Stability of gut microbiome after COVID-19 vaccination in healthy and immuno-compromised individuals.

Bidirectional interactions between the immune system and the gut microbiota are key contributors to various physiological functions. Immune-associated diseases such as cancer and autoimmunity, and efficacy of immunomodulatory therapies, have been linked to microbiome variation. Although COVID-19 infection has been shown to cause microbial dysbiosis, it remains understudied whether the inflammatory response associated with vaccination also impacts the microbiota. Here, we investigate the temporal impact of COVID-19 vaccination on the gut microbiome in healthy and immuno-compromised individuals; the latter included patients with primary immunodeficiency and cancer patients on immunomodulating therapies. We find that the gut microbiome remained remarkably stable post-vaccination irrespective of diverse immune status, vaccine response, and microbial composition spanned by the cohort. The stability is evident at all evaluated levels including diversity, phylum, species, and functional capacity. Our results indicate the resilience of the gut microbiome to host immune changes triggered by COVID-19 vaccination and suggest minimal, if any, impact on microbiome-mediated processes. These findings encourage vaccine acceptance, particularly when contrasted with the significant microbiome shifts observed during COVID-19 infection.

Microbial interactions shape cheese flavour formation.

Cheese fermentation and flavour formation are the result of complex biochemical reactions driven by the activity of multiple microorganisms. Here, we studied the roles of microbial interactions in flavour formation in a year-long Cheddar cheese making process, using a commercial starter culture containing Streptococcus thermophilus and Lactococcus strains. By using an experimental strategy whereby certain strains were left out from the starter culture, we show that S. thermophilus has a crucial role in boosting Lactococcus growth and shaping flavour compound profile. Controlled milk fermentations with systematic exclusion of single Lactococcus strains, combined with genomics, genome-scale metabolic modelling, and metatranscriptomics, indicated that S. thermophilus proteolytic activity relieves nitrogen limitation for Lactococcus and boosts de novo nucleotide biosynthesis. While S. thermophilus had large contribution to the flavour profile, Lactococcus cremoris also played a role by limiting diacetyl and acetoin formation, which otherwise results in an off-flavour when in excess. This off-flavour control could be attributed to the metabolic re-routing of citrate by L. cremoris from diacetyl and acetoin towards α-ketoglutarate. Further, closely related Lactococcus lactis strains exhibited different interaction patterns with S. thermophilus, highlighting the significance of strain specificity in cheese making. Our results highlight the crucial roles of competitive and cooperative microbial interactions in shaping cheese flavour profile.

Clonal cooperation through soluble metabolite exchange facilitates metastatic outgrowth by modulating Allee effect.

Cancers feature substantial intratumoral heterogeneity of genetic and phenotypically distinct lineages. Although interactions between coexisting lineages are emerging as a potential contributor to tumor evolution, the extent and nature of these interactions remain largely unknown. We postulated that tumors develop ecological interactions that sustain diversity and facilitate metastasis. Using a combination of fluorescent barcoding, mathematical modeling, metabolic analysis, and in vivo models, we show that the Allee effect, i.e., growth dependency on population size, is a feature of tumor lineages and that cooperative ecological interactions between lineages alleviate the Allee barriers to growth in a model of triple-negative breast cancer. Soluble metabolite exchange formed the basis for these cooperative interactions and catalyzed the establishment of a polyclonal community that displayed enhanced metastatic dissemination and outgrowth in xenograft models. Our results highlight interclonal metabolite exchange as a key modulator of tumor ecology and a contributing factor to overcoming Allee effect-associated growth barriers to metastasis.

A pathogen-specific isotope tracing approach reveals metabolic activities and fluxes of intracellular Salmonella.

