Dietary fibers are crucial in shaping gut microbial composition and functionality. Physical complexity and chemical interactions between fibers and the gut environment lead to diverse and specialized responses that involve entire food webs of gut bacteria; however, there is comparatively less emphasis on understanding ecological dynamics to predict these outcomes. These responses may potentially promote either a broader (less specific) or narrower (more specific) group of gut bacterial taxa, which may vary across individuals. This review examines fiber specificity at the organismal and community levels by exploring mechanistic interactions among dietary fibers and gut bacteria. We discuss the interplay of exogenous and endogenous factors and the structure–function relationships influencing fiber specificity. We establish a mathematical framework to describe specificity in fiber–microbiome interactions based on directionality, magnitude, and stochasticity of fiber–microbiome ecological responses. Finally, we identify research gaps to enhance fiber–microbiota predictions, with implications for strategies aimed at optimizing fiber design.

Intracellular protein patterns govern essential cellular functions by dynamically redistributing proteins between membrane-bound and cytosolic states, conserving their total numbers. This review presents a theoretical framework for understanding such patterns based on mass-conserving reaction–diffusion systems. The emergence, selection, and evolution of patterns are analyzed in terms of mass redistribution and interface motion, resulting in mesoscale laws of coarsening and wavelength selection. A geometric phase-space perspective provides a conceptual tool to link local reactive equilibria with global pattern dynamics through conserved mass fluxes. The Min protein system of E. coli provides a paradigmatic example, enabling direct comparison between theory and experiment. Successive model refinements capture both the robustness of pattern formation and the diversity of dynamic regimes observed in vivo and in vitro. The Min system thus illustrates how to extract predictive, multiscale theory from biochemical detail, providing a foundation for understanding pattern formation in more complex and synthetic systems.

Recent data suggests one fungus, Aspergillus fumigatus, causes more deaths annually than HIV or malaria combined. Coupled with rapid emergence of antifungal drug resistance, the limited range of effective treatments, and mortality rates of >50%, aspergillosis represents a major challenge in infectious diseases. Recent studies have identified long-noncoding RNAs (lncRNAs) involved in drug resistance and virulence in pathogenic yeasts such as Candida spp. However, there is very limited knowledge of lncRNAs in human pathogenic moulds, including A. fumigatus. Here we exploit transcriptomics data of A. fumigatus exposed to different environments to annotate transcripts mapping to 2388 genomic loci. After manual curation we generate a database of over 1000 lncRNAs. We observe that the lncRNAs display orchestrated transcriptional profiles upon drug treatment and many are proximal to genes involved in azole sensitivity. We knock out a set of intergenic lncRNAs and perform a large-scale phenotypic analysis to identify 60 lncRNA mutants displaying condition-dependent fitness changes with 35 mutants exhibiting a positive growth phenotype under azole stress. Overall, this study generates and experimentally validates an important resource that will enable the wider research community to increase understanding of the functional importance of lncRNAs in A. fumigatus, including their involvement in drug sensitivity. Here, the authors present a bioinformatic pipeline to annotate lncRNA genes in Aspergillus fumigatus. The majority of these lncRNAs are only conserved at the species level, and their expression is highly responsive to antifungal exposure in Aspergillus fumigatus.

Plants and bacteria have coevolved over hundreds of millions of years, forming complex associations ranging from mutualism to pathogenicity that are essential for plant survival and ecosystem function. Bacterial adaptation to plant environments involves dynamic evolutionary mechanisms including horizontal gene transfer, gene regulation, and metabolic specialization, enabling bacteria to persist and specialize within diverse plant-associated niches. Here we review how evolutionary forces such as selection, drift, and gene flow shape bacterial genomes, regulatory networks, and ecological strategies in response to plant-imposed pressures, underpinning both beneficial and pathogenic lifestyles. Understanding these processes provides a unified evolutionary framework for bacterial adaptation to plants and highlights their implications for sustainable agriculture and microbiome-based innovations.

Establishing the biological context of microbial metabolites remains a major challenge. We present microbiomeMASST, a metadata-driven network graph that maps metabolites across 467 available datasets with 144,424 mass spectrometry files from humans, animals, and microbial culture systems. MicrobiomeMASST integrates monocultures, synthetic communities, and host-associated samples across multiple body sites and plants. MS/MS spectra can be queried to trace occurrence across hosts, experimental conditions, and interventions, enabling cross-study integration. We demonstrate this framework by contextualizing microbial-conjugated bile acids and interrogating microbiome-mediated drug metabolism. Screening gut bacteria revealed deprolylation of the angiotensin-converting enzyme (ACE) inhibitor prodrug enalapril. Using microbiomeMASST, we traced this metabolite across human cohorts, microbial isolates, environmental samples, and in Gorilla gorilla. Structural modeling and enzymatic assays showed that microbial deprolylation abolishes ACE inhibition, thereby inactivating its therapeutic effect. Together, microbiomeMASST links MS/MS spectra to biological context, converting isolated observations into an interpretable microbiome map for cross-study analysis.

