Freeing Yeast from Alcohol Addiction (Just) to Make (It) Fat Instead.

Synthetically re-designing eukaryotic metabolism has proven immensely challenging, raising the question of whether evolution has metabolically hardwired eukaryotic cells. Yu et al. now report that, through orchestrating multiple genetic changes and laboratory evolution, Saccharomyces metabolism can be reprogrammed from its evolutionary objective of producing ethanol to produce large amounts of free fatty acids.

Riboregulation of Enolase 1 activity controls glycolysis and embryonic stem cell differentiation.

Differentiating stem cells must coordinate their metabolism and fate trajectories. Here, we report that the catalytic activity of the glycolytic enzyme Enolase 1 (ENO1) is directly regulated by RNAs leading to metabolic rewiring in mouse embryonic stem cells (mESCs). We identify RNA ligands that specifically inhibit ENO1's enzymatic activity in vitro and diminish glycolysis in cultured human cells and mESCs. Pharmacological inhibition or RNAi-mediated depletion of the protein deacetylase SIRT2 increases ENO1's acetylation and enhances its RNA binding. Similarly, induction of mESC differentiation leads to increased ENO1 acetylation, enhanced RNA binding, and inhibition of glycolysis. Stem cells expressing mutant forms of ENO1 that escape or hyper-activate this regulation display impaired germ layer differentiation. Our findings uncover acetylation-driven riboregulation of ENO1 as a physiological mechanism of glycolytic control and of the regulation of stem cell differentiation. Riboregulation may represent a more widespread principle of biological control.

Unravelling the collateral damage of antibiotics on gut bacteria.

Antibiotics are used to fight pathogens but also target commensal bacteria, disturbing the composition of gut microbiota and causing dysbiosis and disease1. Despite this well-known collateral damage, the activity spectrum of different antibiotic classes on gut bacteria remains poorly characterized. Here we characterize further 144 antibiotics from a previous screen of more than 1,000 drugs on 38 representative human gut microbiome species2. Antibiotic classes exhibited distinct inhibition spectra, including generation dependence for quinolones and phylogeny independence for ß-lactams. Macrolides and tetracyclines, both prototypic bacteriostatic protein synthesis inhibitors, inhibited nearly all commensals tested but also killed several species. Killed bacteria were more readily eliminated from in vitro communities than those inhibited. This species-specific killing activity challenges the long-standing distinction between bactericidal and bacteriostatic antibiotic classes and provides a possible explanation for the strong effect of macrolides on animal3-5 and human6,7 gut microbiomes. To mitigate this collateral damage of macrolides and tetracyclines, we screened for drugs that specifically antagonized the antibiotic activity against abundant Bacteroides species but not against relevant pathogens. Such antidotes selectively protected Bacteroides species from erythromycin treatment in human-stool-derived communities and gnotobiotic mice. These findings illluminate the activity spectra of antibiotics in commensal bacteria and suggest strategies to circumvent their adverse effects on the gut microbiota.

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.

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.

Molecular biology for green recovery-A call for action.

Molecular biology holds a vast potential for tackling climate change and biodiversity loss. Yet, it is largely absent from the current strategies. We call for a community-wide action to bring molecular biology to the forefront of climate change solutions.

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.

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.

Extensive impact of non-antibiotic drugs on human gut bacteria.

A few commonly used non-antibiotic drugs have recently been associated with changes in gut microbiome composition, but the extent of this phenomenon is unknown. Here, we screened more than 1,000 marketed drugs against 40 representative gut bacterial strains, and found that 24% of the drugs with human targets, including members of all therapeutic classes, inhibited the growth of at least one strain in vitro. Particular classes, such as the chemically diverse antipsychotics, were overrepresented in this group. The effects of human-targeted drugs on gut bacteria are reflected on their antibiotic-like side effects in humans and are concordant with existing human cohort studies. Susceptibility to antibiotics and human-targeted drugs correlates across bacterial species, suggesting common resistance mechanisms, which we verified for some drugs. The potential risk of non-antibiotics promoting antibiotic resistance warrants further exploration. Our results provide a resource for future research on drug-microbiome interactions, opening new paths for side effect control and drug repurposing, and broadening our view of antibiotic resistance.

Insights and Opinions of Critical Care Healthcare Professionals in the Management of Carbapenem-resistant Enterobacteriaceae Cases and Antibiotic-Resistant Infections in the Intensive Care Unit Setting: A Survey-Based Approach.

