through 84 regulating the expression of host genes in interacting intestinal epithelial cells (Luca et al., 2018; 85 Richards et al., 2016, 2019) . Studies in animal models have demonstrated that gut microbiota can 86 drive changes in host gene expression by altering epigenetic programming, such as histone 87 modification, transcription factor binding, and methylation (Camp et al., 2014; Krautkramer et 88 al., 2016; Pan et al., 2018; Qin et al., 2018) . For example, Camp et al. found that the microbiome 89 drives the differential expression of transcription factors enriched in accessible binding sites 90 (Camp et al., 2014) . In addition, Pan et al. found that the microbiome can alter DNA methylation 91 in the gut epithelial cells of mice (Pan et al., 2018) . Moreover, in cell culture, inter-individual 92 variation in microbiome composition can drive differential responses in host gene expression at 93 the intestinal level (Richards et al., 2019) . However, we do not know how interspecies variation 94 in the microbiome affects gene regulation in host cells. When considering the microbiota 95 variation amongst great ape species and their influences on host gene expression, in vivo studies 96 in experimental animal models are limited. Furthermore, in vivo experiments can be confounded 97 by a multitude of factors, such as differences in diet between the animal model species and the 98 primate species of interest, microbiota colonization history of the animal model, and inherent 99 differences in the genetic backgrounds between the animal model and the primate species (Luca 100 et al., 2018) .

101 Here, we use an in vitro experimental system (Richards et al., 2016, 2019) to assess host 102 gene expression changes in response to diverse gut microbiota from four great ape species: 103 humans (Homo sapiens ), and captive chimpanzees ( Pan troglodytes ), gorillas (Gorilla gorilla 104 gorilla ), and orangutans (Pongo abelii). This experimental design allows us to determine causal 105 relationships between gut microbiome composition and gene expression changes in colonic 106 epithelial cells that are induced by the microbiome while controlling for potentially confounding 107 environmental and technical effects (Richards et al., 2016, 2019) . We have leveraged this design 108 to ascertain how host genes respond to between-species variation in microbiome composition 109 across hominids, characterize the function of host genes that respond to microbiota from each

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110 great ape species, and identify microbial taxa and pathways that likely drive expression of 111 specific host genes.

112 Results 113 To assess how host genes respond to variation in the microbiome, we extracted live 114 microbiota from 19 fecal samples from four hominid species (4 humans, 3 chimpanzees, 6 115 gorillas, and 3 orangutans), and treated human colonic epithelial cells (colonocytes) with the 116 extracted microbiota using an experimental technique from a previously published method (see 117 SI Table 1) (Richards et al., 2016, 2019) . We quantified changes in gene expression in the 118 colonocytes as a response to the primate microbiota using RNA-seq (Fig. 1A). Additionally, we 119 used 16S rRNA sequencing and shotgun metagenomics to characterize the composition of the 120 microbiome in these samples. A principal coordinate analysis of Bray-Curtis dissimilarities 121 confirmed that the microbiome samples cluster by primate host species of origin (Fig 1B; SI Fig. 122 1). This observation is consistent with previous findings showing that the phylogenetic 123 relationship between primate host species is reflected in their microbiomes (Ochman et al., 124 2010) , and that interspecies microbiome distinctions between wild apes is maintained in the 125 captive individuals included in our study.

126 The bacterial composition of the samples confirmed clear distinctions between hominid 127 species at the phylum level ( Fig. 1C, SI Fig. 2), with nine of the most abundant microbial phyla 128 showing significantly different levels between hominid species (Kruskal-Wallis test, 129 Benjamini-Hochberg FDR <0.1, SI Table 2). The human microbiome samples have a high 130 relative abundance of Bacteroidetes and Firmicutes, which have both been previously identified 131 as dominant phyla in the human gut (Human Microbiome Project Consortium, 2012; Turnbaugh 132 et al., 2007) . In addition, Actinobacteria abundance is significantly different between hominid 133 species (Kruskal-Wallis Test, Benjamini-Hochberg q-value = 0.00567; ANOVA, 134 Benjamini-Hochberg q-value = 3.82x10 ), with chimpanzees showing the greatest abundance 135 (see Fig. 1C). Furthermore, we identified 21 microbial species that are differentially abundant 136 between hominid host species (SI Table 3 , Kruskal Wallis, Benjamini-Hochberg FDR <0.1). 137 Examples of several microbes that have variable abundance across species, including 138 Bacteroides ovatus, which shows higher abundance in humans compared to other hominids; 139 Phascolarctobacterium succinatutens, which shows lower abundance in humans compared to 140 other hominids; and Prevotella copri, which has higher abundance in gorilla and orangutan, are 141 shown in Fig. 1D.

142 To characterize the host response to the microbiome, we used likelihood ratio tests 143 combined with a negative binomial model (DESeq2) to identify host genes that change their

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144 expression after inoculation with microbiomes from the four hominid host species (see 145 Methods). We identified 4,329 host genes that respond to the microbiome of at least one hominid 146 species ( Fig. 2A, Benjamini-Hochberg FDR<0.1). The majority of differentially expressed genes 147 (2,261 genes, 52%) respond to the microbiomes of all four hominids (see Fig. 2A, 2B; full 148 dataset available in SI Table 4 ; see Methods). Despite this overall consistent response, we find 149 164 host genes that respond in a species-specific manner; namely, respond to the microbiome of 150 one hominid species but not the other three. For example, SHROOM3 responds to the human 151 microbiome, but shows no response to the chimpanzee, gorilla, and orangutan microbiomes (Fig. 152 2C). Similarly, B3GAT2, DUSP11, and DARS2 respond in a species-specific manner to the 153 chimpanzee, gorilla, and orangutan microbiomes, respectively ( Fig. 2C). We also find 394 host 154 genes that respond to microbiomes from two hominid species; e.g., CBR1 responds to orangutan 155 and gorilla microbiomes ( Fig. 2C). Likewise, 1,313 host genes respond to microbiomes from 156 three hominid species, and 13,531 genes show no response to any of the hominid microbiomes 157 (e.g., INVS ; see Fig. 2C).

158 To understand how genes with a host species-specific response may interact with each 159 other, we visualized interaction networks for differentially expressed host genes that respond to 160 microbiomes from each hominid species (Krämer et al., 2014) (Ingenuity Pathway Analysis, 161 http://www.ingenuity.com ; Fig. 3A, 3B, SI Fig. 3, SI Fig. 4; see Methods). The most significant 162 interaction network of host genes that respond only to human microbiomes is enriched with 163 functional categories related to cancer, cell death and survival, and organismal injuries and 164 abnormalities (Fig. 3A, SI Table 5 ) . This is consistent with previous studies showing that the 165 microbiome may influence host disease through changes in host gene regulation, but also 166 suggests that this effect may be specific to human microbiomes (Camp et al., 2014; Krautkramer 167 et al., 2016; Pan et al., 2018; Qin et al., 2018) . By comparison, the most significant interaction 168 network of genes that respond specifically to orangutan microbiomes is enriched for functional 169 categories related to carbohydrate metabolism, lipid metabolism, and small molecule 170 biochemistry (Fig. 3B, SI Table 6) . This is consistent with the observation that orangutan diets, 171 compared to that of gorillas or chimpanzees, could incorporate a greater proportion of ripe fruits 172 and highly digestible/simple sugars in peak seasons (up to 100% dependence on fruit) (Remis, 173 1997; Taylor, 2006). In addition, previous reports point to a highly diverse archeal community in 174 orangutans compared to other apes, which could be associated with an increased capacity to 175 metabolize highly fermentable plant materials (Raymann et al., 2017). For functions enriched in 176 the most significant networks for genes that respond only to gorilla microbiomes and only to 177 chimpanzee microbiomes, see SI Table 7 and SI Table 8.

178 To further characterize the biological functions represented by host genes that respond to 179 variation in hominid microbiomes, we categorized differentially expressed genes into two 180 groups: low-divergence genes, which show a similar magnitude and direction of response to the

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181 four hominid microbiomes, and high-divergence genes, which show a highly variable response to 182 the four hominid microbiomes (following the approach of Hagai and colleagues (Hagai et al., 183 2018) ; see Methods and SI Table 9). We find that low-divergence genes, namely, differentially 184 expressed genes that show a similar response to microbiomes from all four primate species, tend 185 to be enriched for functions related to basic cell processes, such as RNA processing, cell cycle, 186 and RNA metabolic processing (Fig. 3C, Benjamini-Hochber g FDR<0.1, SI Tables 10-15). This 187 suggests that these genes are likely involved in basic host responses to bacterial cells, rather than 188 response to specific microbial features. Interestingly, high-divergence genes, namely, genes that 189 respond differently to the microbiomes from the four primate host species, tend to be enriched 190 for categories related to disease, inflammation, and cancer ( Fig. 3C, SI Fig. 5, SI Fig. 6). Of 191 note, colorectal cancer, rheumatoid arthritis, and Salmonella infection functional categories are 192 enriched among high-divergence genes, and have all been associated with gut microbiome 193 composition in previous studies (Dahmus et al., 2018; Ferreira et al., 2011; Scher & Abramson, 194 2011) . Moreover, when considering host genes that have been previously associated with 195 complex human traits through genome wide association studies (GWAS) using data in the 196 GWAS Catalog (Buniello et al., 2019) , we find that high-divergence genes are enriched with 197 traits and diseases that have also been linked to the microbiome, such as Crohn’s Disease, 198 Inflammatory Bowel Disease, and body mass index (Fig. 3D; see Methods). This might indicate 199 that these complex disease phenotypes may be modulated by differences in composition of the 200 gut microbial community through the regulation of these key host genes.

201 Next, we sought to identify genes whose response is directly correlated with the 202 abundance of specific microbial taxa. To do so, we used mixed linear models that integrated host 203 response transcriptomic data (via RNA-seq) and microbial species abundance information data 204 (via shotgun metagenomics; see Methods). We identified 25 microbial species that drive the 205 expression of 80 differentially expressed host genes across the four hominids ( Fig. 4A and SI 206 Table 16, 162 host gene-microbial taxon pairs in total, Benjamini-Hochberg FDR<0.05). A 207 heatmap of the interactions reveals two roughly defined major clusters, one of which includes a 208 subcluster of host genes that are downregulated by microbial taxa that are rare or absent in 209 humans but present in the other hominids, such as Pr evotella copri, Methanobrevibacter 210 (unclassified), and Phascolar ctobacterium succinatutens (highlighted in Fig. 4A; also see Fig. 211 1D for P. copri and P . succinatutens abundances across hominids). Genes that are downregulated 212 in the presence of these microbial species are significantly enriched for several immune-related 213 pathways, such as cytokine activity, IL-7 signaling, malaria, Legionellosis, and TNF signaling 214 (SI Tables 17-20). Using a similar method, we identified 89 microbial pathways that drive the 215 expression of 310 unique host genes for a total of 2061 significant microbial pathway-host gene 216 interactions (Benjamini-Hochber g FDR<0.05). For simplicity, we focused on the top 48 217 microbial pathways that drive the expression of the top 44 unique host genes (Fig. 4B and SI 218 Table 21), with a total of 216 microbial pathway-host genes pairs (examples of specific

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219 interactions can be found in SI Fig. 7). Clustering of this interaction data revealed three main 220 clusters (I, II, III), with genes in cluster II associated with pathways that are more abundant in 221 humans compared to other hominid microbiomes. These host genes are enriched in functional 222 categories related to inflammation and infectious disease, including Legionellosis, malaria, and 223 pertussis, and overlap with genes found in the cluster described above in the species-level 224 analysis (SI Table 22).

