Biology subjects

Zhou, W.

Publications and source records attributed to Zhou, W..

At least 19 recordsLinked to original sources

bioRxiv2026

Auditory tracking magnitude, temporal alignment, and anatomical distribution during wakefulness and propofol-based anesthesia in human sEEG

General anesthesia attenuates auditory responses, but response magnitude alone does not describe how residual activity aligns with sound over time or varies across the brain. We jointly characterized these aspects of auditory tracking using stereoelectroencephalography (sEEG) in 13 patients with drug-resistant epilepsy during wakefulness and clinically defined unresponsiveness under propofol-based anesthesia. The same rhythmic humming sequences were presented at approximately 1.61 Hz in both sessions. During anesthesia, stimulus-rate power showed widespread reductions and phase alignment with the stimulus rhythm was less consistent, although responses remained detectable at some recording channels. During wakefulness, response strength decreased with cortical surface distance from posteromedial Heschl's gyrus; this distance-response relationship was weaker during anesthesia. In a contrastive learning analysis (CEBRA), low-dimensional embeddings derived from anesthesia recordings yielded lower acoustic-envelope decoding accuracy on held-out data and lower consistency across model fits. Together, these findings characterize how attenuated auditory tracking is expressed jointly in response magnitude, temporal alignment, and anatomical distribution.

neuroscience↗

bioRxiv2026

Spatiotemporal proteomic profiling of genome editing-associated microenvironment

Genome editing outcomes are influenced by local genomic context, yet the dynamic protein assemblies that form around genome editing sites remain poorly characterized. Here, we developed a Cas9-TurboID proximity labeling platform that combines programmable genomic targeting with rapid and minimally perturbative protein labeling, enabling spatiotemporal mapping of dynamic protein assemblies associated with distinct genome editing processes. Dynamic comparative profiling identified ACTL6A and H2A.Z as candidate regulators of genome editing, and Cas9 fusion with either ACTL6A or H2A.Z enhanced editing efficiency and improved editing outcomes in heterochromatic regions, which are generally refractory to genome editing due to limited DNA accessibility. We further extended the platform to prime editing and identified WRN as a functional regulator. Together, this study provides a strategy for systematically decoding editor-associated protein microenvironments, establishing a framework for understanding genome editing mechanisms and guiding the rational engineering of genome editors.

cell biology↗

bioRxiv2026

A genomic foundation model for interpreting human genetic diversity

Interpreting human genomes requires models that recognize shared biological functions while accounting for variation underlying traits, disease susceptibility and adaptation. Here we present hEVO, a genomic foundation model trained on 4.784 billion distinct haplotype sequences reconstructed from whole genome sequencing (WGS) data for 63,809 individuals selected from 550,237 samples across eight cohorts. Pretraining integrates shared sequence features and naturally occurring allelic differences without clinical or functional labels. Against representatives of the Evo 2, NT, GPN and other genomic model families, hEVO ranked first in 18 of 19 primary comparisons spanning functional annotation, variant interpretation and human evolution. Its pretrained allele scores generalized across all six complementary variant benchmarks without task-specific training, achieving the highest performance across clinical pathogenicity, fine-mapped causal variants and experimentally measured regulatory effects. Retrospective ClinVar reclassification further demonstrated improved identification of pathogenic classifications later downgraded, supporting reassessment of earlier clinical interpretations. Predictive strengths extended to ancestry inference, human accelerated sequences and preference for reported selected alleles. This breadth was achieved with substantially fewer parameters than billion-parameter comparators and low inference overhead in the evaluated workloads. These findings establish human haplotype diversity as a source of transferable biological information for interpreting both the disruption of genome function and the variation retained during human evolution.

genomics↗

bioRxiv2026

Valine Engages a Prefrontal mGluR5 Allosteric Site to Preserve Social Behavior

Branched-chain amino acid metabolism is linked to neurodevelopmental disorders, but whether a defined amino acid directly modulates cortical receptor signaling to control behavior is unknown. Here, we show that valine is uniquely required for sociability through medial prefrontal cortex (mPFC) circuitry. Partial dietary valine restriction, but not comparable leucine or isoleucine restriction, or mPFC-restricted acceleration of BCAA catabolism through Bckdk loss, disrupts social interaction, and local valine supplementation rescues behavior. LiP-SMap, surface plasmon resonance, cryo-EM, and mutagenesis identify metabotropic glutamate receptor 5 (mGluR5) as a direct valine target and reveal a valine-selective allosteric pocket distinct from the glutamate site. Valine is signaling-biased: it drives {beta}-arrestin-linked output yet has little standalone Gq efficacy, instead potentiating glutamate-evoked mGluR5 signaling to recruit endocannabinoid/CB1 feedback, gate inhibitory transmission, and preserve mPFC-dependent social behavior. Together, these findings identify valine as a diet-derived allosteric ligand that links amino acid availability to cortical circuit control of social behavior.

