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Plug-and-play evolution of the Klebsiella pneumoniae capsule locus enables serotype exchange across genetic backgrounds | PLOS

Plug-and-play evolution of the Klebsiella pneumoniae capsule locus enables serotype exchange across genetic backgrounds | PLOS | RMH | Scoop.it

Understanding how complex, multi-gene systems evolve and function across genetic backgrounds is a central question in molecular evolution. While such systems often impose costs through epistatic interactions, some may behave as modular, “plug-and-play” units that retain function with minimal disruption. We used the polysaccharide capsule locus of Klebsiella pneumoniae, a highly exchangeable and fast-evolving locus, as a model. We genetically engineered capsule exchanges (swaps) across diverse genetic backgrounds and combined transcriptomics, fitness assays, and evolution experiments, to show that capsule exchange has negligible effects on global expression and only marginal fitness costs, regardless of capsule type (or K type). Adaptation to capsule-costly environments consistently reduced capsule production, regardless of K type, revealing shared adaptive trajectories rather than K type-specific pathways. Moreover, K type-specific traits involved in bacterial virulence, such as biofilm formation and hypermucoviscosity, were conserved across genetic backgrounds. This reveals that capsule swapping can directly shape host-pathogen interactions and influence within-patient evolution. Our findings provide strong evidence that capsule loci display plug-and-play dynamics: they are transferable, functional across contexts, and minimally disruptive to the host genome. This allows capsules to be seamlessly swapped, and help explain the evolutionary success, ecological versatility, and pervasive exchangeability of capsules in K. pneumoniae.

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Diversity-triggered 2-naphthoic acid exudation recruits keystone microbial taxa to promote soybean drought tolerance | chm

Diversity-triggered 2-naphthoic acid exudation recruits keystone microbial taxa to promote soybean drought tolerance | chm | RMH | Scoop.it
Rhizosphere microbiomes are essential for plant growth and stress tolerance, yet how microbial diversity shapes drought resilience in soybean remains unclear. Here, we demonstrate that high rhizosphere microbial diversity, generated via dilution-to-extinction manipulation of soil microbiome diversity, improves soybean performance under drought. Integrated metabolomic and transcriptomic analyses identify 2-naphthoic acid as a diversity-induced root exudate that accumulates exclusively under drought. This metabolite selectively recruits Sinorhizobium CS204 via chemotaxis and ATP-binding cassette (ABC) transporter-mediated uptake, as confirmed by in vitro substrate utilization assays and targeted mutant construction. Molecular docking and microscale thermophoresis reveal direct interactions between this metabolite and nitrogen-cycling proteins, enhancing denitrification and nitrogen fixation of S.CS204. Co-application of 2-naphthoic acid and S.CS204 significantly improves plant nutrient acquisition and photosynthesis under drought. Collectively, our study underscores the pivotal role of rhizosphere microbial diversity in triggering the exudation of root metabolites to recruit keystone taxa, establishing microbe-plant synergies that bolster drought tolerance.
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Systematic study of genomic loci in Escherichia coli B and K12 for genomic integration: application in plasmid-free astaxanthin production | meg

Systematic study of genomic loci in Escherichia coli B and K12 for genomic integration: application in plasmid-free astaxanthin production | meg | RMH | Scoop.it
E. coli is a major workhorse for industrial biotechnology, yet reliance on plasmids is hindered by intrinsic instability. While genomic integration offers superior stability, it remains challenging for integrating large DNA fragments, particularly in difficult-to-engineer B strains (e.g., BL21) compared to K12 strains like MG1655. Moreover, a systematic study comparing genomic expression across B and K12 strains and different loci is absent. To address these issues, we firstly refined a CRISPR methodology that achieved unprecedented integration efficiencies in both strains: 40% for single-step insertion of a 17.5-kbp fragment in BL21 and 100% for a 9.5-kbp fragment in MG1655. Using this updated workflow, we rationally screened and compared 10 genomic loci in BL21 and MG1655 across varying media. The results demonstrated that host strain and genomic locus selection are more critical for expression than carbon source or medium composition. Although BL21 generally exhibits higher gene expression than MG1655, expression at certain loci can be very low. Therefore, we highlight the need for greater caution in locus selection for BL21 due to its higher variability. We leverage our validated loci for the scarless and marker-less integration of the astaxanthin synthesis pathway (23 genes, including multi-copy key genes, crtYcrtZcrtWcrtEcrtB, and crtI) into the BL21 chromosome. The resulting plasmid-free strain produced 426 mg/L astaxanthin in glucose defined medium, showcasing a robust and generalizable strategy for complex pathway integration and optimization in challenging host strains.
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landing pad

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Metabolic engineering of microbial pathways for 5-aminolevulinic acid biosynthesis: Recent advances and biotechnological applications | meg