INTRODUCTION: A survey-based approach to managing antibiotic-resistant infections in the intensive care unit (ICU) setting, with a focus on carbapenem-resistant Enterobacteriaceae (CRE) cases, was conducted. Among CRE, New Delhi metallo-ß-lactamase 1 (NDM-1) is a carbapenemase that is resistant to ß-lactam antibiotics and has a broader spectrum of antimicrobial resistance than other carbapenemase types. The article explains that healthcare-associated infections (HAIs) are a significant problem, particularly in low- and middle-income countries, and that carbapenem in combination with other antibiotics are the most potent class of antimicrobial agents effective in treating life-threatening bacterial infections, including those caused by resistant strains. AIM: The survey aimed to gather critical care healthcare professionals (HCPs') opinions on their current practices in managing infections acquired in the hospital and ICU settings, with a focus on CRE cases, specifically NDM-1 and other antibiotic-resistant infections. METHODS: Responses from critical care healthcare professionals, including online surveys and in-person interviews, to gain insights into the management of infections caused by multidrug-resistant bacteria. The findings related to the insights on the prevalence of bacterial flora, clinical experiences on efficacy and safety of meropenem sulbactam ethylenediaminetetraacetic acid (EDTA) (MSE) in CRE cases, and various combination therapies of antibiotics used to treat antibiotic-resistant infections in ICU setting were evaluated. RESULTS: Klebsiella pneumoniae bacteria were the most common bacteria in cultures, followed by Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii. NDM-1 was the type of carbapenemase found in around 50% of CRE patients. MSE is among the most preferred antibiotics besides colistin, polymyxin B, and ceftazidime avibactum for CRE cases and specifically for NDM-1 cases due to its high rate of efficacy and safety. CONCLUSION: The article concludes with a discussion on the antibiotics used in response to CRE cases, reporting that critical care HCP considers MSE with high efficacy and safe antibiotic combination and was used as both monotherapy and in combination with other antibiotics. The survey highlights the need for exploring and better understanding the role of MSE in the management of CRE infections, especially in NDM-1.

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.

Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.

Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural similarity with known therapeutic molecules offers a scalable approach. Here, we assessed functional similarity between natural compounds and approved drugs by combining multiple chemical similarity metrics and physicochemical properties using a machine-learning approach. We computed pairwise similarities between 1410 drugs for training classification models and used the drugs shared protein targets as class labels. The best performing models were random forest which gave an average area under the ROC of 0.9, Matthews correlation coefficient of 0.35, and F1 score of 0.33, suggesting that it captured the structure-activity relation well. The models were then used to predict protein targets of circa 11k natural compounds by comparing them with the drugs. This revealed therapeutic potential of several natural compounds, including those with support from previously published sources as well as those hitherto unexplored. We experimentally validated one of the predicted pair's activities, viz., Cox-1 inhibition by 5-methoxysalicylic acid, a molecule commonly found in tea, herbs and spices. In contrast, another natural compound, 4-isopropylbenzoic acid, with the highest similarity score when considering most weighted similarity metric but not picked by our models, did not inhibit Cox-1. Our results demonstrate the utility of a machine-learning approach combining multiple chemical features for uncovering protein binding potential of natural compounds.

Synthesis of Alkyl and Aryl Boronate Esters via CeO2-Catalyzed Borylation of Alkyl and Aryl Electrophiles Including Alkyl Chlorides.

A recyclable protocol using a CeO2-nanorod catalyst for borylation of alkyl halides with B2pin2 (pin = OCMe2CMe2O) is reported. A wide range of synthetically useful alkyl boronate esters are readily obtained from primary and secondary alkyl electrophiles, including unactivated alkyl chlorides, demonstrating broad utility and functional group tolerance. Preliminary investigation revealed an involvement of in situ formed catalytically active boryl species. The catalyst can be reused for up to six runs without appreciable loss in activity. In addition, we have demonstrated the use of this recyclable catalyst for the borylation of aryl halides with B2pin2, providing valuable aryl boronate esters under neat conditions.

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.

Towards a mechanistic understanding of reciprocal drug-microbiome interactions.

