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Title: Interactions between species introduce spurious associations in microbiome studies}
Article:
Abstract:
Microbiota contribute to many dimensions of host phenotype, including disease. To link specific microbes to specific phenotypes, microbiome-wide association studies compare microbial abundances between two groups of samples. Abundance differences, however, reflect not only direct associations with the phenotype, but also indirect effects due to microbial interactions. We found that microbial interactions could easily generate a large number of spurious associations that provide no mechanistic insight. Using techniques from statistical physics, we developed a method to remove indirect associations and applied it to the largest dataset on pediatric inflammatory bowel disease. Our method corrected the inflation of p-values in standard association tests and showed that only a small subset of associations is directly linked to the disease. Direct associations had a much higher accuracy in separating cases from controls and pointed to immunomodulation, butyrate production, and the brain-gut axis as important factors in the inflammatory bowel disease.
Introduction:
Microbes are essential to any ecosystem be it the ocean or the human gut. The sheer impact of microbial processes has however been underappreciated until the advent of culture-independent methods to assess entire communities in situ. Metagenomics and 16S rRNA sequencing identified significant differences in microbiota among hosts, and experimental manipulations established that microbes could dramatically alter host phenotype[1–8]. Indeed, anxiety, obesity, colitis, and other phenotypes can be transmitted between hosts simply by transplanting their intestinal flora[9–13]. New tools and greater awareness of microbiota triggered a wave of association studies between microbiomes and host phenotypes. Microbiome wide association studies( MWAS) have been carried out for diabetes, arthritis, cancer, autism and many other disorders[14–23]. MWAS clearly established that each disease is associated with a distinct state of intestinal dysbiosis, but they often produced conflicting results and identified a very large number of associations both within and across studies[14, 19, 21, 23–26]. For example, a recent study on inflammatory bowel disease( IBD) reported close to 100 taxa associated with IBD[25], a number that is fairly typical[14]. Such long lists of associations defy simple interpretation and complicate mechanistic follow-up studies because one needs to examine the role of almost every species in the microbiota. In fact, one can argue that MWAS are most useful when they can identify a small network of taxa driving the disease. Although extensive dysbiosis might reflect the multifactorial nature of the disease, it is also possible that MWAS detect spurious associations because their statistical methods fail to account for some important aspects of microbiome dynamics. One such aspect is the pervasive nature of microbial interactions: species compete for similar resources, rely on cross-feeding for survival, and even produce their own antibiotics[27–37]. Hence, microbial abundances must be correlated with each other, and even a simple change in host phenotype could manifest as collective responses by the microbiota. Traditional MWAS, however, completely neglect this possibility because they treat each species as an independent manifestation of host phenotype. As a result, MWAS cannot distinguish taxa directly linked to disease from taxa that are affected only through their interactions with other species. The main conclusion of this paper is that realistic microbial interactions produce a large number of spurious associations between particular members of the microbiome and phenotypes. Many of these indirect associations can be removed by a simple procedure based on maximum entropy models from statistical physics[38, 39]. We dubbed this approach Direct Association Analysis, or DAA for short. When applied to the largest MWAS on IBD, DAA shows that many of the previously reported associations could be explained by interspecific interactions rather than the disease. At the genus and species level, the direct associations include only Roseburia, Faecalibacterium prausnitzii, Bifidobacterium adolescentis, Blautia producta, Turicibacter, Oscillospira, Eubacterium dolichum, Aggregatibacter segnis, and Sutterella. Some of these associations are well-known[40–47], while others have received little attention in IBD research. The phenotypes of the taxa directly linked to disease suggest that immunomodulation, butyrate production, and the brain-gut interactions play an important role in the etiology of IBD. Compared to traditional MWAS, DAA corrected the inflation of p-values responsible for the large number of spurious associations and identified taxa most informative of the diagnosis. We found that directly associated taxa are much better at discriminating between cases and controls than an equally-sized subset of indirect associations. In fact, direct associations have the same potential to discriminate between health and disease as the entire set of almost a hundred associations detected by conventional methods.