225 To investigate specific host gene-microbe interactions, we considered the network of all 226 high-divergence host genes for which expression is driven by microbial species (28 host genes 227 and 14 microbial species; Benjamini-Hochberg FDR<0.01). We find that certain microbial taxa 228 are represented in highly connected nodes and likely control the regulation of several 229 high-divergence host genes ( Fig. 4C). For example, two Bacteroides species, B. ovatus and B. 230 uniformis, drive the expression of several host genes, including LIF and DUSP5 respectively , 231 both of which have been previously associated with inflammation (Habibian et al., 2017; Yue et 232 al., 2015) . Bacteroides is a highly abundant microbial genus in the human gut and is known to 233 have mixed ef fects on human health (W exler, 2007) . Notably, B. ovatus is highly abundant in the 234 human microbiome samples, but is at low abundances in the orangutan gut microbiomes and 235 entirely absent in the chimpanzee and gorilla microbiomes (Fig. 1D, FDR<0.1).

236 To explore the possible phenotypic consequences of host genes for which expression is 237 driven by certain microbial species, we integrated gene-trait associations identified through 238 transcription-wide association study (TWAS). TWAS identifies associations between gene 239 expression and complex traits by considering genetically predicted gene expression from 240 expression quantitative trait locus (eQTL) studies and SNP-trait associations from GWAS. We 241 considered genes implicated in 114 complex traits through Probabilistic TWAS (Zhang et al., 242 2019) , and found that expression of 44 out of 57 high-divergence host genes is associated with 243 43 complex phenotypes ( Fig 4D). These include diseases and phenotypes previously linked to 244 the gut microbiome, including Crohn’s disease, inflammatory bowel disease, ulcerative colitis, 245 body mass index, body fat percentage, and schizophrenia ( Fig. 4D, SI Table 23). We found 246 several microbial taxa that have higher abundance in the non-human microbiomes, including P . 247 copri, and P . succinatutens, which have previously been hypothesized to have protective effects, 248 downregulate the expression of host gene LIF , which has been linked to ulcerative colitis, 249 inflammatory bowel disease, and Crohn’s disease in our TWAS analysis (Fig. 4C, Fig.4D) (De 250 Vadder et al., 2016; Morgan et al., 2012) . These results are consistent with findings from the 251 enrichment analysis reported in Fig. 3C and Fig. 3D, where we found phenotypes related to 252 inflammation were driven by high-divergence genes. Furthermore, we find that Eubacterium 253 rectale and B. ovatus , microbes that have higher abundance in humans and that have been 254 previously associated with inflammatory bowel disease (Noor et al., 2010; Zhang et al., 2017) ,

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255 upregulate the expression of CSF3, which has been reported as upregulated in ulcerative colitis 256 patients (de Lange & Barrett, 2015; Hotte et al., 2012)

257 To investigate specific host gene-pathway interactions, we constructed a network of the 258 most significant interactions between microbial pathways and high-divergence host genes as 259 described above (Fig 4E; see Methods). We find that nine of these 17 host genes, including 260 DUSP5, CYR61, NFKBIZ, PTGS2, IL6, CXCL8, IL36G, IL1B , and IL36RN (all displayed at the 261 top layer in Fig. 4E) have been implicated in immune function or inflammation (Cox et al., 262 2004; Emre & Imhof, 2014; Gales et al., 2013; Habibian et al., 2017; Hörber et al., 2016; Müller 263 et al., 2018; Onoufriadis et al., 2011; Ren & Torres, 2009; Rincon, 2012; Wang et al., 2017). We 264 found that these genes are associated with several microbial pathways, including 265 Phosphopanthothenate biosynthesis I, chorismate biosynthesis, UDP-N-acetylmuramoyl 266 pentapeptide biosynthesis II (lysine-containing), and UDP-N-acetylmuramoyl pentapeptide 267 biosynthesis I (meso-diaminopimelate containing).

268 Discussion 269 Interactions between hominid hosts and their microbiomes have been an underexplored 270 area of research, and the complexity of the host-microbiome relationship makes identifying the 271 specific microbial features that causally impact the host phenotype inherently challenging. Here, 272 we use an in vitro model to assess how gut microbiomes from different host species impact gene 273 regulation, which is a likely mechanism for microbes to drive changes in host phenotype and 274 health. Inoculating host colonic epithelial cells with live gut microbiome communities from four 275 great ape species, we find that most host genes are regulated similarly by microbiomes from all 276 four hominid microbiomes. However, some host genes are regulated only by microbiomes from a 277 single hominid; these genes are enriched with immunity functions and are involved in the 278 development of inflammatory bowel disease.

279 Chimpanzees, gorillas, and orangutans are our closest extant relatives, making these 280 species an important study system for understanding human evolution as well as the genetic and 281 environmental etiology of human-specific diseases. Distinct physiological, cognitive, and 282 behavioral differences between primate species are hypothesized to be the result of changes in 283 host gene regulation (Britten & Davidson, 1971; Enard et al., 2002; Gilad et al., 2006; King & 284 Wilson, 1975) . Indeed, studies have identified genes showing a species-specific expression 285 pattern, and genes for which regulation likely evolves under natural selection (Blekhman et al., 286 2008; Brawand et al., 2011) . Here, we show that microbiomes of dif ferent hominid species elicit 287 different gene expression responses in the same type of intestinal epithelial cells (human 288 colonocytes). Although we show that most host genes respond to microbiomes from different 289 hominids in a similar manner, we also identified genes that exhibit a species-specific response. 290 Thus, it may be tempting to hypothesize that some of the species-specific differences in gene

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291 expression observed previously are driven by interactions with the gut microbiome. These 292 species-specific microbiome-regulated host genes might facilitate host-specific adaptations to 293 physiological or dietary constraints; for example, our analysis indicates that genes with a 294 response to only orangutan microbiomes are enriched for carbohydrate metabolism, lipid 295 metabolism, and small molecule biochemistry, which suggests that the interaction of the 296 orangutan microbiome and colonic epithelial cells may aid in digestion of specific 297 macronutrients, especially those associated with diets rich in high-energy, highly digestible plant 298 sources (e.g. ripe fruit).

299 In addition to environmental adaptations, species-specific responses to the microbiota 300 may indicate tightly controlled symbiotic relationships that may result in disease phenotypes 301 when altered. We find that high-divergence genes – namely, genes that respond discordantly to 302 microbiomes from different hominid species – are enriched for traits associated with disease, 303 such as inflammation and aberrant apoptosis. This suggests that genes with a response highly 304 sensitive to the variation across hominid microbiomes may possibly play a role in host disease 305 traits. These genes are also significantly associated with relevant disease traits in the GWAS 306 catalog and in our TWAS analysis, including Crohn’s Disease (CD) and Inflammatory Bowel 307 Disease (IBD). Significant distinctions exist in gut microbiome composition and diversity across 308 apes with marked differences in subsistence strategies: for instance, industrialized human 309 societies and primates in captivity have lower gut microbiome diversity and show higher 310 incidences of noncommunicable diseases than small-scale human populations and wild 311 non-human primates, respectively (Clayton et al., 2016; Gomez, et al., 2016) . Thus, one 312 hypothesis is that these unique features of the microbiome are causal for the development of 313 diseases common in humans living in industrialized areas, but not in non-industrialized human 314 populations or in non-human wild primates, such as IBD. Our results are consistent with this 315 hypothesis, and further suggest that a mechanism by which the microbiome can affect disease 316 risk is through regulating the expression of host genes in interacting colonic epithelial cells. For 317 example, we found that several microbes that have lower abundance in humans compared to the 318 other hominids, including P. copri and P . succinatutens, downregulate the expression of the gene 319 LIF , which has been associated with IBD (SI Fig. 8). This suggests that these microbes may 320 confer a protective effect through regulation of host genes, and their absence in humans is 321 possibly detrimental. Conversely, we found that microbes that have higher abundance in humans 322 compared to the other hominids, including B. ovatus and E. rectale, upregulate the expression of 323 CSF3, which has been associated with inflammatory bowel disease (SI Fig. 8). This suggests that 324 these microbes may have a human-specific pathogenic effect. Moreover, some of the genes we 325 found to be regulated by the microbiome in a species-specific manner, such as IL1B , IL6, IL36G, 326 IL36RN, and CXCL8 , have been previously implicated in IBD (Gijsbers et al., 2004; Khor et al., 327 2011; Müller et al., 2018; Parisinos et al., 2018; Russell et al., 2016; Schulze et al., 2008) , while

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328 others, such as DUSP5 , CYR61, NFKBIZ, and PTGS2 , have rules in immune response (Cox et 329 al., 2004; Emre & Imhof, 2014; Habibian et al., 2017; Hörber et al., 2016).

330 Our ability to interpret these results in a comparative evolutionary context is limited by 331 the unavailability of colonocytes from the non-human hominids in the study. The non-human 332 hominids in the study are all captive, thus their microbiomes might not be representative of wild 333 animals (Clayton et al., 2016); however, we find that the microbiomes used in this study still 334 cluster by host species identity, and between-species variation in microbiome composition is 335 preserved. Another limitation of our analysis is that the taxonomic profiling of metagenomic 336 shotgun sequencing data relies on databases that are biased towards microbes residing in human 337 microbiomes, and might impact our ability to detect and accurately quantify certain microbes in 338 the non-human samples. Moreover, the in vitro approach used here represents a simplified 339 version of the complex interactions occurring at the organismal level. Nevertheless, our approach 340 allows for tightly controlled experimental conditions that can be tailored to the specific question 341 of interest, by focusing on the relevant host cell type and microbiomes, and massively reducing 342 confounding effects of cellular composition and the environment. Indeed, our approach allows 343 controlling for various factors that may affect both the microbiome and host gene regulation, 344 such as organismal-level variables (e.g., infection and hormones), host genetic variation, 345 environmental factors (e.g., host diet), and oscillations and circadian dynamics in the 346 microbiome and host gene expression.