neuroscience↗

bioRxiv2026

Rac1 and CHK1 Converge on Abi-1 to Regulate DNA Repair Dependency and Treatment Response in Human Cancers

Radiation therapy (RT) resistance remains a major clinical challenge, yet biomarkers guiding precision radiosensitization are lacking. We previously demonstrated that Rac1 promotes RT resistance in glioblastoma (GBM) by inducing Abi-1-S323 dephosphorylation and enhancing non-homologous end joining (NHEJ). Here, we identify Abi-1-S323 as a key regulator of DNA repair states and a determinant of therapeutic efficacy in human cancers. Clinically, loss of Abi-1-S323 phosphorylation was associated with poor outcomes in patients with RT-treated GBM. Bioinformatic analyses revealed that non-small cell lung cancer (NSCLC) and head and neck cancer (HNC) are among the cancers with frequent RAC1 amplification, suggesting that these tumor types may have increased Rac1-Abi-1 signaling activity. Loss of Abi-1-S323 phosphorylation also predicted poor outcomes in patients with RT-treated HNC. Consistent with these clinical observations, high Rac1 activity and low Abi-1-S323 phosphorylation were associated with enhanced DNA double-strand break repair and radioresistance in NSCLC and HNC models, whereas genetic or pharmacological inhibition of this signaling impaired DNA repair and radiosensitized tumors in vitro and in vivo. Mechanistically, we identified CHK1 as a kinase that phosphorylates Abi-1 at S323 and defines an alternative homologous recombination (HR)-dependent repair state. Tumors with high Rac1-Abi-1 signaling exhibited elevated NHEJ capacity and were selectively radiosensitized by Rac1 inhibition, whereas tumors with low Rac1-Abi-1 signaling displayed high CHK1 activity, preferentially relied on HR, and were selectively radiosensitized by CHK1 inhibition. These findings establish Abi-1-S323 as a biomarker defining therapeutically distinct DNA repair states and provide a framework for precision radiosensitization.

cancer biology↗

bioRxiv2026

Multiplexed Quantification of Variant Abundance in the Globin Gene Family: Integrating Saturation Mutagenesis with Cross-Paralog Prediction

Widespread genetic testing has expanded variant identification, yet functional characterization remains a bottleneck in genome guided medicine. Here, we present a modified Variant Abundance by Massively Parallel Sequencing (VAMP-seq) platform integrating experimental and computational approaches for high-resolution abundance profiling of protein variants. Utilizing a lentiviral integration system, we systematically assessed the stability effects of 2,696 amino acid substitutions in {zeta}-globin (HBZ) via saturation mutagenesis in human cells, achieving complete variant coverage with high reproducibility. Representative variants showed strong concordance with orthogonal low-throughput validation assays. We further developed a deep learning framework leveraging VAMP-seq derived HBZ data to predict variant abundance across thalassemia-associated globin paralogs (HBA, HBB, and HBG1) not experimentally tractable. Our hybrid framework demonstrates how targeted experimental profiling combined with AI-driven extrapolation can accelerate variant interpretation across protein family members.

genetics↗

bioRxiv2026

sc-pcQTL: hurdle-based co-expression modeling for multi-gene QTL mapping in single-cell RNA-seq data

