How can computation advance microbiome research?

Microbial metabolites and immune regulation: New targets for major depressive disorder

Treatments for depression and mood disorders have been singularly targeted at the brain without consideration for the context of the rest of the body. As evidence mounts for a role of autoimmunity and inflammation as risk factors and contributors to mood disorders, attention has shifted to one of the primary immunoregulatory organs in the body--the gut. Gut-brain interactions have been established and correlative links between the microbiome and mood have been examined, but with novel tools and a base of understanding, focus shifts to the mechanisms of these communications. In this review, we examine how the small molecules produced by metabolic processes of bacteria in the gut influence the host immune system. The gaps in knowledge discussed here include the under characterized diversity of small molecules crossing the gut walls, as well as the need to close the logical loop connecting the microbiome to the immune system, and the immune system to behavior and mood. As we move past the dawn of this field, more precise understanding using novel tools and techniques will help move toward a more informed and systematic process for clinically evaluating the efficacy of probiotics and bacterially derived compounds as antidepressants and mood regulators.

•

Metabolism of the gut microbiota results in diverse molecules available to the host.

•

Small molecules influence inflammation which modulates behavior and mood.

•

Novel targets for mood stabilizers may be produced by the gut microbiota.

Social dynamics modeling of chrono-nutrition

Gut microbiota and human relationships are strictly connected to each other. What we eat reflects our body-mind connection and synchronizes with people around us. However, how this impacts on gut microbiota and, conversely, how gut bacteria influence our dietary behaviors has not been explored yet. To quantify the complex dynamics of this interplay between gut and human behaviors we explore the "gut-human behavior axis" and its evolutionary dynamics in a real-world scenario represented by the social multiplex network. We consider a dual type of similarity, homophily and gut similarity, other than psychological and unconscious biases. We analyze the dynamics of social and gut microbial communities, quantifying the impact of human behaviors on diets and gut microbial composition and, backwards, through a control mechanism. Meal timing mechanisms and "chrono-nutrition" play a crucial role in feeding behaviors, along with the quality and quantity of food intake. Considering a population of shift workers, we explore the dynamic interplay between their eating behaviors and gut microbiota, modeling the social dynamics of chrono-nutrition in a multiplex network. Our findings allow us to quantify the relation between human behaviors and gut microbiota through the methodological introduction of gut metabolic modeling and statistical estimators, able to capture their dynamic interplay. Moreover, we find that the timing of gut microbial communities is slower than social interactions and shift-working, and the impact of shift-working on the dynamics of chrono-nutrition is a fluctuation of strategies with a major propensity for defection (e.g. high-fat meals). A deeper understanding of the relation between gut microbiota and the dietary behavioral patterns, by embedding also the related social aspects, allows improving the overall knowledge about metabolic models and their implications for human health, opening the possibility to design promising social therapeutic dietary interventions.

IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences

BACKGROUND

Microbiome studies often involve sequencing a marker gene to identify the microorganisms in samples of interest. Sequence classification is a critical component of this process, whereby sequences are assigned to a reference taxonomy containing known sequence representatives of many microbial groups. Previous studies have shown that existing classification programs often assign sequences to reference groups even if they belong to novel taxonomic groups that are absent from the reference taxonomy. This high rate of "over classification" is particularly detrimental in microbiome studies because reference taxonomies are far from comprehensive.

RESULTS

Here, we introduce IDTAXA, a novel approach to taxonomic classification that employs principles from machine learning to reduce over classification errors. Using multiple reference taxonomies, we demonstrate that IDTAXA has higher accuracy than popular classifiers such as BLAST, MAPSeq, QIIME, SINTAX, SPINGO, and the RDP Classifier. Similarly, IDTAXA yields far fewer over classifications on Illumina mock microbial community data when the expected taxa are absent from the training set. Furthermore, IDTAXA offers many practical advantages over other classifiers, such as maintaining low error rates across varying input sequence lengths and withholding classifications from input sequences composed of random nucleotides or repeats.

CONCLUSIONS

IDTAXA's classifications may lead to different conclusions in microbiome studies because of the substantially reduced number of taxa that are incorrectly identified through over classification. Although misclassification error is relatively minor, we believe that many remaining misclassifications are likely caused by errors in the reference taxonomy. We describe how IDTAXA is able to identify many putative mislabeling errors in reference taxonomies, enabling training sets to be automatically corrected by eliminating spurious sequences. IDTAXA is part of the DECIPHER package for the R programming language, available through the Bioconductor repository or accessible online ( http://DECIPHER.codes ).