Causal effects in microbiomes using interventional calculus

Causal inference in biomedical research allows us to shift the paradigm from investigating associational relationships to causal ones. Inferring causal relationships can help in understanding the inner workings of biological processes. Association patterns can be coincidental and may lead to wrong conclusions about causality in complex systems. Microbiomes are highly complex, diverse, and dynamic environments. Microbes are key players in human health and disease. Hence knowledge of critical causal relationships among the entities in a microbiome, and the impact of internal and external factors on microbial abundance and their interactions are essential for understanding disease mechanisms and making appropriate treatment recommendations. In this paper, we employ causal inference techniques to understand causal relationships between various entities in a microbiome, and to use the resulting causal network to make useful computations. We introduce a novel pipeline for microbiome analysis, which includes adding an outcome or “disease” variable, and then computing the causal network, referred to as a “disease network”, with the goal of identifying disease-relevant causal factors from the microbiome. Internventional techniques are then applied to the resulting network, allowing us to compute a measure called the causal effect of one or more microbial taxa on the outcome variable or the condition of interest. Finally, we propose a measure called causal influence that quantifies the total influence exerted by a microbial taxon on the rest of the microiome. Our pipeline is robust, sensitive, different from traditional approaches, and able to predict interventional effects without any controlled experiments. The pipeline can be used to identify potential eubiotic and dysbiotic microbial taxa in a microbiome. We validate our results using synthetic data sets and using results on real data sets that were previously published.

Assessing the Genomic Variability of Gardnerella vaginalis through Comparative Genomic Analyses: Evolutionary and Ecological Implications

ABSTRACT Gardnerella vaginalis is described as a common anaerobic vaginal bacterium whose presence may correlate with vaginal dysbiotic conditions. In the current study, we performed phylogenomic analyses of 72 G. vaginalis genome sequences, revealing noteworthy genome differences underlying a polyphyletic organization of this taxon. Particularly, the genomic survey revealed that this species may actually include nine distinct genotypes (GGtype1 to GGtype9). Furthermore, the observed link between sialidase and phylogenomic grouping provided clues of a connection between virulence potential and the evolutionary history of this microbial taxon. Specifically, based on the outcomes of these in silico analyses, GGtype3, GGtype7, GGtype8, and GGtype9 appear to have virulence potential since they exhibited the sialidase gene in their genomes. Notably, the analysis of 34 publicly available vaginal metagenomic samples allowed us to trace the distribution of the nine G. vaginalis genotypes identified in this study among the human population, highlighting how differences in genetic makeup could be related to specific ecological properties. Furthermore, comparative genomic analyses provided details about the G. vaginalis pan- and core genome contents, including putative genetic elements involved in the adaptation to the ecological niche as well as many putative virulence factors. Among these putative virulence factors, particularly noteworthy genes identified were the gene encoding cholesterol-dependent cytolysin (CDC) toxin vaginolysin and genes related to microbial biofilm formation, iron uptake, adhesion to the vaginal epithelium, as well as macrolide antibiotic resistance. IMPORTANCE The identification of nine different genotypes among members of G. vaginalis allowed us to distinguish an uneven distribution of virulence-associated genetic traits within this taxon and thus suggest the potential occurrence of putative pathogen and commensal G. vaginalis strains. These findings, coupled with metagenomics microbial profiling of human vaginal microbiota, permitted us to get insights into the distribution of the genotypes among the human population, highlighting the presence of different structural communities in terms of G. vaginalis genotypes.

Complete Genome Sequence of Strain AC001, a Novel Cultured Member of the Human Oral Microbiome from the Candidate Phylum Saccharibacteria (TM7)

Strain AC001 is a cultured representative of human microbial taxon 488, a bacterium from the candidate phylum Saccharibacteria. It is an obligate parasite with a genome of <0.9 Mb and grows in coculture with its host, Pseudopropionibacterium propionicum. The complete genome sequence is presented here.

Complete Genome Sequence of Strain PM004, a Novel Cultured Member of the Human Oral Microbiome from the Candidate Phylum Saccharibacteria (TM7)

Strain PM004 is a cultured representative of human microbial taxon 955, a bacterium from the phylum Saccharibacteria. It is an obligate parasite with a genome of <0.9 Mb and can be grown in coculture with its host, Pseudopropionibacterium propionicum. The complete genome sequence is presented here.