Metabolic engineering of microbial pathways for 5-aminolevulinic acid biosynthesis: Recent advances and biotechnological applications | meg | RMH | Scoop.it
5-Aminolevulinic acid (5-ALA) is a basic precursor in the bioproduction of tetrapyrrole compounds, such as heme, chlorophyll, and vitamin B12. Its extensive applications in agriculture, medicine, cosmetics and animal feed industry have garnered substantial attention. In agriculture, 5-ALA works as an effective plant growth regulator, improving crop yield, stress tolerance, and product quality. Nonetheless, old chemical production of 5-ALA faces challenges because of complex reaction pathways, low productivity, and environmental concerns. This has directed towards a rising interest in sustainable microbial production approaches. Latest developments in metabolic engineering and synthetic biology have assisted the development of effective bacterial cell factories for 5-ALA bioproduction. Key approaches comprise optimizing precursor supply and cofactor regeneration, reworking inborn C4 and C5 biosynthetic pathways, classifying and alleviating rate-limiting enzymes, engineering transport systems for 5-ALA secretion, decreasing competing pathways, and reducing by-product formation. Furthermore, the addition of biosensor-based vigorous regulation and systems-level metabolic control has considerably boosted production robustness and yield. These technical novelties have accelerated the evolution of microbial 5-ALA production from the laboratory to industrial uses. This review offers a comprehensive overview of recent advancements in bacterial 5-ALA bioproduction, focusing on host selection, pathway engineering strategies, regulatory mechanisms, and evolving biotechnological applications. Lastly, it discusses present challenges and future perspectives to guide the development of next-generation microbial platforms for competent and sustainable 5-ALA synthesis.
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PM-BioPred: A Web-Server for Prediction of Compound Bioactivity Against Plant and Microbial Proteins | jmb

PM-BioPred: A Web-Server for Prediction of Compound Bioactivity Against Plant and Microbial Proteins | jmb | RMH | Scoop.it
Understanding protein–ligand interactions in plants and microbes is essential for advancing agricultural biotechnology and developing antimicrobial strategies. In plants, these interactions govern critical physiological processes such as growth, immunity, and stress response, while in microbes, they influence pathogenicity, virulence, and survival. Experimental determination of bioactive compound–protein interactions is labor-intensive and limited in scope, and existing computational methods often suffer from rigid modeling assumptions. In this study, we collected and curated experimentally validated active and inactive compounds against plant and microbial target proteins from publicly available databases. Using this curated dataset, we developed machine learning-based classification models that predict compound bioactivity with an accuracy of 86% for plant proteins and over 90% for microbial proteins. These models form the core of PM-BioPred, a web-accessible prediction server designed to assist researchers in identifying potentially bioactive compounds against plant and microbial targets. PM-BioPred provides a user-friendly interface for submitting compound queries and retrieving bioactivity predictions. PM-BioPred is freely accessible to the academic community at https://pmbiopred.streamlit.app/ and serves as a valuable resource for plant-pathogen interaction research and compound repurposing efforts. The platform aims to facilitate the early-stage screening of agrochemicals and antimicrobial candidates.
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predict compound bioactivity against plant and microbial proteins via machine learning.

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Towards the targeted activation of silent biosynthetic gene clusters by chemical elicitors | badv

Towards the targeted activation of silent biosynthetic gene clusters by chemical elicitors | badv | RMH | Scoop.it
Systematic genome mining has revealed that microbes encode numerous uncharacterized secondary metabolite biosynthetic gene clusters (BGCs). The efficient and selective activation of these silent or cryptic BGCs is crucial for the high-throughput discovery of novel natural products. Recent influential studies have demonstrated that using small chemical elicitors is a practical and cost-effective method to unlock the secondary metabolic potential of microbes. However, the current approach mainly relies on high-throughput, non-targeted screening methods to discover chemical elicitors capable of activating these silent BGCs. Therefore, this study comprehensively reviews reported cases of small molecules that activate silent BGCs, covering the chemical structures of elicitors, resulting natural products, and target BGCs, thereby constructing an integrated knowledge graph. We also summarize the underlying activation mechanisms. Leveraging relationships captured in this graph, we outline directions for targeted activation of silent pathways using small molecules, thereby facilitating more efficient natural product discovery.
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Advances in Biomolecular Condensates: From Disease Therapy to Synthetic Biology | cbc

Advances in Biomolecular Condensates: From Disease Therapy to Synthetic Biology | cbc | RMH | Scoop.it

Biomolecular condensates (BMCs) are membraneless organelles (MLOs) formed by liquid–liquid phase separation (LLPS) of biomolecules such as proteins and nucleic acids (NAs). They are widely present in organisms and participate in key life processes such as gene transcription regulation. Its dynamic, environmental responsiveness and selective enrichment capabilities lay the foundation for biomedical and engineering biology applications and can be used as innovative targets for new drug research and disease treatment, for building bioreactors, and for developing intelligent biomaterials as well as delivery systems. At present, the related research is converted into various frontier applications, especially providing new intervention strategies for previously undruggable targets. This paper reviews the latest breakthroughs, technical challenges, and future directions from the perspective of frontier applications.

