Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Activation of vitamin D signaling suppresses platinum-induced ovarian cancer stem cell plasticity

Advanced-stage high grade serous ovarian cancer (HGSC) initially responds to platinum-based chemotherapy; however, most HGSC tumors recur and develop chemotherapy-resistance, making recurrent ovarian cancer (OC) a fatal disease. Ovarian cancer stem cells (OCSCs), a subpopulation of cells capable of surviving treatment and repopulating tumors, contribute to disease relapse, and understanding the mechanisms(s) responsible for OCSC survival is critical for developing strategies to overcome acquired resistance and prevent tumor recurrence. However, whether platinum-induced OCSC enrichment results from the selection of pre-existing OCSCs or chemotherapy-induced cellular plasticity, and the molecular mechanisms underlying these processes, remain poorly understood. Here, we demonstrated that cisplatin treatment not only enriched for OCSCs but promoted the conversion of ALDH- cells to ALDH+ cells, revealing chemotherapy-induced plasticity as a potential mechanism for OCSC expansion. This conversion was associated with nuclear receptor signaling pathways, vitamin D receptor signaling (VDR/RXR) and retinoic acid receptor signaling (RAR/RXR). Importantly, activation of VDR/RXR signaling with vitamin D reduced stemness phenotypes and the cisplatin induced conversion to ALDH+ cells. In addition, vitamin D combined with cisplatin decreased tumor volume in vivo compared to either treatment alone. In contrast, cisplatin treatment increased RAR/RXR signaling, supporting an associative role for ligand-dependent nuclear receptor signaling in OCSC plasticity. Together, these findings reveal that platinum chemotherapy can actively promote the acquisition of stem-like properties and identifies VDR/RXR signaling as a potential regulatory mechanism of OCSC plasticity. Targeting this pathway with vitamin D may provide a therapeutic strategy to target OCSCs and improve responses to platinum-based chemotherapy.

cancer biology↗

bioRxiv2026

Targeting MAT2A-S-adenosylmethionine (SAM) Axis Attenuates DNA Damage Response and Cancer Stemness to Increase Platinum Sensitivity in Ovarian Cancer

Abstract Metabolic and epigenetic reprogramming drives development of platinum resistance and disease recurrence in high-grade serous ovarian cancer (HGSC), a major clinical challenge in the field. S-adenosylmethionine (SAM) is the universal methyl group donor, synthesized by methionine adenosyltransferase 2A (MAT2A) from methionine. Prior studies have linked altered SAM-dependent DNA methylation to acquired platinum resistance in HGSC, connecting metabolism and epigenetic regulation. However, how MAT2A-driven SAM synthesis coordinates the metabolic-epigenetic remodeling axis to affect platinum sensitivity remains unclear. Here, we report that MAT2A-driven SAM synthesis is required for platinum-induced alterations in DNA methylation and the enrichment of ovarian cancer stem cells (OCSCs). We found that MAT2A upregulation correlated with poor progression-free survival in OC patients, and both pharmacological inhibition and genetic knockdown of MAT2A increased sensitivity to cisplatin. To investigate the underlying epigenetic mechanism, we profiled genome-wide changes in DNA methylation using OVCAR3 treated with cisplatin (15M, 16hr) and/or MAT2A siRNA (48hr), which showed that MAT2A knockdown reversed platinum-induced DNA methylation dynamics. Subsequent analysis revealed enrichment of pathways associated with platinum resistance, DNA repair, and stemness. Mechanistically, inhibiting MAT2A abrogated both the platinum-induced hypermethylation at promoter regions and the SAMTOR-mTOR-S6K-FANCD2 signaling axis, resulting in accumulated R-loops, attenuated DNA repair activation in response to platinum, and enhanced platinum-induced DNA damage and cell death. Using a functional DNA damage reporter assay, we directly showed that MAT2A knockdown reduced DNA repair through homologous recombination (HR) and non-homologous end joining (NHEJ) pathways. By detecting key DNA damage response kinases governing HR and NHEJ signaling, we further demonstrated that MAT2A inhibition abrogated DNA repair activation in response to platinum. Furthermore, single-sample Gene Set Enrichment Analysis of paired primary and recurrent tumors from HGSC patients revealed an association between MAT2A expression and increased OCSC features in recurrent tumors. In vitro, MAT2A inhibition prevented cisplatin-induced enrichment of OCSCs, reduced stemness markers, and inhibited spheroid-forming ability, all of which were rescued by SAM supplementation. Together, our study reveals a previously unrecognized mechanism linking metabolism to epigenetic regulation through platinum-induced remodeling and establishes the MAT2A-SAM axis as a promising therapeutic target to enhance platinum sensitivity by abrogating DNA damage response and OCSC enrichment and ultimately reduce OCSC-driven disease recurrence in HGSC.

