Super tough microbialites are some of the oldest evidence of life on Earth. Breakthroughs, discoveries, and DIY tips sent every weekday. Among the tricky carnivorous plants, great white shark-killing orca whales, and other remarkable flora and fauna that call South Africa home is a remarkable group of "living rocks." Called microbialites, these communities are similar to coral reefs and are built up by microbes. These tiny living organisms absorb and release dissolved minerals into more solid rock-like forms.

Predicting interspecies interactions is a key challenge in microbial ecology, as these interactions are critical to determining the structure and activity of microbial communities. In this work, we used data on monoculture growth capabilities, interactions with other species, and phylogeny to predict a negative or positive effect of interactions. More precisely, we used one of the largest available pairwise interaction datasets to train our models, comprising over 7,500 interactions be- tween 20 species from two taxonomic groups co-cultured under 40 distinct carbon conditions, with a primary focus on the work of Nestor et al.[28 ]. In this work, we propose Graph Neural Networks (GNNs) as a powerful classifier to predict the direction of the effect. We construct edge-graphs of pairwise microbial interactions in order to leverage shared information across individual co-culture experiments, and use GNNs to predict modes of interaction. Our model can not only predict binary interactions (positive/negative) but also classify more complex interaction types such as mutualism, competition, and parasitism. Our initial results were encouraging, achieving an F1-score of 80.44%. This significantly outperforms comparable methods in the literature, including conventional Extreme Gradient Boosting (XGBoost) models, which reported an F1-score of 72.76%.

Co-occurrence network inference algorithms have significantly advanced our understanding of microbiome communities. However, these algorithms typically analyze microbial associations within samples collected from a single environmental niche, often capturing only static snapshots rather than dynamic microbial processes. Previous studies have commonly grouped samples from different environmental niches together without fully considering how microbial communities adapt their associations when faced with varying ecological conditions. Our study addresses this limitation by explicitly investigating both spatial and temporal dynamics of microbial communities. We analyzed publicly available microbiome abundance data across multiple locations and time points, to evaluate algorithm performance in predicting microbial associations using our proposed Same-All Cross-validation (SAC) framework. SAC evaluates algorithms in two distinct scenarios: training and testing within the same environmental niche (Same), and training and testing on combined data from multiple environmental niches (All). To overcome the limitations of conventional algorithms, we propose fuser, an algorithm that, while not entirely new in machine learning, is novel for microbiome community network inference. It retains subsample-specific signals while simultaneously sharing relevant information across environments during training. Unlike standard approaches that infer a single generalized network from combined data, fuser generates distinct, environment-specific predictive networks. Our results demonstrate that fuser achieves comparable predictive performance to existing algorithms such as glmnet when evaluated within homogeneous environments (Same), and notably reduces test error compared to baseline algorithms in cross-environment (All) scenarios.

We aim to learn the functional co-response group: a group of taxa whose co-response effect (the representative characteristic of the group showing the total topological abundance of taxa) co-responds (associates well statistically) to a functional variable. Different from the state-of-the-art method, we model the soil microbial community as an ecological co-occurrence network with the taxa as nodes (weighted by their abundance) and their relationships (a combination from both spatial and functional ecological aspects) as edges (weighted by the strength of the relationships). Then, we design a method called gFlora which notably uses graph convolution over this co-occurrence network to get the co-response effect of the group, such that the network topology is also considered in the discovery process. We evaluate gFlora on two real-world soil microbiome datasets (bacteria and nematodes) and compare it with the state-of-the-art method. gFlora outperforms this on all evaluation metrics, and discovers new functional evidence for taxa which were so far under-studied. We show that the graph convolution step is crucial to taxa with relatively low abundance (thus removing the bias towards taxa with higher abundance), and the discovered bacteria of different genera are distributed in the co-occurrence network but still tightly connected among themselves, demonstrating that topologically they fill different but collaborative functional roles in the ecological community.

Abstract: Protein subcellular localization is an important factor in normal cellular processes and dis- ease. While many protein localization resources treat it as static, protein localization is dynamic and heavily influenced by biological context. Biological pathways are graphs that represent a specific biological context and can be inferred from large-scale data. We develop graph algorithms to predict the localization of all interactions in a biological pathway as an edge-labeling task. We compare a variety of models including graph neural networks, probabilistic graphical models, and discriminative classifiers for predicting localization an- notations from curated pathway databases. We also perform a case study where we con- struct biological pathways and predict localizations of human fibroblasts undergoing viral infection.

