Global epistasis and the emergence of function in microbial consortia.

The many functions of microbial communities emerge from a complex web of interactions between organisms and their environment. This poses a significant obstacle to engineering microbial consortia, hindering our ability to harness the potential of microorganisms for biotechnological applications. In this study, we demonstrate that the collective effect of ecological interactions between microbes in a community can be captured by simple statistical models that predict how adding a new species to a community will affect its function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise interactions between community members. Our results illuminate an unexplored path to quantitatively predicting the function of microbial consortia from their composition, paving the way to optimizing desirable community properties and bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.

Genomic structure predicts metabolite dynamics in microbial communities.

The metabolic activities of microbial communities play a defining role in the evolution and persistence of life on Earth, driving redox reactions that give rise to global biogeochemical cycles. Community metabolism emerges from a hierarchy of processes, including gene expression, ecological interactions, and environmental factors. In wild communities, gene content is correlated with environmental context, but predicting metabolite dynamics from genomes remains elusive. Here, we show, for the process of denitrification, that metabolite dynamics of a community are predictable from the genes each member of the community possesses. A simple linear regression reveals a sparse and generalizable mapping from gene content to metabolite dynamics for genomically diverse bacteria. A consumer-resource model correctly predicts community metabolite dynamics from single-strain phenotypes. Our results demonstrate that the conserved impacts of metabolic genes can predict community metabolite dynamics, enabling the prediction of metabolite dynamics from metagenomes, designing denitrifying communities, and discovering how genome evolution impacts metabolism.

Personalized Mapping of Drug Metabolism by the Human Gut Microbiome.

The human gut microbiome harbors hundreds of bacterial species with diverse biochemical capabilities. Dozens of drugs have been shown to be metabolized by single isolates from the gut microbiome, but the extent of this phenomenon is rarely explored in the context of microbial communities. Here, we develop a quantitative experimental framework for mapping the ability of the human gut microbiome to metabolize small molecule drugs: Microbiome-Derived Metabolism (MDM)-Screen. Included are a batch culturing system for sustained growth of subject-specific gut microbial communities, an ex vivo drug metabolism screen, and targeted and untargeted functional metagenomic screens to identify microbiome-encoded genes responsible for specific metabolic events. Our framework identifies novel drug-microbiome interactions that vary between individuals and demonstrates how the gut microbiome might be used in drug development and personalized medicine.

Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure.

Understanding the assembly of multispecies microbial communities represents a significant challenge in ecology and has wide applications in agriculture, wastewater treatment, and human healthcare domains. Traditionally, studies on the microbial community assembly focused on analyzing pairwise relationships among species; however, neglecting higher-order interactions, i.e., the change of pairwise relationships in the community context, may lead to substantial deviation from reality. Herein, we have proposed a simple framework that incorporates higher-order interactions into a bottom-up prediction of the microbial community assembly and examined its accuracy using a seven-member synthetic bacterial community on a host plant, duckweed. Although the synthetic community exhibited emergent properties that cannot be predicted from pairwise coculturing results, our results demonstrated that incorporating information from three-member combinations allows the acceptable prediction of the community structure and actual interaction forces within it. This reflects that the occurrence of higher-order effects follows consistent patterns, which can be predicted even from trio combinations, the smallest unit of higher-order interactions. These results highlight the possibility of predicting, explaining, and understanding the microbial community structure from the bottom-up by learning interspecies interactions from simple beyond-pairwise combinations.

Microbiome Metabolome Integration Platform (MMIP): a web-based platform for microbiome and metabolome data integration and feature identification.

A microbial community maintains its ecological dynamics via metabolite crosstalk. Hence, knowledge of the metabolome, alongside its populace, would help us understand the functionality of a community and also predict how it will change in atypical conditions. Methods that employ low-cost metagenomic sequencing data can predict the metabolic potential of a community, that is, its ability to produce or utilize specific metabolites. These, in turn, can potentially serve as markers of biochemical pathways that are associated with different communities. We developed MMIP (Microbiome Metabolome Integration Platform), a web-based analytical and predictive tool that can be used to compare the taxonomic content, diversity variation and the metabolic potential between two sets of microbial communities from targeted amplicon sequencing data. MMIP is capable of highlighting statistically significant taxonomic, enzymatic and metabolic attributes as well as learning-based features associated with one group in comparison with another. Furthermore, MMIP can predict linkages among species or groups of microbes in the community, specific enzyme profiles, compounds or metabolites associated with such a group of organisms. With MMIP, we aim to provide a user-friendly, online web server for performing key microbiome-associated analyses of targeted amplicon sequencing data, predicting metabolite signature, and using learning-based linkage analysis, without the need for initial metabolomic analysis, and thereby helping in hypothesis generation.

