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December 5, 4:52 PM
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Starships are a recently discovered superfamily of extremely large mobile genetic element (MGE)s in fungi that encode diverse gene sequences, many of unknown function. Starships are widespread throughout filamentous Ascomycetes (Pezizomycotina), but relatively little is known about their fine-grained distributions at lower taxonomic levels. As more and more Starships are discovered, it is increasingly important to more effectively catalog their gene contents and taxonomic distributions to better understand their contributions to fungal evolution. To address this, we developed starbase, a web server and comparative toolkit for exploring Starship diversity and hypothesis generation. The starbase database is constructed from Starships identified from existing studies, as well as an exhaustive de novo survey of Starship sequences within a set of 19 863 publicly available fungal genome assemblies. This database consists of 5 493 Starships, their associated nucleotide sequences, captain gene protein sequences, cargo gene annotations, and other metadata pertaining to the annotation and analysis of Starships in fungal genomes. As a resource, starbase provides new avenues for studying structural variation in fungal genomes. starbase provides several key features for the research community: a centralized repository of curated Starship annotations, a standardized accessioning system enabling consistent referencing of elements across studies, tools for searching existing sequences and classifying novel Starships based on established classification schemes, and a submission portal encouraging community contributions. As Starship identification becomes a routine component of fungal genome annotation, starbase provides a framework for organizing this growing body of data and facilitating comparative analyses across the expanding landscape of fungal genomic diversity.
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December 5, 4:44 PM
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Bacterial membrane vesicles (BMVs) have attracted significant attention as highly efficient transport vehicles for molecules crossing biological barriers and as key mediators in infection processes. Based on this increasing interest, the need for standardized isolation protocols and comprehensive analytical approaches becomes apparent. Here, we evaluated BMVs from the human pathogen Pseudomonas aeruginosa, isolated at six distinct growth phases, using physicochemical assays, functional characterization, and Raman spectroscopy. Conventional analyses revealed growth phase-dependent differences in protein content, surface charge, and immunogenicity. Raman spectroscopy provided detailed molecular fingerprints, identifying shifts in protein-to-lipid ratios, increased lipid saturation, and alterations in protein secondary structure during later growth phases. Importantly, the absence of nucleic acid-specific spectral markers confirmed the outer membrane origin of the vesicles. Together, these findings demonstrate that the timing of BMV isolation critically determines their molecular composition and functional properties and establish Raman spectroscopy as a powerful label-free tool for semi-quantitative profiling of BMVs.
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December 5, 4:40 PM
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Transposons impact eukaryotic genome size and evolution. Horizontal transfer of transposable elements (HTT) is important for their long-term persistence, but it has only been systematically studied in animals, and thus the abundance, impact, and factors that shape HTTs in lineages outside animals are unknown. Fungi are at least as ancient and diverse as animals and are characterized by extensive genome size variation caused by transposons. Here, we screened 1,348 genomes across fungal biodiversity, genome sizes, and lifestyles to detect extensive HTTs, which generated on average 7%—but up to 70%—of the transposon content in some taxa. We in total identified at least 5,906 independent HTTs, mostly involving Tc1/Mariner DNA transposons. While the majority of HTTs occur between closely related taxa, irrespective of their lifestyles, HTTs were particularly common in Mucoromycotina, Sordariomycetes, Dothideomycetes, and Leotiomycetes. Importantly, species lacking fungal-specific defense mechanisms against transposons, and those with gene-sparse and repeat-rich genomic compartments, are involved in a significantly higher number of HTTs, unveiling ecological and genomic factors shaping HTTs. Our findings thus illuminate the dynamic landscape of HTTs in fungi, providing the framework to further study the impact of HTTs on genome evolution and the processes that mediate transposon transfers within and between eukaryotic lineages.