Motivation: Single-cell expression quantitative trait locus (eQTL) studies can resolve cell-type-specific genetic effects, but conventional gene-by-gene analyses do not directly capture coordinated genetic regulation of neighboring genes. Principal-component QTL (pcQTL) mapping can summarize such multi-gene effects, but existing approaches were developed for bulk expression and are not designed for sparse single-cell counts. Results: We developed sc-pcQTL, a framework that applies two-component hurdle modeling and sliding-window clustering to identify local co-expression clusters, summarizes each cluster using principal components, and maps cis-pcQTLs. In simulations, the individual hurdle components controlled type I error, while the component-union screening rule was substantially more powerful than donor-level pseudobulk correlation tests. Applied to 1.24 million peripheral blood mononuclear cells from 982 OneK1K donors across 10 cell types, sc-pcQTL identified 2,485 local co-expression clusters and conducted QTL mapping for 4,353 cluster-PC phenotypes at single-cell resolution, of which 2,040 had at least one significant cis-pcQTL association. Fine-mapping and colocalization with genome-wide association study loci across 1,163 phenotypes in the FinnGen study identified 394 colocalized QTL-GWAS signal groups. Each group comprised fine-mapped QTL and GWAS signals connected through one or more colocalization links within the same cell type and local gene cluster. Of these groups, 46 were pcQTL-specific and contained no colocalized single-gene eQTL from a constituent gene. Locus-level analyses further revealed cell-type-specific multi-gene regulatory effects. Thus, sc-pcQTL complements conventional single-gene eQTL analysis by identifying trait-relevant regulatory signals shared across neighboring genes.

bioinformatics↗

bioRxiv2026

Metabolic disease-relevant stimuli unmask context-dependent genetic regulation of cardiometabolic loci in human adipocytes

Genome-wide association studies (GWAS) have identified thousands of loci associated with cardiometabolic disease, yet translating these associations into regulatory mechanisms, effector genes, and cellular programs remains a major challenge. A key limitation is that genetic effects are often highly context dependent, varying across cell states and environmental conditions that are difficult to model at scale. Here, we leverage CellGenBank, a population-scale biobank of primary human adipose-derived mesenchymal stem cells (AMSCs), to implement a multi-donor cell village in vitro system to map cardiometabolic disease genetic variation across adipocyte differentiation and metabolic stress conditions. We pooled AMSCs from 118 donors into multiplexed villages, differentiated them toward adipocytes, and profiled chromatin accessibility and gene expression using single-nucleus multiome sequencing under four disease-relevant conditions: basal, elevated free fatty acids, low glucose, and hypoxia. By combining with genetic demultiplexing, we quantify how regulatory element activity, gene expression, and higher-order cellular programs are modulated by both genotype and environmental context. Across conditions, we identify widespread context-specific cis-regulatory effects, including expression and chromatin accessibility quantitative trait loci that are masked in baseline states. Genetic effects frequently converge on coordinated transcriptional programs linked to lipid metabolism, insulin responsiveness, and stress adaptation, enabling the identification of cellular program QTLs that bridge variants, genes, and disease-relevant phenotypes. Integration with cardiometabolic GWAS reveals enhanced colocalization in condition- and state-resolved analyses, highlighting the importance of modeling environmental context to resolve disease mechanisms. Together, our study establishes large-scale adipocyte cell villages as a powerful and generalizable framework to map the context-dependent regulatory architecture of cardiometabolic disease and provides a resource linking human genetic variation to adipocyte cellular programs.

genomics↗

bioRxiv2026

An Integrated Atlas of the Human Kidney Spanning Health and Diseases

The human kidney contains highly specialized cell populations. Despite numerous single-cell and single-nucleus transcriptomics studies, differences in cohorts, technologies, analytical pipelines, and annotation frameworks have limited the ability to define consensus kidney cell states, identify disease-associated populations and interpret kidney disease genetic susceptibility. Here, we assembled 18 human kidney single-cell and single-nucleus RNA-sequencing datasets spanning 232 donors and five major disease contexts into a uniformly processed and computationally integrated Human Kidney Cell Atlas (HKCA), comprising over one million high-quality cells (816,895) and nuclei (215,308). The HKCA resolves 63 cell types and 120 harmonized cell states, including rare epithelial and stromal populations associated with kidney diseases . Integration with spatial transcriptomics, intercellular communication networks, and human genetic association data further defined the anatomical context and disease relevance of these populations. The HKCA also provides a framework for automated annotation of independent kidney human and mouse datasets. Together, the HKCA establishes a comprehensive reference for human kidney biology, enabling disease interpretation and genetic risk localization at cellular resolution.