The current status and trends of DNA extraction.

DNA extraction, playing an irreplaceable role in molecular biology as it is an essential step prior to various downstream biological analyses. Thus, the accuracy and reliability of downstream research outcomes depend largely on upstream DNA extraction methodology. However, with the advancement of downstream DNA detection techniques, the development of corresponding DNA extraction methods is lagging behind. The most innovative DNA extraction techniques are silica- or magnetic-based. Recent studies have demonstrated that plant fiber-based adsorbents (PF-BAs) have stronger DNA capturing ability than classic materials. Moreover, magnetic ionic liquid (MIL)-based DNA extraction has gathered attention lately, and extrachromosomal circular DNA (eccDNA), cell-free DNA (cfDNA), and microbial community DNA are current research hotspots. These require specific extraction methods, along with constant improvements in the way they are used. This review discusses the significance as well as the direction of innovation of DNA extraction methods to try to provide valuable references including current status and trends for DNA extraction.

Geological activity shapes the microbiome in deep-subsurface aquifers by advection.

Subsurface environments host diverse microorganisms in fluid-filled fractures; however, little is known about how geological and hydrological processes shape the subterranean biosphere. Here, we sampled three flowing boreholes weekly for 10 mo in a 1478-m-deep fractured rock aquifer to study the role of fracture activity (defined as seismically or aseismically induced fracture aperture change) and advection on fluid-associated microbial community composition. We found that despite a largely stable deep-subsurface fluid microbiome, drastic community-level shifts occurred after events signifying physical changes in the permeable fracture network. The community-level shifts include the emergence of microbial families from undetected to over 50% relative abundance, as well as the replacement of the community in one borehole by the earlier community from a different borehole. Null-model analysis indicates that the observed spatial and temporal community turnover was primarily driven by stochastic processes (as opposed to deterministic processes). We, therefore, conclude that the observed community-level shifts resulted from the physical transport of distinct microbial communities from other fracture(s) that outpaced environmental selection. Given that geological activity is a major cause of fracture activity and that geological activity is ubiquitous across space and time on Earth, our findings suggest that advection induced by geological activity is a general mechanism shaping the microbial biogeography and diversity in deep-subsurface habitats across the globe.

Stabilizing microbial communities by looped mass transfer.

Building and changing a microbiome at will and maintaining it over hundreds of generations has so far proven challenging. Despite best efforts, complex microbiomes appear to be susceptible to large stochastic fluctuations. Current capabilities to assemble and control stable complex microbiomes are limited. Here, we propose a looped mass transfer design that stabilizes microbiomes over long periods of time. Five local microbiomes were continuously grown in parallel for over 114 generations and connected by a loop to a regional pool. Mass transfer rates were altered and microbiome dynamics were monitored using quantitative high-throughput flow cytometry and taxonomic sequencing of whole communities and sorted subcommunities. Increased mass transfer rates reduced local and temporal variation in microbiome assembly, did not affect functions, and overcame stochasticity, with all microbiomes exhibiting high constancy and increasing resistance. Mass transfer synchronized the structures of the five local microbiomes and nestedness of certain cell types was eminent. Mass transfer increased cell number and thus decreased net growth rates µ'. Subsets of cells that did not show net growth µ'SCx were rescued by the regional pool R and thus remained part of the microbiome. The loop in mass transfer ensured the survival of cells that would otherwise go extinct, even if they did not grow in all local microbiomes or grew more slowly than the actual dilution rate D would allow. The rescue effect, known from metacommunity theory, was the main stabilizing mechanism leading to synchrony and survival of subcommunities, despite differences in cell physiological properties, including growth rates.

Characterizing a stable five-species microbial community for use in experimental evolution and ecology.