347 In conclusion, we find that gut microbial communities from different hominids mostly 348 elicit a conserved regulatory response in host cells, whereby most host genes respond similarly to 349 hominid microbiomes. However, we also find that some host genes show a divergent response, 350 and a number of host genes respond only to microbiomes from one hominid species and not the 351 others. These genes are enriched in functional categories related to immunity and inflammation, 352 and are over-represented in pathways involved in autoinflammatory diseases, such as IBD and 353 Crohn’s disease. These results represent an important step towards understanding the causal 354 relationships between variation in the gut microbiome across hominids and the regulation of 355 intestinal epithelial cells. We hope that future studies will expand on this work using organoid 356 culture and animal models to characterize the contribution of specific microbes to the 357 development of disease through regulation of host genes.

358 Acknowledgements 359 We would like to thank the primate zookeepers at the Ostrava Zoo, and especially Jana 360 Pluhackova, for their help with chimpanzee fecal sample collection. We would also like to thank 361 the primate zookeepers at the Como Park Zoo and Conservatory for their help with gorilla and 362 orangutan fecal sample collection. Finally, we would like to thank Blekhman Lab members, and 363 especially Rich Abdill, Beth Adamovicz, Laura Grieneisen, and Sambhawa Priya, for their

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364 comments and advice on the manuscript. This work is supported by NIH award R35-GM128716 365 (to R. B.) and NIH award R01-GM109215 (to F. L. and R. P.-R.). Partial funding was provided 366 by the Czech-American Scientific cooperation (LH15175) supported by the Ministry of 367 Education, Youth and Sports of the Czech Republic (to K. P.). This work was carried out, in part, 368 by resources provided by the Minnesota Supercomputing Institute.

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369 Methods

370 Sample acquisition and live microbiota extraction 371 See SI Table 1 for full details about the human and non-human primate fecal samples 372 used in this analysis. Non-human fecal samples from gorillas and orangutans were collected from 373 captive animals immediately after defecation. One orangutan who donated two samples was on a 374 low dose of antibiotics for chronic colitis. Samples were collected as soon as possible (within an 375 hour of defecation) into a 50mL conical tube containing 20mL of cryoprotectant solution 376 consisting of a 50:50 mixture of glycerol and saline solution. The cryoprotectant was filter 377 sterilized through a 0.22 μ m filter. Samples were shaken vigorously to distribute the 378 cryoprotectant. Gorilla and orangutan samples were stored at -80°C within 1 hour after collection 379 and shipped to the lab on dry ice. Chimpanzee samples were stored at -20°C within 1 hour of 380 collection and then shipped to the U.S. lab on dry ice within one day. Human fecal samples were 381 purchased from OpenBiome and arrived frozen on dry ice. T he following briefly describes the 382 protocol by which OpenBiome processes stool samples. The sample is collected by OpenBiome 383 and given to a technician within 1 hour of defecation. The mass of the sample is measured and 384 transferred to a sterile biosafety cabinet. The stool sample is put into a sterile filter bag, and a 385 sterile filtered dilutant of 12.5% glycerol is added with a normal saline buffer (0.90% [wt/vol] 386 NaCl in water). The sample solution is then introduced to a homogenizer blender for 60 s and 387 aliquoted into sterile bottles. The bottles are then immediately frozen at −80°C. Any sample not 388 fully processed within 2 hours of passage is destroyed. 389 To extract fecal microbiota from the non-human primate samples, inside a sterile 390 low-oxygen cabinet we placed fecal material into a sterilized disposable standard commercial 391 blender cup, added 20ml glycerol to reach approximately 30mL glycerol and 200mL normal 392 saline buffer (0.90% [wt/vol] NaCl in water). Fecal material was blended until fully 393 homogenized (about 1-2 min). Blended material was transferred to the same side of the 394 membrane in a 330-micron filter bag and the liquid suspension of the bacterial community was 395 collected on the other side of the filter. The resulting microbiota suspension was then mixed and 396 aliquoted into small tubes and stored at -80°C. 397 The research and sample collection in this study complied with protocols approved 398 through the University of Minnesota Institutional Animal Care and Use Committee.

399 Colonocyte with hominid-derived microbiota treatment experiment 400 The experimental protocol used for the treatment of colonocytes with microbiota has 401 previously been described in Richards et al., 2016 (Richards et al., 2016) . Experiments were 402 conducted using primary human colonic epithelial cells (HCoEpiC, lot: 9763), hereby called 403 colonocytes (ScienCell Research Laboratories, Carlsbad, California, USA, 2950). The cells were 404 cultured on plates or flasks coated with poly-l-lysine (PLL), according to the supplier’s 405 specifications (ScienCell 0413). Colonocytes were cultured in colonic epithelial cell medium

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406 supplemented with colonic epithelial cell growth supplement and penicillin-streptomycin

407 according to the manufacturer’s protocol (ScienCell 2951) at 37 °C with 5% CO2 . At 24 hours 408 before treatment, cells were changed to antibiotic-free medium and moved to an incubator at 37

409 °C, 5% CO2 , and a reduced 5% O2 . 410 Fecal microbiota were not thawed until the day of the experiment. Prior to treatment, the

411 microbiota was thawed at 30 °C, and the microbial density (OD6 00) was assessed via a 412 spectrophotometer (Bio-Rad SmartSpec 3000). Medium was removed from the colonocytes and 413 fresh antibiotic-free medium was added to the cells, with a final microbial ratio of 10:1 414 microbe:colonocyte in each well. Additional wells containing only colonocytes were also 415 cultured in the same 24-well plate for use as controls. 416 After 2 hours, the wells were scraped on ice, pelleted, and washed with cold 417 phosphate-buffered saline (PBS) and then resuspended in lysis buffer (Dynabeads mRNA Direct 418 kit, ThermoFisher Scientific, Waltham, Massachusetts, USA) and stored at −80 °C until 419 extraction of colonocyte RNA for RNA-seq. We conducted both metagenomic shotgun 420 sequencing and 16s rRNA sequencing on the microbiomes at four points: before preparation 421 (raw), after preparation (prepared), cultured with colonocytes (colonocytes) and cultured without 422 colonocytes (control). Human fecal microbiome samples were purchased as "prepared" from 423 Openbiome and therefore were not sequenced raw.

424 RNA-seq experiment and data processing 425 Poly-adenylated mRNA was isolated from thawed cell lysates using the Dynabeads 426 mRNA Direct Kit (Ambion) following the manufacturer’s instructions. RNA-seq libraries were 427 prepared using a protocol modified from the NEBNext Ultradirectional (NEB) library 428 preparation protocol to use Barcodes from BIOO Scientific added by ligation, as described in 429 Richards et al. (Richards et al., 2019) . The libraries were then pooled and sequenced on two 430 lanes of the Illumina Next-seq 500 in the Luca/Pique-Regi laboratory using the high output kits 431 for 75 cycles to obtain paired-end reads. Reads were 80 bp in length. Read counts ranged 432 between 12,632,223 and 36,747,968 reads per sample, with a mean of 18,726,038 and median of 433 16,993,999 reads per sample. 434 FastQC was used to determine quality of reads from raw data (FastQC, version 0.11.5). 435 Trimmomatic was used to trim adapters. FastQC was again used to determine quality of reads 436 after trimming of adapters (Trimmomatic version 0.33). Transcripts were aligned to database 437 GRCh38 and was performed using HISAT2 (HISAT2 version 2.0.2)(Kim et al., 2019). After 438 alignment, read counts ranged between 10,817,737 and 33,592,529 aligned reads per sample, 439 with a mean of 17,142,585.72 and a median of 15,542,693.5 aligned reads per sample. Overall, 440 the average alignment rate was ~70% across samples (SI Fig. 9). The R ‘Subread’ package with 441 the ‘featureCounts’ program was used to make the transcript abundance file (R version 3.3.3, 442 Subread version 1.4.6).

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443 16s rRNA sequencing 444 Sequencing on the 16s rRNA V4 region was performed at the University of Minnesota 445 Genomics Center using the protocol described in Gohl et. al, 2016 (Gohl et al., 2016) . DNA 446 isolated from fecal samples was quantified with qPCR and the V4 region of the 16s rRNA gene 447 was amplified using PCR with barcodes for multiplexing. 448 The forward indexing primer sequence is - 449 AATGATACGGCGACCACCGAGA TCTACAC[i5]TCGTCGGCAGCGTC and the reverse 450 indexing primer sequence is - 451 CAAGCAGAAGACGGCATACGAGA T[i7]GTCTCGTGGGCTCGG (where the bolded 452 regions are the p5 and p7 flow cell adapters and [i5] and [i7] refer to the index sequence codes 453 used by Illumina). The qPCR step starts with an initial denaturing step at 95 °C for 5 min 454 followed by 35 cycles of denaturation (20s at 98 °C), annealing (15s at 66 °C) and elongation (1 455 min at 72 °C). After qPCR, samples are normalized to 167,000 molecules/µl. This is based on 456 the volume of sample used for PCR1 (3ul), so 500,000 molecules is roughly 10x the target 457 sequencing coverage. The next PCR (PCR1) step is similar to the qPCR step, except with only 458 25 cycles of denaturation, annealing and elongation. After the first round of amplification, PCR1 459 products are diluted 1:100 and 5µl of 1:100 PCR1 is used in the second PCR reaction. The next 460 step (PCR2) is similar to the previous two PCR protocols, except with only 10 cycles of 461 denaturation, annealing and elongation. Next, Pooled samples were denatured with NaOH, 462 diluted to 8 pM in Illumina’s HT1 buffer, spiked with 15% PhiX, and heat denatured at 96 °C for 463 2 minutes immediately prior to loading. A MiSeq 600 cycle v3 kit was used to sequence the 464 sample. The following Nextera adapter sequences for post-run trimming are also used. For read 1 465 - 466 CTGTCTCTTATACACATCTCCGAGCCCACGAGACNNNNNNNNATCTCGTATGCCGTCT 467 TCTGCTTG and for read 2 - 468 CTGTCTCTTATACACATCTGACGCTGCCGACGANNNNNNNNGTGTAGATCTCGGTGGT 469 CGCCGTATCATT

470 Metagenomic shotgun sequencing 471 Metagenomic shotgun sequencing on prepared microbiota samples was performed at the 472 University of Minnesota Genomics Center (UMGC). DNA samples were quantified using a 473 fluorimetric PicoGreen assay. For a sample to pass QC, it needs to quantify greater than 0.2 474 ng/ul. If the samples pass QC they enter the TruSeq NexteraXT DNA library preparation queue. 475 gDNA samples were converted to Illumina sequencing libraries using Illumina’s NexteraXT 476 DNA Sample Preparation Kit (Cat. # FC-130-1005). 1 ng of gDNA is simultaneously 477 fragmented and tagged with a unique adapter sequence. This “tagmentation” step is mediated by 478 a transposase. The tagmented DNA is simultaneously indexed and amplified 12 PCR cycles. 479 Final library size distribution is validated using capillary electrophoresis and quantified using 480 fluorimetry (PicoGreen). Truseq libraries were hybridized to a NextSeq (either Single Read or

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481 Paired End). Clustering occurs on-board where the bound library molecules are clonally 482 amplified and sequenced using Illumina’s SBS chemistry. NextSeq uses 2-color chemistry to 483 image the clusters. Upon completion of read 1, a 7 base paired index read is performed in the 484 case of single indexed libraries. If dual indexing was used during library preparation, 2 separate 485 8 or 10 base pair index reads are performed. Finally, clustered library fragments were 486 re-synthesized in the reverse direction thus producing the template for paired end read 2. Base 487 call (.bcl) files for each cycle of sequencing are generated by Illumina Real Time Analysis (RTA) 488 software. The base call files are demultiplexed and then converted to index specific fastq files 489 using the MiSeq Reporter software on-instrument.