The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data – being compositional, sparse, and high-dimensional – necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.

The human microbiota is a complex microbial community living on and in our bodies. Its impact on a host's health is immense, affecting digestion ([ 1 ][1]), the immune system ([ 2 ][2]), behavior ([ 3 ][3]), metabolic diseases ([ 4 ][4]), and responses to drugs ([ 5 ][5]–[ 7 ][6]). Rapid advances in experimental and computational methods have moved the human microbiome field from identifying associations between microbiota composition and host health to unraveling the underlying molecular mechanisms ([ 8 ][7]–[ 10 ][8]). However, exactly how much the microbiota contributes to host health is a very difficult question to answer. By focusing on mechanistic and quantitative questions about the microbiome's contributions to host metabolism, I leverage my background in applied mathematics and systems biology to develop computational models describing host-microbiota interactions. Good models require good data from controlled experiments—a challenging proposition in complex host-microbiota systems. As a postdoc, I joined Andy Goodman's lab at Yale University and found myself in a perfect position to collect such data. By combining bacterial genetics with gnotobiotic mouse models, I learned how to modify the microbiome of germ-free, sterile mice. In the Goodman lab, we used these mice to study the contribution of microbiota to host metabolism of a number of pharmaceutical drugs. We found that this was also a good system to quantify host-microbiome interactions in vivo, because the compounds we used can be introduced into the system in a controlled way. We first focused on brivudine, an antiviral compound that can be converted into a potentially toxic metabolite, bromovinyluracil (BVU), by either a host or its microbiome ([ 11 ][9]). To identify bacteria capable of converting brivudine to BVU, we incubated individual bacterial species with the drug in vitro. One of the most potent brivudine metabolizers was Bacteroides thetaiotaomicron , a common gut bacterium with a genetic deletion library readily available. By incubating this library with the drug, we identified one bacterial mutant that had lost the capacity to convert brivudine to BVU. We then colonized germ-free mice with either the wild-type or mutant B. thetaiotaomicron , which provided us with a controllable host-microbiome system and two mouse groups that were identical, save for a single bacterial gene. When we administered brivudine to these two groups, the observed outcome was somewhat puzzling. Although drug levels in the intestine were much higher in mice colonized with the mutant bacterium, serum levels were comparable between the two mouse groups. The metabolite levels showed the opposite pattern: no difference (and very low levels) in the intestine but much higher metabolite levels in the sera of mice colonized with the wild-type bacterium (see the figure). These data could potentially be explained by bacterial conversion of the drug in the intestine and the rapid metabolite absorption into the serum. To test this explanation, we started with a simple kinetic model with two equations describing host drug metabolism in the liver and bacterial drug metabolism in the intestine. Once solved, this equation system showed that the difference between the amounts of metabolite absorbed into the sera of each of the two mouse groups was determined by the amount of BVU produced by microbes in the gut. This controlled experimental setup allowed us to quantify that the bacterial contribution to the toxic drug metabolite in vivo was about 70% ([ 12 ][10]) (see the figure). We expanded the model to describe drug metabolism processes in eight different tissues and in enterohepatic circulation (when the drug metabolized in the liver is secreted back into the small intestine via bile). We then demonstrated that our approach can be generalized to estimate the bacterial contribution to drug metabolism even if the metabolizing species remain unknown by using data from germ-free mice and mice harboring a complex microbial community. We also showed that microbial contribution to the drug metabolite far exceeds the host for sorivudine, an antiviral drug with different host and microbiome metabolism rates, and for clonazepam, an anxiolytic and anticonvulsant drug converted to multiple metabolites ([ 12 ][10]). ![Figure][11] Experimental and computational approaches that quantify host and microbial contributions to drug metabolism Oral drugs are administered to gnotobiotic mice that differ in a single microbial drug-metabolizing enzyme (GNMUT, mutant; GNWT, wild type); drug and drug metabolite kinetics are then quantified across tissues. A microbiome-host pharmacokinetic model developed from these measurements accurately predicts serum metabolite exposure and untangles host and microbiome contributions to drug metabolism. GRAPHIC: ADAPTED FROM M. ZIMMERMANN-KOGADEEVA BY N. CARY/ SCIENCE Quantifying the metabolic host-microbiome interactions is not the only purpose of our model. Having a robust model of host-microbiome interaction allows us to study, explain, and predict the system's behavior in different conditions. By analyzing how drug and metabolite profiles change when model parameters are varied, we found that the similarity of drug serum profiles between germ-free and colonized mice can be explained by the fast and microbiota-independent drug absorption from the small