Model microbial communities are regularly used to test ecological and evolutionary theory as they are easy to manipulate and have fast generation times, allowing for large-scale, high-throughput experiments. A key assumption for most model microbial communities is that they stably coexist, but this is rarely tested experimentally. Here we report the (dis)assembly of a five-species microbial community from a metacommunity of soil microbes that can be used for future experiments. Using reciprocal invasion-from-rare experiments we show that all species can coexist and we demonstrate that the community is stable for a long time (~600 generations). Crucially for future work, we show that each species can be identified by their plate morphologies, even after >1 year in co-culture. We characterise pairwise species interactions and produce high-quality reference genomes for each species. This stable five-species community can be used to test key questions in microbial ecology and evolution.

Analysis of endophytic bacterial diversity in seeds of different genotypes of cotton and the suppression of Verticillium wilt pathogen infection by a synthetic microbial community.

BACKGROUND: In agricultural production, fungal diseases significantly impact the yield and quality of cotton (Gossypium spp.) with Verticillium wilt posing a particularly severe threat. RESULTS: This study is focused on investigating the effectiveness of endophytic microbial communities present in the seeds of disease-resistant cotton genotypes in the control of cotton Verticillium wilt. The technique of 16S ribosomal RNA (16S rRNA) amplicon sequencing identified a significant enrichment of the Bacillus genus in the resistant genotype Xinluzao 78, which differed from the endophytic bacterial community structure in the susceptible genotype Xinluzao 63. Specific enriched strains were isolated and screened from the seeds of Xinluzao 78 to further explore the biological functions of seed endophytes. A synthetic microbial community (SynCom) was constructed using the broken-rod model, and seeds of the susceptible genotype Xinluzao 63 in this community that had been soaked with the SynCom were found to significantly control the occurrence of Verticillium wilt and regulate the growth of cotton plants. Antibiotic screening techniques were used to preliminarily identify the colonization of strains in the community. These techniques revealed that the strains can colonize plant tissues and occupy ecological niches in cotton tissues through a priority effect, which prevents infection by pathogens. CONCLUSION: This study highlights the key role of seed endophytes in driving plant disease defense and provides a theoretical basis for the future application of SynComs in agriculture.

Effects of temperature and lactic acid Bacteria additives on the quality and microbial community of wilted alfalfa silage.

This study investigated the influence of different temperatures (35℃ High temperature and average indoor ambient temperature of 25℃) and lactic acid bacterial additives (Lactiplantibacillus plantarym, Lentilactobacillus buchneri, or a combination of Lactiplantibacillus plantarym and Lentilactobacillus buchneri) on the chemical composition, fermentation quality, and microbial community of alfalfa silage feed. After a 60-day ensiling period, a significant interaction between temperature and additives was observed, affecting the dry matter (DM), crude protein (CP), acid detergent fiber (ADF), and neutral detergent fiber (NDF) of the silage feed (p < 0.05). Temperature had a highly significant impact on the pH value of the silage feed (p < 0.0001). However, the effect of temperature on lactic acid, acetic acid, propionic acid, and butyric acid was not significant (p > 0.05), while the inoculation of additives had a significant effect on lactic acid, acetic acid, and butyric acid (p > 0.05). As for the dynamic changes of microbial community after silage, the addition of three kinds of bacteria increased the abundance of lactobacillus. Among all treatment groups, the treatment group using complex bacteria had the best fermentation effect, indicating that the effect of complex lactic acid bacteria was better than that of single bacteria in high temperature fermentation. In summary, this study explained the effects of different temperatures and lactic acid bacterial additives on alfalfa fermentation quality and microbial community, and improved our understanding of the mechanism of alfalfa related silage at high temperatures.

Effects of a co-bacterial agent on the growth, disease control, and quality of ginseng based on rhizosphere microbial diversity.