genomics↗

bioRxiv2026

A shared cortical manifold links sensory error to motor planning

The ability to detect and correct errors is central to learning and executing skilled behaviors such as speech and musical performance. Accordingly, sensory and motor regions of the brain must communicate and coordinate their activity in order to both detect errors and adapt in response to them. However, the neural mechanisms by which sensory feedback is transformed into updated motor plans across distributed cortical circuits remain unclear. Here, we identify how the mouse brain encodes error signals during a skilled acoustic task and how these signals guide motor adaptation. We developed a novel, sound-dependent behavior in which mice use real-time auditory feedback to adjust ongoing forelimb movements. Task performance critically depends on auditory cortex, where neurons encode error-related feedback that predicts both within-trial and across-trial behavioral adaptations. Acoustic error feedback alters secondary motor cortex (M2) dynamics, selectively pushing activity along dimensions that encode planning signals for upcoming movements. Notably, auditory cortex and M2 share a low-dimensional manifold that emerges only after learning the skilled behavior. This shared geometry is not engaged during simpler forelimb tasks, suggesting that it reflects a learned, context-dependent solution for coordinating distributed cortical activity. In trained mice, silencing auditory cortex disrupts M2 dynamics, consistent with a model of continuously coupled cortical interactions during skilled performance. Together, these findings identify a learned coordinate transformation that maps sensory error onto corrective motor plans. More broadly, they point to a candidate general principle for how learning can establish a shared cortical manifold that links distant brain regions, enabling sensory feedback to selectively reshape future actions and support flexible, goal-directed control.

neuroscience↗

bioRxiv2026

Grant writing coaching groups: A randomized trial to discern the influences of coaching duration and engagement of scientific mentors on participants proposal submission and success rates

IntroductionThe acquisition of National Institutes of Health (NIH) grants is an increasingly competitive undertaking. Many early-career biomedical investigators lack sufficient grant writing skills. This pragmatic, 2x2 factorial, group-randomized trial tested the effects of coaching duration and mode of engagement of participants scientific mentors in a grant writing coaching group intervention on grant application outcomes. MethodsA national US sample of 367 faculty and postdoctoral fellows, organized into 76 coaching groups across 6 cohorts (2020-2022), was randomized to 1 of 4 arms that differed by coaching duration (5 months vs 5 months plus 18 months of access to 1:1 coaching) and mode of scientific mentor engagement (structured vs unstructured). Primary outcomes were national-level grant application submissions and awards, and success rate among submitters, assessed via surveys and verified through award databases 30 months after study initiation. ResultsAmong 271 faculty and 96 postdoctoral fellows, 69% submitted at least 1 national application and 32% were awarded at least 1 grant. Submission rates did not differ by experimental arm. Among faculty, the structured engagement of a mentor in coaching was associated with significantly higher odds of any award (adjusted OR = 1.87) and any NIH mentored career development (K) award (adjusted OR = 3.32), and with higher success rates for these proposal types. Extended coaching duration showed a positive but not statistically significant association with faculty awards after adjustment. Neither factor significantly affected outcomes for postdoctoral fellows. Faculty success rates exceeded national NIH-reported success rates for research project grants equivalent to the R01 mechanism (43% vs 17.6%) and K awards (51% vs 36.5%). ConclusionParticipation in a national, cross-institutional, group coaching model yielded high grant submission outcomes, and award rates were higher relative to NIH benchmarks. Structured integration of scientific mentors with the coaching intervention had a greater effect than extended coaching duration on faculty outcomes.

scientific communication and education↗

bioRxiv2026

Network-scale disruption of RNA-protein interactions by a bioaccumulating small-molecule drug

Many small-molecule drugs accumulate in both host and gut microbial cells. Yet, the molecular consequences of intracellular drug accumulation are largely unknown. Here we show that the antidepressant duloxetine broadly disrupts RNA-protein interactions revealing a previously unrecognized mode of drug effect. Across phylogenetically diverse bacteria and human intestinal cells, duloxetine consistently reduced RNA-binding capacity, with approximately 80% of RNA-binding proteins responding to duloxetine in bacteria (E. coli IAI1) and human (Caco-2) cells. As a mechanistic example, we show how duloxetine disrupts interactions between the pyrimidine biosynthesis enzyme PyrB and transcripts encoding metabolically linked functions. Biochemical and structural analyses show that duloxetine competes with aspartate, the natural substrate of PyrB, weakening the enzymes binding with 3' UTR-localized stem-loop structures in its RNA partners. Overall, our results uncover the off-target disruption of RNA-protein interactions due to drug accumulation and have implications for understanding molecular basis of variation in drug efficacy and toxicity.