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December 5, 4:12 PM
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Biological discovery and design are increasingly being guided by predictive models in place of costly experimentation. However, existing datasets are often biased by overrepresentation from model organisms, leading to failures in evolutionary studies of non-model species. We present a hybrid framework that leverages high-throughput molecular assays and active learning to quantify biological properties across evolutionary space. We focus on transcriptional activators, which contain activation domains (ADs) that promote gene expression. ADs are intrinsically disordered and poorly conserved, which limits their study using comparative genomics. Here, we developed ADhunter, a high-capacity regression model that outperforms state-of-theart algorithms in identifying and quantifying the strength of transcriptional activators. Model uncertainty was used to guide evolutionary sampling across 7.8 million proteins from 2,400 fungal genomes. We functionally characterized 9,836 ADs from 1,071 fungal genomes, providing a 15.5-fold expansion in genome representation compared to existing datasets. Comprehensive sampling from non-model genomes improved model generalizability and provides the first functional annotation for 3,416 proteins from 670 non-model fungi. Model interpretability analysis aligns with the biophysical model of AD function and reveals novel, underrepresented protein codes, highlighting the importance of sampling from non-model organisms to build evolutionarily robust models for predicting biological properties.
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December 5, 9:08 AM
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Aneuploidy induces chromosomal scale alterations in gene dosage, impacting organismal proliferation yet serving as a driver for adaptive evolution. The complexity of gene dosage effects makes it challenging to elucidate the causal genetic basis of aneuploid consequence. Here, using loss-of-function screening with Synthetic Chromosome Rearrangement and Modification by LoxP-mediated Evolution (SCRaMbLE) in synthetic aneuploid yeast, in conjunction with gain-of-function testing in euploid yeast, we established a sufficient and necessary framework and discovered cases of nonlinear polygenic dosage effects in driving aneuploid phenotypes. We identified an emergent effect resulting from copy number alterations in a locus of five genes, which enhance trehalose biosynthesis and confer heat tolerance in aneuploid yeast with additional chromosome III. Additionally, a gene dosage-dependent antagonistic epistasis effect of two genes YCL039W and YCL037C determines rapamycin resistance in aneuploid yeast by regulating the Ras signaling pathway. Moreover, several cases of sufficiency-necessity asymmetries were found for other aneuploid traits. Together, our findings provide direct evidence of various dosage-dependent nonlinear polygenic interactions in shaping aneuploid phenotypes and advance understanding of the genetic basis of cellular adaptive evolution. Elucidating the genetic basis of aneuploid phenotypes has posed a challenge. Here, the authors use loss-of-function screening in synthetic aneuploid yeast and gain-of-function testing in euploid yeast to identify various dosage-dependent nonlinear polygenic interactions driving aneuploid traits.
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December 5, 12:36 AM
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Host-associated bacteria live amongst eukaryotes within varied niches and form relationships ranging from facultative to obligate. With advancement in studies of such symbiotic associations, fastidious bacteria are increasingly becoming targets for genetic manipulation. However, there are limited resources for screening possible agents enabling in vitro culturing and storage of these microbes. In this study, we present a simple protocol for optimizing cryopreservation of non-model organisms in laboratory settings using conventional chemicals. Our initial motivation for this observation was to discover a cryoprotection agent for independently cultured Mycetohabitans spp., a fungal endosymbiont. We tested several common bacterial cryoprotection agents like glycerol, bovine serum albumin (BSA), and dimethyl sulfoxide (DMSO) over an ultra-low freeze-thaw cycle to determine an adequate method of cryoprotection for assorted bacteria. We observed different recovery rates across bacterial species and cryopreservation methods, and identified cryoprotectants that reliably resulted in viable bacteria for each of the strains tested. We present this as a resource for those working with other fastidious and host-associated bacteria that may be missing effective cryopreservation methods.
