Biology subjects

Yang, H.

Publications and source records attributed to Yang, H..

At least 145 records · Page 8Linked to original sources

bioRxiv2025

A Population-scale Single-cell Spatial Transcriptomic Atlas of the Human Cortex

The genetic and spatial determinants of cell type diversity in human cerebral cortices remain poorly defined. Here, we present a population-level single-cell spatial transcriptomic atlas of human cortices from 71 donors across the lifespan. We identified 906 layer-specific genes showing conserved and divergent laminar expression patterns between humans and other species. Spatial analysis revealed neuronal vulnerability and glial activation during aging, together with a decline in the proportion of superficial SST neurons and their interactions with other cells. Disease-associated genes exhibited high cell-type and layer-specific expression, implicating the pathogenic role of spatially specific gene expression. Spatial cis-eQTL analysis identified regulatory variants linked to genes related to diseases like Tourette syndrome. Cross-species comparison demonstrated glial expansion in the human cortex, accompanied by enhanced neuron-glia communication via the neuregulin signaling. Together, we provide a comprehensive single-cell atlas of the human cortex that is essential for understanding aging, evolution, and disease pathogenesis.

neuroscience↗

bioRxiv2025

Computer Vision Methods for Spatial Transcriptomics: A Survey

Spatial transcriptomics (ST) enables the simultaneous measurement of gene expression and spatial localization within tissue sections, providing unprecedented opportunities to dissect tissue architecture and functional organization. As a relatively new omics technology, bioinformatics has driven much of the innovation in ST. However, within these frameworks, "spatial" information is often reduced to locations and relationships between molecular profiles, without fully leveraging the wealth of submicron morphological detail and histological knowledge available. Advances in computer vision-based artificial intelligence (AI) are opening exciting new avenues beyond conventional bioinformatics approaches by modeling complex histological patterns and linking morphology to molecular states. More excitingly, they bring fresh perspectives to potentially address key limitations of ST, including its high cost, limited clinical applicability, and reliance on twodimensional (2D) analysis of inherently three-dimensional (3D) tissues. For instance, models that predict ST directly from histology images enable "virtual sequencing," drastically reducing costs while integrating morphological insights from pathology with molecular biomarkers, thus accelerating clinical translation. Moreover, computer vision techniques can reconstruct pixel-aligned 3D tissue models, overcoming the technical barriers of 2D acquisition and advancing 3D spatial omics analytics. In this paper, we present the first systematic survey of computer vision AI models for ST analytics, categorizing approaches across architectures, learning paradigms, tasks, and datasets, and tracing their technological evolution. We highlight key challenges and future directions, offering a panoramic perspective on vision-driven ST and its potential to transform both basic research and clinical practice. The curated collection of vision-driven spatial transcriptomics papers is available at https://github.com/hrlblab/computer_vision_spatial_omics.

bioinformatics↗

bioRxiv2025

Generative design of antibody Fc-variants with synthetic and programmable functional profiles

Beyond antigen recognition, antibodies direct diverse immune effector functions through their constant (Fc) domain. While the Fc domain is central to antibody biology and therapeutic efficacy, our understanding of how Fc sequence encodes function remains limited, as most of Fc sequence space has not been experimentally mapped or linked to Fc-receptor engagement. Furthermore, the extensive overlap in Fc-receptor binding sites on the Fc domain has impeded efforts to engineer antibodies with tailored, multi-receptor engagement profiles that can precisely control downstream immunity. Here we introduce a novel framework for Fc engineering that integrates protein engineering with deep learning to rationally predict and engineer antibody Fc function. Using a yeast-based, aglycosylated Fc display system, we performed deep mutational scanning across the entire human IgG1 Fc domain, allowing the rational design of a diverse combinatorial library of more than 108 Fc-variants. This library was sorted based on binding to a panel of eight canonical Fc-receptors, and the resulting populations were deep sequenced to generate a high-quality dataset comprising millions of unique Fc sequences annotated with their respective Fc-receptor binding profiles. Deep learning-based classifiers trained on this dataset accurately predicted Fc-receptor binding activity from Fc sequence across all Fc-receptors tested. We further developed FcGPT, a domain-specific autoregressive protein language model pre-trained on over three million unique Fc sequences, and refined by post-training through reinforcement learning with experimental feedback (RLXF) and synthetic verifiers. FcGPT enables the computational design of novel Fc-variants with user-defined Fc-receptor binding profiles, providing a foundational tool for understanding and programming antibody-mediated immunity.