systems biology↗

bioRxiv2026

Flux-species sampling enables holistic exploration of biocircuit behavior

Biocircuit behavior is usually discovered by drawing uncertain kinetic parameters and simulating, but the scored phenotypes are functions of steady-state species, catalytic fluxes, dominance relations, and local response matrices. We introduce flux-species sampling (FSS), which samples that functional layer directly and algebraically inverts to parameters. This coordinate change turns regulatory modes into simple dominance chambers, gives every full-dimensional behavior-realizing regime positive measure, and can replace the sampling stage of existing pipelines without changing downstream stability or phenotype tests. A sequestration toy shows how a large species regime can collapse into a narrow parameter strip. In an enzymatic feedback motif, FSS rescues free-A* adaptation missed by parameter sampling and identifies the regime as non-Michaelian and complex-dominated. In MultiFate-N, FSS separates a sampling limitation from a structural limitation and rescues parameter sets realizing all 2N states. HighlightsO_LIFlux-species sampling (FSS) draws the control/function layer where biocircuit behavior is read C_LIO_LIFSS boxes intersect every full-dimensional behavior-realizing regime with positive measure C_LIO_LIFSS is no slower than parameter-space sampling and is a hot fix for existing pipelines C_LIO_LIFSS rescues non-Michaelian free-A* adaptation missed by parameter sampling C_LIO_LIFSS rescues the all-2N symmetric MultiFate regime missed by fixed parameter boxes C_LI eTOC blurbThis study reframes biocircuit exploration by sampling steady-state species and fluxes rather than raw parameters. The resulting flux-species coordinate exposes regulatory dominance regimes, rescues adaptation and multistability regimes missed by parameter sampling, and gives finite sampling nulls an explicit scope.

systems biology↗

bioRxiv2026

Development of Deep-Learning Models that Predict Quantitative Protein-Ligand Interac-tions in Glycobiology as a part of a Capstone Course

Glycans coat the surface of all cells, and every glycan is recognised by specific glycan-binding proteins (GBPs). There are no general tools that can accurately estimate the binding strength between glycan and GBP from the amino acid sequence of the GBP and the molecular structure of the glycan, represented as SMILES string. We describe models for predicting such binding strengths developed as a part of a Capstone Course at the University of Alberta. The models are trained on a dataset that combines BindingDB, a published database of small-molecule protein interactions, and data from glycan arrays measured by Consortium of Functional Glycomics (CFG). In this hybrid dataset of protein-ligand interactions the ligands are both glycans from CFG and small molecules from BindingDB; similarly, proteins include GBP and proteins from BindingDB. Three models are presented (i) ProMax which fuses ESM-2, MolFormer, and MolCLR features; (ii) APEX which constrains learning to a predetermined form, a physical model of binding; (iii) UltraMax adds inter-atomic distances for the ligands. To address the datasets severe long-tail distribution, the models employ tail-aware losses for rare high-binding instances. Trained and evaluated on approximately one million protein-ligand pairs using hold-out splits for unseen molecules, the three models provide a unified framework for quantitative glycan-protein binding prediction. We observed that learning glycan-protein binding is harder than the similar task of learning small-molecule-protein interactions. Simple mirror-inversion tests led us to postulate that insufficient use of chiral features is an important source of difficulty in learning these interactions.

bioinformatics↗

bioRxiv2026

Ecosystem disservices are underrepresented in literature on annual crop agroecosystems

Maximising ecosystem service (ES) benefits while minimising ecosystem disservices (EDS) is essential for ecological intensification in annual crop systems. Yet ecosystem disservices are often underreported in the scientific literature, potentially biasing how agroecosystem functioning is understood. To test this, we built a predicted network of plausible links between ecosystem service providers (taxa or functional groups that can deliver ecosystem services or disservices), and the ES or EDS they may provide. We then compared this with a realised network based on systematic Scopus searches of annual crop literature. The predicted network contained 47 nodes and 103 links, whereas the realised network contained 33 nodes and 58 links, representing declines of 29.8% in nodes and 43.7% in links. Overall connectivity declined, especially for highly connected nodes, and four of the nine predicted disservice nodes were absent from the literature. ES links were more likely to be documented than EDS links, and EDS links were three times more likely to be absent. Across all links, ES were reported in 6.6 times more papers than EDS. Projected networks, which map indirect connections by linking ES directly to EDS if they share common providers, showed that these bundled interactions were strongly reduced, obscuring multifunctionality and trade-offs. This systematic underrepresentation of EDS, reflecting a cognitive bias, can inflate perceived benefits, distort the evaluation of key taxa and interactions, and create unrealistic expectations about intervention outcomes in biological crop protection. Addressing EDS alongside ES is therefore essential for more balanced assessments of crop-system management and better-informed decisions.