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December 4, 11:37 PM
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The rhizosphere, often defined as the narrow layer of soil around plant roots, is a hotbed of microbial activity and is enriched with plant-derived metabolites that shape the root-associated microbiome. Several species of free-living rhizosphere microbes (known as rhizobacteria) have been identified in laboratory and small-scale experiments that enhance plant growth and adaptation to challenging environments. However, efforts to utilize these beneficial microbes on large scales have not always produced the anticipated results. A key bottleneck is the low rhizosphere competence of many of these rhizobacteria, described as their inability to effectively outcompete other soil-resident microbes and to colonize and thrive in the rhizosphere. Yet, root exudates contain metabolites that select for beneficial microbes, suggesting that the rhizosphere could be engineered to enable beneficial microbes applied in the field overcome their low rhizosphere competence and ultimately improve plant performance. This Review summarizes our current knowledge of how root exudates modulate root–microbe associations and discusses some outstanding questions, namely: (i) whether root exudation profiles could be rationally engineered to enhance the accumulation of specific metabolites in the rhizosphere to promote plant–beneficial microbe interactions, and the challenges that may come with this endeavour, and (ii) whether root exudation can be temporally engineered to benefit the plant at different developmental stages. Opportunities for rhizosphere engineering based on the dynamic nature of root exudate compositions are briefly discussed. Thus, this Review largely focuses on the significant promise of rhizosphere engineering to promote effective plant–beneficial microbe associations for improved plant performance and yield, while highlighting some potential pitfalls.
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December 4, 11:31 PM
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Microorganisms play important ecological roles during interactions with plants, with some strains promoting plant performance. However, the molecular basis of bacterial adaptation to the plant environment remains poorly understood. Microbial plant growth promotion is a complex process that likely involves numerous bacterial genes, many of which remain uncharacterized. In this study, we aimed to identify genes tightly associated with the bacterial adaptation to plant hosts by integrating transcriptomic data from bacteria colonizing roots with comparative genomic and metagenomic analyses. Here, we identified a set of bacterial genes that were significantly upregulated during root colonization and are more abundant in rhizosphere communities than in bulk soils. Many of these genes had not been previously linked to plant-bacteria interactions. Comparative genomic analyses revealed some of these genes as more prevalent in plant-associated Pseudomonas genomes than in genomes from other environments. We argue that these genes may play relevant biological roles in this host, although only a few have been previously associated with plant colonization. Among them, we focused on a gene homologous to yafL, which encodes a cysteine peptidase of the NlpC/P60 family, known for its role in peptidoglycan remodelling. This gene is more abundant in rhizosphere microbiomes than in bulk soils, and it showed induced expression on the root surface, supporting its ecological relevance in root-associated environments. Functional validation using a knockout mutant confirmed its contribution to plant-bacteria interactions by affecting root architecture and plant growth. This study provides new insights into the genetic basis of bacterial adaptation to the plant root environment. By integrating transcriptomic and comparative genomic analyses, we identified numerous genes upregulated during root colonization that are enriched in plant-associated Pseudomonas genomes. Our findings highlight previously overlooked bacterial functions with potential roles in plant–microbe interactions. The functional validation of a protein of the NlpC/P60 family supports its involvement in plant-bacteria interactions and underscores the importance of uncharacterized genes in shaping beneficial associations in the rhizosphere.
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December 4, 11:11 PM
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Genetically engineered microbes have the potential to increase efficiency in the bioeconomy by overcoming growth-limiting production stress. Screens of gene perturbation libraries against production stressors can identify high-value engineering targets, but follow-up experiments needed to guard against false positives are slow and resource-intensive. In principle, the use of orthogonal gene perturbation approaches could increase recovery of true positives over false positives because the strengths of one technique compensate for the weaknesses of the other, but, in practice, two parallel screens are rarely performed at the genome scale. Here, we screen genome-scale CRISPRi (CRISPR interference) knockdown and transposon insertion libraries of the bioenergy-relevant Alphaproteobacterium, Zymomonas mobilis, against growth inhibitors commonly found in deconstructed plant material. Integrating data from the two gene perturbation techniques, we established an approach for defining engineering targets with high specificity. This allowed us to identify all known genes in the cytochrome bc1 and cytochrome c synthesis pathway as potential targets for engineering resistance to phenolic acids under anaerobic conditions, a subset of which we validated using precise gene deletions. Strikingly, this finding is specific to the cytochrome bc1 and cytochrome c pathway and does not extend to other branches of the electron transport chain. We further show that exposure of Z. mobilis to ferulic acid causes substantial remodeling of the cell envelope proteome, as well as the downregulation of TonB-dependent transporters. Our work provides a generalizable strategy for identifying high-value engineering targets from gene perturbation screens that is broadly applicable.