intestine. Our model further suggests that even for rapidly absorbed drugs, microbiome contributions to a host's metabolism can be substantial under certain conditions (e.g., a high microbiome to host ratio of drug metabolism or extensive enterohepatic circulation of the drug and its metabolites) ([ 13 ][12]). Such computational models enable us to investigate host-microbiota interactions in silico, guide experimental design, and help reduce the number of experiments needed to confirm model predictions. To systematically investigate microbial capacity to metabolize drugs, we next conducted a high-throughput in vitro screen. We found that microbiota contribution to drug metabolism might even be more widespread than we anticipated—two-thirds (176 out of 271) of the human-targeted drugs we examined were metabolized by at least one of the 76 tested bacteria ([ 14 ][13]). Although follow-up studies are required to test these microbiota-drug interactions in vivo, our findings emphasize that the microbiota should be considered when developing new drugs, stratifying patients, and choosing the most efficient treatment strategies. In the future, I believe that computational models combined with quantitative experimental data will allow us to measure host-microbiome interactions beyond drug metabolism and to better understand, predict, and control the effect of the microbiome on our health in everyday life. FINALIST Maria Zimmermann-Kogadeeva Maria Zimmermann-Kogadeeva received undergraduate degrees from Lomonosov Moscow State University in Russia and a PhD from ETH Zürich, Switzerland. After completing her postdoctoral fellowships at Yale University in the Goodman group and at European Molecular Biology Laboratory (EMBL) Heidelberg in the Bork group, Maria will start her laboratory in the Genome Biology Unit at EMBL Heidelberg in 2021. 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There are hundreds of trillions of microbes within the human body, which have a profound impact on modulating host function. Many of these microbes reside in the gastrointestinal tract and have been shown to influence normal physiology across all body systems ([ 1 ][1]). Disruptions in the delicate balance of microbes within the gut and other niches are associated with numerous disease states—including neurologic disorders, cardiovascular disease, gastrointestinal disorders, and even cancer ([ 2 ][2]). Accordingly, there is intense interest in targeting these microbes to promote overall health and to abrogate disease, with considerable advances made recently. Strategies to modulate gut microbes include fecal microbiota transplant (FMT), which involves the transfer of fecal material from one individual to another for a desired physiologic effect. This approach, among other gut microbiota modulation strategies, has shown promise in treating several disease conditions, although opportunities exist to iterate and build on these approaches. The idea that disruptions in the gastrointestinal tract could contribute to systemic disease was championed centuries ago by Hippocrates, a physician in ancient Greece. Strategies to modulate the composition of the gut have also been around for centuries, with the first reports of the use of FMT dating back to the fourth century BCE in China where fecal preparations were used to treat gastrointestinal disorders ([ 3 ][3]). Parallels have also been observed in the animal kingdom, where coprophagia (ingesting fecal material) is common and may confer an increase in gut microbial diversity and associated enhancements in host function for digestion and other physiologic processes. However, the first successful clinical application of FMT was not published until 1958 with the report of FMT from healthy donors used for patients with pseudomembranous enterocolitis from Clostridioides difficile infection (CDI) ([ 4 ][4]). Numerous clinical trials have since been undertaken, using FMT and other gut microbiota modulation strategies to treat diseases of the gut (such as CDI, and inflammatory bowel disease, IBD) as well as other systemic diseases—including metabolic syndrome, autism, multiple sclerosis, Parkinson's disease, and even cancer ([ 2 ][2]). ![Figure][5] Strategies to alter gut microbiota Fecal microbiota transplant (FMT) involves transfer of fecal microbiota from a donor to another individual . Alternatively, microbial consortia (targeted formulations used to augment host microbiota) are being developed. Diet, prebiotics, and postbiotics can also influence the microbial community. GRAPHIC: N. CARYI/ SCIENCE To date, many of the strategies to target gut microbes have involved the two extremes: either transfer of entire microbial communities (by using FMT) or transfer of a single microbial taxon. However, a growing number of approaches are now being developed as more is learned about the functional aspects and physiologic impact of microbes throughout the body. These iterative approaches transcend efforts that focus on taxonomic characterization of microbial niches through next-generation genomic sequencing, incorporating interrogation of functional characteristics of gut microbes (by metabolomic profiling and studies in preclinical models) to mediate the desired physiologic response. This has led to a host of therapeutic strategies from microbial consortia to pre-, pro-, and postbiotic interventions. Nonetheless, much still needs to be learned to implement true “precision” modulation of the gut microbiota. When considering strategies to modulate the gut microbiota, the indication for