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review provides a systematic elucidation of the applications of biomolecular condensates, an emerging frontier in biomedical research. The content is structured around 4 core dimensions: 1. Disease Diagnosis and Targeted Therapy: revealing the role of condensate dysregulation in pathogenesis and exploring therapeutic strategies that target them. 2. Novel Drug Delivery Systems: introducing the design of intelligent and efficient drug delivery platforms using bio-inspired or engineered condensates. 3. Synthetic Biology and Artificial Cells: discussing the programming and control of condensates to construct synthetic modules and even artificial cells with life-like functions. 4. Advances in Tools and Technologies: highlighting key technological innovations, from high-resolution imaging to computational modeling, that are driving the field forward.

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Benchmarking and Experimental Validation of Machine Learning Strategies for Enzyme Engineering | brvai

Benchmarking and Experimental Validation of Machine Learning Strategies for Enzyme Engineering | brvai | RMH | Scoop.it

Enzyme-directed evolution increasingly relies on computational tools to prioritize mutations, yet their practical value is difficult to assess because kinetic data are often aggregated across heterogeneous assay conditions, inflating apparent generalization. Here we introduce EnzyArena, a curated benchmark that groups kinetic parameters (kcat, Km, kcat/Km) into condition-matched experimental subsets to enable realistic evaluation. Using this resource, we benchmark 10 representative models from two arising strategy families-zero-shot fitness prediction and supervised kinetic-parameter prediction-across BRENDA- and SABIO-RK-derived subsets and 25 independent mutagenesis datasets. Kinetic-parameter predictors perform strongly on database-derived subsets but lose their advantage on independent datasets, whereas zero-shot predictors show more consistent generalization. A simple consensus of multiple zero-shot models further improves the precision of identifying beneficial mutants. We prospectively validated these findings in a wet-lab campaign (150 mutants) comparing random mutants, UniKP-prioritized mutants and ESM-1v-prioritized mutants (representing supervised kinetic-parameter prediction and zero-shot fitness prediction, respectively), where ESM-1v achieved the highest utility and UniKP underperformed the random baseline. Together, this study establishes realistic baselines for computational mutant prioritization and highlights consensus zero-shot strategies as a practical starting point for enzyme engineering.

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Plug-and-play evolution of the Klebsiella pneumoniae capsule locus enables serotype exchange across genetic backgrounds | PLOS

Plug-and-play evolution of the Klebsiella pneumoniae capsule locus enables serotype exchange across genetic backgrounds | PLOS | RMH | Scoop.it

Understanding how complex, multi-gene systems evolve and function across genetic backgrounds is a central question in molecular evolution. While such systems often impose costs through epistatic interactions, some may behave as modular, “plug-and-play” units that retain function with minimal disruption. We used the polysaccharide capsule locus of Klebsiella pneumoniae, a highly exchangeable and fast-evolving locus, as a model. We genetically engineered capsule exchanges (swaps) across diverse genetic backgrounds and combined transcriptomics, fitness assays, and evolution experiments, to show that capsule exchange has negligible effects on global expression and only marginal fitness costs, regardless of capsule type (or K type). Adaptation to capsule-costly environments consistently reduced capsule production, regardless of K type, revealing shared adaptive trajectories rather than K type-specific pathways. Moreover, K type-specific traits involved in bacterial virulence, such as biofilm formation and hypermucoviscosity, were conserved across genetic backgrounds. This reveals that capsule swapping can directly shape host-pathogen interactions and influence within-patient evolution. Our findings provide strong evidence that capsule loci display plug-and-play dynamics: they are transferable, functional across contexts, and minimally disruptive to the host genome. This allows capsules to be seamlessly swapped, and help explain the evolutionary success, ecological versatility, and pervasive exchangeability of capsules in K. pneumoniae.

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rocha 

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Bioproduction, bioprotection, and biocontainment in multi-Kingdom microbial systems with 3D spatial control

Bioproduction, bioprotection, and biocontainment in multi-Kingdom microbial systems with 3D spatial control | RMH | Scoop.it

Engineered living materials (ELMs) are a class of hybrid materials that include engineered microbes encapsulated by a polymer matrix. The biotic and abiotic components define the ELMs design space and can be altered to improve performance and function. While current synthetic materials in the field display robust biocompatibility with both native and engineered living systems, we have a limited understanding of how to leverage 3D form factors to spatially organize and control microbial dynamics within the material. Motivated by this knowledge gap, we employed extrusion-based 3D printing to fabricate multi-material hydrogel constructs for the encapsulation of both single and dual-microbial systems. Core-shell cubic constructs enabled the spatial organization of a constitutive multi-kingdom system of levodopa (L-DOPA)-producing E. coli and betaxanthins-producing S. cerevisiae. This deliberate spatial organization in 3D materials can introduce precise control over bioproduction, bioprotection, and biocontainment, features that are critical to the efficacy of current ELMs. The relative spatial organization of the organisms, as well as the surface area-to-volume ratio were investigated to determine how these design elements impact microbial behavior (metabolite production, growth, expression and cell distribution) over time. We demonstrated that F127-BUM core-shell geometries enable the hierarchical 3D printing of multi-kingdom constructs, offering customizable control over bioproduction, bioprotection, and biocontainment. With the optimization of these core-shell structures for continuous bioproduction, these ELMs could be deployed as compact, sustainable bioreactors in remote environments.