Discussion:
The primary goal of MWAS is to guide the study of disease etiology by detecting microbes that have a direct effect on the host. These direct effects could be very diverse and include secretion of toxins, production of nutrients, stimulation of the immune system, and changes in mucus and bile[67, 68]. In addition to the host-microbe interactions, the composition of microbiota is also influenced by the interspecific interactions among the microbes such as competition for resources, cross-feeding, and production of antibiotics[27–37]. In the context of MWAS, microbial interactions contribute to indirect changes in microbial abundances, which are less informative of the disease mechanism and are less likely to be valuable for follow-up studies or in interventions. Here, we estimated the relative contribution of indirect associations to MWAS and showed how to isolate direct from indirect associations. Our main result is that interspecific interactions are sufficiently strong to generate detectable changes in the abundance of many microbes that are not directly linked to host phenotype. As a result, conventional approaches to MWAS detect a large number of spurious associations and produce inflated p-values that do not match their expected distribution( Fig 4A). These challenges are resolved by Direct Association Analysis( DAA), which uses maximum entropy models to explicitly account for interspecific interactions. We applied DAA to a large data set of pediatric Crohn’s disease and found that it restores the distribution of p-values and substantially simplifies the pattern of dysbiosis while retaining full classification power of a conventional MWAS. The relatively simple dysbiosis identified by DAA in IBD has strong support in the literature and offers interesting insights into disease etiology. Four of the taxa identified by our method have a well-established role in IBD: B. adolescentis, F. prausnitzii, B. producta, and Roseburia. They have been repeatedly found to have lower abundance in both Crohn’s disease and ulcerative colitis[40–47], and several studies have demonstrated their ability to suppress inflammation and alleviate colitis[43, 69–73]. Bifidobacterium species occupy a low trophic level in the gut and ferment complex polysaccharides such as fiber[74, 75]. Fermentation products include lactic acid, which promotes barrier function, and maintains a healthy, slightly acidic environment in the colon[76]. Due to these properties Bifidobacterium species are commonly used as probiotics[74]. F. prausnitzii, Blautia producta and Roseburia occupy a higher trophic level and ferment the byproducts of polysaccharides digestion into short-chain fatty acids( SCFA), which are an important energy source for the host[42, 43, 77, 78]. The ability of DAA to detect taxa strongly associated with IBD is reassuring, but not surprising. What is surprising is that many strong associations are classified as indirect by our method. For example, Roseburia and Blautia are the only genera of Lachnospiraceae that DAA finds to be directly linked to the disease. In sharp contrast, traditional MWAS report seven genera in this family that are strongly associated with IBD[25]. All seven genera are involved in SCFA metabolism, but their specializations differ. Species in Blautia genus are major producers of acetate, a SCFA that is commonly involved in microbial crossfeeding[79, 80]. In particular, many species extract energy from acetate by converting it into butyrate, another SCFA that plays a major role in gut health by nourishing colonocytes and regulating the immune function[77, 80]. Roseburia genus specializes almost exclusively in the production of butyrate and acts as a major source of butyrate for the host[77, 81]. Thus, our findings suggest that butyrate production plays an important role in IBD etiology and that the dysregulation of this process is directly linked to the depletion of Roseburia and possibly Blautia. The important role of butyrate is further supported by our detection of E. dolichum and Oscillospira, which are known to produce butyrate[82–84]. The latter taxon has not been detected in three independent analyses of this IBD data set[21, 25, 85] presumably because its involvement is masked by indirect associations and interactions with other microbes. Several other studies support this DAA finding and confirm that Oscillospira is suppressed in IBD[86, 87]. Oscillospira was also found to be positively associated with leanness and negatively associated with the inflammatory liver disease[88–90]. The interactions between Oscillospira and the host appears to be quite complex and involve the consumption of host-derived glycoproteins including mucin, production of SCFA, and modulation of bile-acid metabolism[84, 91]. The latter interaction was suggested to be a major factor in the protective role of Oscillospira against infections with Clostridium difficile[91–93]. The final taxon that was suppressed in IBD is Turicibacter. This genus is not very well characterized, and few MWAS studies point to its involvement in IBD[21, 25, 94]. Two studies in animal models, however, directly looked into the connection between