intervention in the intended population must be considered. Gut dysbiosis, an imbalance in the composition of commensal microbial communities, has been linked to numerous disease states, substantiating the use of FMT and other gut microbiota modulation strategies ([ 5 ][6]). This link is fortified by data demonstrating that although there has been a decrease in infectious diseases over the past several decades with the widespread use of antibiotics, there has been a concurrent increase in allergy and autoimmune diseases ([ 6 ][7]) presumably at least partially due to disruption of the gut microbiota. Notably, some of the diseases being treated by gut microbiota modulation have a profound dysbiosis (such as CDI), whereas others have a more subtle disruption of gut microbes, which has implications for choosing the appropriate strategy for gut microbiota modulation. Numerous other factors should be taken into account when contemplating modulation of the gut microbiota. These include the means of gut microbiota modulation, preparative regimen, measurement of engraftment of gut microbes and of the desired physiologic effect, and concurrent dietary intake ([ 7 ][8]). In general, the approach aims to restore a more “healthy” gut microbial community—although the definition of a “healthy” gut microbiota is not clearly established. However, data suggest that a diverse microbial community with a high degree of functional redundancy is associated with better overall health ([ 2 ][2]) and better outcomes in several disease states ([ 8 ][9], [ 9 ][10]). The most successful application of FMT thus far is in the treatment of refractory CDI, where treatment with FMT has been shown to be generally safe and highly effective ([ 2 ][2]). Nonetheless, guidelines for proper treatment and screening of donor stool are critical for safety and include screening for infectious diseases and disorders that are associated with perturbations of the gut microbiota, as well as the use of medications that can affect gut microbes such as antibiotics and proton pump inhibitors ([ 10 ][11]). Notably, these guidelines are iterative, as new recommendations are made to expand screening and testing of donors based on insights gained from ongoing trials. For example, screening of donors for multidrug-resistant organisms and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is now recommended. This follows reports of several patients with CDI who developed systemic infection with antibiotic-resistant bacterial infections following FMT ([ 11 ][12]), as well as concerns about possible infections with SARS-CoV-2. The use of FMT is being investigated across numerous other disease conditions, although most of these are associated with a less profound dysbiosis and greater heterogeneity in assessed endpoints and outcomes. However, there is clear evidence of success in some trials across a number of indications, including IBD, after hematopoietic stem cell transplant and in autism spectrum disorders ([ 5 ][6]). Limitations in measuring efficacy in FMT trials may arise from “true negative” results, or from numerous other confounding factors, including features not dependent on gut microbes that contribute to the development and persistence of disease in the recipient, as well as variability in trial design and outcome measures. Additionally, there may be factors inherent to the FMT donor that may affect efficacy (such as composition and functional aspects of the transplanted microbiota); however, such “donor effects” may be less prominent for indications in which a more profound dysbiosis is present, such as in CDI and even IBD ([ 12 ][13]). Optimal dosing and route of delivery for FMT are also incompletely understood and may be context dependent. Additional studies are critically needed to interrogate the success (or failure) of this approach for these indications and to develop optimal strategies for use of FMT. One attribute of FMT not possessed by other strategies to modulate the gut microbiota is the diversity of microbes that may be administered (including not only bacteria, but also viruses, fungi, and archaea) (see the figure). This provides potential functional redundancy for favorable impact on host physiology. In a profoundly dysbiotic state, this diversity represents a potential advantage over strategies that administer minimal-complexity microbial consortia, which may not engraft and may not be sufficient in reestablishing a “favorable” gut microbiota. However, the same attribute of increased diversity and complexity of FMT is also a limitation that creates issues with reproducibility and scalability. There are also concerted efforts under way to develop consortia of microbes that can be reliably and consistently manufactured and administered to favorably modulate the gut microbiota to address gastrointestinal and systemic disease, offering improved scalability over FMT. This includes commercially available probiotics, which are live microorganism preparations with presumed health benefits. The impact of administration of many of these formulations across disease indications has been studied in clinical trials with mixed results, and to date none of these commercially available formulations are approved for use by major regulatory bodies such as the U.S. Food and Drug Administration ([ 13 ][14]). However, next-generation live biotherapeutics (live microorganisms developed as therapeutic agents with defined clinical benefit claims) are now being developed based on insights gained from sequencing data in human cohorts and