BACKGROUND: The ginseng endophyte Paenibacillus polymyxa Pp-7250 (Pp-7250) has multifaceted roles such as preventing ginseng diseases, promoting growth, increasing ginsenoside accumulation, and degrading pesticide residues, however, these effects still have room for improvements. Composite fungicides are an effective means to improve the biocontrol effect of fungicides, but the effect of Pp-7250 in combination with its symbiotic bacteria on ginseng needs to be further investigated, and its mechanism of action has not been elucidated. In this study, a series of experiments was conducted to elucidate the effect of Paenibacillus polymyxa and Bacillus cereus co-bacterial agent on the yield and quality of understory ginseng, and to investigate their mechanism of action. RESULTS: The results indicated that P. polymyxa and B. cereus co-bacterial agent (PB) treatment improved ginseng yield, ginsenoside accumulation, disease prevention, and pesticide degradation. The mechanism is that PB treatment increased the abundance of beneficial microorganisms, including Rhodanobacter, Pseudolabrys, Gemmatimonas, Bacillus, Paenibacillus, Cortinarius, Russula, Paecilomyces, and Trechispora, and decreased the abundance of pathogenic microorganisms, including Ellin6067, Acidibacter, Fusarium, Tetracladium, Alternaria, and Ilyonectria in ginseng rhizosphere soil. PB co-bacterial agents enhanced the function of microbial metabolic pathways, biosynthesis of secondary metabolites, biosynthesis of antibiotics, biosynthesis of amino acids, carbon fixation pathways in prokaryotes, DNA replication, and terpenoid backbone biosynthesis, and decreased the function of microbial plant pathogens and animal pathogens. CONCLUSION: The combination of P. polymyxa and B. cereus may be a potential biocontrol agent to promote the resistance of ginseng to disease and improve the yield, quality, and pesticide degradation.

EXPERT: transfer learning-enabled context-aware microbial community classification.

Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.

Revealing the dynamics and mechanisms of bacterial interactions in cheese production with metabolic modelling.

Cheese taste and flavour properties result from complex metabolic processes occurring in microbial communities. A deeper understanding of such mechanisms makes it possible to improve both industrial production processes and end-product quality through the design of microbial consortia. In this work, we caracterise the metabolism of a three-species community consisting of Lactococcus lactis, Lactobacillus plantarum and Propionibacterium freudenreichii during a seven-week cheese production process. Using genome-scale metabolic models and omics data integration, we modeled and calibrated individual dynamics using monoculture experiments, and coupled these models to capture the metabolism of the community. This model accurately predicts the dynamics of the community, enlightening the contribution of each microbial species to organoleptic compound production. Further metabolic exploration revealed additional possible interactions between the bacterial species. This work provides a methodological framework for the prediction of community-wide metabolism and highlights the added value of dynamic metabolic modeling for the comprehension of fermented food processes.

Difference in microbial community structure along a gradient of crater altitude: insights from the Nushan volcano.

The variation in the soil microbial community along the altitude gradient has been widely documented. However, the structure and function of the microbial communities distributed along the altitude gradient in the crater still need to be determined. We gathered soil specimens from different elevations within the Nushan volcano crater to bridge this knowledge gap. We investigated the microbial communities of bacteria and fungi in the soil. It is noteworthy that the microbial alpha diversity peaks in the middle of the crater. However, network analysis shows that bacterial (nodes 760 vs 613 vs 601) and fungal (nodes 328 vs 224 vs 400) communities are most stable at the bottom and top of the crater, respectively. Furthermore, the soil microbial network exhibited a decline, followed by an increase across varying altitudes. The core microorganisms displayed the highest correlation with pH and alkaline phosphatase (AP, as determined through redundancy analysis (RDA) and Mantel tests for correlation analysis. The fungal community has a higher number of core microorganisms, while the bacterial core microorganisms demonstrate greater susceptibility to environmental factors. In conclusion, we utilized Illumina sequencing techniques to assess the disparities in the structure and function of bacteria and fungi in the soil.IMPORTANCEThese findings serve as a foundation for future investigations on microbial communities present in volcanic soil.

The increase of particle size shifts the biogeochemical cycle functions of mineral-associated microorganisms and weakens the mineral-associated organic carbon sink in mangrove soils.