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December 4, 7:30 PM
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DNA strand displacement (SD) reactions are central to the operation of many synthetic nucleic acid systems, including molecular circuits, sensors, and machines. Over the years, a broad set of design frameworks has emerged to accommodate various functional goals, initial configurations, and environmental conditions. Nevertheless, key challenges persist, particularly in reliably predicting reaction kinetics. In contrast to reviews centred on network-level architectures, this article focuses on the design and analysis of individual SD reactions, highlighting kinetic mechanisms, structural determinants, and the current limits of predictive modelling. We identify promising innovations while analysing the factors that continue to hinder predictive accuracy. We conclude by outlining future directions for achieving more robust and programmable behavior in DNA-based systems.
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December 4, 7:21 PM
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Efficient enzymes and microbial factories are essential to promote the transition toward a sustainable bioeconomy. This review focuses on the progress of artificial intelligence (AI) methods in accelerating the development of optimized biocatalysts and genetic networks in cells. Recent advances in AI in the field of enzyme discovery, engineering, and de novo design are discussed. Additionally, we highlight examples of successful applications of AI in optimizing different components in cells, from gene expression regulation to metabolic pathway optimization and design. Finally, this review emphasizes the challenges limiting the reliability and generalizability of current AI methods.
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December 4, 4:43 PM
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In microbial communities, bacteria can inhibit or facilitate each other by altering their shared environment. Most studies of these interactions have focused on well-mixed environments, leaving spatial effects underexplored. Here, we show that in an antibiotic-treated community, bacterial spread depends on a facilitation mechanism that only emerges in spatial settings. The facilitating species enables the community’s range expansion but is then suppressed to a minority, making it a hidden initiator of the expansion. Focusing on two pathogens, immotile Klebsiella pneumoniae and motile Pseudomonas aeruginosa, we found that both tolerate a β-lactam antibiotic, with Pseudomonas being more resilient and dominating in well-mixed cultures. During range expansion, however, the antibiotic inhibits Pseudomonas’ ability to spread unless it is near Klebsiella—which creates a clear zone by degrading the antibiotic, at the expense of its own growth. As Pseudomonas spreads, it competitively suppresses Klebsiella. Our modeling and experimental analyses reveal that this facilitation operates at a millimeter scale. We also observed similar facilitation by a Bacillus species isolated from a hospital sink, in both pairwise and eight-member bacterial communities with its co-isolates. These findings suggest that spatially explicit experiments are essential to understand certain facilitation mechanisms and have implications for surface-associated microbial communities like biofilms and for polymicrobial infections involving drug-degrading immotile and drug-tolerant motile bacteria. Here, the authors show that immotile bacteria degrade antibiotics to alter their environment and facilitate nearby motile competitors to expand. As expansion proceeds, the immotile degrader is outcompeted, becoming a hidden driver of community spread.
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December 4, 4:23 PM
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The interplay between diet and gut microbiome composition is complex. Faecal metabolites, the end products of human and microbial metabolism, provide insights into these interactions. Here, we integrate faecal metabolomics, metagenomics, and habitual dietary data from 1810 individuals from the TwinsUK and 837 from the ZOE PREDICT1 cohorts. Using machine learning models, we find that faecal metabolites accurately predict reported intakes of 20 food groups (area under the curve (AUC) > 0.80 for meat, nuts and seeds, wholegrains, tea and coffee, and alcohol) and adherence to seven dietary patterns (AUC from 0.71 for the Plant-based Diet Index to 0.83 for the Dietary Approaches to Stop Hypertension score). Notably, the faecal metabolome is a stronger predictor of atherosclerotic cardiovascular disease risk (AUC = 0.86) than the Dietary Approaches to Stop Hypertension score (AUC = 0.66). We identify 414 associations between 19 food groups and 211 metabolites, that significantly correlate with microbial α-diversity and 217 species. Our findings reveal that faecal metabolites capture mediations between diet and the gut microbiome, advancing our understanding of diet-related disease risk and informing metabolite-based interventions. Here, by integrating faecal metabolomics, metagenomics, and habitual dietary data of two large human cohorts, the authors show that faecal metabolites reflect diet and gut microbiome interactions, predict dietary patterns, and indicate cardiovascular risk, offering insights for diet-based health interventions.