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Go Big or go home: a new gene ontology subset that improves plant gene function prediction | Pmet

The availability of gene function prediction datasets helps researchers to consider possible functions for uncharacterized genes for hypothesis generation, candidate gene prioritization, and many other applications. Many such datasets are based on the Gene Ontology (GO) function graph. For plants this can be problematic because the most specific GO terms available are often derived from the biology of non-plant taxa (e.g., functions specific to nerve function would not seem likely to map to plant biological processes given that plants lack nerves). To balance the need for functional specificity while limiting to functions relevant to plant biology, researchers often limit to the GO Slim plant subset, but, by design, that subset consists of very general terms and limits real utility for, e.g., specific hypothesis generation. Worse yet, sometimes researchers choose to simply throw out terms if they are not relevant to plant biology (rather than traversing the GO graph to select the most specific term in that hierarchy that is compatible with plant biology). We created GO Big, a Gene Ontology subset type, to improve the biological relevance of gene function predictions for taxon-specific biology applications. GO Big plant subsets retain maximal functional specificity for hypothesis generation while limiting to terms applicable to the biology of plants. In brief, we used a curatorial approach to generate two GO Big subsets, a general subset derived from terms with experimentally validated functions across Viridiplantae species, and a species-specific subset for maize (Zea mays ssp. mays). Annotating genes with assignments that better reflect the biology of a taxon can pave the way for more biologically accurate and testable hypotheses for genes of interest. The subsets produced here can help plant biologists limit genome-wide gene function prediction sets to functions possible for plant genes, and the process to generate GO Big subsets is described in detail to enable others to create GO Big subsets for additional taxon sets, including ones for protists, fungi, and other phylogenetic categories. annotation

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CrisprPr: a hybrid-driven framework for CRISPR/Cas9 off-target prediction with analysis of prior-information updates | bft

CRISPR/Cas9 specificity is critically affected by off-target effects. However, the complex patterns of mismatches and their combinations at off-target sites remain difficult to capture, and existing approaches show limited capacity to identify informative features. Here, we present CrisprPr, a hybrid-driven off-target prediction framework that integrates both prior information and data-driven modeling to improve the characterization of off-target activity. CrisprPr employs a synchronous updating strategy that jointly optimizes prior-knowledge and deep-learning modules, together with multi-source integration, to deliver accurate and stable off-target predictions. Evaluations on independent test sets indicate that CrisprPr achieves competitive predictive performance and generalization compared with existing deep learning methods, with statistically significant improvements observed on several datasets. Beyond predictive performance, its analysis module examines the patterns of prior embedding-space updates to reveal distinctive target-site features supported by literature evidence. Overall, CrisprPr proposes a novel framework that demonstrates competitive predictive performance while offering new insights into the characteristics of off-target effects.

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grna design

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SalmonAct deciphers transcription factor regulatory activity in Salmonella transcriptomics | mSys

SalmonAct deciphers transcription factor regulatory activity in Salmonella transcriptomics | mSys | RMH | Scoop.it
Foodborne Salmonella enterica infection remains a major public health threat due to its prevalence in food, ease of transmission, and increasing antibiotic resistance. Consequently, understanding the molecular mechanisms underlying Salmonella pathogenesis is crucial for guiding novel diagnostic, preventative, and therapeutic approaches. High-throughput transcriptomic technologies are now often employed in Salmonella research to quantify how gene expression changes in response to various conditions or mutations. But due to the high dimensionality of this data, resulting from the comparison of the expression of thousands of genes from multiple strains or culture conditions with complex interactions, interpretation remains a demanding task. To address this issue, we present SalmonAct, a comprehensive signed and directed prior knowledge resource for inferring transcription factor activities in Salmonella. This new resource expands the toolkit available for Salmonella functional analysis. Built as an extension of the SalmoNet2 database, SalmonAct can be used to infer the activity of 191 transcription factors in 5,991 regulatory interactions based on publicly available interaction and regulatory knockout data. SalmonAct enhances the interpretation of highly dimensional transcriptomic data by identifying both highly influential and minimally active transcription factors that drive the observed expression state. SalmonAct aids in bridging the gap between model and non-model organisms' functional analysis, and together with the SalmoNet2 resource, can be used for further downstream data analyses.
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tfs http://salmonet.org/ 

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Two new factors modulating Bacillus subtilis spore resistance and germination | mSph

