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The ecological forces that govern the assembly and stability of the human gut microbiota remain unresolved. We developed a generalizable model‐guided framework to predict higher‐dimensional consortia from time‐resolved measurements of lower‐order assemblages. This method was employed to decipher microbial interactions in a diverse human gut microbiome synthetic community. We show that pairwise interactions are major drivers of multi‐species community dynamics, as opposed to higher‐order interactions. The inferred ecological network exhibits a high proportion of negative and frequent positive interactions. Ecological drivers and responsive recipient species were discovered in the network. Our model demonstrated that a prevalent positive and negative interaction topology enables robust coexistence by implementing a negative feedback loop that balances disparities in monospecies fitness levels. We show that negative interactions could generate history‐dependent responses of initial species proportions that frequently do not originate from bistability. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis of microbial interactions. In sum, these methods defined the ecological roles of major human‐associated intestinal species and illuminated design principles of microbial communities. Synopsis Analysis of microbial interactions in a synthetic human gut microbiome community shows that pairwise microbial interactions are major drivers of multi‐species community dynamics. The study reveals ecological drivers, metabolite hub species and ecologically sensitive organisms in the network. A data‐driven pipeline is used to construct a predictive dynamic model of a diverse anaerobic human gut microbiome community. Design principles of stable coexistence and history‐dependence are elucidated. Ecological roles and metabolite profiles are analyzed for each organism. The study highlights challenges in using phylogenetic and exo‐metabolomic “signals” to predict microbial interactions and community functions. Graphical Abstract Analysis of microbial interactions in a synthetic human gut microbiome community shows that pairwise microbial interactions are major drivers of multi‐species community dynamics. The study reveals ecological drivers, metabolite hub species and ecologically sensitive organisms in the network.

Metagenomic sequencing provides a window into microbial community structure and metabolic potential; however, linking these data to exogenous metabolites that microorganisms process and produce (the exometabolome) remains challenging. Previously, we observed strong exometabolite niche partitioning among bacterial isolates from biological soil crust (biocrust). Here we examine native biocrust to determine if these patterns are reproduced in the environment. Overall, most soil metabolites display the expected relationship (positive or negative correlation) with four dominant bacteria following a wetting event and across biocrust developmental stages. For metabolites that were previously found to be consumed by an isolate, 70% are negatively correlated with the abundance of the isolate’s closest matching environmental relative in situ, whereas for released metabolites, 67% were positively correlated. Our results demonstrate that metabolite profiling, shotgun sequencing and exometabolomics may be successfully integrated to functionally link microbial community structure with environmental chemistry in biocrust. Metagenomic sequencing provides a window into microbial community structure and metabolic potential. Here, Swenson et al. integrate metabolomics and shotgun sequencing to functionally link microbial community structure with environmental chemistry in biological soil crust (biocrust).

Like all higher organisms, plants have evolved in the context of a microbial world, shaping both their evolution and their contemporary ecology. Interactions between plant roots and soil microorganisms are critical for plant fitness in natural environments. Given this co-evolution and the pivotal importance of plant–microbial interactions, it has been hypothesized, and a growing body of literature suggests, that plants may regulate the composition of their rhizosphere to promote the growth of microorganisms that improve plant fitness in a given ecosystem. Here, using a combination of comparative genomics and exometabolomics, we show that pre-programmed developmental processes in plants ( Avena   barbata ) result in consistent patterns in the chemical composition of root exudates. This chemical succession in the rhizosphere interacts with microbial metabolite substrate preferences that are predictable from genome sequences. Specifically, we observed a preference by rhizosphere bacteria for consumption of aromatic organic acids exuded by plants (nicotinic, shikimic, salicylic, cinnamic and indole-3-acetic). The combination of these plant exudation traits and microbial substrate uptake traits interact to yield the patterns of microbial community assembly observed in the rhizosphere of an annual grass. This discovery provides a mechanistic underpinning for the process of rhizosphere microbial community assembly and provides an attractive direction for the manipulation of the rhizosphere microbiome for beneficial outcomes. Using comparative genomics and exometabolomics, the authors characterize the chemical composition of plant root exudates and show that this chemical succession is a likely driver of microbial community assembly in the rhizosphere.

Soils are arguably the most microbially diverse ecosystems. Physicochemical properties have been associated with the maintenance of this diversity. Yet, the role of microbial substrate specialization is largely unexplored since substrate utilization studies have focused on simple substrates, not the complex mixtures representative of the soil environment. Here we examine the exometabolite composition of desert biological soil crusts (biocrusts) and the substrate preferences of seven biocrust isolates. The biocrust's main primary producer releases a diverse array of metabolites, and isolates of physically associated taxa use unique subsets of the complex metabolite pool. Individual isolates use only 13−26% of available metabolites, with only 2 out of 470 used by all and 40% not used by any. An extension of this approach to a mesophilic soil environment also reveals high levels of microbial substrate specialization. These results suggest that exometabolite niche partitioning may be an important factor in the maintenance of microbial diversity. Production and consumption of metabolites by soil microorganisms are important for nutrient cycling and maintenance of microbial diversity. Here, Baran et al . study metabolite uptake and release by desert soil microorganisms, showing that coexisting microbes can have divergent substrate preferences.