bioengineering↗

bioRxiv2025

Force loading on molecular clutches governs the stability of cell lamellipodia

Cells can utilize the lamellipodia, a thin actin-rich membrane protrusion, to probe the mechanical properties of microenvironments. During the mechanosensing process, the lamellipodium usually exhibits instability in dynamics, i.e., protrusion-retraction cycles. However, how mechanical instability arises in lamellipodia, along with the functional role of dynamic instability in mechanosensing, is poorly understood. Here, we develop a minimal mechanochemical model for lamellipodia dynamics that integrates membrane deformation, myosin contractility, and binding kinetics of adhesion molecules (molecular clutches). Through stochastic simulations and analytical mean-field analysis, we demonstrate that both force-loading rate and magnitude applied by myosin-driven retrograde flow mediate the clutch binding kinetics, governing lamellipodial stability and hence the mechanosensing. Specifically, a slow force loading rate allows the clutches to bind and traction to accumulate, while a high loading magnitude collapses the bound clutches, causing protrusion-retraction cycles (instability) in lamellipodia. Our model predicts that a stiffer substrate stabilizes the lamellipodia by increasing the force loading rate, consistence with previous experiments. Furthermore, our model predictions on the biphasic regulation effect of myosin perturbations have been quantitatively validated by experimental results. Overall, the theoretical framework highlights the force loading as the key mechanical input driving lamellipodial instability and cell mechanosensing, advancing our understanding of mechanotransductions role in cell behaviors. SignificanceCell behavior can be highly dynamic, showing periodic protrusion and retraction at the leading edge, and this dynamic instability is key to migration, immune response, and cancer invasion. How such instability arises from mechanotransduction between intercellular components and extracellular matrices remains unclear. Here, through theoretical modeling, we identify the rate and magnitude of force loading on adhesion molecules (molecular clutches) as the key factors for controlling lamellipodial stability. We find that a slow loading rate enables more clutches to bind and traction to accumulate, while a high loading magnitude collapses the bound clutches, causing protrusion-retraction cycles in lamellipodia. This work reveals a physical mechanism of how cells sense mechanical cues and adapt their dynamics, offering insights into diverse biological processes.

biophysics↗

bioRxiv2025

Pharmacological Induction of Irreversible Senescence in Drug-Tolerant Persister Cells Prevents Tumor Relapse

Despite substantial advances in targeted therapies, most patients with advanced cancers ultimately relapse. This relapse is frequently seeded by drug-tolerant persister cells (DTPs), which persist as minimal residual disease (MRD) by adopting a reversible quiescent state and later reacquire proliferative capacity under ongoing treatment. To address this, we sought to convert quiescent DTPs into an irreversible, non-proliferative state by pharmacologically inducing senescence. Through large-scale chemical screening and combination optimization, we identified SAHA plus tilorone dihydrochloride (SIC) as a regimen that robustly induces senescence in melanoma DTPs and abolishes their proliferative potential in vitro. Mechanistically, SAHA relieves HDAC1-mediated repression of HMGA2, a key senescence driver, while tilorone synergistically inhibits autophagy to promote a fully senescent state. In a melanoma xenograft mouse model receiving MAPK-targeted therapy, SIC treatment significantly suppressed MRD progression. Importantly, SIC also induced robust senescence across diverse DTP models irrespective of tumor origin, mutational background, or prior treatment. Collectively, these findings provide proof-of-concept that converting DTPs from reversible quiescence to permanent senescence represents an effective strategy to prevent relapse under targeted therapy and position SIC as a broadly applicable pharmacological approach for durable control of residual disease in advanced cancers.