cancer biology↗

bioRxiv2026

Online synaptic credit assignment in active dendrites

Credit assignment in neural networks is usually formulated as the computation of an abstract error gradient. Whether such a gradient can take a physical, causal form in biophysically detailed multi-compartment neuron models, and enable online, supervised learning, remains unclear. Here we show that the gradient of a detailed neuron's voltage with respect to a synaptic weight is itself a voltage. Differentiating the discrete backward-Euler update solved by standard simulators yields equations of the same form as the original voltage dynamics, driven by gradient currents. Replaying weight-specific gradient currents forward in time reproduces the exact gradient with high fidelity in an L5 pyramidal neuron across diverse input regimes (R2 > 0.998). Pairing the replayed gradient voltage with a local learning signal yields a causal, online learning rule. A single L5 pyramidal neuron with active dendrites learns to reproduce target voltage trajectories containing calcium plateaus and bursts, and recurrent networks of detailed neurons learn to generate target temporal patterns, far outperforming a readout-only control. These results demonstrate that synaptic credit assignment can be implemented online by the voltage dynamics of detailed neurons, potentially suggesting a physical substrate for gradient-based supervised learning in the brain.

neuroscience↗

bioRxiv2026

Machine learning reveals sequence and genomic context features underlying Alu-specific effects on genome folding

The Alu transposable element is among the most abundant classes of mobile DNA in the human genome, and has been linked to gene regulation and chromatin organization. Yet how individual Alu insertions influence nearby chromatin interactions remains poorly understood. To investigate this, we used deep learning to perform a genome-wide in silico deletion screen of ~1.1 million Alus, predicting each element's importance to local genome folding. We identified a subset of high-scoring Alus that span multiple Alu subfamilies and are enriched in loci that are fast-evolving and gene-dense, especially loci encoding genes that are actively transcribed and/or related to Alu biology. We further found that polymorphic Alus preferentially occur in regions tolerant of sequence variation but predicted to be resistant to changes in chromatin structure. Targeted in silico mutagenesis showed that the importance of individual Alus to local chromatin interactions depends on both intrinsic Alu sequence properties and genomic context. Finally, we identified Alu sequences predicted to alter CTCF-mediated boundary strength and, in some cases, to promote the formation of new loops and boundaries. Together, these results position Alus as key modulators of genome architecture, while underscoring that the fate of a new Alu depends on where it inserts and how it interacts with other determinants of chromatin state and structure.

genomics↗

bioRxiv2026

Inverted logic of ecDNA localization converts nuclear periphery into oncogenic transcription hubs in cancer

Extrachromosomal DNA (ecDNA) is a major driver of cancer pathogenesis, however, its spatial organization in the nucleus is not currently understood. Here we show that ecDNAs, in contrast to chromosomal DNA, preferentially localize to the nuclear periphery yet resist peripheral gene silencing. Single-molecule multi-omic sequencing and imaging-based CRISPR screening demonstrate enhanced ecDNA hub formation, H3K27ac and H3K9me3 bivalency, late DNA replication, long-range CpG hypomethylation, and increased transcriptional output when ecDNAs localize to the nuclear periphery. EcDNA anchors to the nuclear lamina through H3K9me3-dependent interactions with the Lamin B Receptor (LBR), which if interrupted, results in inward migration of ecDNA particles, reduced ecDNA congregation, and decreased transcription of ecDNA-encoded genes. These results reveal inherent advantages of the spatial organization of ecDNA that are not available to chromosomal DNA, enhancing ecDNA congregation and amplifying oncogenic transcriptional output, by localizing to the nuclear periphery of cancer cells.