ecology↗

bioRxiv2026

Scalable in vivo cardiac functional genomics with compressed AAV-Perturb-seq reveals a common mitochondrial response to perturbation

Efficient identification of new targets to treat human disease requires a scalable way to link genotype to phenotype directly in the target organ. Pooled CRISPR screening with single-cell RNA sequencing as a readout (Perturb-seq) has emerged as a method for functional genomics but is typically applied in vitro and is limited in scale. Here, we combine in vivo Perturb-seq via adeno-associated virus (AAV)-mediated delivery with a statistical framework allowing for signal deconvolution after multiple random perturbations per cell (compressed Perturb-seq), to develop in vivo compressed AAV-Perturb-seq, a scalable way to perform high-throughput, cell-autonomous functional genomics in a desired target organ. We apply this approach to study the effect of 585 gene knockouts on the cardiomyocyte transcriptome. We identify that alterations in the mitochondrial transcriptome are a common response to genetic perturbation, a finding independently validated across species and perturbation modalities. We identify very few synergistic perturbations, despite observing frequent combinatorial effects. Broadly, our work establishes a platform to facilitate in vivo functional genomics in a target organ with direct applicability to identifying therapeutic targets for the treatment of human disease.

genomics↗

bioRxiv2026

Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features

Cancer arises from extensive genetic and epigenetic alterations that reshape chromatin, transcriptional regulation, and malignant cell states. To systematically chart cancer-intrinsic regulatory programs, we constructed a pan-cancer single-cell transcriptomic and epigenomic atlas encompassing 60 human cell lines representing 16 tissue origins and 20 cancer types, comprising 240,957 single-nucleus RNA-seq and 223,347 single-nucleus ATAC-seq profiles. Integrative analyses revealed extensive pan-cancer cell-state heterogeneity, core gene-regulatory networks, and a conserved epithelial-mesenchymal transition (EMT) axis that transcends tissue of origin. Copy-number variation analysis identified transcription factor amplification and downstream hyperactivation as key drivers of cancer cell-state reprogramming. To further examine how regulatory programs diverge within a cancer lineage and contribute to clinically divergent outcomes, we performed a focused comparison of cutaneous melanoma with acral melanoma, a rare, UV-independent subtype underrepresented in existing pan-cancer atlases. The comparison uncovered a universal inflammation-suppressive program in acral melanoma and an inflamed regulatory landscape in cutaneous melanoma, with the JAK-STAT pathway and downstream transcriptional responses as central discriminators. Integration of single-cell and bulk datasets across models and patient cohorts further linked in vitro tumor-intrinsic gene regulations with in vivo microenvironmental composition and immunotherapy responses. Together, by extending single-cell multi-omic profiling to rare alongside common cancer subtypes, this atlas offers a resource for mapping pan-cancer and subtype-specific gene-regulatory programs that shape cancer cell-state plasticity.

genomics↗

bioRxiv2026

Sea-level and climate changes drive lineage diversification in the imperiled Venus flytrap (Dionaea muscipula J. Ellis, Droseraceae)

Venus flytrap (Dionaea muscipula) is an insectivorous plant in the monotypic genus Dionaea, endemic to the Southeastern United States, and found in a small geographic range in North and South Carolina. Its unique morphology and natural history have intrigued biologists for centuries, yet the evolution of populations has not been adequately characterized. Population decline, driven primarily by disruptive land conversion, fire suppression, and illegal harvesting/poaching underscores the urgency of defining the genetic diversity in the present population to guide species conservation. We applied a genome-wide SNP dataset to analyze admixture and population structure and used coalescent modeling to trace the ancestry and migration of geographically distinct population clusters. Our population dynamics model supports four lineages, with the North Cape Fear Arch lineage as the ancestor of all lineages and two genetically distinct Sandhills lineages derived from the South Cape Fear Arch lineage independently at [~]13 Ma and [~]3 Ma, respectively, during peak climate optima periods and maximal sea level rise. Severe population decline events occurred among all lineages, with two ancient instant bottleneck population decline events in Sandhills lineages and two recent ones in the two Cape Fear Arch lineages, which contributed to the current genetic structure. Shorter peduncle lengths in Sandhills populations correspond with suspected local adaptation to Sandhills habitats.

evolutionary biology↗