490 Characterizing microbiota 491 To identify microbial features from the metagenomic shotgun sequencing data, including 492 taxa and pathway abundances, we used the HUMAnN2 pipeline with Metaphlan2 (HUMAnN2 493 v0.11.1, Metaphlan2 v0.2.6.0) (Franzosa et al., 2018; Truong et al., 2015). FastQC v0.11.7 was 494 used to determine quality of sequencing reads before trimming. Sequencing adapters were 495 trimmed from the raw reads using Trimmomatic (Trimmomatic v0.33) (Bolger et al., 2014) . 496 FastQC v0.11.7 was again used to determine quality of sequencing reads after trimming the 497 sequencing adapters from the reads (SI Fig. 10). Metaphlan2 was used to assign taxonomy at all 498 taxonomic levels to the sequencing reads in each sequencing file, and in particular to get relative 499 abundances of microbial taxa for each sample. The HUMAnN2 pipeline utilizes bowtie v0.2.2 500 for read alignment (Langmead & Salzberg, 2012) , DIAMOND v0.8.22 for high throughput 501 protein alignment (Buchfink et al., 2015) , MinPath (Y e & Doak, 2009) for pathway 502 reconstruction from protein family predictions. The UniRef90 database was used for determining 503 gene family abundances (Suzek et al., 2015). We found a total of 166 named microbial species 504 detected in at least one sample (SI Fig. 11).

505 Principal Coordinate Analysis of Samples 506 Using the 16s rRNA data from the fecal microbiota samples, we used the R package 507 ‘DADA2’ (DADA2, version 1.2.2) to identify amplicon sequence variants (ASVs) from the 508 reads(Callahan et al., 2016). DADA2 was used to filter and trim sequences from raw reads. 509 Forward reads were trimmed to position 240 and reverse reads were trimmed to position 160. 510 Reads were truncated at the first quality score less than or equal to 2. Reads with more than two 511 errors were discarded after truncation. Amplicon sequences were dereplicated using the function 512 ‘derepFastq.’ Sample composition was inferred using the ‘dada’ function. Chimeras were 513 removed using ‘removeBimeraDenovo.’ We assigned taxonomy to the resulting ASVs using 514 ‘assignTaxonomy.’ Using the R package ‘vegan’ (version 2.5-3), we calculated Bray-Curtis 515 dissimilarities and plotted these as a principal coordinate analysis plot (Fig. 1B).

516 Species-specific differential expression analysis

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517 We filtered the RNA-seq counts table so that we only consider protein coding genes, 518 reducing the number of considered genes from 60,674 to 19,715. Host genes were filtered for 519 only protein coding genes using the R package ‘biomaRt’ with ensembl build 37. Within 520 DESeq2 (DESeq2 version 1.14.1), RNA-seq counts were further filtered such that each gene had 521 to be present at least once over all the samples, leaving 17860 tested genes (Love et al., 2014) . 522 DESeq2 uses a negative binomial model to model the count data while it also estimates an 523 appropriate size factor to normalize each sample by its sequencing depth. Additionally, the 524 overdispersion parameter governing the negative binomial distribution is estimated per each gene 525 and using a regularization approach that can monitor outliers and adjust for the mean-variance 526 dependency. The parameter governing the mean gene expression after adjusting to its sequencing 527 depth is modeled as a linear combination that incorporates known batch effects (i.e., plate) and 528 the effect of the biological variable of interest (i.e., each microbiome):

529 Host gene expression ~ ExperimentPlate + Microbiome effects.

530 or, in mathematical terms:

S P 531 Y nj = ∑ βjsM ns + ∑βjpP np s p

532 Where Y nj represents the internal DEseq parameter for mean gene expression for gene j and M 533 experiment n , M ns is the treatment indicator (control or microbiome for species s ), and the βjs 534 parameter is the microbiome effect for each species. To model plate as a known batch effect we P 535 use P np and βjp for the plate indicator variable and its effect on gene expression.

536 For four hominid microbiomes, 24 =16 effect configurations are possible (for each species M 537 combination of which parameters βjs = 0 ), and we ran a likelihood test for each configuration

538 Li : a gene can respond to a single primate microbiome (chimp, gorilla, human, or orangutan), a 539 gene can respond to two of the four primate microbiomes (chimp-gorilla, chimp-human, 540 chimp-orangutan, gorilla-human, gorilla-orangutan, human-orangutan), a gene can respond to 541 three of the four primate microbiomes (chimp-gorilla-human, chimp-human-orangutan, 542 chimp-gorilla-orangutan, or gorilla-human-orangutan), a gene can respond to all four primate 543 microbiomes, or a gene can show no response to any of the four primate microbiomes. The no 544 response case is considered the base case, or null model for all the likelihood ratio tests 545 performed.

546 To identify genes that respond to microbiomes from a specific primate species and to 547 detect the total number of differentially expressed genes that respond to each of the fifteen 548 possible non-null combinations of primate microbiomes we ran a likelihood ratio test against the

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549 base model, which assumes that the host gene shows no response: Host gene expression ~ 550 Experiment Plate and all the coefficients are zero. After determining across all genes and 551 configurations which were statistically significant FDR<10%. We used the likelihood statistics

552 Lji for each gene j and configuration i to calculate the most probably configuration

553 P (Hji|D) = Lji/ΣiLji .

554 Enrichment analysis 555 Enrichment analysis was performed using Ingenuity Pathway Analysis (IPA, QIAGEN 556 Inc., https://www .qiagenbioinformatics.com/products/ingenuity-pathway-analysis , ). We 557 analyzed genes that show a response to microbiomes from a specific primate species. Here, we 558 define those genes as genes that are upregulated or downregulated in response to a specific 559 primate host species, or that show no response to microbiomes from that primate species and 560 show a response to the other three primate host species. For example, genes that show a response 561 only to human microbiomes will be upregulated or downregulated in response to human 562 microbiomes, or show no response to human microbiomes and a response to chimpanzee, gorilla, 563 and orangutan microbiomes. Genes that show a response to three species but not the fourth are 564 also showing a species specific response to the fourth primate species. 565 We further validated these results using the R package ‘ClusterProfiler’ for enrichment 566 analysis using all detected genes present in at least one sample as the background set 567 (ClusterProfiler v3.2.14, Fig. 3C) (Yu et al., 2012) . We used ENRICHR for enrichment analysis 568 of the high and low-divergence genes and extracted the top ten response categories from the GO 569 Biological, GO Molecular, KEGG, and Reactome databases (SI Fig. 5, SI Fig. 6) (Chen et al., 570 2013; Kuleshov et al., 2016). 571 To identify enrichment of high-divergence genes among genes that were previously found 572 to be associated with complex human disease and traits, we used data from the GWAS catalog 573 (Buniello et al., 2019) . Since each GWAS has a different distribution of p-values and

574 significance cutoffs, we chose to use a set of −log1 0(p-value) cutoffs in the range of 8-50 (plotted 575 along the x axis in Fig. 3D). For a given trait, we identified the overlap between the genes 576 significantly associated with the disease at each cutoff and high-divergence genes 34 , and 577 calculated a fold enrichment (plotted along the y axis in Fig. 3D), defined as the ratio of 578 observed/expected overlap between the two gene sets. We used a Fisher Exact Test to calculate a 579 p-value for each cutoff, and traits for which this value was significant after FDR corrections were 580 marked with a colored line in Fig. 3D. 581 K-means clustering was performed using the ‘kmeans’ function in base R (version 3.3.3) 582 on the cluster of microbes P. copri , Methanobrevibacter and P . succinatutens for the genes in 583 Fig. 4A. Enrichment analysis was performed using ENRICHR on the two clusters of genes. A 584 k-means clustering analysis was also performed on the full set of microbial pathway-host gene 585 correlations in Fig. 4B to produce three clusters of genes.

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586 Log fold change of genes by primate species 587 To calculate the fold changes for each gene for each of the four primate species, we used 588 a similar DESeq2 model to the one described above:

589 Gene expression ~ ExperimentPlate + Species

590 or, in mathematical terms:

S P 591 Y nj = ∑ βjsM ns + ∑βjpP np s p

592 Here, Species is a vector indicating which primate species the microbiome sample originated 593 from, and ExperimentPlate controls for the batch effect as before, but we just test the marginal S C 594 effect of each species-specific parameter βjs being not different than the untreated control βj . 595 We use the contrast argument in DESeq2 to extract comparisons of each primate species against 596 the control. Thus, this resulted in log fold change calculations for each gene as it responds to 597 each of the four primate species’ microbiomes. These values are available in SI Tables 24-27 .

598 Divergence scores for differentially expressed, conserved genes 599 Using DESeq2, we identified genes that responded to microbiome treatment. The model 600 to determine whether a gene responds to treatment, we used the following model:

601 Gene expression ~ ExperimentPlate + Treatment

602 Where ExperimentPlate controls for the batch effect of the experiment, and Treatment is a binary 603 vector indicating whether the colonocytes are treated with a microbiome or act as a control for 604 the experiment. Mathematically:

T P 605 Y nj = μ + βj T n + ∑βjpP np p

606 Where Y nj represents the internal DEseq mean gene expression parameter for gene j and

607 experiment n as before, T n is the treatment indicator (control = 0 or microbiome = 1), and the 608 T βj parameter is the microbiome effect. Plate effects are modeled as before. To model plate as a P 609 known batch effect we use P np and βjp for the plate indicator variable and its effect on gene 610 expression.

611 Log fold changes for each gene were calculated as described above, and then used to 612 calculate a divergence metric for each gene. We used a similar divergence calculation as 613 described in Hagai et al. (Hagai et al., 2018) . Namely, for the genes identified as responding to

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614 treatment with microbiomes, we used the log fold changes for each species in the following 615 equation:

616 1 2 Divergence = log2[ 6 Σi,j(logF C primatei − logF C primatej) ]

617 Following Hagai et al., the top 25% of genes were assigned a “high-divergence” status, 618 and the lowest 25% of genes were assigned a “low-divergence” status. These genes were used in 619 the enrichment analyses described below. 620 The rest of the genes are considered “medium divergence” genes. These genes are used in 621 the enrichment analysis as a background set (Fig. 3A, Fig. 3C).