cancer biology↗

bioRxiv2026

High-throughput physics-based enzyme engineering

Enzyme engineering aims to tailor natural enzymes for industrial and therapeutic applications, yet physically grounded rational design has been limited by a trade-off between accuracy and cost, leaving the field heavily dependent on expert intuition. Here we present a scalable physics-based framework that combines field-aware machine learning with molecular mechanics to capture enzyme electrostatics at quantum-mechanical accuracy while enabling efficient, atomistic exploration of reaction free-energy landscapes. Coupled with microkinetic modelling, the framework translates molecular free-energy landscapes into catalytic rates and selectivity across competing, multistep reaction pathways. Applied to a newly engineered oxidative amidase (OxiAm), the framework predicts catalytic rate constants with near-experimental accuracy, quantitatively resolves the selectivity between hydrolysis and aminolysis, and generalizes across substrates, mutations and enzyme homologues. Transition-state ensemble analysis further reveals the reaction mechanism and guides the design of enzyme variants for pharmaceutical synthesis. By bringing chemical accuracy and high-throughput sampling to enzyme catalysis, this approach shifts rational design from static, empirical practice toward dynamic, free-energy-driven design, and should accelerate the engineering of biocatalysts.

biophysics↗

bioRxiv2026

Generation and application of human pluripotent stem cells derived peri-nervoids

Autologous nerve grafts remain the gold standard for peripheral nerve repair, but donor scarcity, donor-site morbidity and incomplete functional recovery limit their use. Schwann cells (SCs) drive autograft-mediated regeneration, yet their limited availability and expansion impede SC-based therapies. Here, we generated human pluripotent stem cell-derived SCs, motor neuron-enriched spinal cord organoids and nociceptive sensory neuron-enriched dorsal root ganglion organoids, and assembled them into a nerve-like construct termed the Peri-nervoid. Single-cell RNA sequencing revealed context-dependent SC plasticity: axonal co-culture promoted myelination, whereas axonal transection induced a repair phenotype resembling SC dedifferentiation during Wallerian degeneration. Following transplantation into 7-mm sciatic nerve defects in NSG mice, human SCs survived and remyelinated regenerating host axons, while axon-containing grafts enhanced regeneration relative to SC-only grafts. No abnormal proliferation, systemic toxicity or major-organ histopathology was detected. These findings position the Peri-nervoid as a scalable, developmentally inspired platform for developing engineered grafts for peripheral nerve repair.

bioengineering↗

bioRxiv2026

Human skin fibrosis with iPSC-derived organoids reveals RUNX2-mediated fibroblast reprogramming

Fibrotic skin diseases are characterized by persistent fibroblast activation and extracellular matrix remodeling, yet the mechanisms governing fibroblast state transitions remain incompletely understood. Here, we established a human iPSC-derived skin organoid model of fibrosis through chronic TGF-{beta} stimulation. Single-cell RNA sequencing combined with immunofluorescence-based spatial analysis revealed dynamic fibroblast state transitions, spatial reorganization, and expansion of activated fibroblast populations during fibrotic remodeling. Integration with human scleroderma single-cell datasets demonstrated conserved fibroblast states and transcriptional programs between organoids and patient tissues. We further identified broad induction of RUNX2 in the dermal compartment during fibrosis, and RUNX2 depletion attenuated fibrotic marker expression. CUT&RUN profiling revealed RUNX2 occupancy at fibrosis-associated loci, including RUNX1 and LOXL2. Using a machine learning-guided screening approach, we identified F0565-0303, a small molecule that suppressed RUNX2-dependent fibrotic programs in vitro and reduced fibrosis in a bleomycin-induced mouse model. Together, these findings establish human skin organoids as a platform for modeling fibrosis and nominate RUNX2 as a potential therapeutic target.

bioengineering↗

bioRxiv2026

Sex-dimorphic immune trajectories across the canine lifespan and early-life signatures of geroprotective interventions