IBD and Turicibacter[95, 96]. The first study found that iron limitation eliminates colitis in mice while at the same time restoring the abundance of Turicibacter, Bifidobacterium, and four other genera[95]. The second study identified Turicibacter as the only genus that is fully correlated with immunological differences between mice resistant and susceptible to colitis: high abundance of Turicibacter in the colon predicted high levels of MZ B and iNK T cells, which are potent regulators of the immune response[96]. Moreover, Turicibacter was the only genus positively affected by the reduction in CD8+ T cells. Thus, our method identified a taxon that is potentially directly linked to IBD via the modulation of the immune system. Perhaps the most unexpected finding was our detection of A. segnis and Sutterella as the only species and genus increased in disease compared to 26 positive associations detected by the previous analysis[25]. All other associations were classified as indirect even though they often corresponded to much more significant changes in abundance between IBD and control groups. Thus, our results indicate that expansion of many taxa including opportunistic pathogens is driven by their interactions with the core IBD network shown in Fig 3. One possibility is that the dysbiosis of the symbiotic microbiota makes it less competitive against other bacteria and opens up niches that can be colonized by opportunistic pathogens. The other, less explored possibility, is that commensal microbiota can not only protect from pathogens, but also facilitate their invasion, a phenomenon that has been recently demonstrated in bees[97]. Little is known about the specific roles that A. segnis and Sutterella play in IBD, and more generally in gut health. Aggregatibacter is a common member of the oral microbiota that thrives in local infections such as periodontal disease and bacterial vaginosis[98–100]. The high abundance of A. segnis is also associated with an increased risk of IBD recurrence[101]. Sutterella, on the other hand lacks overt pathogenicity, and MWAS produced inconsistent findings[102–108] on its involvement in IBD. Some studies reported that Sutterella is increased in patients with good outcomes[21, 105] while other studies found positive or no association between Sutterella and IBD[25, 103, 106–108]. Experimental investigations showed that Sutterella lacks many pathogenic properties; in particular, it does not induce a strong immune-response and has only moderate ability to adhere to mucus[107, 108]. Further, Sutterella strains from IBD and control patients showed no phenotypic differences in metabolomic, proteomic, and immune response assays[108]. Nevertheless, Sutterella is strongly associated with worse behavioral scores in children with autism spectrum disorder and Down syndrome[19, 20, 109]. Therefore, the direct link between Sutterella and IBD could involve the gut-brain axis. In summary, we found a small number of taxa can explain extensive dysbiosis in IBD and accurately predict disease status. Directly associated taxa have strains with dramatically different abilities to trigger colitis and are specifically targeted by the immune system of patients and animals with IBD[12]. Previous studies of these taxa point to facilitated colonization by pathogens, butyrate production, immunomodulation, bile metabolism, and the gut-brain axis as the primary factors in the etiology of IBD. Many disorders are accompanied by substantial changes in host microbiota, but our work shows that only a small subset of these changes could be directly related to the disease. Similarly, only a handful of taxa could drive the dynamics of the ecosystem-level changes in the environment. To untangle the complexity of such dysbioses, it is important to account for microbial interactions using mechanistic or statistical methods. Direct association analysis proposed in this paper is a simple statistical approach based on the principle of maximum entropy. DAA can be applied to any microbiome data set that is sufficiently large to infer interspecific interactions.
Microbiomes are important for better health , sustainable agriculture , and climate management . Since experimental studies of natural microbial communities are often challenging , microbiome wide association studies ( MWAS ) have been the primary method to reveal the connection between specific microbes and host phenotype . MWAS have established that many diseases are associated with a complex dysbiosis driven by a large number of microbes . We show that many of these associations may not reflect a mechanistic link with the disease , but arise instead due to interspecific interactions such as cross-feeding and competition for resources . We also propose a method grounded in the maximum entropy models of statistical physics to separate direct from indirect associations . Using both synthetic and real microbiome data , we show that this method detects only direct associations , identifies most predictive features of microbiomes , and avoids p-value inflation . We demonstrate the power of this method on one of the largest microbiome data sets on inflammatory bowel disease .