Most microbes grow in spatially structured communities, and this profoundly shapes their ecology and evolution. At the microscale, short interaction ranges and steep nutrient gradients underlie cross-feeding, quorum sensing, and niche construction, generating spatial patterns that influence microbial behavior, community assembly, and stability. Here, we review theoretical and experimental evidence for how spatial organization drives eco-evolutionary processes, including founder effects during colonization, allele surfing during range expansion, emergent patterns that facilitate multilevel selection, and the exploration of rare epistatic genotypes. While the ecological and evolutionary consequences of spatial structure at the microscale are becoming clearer, linking these processes across scales to predict community- and ecosystem-level outcomes remains a major challenge. Addressing spatial interactions explicitly in microbiome research will be key. Recent advances in computational modeling, cultivation approaches, and omics now offer unprecedented opportunities to meet this challenge, providing fresh insights into how spatial structure governs the organization and dynamics of the microbial world across scales.

Intrinsically disordered regions (IDRs) in proteins drive phase separation (PS) to form biomolecular condensates, which organize cellular matter. While IDRs are recognized as critical drivers of PS, the systematic identification of sequence motifs governing this phenomenon and their compositional determinants remain a key challenge. Here we develop PhaSeMotif, a deep learning framework for interpretable and precise predictions of essential phase-separating motifs within IDRs. We experimentally validate PhaSeMotif, demonstrating that mutations of predicted motifs significantly reduce or eliminate the PS capabilities of IDRs. The identified motifs possess diverse amino acid compositional features that are critical for determining PS propensities and condensate partitioning. Furthermore, PhaSeMotif integrates generative models to create validation-ready motifs that preserve these critical compositional features, empowering direct experimental verification and deeper mechanistic investigation of PS-driving IDR motifs. Overall, by combining motifs prediction, generation, and validation, PhaSeMotif provides an open-access toolkit to facilitate more efficient IDR motifs investigation and provides insights into the molecular determinants of PS. Deciphering the rules of protein phase separation remains a challenge. Here, the authors develop PhaSeMotif, an interpretable and generative deep learning framework that identifies essential sequence motifs and designs functional synthetic variants.

All pathogens must sense that they have arrived at their host. This is a necessary part of infection in order to effect the changes in pathogen biology required to progress through their life cycle. How the information that they have arrived is transmitted, and what molecules/media convey the information, is poorly understood. Here, we review recent literature and provide speculation as to how this might happen, by analogy to the five human senses. Our criteria center on natural selection: we consider host-derived signals—in the broadest sense—to be those that carry some information and that can be detected by the pathogen, in principle. For each, we identify supporting literature and speculate on areas of possible expansion. We conclude, on the one hand, that there is a diversity of understudied but compelling signals, but, on the other hand, that not all signals are equal. The magnitude of the response is likely a function of the fidelity of the signal/detection. Although knowledge is currently incomplete, the prospect of understanding perception of arrival at the host may allow us to perturb pathogen perception of the host and thereby thwart this early and fundamental step in pathogen development.

Plant-parasitic nematodes secrete molecules to manipulate their hosts, but little is known about their mode of delivery and packaging. Here, we describe microRNA-containing exosomes that are secreted by root-knot nematodes and systemically increase host susceptibility. By revealing a novel mode of nematode-plant communication, our findings outline a mechanism for the delivery of nematode patho-molecules, offering a new target for disrupting parasitism at the level of vesicle-mediated delivery.

Since its inception, the CRISPR-Cas system, particularly Cas9, has demonstrated immense potential for life science applications, but expansion of the Cas9 toolkit is constrained by sequence alignment-based strategies for mining and optimization. Here, we developed CasMiner, a deep learning model for discovering and engineering novel Cas9 proteins. CasMiner achieved 99.63% accuracy in predicting Cas9s, and identified VpCas9 from public databases. Experimental validation showed that VpCas9 exhibits robust double-strand cleavage activity. Combining CasMiner and evolutionary analysis, we engineered three mutants with markedly increased structural rigidity and positive charge. In vivo cleavage assays revealed that the mutant VPM2-3 achieved a higher average editing efficiency in rice callus and maize protoplasts than the wild-type VpCas9, whose editing efficiency rivals that of SpCas9. This study thus establishes a comprehensive platform for mining and engineering Cas9 proteins, and provides VpCas9 and derivative nucleases as powerful tools that greatly broaden the horizon for genome editing applications.