Broad-spectrum antibiotics target multiple gram-positive and gram-negative bacteria, and can collaterally damage the gut microbiota. Yet, our knowledge of the extent of damage, the antibiotic activity spectra, and the resistance mechanisms of gut microbes is sparse. This limits our ability to mitigate microbiome-facilitated spread of antibiotic resistance. In addition to antibiotics, non-antibiotic drugs affect the human microbiome, as shown by metagenomics as well as in vitro studies. Microbiome-drug interactions are bidirectional, as microbes can also modulate drugs. Chemical modifications of antibiotics mostly function as antimicrobial resistance mechanisms, while metabolism of non-antibiotics can also change the drugs' pharmacodynamic, pharmacokinetic, and toxic properties. Recent studies have started to unravel the extensive capacity of gut microbes to metabolize drugs, the mechanisms, and the relevance of such events for drug treatment. These findings raise the question whether and to which degree these reciprocal drug-microbiome interactions will differ across individuals, and how to take them into account in drug discovery and precision medicine. This review describes recent developments in the field and discusses future study areas that will benefit from systems biology approaches to better understand the mechanistic role of the human gut microbiota in drug actions.

Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion.

Adaptive laboratory evolution has proven highly effective for obtaining microorganisms with enhanced capabilities. Yet, this method is inherently restricted to the traits that are positively linked to cell fitness, such as nutrient utilization. Here, we introduce coevolution of obligatory mutualistic communities for improving secretion of fitness-costly metabolites through natural selection. In this strategy, metabolic cross-feeding connects secretion of the target metabolite, despite its cost to the secretor, to the survival and proliferation of the entire community. We thus co-evolved wild-type lactic acid bacteria and engineered auxotrophic Saccharomyces cerevisiae in a synthetic growth medium leading to bacterial isolates with enhanced secretion of two B-group vitamins, viz., riboflavin and folate. The increased production was specific to the targeted vitamin, and evident also in milk, a more complex nutrient environment that naturally contains vitamins. Genomic, proteomic and metabolomic analyses of the evolved lactic acid bacteria, in combination with flux balance analysis, showed altered metabolic regulation towards increased supply of the vitamin precursors. Together, our findings demonstrate how microbial metabolism adapts to mutualistic lifestyle through enhanced metabolite exchange.

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.

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.

Targeting RNA editing of antizyme inhibitor 1: A potential oligonucleotide-based antisense therapy for cancer.

Dysregulated adenosine-to-inosine (A-to-I) RNA editing is implicated in various cancers. However, no available RNA editing inhibitors have so far been developed to inhibit cancer-associated RNA editing events. Here, we decipher the RNA secondary structure of antizyme inhibitor 1 (AZIN1), one of the best-studied A-to-I editing targets in cancer, by locating its editing site complementary sequence (ECS) at the 3' end of exon 12. Chemically modified antisense oligonucleotides (ASOs) that target the editing region of AZIN1 caused a substantial exon 11 skipping, whereas ECS-targeting ASOs effectively abolished AZIN1 editing without affecting splicing and translation. We demonstrate that complete 2'-O-methyl (2'-O-Me) sugar ring modification in combination with partial phosphorothioate (PS) backbone modification may be an optimal chemistry for editing inhibition. ASO3.2, which targets the ECS, specifically inhibits cancer cell viability in vitro and tumor incidence and growth in xenograft models. Our results demonstrate that this AZIN1-targeting, ASO-based therapeutics may be applicable to a wide range of tumor types.

Simultaneous determination of novel ß-lactamase inhibitor WCK 4234 and Meropenem in dog plasma by LC-MS/MS and its application to preclinical pharmacokinetic study.

A precise and accurate liquid chromatography-tandem mass spectrometric (LC-MS/MS) bioanalytical method has been developed and validated for the simultaneous quantification of WCK 4234 and meropenem (MEM) in dog plasma. Protein precipitation using acetonitrile was employed as a sample preparation approach. Cefepime was used as an internal standard. The developed method was selective, sensitive (limit of quantification, 0.075 µg/ml for both drugs), accurate (recovery > 90%), precise (CV < 10%) and linear (r2  ≥ 0.99, concentration range 0.075-120 µg/ml for both analytes). The developed method was successfully applied for the determination of both drugs in plasma to assess the pharmacokinetics in beagle dogs. WCK 4234 + MEM in a 1:1 ratio at 15 + 15 and 30 + 30 mg/kg doses were administered by the intravenous route. The mean plasma concentration and area under the concentration-time curve of WCK 4234 ranged from 38.3 to 77.4 µg/ml and from 47.8 to 77.1 µg h/ml, respectively, and the values for MEM ranged from 52.2 to 115.3 µg/ml and 70.5 to 133.6 µg h/ml respectively. The elimination half-life of WCK 4234 and MEM was around 0.8 h.