cancer biology↗

bioRxiv2025

AGO1 in neural progenitor cells orchestrates brain development and sociability via LIN28A-REELIN axis

AGO1, an essential RNA binding protein (RBP) in RNA interference, is associated with autism spectrum disorder (ASD). However, the precise functions of AGO1 in brain development and related disorders remain largely unexplored. Here, we report the critical roles of AGO1 in neural progenitor cells (NPCs) in promoting sociability and shaping the structure of the developing brain. Ago1 knockout (KO) in the mouse brain leads to hyposociability, a characteristic symptom of ASD. In human forebrain organoids, AGO1 KO disrupts the formation of ventricle-like structures and delays cortical layer development. We discovered that AGO1 KO results in a loss of polarity in NPCs, which subsequently reduces neuronal development. These AGO1 KO phenotypes originate from the failure to suppress LIN28A in NPCs. AGO1 is primarily localized in the nucleus within NPCs and binds to the LIN28A promoter region, thereby inhibiting LIN28A transcription. We found that increased LIN28A in AGO1 KO NPCs reduces REELIN expression, a key regulator of brain development, by binding directly to the REELIN mRNA. The aberrant polarity phenotype in AGO1 KO NPCs was successfully ameliorated by either LIN28A knockdown or recombinant REELIN treatment. Collectively, our findings elucidate the intricate molecular mechanisms orchestrated by nuclear AGO1 in NPCs and underscores its pivotal roles in brain development, providing significant insights into ASD.

neuroscience↗

bioRxiv2025

Mass spectrometry-based proteomic and glycoproteomic drafts of fifteen human body fluids

The study of proteins and N-glycosylation in human body fluids (HBFs) is crucial for understanding both physiological and pathological states, offering valuable insights for disease diagnostics and monitoring. In this study, we present a comprehensive proteomics and glycoproteomics dataset featuring 15 types of precious HBFs (plasma (P), urine (U), cerebrospinal fluid (CSF), pleural fluid (PF), ascitic fluid (AF), synovial fluid (SF), pericardial fluid (PCF), peritoneal dialysis effluent (PDE), bile (B), gastric juice (GJ), seminal plasma (SP), saliva (SA), pancreatic juice (PJ), sweat (SW), and tear (T)). We also provide detailed protocols for protein processing specific to each type of HBF. The resulting peptides and enriched intact N-glycopeptides were analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data independent acquisition (DIA) and combined electron transfer/higher-energy collisional dissociation and stepped collision energy/higher-energy collisional dissociation (EThcD-sceHCD) mode, respectively. The resulting spectra and searched files have been uploaded to the online repository iProX (www.iprox.cn) and made easily accessible (PXD068799) to facilitate further research into HBF proteomics and glycoproteomics.

systems biology↗

bioRxiv2025

Inflammatory macrophages drive smooth muscle dedifferentiation via YAP signaling in murine deep vein thrombosis