622 Pairwise correlations between host genes and microbial species and pathways 623 Using the microbial species abundances calculated from the metagenomic shotgun 624 sequencing, we ran correlation analysis between genes that are differentially expressed with 625 respect to treatment with microbiota and abundances of microbial species. Metaphlan2 reports 626 microbial species as a proportion of the total microbial community per sample. Microbial species 627 were filtered such that only microbial species present in at least half of the samples and that 628 reached a total summed relative abundance of 9% were included in the analysis, leaving 36 629 microbial species. We applied a center log-ratio transformation to the filtered microbial species 630 abundance data. Microbial pathways were filtered such that the total of each pathway had to be 631 greater than a summed threshold of 8000 reads per kilobase (RPK), leaving 95 microbial 632 pathways to be included in the analysis. Microbial pathways were normalized using the centered 633 log ratio transformation in a similar manner to the microbial species. 634 Using DESeq2, we identified which microbial species or pathways are associated with 635 differentially expressed genes using the following model:

636 Gene Expression ~ ExperimentPlate + Treatment + Microbial feature abundance

637 Mathematically:

A(f) 638 ∑ P T Y nj = μ + βjpP np + βj T n + βj A p fn

639 Where Y nj represents the internal DEseq parameter for gene expression for gene j and

640 experiment n as before, T n is the treatment indicator (control = 0 or microbiome = 1), and the 641 T βj parameter is the microbiome effect. Plate effects are modeled as before. The parameter βA(f)A is used to model the effect of the microbiome feature (i.e., microbial species or 642 j fn A(f) 643 pathway) f on gene expression. We statistically test effect β = 0 in a separate DESeq model j /

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644 run for each feature f. We used an FDR correction on the combined results from all models. The 645 microbial species abundance is a continuous numeric value that represents that center log ratio

646 transformed relative abundance of the microbial feature f Afn for each sample n .

647 TW AS analysis 648 To directly investigate whether discovered effects on gene expression may contribute to 649 complex traits, we considered PTWAS gene-trait associations for 114 traits from Zhang et 650 al. (Zhang et al., 2019) . PTWAS utilizes probabilistic eQTL annotations derived from 651 multi-variant Bayesian fine-mapping analysis of eQTL data across 49 tissues from GTEx v8 to 652 detect associations between gene expression levels and complex trait risk. Using the host genes 653 that were highly correlated with a microbial species and fell into the high-divergence category 654 (FDR<0.05), we overlapped the significant results with genes causally implicated in complex 655 traits across all tissues by Zhang et al. (PTWAS scan, 5% FDR). We repeated the same analysis 656 with the host genes that were highly correlated with a microbial pathway (FDR<0.01) and fell 657 into the high-divergence category.

658 Data Availability 659 Raw data for 16s rRNA sequencing, RNA-sequencing, and metagenomic shotgun 660 sequencing are available on the Sequence Read Archive (SRA) under submission ID 661 SUB7918466. For data tables used in this analysis, including tables for RNA-seq gene 662 expression counts, metagenomic shotgun sequencing species abundances, amplicon sequence 663 variant abundances, pathway abundances and metadata, see our Figshare project under the same 664 title as the manuscript at https://figshare.com/account/home#/projects/87626 .

665 Competing interests 666 The authors have no competing interests to report.

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A B 0.250.25 Extract Treat Chimpanzee Microbiome Colonocytes Gorilla

0.000.00

Orangutan Treatment

Colonocytes −-0.250.25 Control PC2: 11.05% variance PC2:11.05% Prepared PC2: 11.05% variance Raw Human

−-0.500.50 −-0.500.50 − -0.250.25 0.00 0.00 0.250.25 PC1: 14.92% variance PC1: 14.92% variance Metagenomic RNA-seq Shotgun Sequencing Colonocytes D C Phascolarctobacterium Bacteroides ovatus succinatutens 100 ● ● ● 7.57.5 ● ●● ● ● 2020 ●●● ● ●

5.05.07.5 ● ● Phylum ●● ● ● 20 ●●● ● 7575 Actinobacteria 1010 2.52.5 ● 5.0 ● ● Ascomycota ● ● ● ● 10 ●● Bacteroidetes 0.00.02.5 ●●●● ●●●●●●● ●●●● 00 ●●●● ● ●

Euryarchaeota (%) Abundance Relative ● ●● 5050 ● Firmicutes 0.0 ●●●● ●●●●●●● ●●●● 0 ●●●● 16 Proteobacteria Faecalibacterium● ● ● prausnitzii ● Prevotella copri Spirochaetes ● 12 ● ● 1616 ● ● 20 ● ● ● Synergistetes ● 2525 ●● ●● ●● 8 ● ● ● ● Verrucomicrobia 1212 ● ● ● ● 2020 ● ● 10 ●● ● ●● ●

Baseline Relative Abundance BaselineRelative ●● ● ● 4 ● ● ●● 8 8 ● ● ● ● ● ●● ● 1010 ●● ● ●● ●●●● ●● 0 ● 00 ● 4 4 ● ●● ● ● ● 0 0 ●●●● ●●

Human Chimpanzee Gorilla Orangutan (%) Abundance Relative

Chimpanzee Gorilla Human Orangutan bioRxiv preprint doi: https://doi.org/10.1101/2020.08.17.255059; this version posted August 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is31 made available under aCC-BY-NC-ND 4.0 International license.

981 Figure 1. Experimental design and gut microbiome composition 982 (A) Experimental design. Live microbiomes were extracted from fecal samples from 983 chimpanzees (n=4), gorillas (n=7), humans (n=4), and orangutans (n=4). Microbes were 984 incubated with human colonic epithelial cells for 2 hours, after which RNA-seq was performed 985 on the epithelial cells. Metagenomic shotgun sequencing and 16s rRNA sequencing were 986 performed on the microbiome samples to determine microbiome composition. 987 (B) Bray-Curtis dissimilarity of the all microbiome samples from all four primate species at 988 different stages of the experiment: in raw fecal samples (raw), after extraction from fecal samples 989 and before the experiment (prepared), after incubation with colonocytes (colonocytes), and after 990 incubation without colonocytes (control). 991 (C) Stacked barplot showing the relative abundances of microbial phyla for each hominid fecal 992 sample prior to culturing but after extracting the microbiota for treatment. 993 (D) Examples of microbial species (from shotgun metagenomics) that show various patterns of 994 abundance across hominid species. Bacter oides ovatus shows a high abundance in humans 995 relative to the other hominid species. Phascolarctobacterium succinatutens is highly abundant in 996 the non-human hominid but not present in the human fecal samples. Faecalibacterium 997 prausnitzii is highly abundant in all four hominid species. Prevotella copri is highly abundant in 998 the chimpanzee and gorilla samples, has a lower abundance in the orangutan samples, and is not 999 present in the human samples.