Canine models are invaluable for translational geroscience, but widespread gonadectomy in companion dogs obscures natural sex-specific aging trajectories. Here, we characterize the age-associated hematologic and serum cytokine profiles of 80 intact laboratory Beagles spanning 1 to 11 years. Contrary to classical linear models of immunosenescence and inflammaging, we reveal that canine immune aging is highly dynamic and sexually dimorphic. While absolute leukocyte counts declined continuously, cytokine remodeling was sex-specific: intact males exhibited extensive age-dependent inflammatory restructuring, whereas females demonstrated restricted fluctuations. To establish baseline geroprotective signatures, we evaluated 24 young beagles following a 90-day intervention with rapamycin, canagliflozin, or dietary restriction. Rapamycin induced robust immune modulation, while canagliflozin prompted early, sex-biased weight loss and narrower cytokine responses. Together, our findings demonstrate that canine immune aging involves complex, sexually dimorphic remodeling. By utilizing an intact cohort to isolate natural dimorphic trajectories, we provide a crucial foundation for developing precision, sex-optimized geroprotectors.

physiology↗

bioRxiv2026

Atlas-scale single-cell analysis beyond in-memory paradigm with scAtlasPy

Single-cell atlases are rapidly outgrowing the memory capacity of standard workstations, challenging the in-memory paradigm underlying mainstream computational ecosystems. Here, scAtlasPy decouples scale of atlas from memory capacity by leveraging the disk-resident computing. It enables full-resolution analysis of a 100-million-cell atlas with only 42.9 GB peak memory, whereas state-of-the-art platforms are limited at 3 million cells with 512 GB memory. scAtlasPy achieves 137,745 cells/s, 10.4x faster than scDataset with 82.6% lower memory usage for random minibatch retrieval. Its extensible architecture offers a flexible platform for diverse atlas-scale analytical tasks, facilitating the discovery of complex cellular heterogeneity and functions in massive cell atlases.

bioinformatics↗

bioRxiv2026

Structural basis of endogenous inverse agonism and subtype selectivity at melanocortin receptors

Endogenous inverse agonists suppress constitutive G protein-coupled receptor (GPCR) signaling, but their mechanisms remain poorly understood. Here we report cryo-EM structures of melanocortin-1 receptor (MC1R) bound to its endogenous inverse agonist, agouti signaling protein (ASIP) and melanocortin-4 receptor (MC4R) bound to agouti-related protein (AgRP). Together with previously reported structures and those we determined using extracellular nanobodies developed here, these data delineate a conformational continuum underlying receptor activation and silencing. Both inverse agonists occlude the orthosteric pocket as molecular corks and drive a shared TM3-centered extracellular remodeling. Against this common mechanism, subtype selectivity is encoded not by the conserved orthosteric pocket but by the divergent extracellular receptor surface, engaged through an ASIP C-terminal loop-dependent clasp. These findings establish a mechanistic framework for endogenous inverse agonism and identify the receptor periphery as a tractable target for subtype-selective modulation.

biophysics↗

bioRxiv2026

Chimeric Induced Cooperativity Opens the Design Space of Eukaryotic Gene Regulation

Predictive engineering of eukaryotic transcription is limited by the coupling of signal sensing, DNA binding, TF abundance and promoter output. Here we establish chimeric induced cooperativity (CIC), a modular architecture that separates LBD, DBD and AD functions and links them to promoters with tunable basal and maximal output. Module parameters can be recombined to predict new CIC-TF configurations and guide design before construction. Ligand-induced cooperativity reduces basal DNA occupancy while increasing induced occupancy, and effective DBDs combine low OFF-state activity with strong ON-state promoter occupancy rather than binding strength alone. Synthetic promoters independently control occupancy gain and output range. The same framework extends to repression and can be recalibrated with limited measurements in mammalian cells. In yeast, CIC-12 achieved a mean fold induction of 298-fold across 12 orthogonal sensors; an earlier CIC-10 chassis enabled model-guided optimization of an eight-gene vitamin B5 biosynthetic pathway. CIC establishes a programmable, model-guided design space for eukaryotic transcriptional control.

synthetic biology↗

bioRxiv2026

Cross-species discovery of large structural variants reveals distinct evolutionary dynamics in primates