Two new factors modulating Bacillus subtilis spore resistance and germination | mSph | RMH | Scoop.it
New effectors of spore properties have been identified in two Bacillus subtilis strains—bacillithiol, the major low-molecular-weight thiol in B. subtilis, and sporulene, a multi-ring compound synthesized from curcumene in spores’ outer layers. The absence of either bacillithiol or sporulene caused faster germination of spores of one wild-type B. subtilis strain, PS832, with two germinant receptors (GR)-dependent germinants, L-valine for the GerA GR, and the AGFK mixture for the cooperative action of the GerB and GerK GRs, as well as the GR-independent germinant dodecylamine. However, these effects on PS832 spore germination were due to the presence of the antibiotic marker used to replace the genes for bacillithiol or sporulene synthesis. The absence of bacillithiol also caused reduced spore resistance to wet heat but not to hydrogen peroxide or UV radiation, while sporulenes’ absence had no effect on spore wet heat resistance, but reduced spore resistance to hydrogen peroxide as found previously and to UV radiation, but the presence of the antibiotic markers in these mutant strains was not responsible for these effects. Notably, spores lacking sporulene were much whiter than wild-type (wt) spores, suggesting that sporulene and its precursor curcumene contribute to spore pigmentation that can absorb UV, thus reducing spores’ UV resistance. Analyses of reasons for the effects of bacillithiol’s or sporulene’s absence on spore resistance, specifically core water content and/or altered spore inner membrane (IM) permeability, found no major differences from values in wt spores. However, other analyses showed that sporulene’s absence led to slight changes in spore IM fluidity.
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Cereal protein biofortification at the interface of nutrition, yield and sustainability | Npt

Cereal protein biofortification at the interface of nutrition, yield and sustainability | Npt | RMH | Scoop.it

Protein malnutrition remains a major global health challenge, particularly in regions where cereal grains dominate daily diets and access to diverse protein sources is limited. Cereals such as rice, wheat and maize provide most of the world’s calories, yet their grain proteins are often low in essential amino acids and poorly balanced for human nutrition. Improving both the quantity and quality of cereal protein therefore represents a critical opportunity to enhance human health while reducing reliance on environmentally intensive animal-based foods. In this Review, we synthesize recent advances in understanding how grain protein content and composition are regulated in cereals, and why protein enhancement has historically been constrained by trade-offs with starch accumulation and yield. We discuss how domestication and modern breeding reshaped carbon and nitrogen allocation in cereal grains, creating a starch-dominant optimum that limits protein concentration. Drawing on genetic studies from rice, maize and wheat, we highlight emerging strategies that improve nitrogen acquisition, amino acid transport, storage protein composition and endosperm buffering capacity, enabling partial decoupling of protein accumulation from yield penalties. Finally, we place cereal protein biofortification within a broader nutritional and environmental context. Enhancing protein density and amino acid balance in staple cereals can improve dietary adequacy for vulnerable populations while lowering greenhouse gas emissions per unit of nutrition. Together, these insights position cereal protein biofortification as a scalable and equitable pathway towards healthier diets and more sustainable food systems under global climate and population pressures. Cereal protein biofortification can improve nutrition while maintaining yield and lowering environmental impact. This Review shows how genetics and breeding can enhance protein quality in staple cereals to support healthier and more sustainable diets.

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high protein cereal

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Engineering synthetic biology sensors with artificial intelligence: From programmable circuits to next-generation biosensing | Badv

Engineering synthetic biology sensors with artificial intelligence: From programmable circuits to next-generation biosensing | Badv | RMH | Scoop.it
Artificial intelligence (AI) is advancing synthetic biology biosensors (SBBs), driving a fundamental shift from rational design to AI-driven prediction. This review establishes a systematic framework linking AI algorithms to the Design-Build-Test-Learn (DBTL) cycle. We explicitly analyze the engineering paradigms of AI-enabled cell-based SBBs and AI-optimized cell-free SBBs, highlighting how computational intelligence addresses platform-specific bottlenecks. Crucially, we synthesize the AI-driven workflow into three core frontiers: AI-guided robust sensor element design, AI-assisted signal processing for accurate performance characterization, and AI-driven closed-loop optimization to accelerate autonomous evolution. Furthermore, representative applications of SBBs are investigated, including multi-pollutant environmental detection, continuous biomarker monitoring, food safety tracking, and intelligent biomanufacturing. Beyond achievements, we critically evaluate unresolved obstacles, notably the “reality gap” and the “small-data dilemma”. Finally, we propose a roadmap centered on bio-digital hybrid interfaces, explainable AI, and data standardization to accelerate the transformation of SBBs into robust, field-deployable intelligent sensing systems.
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Recent advances in balancing the growth-production trade-off in Bacillus subtilis: A systematic resource reallocation perspective | badv