BackgroundDeep vein thrombosis (DVT) is a common clinical problem characterized by the formation of blood clots in deep veins. The contribution of venous smooth muscle cells (SMCs) to the onset of DVT, particularly the mechanisms driving their phenotypic changes, remains poorly understood. MethodsWe employed a murine stasis model of DVT via inferior vena cava (IVC) ligation in both male and female mice. Single-cell RNA sequencing (scRNA-seq) was used to analyze the cellular and transcriptomic landscape of the vein wall 24 hours post-ligation. To investigate cellular crosstalk, we utilized an in vitro system where primary mouse aortic SMCs were treated with conditioned media from inflammatory (M1-like) bone marrow-derived macrophages (BMDMs). The effects on SMC phenotype and signaling were assessed using bulk RNA-seq, Western blotting, qRT-PCR, and functional assays. ResultsscRNA-seq analysis revealed that DVT induction promotes a rapid inflammatory response characterized by neutrophil influx and a shift in SMCs from a contractile to a synthetic phenotype in both sexes. Infiltrating myeloid cells were identified as a primary source of signaling to SMCs. In vitro, conditioned media from M1-like macrophages was sufficient to suppress SMC contractile gene expression and function. Mechanistically, this effect was linked to the inhibition of the Hippo pathway effector YAP, evidenced by increased YAP phosphorylation (S127/S397) and a subsequent reduction of total YAP protein in SMCs. We identified macrophage-derived IL-1{beta} as a key ligand that mimics these effects. Importantly, pharmacological activation of YAP rescued contractile gene expression in SMCs exposed to the inflammatory macrophage secretome. ConclusionsOur findings demonstrate that SMC dedifferentiation occurs early in DVT, driven by inflammatory macrophage-mediated suppression of YAP signaling. This study uncovers a critical macrophage-SMC crosstalk mechanism in early DVT pathogenesis and highlights YAP as a potential therapeutic target to preserve vein wall integrity and prevent long-term complications.

cell biology↗

bioRxiv2025

A neural circuit for female-specific defensive homeostasis in risk assessment

HighlightsSC CB1R+ neurons encode visual survival threats and initiate risk assessment in both sexes CB1R+ SC-LHb GABAergic circuit maintains defensive homeostasis in risk assessment in females eCBs gate this circuit by disinhibiting presynaptic GABA release exclusively in females Disruption of this mechanism female-specifically impairs risk assessment and stress coping In BriefLiu et al. identify a female-specific subcortical circuit in which eCB signaling, via CB1R on GABAergic terminals in the SC, suppresses transmission to the LHb to maintain precise defensive homeostasis in risk assessment. These findings uncover the circuit and neuronal mechanisms that contribute to the sexually dimorphic maintenance of defensive homeostasis. Risk assessment in defensive behavior is an adaptive mechanism shaped by natural selection, enabling individuals to evaluate potential threats and thereby maintain defensive homeostasis. However, it remains unknown whether specific neural circuits maintain their behavioral homeostasis in a sex-specific manner. To address this, we investigated its neural basis by hierarchical behavior analysis framework with neural circuit dissection. In mice of both sexes, visual survival threats activated cannabinoid 1 receptor (CB1R)-expressing neurons in the superior colliculus (SC) to initiate consistent risk assessment. Deletion of CB1R in SC GABAergic neurons female-specifically impairs risk assessment by disinhibiting GABA release in SC-lateral habenula (LHb) projections, resulting in shortened risk assessment. This disruption furthermore increases the occurrence of abnormal spontaneous behavior following chronic stress exclusively in females. We identified a female-specific SC-LHb GABAergic circuit gated by CB1R maintains defensive homeostasis in risk assessment. Our findings reveal how a conserved neuromodulatory system sex-specifically gates a subcortical circuit to orchestrate distinct survival strategies.

neuroscience↗

bioRxiv2025

A deep-time landscape of plant cis-regulatory sequence evolution

Developmental gene function is often conserved over deep time, but cis-regulatory sequence conservation is difficult to identify. Rapid sequence turnover, paleopolyploidy, structural variation, and limited phylogenomic sampling have impeded conserved non-coding sequence (CNS) discovery. Using Conservatory, an algorithm that leverages microsynteny and iterative alignments to map CNS-gene associations over evolution, we uncovered [~]2.3 million CNSs, including over 3,000 predating angiosperms, from 284 plant species spanning 300 million years of diversification. Ancient CNSs were enriched near developmental regulators, and mutating CNSs near HOMEOBOX genes produced strong phenotypes. Tracing CNS evolution uncovered key principles: CNS spacing varies, but order is conserved; genomic rearrangements form new CNS-gene associations; and ancient CNSs are preferentially retained among paralogs, but are often lost as cohorts or evolve into lineage-specific CNSs. One Sentence SummaryConservatory maps ancient cis-regulatory elements and uncovers regulatory evolution dynamics.

