Emergence of collective territorial defense in bacterial communities: horizontal gene transfer can stabilize microbiomes

Role of horizontal gene transfer and cooperation in rhizosphere microbiome assembly

Microbes employ a variety of mechanisms, encompassing chemical signaling (e.g., quorum-sensing molecules) and genetic processes like horizontal gene transfer (HGT), to engage in interactions. HGT, in particular, holds a pivotal role as it facilitates the generation of metabolic diversity, thus directly or indirectly influencing microorganisms' interactions and functioning within their habitat. In this study, we investigate the correlations between enhanced metabolic diversity through HGT and cooperative behavior in the rhizosphere. Despite the potential drawbacks of cooperative behavior, which renders it susceptible to exploitation by cheaters based on evolutionary theory, HGT emerges as a mitigating factor. It serves as a valuable and adaptive tool for survival in competitive environments, notably the rhizosphere. By initiating a comprehensive discussion on these processes combined, we anticipate achieving a profound understanding of the rhizosphere microbiome, ultimately enhancing soil microbiology management and the exploitation of this ecological niche.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Differential signal sensitivities can contribute to the stability of multispecies bacterial communities

Background

Bacterial species present in multispecies microbial communities often react to the same chemical signal but at vastly different concentrations. The existence of different response thresholds with respect to the same signal molecule has been well documented in quorum sensing which is one of the best studied inter-cellular signalling mechanisms in bacteria. The biological significance of this phenomenon is still poorly understood, and cannot be easily studied in nature or in laboratory models. The aim of this study is to establish the role of differential signal response thresholds in stabilizing microbial communities.

Results

We tested binary competition scenarios using an agent-based model in which competing bacteria had different response levels with respect to signals, cooperation factors or both, respectively. While in previous scenarios fitter species outcompete slower growing competitors, we found that stable equilibria could form if the fitter species responded to a higher chemical concentration level than the slower growing competitor. We also found that species secreting antibiotic could form a stable community with other competing species if antibiotic production started at higher response thresholds.

Conclusions

Microbial communities in nature rely on the stable coexistence of species that necessarily differ in their fitness. We found that differential response thresholds provide a simple and elegant way for keeping slower growing species within the community. High response thresholds can be considered as self-restraint of the fitter species that allows metabolically useful but slower growing species to remain within a community, and thereby the metabolic repertoire of the community will be maintained.

Reviewers

This article was reviewed by Michael Gromiha, Sebastian Maurer-Stroh, István Simon and L. Aravind.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-017-0192-3) contains supplementary material, which is available to authorized users.

Encyclopedia of bacterial gene circuits whose presence or absence correlate with pathogenicity--a large-scale system analysis of decoded bacterial genomes

BACKGROUND

Bacterial infections comprise a global health challenge as the incidences of antibiotic resistance increase. Pathogenic potential of bacteria has been shown to be context dependent, varying in response to environment and even within the strains of the same genus.

RESULTS

We used the KEGG repository and extensive literature searches to identify among the 2527 bacterial genomes in the literature those implicated as pathogenic to the host, including those which show pathogenicity in a context dependent manner. Using data on the gene contents of these genomes, we identified sets of genes highly abundant in pathogenic but relatively absent in commensal strains and vice versa. In addition, we carried out genome comparison within a genus for the seventeen largest genera in our genome collection. We projected the resultant lists of ortholog genes onto KEGG bacterial pathways to identify clusters and circuits, which can be linked to either pathogenicity or synergy. Gene circuits relatively abundant in nonpathogenic bacteria often mediated biosynthesis of antibiotics. Other synergy-linked circuits reduced drug-induced toxicity. Pathogen-abundant gene circuits included modules in one-carbon folate, two-component system, type-3 secretion system, and peptidoglycan biosynthesis. Antibiotics-resistant bacterial strains possessed genes modulating phagocytosis, vesicle trafficking, cytoskeletal reorganization, and regulation of the inflammatory response. Our study also identified bacterial genera containing a circuit, elements of which were previously linked to Alzheimer's disease.

CONCLUSIONS

Present study produces for the first time, a signature, in the form of a robust list of gene circuitry whose presence or absence could potentially define the pathogenicity of a microbiome. Extensive literature search substantiated a bulk majority of the commensal and pathogenic circuitry in our predicted list. Scanning microbiome libraries for these circuitry motifs will provide further insights into the complex and context dependent pathogenicity of bacteria.