Large-scale structural variants (SVs) are major drivers of genome evolution and disease susceptibility, shaping lineage-specific gene content, genome architecture, and phenotypic diversity. Yet, their systematic detection across divergent species has been hindered by complex rearrangements and reference bias, limiting our understanding of their evolutionary impact. Here we present LGvar, a divergence-aware tool for identifying SVs in population-scale and cross-species genome assemblies, enabling up to a 7-fold increase in the detection of large deletions, insertions, inversions and complex rearrangements ([≥]10 kbp). Application of LGvar uncovers 112 previously undetected large SVs overlooked by conventional methods and quantifies the effect of evolutionary divergence on SV discovery across primate euchromatic genomes, revealing an approximately 30% overall decline in detection sensitivity with increasing phylogenetic distance. Mapping SVs from comparable syntenic regions onto the primate phylogeny shows that large deletions ([≥]10 kbp) accumulate approximately two-fold faster than insertions and eight-fold faster than inversions, in contrast to the dynamics of smaller SVs (<10 kbp). Integrating structurally divergent regions with the synteny region analysis reveals over 606 previously unreported gene gains and losses in great apes and refined the totals to 134 human-lineage-specific and 51 great-ape-lineage-specific protein coding genes. These results suggest that large SVs follow distinct evolutionary trajectories, with potential selective constraints shaping genome structure. Our study establishes a generalizable strategy for cross-species SV discovery and provides a high-resolution view of how large SVs contribute to genome evolution in primates.

genomics↗

bioRxiv2026

Active control modulates memory-related activity during encoding and encoding-retrieval similarity

Self-directed learning allows learners to actively control their learning experience and has been shown to enhance memory compared with matched yoked learning. However, it remains unclear when and how active control modulates memory-related neural activity during learning and retrieval. We recorded electroencephalography (EEG) while participants encoded objects under active and yoked learning conditions and again during a delayed recognition test approximately 24 h later. We examined event-related potential (ERP) activity across earlier processing windows, including pre-stimulus slow potentials and early N2 activity, and later processing windows, including P300, late slow-wave, and post-stimulus slow-potential activity. We also examined encoding-retrieval similarity (ERS) between neural patterns during encoding and retrieval. Behaviorally, active control improved delayed recognition, with the advantage selectively expressed in detailed recognition. In the ERP analyses, earlier processing windows showed memory-related effects, with pre-stimulus slow-potential and N2 activity differentiating subsequently remembered from forgotten items, but were not modulated by active control. By contrast, active control modulated later memory-related ERP activity, with remembered-forgotten differences expressed during late stimulus-related and immediate post-stimulus processing only in the active condition. ERS showed a similar active-control modulation: memory-related encoding-retrieval pattern similarity was evident under active learning, but not under yoked learning, with this effect involving relatively late encoding and retrieval windows. Together, these findings support a constructive-processing account, suggesting that memory formation under active control depends more strongly on rich, detailed encoding representations that can be reinstated during retrieval.

neuroscience↗

bioRxiv2026

A multi-scale structural and biophysical atlas of TCR-peptide-HLA recognition dynamics

Dynamic interactions between T cell receptor (TCR) and peptide-human leukocyte antigen (pHLA) complexes are central to peptide-specific immune recognition, influencing T cell activation and immune responses. While structural biology has provided valuable static structures of TCR-pHLA complexes, systematic datasets capturing their dynamic and interaction patterns remain limited. Here, we present DynaTPH, a curated structural dynamics dataset of human TCR-pHLA complexes. DynaTPH integrates TCR-pHLA structures, covering both HLA class I and class II complexes, and extends these static structural resources with standardized molecular dynamics simulations and derived biophysical properties. Through a multi-stage filtering procedure, we identified 256 representative complexes and performed standardized all-atom molecular dynamics simulations for each system, corresponding to a cumulative simulation time of 38.4 s. The dataset includes static structures, trajectories, corresponding frames, and derived physicochemical properties, including hydrogen bonds, intermolecular contacts, solvent accessibility, and backbone flexibility. By capturing the conformational flexibility and dynamic interaction patterns across diverse TCR-pHLA interfaces, DynaTPH extends static structural resources with multidimensional biophysical information. This dataset enables systematic investigation of TCR-pHLA recognition dynamics and supports applications in TCR engineering, vaccine design, and immune tolerance research and artificial intelligence-driven computational immunology.