Molecular recording is an emerging paradigm for measuring biology over time. Enhancer-mediated genomic recording of activity in multiplex (ENGRAM) is a recently described synthetic biology circuit architecture that converts the transient activity of cis-regulatory elements (CREs) into stable genomic records that can be retrospectively recovered via DNA sequencing. Here we provide a step-by-step protocol for conducting ENGRAM experiments and analyzing the resulting data. We also describe key design considerations for ENGRAM recorders, summarize the strengths and limitations of ENGRAM, and highlight applications, including multiplex signal recording and high-throughput CRE screening. In contrast to other systems for DNA-based recording in mammalian systems, ENGRAM relies on prime editing-mediated insertions to record the activity of a given CRE, such that it is inherently multiplexable—for example, four-base-pair insertions can represent the activities of up to 256 distinct CREs. A further contrast lies with ENGRAM’s compatibility with DNA Typewriter, which facilitates the capture of signal order. For users with basic skills in molecular biology, mammalian cell culture and DNA sequencing analysis, ENGRAM experiments can typically be completed within 5–6 weeks. This Protocol details the use of enhancer-mediated genomic recording of activity in multiplex (ENGRAM) for molecular recording. This system converts the transient activity of cis-regulatory elements into stable genomic records that can be retrospectively recovered via DNA sequencing.

It has become increasingly appreciated that gut microbes influence host stress hormone responses through direct and indirect mechanisms. These relationships may have broad implications on hormone bioavailability, receptor signaling, and stress resilience. In this review, we summarize current evidence for microbe-stress factor interactions and their consequences for host physiology. We further examine how microbiota-stress crosstalk may contribute to inflammatory bowel disease, highlighting emerging mechanisms and potential microbiota-targeted therapies.

Interactions between genes or cis-regulatory elements (CREs) underlie many biological processes. High-throughput CRISPR screens have allowed researchers to assess the impact of activation or repression of gene and regulatory elements on many phenotypes. However, assessment of interactions between those genes or elements remains limited. To enable efficient highly-multiplexed control of regulatory element activity, we combine a hyper-efficient version of Lachnospiraceae bacterium dCas12a (dHyperLbCas12a) with RNA Polymerase II expression of long CRISPR RNA (crRNA) arrays. We demonstrate this system with several activation and repression domains, in cultured primary immune cells, and to differentiate induced pluripotent stem cells. We also develop approaches to use dCas12a for simultaneous activation and repression. Lastly, we demonstrate that dHyperLbCas12a effectors can be used to dissect the independent and combinatorial contributions of CREs to gene expression. These tools create possibilities for highly multiplexed control of gene expression in many biological systems. CRISPR/Cas9 screens have identified genetic contributions to many phenotypes. However, studying combinations of genes or regulatory elements remains challenging. Here, the authors use CRISPR/Cas12a to overcome those challenges and enable new approaches to study combinatorial genetic mechanisms.

Predator-prey interactions are intricately linked to ecological systems, from microorganisms to large animals. Most predator-prey studies use simplified pairwise interactions, constraining our ability to identify general principles. Here, predator prey choices are examined across scales and levels of environmental complexity. We review current knowledge and emphasize the diversity and complexity of predator-prey systems, point to challenges in integrating them, and propose a framework that could benefit predictive modelling for ecosystem functioning and resilience. To do so, we compare the tools, mechanisms and strategies deployed by micro- and macro- predators and prey defenses to show that commonalities become identifiable, and suggest structural and functional links between micro- and macro-scales. This provides arguments for both descriptive, and mathematical models. We propose that the use of microbial predators like the Bdellovibrio and like organisms (BALOs) can greatly advance the integration of experimental and mathematical modeling research, as they can provide robust empirical observations of predator-prey interactions tested under multiple conditions and levels of complexity. This facilitates model development, in turn leading to new hypotheses. We conclude by showing examples of current developments, that predator-prey interaction-based knowledge has the potential to provide novel medical tools and to improve environmental and agricultural management.

Horizontal transfer of transposable elements (TEs) is widespread in eukaryotes, driving genetic variation and often associated with bursts of TE activity. Here, we report a recent TE burst in the insect-pathogenic fungus Metarhizium anisopliae. The actively transposing TEs were likely introduced via hitchhiking on a so-called Starship, a class of large, horizontally transferable transposons. This TE burst likely triggered extensive structural reshuffling across all chromosomes, which was associated with loss of pathogenicity. Expanding our analysis to other fungi, we found that Starship-mediated horizontal transfer of TEs is a general phenomenon. Most (75%) of 522 reported Starships harbor TEs; many of which show evidence of a recent burst, in some cases likely starting from the TE copies on the Starship itself. A high fraction of TEs located on Starships also shows signatures of past horizontal transfer. Collectively, our results establish Starships as major vectors of horizontal TE transfer. Large mobile genetic elements known as Starships act as vehicles for transferring transposable elements (TEs) between fungi. Here, Griem-Krey et al. show that these ‘hitchhiking’ TEs can drive rapid evolution through genome reshuffling, which can alter fungal pathogenicity.