RMH

Most of our current knowledge about yeast is based on the workhorse Saccharomyces cerevisiae. However, can this yeast represent the vast array of natural yeast life-forms? This review discusses significant recent advances in the study of non-Saccharomyces yeasts, also known as non-conventional yeasts (NCYs). We (a) review recent literature on bioprospecting methodologies and on population genomics that have expanded our understanding of NCY diversity, (b) highlight critical species with industrial applications, and (c) offer insights into how NCYs’ genetic diversity translates into phenotypic plasticity and adaptation to extreme environments. We assess the limitations that are delaying the widespread use of NCYs in biotechnology, including the need for ambitious bioprospecting efforts and robust genetic tools in the scaling up of NCY-based processes for industry. NCYs could offer novel sustainable solutions in the food, beverage, pharmaceutical, and bioenergy sectors and could open a new frontier of commercial opportunities.

As a key bacterial actin-like protein, MreB plays crucial roles in maintaining cell shape, regulating peptidoglycan synthesis, and coordinating chromosome segregation, making it a promising target for novel antibiotics. This review comprehensively explores MreB’s molecular architecture, its assembly into antiparallel protofilaments, and its pivotal roles in bacterial cell morphology and division. We also delve into how MreB interacts with membrane-associated proteins such as RodZ and MreC/D to coordinate cell wall synthesis and respond to environmental signals like ion gradients and temperature changes. Furthermore, we highlight the cooperation and functional divergence between MreB and FtsZ, underscoring the evolutionary adaptability of bacterial cytoskeletal structures. The structural and functional parallels between MreB and eukaryotic cytoskeletal proteins are also examined, offering new insights into the evolution of cytoskeletal systems. By integrating insights from structural biology, synthetic biology, and microbial ecology, this review aims to provide a deeper understanding of MreB’s role in bacterial biology, its dynamic responses to environmental cues, and its implications for therapeutic innovation. This comprehensive analysis not only enhances our knowledge of bacterial self-organization mechanisms but also paves the way for the development of innovative antimicrobial strategies to address the growing challenge of antibiotic resistance.

Transfer RNA (tRNA) plays a central role in translation. The simultaneous in vitro synthesis of minimal yet sufficient tRNA species (at least 21) poses a challenge for constructing a self-reproducible artificial cell. A key obstacle is the processing of the 5’ and 3’ ends, which requires a multi-step reaction in the natural cells. In this study, we develop a simplified processing method that allows simultaneous expression of all 21 tRNAs in a reconstituted transcription/translation system (PURE system). We test two methods for 5’-end processing (the leader and 5’-G variants methods) and two methods for 3’-end processing (the direct tRNA linkage and HDVR attachment methods). Finally, by combining the direct tRNA linkage and HDVR attachment methods (newly termed the tRNA array method), we succeed in simultaneously expressing all 21 tRNAs from a single polycistronic DNA template in the PURE system. The tRNA mixture produced by the tRNA array method supports a similar level of translation to the individually synthesized tRNA mixture for luciferase and GFP. This study represents a step toward the realization of self-reproducible artificial cells and also provides an easy method for preparing all tRNAs useful for genetic code engineering. tRNAs are essential for translating genetic information into proteins. Here, the authors develop a method to synthesize all 21 essential tRNAs from a single DNA in vitro, enabling protein production and providing a foundation for artificial cells and genetic code reprogramming.

Cell signaling and communication are fundamental to living cellular communities. Over the past two decades, there has been a continuous development of bottom-up engineered synthetic cells, which have become increasingly similar to their natural counterparts. However, we are only scratching the surface with the development of synthetic cellular communities and their integration into natural tissues. Here we review different intercellular communication mechanisms engineered for synthetic cells and classify them based on their resemblance to natural cell signaling mechanisms—autocrine, paracrine and juxtacrine. In particular, we highlight recent advances in molecular tools for intercellular communication strategies and discuss potential applications of engineering synthetic cellular communities and synthetic cell–natural cell communication. With further advances in this area, synthetic cellular communities will be powerful tools for understanding and manipulating cellular functions, thus unlocking potential applications in biosensing, cellular reprogramming and sustainability. Recent advances in engineering bottom-up synthetic cells have created powerful compartmentalized biochemical reactors with cell-like abilities. To push the boundaries of the collective capabilities of synthetic cells and unlock biomedical applications, biomimicry of signaling and communication is imperative. This Review highlights state-of-the-art communication mechanisms between synthetic cells and discusses potential applications.