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Color Key Color Key

Log Fold Change −1 −0.5 0 0.5 1 −1 −0.5 0 Color0.5 1Key Value Value 25002500 2500 -1 -0.5 0 0.5 1 A −1 −0.5 0 0.5 ENSG000001166791 B ENSG00000213865ENSG00000198453ENSG00000164751ENSG00000144476ENSG00000185838ENSG00000105875ENSG00000242950ENSG00000119929ENSG00000138380ENSG00000115271ENSG00000184898ENSG00000130997ENSG00000106009ENSG00000243667ENSG00000160712ENSG00000178573ENSG00000132854ENSG00000164707ENSG00000106003 ENSG00000144476ENSG00000185838ENSG00000105875ENSG00000242950ENSG00000119929ENSG00000138380ENSG00000115271ENSG00000184898ENSG00000130997ENSG00000106009ENSG00000243667ENSG00000160712ENSG00000178573ENSG00000132854ENSG00000164707ENSG00000106003ENSG000001166792261 2261 ENSG00000166342ENSG00000112624ENSG00000110851ENSG00000003989ENSG00000118260ENSG00000100403ENSG00000124831ENSG00000130147ENSG00000161011ENSG00000128203ENSG00000153246ENSG00000166436ENSG00000112659ENSG00000162885ENSG00000113812ENSG00000149212ENSG00000107104ENSG00000163728ENSG00000167703ENSG00000205268ENSG00000156521ENSG00000148120ENSG00000125848 ENSG00000110851ENSG00000003989ENSG00000118260ENSG00000100403ENSG00000124831ENSG00000130147ENSG00000161011ENSG00000128203ENSG00000153246ENSG00000166436ENSG00000112659ENSG00000162885ENSG00000113812ENSG00000149212ENSG00000107104ENSG00000163728ENSG00000167703ENSG00000205268ENSG00000156521ENSG00000148120ENSG00000125848ENSG00000213865ENSG00000198453ENSG00000164751 ENSG00000179304ENSG00000108691ENSG00000141682ENSG00000059804ENSG00000031081ENSG00000164442ENSG00000162892ENSG00000136492ENSG00000198369ENSG00000138639ENSG00000124171ENSG00000119900ENSG00000131016ENSG00000104689ENSG00000100077ENSG00000175567ENSG00000150938ENSG00000176907ENSG00000130559ENSG00000166923ENSG00000175745ENSG00000138434ENSG00000121879ENSG00000160201 2261ENSG00000059804ENSG00000031081ENSG00000164442ENSG00000162892ENSG00000136492ENSG00000198369ENSG00000138639ENSG00000124171ENSG00000119900ENSG00000131016ENSG00000104689ENSG00000100077ENSG00000175567ENSG00000150938ENSG00000176907ENSG00000130559ENSG00000166923ENSG00000175745ENSG00000138434ENSG00000121879ENSG00000160201ENSG00000166342ENSG00000112624 ENSG00000183431ENSG00000075415ENSG00000114450ENSG00000104408ENSG00000265241ENSG00000107262ENSG00000136897ENSG00000136810ENSG00000117748ENSG00000145425ENSG00000182013ENSG00000230989ENSG00000143947ENSG00000093144ENSG00000179240ENSG00000138835ENSG00000155959ENSG00000187118ENSG00000142686ENSG00000102760ENSG00000225614ENSG00000138771ENSG00000140285 ENSG00000114450ENSG00000104408ENSG00000265241ENSG00000107262ENSG00000136897ENSG00000136810ENSG00000117748ENSG00000145425ENSG00000182013ENSG00000230989ENSG00000143947ENSG00000093144ENSG00000179240ENSG00000138835ENSG00000155959ENSG00000187118ENSG00000142686ENSG00000102760ENSG00000225614ENSG00000138771ENSG00000140285ENSG00000179304ENSG00000108691ENSG00000141682 ENSG00000134824ENSG00000151834ENSG00000160007ENSG00000198001ENSG00000187778ENSG00000214595ENSG00000136840ENSG00000228672ENSG00000214022ENSG00000149357ENSG00000155760ENSG00000196455ENSG00000167081ENSG00000087338ENSG00000108106ENSG00000204311ENSG00000169169ENSG00000010361ENSG00000164093ENSG00000104936ENSG00000135119ENSG00000187699ENSG00000112309ENSG00000007038Human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alue ENSG00000119421ENSG00000121440ENSG00000181007ENSG00000197461ENSG00000175376ENSG00000160932ENSG00000168398ENSG00000082258ENSG00000118873ENSG00000205758ENSG00000168538ENSG00000117593ENSG00000135315ENSG00000196911ENSG00000143248ENSG00000167384ENSG00000184731ENSG00000175155ENSG00000086598ENSG00000111266ENSG00000027075ENSG00000076944ENSG00000131171ENSG00000144048 ENSG00000197461ENSG00000175376ENSG00000160932ENSG00000168398ENSG00000082258ENSG00000118873ENSG00000205758ENSG00000168538ENSG00000117593ENSG00000135315ENSG00000196911ENSG00000143248ENSG00000167384ENSG00000184731ENSG00000175155ENSG00000086598ENSG00000111266ENSG00000027075ENSG00000076944ENSG00000131171ENSG00000144048ENSG00000153310ENSG00000140992 ENSG00000079332ENSG00000094880ENSG00000143149ENSG00000149557ENSG00000116918ENSG00000165609ENSG00000085788ENSG00000103365ENSG00000107874ENSG00000107021ENSG00000136720ENSG00000153574ENSG00000125863ENSG00000171314ENSG00000164405ENSG00000137824ENSG00000109854ENSG00000131650ENSG00000183579ENSG00000198746ENSG00000159228ENSG00000183513ENSG00000152049ENSG00000137806 ENSG00000149557ENSG00000116918ENSG00000165609ENSG00000085788ENSG00000103365ENSG00000107874ENSG00000107021ENSG00000136720ENSG00000153574ENSG00000125863ENSG00000171314ENSG00000164405ENSG00000137824ENSG00000109854ENSG00000131650ENSG00000183579ENSG00000198746ENSG00000159228ENSG00000183513ENSG00000152049ENSG00000137806ENSG00000119421ENSG00000121440ENSG00000181007 ENSG00000184635ENSG00000067798ENSG00000169246ENSG00000172638ENSG00000117586ENSG00000165259ENSG00000137801ENSG00000068878ENSG00000141458ENSG00000156983ENSG00000122515ENSG00000106546ENSG00000113448ENSG00000126218ENSG00000175470ENSG00000161021ENSG00000198121ENSG00000237172ENSG00000164535ENSG00000166483ENSG00000186566ENSG00000187720ENSG00000149256 ENSG00000169246ENSG00000172638ENSG00000117586ENSG00000165259ENSG00000137801ENSG00000068878ENSG00000141458ENSG00000156983ENSG00000122515ENSG00000106546ENSG00000113448ENSG00000126218ENSG00000175470ENSG00000161021ENSG00000198121ENSG00000237172ENSG00000164535ENSG00000166483ENSG00000186566ENSG00000187720ENSG00000149256ENSG00000079332ENSG00000094880ENSG00000143149 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ENSG00000155903ENSG00000092969ENSG00000139278ENSG00000143322ENSG00000137936ENSG00000132003 ENSG00000145390ENSG00000134954ENSG00000087128ENSG00000163221ENSG00000151014ENSG00000143367 ● ● ● ENSG00000127124ENSG00000103067ENSG00000148154ENSG00000181467ENSG00000164741ENSG00000160213● ● ● ● ● OrangutanOrangutan ● ● ENSG00000174136ENSG00000248458ENSG00000135373ENSG00000167470ENSG00000163209ENSG00000180758● ● ● ● ● ● ENSG00000124102ENSG00000176845ENSG00000137033ENSG00000155324ENSG00000112033ENSG00000067082 O ENSG00000146592ENSG00000133710ENSG00000162981ENSG00000163235ENSG00000172738ENSG00000006432 ENSG00000100311ENSG00000095752ENSG00000169252ENSG00000057657ENSG00000170345ENSG00000020577 ENSG00000225614ENSG00000128283ENSG00000179059ENSG00000175984ENSG00000188505ENSG00000198853 ENSG00000114315ENSG00000116285ENSG00000183347ENSG00000143514ENSG00000182585ENSG00000112658 ENSG00000070444ENSG00000139132ENSG00000115008ENSG00000136153ENSG00000241794ENSG00000119862 Chimp Gorilla Chimp ENSG00000163545 Gorilla ENSG00000127666ENSG00000181788ENSG00000171522ENSG00000069020ENSG00000163110ENSG00000102554 Human ENSG00000163874 Human ENSG00000169469ENSG00000179820 ENSG00000221869ENSG00000102760ENSG00000073712ENSG00000155034ENSG00000198142ENSG00000145632 ENSG00000128016ENSG00000142273ENSG00000163734ENSG00000170542ENSG00000117595ENSG00000142627 ENSG00000185950ENSG00000188112ENSG00000175505ENSG00000163947ENSG00000100906ENSG00000171056 ENSG00000170802ENSG00000232810ENSG00000168575ENSG00000162757ENSG00000149798ENSG00000184292 ● ● ● ●ENSG00000002587ENSG00000147676ENSG00000182580ENSG00000182795ENSG00000173281ENSG00000150347● ● ● ● Orangutan ● ● ● ● ● ● ● ● GorillaOrangutan Gorilla ENSG00000138675ENSG00000197822ENSG00000152804ENSG00000070190ENSG00000116741 ENSG00000008086ENSG00000168386ENSG00000137331ENSG00000128965ENSG00000136689ENSG00000141579 G ENSG00000134317ENSG00000115963ENSG00000164400ENSG00000011422ENSG00000163739ENSG00000081041 ENSG00000183742ENSG00000013588ENSG00000087074ENSG00000131737ENSG00000087495ENSG00000117228 ENSG00000125266ENSG00000134363ENSG00000163347ENSG00000138166ENSG00000103044ENSG00000197632 ENSG00000118503ENSG00000157168ENSG00000129514ENSG00000169991ENSG00000147050ENSG00000158055 ENSG00000114019ENSG00000125538ENSG00000122641ENSG00000212724ENSG00000136688ENSG00000189143 ENSG00000073756ENSG00000107984ENSG00000120217ENSG00000206538ENSG00000169429ENSG00000138623 ENSG00000078401ENSG00000128342ENSG00000057704ENSG00000136244ENSG00000120129ENSG00000134668 01000200030004000 01000200030004000 ENSG00000113070ENSG00000144802ENSG00000172818ENSG00000118523ENSG00000142871ENSG00000137193 Set SizeSet Size 0 4000 3000 2000 1000 Chimp Gorilla Human Orangutan C SHROOM3 B3GAT2 DUSP11 DARS2 0.3 0.5 0.2 ● ● 0.0 ● ● ● ●● 0.4 ● 0.2 ● 0.1 ● ● -0.1 ● 0.3 0.0 0.1 0.2 ● ● ● -0.2 ● ● ●● ● -0.1 ● ● ● ●● ● ● 0.1 ● ● ● ● Log Fold Change ● 0.0 -0.3 -0.2

INVS NDUFB6 SERPINB13 CBR1 0.0 0.2 0.0 0.6 ●● ●● -0.2 ● -0.1 ● ● ● ● ● ● ● ● 0.0 ● ● ● ● 0.4 ● ● ● ●● ● ● ● ● ● ● -0.2 ● -0.4 ● 0.2 ● -0.2 -0.3 ● Log Fold Change -0.6 0.0 bioRxiv preprint doi: https://doi.org/10.1101/2020.08.17.255059; this version posted August 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is32 made available under aCC-BY-NC-ND 4.0 International license.

1000 Figure 2. Patterns of host gene expression changes in response to hominid microbiome 1001 treatment

1002 (A) Log2 fold change for all differentially expressed genes (rows), grouped by expression 1003 pattern. The colored bar on the right hand side indicates in response to which hominid 1004 microbiome these genes change their expression. 1005 (B) UpSet plot visualizing the intersections among the sets of host genes that respond to hominid 1006 microbiomes. 1007 (C) Examples of the expression pattern of 8 differentially expressed genes. Each panel shows the 1008 change in expression (y-axis) in response to the four hominid microbiomes (x-axis) of a single 1009 host gene, with the gene name listed at the top of each panel. Error bars indicate 1X the standard 1010 error.

bioRxiv preprint doi: https://doi.org/10.1101/2020.08.17.255059; this version posted August 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A C ● TNF signaling pathway ● ● ILTNF−17 signaling signaling pathway pathway ●● SalmonellaIL−17 signaling infection pathway ●● CytokineSalmonella−cytokine infection receptor interaction ●● FluidCytokine shear−cytokine stress and receptor atherosclerosis interaction ●● ViralFluid proteinshear stress interaction and atherosclerosis with cytokine and cytokine receptor ● ● RheumatoidViral protein interactionarthritis with cytokine and cytokine receptor ● ● ColorectalRheumatoid cancer arthritis ●● MAPKColorectal signaling cancer pathway KEGG ●● AGEMAPK−RAGE signaling signaling pathway pathway in diabetic complications ● TNF signaling● pathwayAGE−RAGE signaling pathway in diabetic complications ● IL−17 signaling● Spliceosome pathway ● ● Spliceosome ● Adherens junction Salmonella● infection ● ILAdherens−17 signaling junction pathway ● Cytokine● −cytokineIL−17 receptor signaling interaction pathway ● Fluid● shear stressAntigen and atherosclerosisprocessing and presentation ● Antigen processing and presentation ● Viral protein ●interaction with cytokine and cytokine receptor Interleukin−10 signaling Number of Genes ● Rheumatoid ●arthritisInterleukin−10 signaling ● PI5P, PP2A and IER3 Regulate PI3K/AKT Signaling Number● 10 of Genes ● ● ● 2010 Colorectal● cancerConstitutivePI5P, PP2A andSignaling IER3 byRegulate Aberrant PI3K/AKT PI3K in CancerSignaling ● 20 ● MAPK signaling● Constitutive pathway Signaling by Aberrant PI3K in Cancer ●● 30 Signaling by Interleukins 30 ● ● 40 AGE−RAGE● signalingSignaling pathway by Interleukins in diabetic complications RAF−independent MAPK1/3 activation 40 ●● NegativeRAF−independent regulation MAPK1/3 of the PI3K/AKT activation network ● Spliceosome● Negative regulation of the PI3K/AKT network ● ● MAPK family signaling cascades Adherens● junctionMAPK family signaling cascades ● IL−17 ●signalingMAPK1/MAPK3 pathway signaling ● MAPK1/MAPK3 signaling ● Antigen processingInterleukin and presentation−4 and Interleukin−13 signaling ●● Interleukin−4 and Interleukin−13 signaling Cytokine/Growth Factor Transporter Upregulated PI3K/AKT Signaling in Cancer ● PI3K/AKT Signaling in Cancer Enzyme ● Interleukin−10 signaling Protein/Other Complex Downregulated Processing of Capped Intron−Containing Pre−mRNA NumberNumber of of Genes Genes ● PI5P, PP2A and IER3 Regulate PI3K/AKT Signaling ● 1010 Group/Complex Ion Channel Processing of Capped Intron−Containing Pre−mRNA 20 ● Constitutive● SignalingmRNA Splicingby Aberrant PI3K in Cancer ● 20 B Transcription Regulator Kinase mRNA Splicing