Mineral-organic matter-microbe interactions greatly impact the biogeochemical processes and biodiversity in soils. An increasing trend of particle size (PS) in mangrove soils has been observed because of the relative sea level rise. However, the impacts of PS increase on the microbial biogeochemical functions and carbon sink in the mineral-associated microcosms are exceedingly nebulous. This work showed a remarkable difference in the communities of mineral-associated microorganisms (MMOs) in various PS fractions. Heavy metal contents and urease activity were the factors that mostly driven the MMO community variation in different PS fractions. Large PS fraction attenuated the stability of MMO communities according to the co-occurrence network characteristics. The PS increase significantly (P < 0.05) lowered the gene abundances for carbon input (e.g., carbon fixation) and raised the gene abundances for carbon loss (e.g., aerobic respiration). Combined with the significant decrease of mineral-associated organic carbon (MOC) in large PS fraction (P < 0.05), this work inferred that the PS increase could weaken the MOC sink partially due to the MMO function shift for carbon cycle. The current work indicated unhealthy changes of MMO communities and MOC storage in mangrove soils, and PS was of significance as an indicator for predicting the carbon sink function, especially for the stable form, such as MOC, in the soils of mangroves under the ecological background of climate migration. IMPORTANCE: Carbon with stable forms, such as mineral-associated organic carbon (MOC), is crucial for the sink capabilities in mangrove soils, and mineral-associated microorganisms (MMOs) are important players for the formation and metabolism of MOC. Therefore, the future successions of the MMO functions and MOC contents under the background of climate change are of value for a deeper understanding of mangrove ecology. The relative sea level rise caused by the global warming results in the increase of mangrove soil particle size (PS), which provides distinct microcosms for MMOs and MOC. However, the responses of MMO functions and MOC content to the PS increase of mangrove soils are unknown. The current study aims to reveal the succession regulations of MMO functions and their potential ecological impacts for the storages of MOC in different PS fractions, therefore widening our knowledge of future function migration and promoting the research development of mangrove.

Successional changes in bacterial phyllosphere communities are plant-host species dependent.

Phyllosphere microbial communities are increasingly experiencing intense pulse disturbance events such as drought. It is currently unknown how phyllosphere communities respond to such disturbances and if they are able to recover. We explored the stability of phyllosphere communities over time, in response to drought stress, and under recovery from drought on temperate forage grasses. Compositional or functional changes were observed during the disturbance period and whether communities returned to non-stressed levels following recovery. Here, we found that phyllosphere community composition shifts as a result of simulated drought but does not fully recover after irrigation is resumed and that the degree of community response to drought is host species dependent. However, while community composition had changed, we found a high level of functional stability (resistance) over time and in the water deficit treatment. Ecological modeling enabled us to understand community assembly processes over a growing season and to determine if they were disrupted during a disturbance event. Phyllosphere community succession was characterized by a strong level of ecological drift, but drought disturbance resulted in variable selection, or, in other words, communities were diverging due to differences in selective pressures. This successional divergence of communities with drought was unique for each host species. Understanding phyllosphere responses to environmental stresses is important as climate change-induced stresses are expected to reduce crop productivity and phyllosphere functioning. IMPORTANCE: Leaf surface microbiomes have the potential to influence agricultural and ecosystem productivity. We assessed their stability by determining composition, functional resistance, and resilience. Resistance is the degree to which communities remain unchanged as a result of disturbance, and resilience is the ability of a community to recover to pre-disturbance conditions. By understanding the mechanisms of community assembly and how they relate to the resistance and resilience of microbial communities under common environmental stresses such as drought, we can better understand how communities will adapt to a changing environment and how we can promote healthy agricultural microbiomes. In this study, phyllosphere compositional stability was highly related to plant host species phylogeny and, to a lesser extent, known stress tolerances. Phyllosphere community assembly and stability are a result of complex interactions of ecological processes that are differentially imposed by host species.

Activated sludge microbial community assembly: the role of influent microbial community immigration.