Recent breakthroughs in protein structure prediction have led to a surge in high-quality 3D models, highlighting the need for efficient computational solutions. In our work, we examine the structural clusters from the AlphaFold Protein Structure Database (AFDB), a high-quality subset of ESMAtlas, and the Microbiome Immunity Project (MIP). We create a single cohesive low-dimensional representation of the resulting protein space. We show that, while each database occupies distinct regions, they collectively exhibit significant overlap in their functional profiles. High-level biological functions tend to cluster in particular regions, revealing a shared functional landscape despite the diverse sources of data. By creating a representation of protein structure space, localizing functional annotations within this space, and providing an open-access web-server for exploration, this work offers insights for future research concerning protein sequence-structure-function relationships, enabling biological questions to be asked about taxonomic assignments, environmental factors, or functional specificity. This approach is generalizable, thus enabling further discovery beyond findings presented here. Researchers mapped the protein structure landscape, revealing structural complementarity across databases and functional clustering in specific regions. Their web tool helps explore this space, unlocking new insights into protein roles, evolution, and diversity.

Enzymes are essential biological catalysts that drive nearly all biochemical reactions. Understanding their efficiency and specificity involves studying enzyme kinetics, particularly the parameters kcat and Km. However, there is limited data linking these kinetic parameters with the three-dimensional (3D) structures of enzyme-substrate complexes. Since enzyme function is determined by its structure, such mapping enhances insight into structural basis of enzymatic function and supports applications in enzyme design, synthetic biology and metabolic engineering. To address this critical gap, this work presents SKiD (Structure-oriented Kinetics Dataset), a comprehensive, structured dataset integrating kcat and Km values with the corresponding 3D structural data. This is accomplished by integrating data from existing bioinformatics resources using automated programs to process the data and enhancing it with computational predictions. The erroneous data encountered during data integration is manually resolved. Metadata such as literature and assay conditions (e.g., pH and temperature) are preserved. The 3D coordinates of the modelled enzyme-substrate complexes are provided along with their UniProtKB identifier.

Axenic cultures are essential for studying microbial ecology, evolution, and genomics. Despite the importance of pure cultures, public culture collections are biased towards fast-growing copiotrophs, while many abundant aquatic prokaryotes remain uncultured due to uncharacterized growth requirements and oligotrophic lifestyles. Here, we applied high-throughput dilution-to-extinction cultivation using defined media that mimic natural conditions to samples from 14 Central European lakes, yielding 627 axenic strains. These cultures include 15 genera among the 30 most abundant freshwater bacteria identified via metagenomics, collectively representing up to 72% of genera detected in the original samples (average 40%) and are widespread in freshwater systems globally. Genome-sequenced strains are closely related to metagenome-assembled genomes (MAGs) from the same samples, many of which remain undescribed. We propose a classification of several novel families, genera, and species, including many slowly growing, genome-streamlined oligotrophs that are notoriously underrepresented in public repositories. Our large-scale initiative to cultivate the “uncultivated microbial majority” has yielded a valuable collection of abundant freshwater microbes, characterized by diverse metabolic pathways and lifestyles. This culture collection includes promising candidates for oligotrophic model organisms, suitable for a wide array of ecological studies aimed at advancing our ecological and functional understanding of dominant, yet previously uncultured, taxa. A large fraction of aquatic bacteria remains uncultured. Here, the authors cultivated 627 strains of abundant freshwater bacteria from 14 European lakes, thus generating a collection that includes many previously uncultured, oligotrophic bacteria that may serve as model organisms.

Post-translational modifications (PTMs) are critical regulators of protein function, and their disruption is a key mechanism by which missense variants contribute to disease. Accurate PTM site prediction using deep learning can help identify PTM-altering variants, but progress has been limited by the lack of large, high-quality training datasets. Here, we introduce PTMAtlas, a curated compendium of 397,524 PTM sites generated through systematic reprocessing of 241 public mass-spectrometry datasets, and DeepMVP, a deep learning framework trained on PTMAtlas to predict PTM sites for phosphorylation, acetylation, methylation, sumoylation, ubiquitination and N-glycosylation. DeepMVP substantially outperforms existing tools across all six PTM types. Its application to predicting PTM-altering missense variants shows strong concordance with experimental results, validated using literature-curated variants and cancer proteogenomic datasets. Together, PTMAtlas and DeepMVP provide a robust platform for PTM research and a scalable framework for assessing the functional consequences of coding variants through the lens of PTMs. DeepMVP is a deep learning framework for predicting PTM sites and variant-induced alterations across six modification types, including phosphorylation, acetylation, methylation, sumoylation, ubiquitination and N-glycosylation.