from studies in preclinical models ([ 13 ][14])—with many now in clinical trials. The first wave of these next-generation live biotherapeutics focused mainly on taxonomy—incorporating single or several bacterial taxa within a consortia based on insights gained from profiling gut microbial species in human cohort studies and in preclinical models. An example of this is in cancer immunotherapy: Clinical trials are now under way using modulation of the gut microbiota through administration of microbial consortia ([ 7 ][8]). These formulations range from simple (monoclonal microbial formulations) to complex (involving consortia of 50 or more bacterial taxa and strains). However, there is a growing appreciation that focusing on the functional aspects of these microbes may be far more important than simply focusing on taxonomy, and genetically modified organisms are now being developed with a wide range of functional attributes ([ 13 ][14]). Although overall these formulations are generally well-tolerated, safety still needs to be taken into account because there are reports of bacterial translocation of these organisms from the gut into the bloodstream in critically ill patients receiving gut microbiota modulation through administration of commercially available probiotics ([ 14 ][15]). Another strong consideration in gut microbiota modulation is the role of diet and prebiotics, as these can profoundly influence existing commensal gut microbes and those administered for therapeutic intent. These may ultimately serve as a stand-alone intervention in appropriate individuals with more subtle gut dysbiosis. Short-term studies have shown that large changes in diet can have a marked impact on gut microbes and associated physiology in the short term ([ 15 ][16]). However, this reliably reverts to a preintervention state if the instituted change in diet is not sustained. Nonetheless, numerous dietary intervention studies are currently under way ([ 7 ][8]), ranging from a somewhat simple intervention of adding one cup of canned beans per day to existing diets (NCT02843425) to extended (or longer-term) dietary interventions, where meals are prepared for (and shipped to) participants (NCT03950635). Such dietary modifications have potential relevance even if recipients are also treated with other gut microbiota modulation strategies such as FMT or live biotherapeutics, as they may sustain and promote optimal function of the transferred gut microbes, although optimal approaches of dietary intervention in these scenarios has yet to be defined. The use of prebiotic supplementation (such as resistant starches, polyphenols, and polyunsaturated fatty acids) is also being studied, because these compounds may provide optimal substrate to beneficial commensal (or administered) microbes. It is becoming evident that modulation of gut microbes will be increasingly employed to promote overall health and to help treat disease, although optimal strategies for “precision” gut microbiota modulation remain incompletely understood. It is probable that a personalized approach will be needed, incorporating strategies such as FMT, administration of live biotherapeutics, dietary strategies, and prebiotics—although it is not inconceivable that an ideal “one-size-fits-all” approach could be identified. Through additional research and collaborative efforts, the true definition of dysbiosis in the gut microbiota as it relates to disease states can be better understood, as well as what constitutes an optimal gut microbiota to promote overall health, which could have broad impact for public health. 1. [↵][17]1. I. Cho, 2. M. J. Blaser , Nat. Rev. Genet. 13, 260 (2012). [OpenUrl][18][CrossRef][19][PubMed][20] 2. [↵][21]1. J. R. Allegretti, 2. B. H. Mullish, 3. C. Kelly, 4. M. Fischer , Lancet 394, 420 (2019). [OpenUrl][22][CrossRef][23][PubMed][24] 3. [↵][25]1. F. Zhang et al ., Am. J. Gastroenterol. 107, 1755 (2012). [OpenUrl][26][CrossRef][27][PubMed][28] 4. [↵][29]1. B. Eiseman, 2. W. Silen, 3. G. S. Bascom, 4. A. J. 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Intelligence is not a quality to attribute lightly to microbes. There is no reason to think that bacteria, slime molds and similar single-cell forms of life have awareness, understanding or other capacities implicit in real intellect. But particularly when these cells commune in great numbers, their startling collective talents for solving problems and controlling their environment emerge. Those behaviors may be genetically encoded into these cells by billions of years of evolution, but in that sense the cells are not so different from robots programmed to respond in sophisticated ways to their environment. If we can speak of artificial intelligence for the latter, perhaps it's not too outrageous to refer to the underappreciated cellular intelligence of the former.

Used to be you knew which city you were in from the food, the sports team, the historic sites, even the local brew. Now a team of microbiologists discovered they can tell cities apart by their unique bacterial fingerprints. The surprising finding was made after an intense study led by John Chase of Northern Arizona University's Department of Biological Sciences and Center for Microbial Genetics and Genomics. He and his colleagues spent a year swabbing for samples at nine offices in San Diego, Flagstaff, and Toronto. They wanted to find out what kind of impact factors like geography, location in a room, seasons, and human interaction have on the microbial communities we spread around, called microbiomes.