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December 5, 4:49 PM
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Designing biological sequences that fold into predefined conformations is a central challenge in bioengineering. Although deep learning has enabled significant advances in protein and RNA sequence design, progress in single-stranded DNA (ssDNA) design has been constrained by the limited availability of structural data. To address this challenge, we introduce InvDNA, a deep learning-based method that designs ssDNA sequences directly from backbone atomic coordinates. This end-to-end formulation avoids the loss of structural information during backbone-to-feature conversion and further accommodates flexible backbone representations, dynamic sequence masking, and structural reconstruction objectives. All these training strategies enhance the capacity of InvDNA to generalize across diverse ssDNA structural contexts while enabling additional functionalities, including generating diversity sequences for a given backbone, reconstructing base conformations from backbone and preserving functional sites. In benchmarks using experimentally determined ssDNA structures, InvDNA achieves over a twofold improvement in sequence recovery compared with existing ssDNA design approaches. Further computational validation using AlphaFold3 shows that 44.4% of InvDNA-designed sequences successfully fold into their predefined conformations. Notably, this success rate increases when backbone coordinates are perturbed to diversify the InvDNA-designed sequences. Collectively, these results establish InvDNA as a robust framework for rational ssDNA engineering.
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December 5, 4:42 PM
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Aspergillus niger is a filamentous fungus widely used in industrial fermentation due to its strong safety record, genetic manipulability, and metabolic flexibility. These characteristics make it an ideal host for producing high-value metabolites such as organic acids, industrial enzymes, and secondary metabolites. Recent advancements in gene editing, genomics, and synthetic biology have significantly enhanced the development of A. niger as an efficient cell factory. This review provides a comprehensive overview of recent breakthroughs in metabolite production using A. niger, with a focus on engineering strategies that improve carbon source utilization, regulate metabolic pathways, enhance transport systems, and facilitate product secretion. It also highlights emerging approaches, including coculture induction and the activation of silent gene clusters, which further expand its biosynthetic potential. By analyzing representative case studies, we offer insights into the diverse industrial applications of A. niger in food, pharmaceuticals, and sustainable manufacturing, and suggest future directions for developing more robust synthetic biology platforms.
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December 5, 4:30 PM
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The widespread expression of therapeutic targets in both diseased and healthy tissues poses a major challenge for protein-based therapeutics, often leading to dose-limiting side effects. One promising strategy to enhance selectivity is reversible inactivation via affinity masks tethered through cleavable linkers responsive to disease-specific cues. Here, we introduce a workflow for the de novo design of peptide masks that reversibly inactivate miniprotein binders. By extending the C-terminus of the binder with a protease-cleavable linker and a masking helix, we generated minimal constructs that sterically block the receptor-binding interface. We applied this strategy to four therapeutically relevant targets, EGFR domains I and III, FGFR2, and IL7Rα, demonstrating broad applicability. Nearly half of the 20 designs achieved >100-fold affinity reduction, with the most effective mask decreasing EGFR binding by over 3 orders of magnitude. Upon cleavage by tumor-associated proteases, binding was restored in 19 out of 20 cases, confirming reversibility. We further show that micromolar or weaker affinity between the binder and the isolated mask is sufficient for robust inactivation and rapid activation. Additionally, by chemically conjugating a photocleavable linker, we created a light-responsive version of the masked binder, enabling external control with comparable efficiency to protease-sensitive designs. This work establishes a generalizable, rapid, and efficient platform for designing cleavable peptide masks from scratch, paving the way for conditionally active protein therapeutics responsive to endogenous or exogenous stimuli.