Pathogenic bacteria proliferating inside mammalian host cells need to rapidly adapt to the intracellular environment. How they achieve this and scavenge essential nutrients from the host has been an open question due to the difficulties in distinguishing between bacterial and host metabolites in situ. Here, we capitalized on the inability of mammalian cells to metabolize mannitol to develop a stable isotopic labeling approach to track Salmonella enterica metabolites during intracellular proliferation in host macrophage and epithelial cells. By measuring label incorporation into Salmonella metabolites with liquid chromatography-mass spectrometry (LC-MS), and combining it with metabolic modeling, we identify relevant carbon sources used by Salmonella, uncover routes of their metabolization, and quantify relative reaction rates in central carbon metabolism. Our results underline the importance of the Entner-Doudoroff pathway (EDP) and the phosphoenolpyruvate carboxylase for intracellularly proliferating Salmonella. More broadly, our metabolic labeling strategy opens novel avenues for understanding the metabolism of pathogens inside host cells.

Consistency across multi-omics layers in a drug-perturbed gut microbial community.

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

The Pioneer platform: A novel approach for selection of selective anti-cancer cytotoxic activity in bacteria through co-culturing with engineered human cells.

Most of the small-molecule drugs approved for the treatment of cancer over the past 40 years are based on natural compounds. Bacteria provide an extensive reservoir for the development of further anti-cancer therapeutics to meet the challenges posed by the diversity of these malignant diseases. While identifying cytotoxic compounds is often easy, achieving selective targeting of cancer cells is challenging. Here we describe a novel experimental approach (the Pioneer platform) for the identification and development of 'pioneering' bacterial variants that either show or are conduced to exhibit selective contact-independent anti-cancer cytotoxic activities. We engineered human cancer cells to secrete Colicin M that repress the growth of the bacterium Escherichia coli, while immortalised non-transformed cells were engineered to express Chloramphenicol Acetyltransferase capable of relieving the bacteriostatic effect of Chloramphenicol. Through co-culturing of E. coli with these two engineered human cell lines, we show bacterial outgrowth of DH5α E. coli is constrained by the combination of negative and positive selection pressures. This result supports the potential for this approach to screen or adaptively evolve 'pioneering' bacterial variants that can selectively eliminate the cancer cell population. Overall, the Pioneer platform demonstrates potential utility for drug discovery through multi-partner experimental evolution.

Age-associated B cells predict impaired humoral immunity after COVID-19 vaccination in patients receiving immune checkpoint blockade.

Age-associated B cells (ABC) accumulate with age and in individuals with different immunological disorders, including cancer patients treated with immune checkpoint blockade and those with inborn errors of immunity. Here, we investigate whether ABCs from different conditions are similar and how they impact the longitudinal level of the COVID-19 vaccine response. Single-cell RNA sequencing indicates that ABCs with distinct aetiologies have common transcriptional profiles and can be categorised according to their expression of immune genes, such as the autoimmune regulator (AIRE). Furthermore, higher baseline ABC frequency correlates with decreased levels of antigen-specific memory B cells and reduced neutralising capacity against SARS-CoV-2. ABCs express high levels of the inhibitory FcγRIIB receptor and are distinctive in their ability to bind immune complexes, which could contribute to diminish vaccine responses either directly, or indirectly via enhanced clearance of immune complexed-antigen. Expansion of ABCs may, therefore, serve as a biomarker identifying individuals at risk of suboptimal responses to vaccination.

Glycolytic flux-signaling controls mouse embryo mesoderm development.

How cellular metabolic state impacts cellular programs is a fundamental, unresolved question. Here, we investigated how glycolytic flux impacts embryonic development, using presomitic mesoderm (PSM) patterning as the experimental model. First, we identified fructose 1,6-bisphosphate (FBP) as an in vivo sentinel metabolite that mirrors glycolytic flux within PSM cells of post-implantation mouse embryos. We found that medium-supplementation with FBP, but not with other glycolytic metabolites, such as fructose 6-phosphate and 3-phosphoglycerate, impaired mesoderm segmentation. To genetically manipulate glycolytic flux and FBP levels, we generated a mouse model enabling the conditional overexpression of dominant active, cytoplasmic PFKFB3 (cytoPFKFB3). Overexpression of cytoPFKFB3 indeed led to increased glycolytic flux/FBP levels and caused an impairment of mesoderm segmentation, paralleled by the downregulation of Wnt-signaling, reminiscent of the effects seen upon FBP-supplementation. To probe for mechanisms underlying glycolytic flux-signaling, we performed subcellular proteome analysis and revealed that cytoPFKFB3 overexpression altered subcellular localization of certain proteins, including glycolytic enzymes, in PSM cells. Specifically, we revealed that FBP supplementation caused depletion of Pfkl and Aldoa from the nuclear-soluble fraction. Combined, we propose that FBP functions as a flux-signaling metabolite connecting glycolysis and PSM patterning, potentially through modulating subcellular protein localization.