Recent advances in balancing the growth-production trade-off in Bacillus subtilis: A systematic resource reallocation perspective | badv | RMH | Scoop.it
As an excellent industrial chassis, Bacillus subtilis plays a pivotal role in promoting the efficient and sustainable manufacturing of high-value biological products. However, the intrinsic resource trade-off between growth and production remains a formidable barrier for biosynthetic efficiency. Therefore, from the perspective of cellular resource allocation, this review provides a comprehensive analysis through a holistic framework encompassing synthetic toolkits, mechanisms, engineering strategies, and industrial applications. We analyze the fundamental limitations of resource partitioning, focusing on the proteome allocation competition driven by heterologous synthesis and the global physiological feedback responses triggered by metabolic burden. To address resource constraints, the paper discusses fundamental engineering modules to enhance strain robustness and metabolic capacity, as well as system-level strategies for rational resource allocation. Finally, by showcasing diverse industrial applications, this work provides profound insights into overcoming metabolic trade-offs and lays the foundation for the rational design of high-performance B. subtilis cell factories.
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Orthogonal quorum sensing circuits enable dynamic regulation in Escherichia coli | meg

Orthogonal quorum sensing circuits enable dynamic regulation in Escherichia coli | meg | RMH | Scoop.it
Engineers have effectively employed quorum sensing (QS) in a variety of applications to dynamically regulate gene expression. Particular emphasis has been placed on the class of well-studied systems that use acyl homoserine lactones (AHL) as signaling molecules due to their ease of implementation, high expression level, and previous optimization efforts. However, many of these AHL systems respond to ligands with similar structures, causing crosstalk when combined in multi-layered regulation strategies. Here, we first confirmed the functional orthogonality of the previously identified Tra and Rpa quorum sensing circuits within a single strain of E. coli MG1655 by analyzing the pairwise interactions of several AHL systems. The orthogonality of the systems allowed for independent tuning of two control strategies, which was then applied to the naringenin biosynthetic pathway. The Tra system was used to activate expression of tyrosine-ammonia lyase (TAL) and 4-coumaroyl-CoA ligase (4CL), controlling the expression of the upstream pathway. Meanwhile, Rpa dynamically downregulated competing pathways of native metabolism via CRISPRi to increase availability of malonyl-CoA. This multi-layered approach provided finely-tuned metabolic control that allowed for a combinatorial screening of optimal dynamic regulation. A strain library with varying promoter strengths was then built to test AHL induction timings and screened for target compound production. Naringenin production from this autoinducible method reached a final titer of 71.02 ± 3.96 mg/L in flask-scale fermentation.
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IDBSpred: An intrinsically disordered binding site predictor using machine learning and protein language model | brvai

Intrinsically disordered proteins (IDPs) mediate many cellular functions through interactions with structured protein partners, but predicting the corresponding binding sites on the structured partner remains challenging. Here, we present IDBSpred, a sequence-based method for residue-level prediction of IDP-binding sites on structured proteins. Training and test data were collected from the DIBS database, which contains more than 700 non-redundant IDP-protein complexes. Residue-level embeddings of structured partner sequences were generated using the ESM-2 protein language model and used as input to a multilayer perceptron classifier for binary prediction of binding versus non-binding residues. Analysis of amino acid composition showed that IDP-binding sites are enriched in aromatic residues, especially Trp, Tyr, and Phe, as well as several charged and polar residues, whereas Ala and several small or conformationally restrictive residues are depleted. The classifier achieved an ROC AUC of 0.87 and an average precision of 0.61. Structural case studies further showed that the predicted sites largely recapitulate the major experimentally defined binding interfaces. These results demonstrate that protein language model embeddings plus machine learning algorithms can effectively capture sequence features associated with IDP recognition on structured proteins. IDBSpred provides a practical framework for studying IDP-mediated interfaces and identifying potential therapeutic hotspots.

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Advances in large-scale DNA engineering with the CRISPR system

Advances in large-scale DNA engineering with the CRISPR system | RMH | Scoop.it

In recent years, DNA engineering technology has undergone significant advancements, with CRISPR-based target-specific DNA insertion emerging as one of the most rapidly expanding and widely studied approaches. Traditional DNA insertion technologies employing recombinases typically involve introducing foreign DNA into genes in vivo by either pre-engineering recognition sequences specific to the recombinase or through genetic crossing to incorporate the requisite recognition sequence into the target gene. However, CRISPR-based gene insertion technologies have advanced to streamline this engineering process by combining the CRISPR–Cas module with recombinase enzymes. This process enables accurate and efficient one-step insertion of foreign DNA into the target gene in vivo. Here we provide an overview of the latest developments in CRISPR-based gene insertion technologies and discusses their potential future applications. As scientists work to understand complex traits and develop new therapies, they need tools to edit large sections of DNA. Traditional methods such as recombinases are constrained by their requirement for highly specific DNA sequence recognition, which limits their versatility and applicability. This study focuses on CRISPR systems, which use reprogrammed guide RNA for precise changes. However, CRISPR can create double-strand breaks, which can cause unintended changes. To address this, scientists are developing methods like prime editing, which makes precise edits without breaking both DNA strands. The study also highlights recent advances in CRISPR-based approaches for large-scale DNA integration. These methods show promise but need further refinement for use in human cells. Researchers conclude that while progress is being made, more work is needed to improve efficiency and reduce unintended effects. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