Anaerobic fungi (class Neocallimastigomycetes) thrive as low-abundance members of the herbivore digestive tract. The genomes of anaerobic gut fungi are poorly characterized and have not been extensively mined for the biosynthetic enzymes of natural products such as antibiotics. Here, we investigate the potential of anaerobic gut fungi to synthesize natural products that could regulate membership within the gut microbiome. Complementary ’omics’ approaches were combined to catalog the natural products of anaerobic gut fungi from four different representative species: Anaeromyces robustus (A. robustus), Caecomyces churrovis (C. churrovis), Neocallimastix californiae (N. californiae), and Piromyces finnis (P. finnis). In total, 146 genes were identified that encode biosynthetic enzymes for diverse types of natural products, including nonribosomal peptide synthetases and polyketide synthases. In addition, N. californiae and C. churrovis genomes encoded seven putative bacteriocins, a class of antimicrobial peptides typically produced by bacteria. During standard laboratory growth on plant biomass or soluble substrates, 26% of total core biosynthetic genes in all four strains were transcribed. Across all four fungal strains, 30% of total biosynthetic gene products were detected via proteomics when grown on cellobiose. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) characterization of fungal supernatants detected 72 likely natural products from A. robustus alone. A compound produced by all four strains of anaerobic fungi was putatively identified as the polyketide-related styrylpyrone baumin. Molecular networking quantified similarities between tandem mass spectrometry (MS/MS) spectra among these fungi, enabling three groups of natural products to be identified that are unique to anaerobic fungi. Overall, these results support the finding that anaerobic gut fungi synthesize natural products, which could be harnessed as a source of antimicrobials, therapeutics, and other bioactive compounds.

Plants deploy both primary and species-specific, specialized metabolites to communicate with other organisms and adapt to environmental challenges, including interactions with soil-dwelling microbial communities. However, the role of specialized metabolites in modulating plant-microbiome interactions often remains elusive. In this study, we report that maize ( Zea mays ) diterpenoid metabolites with known antifungal bioactivities also influence rhizosphere bacterial communities. Metabolite profiling showed that dolabralexins, antibiotic diterpenoids that are highly abundant in roots of some maize varieties, can be exuded from the roots. Comparative 16S rRNA gene sequencing determined the bacterial community composition of the maize mutant Zman2 ( anther ear 2 ), which is deficient in dolabralexins and closely related bioactive kauralexin diterpenoids. The Zman2 rhizosphere microbiome differed significantly from the wild-type sibling with the most significant changes observed for Alphaproteobacteria of the order Sphingomonadales. Metabolomics analyses support that these differences are attributed to the diterpenoid deficiency of the Zman2 mutant, rather than other large-scale metabolome alterations. Together, these findings support physiological functions of maize diterpenoids beyond known chemical defenses, including the assembly of the rhizosphere microbiome.

Light and nutrients are critical regulators of photosynthesis and metabolism in plants and algae. Many algae have the metabolic flexibility to grow photoautotrophically, heterotrophically, or mixotrophically. Here, we describe reversible Glc-dependent repression/activation of oxygenic photosynthesis in the unicellular green alga Chromochloris zofingiensis. We observed rapid and reversible changes in photosynthesis, in the photosynthetic apparatus, in thylakoid ultrastructure, and in energy stores including lipids and starch. Following Glc addition in the light, C. zofingiensis shuts off photosynthesis within days and accumulates large amounts of commercially relevant bioproducts, including triacylglycerols and the high-value nutraceutical ketocarotenoid astaxanthin, while increasing culture biomass. RNA sequencing reveals reversible changes in the transcriptome that form the basis of this metabolic regulation. Functional enrichment analyses show that Glc represses photosynthetic pathways while ketocarotenoid biosynthesis and heterotrophic carbon metabolism are upregulated. Because sugars play fundamental regulatory roles in gene expression, physiology, metabolism, and growth in both plants and animals, we have developed a simple algal model system to investigate conserved eukaryotic sugar responses as well as mechanisms of thylakoid breakdown and biogenesis in chloroplasts. Understanding regulation of photosynthesis and metabolism in algae could enable bioengineering to reroute metabolism toward beneficial bioproducts for energy, food, pharmaceuticals, and human health.

Interrelating small molecules according to their aligned fragmentation spectra is central to tandem mass spectrometry-based untargeted metabolomics. Current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences and therefore often fail to have their fragment ions aligned. Here we align fragmentation spectra with both statistical significance and allowance for multiple chemical differences using Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE). SIMILE yields spectral alignment inferred structural connections in molecular networks that are not found with cosine-based scoring algorithms. In addition, it is now possible to rank spectral alignments based on p-values in the exploration of structural relationships between compounds and enhance the chemical connectivity that can be obtained with molecular networking. Interrelating metabolites by their fragmentation spectra is central to metabolomics. Here the authors align fragmentation spectra with both statistical significance and allowance for multiple chemical differences using Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE).

Metabolomics has a long history of using cosine similarity to match experimental tandem mass spectra to databases for compound identification. Here we introduce the Blur-and-Link (BLINK) approach for scoring cosine similarity. By bypassing fragment alignment and simultaneously scoring all pairs of spectra using sparse matrix operations, BLINK is over 3000 times faster than MatchMS, a widely used loop-based alignment and scoring implementation. Using a similarity cutoff of 0.7, BLINK and MatchMS had practically equivalent identification agreement, and greater than 99% of their scores and matching ion counts were identical. This performance improvement can enable calculations to be performed that would typically be limited by time and available computational resources.

Public untargeted metabolomics data is a growing resource for metabolite and phenotype discovery; however, accessing and utilizing these data across repositories pose significant challenges. Therefore, here we develop pan-repository universal identifiers and harmonized cross-repository metadata. This ecosystem facilitates discovery by integrating diverse data sources from public repositories including MetaboLights, Metabolomics Workbench, and GNPS/MassIVE. Our approach simplified data handling and unlocks previously inaccessible reanalysis workflows, fostering unmatched research opportunities. Public untargeted metabolomics data hold great promise for discovery but are difficult to access across repositories. Here, the authors develop universal identifiers and harmonized metadata to integrate major databases, enabling streamlined analysis and expanded research possibilities.