Predictive evolution of metabolic phenotypes using model-designed environments.

Adaptive evolution under controlled laboratory conditions has been highly effective in selecting organisms with beneficial phenotypes such as stress tolerance. The evolution route is particularly attractive when the organisms are either difficult to engineer or the genetic basis of the phenotype is complex. However, many desired traits, like metabolite secretion, have been inaccessible to adaptive selection due to their trade-off with cell growth. Here, we utilize genome-scale metabolic models to design nutrient environments for selecting lineages with enhanced metabolite secretion. To overcome the growth-secretion trade-off, we identify environments wherein growth becomes correlated with a secondary trait termed tacking trait. The latter is selected to be coupled with the desired trait in the application environment where the trait manifestation is required. Thus, adaptive evolution in the model-designed selection environment and subsequent return to the application environment is predicted to enhance the desired trait. We experimentally validate this strategy by evolving Saccharomyces cerevisiae for increased secretion of aroma compounds, and confirm the predicted flux-rerouting using genomic, transcriptomic, and proteomic analyses. Overall, model-designed selection environments open new opportunities for predictive evolution.

Ecological modelling approaches for predicting emergent properties in microbial communities.

Recent studies have brought forward the critical role of emergent properties in shaping microbial communities and the ecosystems of which they are a part. Emergent properties-patterns or functions that cannot be deduced linearly from the properties of the constituent parts-underlie important ecological characteristics such as resilience, niche expansion and spatial self-organization. While it is clear that emergent properties are a consequence of interactions within the community, their non-linear nature makes mathematical modelling imperative for establishing the quantitative link between community structure and function. As the need for conservation and rational modulation of microbial ecosystems is increasingly apparent, so is the consideration of the benefits and limitations of the approaches to model emergent properties. Here we review ecosystem modelling approaches from the viewpoint of emergent properties. We consider the scope, advantages and limitations of Lotka-Volterra, consumer-resource, trait-based, individual-based and genome-scale metabolic models. Future efforts in this research area would benefit from capitalizing on the complementarity between these approaches towards enabling rational modulation of complex microbial ecosystems.

Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance.

Microbial communities are composed of cells of varying metabolic capacity, and regularly include auxotrophs that lack essential metabolic pathways. Through analysis of auxotrophs for amino acid biosynthesis pathways in microbiome data derived from >12,000 natural microbial communities obtained as part of the Earth Microbiome Project (EMP), and study of auxotrophic-prototrophic interactions in self-establishing metabolically cooperating yeast communities (SeMeCos), we reveal a metabolically imprinted mechanism that links the presence of auxotrophs to an increase in metabolic interactions and gains in antimicrobial drug tolerance. As a consequence of the metabolic adaptations necessary to uptake specific metabolites, auxotrophs obtain altered metabolic flux distributions, export more metabolites and, in this way, enrich community environments in metabolites. Moreover, increased efflux activities reduce intracellular drug concentrations, allowing cells to grow in the presence of drug levels above minimal inhibitory concentrations. For example, we show that the antifungal action of azoles is greatly diminished in yeast cells that uptake metabolites from a metabolically enriched environment. Our results hence provide a mechanism that explains why cells are more robust to drug exposure when they interact metabolically.

Multimodal interactions of drugs, natural compounds and pollutants with the gut microbiota.