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Host-transposon mutualism supports regeneration in planarians | devC

Host-transposon mutualism supports regeneration in planarians | devC | RMH | Scoop.it
Transposons make up a significant fraction of eukaryotic genomes. They are commonly viewed as selfish elements that are detrimental to their hosts, and they are prime targets of specialized host defenses that constrain their expansion. Mutualistic interactions, in which elements co-exist and benefit each other, have so far not been found between transposable elements and eukaryote hosts. Here, we present evidence for an active transposon that confers a direct benefit to its planarian host. We find that the Ty3-like giant transposon Burro1 is an ancient element that retains its mobility. Burro1 has incorporated a host-derived anti-apoptotic protein that upregulates upon stress and improves stem cell resilience, resulting in enhanced regenerative abilities in its host. Apart from the surprising finding of a transposon’s involvement in planarian regeneration, our data also uncover a true mutualistic interaction between a transposon and a eukaryote host.
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Directed Evolution of Gram-Negative Klebsiella michiganensis M5al for Desiccation Tolerance and Survivability of a Spray Dry Encapsulation Process

Spray-drying is a scalable method for producing dry microbial inoculants, but Gram-negative bacteria often exhibit poor survival under desiccation stress. We employed a directed-evolution approach to improve the survivability of diazotroph Klebsiella michiganensis M5al during spray dry encapsulation in cross-linked alginate microcapsules (CLAMs). Wildtype K. michiganensis M5al was serially passaged through the CLAMs spray-drying process under incrementally increasing selection pressure. Viable cell density in powders improved over 1000-fold (greater than three log10) compared to baseline, reaching 3.07 x 1010 CFU/g over the course of the directed evolution experiment. Paired trials confirmed a statistically significant increase in survival of evolved isolates relative to the parental strain (mean difference of 1.39 log10, p < 0.05). Whole-genome sequencing and analysis using breseq identified candidate mutations associated with desiccation tolerance, including a single point mutation in the phosphoenolpyruvate carboxylase gene (pepC), suggesting a potential link between central carbon metabolism and stress adaptation. This work demonstrates that iterative selection under industrial drying conditions can rapidly enhance microbial fitness and provide genomic insights for metabolic and formulation strategies. The approach offers a framework for improving the manufacturability of Gram-negative biostimulants and other microbial products.

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r-2st

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March 30, 10:56 PM
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Post-translational modifications of GlmR integrate metabolic and stress signals to maintain cell envelope homeostasis in Bacillus subtilis | PLOS

Post-translational modifications of GlmR integrate metabolic and stress signals to maintain cell envelope homeostasis in Bacillus subtilis | PLOS | RMH | Scoop.it

The metabolic networks of most life forms integrate cost-benefit analysis to properly budget carbon and other essential nutrients. Bacillus subtilis is a Gram-positive model bacterium found in diverse ecological niches such as soil, marine environments, and the human gut. As such, B. subtilis cells fine-tune metabolic pathways by monitoring signals indicating the presence of nutrients and stressors. A highly conserved protein, GlmR, is a key player in rationing carbon for the production of cell envelope precursors. This function of GlmR can be attributed to its role in cell shape regulation and antibiotic resistance. Given its central position in carbon utilization, GlmR is under post-translational regulation by phosphorylation and UDP-N-acetylglucosamine (UDP-GlcNAc) binding. GlmR is also linked to cyclic-di-AMP (c-di-AMP), a nucleotide second messenger involved in osmotic and cell wall stress response. In this study, we probed the importance of GlmR in cell morphogenesis, c-di-AMP signaling, and investigated the physiological significance of post-translational regulation. Our results reveal that cells lacking glmR exhibit: (i) increased susceptibility to tunicamycin, a cell envelope targeting antibiotic; (ii) impaired division site positioning; and (iii) reduced intracellular c-di-AMP concentration. Furthermore, we show that the function of GlmR is fine-tuned by UDP-GlcNAc binding, phosphorylation, and acetylation. Additionally, we provide evidence showing that the recently discovered uridyltransferase activity of GlmR is integral for its function. We show that GlmR is a cell width determinant and propose a model suggesting close cooperation with an actin-like protein, MreB. Overall, our studies highlight the importance of the enzymatic function of GlmR and elucidate the mechanism behind the multiple post-translational means to regulate this crucial protein which is at the crux of carbon flux with an important role in maintaining cell envelope integrity.