Reactome ● Signaling by● InterleukinsmRNA Splicing − Major Pathway ● 3030 ● ● mRNA Splicing − Major Pathway 4040 ● Adherens junctions interactions RAF−independent● MAPK1/3 activation ● Adherens junctions interactions Divergence ● Cytosolic tRNA aminoacylation Negative● regulationCytosolic of the tRNA PI3K/AKT aminoacylation network ● MAPK● family signalingCell Cycle cascades Checkpoints x High ●● Cell Cycle Checkpoints Low ● MAPK1/MAPK3Apoptotic signaling cleavage of cellular proteins ●● Apoptotic cleavage of cellular proteins ● Interleukin−4 andTransport Interleukin of Mature−13 signaling Transcript to Cytoplasm ● Transport of Mature Transcript to Cytoplasm ● Transport of Mature mRNA derived from an Intron−Containing Transcript PI3K/AKT● Signaling in Cancer ● mRNATransport 3'− ofend Mature processing mRNA derived from an Intron−Containing Transcript ● Processing1 2 3 4 of5 CappedmRNA 3'Intron−end− processingContaining Pre−mRNA ● mRNA−log(Q1 2 Splicing3−value)4 5 ● mRNA−-log(Q-value)log(Q Splicing−value) − Major Pathway ● Adherens junctions interactions B ● Cytosolic tRNA aminoacylation ● DCell Cycle Checkpoints ● Apoptotic cleavage of cellular proteins ● Transport ofBody Mature mass Transcript index to Cytoplasm ●

Transport15 of Mature mRNA derived from an Intron−Containing Transcript ● mRNA 3'−end processing 1 2 3 4 5 −log(Q−value)

10 Menarche (age at onset) Age-related macular degeneration Crohn's disease Inflammatory bowel disease 5 FoldEnrichment Rheumatoid arthritis Fold Enrichment Fold 0

10 20 30 40 GWAS -log(P-value) GWAS -log(P-value) bioRxiv preprint doi: https://doi.org/10.1101/2020.08.17.255059; this version posted August 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is33 made available under aCC-BY-NC-ND 4.0 International license.

1011 Figure 3. Interaction networks and functional enrichment categories for host genes 1012 responding to hominid gut microbiomes 1013 (A) Interaction network showing host genes that respond only to human microbiomes, generated 1014 using Ingenuity Pathway Analysis. 1015 (B) Similar to (A), but including host genes that respond only to orangutan microbiomes. 1016 (C) Functional categories in the KEGG (top) and Reactome (bottom) databases enriched among 1017 high-divergence genes (red) and low-divergence genes (blue). X-axis indicates the statistical 1018 significance of enrichment, and the circle size corresponds to the number of genes in each 1019 category. 1020 (D) Complex disease enriched among genes that respond to hominid microbiomes. Fold 1021 enrichment (y-axis) is shown for a given P value threshold (x-axis) to define genes that are 1022 associated with each complex disease in the GWAS Catalog. Each colored line represents a 1023 different complex disease with an enrichment of at least three-fold, with a circle indicating the 1024 most significant P-value threshold. Diseases that did not reach significance are shown in grey 1025 lines.

bioRxiv preprint doi: https://doi.org/10.1101/2020.08.17.255059; this version posted August 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A Pearson’s R B

-1 -0.5 0 0.5 1 superpathway of aromatic amino acid biosynthesis superpathway of aromatic amino acid biosynthesis Superpathway of aromatic amino acid biosynthesis chorismate biosynthesis from 3−dehydroquinate chorismate biosynthesis from 3−dehydroquinate Chorismate biosynthesis from 3−dehydroquinate chorismate biosynthesis I chorismate biosynthesis I Chorismate biosynthesis I starch degradation V starch degradation V Starch degradation V Lachnospiraceae_bacterium_7_1_58FAA Lachnospiraceae_bacterium_7_1_58FAA Lachnospiraceae bacterium 7-1-FAA coenzyme A biosynthesis II coenzyme A biosynthesis II Coenzyme A biosynthesis II CDP−diacylglycerol biosynthesis I CDP−diacylglycerol biosynthesis I CDP−diacylglycerol biosynthesis I Ruminococcus_torques Ruminococcus_torques Ruminococcus torques S−adenosyl−L−methionine cycle I S−adenosyl−L−methionine cycle I S−adenosyl−L−methionine cycle I L−lysine biosynthesis III L−lysine biosynthesis III Bacteroides_ovatus L−lysine biosynthesis III Bacteroides_ovatus Bacteroides ovatus UDP−N−acetylmuramoyl−pentapeptide biosynthesis I UDP−N−acetylmuramoyl−pentapeptide biosynthesis I UDP−N−acetylmuramoyl−pentapeptide biosynthesis I peptidoglycan biosynthesis I Peptidoglycan biosynthesis I Bacteroides_uniformis Bacteroides_uniformis Bacteroides uniformis peptidoglycan biosynthesis I UDP−N−acetylmuramoyl−pentapeptide biosynthesis II UDP−N−acetylmuramoyl−pentapeptide biosynthesis II UDP−N−acetylmuramoyl−pentapeptide biosynthesis II L−lysine biosynthesis VI Oscillibacter_unclassified L−lysine biosynthesis VI L−lysine biosynthesis VI

Oscillibacter_unclassified Oscillibacter (unclassified) Relative Species Microbial (%) Abundance UMP biosynthesis UMP biosynthesis UMP biosynthesis methylerythritol phosphate pathway I Human matrix_77 Human Roseburia_intestinalis Roseburia_intestinalis Roseburia intestinalis methylerythritol phosphate pathway I Methylerythritol phosphate pathway I L−ornithine de novo biosynthesis L−ornithine de novo biosynthesis1 L−ornithine1 de novo1 biosynthesis Pathway (RPK) Abundance Bacteroides_xylanisolvens Bacteroides_xylanisolvens Bacteroides xylanisolvens superpathway of L−aspartate and L−asparagine biosynthesis superpathway of L−aspartate and L−asparagine biosynthesisSuperpathway of L−aspartate and L−asparagine biosynthesis L−rhamnose degradation I 0 L−rhamnose0.5 degradation0 I Collinsella_aerofaciens L−rhamnose degradation I Collinsella_aerofaciens Collinsella aerofaciens phosphopantothenate biosynthesis I phosphopantothenate biosynthesisOrangutan I phosphopantothenate0 Orangutan biosynthesis I cis−vaccenate biosynthesis cis−vaccenate biosynthesis Faecalibacterium_prausnitzii Faecalibacterium_prausnitzii Faecalibacterium prausnitzii cis−vaccenate biosynthesis −0.5 preQ0 biosynthesis preQ0 biosynthesis 1 preQ0 biosynthesis1 N10−formyl−tetrahydrofolate biosynthesis −1 Roseburia_inulinivorans Roseburia_inulinivorans Roseburiamatrix_8 inulinivorans N10−formyl−tetrahydrofolate biosynthesis0 N10−formyl−tetrahydrofolate0 biosynthesis folate transformations II Folate transformations II 1 folate transformations II Eubacterium_rectale Eubacterium_rectale Eubacterium rectale peptidoglycan biosynthesis III peptidoglycan biosynthesisGorilla III Peptidoglycan biosynthesisGorilla III 0.5 dTDP−L−rhamnose biosynthesis I dTDP−L−rhamnose biosynthesis I dTDP−L−rhamnose biosynthesis I Subdoligranulum_unclassified 1 Subdoligranulum_unclassified Subdoligranulum0 (unclassified) superpathway of L−threonine biosynthesis superpathway of L−threonine 1biosynthesis Superpathway of L−threonine biosynthesis pantothenate and coenzyme A biosynthesis I pantothenate and coenzyme A0 biosynthesis I Pantothenate and coenzyme0 A biosynthesis I Eubacterium_ramulus −0.5 Eubacterium_ramulus Eubacterium ramulus coenzyme A biosynthesis I Coenzyme A biosynthesis I −1 coenzyme A biosynthesis I gluconeogenesis I gluconeogenesis I Chimp GluconeogenesisChimp I Roseburia_hominis Roseburia_hominis Roseburia hominis glycogen biosynthesis I glycogen biosynthesis I 1 Glycogen biosynthesis1 I tetrapyrrole biosynthesis I Tetrapyrrole biosynthesis I Coprococcus_catus Coprococcus_catus Coprococcus catus tetrapyrrole biosynthesis I L−arginine biosynthesis II L−arginine biosynthesis II 0 L−arginine biosynthesis0 II Phascolarctobacterium_succinatutens Phascolarctobacterium_succinatutens Phascolarctobacterium succinatutens L−ornithine biosynthesis L−ornithine biosynthesis L−ornithine biosynthesis pyruvate fermentation to acetate and lactate II pyruvate fermentation to acetate and lactate II Pyruvate fermentation to acetate and lactate II Prevotella_copri Prevotella_copri Prevotella copri 5−aminoimidazole ribonucleotide biosynthesis I 5−aminoimidazole ribonucleotide biosynthesis I 5−aminoimidazole ribonucleotide biosynthesis I 5−aminoimidazole ribonucleotide biosynthesis II 5−aminoimidazole ribonucleotide biosynthesis II 5−aminoimidazole ribonucleotide biosynthesis II Methanobrevibacter_unclassified Methanobrevibacter_unclassified Methanobrevibacter (unclassified) superpathway of 5−aminoimidazole ribonucleotide biosynthesis superpathway of 5−aminoimidazole ribonucleotide biosynthesisSuperpathway of 5−aminoimidazole ribonucleotide biosynthesis glycolysis IV glycolysis IV Glycolysis IV Ruminococcus_flavefaciens Ruminococcus_flavefaciens Ruminococcus flavefaciens Calvin−Benson−Bassham cycle Calvin−Benson−Bassham cycle Calvin−Benson−Bassham cycle L−histidine degradation I L−histidine degradation I Parabacteroides_unclassified Parabacteroides_unclassified Parabacteroides (unclassified) L−histidine degradation I tRNA charging tRNA charging tRNA charging Eubacterium_siraeum Eubacterium_siraeum Eubacterium siraeum L−proline biosynthesis II L−proline biosynthesis II L−proline biosynthesis II superpathway of branched amino acid biosynthesis superpathway of branched amino acid biosynthesis Superpathway of branched amino acid biosynthesis Eubacterium_biforme Eubacterium_biforme Eubacterium biforme homolactic fermentation homolactic fermentation Homolactic fermentation pyruvate fermentation to isobutanol pyruvate fermentation to isobutanol Pyruvate fermentation to isobutanol Coprococcus_comes Coprococcus comes Coprococcus_comes L−isoleucine biosynthesis IV L−isoleucine biosynthesis IV L−isoleucine biosynthesis IV pantothenate and coenzyme A biosynthesis III Pantothenate and coenzyme A biosynthesis III Eubacterium_eligens Eubacterium eligens pantothenate and coenzyme A biosynthesis III Eubacterium_eligens glycolysis I glycolysis I Glycolysis I superpathway of purine nucleotides de novo biosynthesis II Superpathway of purine nucleotides de novo biosynthesis II Ruminococcus_obeum Ruminococcus_obeum Ruminococcus obeum superpathway of purine nucleotides de novo biosynthesis II 3 3 − − IL6 LIF IL6 LIF TNF TNF MAL IL1B MAL IL1B PIM1 3 5 PIM1 SOX7 MALL DKK1 LFNG SOX7 MALL DKK1 SBSN IL36G CTGF LFNG 3 5 SBSN IL36G CTGF IL1RN IFFO2 − − IL1RN IFFO2 KRT24 IL6 LIF KRT24 FOXA1 CXCL8 CYR61 OVOL1 CLDN4 PTGS2 FOXA1 SPRR3 CRCT1 DUSP5 GRHL3 CXCL8 CYR61 OVOL1 − − IL36RN CLDN4 PTGS2 TMCC3 SPRR3 CRCT1 DUSP5 GRHL3 HBEGF IL36RN TMCC3 NFKBIZ HBEGF ZNF750 IL6 TNF NFKBIZ LIF SRF ZNF750 MAF LIPH IRS2 SPRR2F AMOTL2 SPRR1A SPRR2A SPRR1B PIM1 SPRR2D ELF3 SPRR2F AMOTL2 S100A11 SAMD4A SPRR1A SPRR2A SPRR1B SPRR2D TNF CSF3 FGF7 S100A11 SAMD4A SRF MALL DKK1 PDP1 PHC1 FGD4 MAF FAM156B SBSN CNN2 RGS2 CTGF OCLN LIPH IRS2 TRIB2 FAM156B CRNN RGCC PIM1 0 2 4 6 810 14 0 2 4 6 810 14 0 2 4 6 810 14 0 2 4 6 810 14 TMEM100 ELF3 VGLL3 KRT13 KRT24 KRT34 TMEM100 CD274 CSF3 FGF7 ARL4C EFNA1 CYR61 CXCL1 CDKL5 CXCL8 NR4A1 OVOL1 MALL DKK1 PDP1 KRTAP2 FOXN2 KANK4 PTGS2 CLDN4 EPHB3 RFLNB PHC1 FGD4 SPRR3 CREB5 DUSP5 SERPINB2 SBSN CNN2 RGS2 CTGF MUC16 KRTAP2 OCLN HBEGF KDM6A TMCC3 SERPINB2 TRIB2 I II III CRNN ERRFI1 RGCC SLC2A3 VGLL3 KRT13 KRT24 KRT34 HMGCR HS3ST1 AKAP12 CD274 CAPN14 ARL4C EFNA1 CYR61 CXCL1 CDKL5 CXCL8 NR4A1 OVOL1 SPRR2F AMOTL2 FOXN2 KANK4 PTGS2 CLDN4 EPHB3 RFLNB SPRR1A SPRR1B SPRR2E BDKRB1 SPRR3 CREB5 DUSP5 SPRR2D MUC16 ARRDC3 C16orf52 SMURF2 SPOCD1 SAMD4A HBEGF KDM6A TMCC3 ERRFI1 SLC15A2 FAM156B TMPRSS11B