Promoting Beneficial and Inhibiting Undesirable Biofilm Formation with Mangrove Extracts

The extracts of two mangrove species, Bruguiera cylindrica and Laguncularia racemosa, have been analyzed at sub-lethal concentrations for their potential to modulate biofilm cycles (i.e., adhesion, maturation, and detachment) on a bacterium, yeast, and filamentous fungus. Methanolic leaf extracts were also characterized, and MS/MS analysis has been used to identify the major compounds. In this study, we showed the following. (i) Adhesion was reduced up to 85.4% in all the models except for E. coli, where adhesion was promoted up to 5.10-fold. (ii) Both the sum and ratio of extracellular polysaccharides and proteins in mature biofilm were increased up to 2.5-fold and 2.6-fold in comparison to the negative control, respectively. Additionally, a shift toward a major production of exopolysaccharides was found coupled with a major production of both intracellular and extracellular reactive oxygen species. (iii) Lastly, detachment was generally promoted. In general, the L. racemosa extract had a higher bioactivity at lower concentrations than the B. cylindrica extract. Overall, our data showed a reduction in cells/conidia adhesion under B. cylindrica and L. racemosa exposure, followed by an increase of exopolysaccharides during biofilm maturation and a variable effect on biofilm dispersal. In conclusion, extracts either inhibited or enhanced biofilm development, and this effect depended on both the microbial taxon and biofilm formation step.

Interpretable and Accurate Prediction Models for Metagenomics Data

ABSTRACTBiomarker discovery using metagenomic data is becoming more prevalent for patient diagnosis, prognosis and risk evaluation. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes. Moreover, they seldom generalize well when learned on small datasets. Here, we introduce an original approach that focuses on three models inspired by microbial ecosystem interactions: the addition, subtraction, and ratio of microbial taxon abundances. While being extremely simple, their performance is surprisingly good and compares to or is better than Random Forest, SVM or Elastic Net. Such models besides being interpretable, allow distilling biological information of the predictive core-variables. Collectively, this approach builds up both reliable and trustworthy diagnostic decisions while agreeing with societal and legal pressure that require explainable AI models in the medical domain.

Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies

Correlation-based analysis of paired microbiome-metabolome datasets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach have not been comprehensively evaluated. To address this challenge, we introduce a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally use a multi-species metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome datasets. We then compare observed taxon-metabolite correlations in these datasets to calculated ground-truth taxonomic contribution values. We find that in simulations of both a model 10-species community and of complex human gut microbiota, correlation-based analysis poorly identifies key contributors, with extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.ImportanceIdentifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step towards understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples, and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation, and then examining these contributions in simulated datasets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome datasets identifies true contributions with very low predictive value, and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

Interspecies Interactions Determine the Impact of the Gut Microbiota on Nutrient Allocation in Drosophila melanogaster

ABSTRACTThe animal gut is perpetually exposed to microorganisms, and this microbiota affects development, nutrient allocation, and immune homeostasis. A major challenge is to understand the contribution of individual microbial species and interactions among species in shaping these microbe-dependent traits. Using theDrosophila melanogastergut microbiota, we tested whether microbe-dependent performance and nutritional traits ofDrosophilaare functionally modular, i.e., whether the impact of each microbial taxon on host traits is independent of the presence of other microbial taxa. Gnotobiotic flies were constructed with one or a set of five of theAcetobacterandLactobacillusspecies which dominate the gut microbiota of conventional flies (Drosophilawith untreated microbiota). Axenic (microbiota-free) flies exhibited prolonged development time and elevated glucose and triglyceride contents. The low glucose content of conventional flies was recapitulated in gnotobioticDrosophilaflies colonized with any of the 5 bacterial taxa tested. In contrast, the development rates and triglyceride levels in monocolonized flies varied depending on the taxon present:Acetobacterspecies supported the largest reductions, while mostLactobacillusspecies had no effect. Only flies with bothAcetobacterandLactobacillushad triglyceride contents restored to the level in conventional flies. This could be attributed to two processes:Lactobacillus-mediated promotion ofAcetobacterabundance in the fly and a significant negative correlation between fly triglyceride content andAcetobacterabundance. We conclude that the microbial basis of host traits varies in both specificity and modularity; microbe-mediated reduction in glucose is relatively nonspecific and modular, while triglyceride content is influenced by interactions among microbes.