Summary: Microbial communities are increasingly utilized in biotechnology. Efficiency and productivity in many of these applications depends on the presence of cooperative interactions between members of the community. Two key processes underlying these interactions are the production of public goods and metabolic cross‐feeding, which can be understood in the general framework of ecological and evolutionary (eco‐evo) dynamics. In this review, we illustrate the relevance of cooperative interactions in microbial biotechnological processes, discuss their mechanistic origins and analyse their evolutionary resilience. Cooperative behaviours can be damaged by the emergence of ‘cheating’ cells that benefit from the cooperative interactions but do not contribute to them. Despite this, cooperative interactions can be stabilized by spatial segregation, by the presence of feedbacks between the evolutionary dynamics and the ecology of the community, by the role of regulatory systems coupled to the environmental conditions and by the action of horizontal gene transfer. Cooperative interactions enrich microbial communities with a higher degree of robustness against environmental stress and can facilitate the evolution of more complex traits. Therefore, the evolutionary resilience of microbial communities and their ability to constraint detrimental mutants should be considered to design robust biotechnological applications.

Global quantification of protein abundances in single cells could provide direct information on cellular phenotypes and complement transcriptomics measurements. However, single-cell proteomics is still immature and confronts many technical challenges. Herein we describe a nested nanoPOTS (N2) chip to improve protein recovery, operation robustness, and processing throughput for isobaric-labeling-based scProteomics workflow. The N2 chip reduces reaction volume to 240 single cells on a single microchip. The tandem mass tag (TMT) pooling step is simplified by adding a microliter droplet on the nested nanowells to combine labeled single-cell samples. In the analysis of ~100 individual cells from three different cell lines, we demonstrate that the N2 chip-based scProteomics platform can robustly quantify ~1500 proteins and reveal membrane protein markers. Our analyses also reveal low protein abundance variations, suggesting the single-cell proteome profiles are highly stable for the cells cultured under identical conditions.

A key prerequisite to achieve a deeper understanding of microbial communities and to engineer synthetic ones is to identify the individual metabolic interactions among key species and how these interactions are affected by different environmental factors. Deciphering the physiological basis of species–species and species–environment interactions in spatially organized environments requires reductionist approaches using ecologically and functionally relevant species. To this end, we focus here on a defined system to study the metabolic interactions in a spatial context among the plant-beneficial endophytic fungus Serendipita indica, and the soil-dwelling model bacterium Bacillus subtilis. Focusing on the growth dynamics of S. indica under defined conditions, we identified an auxotrophy in this organism for thiamine, which is a key co-factor for essential reactions in the central carbon metabolism. We found that S. indica growth is restored in thiamine-free media, when co-cultured with B. subtilis. The success of this auxotrophic interaction, however, was dependent on the spatial and temporal organization of the system; the beneficial impact of B. subtilis was only visible when its inoculation was separated from that of S. indica either in time or space. These findings describe a key auxotrophic interaction in the soil among organisms that are shown to be important for plant ecosystem functioning, and point to the potential importance of spatial and temporal organization for the success of auxotrophic interactions. These points can be particularly important for engineering of minimal functional synthetic communities as plant seed treatments and for vertical farming under defined conditions.

Thiol-based post-translational modifications (PTMs) play a key role in redox-dependent regulation and signaling. Functional cysteine (Cys) sites serve as redox switches, regulated through multiple types of PTMs. Herein, we aim to characterize the complexity of thiol PTMs at the proteome level through the establishment of a direct detection workflow. The LC-MS/MS based workflow allows for simultaneous quantification of protein abundances and multiple types of thiol PTMs. To demonstrate its utility, the workflow was applied to mouse pancreatic β-cells (β-TC-6) treated with thapsigargin to induce endoplasmic reticulum (ER) stress. This resulted in the quantification of >9000 proteins and multiple types of thiol PTMs, including intra-peptide disulfide (S–S), S-glutathionylation (SSG), S-sulfinylation (SO₂H), S-sulfonylation (SO₃H), S-persulfidation (SSH), and S-trisulfidation (SSSH). Proteins with significant changes in abundance were observed to be involved in canonical pathways such as autophagy, unfolded protein response, protein ubiquitination pathway, and EIF2 signaling. Moreover, ~500 Cys sites were observed with one or multiple types of PTMs with SSH and S–S as the predominant types of modifications. In many cases, significant changes in the levels of different PTMs were observed on various enzymes and their active sites, while their protein abundance exhibited little change. These results provide evidence of independent translational and post-translational regulation of enzyme activity. The observed complexity of thiol modifications on the same Cys residues illustrates the challenge in the characterization and interpretation of protein thiol modifications and their functional regulation.

Bistability, and more generally multistability, is a key system dynamics feature enabling decision-making and memory in cells. Deciphering the molecular determinants of multistability is thus crucial for a better understanding of cellular pathways and their (re)engineering in synthetic biology. Here, we show that a key motif found predominantly in eukaryotic signalling systems, namely a futile signalling cycle, can display bistability when featuring a two-state kinase. We provide necessary and sufficient mathematical conditions on the kinetic parameters of this motif that guarantee the existence of multiple steady states. These conditions foster the intuition that bistability arises as a consequence of competition between the two states of the kinase. Extending from this result, we find that increasing the number of kinase states linearly translates into an increase in the number of steady states in the system. These findings reveal, to our knowledge, a new mechanism for the generation of bistability and multistability in cellular signalling systems. Further the futile cycle featuring a two-state kinase is among the smallest bistable signalling motifs. We show that multi-state kinases and the described competition-based motif are part of several natural signalling systems and thereby could enable them to implement complex information processing through multistability. These results indicate that multi-state kinases in signalling systems are readily exploited by natural evolution and could equally be used by synthetic approaches for the generation of multistable information processing systems at the cellular level.

Recent years have witnessed the remarkable progress of deep learning within the realm of scientific disciplines, yielding a wealth of promising outcomes. A prominent challenge within this domain has been the task of predicting enzyme function, a complex problem that has seen the development of numerous computational methods, particularly those rooted in deep learning techniques. However, the majority of these methods have primarily focused on either amino acid sequence data or protein structure data, neglecting the potential synergy of combining both modalities. To address this gap, we propose a Contrastive Learning framework for Enzyme functional ANnotation prediction combined with protein amino acid sequences and Contact maps (CLEAN-Contact). We rigorously evaluate the performance of our CLEAN-Contact framework against the state-of-the-art enzyme function prediction models using multiple benchmark datasets. Using CLEAN-Contact, we predict previously unknown enzyme functions within the proteome of Prochlorococcus marinus MED4. Our findings convincingly demonstrate the substantial superiority of our CLEAN-Contact framework, marking a significant step forward in enzyme function prediction accuracy.