SLC2A3 −8 −4 0 2 4 −8 −4 0 2 4 −8 −4 0 2 4 −8 −4 0 2 4 HMGCR HS3ST1 AKAP12 TMPRSS11B ANKRD22 CAPN14 ADAMTS9 SPRR2F AMOTL2 SPRR1A SPRR1B SPRR2E BDKRB1 SPRR2D CEACAM5 CEACAM6 ARRDC3 C16orf52 SMURF2 SPOCD1 SAMD4A KRTAP2 KRTAP1 IVNS1ABP SHROOM3 SLC15A2 FAM156B ADAMTS15 ARHGAP29 ANKRD22 ADAMTS9 CEACAM5 CEACAM6 KRTAP2 KRTAP1 IVNS1ABP SHROOM3 ADAMTS15 ARHGAP29 Host Gene Expression log2(Abundance) Host Gene Expression log2(Abundance)

Microbe Gene Phenotype ● C D LDL E DUSP5 CYR61 NFKBIZ PTGS2 IL6 CXCL8 IL36G IL1B IL36RN ● Self-reported high cholesterol NR4A1 ● ● ● OCLN LDL-C HS3ST1 E. siraeum ● WC combined UDP-N-acetylmuramoyl HMGCR ● Subdioligranulum (unclassified) ● ● BMI pentapeptide

interacts with ● interacts with interacts with IDL-TG biosynthesis II interacts with

interacts with SHROOM3 interacts with ● Self-reported hypertension interacts with

interacts with interacts with interacts with interacts with (lysine-containing) interacts with

● interacts with Chorismate ER negative breast cancer interacts with interacts with DKK1 interacts with ● Superpathway of ● Chronic kidney disease Phosphopanthothenate biosynthesis VGLL3 interacts with interacts with R. inulinivorans ● ● aromatic amino F. prausnitzii ● Eczema AMOTL2 biosynthesis I from 3-dehydroquinate IRS2 ANKRD22 ● interacts with E. ramulus Self-reported gout acid biosynthesis interacts with ● ● interacts with interacts with Oscillibacter interacts with OVOL1 ● Overall breast cancer interacts with (unclassified) R. inulinivorans AKAP12 IL6 cis-vaccenate Chorismate UDP-N-acetylmuramoyl Subdoligranulum ● Hayfever allergic rhinitis or eczema interacts with ZNF750 interacts with R. torques biosynthesis I interacts with

interacts with pentapeptide (unclassified) ● Alzheimers Disease biosynthesis CTGF interacts with

interacts with interacts with HBEGF interacts with biosynthesis I interacts with ● Eosinophil counts interacts with OVOL1 interacts with ● (meso-diaminopimelate ● interacts with interacts with interacts with Diagnosed asthma interacts with interacts with interacts with ● containing) Sum eosinophil basophil counts interacts with

interacts with interacts with interacts with Pantothenate & OCLN ● interacts with interacts with Self-Reported asthma interacts with L-ornithine interacts with interacts with coenzymeinteracts with A interacts with interacts with ● Schizophrenia

biosynthesis interacts with interacts with HS3ST1 ● ● Sum basophil neutrophil counts interacts with biosynthesis III B. ovatus ● interacts with C. catus ● interacts with interacts with Neutrophil counts interacts with

interacts with CLDN4 VGLL3 IL6 ● ● BMI active interacts with RGCC ANKRD22 interacts with interacts with HBEGF CXCL1 S-adenosyl-L-methionine interacts with interacts with interacts with ● ● Sum neutrophil eosinophil counts interacts with interacts with ADAMTS15 C. catus CXCL8 ● cycle I interacts with interacts with ● White blood cell count OVOL1 CDKL5 F. prausnitzii ● CREB5 ● interacts with ● interacts with ● Granulocyte counts

P. succinatutens interacts with CSF3 ● Myeloid white cell count FOXN2 ● interacts with interacts with ● ● Neuroticism score interacts with Gluconeogenesis I E. rectale ● interacts with Asthma Coenzyme A interacts with ● interacts with Self-reported hypothyroidism or myxoedema E. siraeum SAMD4A LIF biosynthesis I interacts with

interacts with interacts with

interacts with interacts with

interacts with EFNA1 interacts with IVNS1ABP ● interacts with interacts with RGS2 ●

interacts with interacts with SHROOM3 ● interacts with Calvin-Benson-Bassham cycle P. copri ● CDP-diacylglycerol interacts with KRTAP2-3 UMP DUSP5 ● Body fat percentage interacts with ● CD274 interacts with interacts with B. ovatus ● ●Lymphocyte counts biosynthesisinteracts with I biosynthesis SPOCD1 R. intestinalis interacts with

interacts with interacts with interacts with Methanobrevibacter LIF interacts with ● interacts with interacts with L. bacterium interacts with CTGF L-lysineinteracts with

interacts with interacts with (unclassified) ● ● IVNS1ABP ● Red blood cell count interacts with 7-1-58FAA R. intestinalis biosynthesis VI interacts with ● Platelet count B. uniformis ● EGD4 ● interacts with B. uniformis ● Starch interacts with Crohn’s disease ● ● Oscillibacter (unclassified) TRIB2 ● Methyerythritol interacts with KDM6A ● PIM1● Monocyte counts L-lysine degradation V interacts with P. succinatutens ● CYR61 PTGS2 AKAP12 ● Birthweight phosphate ●Ulcerative colitis biosynthesis III MALL interacts with IRS2● pathway I

interacts with interacts with ERRE11

interacts with interacts with ● ●Standing height interacts with SRF ● Coenzyme A interacts with ●Inflammatory bowel disease Methanobrevibacter (unclassified) ADAMTS9 ● PDP1 ● ● biosynthesis II E.biforme E. biforme ● SMURF2 5-aminoimidazole (mammalian) SAMD4A ● ●Height ribonucleotide Peptidoglycan ●Reticulocyte count biosynthesis I biosynthesis I (meso- TRIB2 SPCOD1 ● dTDP-L-rhamnose TMCC3 ●WHR combined biosynthesis I diaminopimelate ●HIP combined ARRDC3 L. bacterium 7-1-58FAA ● AMOTL2 ● ●High light scatter reticulocyte counts containing) R. obeum ● KRTAP2-3 bioRxiv preprint doi: https://doi.org/10.1101/2020.08.17.255059; this version posted August 17, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is34 made available under aCC-BY-NC-ND 4.0 International license.

1026 Figure 4. Relationship between host gene expression and specific microbiome features 1027 (A) Heatmap showing correlations between microbial species (rows) and host genes (columns). 1028 Colors at the top indicate to which hominid microbiome a gene responds to. Boxplots to the right 1029 show the abundance of each microbial species in each hominid microbiome (microbial

1030 abundance transformed by log 2). 1031 (B) Similar to (A), but showing microbial pathways instead of species. 1032 (C) Network visualization of microbial species (green) and high-divergence host genes (purple) 1033 that respond to each species connected with an arrow. Node size of microbial species and host

1034 genes corresponds to species abundance and log 2 fold change of the differential expression, 1035 respectively. Arrow colors indicate whether a microbial species increases (blue) or decreases 1036 (red) the expression of the connected host gene. 1037 (D) Three tier network showing microbial species (left column), the host genes they each 1038 regulate (middle column), and TWAS phenotypes these genes are associated with (right column). 1039 Microbial species and host gene node size indicates microbial abundance and differential 1040 expression, respectively, correlated with high-divergence genes and TWAS phenotypes. 1041 (E) Similar to (C), but showing microbial pathways instead of species.