The gut microbiota contributes to diverse aspects of host physiology, ranging from immunomodulation to drug metabolism. Changes in the gut microbiota composition are associated with various diseases as well as with the response to medications. It is therefore important to understand how different lifestyle and environmental factors shape gut microbiota composition. Beyond the commonly considered factor of diet, small-molecule drugs have recently been identified as major effectors of the microbiota composition. Other xenobiotics, such as environmental or chemical pollutants, can also impact gut bacterial communities. Here, we review the mechanisms of interactions between gut bacteria and antibiotics, host-targeted drugs, natural food compounds, food additives and environmental pollutants. While xenobiotics can impact bacterial growth and metabolism, bacteria in turn can bioaccumulate or chemically modify these compounds. These reciprocal interactions can manifest in complex xenobiotic-microbiota-host relationships. Our Review highlights the need to study mechanisms underlying interactions with pollutants and food additives towards deciphering the dynamics and evolution of the gut microbiota.

Quantification of Duloxetine in the Bacterial Culture and Medium to Study Drug-gut Microbiome Interactions.

Expanding our understanding of drug-gut bacteria interactions requires high-throughput drug measurements in complex bacterial cultures. Quantification of drugs in the cultures, media, and cell pellets is prone to strong matrix effects. We have developed a liquid chromatography-high resolution mass spectrometry (LC-HRMS) method for quantifying duloxetine from high-throughput gut-drug interaction experiments. The method is partially validated for its reproducibility, sensitivity, and accuracy, which makes it suitable for largescale drug screens. We extensively used this method to study biotransformation and bioaccumulation of duloxetine and other drugs in several species of gut bacteria.

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes.

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

Metabolic memory underlying minimal residual disease in breast cancer.

Tumor relapse from treatment-resistant cells (minimal residual disease, MRD) underlies most breast cancer-related deaths. Yet, the molecular characteristics defining their malignancy have largely remained elusive. Here, we integrated multi-omics data from a tractable organoid system with a metabolic modeling approach to uncover the metabolic and regulatory idiosyncrasies of the MRD. We find that the resistant cells, despite their non-proliferative phenotype and the absence of oncogenic signaling, feature increased glycolysis and activity of certain urea cycle enzyme reminiscent of the tumor. This metabolic distinctiveness was also evident in a mouse model and in transcriptomic data from patients following neo-adjuvant therapy. We further identified a marked similarity in DNA methylation profiles between tumor and residual cells. Taken together, our data reveal a metabolic and epigenetic memory of the treatment-resistant cells. We further demonstrate that the memorized elevated glycolysis in MRD is crucial for their survival and can be targeted using a small-molecule inhibitor without impacting normal cells. The metabolic aberrances of MRD thus offer new therapeutic opportunities for post-treatment care to prevent breast tumor recurrence.

Bioaccumulation of therapeutic drugs by human gut bacteria.

Bacteria in the gut can modulate the availability and efficacy of therapeutic drugs. However, the systematic mapping of the interactions between drugs and bacteria has only started recently1 and the main underlying mechanism proposed is the chemical transformation of drugs by microorganisms (biotransformation). Here we investigated the depletion of 15 structurally diverse drugs by 25 representative strains of gut bacteria. This revealed 70 bacteria-drug interactions, 29 of which had not to our knowledge been reported before. Over half of the new interactions can be ascribed to bioaccumulation; that is, bacteria storing the drug intracellularly without chemically modifying it, and in most cases without the growth of the bacteria being affected. As a case in point, we studied the molecular basis of bioaccumulation of the widely used antidepressant duloxetine by using click chemistry, thermal proteome profiling and metabolomics. We find that duloxetine binds to several metabolic enzymes and changes the metabolite secretion of the respective bacteria. When tested in a defined microbial community of accumulators and non-accumulators, duloxetine markedly altered the composition of the community through metabolic cross-feeding. We further validated our findings in an animal model, showing that bioaccumulating bacteria attenuate the behavioural response of Caenorhabditis elegans to duloxetine. Together, our results show that bioaccumulation by gut bacteria may be a common mechanism that alters drug availability and bacterial metabolism, with implications for microbiota composition, pharmacokinetics, side effects and drug responses, probably in an individual manner.

Model-guided development of an evolutionarily stable yeast chassis.

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories.