Mechanical force has recently emerged as a microbial energy source through direct mechanical-to-electrical energy conversion. However, the flexoelectric effect, a universal mechanoelectrical coupling phenomenon, remains underexplored as an energy source for microbial life. Here, we report a mechanically-driven microbial growth mechanism whereby mechanical energy is converted into electrical energy via the flexoelectric effect of a mineral. This process supports microbial growth in the absence of sunlight and chemical energy sources. Using flexoelectric manganese oxide and electroactive Rhodopseudomonas palustris as a model biohybrid, we demonstrate that mechanical force bends manganese oxide nanosheets to generate free electrical charge. R. palustris captures and utilizes this electrical energy for growth by coupling carbon fixation and denitrification in the absence of chemical electron donors under dark conditions. This pathway is accessible to various electroactive microorganisms and flexoelectric minerals. These findings reveal a noteworthy energy acquisition pathway for microbial survival in energy-limited settings, providing crucial evidence for the diversity of mechanical-to-metabolic coupling in biological processes. Mechanical forces are ubiquitous in natural environments, yet the ability of microbes to harness this energy is underexplored. Here, the authors reveal that flexoelectric minerals generate free electrons under mechanical strain, providing electroactive microbes with an energy source for growth independent of sunlight or chemical substrates.

A key challenge in addressing the antibiotic resistance crisis is identifying new antimicrobial compounds. Although natural products produced by fungi and bacteria, particularly actinomycetes, have been the source of most antibiotics discovered over the past 80 years, they have fallen out of favor owing to the frequent rediscovery of known drug scaffolds. The current perception is that antibiotic-producing actinomycetes have been over-mined and possess little novelty left to yield. Here we demonstrate that by using improved fractionation approaches that enrich previously overlooked minor products, even well-studied strains of antibiotic-producing actinomycetes can provide new chemical scaffolds with unique modes of action. By fractionating a library of natural product extracts from soil bacteria, we show that Streptomyces rimosus, the source of the well-known antibiotic oxytetracycline, produces a cyclic depsipeptide antibiotic that we call manikomycin. Manikomycin can kill multidrug-resistant Enterobacteriaceae and is not susceptible to resistance associated with clinically used antibiotics. Biochemical, genetic and structural analyses reveal that manikomycin binds in the E-site of the large subunit of the bacterial ribosome, preventing entry of the 3′ end of the tRNA into the E-site and effectively hindering the translocation step of protein synthesis in a sequence-context-specific manner. Manikomycin is the first antibacterial agent, to our knowledge, to target the critical but underexplored E-site in the large ribosomal subunit, highlighting its value as a lead for developing new antibiotics. Improved fractionation strategies can identify antibiotics with previously unseen scaffolds and mechanisms, exemplified by manikomycin from Streptomyces rimosus, which acts by targeting the E-site of the bacterial large ribosomal subunit.

The microbiota produces thousands of potentially bioactive small molecules. High-throughput bioactivity screens of in vitro commensal cultures have exposed microbiota metabolites that shape host physiology by activating diverse G-protein-coupled receptors (GPCRs). However, owing to technical limitations, the GPCRome-wide bioactivities of in vivo metabolomes, which result from complex diet–microorganism–host interactions, remain unclear. Here we used a multiplexed GPCR screening technology to assess GPCRome-wide bioactivities of 100 commensal strains grown in vivo in monoassociated germ-free mice or in vitro in bacterial culture medium. In vivo and in vitro commensal metabolomes exhibited distinct GPCR activation patterns due to (1) host-mediated metabolite degradation; (2) in vivo microbial metabolic reprogramming; and (3) biotransformation of dietary substrates. Notably, we found that multiple commensal strains produced acetylcholine (ACh) in vivo through the conversion of dietary choline, including select Bifidobacterium strains that dominate the microbiome in early life and a probiotic Pediococcus strain. Mechanistically, we identified and characterized the bacterial enzymes that mediate this biotransformation in Bifidobacterium breve and Pediococcus pentosaceus, and generated an isogenic mutant B. breve strain lacking ACh production. Mice colonized with ACh-producing B. breve exhibited enhanced intestinal immunoglobulin A (IgA) production, altered microbiota composition and increased resistance to enteric infection. These findings underscore the profound impacts of the in vivo environment on microbiota metabolism and reveal a diet–microbiome–host axis that strengthens mucosal immune defences and reinforces host–microbiota mutualism. A diet–microbiome–host axis strengthens mucosal immune defences and reinforces host–microbiota mutualism.

Current long-read single-nucleotide variant callers were designed primarily for genomic data—particularly human genomes. While some have been used on metagenomic data, their underlying assumptions and training procedures fail to account for the inherent complexity of metagenomic samples. To date, no long-read variant caller has been purpose-built for metagenomic applications. To address this gap, we present SNooPy, a single nucleotide polymorphism (SNP)-calling tool that implements a new statistical framework tailored to long-read metagenomic data. Unlike previous genomic methods, our approach makes no assumptions about the number of haplotypes present, their evolutionary relationships, or their sequence divergence. We demonstrate that SNooPy outperforms both traditional statistical and deep learning–based SNP callers. Our results suggest that future integration of this framework with deep learning approaches could further enhance variant-calling performance. SNooPy is freely available on github.com/rolandfaure/snoopy.

Connecting mathematical models with empirically measured microbial growth has remained challenging, as numerous competing models based on different theoretical approaches can fit observations. Therefore, we develop a method to automatically propose growth models from microbial data alone. We validate this approach using an available dataset of E. coli grown on known resources, and study 14 species across various concentrations of a rich medium. The inherently interpretable approach of symbolic regression infers explicit dynamical models directly from growth data. Using symbolic regression natively, does not favor biologically interpretable models, but we find cumulative population gain to be a more informative machine learning feature than population size. Random Forest machine learning allows us to relate this finding to the approximation of a constant-rate per capita resource consumption. This suggests that the area under the growth curve (AUC) measured in routine experiments provides information on the effective resource dynamics governing microbial growth. Finally, we use theoretical insights to inform the symbolic regression algorithm and favor biologically interpretable models. Overall, we found that balancing between data fit, parsimony and biological relevance favored both the simplest, linear approximation and models based on Monod dynamics, with either one or two underlying resources. Therefore, our approach to read growth laws off of microbial batch cultures provides insights on data-driven modelling.

CRISPR-Cas13d RNA nucleases are powerful tools for programmable RNA targeting. A light-controlled RNA nuclease could be transformative by enabling researchers to selectively knock down transcripts at desired positions in a cell or tissue or at timepoints of interest. Here, we develop a set of RfxCas13d tools that can be multimodally controlled by either light or small molecule addition. By screening an RfxCas13d library containing insertions of the AsLOV2 photoswitchable domain, we identify an OptoCas13d-off variant that induced target RNA cleavage in the dark and switched to an inactive state under blue light. We show that the same allosteric hotspot can be exploited to generate an OptoCas13d-on with an inverted light response and a ChemoCas13d that is activated by rapamycin analogs, enabling knockdown of endogenous mRNA and protein targets. Overall, our study shows that engineered allostery can produce stimulus-controlled Cas13d variants to modulate RNA with high spatial and temporal precision. The Cas13 family of RNA-guided RNA nucleases are powerful tools for targeted RNA perturbations, but their activity is difficult to control in space and time. Here, authors present a set of engineered optogenetic and chemogenetic Cas13 variants to precisely control RNA cleavage.

During maize (Zea mays L.) domestication, seed protein content sharply declined. In plants, glutamine and asparagine levels are closely correlated with protein content. Asparagine is synthesized from glutamine, a process catalysed by asparagine synthase. Teosinte harbors a superior haplotype of asparagine synthase 4 (ASN4). Here, we report that teosinte also possesses a superior haplotype gene promoting glutamine synthesis. We identify and clone teosinte high protein 3 (THP3), which encodes glutamate-oxaloacetate transaminase 1 (GOT1), a key enzyme involved in nitrogen assimilation and carbon–nitrogen balance. The superior THP3-T allele, subjected to negative selection during domestication, has natural variations that boost both its expression and enzymatic activity. Overexpressing THP3-T, but not the modern THP3-B allele, significantly increases seed protein, representing altered carbon–nitrogen composition. Pyramiding THP3-T with THP9-T (the latter encoding asparagine synthase 4 (ASN4)) synergistically elevates both seed and whole-plant protein in elite hybrids while maintaining yield. Our findings demonstrate a powerful strategy for crop improvement by reintroducing beneficial rare alleles disfavoured during domestication from wild relatives. Teosinte alleles enhance nitrogen assimilation and seed protein without lowering crop yield when expressed in modern maize, providing a powerful strategy for crop improvement to meet future population demands.

mRNA technology, which has enabled the rapid development of vaccines for infectious diseases, also holds great promise for a new generation of therapies for a host of rare and common diseases. A decade of clinical trials are beginning to clarify the key barriers to unlock the transformative potential of mRNA drugs, which are being addressed with novel interdisciplinary technical advances that, in some cases, are integrating the fields of gene, cell and mRNA therapies. Here, we review the scientific insights from a select group of clinical studies on mRNA-based drugs, including enzyme replacement therapies for rare diseases, cancer immunotherapies, genome-modifying therapies, and immune cell reprogramming therapies for cancer and autoimmune diseases. Several innovative approaches such as clinically tractable in vivo delivery systems, the development of completely ‘immune-silent’ mRNA–vehicle formulations that allow repeated administration and the development of approaches for preferential delivery to organs other than the liver would expedite the development of mRNA therapeutics 2.0. mRNA technology is being applied to develop a new generation of therapies for rare and common diseases. This Review discusses insights from clinical studies on mRNA-based therapeutics, including enzyme replacement therapies, cancer immunotherapies, genome-modifying therapies and immune cell reprogramming therapies, which illustrate how technological advances are paving the way for the wider adoption of mRNA technology in drug development.