Wastewater treatment plants (WWTPs) are host to diverse microbial communities and receive a constant influx of microbes from influent wastewater. However, the impact of immigrants on the structure and activities of the activated sludge (AS) microbial community remains unclear. To gain insight on this phenomenon known as perpetual community coalescence, the current study utilized controlled manipulative experiments that decoupled the influent wastewater composition from the microbial populations to reveal the fundamental mechanisms involved in immigration between sewers and AS-WWTP. The immigration dynamics of heterotrophs were analyzed by harvesting wastewater biomass solids from three different sewer systems and adding to synthetic wastewater. Immigrating influent populations were observed to contribute up to 14% of the sequencing reads in the AS. By modeling the net growth rate of taxa, it was revealed that immigrants primarily exhibited low or negative net growth rates. By developing a protocol to reproducibly grow AS-WWTP communities in the lab, we have laid down the foundational principles for the testing of operational factors creating community variations with low noise and appropriate replication. Understanding the processes that drive microbial community diversity and assembly is a key question in microbial ecology. In the future, this knowledge can be used to manipulate the structure of microbial communities and improve system performance in WWTPs.IMPORTANCEIn biological wastewater treatment processes, the microbial community composition is essential in the performance and stability of the system. This study developed a reproducible protocol to investigate the impact of influent immigration (or perpetual coalescence of the sewer and activated sludge communities) with appropriate reproducibility and controls, allowing intrinsic definitions of core and immigrant populations to be established. The method developed herein will allow sequential manipulative experiments to be performed to test specific hypothesis and optimize wastewater treatment processes to meet new treatment goals.

Responses of microbial community composition and CAZymes encoding gene enrichment in ensiled Elymus nutans to altitudinal gradients in alpine region.

High-throughput metagenomic sequence technology was employed to evaluate changes in microbial community composition and carbohydrate-active enzymes encoding gene enrichment status in Elymus nutans silages to altitudinal gradients in the world's highest alpine region of Qinghai-Tibetan Plateau (QTP). E. nutans were collected from three different altitudes in QTP: 2,600 m (low altitude), 3600 m (moderate altitude), and 4,600 m [high (H) altitude], and ensiled for 7, 14, 30, and 60 d. Results indicated an improvement in silage quality with the increasing altitude, although the acetic acid concentration and dry matter loss were greater in H altitude silages after 30 d of ensiling. Harmful bacteria or potential pathogens predominated in the microbial community on d 7 and 14 of fermentation, while genera belonging to lactic acid bacteria gradually became the main microorganisms with the increasing altitude on d 30 and 60 of ensiling. The abundance of carbohydrate-active enzymes genes responsible for macromolecular carbohydrate degradation in silage increased with increasing altitude, and those genes were mainly carried by Lactiplantibacillus and Pediococcus at 30 and 60 d of ensiling. The abundance of key enzymatic genes associated with glycolysis and organic acid production in carbohydrate metabolism pathway was higher in H altitude silages, and Lactiplantibacillus and Pediococcus were also the main hosts after 30 d of silage fermentation, except for the fact that acetic acid production was also related to genera Leuconostoc, Latilactobacillus, and Levilactobacillus. IMPORTANCE: The fermentation quality of Elymus nutans silage was getting better with the increase of altitude in the Qinghai-Tibetan Plateau. The abundance of hosts carrying carbohydrate-active enzymes genes and key enzyme genes related to organic acid production increased with increasing altitude during the later stages of fermentation. Lactiplantibacillus and Pediococcus were the core microorganisms responsible for both polysaccharide hydrolysis and silage fermentation in the late stage of ensiling. This study provided insights on the influence of different altitudes on the composition and function of silage microbiome in the Qinghai-Tibetan Plateau, and provided a reference approach for improving the quality and controllability of silage production in high altitude areas of the Qinghai-Tibetan Plateau.

Unravelling metabolic cross-feeding in a yeast-bacteria community using 13 C-based proteomics.

Cross-feeding is fundamental to the diversity and function of microbial communities. However, identification of cross-fed metabolites is often challenging due to the universality of metabolic and biosynthetic intermediates. Here, we use 13 C isotope tracing in peptides to elucidate cross-fed metabolites in co-cultures of Saccharomyces cerevisiae and Lactococcus lactis. The community was grown on lactose as the main carbon source with either glucose or galactose fraction of the molecule labelled with 13 C. Data analysis allowing for the possible mass-shifts yielded hundreds of peptides for which we could assign both species identity and labelling degree. The labelling pattern showed that the yeast utilized galactose and, to a lesser extent, lactic acid shared by L. lactis as carbon sources. While the yeast provided essential amino acids to the bacterium as expected, the data also uncovered a complex pattern of amino acid exchange. The identity of the cross-fed metabolites was further supported by metabolite labelling in the co-culture supernatant, and by diminished fitness of a galactose-negative yeast mutant in the community. Together, our results demonstrate the utility of 13 C-based proteomics for uncovering microbial interactions.