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December 5, 4:08 PM
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Accurate prediction of biomolecular complex structures is fundamental for understanding biological processes and rational therapeutic design. Recent advances in deep learning methods, particularly all-atom structure prediction models, have significantly expanded their capabilities to include diverse biomolecular entities, such as proteins, nucleic acids, ligands, and ions. However, comprehensive benchmarks covering multiple interaction types and molecular diversity remain scarce, limiting fair and rigorous assessment of model performance and generalizability. To address this gap, we introduce FoldBench, an extensive benchmark dataset consisting of 1522 biological assemblies categorized into nine distinct prediction tasks. Our evaluations reveal critical performance dependencies, showing that ligand docking accuracy notably diminishes as ligand similarity to the training set decreases, a pattern similarly observed in protein-protein interaction modeling. Furthermore, antibody-antigen predictions remain particularly challenging, with current methods exhibiting failure rates exceeding 50%. Among evaluated models, AlphaFold 3 consistently demonstrates superior accuracy across the majority of tasks. In summary, our results highlight significant advancements yet reveal persistent limitations within the field, providing crucial insights and benchmarks to inform future model development and refinement. Accurate all-atom structure prediction is essential for biology and medicine, yet systematic benchmarks remain limited. Here, authors introduce FoldBench, a dataset of 1,522 targets across nine tasks, mapping strengths and challenges across diverse biomolecular interactions.
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December 5, 12:59 AM
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Strigolactones (SLs) are carotenoid-derived phytohormones that regulate plant development and mediate rhizosphere interactions. Synthesized from β-carotene through a multistep enzymatic pathway, SLs modulate key physiological processes, including shoot branching, leaf development, flowering, and root growth. In addition to performing endogenous hormonal roles, SLs are exuded into the soil, where they serve as ecological signals. Exuded SLs facilitate symbiotic relationships with arbuscular mycorrhizal fungi AMF to enable nutrient exchange and are also exploited by parasitic weeds to locate host plants. Although the core SL biosynthesis and signaling pathways have been extensively characterized, research continues to uncover new layers of complexity in their regulation and function. Here, we present a comprehensive overview of SLs, summarizing the past major achievements and recent advances in their biosynthesis, transport, perception, and signal transduction, together with their multifaceted functions. We discuss current challenges in SL research and highlight important questions for future investigations. Addressing these issues can further enhance our understanding of the SL pathway and promote its application in agriculture. review
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December 4, 11:44 PM
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Saccharomyces cerevisiae is a widely used chassis in metabolic engineering. Due to the Crabtree effect, it preferentially produces ethanol under high-glucose conditions, limiting the synthesis of other valuable metabolites. Conventional metabolic engineering approaches typically rely on irreversible genetic modifications, making it insufficient for dynamic metabolic control. In contrast, optogenetics offers a reversible and tunable method for regulating cellular metabolism with high temporal precision. In this study, we engineered the pyruvate decarboxylase isozyme 1 (Pdc1) by inserting the photosensory modules (AsLOV2 and cpLOV2 domains) into rationally selected positions within the enzyme. Through a growth phenotype-based screening system, we identified two blue light-responsive variants, OptoPdc1D1 and OptoPdc1D2, which enable light-dependent control of enzymatic activity. Leveraging these OptoPdc1 variants, we developed opto-S. cerevisiae strains, MLy-9 and MLy-10, which demonstrated high efficiency in modulating both cell growth and ethanol production. These strains allow reliable regulation of ethanol biosynthesis in response to blue light, achieving a dynamic control range of approximately 20- to 120-fold. The opto-S. cerevisiae strains exhibited dose-dependent production in response to blue light intensity and pulse patterns, confirming their potential for precise metabolic control. This work establishes a novel protein-level strategy for regulating metabolic pathways in S. cerevisiae and introduces an effective method for controlling ethanol metabolism via optogenetic regulation.
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December 4, 11:33 PM
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AlphaGenome, recently announced in a preprint by Avsec et al., is Google DeepMind’s powerful ‘Swiss army knife’ for predicting molecular effects from non-coding DNA. Remarkably, it does so with base-pair resolution while maintaining long-range context. Here, we discuss AlphaGenome’s promise and limitations in prioritizing and functionally interpreting non-coding variants underlying human traits and diseases.