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March 30, 10:44 PM
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Controlling heterologous protein synthesis through a plant RNA ThermoSwitch | Pmet

Plants are far from passive in the face of changing temperatures and have evolved transcriptional, post-transcriptional, and post-translational strategies to stay one step ahead of the environment. Among the most exciting recent discoveries are RNA ThermoSwitches, embedded within 5′ untranslated regions (UTR) of several mRNAs. These cis-acting RNA elements sense temperature shifts and instantly tune translational output, acting as molecular thermostats inside the cell. First uncovered in Arabidopsis thaliana, ThermoSwitches are now emerging as a powerful new frontier for biotechnology, offering a programmable way to modulate protein production through temperature adjustments. Using a dual fluorescence reporter construct introduced by Agrobacterium-mediated transient expression, we have demonstrated that the native ThermoSwitch within the 5′ UTR of the Arabidopsis phytochrome-interacting factor 7 (PIF7) mRNA functions as a potent temperature-responsive module in Nicotiana benthamiana leaves. A shift from 17 to 27 °C triggered a pronounced increase in reporter translation, delivering ~ 70% higher output within one day. This magnitude of enhancement mirrors the rise in endogenous PIF7 translation observed in Arabidopsis following the same temperature shift. The response remained even when the native upstream 5′ UTR context was replaced with an unrelated sequence, demonstrating that the ThermoSwitch operates autonomously. Crucially, locking the hairpin into a strengthened, rigid conformation abolished the temperature response entirely. This work provides the first in vivo evidence that a native plant RNA ThermoSwitch functions effectively in an Agrobacterium-mediated transient expression system, establishing a homogeneous, temperature-responsive gene regulation system, free from chemical inducers or repressors. Demonstrating that the Arabidopsis PIF7 ThermoSwitch operates autonomously in Nicotiana benthamiana highlights its value as a versatile plug-and-play module that can be readily deployed across plant systems for broad biotechnological applications.

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March 30, 10:33 PM
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Widespread atypical response regulator SecR governs bacterial growth on methylamines through the serine cycle | ComB

Widespread atypical response regulator SecR governs bacterial growth on methylamines through the serine cycle | ComB | RMH | Scoop.it

Microbial utilization of methylamines (MAs) exerts profound effects on biogeochemical carbon cycles, yet the regulatory mechanisms enabling bacterial adaptation to fluctuating MAs availability remain elusive. Here, we report the discovery of previously unknown atypical response regulators, designated SecRs. They exhibit homology to RcsB from the two-component regulatory system but lack the conserved phosphorylation motif and a cognate kinase. SecRs serve as the gatekeeper of the serine cycle, an essential one-carbon assimilation pathway required for bacterial growth on MAs. Remarkably, Aminobacter sp. strain NyZ550 encodes both chromosomal and plasmid-borne secR homologs that differentially regulate identical targets encoding the serine cycle enzymes. Inactivation of secRs in strain NyZ550 abolishes its growth on MAs. Biochemical analyses demonstrate that purified SecRs form stable dimers capable of direct binding to the promoters of their own genes and those of the serine cycle genes, consequently activating transcription. SecR homologs are distributed among diverse terrestrial and marine methylotrophs, and frequently adjacent to the serine cycle genes in four distinct genomic arrangements, demonstrating that SecR-mediated regulation is conserved despite divergent evolutionary rearrangements of the serine cycle genes. The findings highlight the crucial role of SecR regulation in enabling microbial adaptation to biogeochemically important one-carbon compounds across diverse environments. Discovery of uncharacterized atypical response regulators (SecRs) lacking phosphorylation domains that directly activate the essential serine cycle, enabling microbial methylamine metabolism in widespread terrestrial and marine methylotrophs.

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High-throughput generation and comparison of genome-scale metabolic models reveal strain-specific metabolic diversity in 439 Lactococcus strains | mSys

High-throughput generation and comparison of genome-scale metabolic models reveal strain-specific metabolic diversity in 439 Lactococcus strains | mSys | RMH | Scoop.it
The emergence of automated methods for the generation of genome-scale metabolic models (GEMs) has enabled the use of these models to study metabolic differences between large sets of different microorganisms. Current methods are often optimized for either handling large sets of strains or highly accurately reflecting the metabolism of selected strains; however, both aspects are necessary for analyzing the metabolic differences among large numbers of strains of the same or closely related species. In this study, we present a workflow for the high-throughput generation of high-quality GEMs of closely related strains, which has been applied for the generation of 439 GEMs of Lactococcus lactis and Lactococcus cremoris strains. Comparison of the resulting GEMs under different growth conditions revealed metabolic differences between the strains in carbon source utilization, fermentation products, and nutrient requirements. Notably, L. lactis and L. cremoris showed differences in xylose and ribose utilization pathways, with over 90% of L. cremoris strains unable to utilize xylose or showing limited ribose utilization due to a lack of key enzymes in the pentose phosphate pathway. Additionally, the strain-specific GEMs predicted differences in amino acid auxotrophies known in Lactococcus, including cysteine, which was possible to synthesize in only 11% of the strains and was found advantageous for growth in milk. The workflow presented in this study enables the generation of GEMs that can be used for comparing a large number of closely related strains and facilitates the assessment of their suitability in different biotechnological applications.
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workflow based on CarveMe, which can be used to generate high-quality strain-specific GEMs for a large set of strains of the same or closely related species. This workflow was implemented for the generation of strain-specific GEMs for 439 Lactococcus strains with publicly available genome sequences.

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