plant biology↗

bioRxiv2025

Size-Modulated Mesoderm-Endoderm Divergence and Myocardial Cavitation in Micropatterned Cardioids

The human heart, originating from the splanchnic mesoderm, is the first functional organ to develop, co-evolving with the foregut endoderm through reciprocal signaling. Previously, cardioid models offered new insights on cardiovascular cell lineages and tissue morphogenesis during heart development, while mesoderm-endoderm crosstalk remain incompletely understood. Here, we integrated micropatterned cardioids, CRISPR-engineered reporter hiPSCs, deep-tissue imaging, and single-cell RNA sequencing (scRNA-seq) to explore synergistic mesoderm-endoderm co-development. scRNA-seq with PHATE trajectory mapping reconstructed lineage bifurcations of mesoderm-heart and endoderm-foregut lineages, identifying key cell types in cardiac and hepatic development. Ligand-receptor interaction analysis highlighted mesodermal cells enriched in non-canonical WNT, NRG, and TGF-{beta} signaling, while endodermal cells exhibited VEGF and Hedgehog activity. We found that micropattern sizes influenced cellular composition, cardioid cavitation, contractile functions, and mesoderm-endoderm signaling crosstalk. The cardioids generated from 600 {micro}m diameter circle patterns showed larger cavity formation resembling early heart chamber formation. Our findings establish micropatterned cardioids as a model for mesoderm-endoderm co-development, enhancing our understanding of heart-foregut synergy during early embryogenesis.

bioengineering↗

bioRxiv2025

An Interactive AI Agent for Adaptive Modeling of RNA Region-Ligand Interactions via LLM-Generated Machine Learning Workflows

Precise modeling of RNA-ligand interactions is essential for understanding RNA functionality and designing RNA-targeted therapeutics. Current computational approaches largely focus on predicting discrete binding sites, limiting their applicability to complex RNA regions that may harbor multiple or diffuse ligand binding motifs. Here, we present RLAgent, an interactive agent framework designed to predict ligand interactions at the RNA region level, enabling higher-resolution and more flexible modeling than conventional site-centric approaches. RLAgent reframes the RNA-ligand prediction workflow as a dialogue-driven process. Through a natural language interface, users can interactively configure modeling preferences without writing code. A locally hosted large language model (LLM) acts as the core orchestration agent, automating all key components of the modeling pipeline, including data validation, feature encoding, model training, evaluation, and visualization. This agent-based design lowers technical barriers and enhances reproducibility, making RNA-ligand prediction more accessible for both computational and experimental researchers.

bioinformatics↗

bioRxiv2025

DNA Data Storage Architecture via Ligation of Dynamic DNA Bytes

The explosive growth of digital data is overwhelming conventional storage media, creating an urgent need for more efficient solutions. DNA offers immense potential for digital data storage, yet most systems remain static and archival. Here, we present a modular DNA storage architecture based on dynamic DNA bytes (DynaBytes)-- pre-fabricated DNA segments that can be ligated into reconfigurable information units. Utilizing core, functional and control DynaBytes, we stored 210,776 bits (26,347 bytes) of digital information organized within a file-system, and demonstrated CRUD (Create-Read-Update-Delete)-like operations, hierarchical access and nanopore-based realtime retrieval. Robust data recovery was achieved under [~]100x error-prone sequencing using streamlined error correction and fuzzy decoding. By relying on in vitro ligation of standardized components, DynaBytes reduces cost, scales efficiently, and allows interactive, rewritable storage. These features advance DNA storage beyond passive archiving toward a reconfigurable framework, opening new possibilities for dynamic, practical and large-scale DNA-based data systems.