molecular biology↗

bioRxiv2026

A multi-tissue epigenomic atlas links the sheep non-coding genome to domestication and complex traits

Non-coding regulatory variation drives complex traits, domestication, and evolutionary adaptation, yet the sheep genome lacks high-resolution functional annotation. Here we present SheepEpimap, a multi-tissue regulatory atlas harmonizing 516 CUT&Tag histone modifications, ATAC-seq, and RNA-seq datasets across 43 adult tissues in sheep. We annotated 2.93 million cis-regulatory elements, yielding 557,441 enhancer-gene pairs and 145,407 variants with allele-specific effects. By training a sequence-to-function deep-learning model, we decoded the base-pair syntax of chromatin accessibility, annotated transcription factor motif instances genome-wide, and constructed 12,210 tissue-specific gene regulatory networks (GRNs). Integrating this resource with multi-tissue expression quantitative trait loci, selection sweeps, and genome-wide association studies prioritized non-coding variants driving domestication and complex traits. Finally, cross-species analysis revealed that sequence-conserved, tissue-matched enhancers were significantly enriched in the heritability of complex traits and diseases in humans. In summary, SheepEpimap (https://genome.ucsc.edu/s/mengzhu/SheepEpimap) provides an open-access foundational ecosystem for sheep functional genomics, precision breeding, and comparative biology.

genetics↗

bioRxiv2026

Predicting Protein-RNA Binding Affinity Changes via Spatial Coupling-Aware State Space Modeling

Accurately predicting the effects of mutations on protein-RNA binding is crucial for elucidating disease mechanisms. Yet, exhaustively exploring the space of all possible variants is prohibitively expensive, motivating computational methods that can quantify mutation-induced changes in binding affinity (aka {Delta}{Delta}G) accurately and efficiently. We present iSCALE, an interpretable and generalizable deep learning method that adopts an implicit Spatial Coupling-Aware Ligand Encoding strategy to predict mutation-induced binding affinity changes. By injecting this implicit multiscale encoding scheme into a bidirectional state space modeling architecture, iSCALE learns a generalizable multiscale coupling pattern that achieves superior performances on not only the protein-RNA binding {Delta}{Delta}G, but also the protein stability {Delta}{Delta}G and protein-protein binding {Delta}{Delta}G predictions. Detailed analyses demonstrate that the model attention scores align well with structural characteristics. In addition, iSCALE shows good discriminative ability when predicting close samples such as complexes of same mutation but with different ligands or the same complex but with different mutation sites. In summary, iSCALE serves as an effective in silico tool for large-scale protein-RNA binding {Delta}{Delta}G prediction, which pushes the border of understanding in mutation-induced pathological outcomes.

bioinformatics↗

bioRxiv2026

Prior low-severity fires reduce the risk of persistent forest loss from subsequent fires

Intensifying fire regimes threaten forests globally, but the risk of persistent post-fire forest loss and its potential mitigation remains poorly quantified. We analyzed millions of wildfires worldwide from 2001 to 2024 and tracked recovery in satellite-observed forest structure and ecosystem function. Post-fire persistent forest loss, indicated by modeled non-recovery to pre-fire conditions over decadal timescales, affected 57.1% of burned forest area globally since 2001, with hotspots in Pacific temperate and southern boreal forests. We then identified 'crucial fires' as events exceeding a stringent modeled-risk probability threshold for persistent structural or functional non-recovery, with fire severity strongly predicting this loss. This severity dependence revealed a management pathway, as locations with prior low-severity fire experienced lower severity in subsequent wildfires and had lower modeled probability of becoming crucial. Under a model-based counterfactual scenario, applying the estimated severity attenuation was associated with a 7.6% reduction; the top 1% of road-accessible areas accounted for 35% of this reduction. These results provide a global framework for identifying where wildfire threatens forest resistance and where targeted low-severity fire management like prescribed fire might be used to combat global forest loss.

ecology↗