Engineering agricultural soil microbiomes and predicting plant phenotypes

Plant growth-promoting rhizobacteria (PGPR) can improve crop yields, nutrient use efficiency, plant tolerance to stressors, and confer benefits to future generations of crops grown in the same soil. Unlocking the potential of microbial communities in the rhizosphere and endosphere is therefore of great interest for sustainable agriculture advancements. Before plant microbiomes can be engineered to confer desirable phenotypic effects on their plant hosts, a deeper understanding of the interacting factors influencing rhizosphere community structure and function is needed. Dealing with this complexity is becoming more feasible using computational approaches. In this review, we discuss recent advances at the intersection of experimental and computational strategies for the investigation of plant-microbiome interactions and the engineering of desirable soil microbiomes.

BiG-MAP: an Automated Pipeline To Profile Metabolic Gene Cluster Abundance and Expression in Microbiomes

Microbial gene clusters encoding the biosynthesis of primary and secondary metabolites play key roles in shaping microbial ecosystems and driving microbiome-associated phenotypes. Although effective approaches exist to evaluate the metabolic potential of such bacteria through identification of these metabolic gene clusters in their genomes, no automated pipelines exist to profile the abundance and expression levels of such gene clusters in microbiome samples to generate hypotheses about their functional roles, and to find associations with phenotypes of interest. Here, we describe BiG-MAP, a bioinformatic tool to profile abundance and expression levels of gene clusters across metagenomic and metatranscriptomic data and evaluate their differential abundance and expression under different conditions. To illustrate its usefulness, we analyzed 96 metagenomic samples from healthy and caries-associated human oral microbiome samples and identified 252 gene clusters, including unreported ones, that were significantly more abundant in either phenotype. Among them, we found the muc operon, a gene cluster known to be associated with tooth decay. Additionally, we found a putative reuterin biosynthetic gene cluster from a Streptococcus strain to be enriched but not exclusively found in healthy samples; metabolomic data from the same samples showed masses with fragmentation patterns consistent with (poly)acrolein, which is known to spontaneously form from the products of the reuterin pathway and has been previously shown to inhibit pathogenic Streptococcus mutans strains. Thus, we show how BiG-MAP can be used to generate new hypotheses on potential drivers of microbiome-associated phenotypes and prioritize the experimental characterization of relevant gene clusters that may mediate them. IMPORTANCE Microbes play an increasingly recognized role in determining host-associated phenotypes by producing small molecules that interact with other microorganisms or host cells. The production of these molecules is often encoded in syntenic genomic regions, also known as gene clusters. With the increasing numbers of (multi)omics data sets that can help in understanding complex ecosystems at a much deeper level, there is a need to create tools that can automate the process of analyzing these gene clusters across omics data sets. This report presents a new software tool called BiG-MAP, which allows assessing gene cluster abundance and expression in microbiome samples using metagenomic and metatranscriptomic data. Here, we describe the tool and its functionalities, as well as its validation using a mock community. Finally, using an oral microbiome data set, we show how it can be used to generate hypotheses regarding the functional roles of gene clusters in mediating host phenotypes.