synthetic biology↗

bioRxiv2025

A Benchmark of Evo2 Genomic AI Models for Efficient and Practical Deployment

The rapid advancement of DNA foundation language models has brought about a transformative shift in genomics, allowing for the deciphering of intricate patterns and regulatory mechanisms embedded within DNA sequences. The genomic foundation model Evo2 demonstrates remarkable capabilities in decoding DNA functional patterns through cross-species pretraining. However, despite the great potential of Evo2 in basic genomics research, there is currently no clear and systematic guidance on its specific application scenarios, performance, and optimization directions in the field of tumor genomics, and its performance dependency on specialized hardware (such as FP8 precision on H800 GPUs) has not been empirically benchmarked. Here, we present a focused validation of Evo2 using two independent cancer genomic datasets (Bladder Urothelial Carcinoma and Ovarian Cancer), we tested the downstream tasks of Evo2, including the prediction of tumor pathogenic variants and the prediction of mutational effects, and compared its performance on A100 and H800 GPUs. The results show that critical importance of FP8 precision, enabling the H800 to achieve a 4x faster inference speed than the A100 with stable accuracy (AUC 0.88-0.95). The 7B-parameter model emerged as the top performer, whereas the 40B model experienced a severe performance drop (AUC to 0.48) on non-FP8 hardware like the A100. These findings empirically validated Evo2s hardware specifications and provided practical insights for researchers implementing the model with similar computational resources. Futhermore, our findings provide a framework for the application and optimization of downstream tasks of the DNA language model Evo2 in cancer, and can guide researchers in effectively applying it in genomic studies. Key PointsO_LIHardware Precision Impact: FP8 precision on H800 GPUs is critical for Evo 2s performance, enabling 4x faster inference than A100 (without FP8 support) while maintaining high accuracy (AUC 0.88-0.95). C_LIO_LIModel Scale Optimization: The 7B-parameter model outperformed larger variants (e.g., 40B), which suffered severe accuracy drops (AUC as low as 0.48) on non-FP8 hardware, highlighting a balance between efficiency and performance. C_LIO_LIPractical Guidelines: We provide a framework for deploying Evo 2 in cancer genomics, including hardware recommendations, dataset curation, and downstream task optimization--valuable for researchers with varied computational resources. C_LI

genomics↗

bioRxiv2025

PhyloAug: An Evolutionary and Structure-Aware Data Augmentation Tool

Genomic Language Models (GLMs) suffer from the inherent problem of data scarcity, due to the cost, time and complexity of wet-lab experiments. Data augmentation offers a solution; however traditional methods may unintentionally affect the underlying structure or function. By combining evolutionary signals with the RNA secondary structure, augmentations can retain original function, and remain structurally coherent. To implement this, we developed PhyloAug, a structure-aware, evolution-inspired augmentation method grounded in neutral theory. We employ Genomic Foundation Models (GFMs) to accurately perturb RNA sequences, and utilise phylogenetic analysis via PAML to provide site-wise restrictions based on evolutionary principles. These principles are obtained through the identification of evolutionarily neutral sites (sequence positions where mutations are unlikely to alter function), which are concatenated with the predicted (or known) secondary structure. We thereby ensure adherence to the underlying structure whilst enabling biologically plausible variation. To validate the biological validity of our augmentations, we compare our predicted neutral sites with Rfam-annotated conserved regions and assess sequence similarity to the underlying multiple sequence alignments. We next fine-tune GLMs on augmented data, yielding significant performance improvements up to 12.9% MCC, and 17.2% F1-Score.

bioinformatics↗

bioRxiv2025

MAIT cell enrichment in Lynch syndrome is associated with immune surveillance and colorectal cancer risk

Tissue microenvironment characteristics associated with elevated risk of colorectal cancer (CRC) in Lynch syndrome (LS) are poorly characterized. We applied the multimodal single cell sequencing platform ExCITE-seq to define the colonic cellular composition and transcriptome of LS carriers with and without a history of CRC compared with general population controls. Our analysis revealed widespread remodeling in LS that included striking expansion of epithelial stem and progenitor cells, and loss of fibroblast populations. Although clonally expanded and terminally exhausted CD8 T cells were more prominent in individuals with a history of CRC, LS carriers without CRC displayed enrichment of cytotoxic mucosal-associated invariant T (MAIT) cells associated with CCL20 expression in epithelial progenitors, validated by orthogonal techniques including demonstration of a protective function in a murine model of CRC. These findings highlight cellular features that distinguish LS carriers and suggest a protective role of MAIT cells in human CRC surveillance.