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December 4, 11:15 PM
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The human microbiome plays a crucial role in maintaining homeostasis and influencing disease development, yet its composition varies across geography, age, and lifestyle. These differences challenge the efficacy of universal probiotic treatments and call for more personalized or regionally adapted approaches. In this review we examine the limitations of universal probiotics, emphasizing the importance of considering host–microbe co-adaptation, local dietary practices, and ecological context. We argue that probiotic design must account for microbial diversity, strain-level adaptation, and functional redundancy, and we explore how these factors affect colonization success and therapeutic potential. Finally, we discuss ways to re-center microbiome knowledge within diverse ecological, cultural, and epistemic traditions for a global, inclusive approach allowing for microbiome-targeted therapies that are both effective and accessible.
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December 4, 8:03 PM
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Gene expression shapes phenotypes and evolution. However, studies of gene regulation focus on transcription factors, overlooking core promoters. To investigate how promoters emerge and regulate transcription, we determined the sequence−function landscapes of core elements, -35 and -10, in constitutive and transcription factor-regulated promoters in Escherichia coli. Characterization of in vivo transcriptional landscapes and in vitro RNA polymerase−promoter interactions showed the -10 element as essential for promoter evolution from random sequences. In contrast, the -35 element, though broadly conserved, is dispensable for promoter birth. Instead, it exerts greater impact on gene regulation via coordinated interactions with transcription activators and RNA polymerase. We further showed that evolution fine-tunes the -35 and -10 sequences of transcription factor-regulated promoters to achieve near-maximal fold changes by lowering basal while elevating induced expression. A notable exception is PluxI, whose leaky expression provides a crucial baseline for initiating quorum sensing. These findings elucidate promoter design principles and underscore the interdependence and coevolution of core elements, RNA polymerase, and transcription factors.
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December 4, 7:29 PM
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Engineered living materials (ELMs) combine living cells, typically microorganisms, such as bacteria, yeasts, or filamentous fungi, with structural carrier matrices to form systems capable of sensing, growth, and self-repair. Most current designs emphasize programming the microbes to render otherwise static materials functional. A less explored dimension is leveraging reciprocal microbial–material interactions themselves to engineer adaptive and evolving living materials as integrated systems. Achieving such dynamic behavior requires understanding how support matrices influence microbial behavior and how cells, in turn, reshape material properties over time. This review outlines key modes of cell–material interactions as a framework for expanding the functional toolbox of ELMs and for creating sustainable and programmable materials that respond to their environments and evolve.
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December 4, 7:18 PM
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Nucleocytoplasmic large DNA viruses (NCLDVs) encode multi-subunit RNA polymerases (msRNAPs) that challenge conventional views of viral evolution. Phylogenetic and structural studies reveal that NCLDV RNAP catalytic cores share deep evolutionary roots with eukaryotic counterparts, implicating ancient gene transfers that shaped the last eukaryotic common ancestor (LECA), underscoring NCLDVs’ pivotal role in eukaryotic origins. NCLDV RNAP retains the fundamental architecture of cellular RNAPs while evolving and adapting for viral gene regulation. This review summarizes structural and functional divergences between viral and cellular RNAPs, synthesizes evidence for virus-driven RNAP evolution, and evaluates emerging hypotheses of viral eukaryogenesis. Viewing viruses as evolutionary collaborators offers new insights into RNAP adaptability and bridges virology, evolutionary biology, and synthetic biology across diverse biological contexts.
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December 4, 4:34 PM
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The coexistence of diverse microbial communities despite the common presence of antimicrobial weapons presents a fundamental puzzle in ecology. To address this issue, we investigate the role of antibiotic-mediated interactions in driving microbial diversity using methods from graph theory and theoretical ecology. Our exhaustive analysis of small interaction graphs involving antibiotic production, resistance, and degradation reveals that two factors together determine whether an interaction pattern can support coexistence or not: a certain producer-sensitive-degrader (PSD) motif is critical, and a form of cyclicity between the action of different antibiotics is necessary. Using individual-based simulations, we also explore the role of the PSD motif in spatially structured populations and demonstrate that community coexistence is robust over a wide range of antibiotic and degrader diffusivities. Our findings provide a deeper understanding of the interaction patterns that drive diversity in complex microbial communities. Specifically, we emphasize how antagonism does not preclude biodiversity. These results offer clear pathways for cultivating synthetic microbial consortia, enabling the design of more effective strategies for manipulating microbial communities.
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