immunology↗

bioRxiv2025

Efficient biosynthesis of 3-hydroxypropionic acid in recombinant Escherichia coli by metabolic engineering

3-Hydroxypropionic acid (3-HP) is a platform compound that can produce many chemical commodities. This study focuses on establishing and optimizing the production of 3-HP in E. coli. We constructed a series of engineered E. coli strains which can produce 3-HP via the malonyl-CoA pathway. To increase the metabolic flux of acetyl-CoA, a precursor for the synthesis of 3-HP, CRISPR/Cas9-based DNA editing technique was used to knock out the genes encoding pyruvate oxidase (poxB), lactate dehydrogenase (ldhA) and phosphate transacetylase (pta), thereby reducing the formation of by-products. Concurrently, the acetyl coenzyme a carboxylase gene (accDABC) is overexpressed on the chromosome with the objective of augmenting intracellular acetyl-CoA levels and, consequently, 3-HP production. Next, we introduced a plasmid containing a codon-optimized malonyl-CoA reductase gene (mcr) into the engineered strain. Finally, we constructed a transcription factor-based metabolite biosensor utilizing the PpHpdR/PhpdH system, followed by the screening of mutant strains for enhanced 3-HP production through adaptive laboratory evolution. Combining the above metabolic engineering efforts with optimisation of media and fermentation conditions, the 3-HP titer of the engineered strain WY7 increased from an initial titer 0.34 g/L to 48.8 g/L. This study encourages further research in metabolic pathway optimizationto produce 3-HP. HighlightsO_LISynthesis 3-HP in the malonyl-CoA pathway. C_LIO_LIEdit the Escherichia coli genome using the CRISPR/Cas9 system. C_LIO_LIElevated production of 3-HP by knocking out bypass genes ldhA/pta/poxB. C_LIO_LIA biosensor was designed to respond to 3-HP concentration. C_LIO_LIAdaptive laboratory evolutionary strategies increase 3-HP production. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/672340v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1f8b65borg.highwire.dtl.DTLVardef@18c23d6org.highwire.dtl.DTLVardef@184edc3org.highwire.dtl.DTLVardef@15bcc2_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2025

pH-driven evolutionary divergence and ongoing speciation in the cosmopolitan cyanobacterium Microcoleus vaginatus

Speciation models and the concept of speciation continuum have been well established in multicellular eukaryotes. However, the mechanisms underlying population differentiation and their environmental drivers in prokaryotes remain elusive. Here, we explored the population diversification of the filamentous cyanobacterium Microcoleus vaginatus, a photoautotrophic edificator with significant importance for dryland ecosystems worldwide. By constructing a 132-genome dataset spanning multiple climate zones across four continents, we integrated intraspecific genomics with environmental association analyses to elucidate the population genetic structure and molecular mechanisms underlying the divergence of the M. vaginatus continuum. Our findings reveal that adaptive differentiation is driven by the combined effects of natural selection, homologous recombination, and gene family turnover, with varying contributions across different phylogroups. The evolution of core and accessory genomes exhibits complementary patterns, with genome-wide association studies demonstrating the predominant role of the core genome in population divergence. Selection, followed by neutral evolution and recombination, mediates trade-offs in life-history strategies, while environmental factors shape these evolutionary processes. Notably, while local edaphic variables exert direct influences on Tajimas D values, they demonstrate predominantly indirect modulation through alterations in the selection-to-gene gain ratio (S/G). pH emerges as the principal abiotic driver governing population genetic signatures, operating through coordinated effects on both S/G and the proportion of selected genes undergoing recombination (SUR). This study offers novel insights into the diversification mechanisms of cosmopolitan, dominant cyanobacteria, thereby advancing our understanding of prokaryotic speciation in complex natural settings and providing a framework for predicting microbial adaptation in a changing world.

evolutionary biology↗