Biology subjects

Zhang, S.

Publications and source records attributed to Zhang, S..

At least 91 records · Page 5Linked to original sources

bioRxiv2026

AART enables fast and accurate cross-platform proteomic translation

Plasma proteomic profiling has been widely used for biomarker discovery, disease prediction and diagnosis, and patient stratification. However, technical differences across assay platforms often result in low-to-moderate agreement, limiting study reproducibility, data integration, and model transferability. Here we present AART, a cross-platform proteomic translation framework that integrates matched-protein ridge regression with proteome-wide residual learning. We benchmarked AART spanning three independent cohorts profiled using three major platforms, including Olink, SomaScan, and mass spectrometry. Across all six translation directions, AART achieved the best performance compared with baseline methods for both overlapping and non-overlapping protein translations, with a relative improvement of 92.0% on average over direct mapping and by up to 31.6% over cpiVAE, the strongest baseline. Proteins that were accurately translated and improved by AART were enriched for extracellular, vesicle-associated, and tissue-restricted plasma biology. In downstream applications, AART improved the reproducibility of proteomic association analyses relative to direct cross-platform comparison by 75.5% for type 2 diabetes and 370.6% for Alzheimers disease. AART-enabled cohort integration enhanced diagnostic accuracy for amyotrophic lateral sclerosis by 92.6% compared with non-integration analysis. AART was overall one to three orders of magnitude faster than cpiVAE, facilitating biobank-scale applications. Together, these results establish AART as a fast, accurate, and scalable framework for cross-platform proteomic translation, enabling more reproducible, transferable, and integrated proteomic research.

bioinformatics↗

bioRxiv2026

mirCCC: Repression-aware graph learning for miRNA-mediated cell-cell communication inference

Cell-cell communication analyses usually focus on protein ligands and receptors and therefore miss extracellular vesicle transfer of microRNAs, an important route of signalling in cancer. Here we show that microRNA-mediated communication can be inferred from standard single-cell RNA sequencing by detecting coordinated decreases in the expression of validated miRNA target genes. We developed mirCCC, a computational framework that estimates cell-specific microRNA activity, models cellular sending and receiving capacity for extracellular vesicle transfer, and learns microRNA-resolved communication graphs from transcriptomic data. In synthetic benchmarks with strong confounding signals, mirCCC improved whereas all comparison methods declined. Applied to a human colorectal cancer atlas, mirCCC recovered known colorectal cancer-associated microRNAs and identified stromal- and myeloid-to-epithelial communication converging on a plasticity program linked to TGF-{beta} and Wnt/{beta}-catenin signalling. These results provide a practical route for studying extracellular vesicle-mediated communication in existing single-cell atlases. Author summaryCell-cell communication plays important roles across a wide range of biological processes. Most studies of cell-cell communication focus on interactions between ligands and receptors. However, cells can also use extracellular vesicles to deliver microRNAs (miRNAs), which regulate gene activity in receiving cells and contribute to cancer progression, immune regulation, and changes in the tissue environment. Since standard single-cell sequencing does not directly capture miRNAs, this mode of communication remains largely unexplored. Here we developed mirCCC, a computational method for inferring miRNA-mediated communication from standard single-cell transcriptomic data. Instead of detecting miRNAs directly, mirCCC examines the effects they leave in receiving cells. Active miRNAs often cause coordinated suppression of their target genes. mirCCC uses these coordinated reductions in the expression of known target genes to estimate miRNA activity and combines them with the ability of sending cells to package and release miRNAs and of receiving cells to process them. It can therefore reconstruct directional communication networks for individual miRNAs. Our evaluations show that mirCCC remains reliable in the presence of misleading signals and reveals biologically meaningful regulatory patterns in human colorectal cancer data. Overall, mirCCC provides a practical way to study miRNA-mediated cell-cell communication using existing single-cell transcriptomics datasets.

bioinformatics↗

bioRxiv2026

Structural Bioinformatics of Four Human Aquaporins and Their Water-Soluble QTY Analogs

Human aquaporins (AQPs) are essential membrane channels, yet their inherent hydrophobicity complicates structural and functional studies. We present the systematic application of the QTY code to human AQPs, integrating it with AlphaFold 3 structure prediction to design and validate that four-representative human AQPs (AQP1, AQP3, AQP4, AQP7) can be converted into water-soluble analogs while maintaining their conformation. This approach features a novel platform for editing challenging membrane proteins. The QTY code was applied to the transmembrane regions of the selected four AQPs. Subsequently, the water-soluble QTY analogs of the four AQPs were predicted using AlphaFold 3. The predicted structures were superposed with CyroEM- or X-ray-determined native structures in PyMOL. Further analyses included root-mean-square deviation (RMSD) calculations, visualization of hydrophobic surface reduction, and inspection of conserved protein-ligand binding ability. After applying the QTY code, sequence changes between native AQPs and their QTY analogs was significant (42.86-48.80%). Nevertheless, their structures superposed well in analyses, with only slight deviations (RMSD < 0.6 [A]). In addition, the surface hydrophobicity of all QTY-edited AQPs was significantly reduced. Importantly, molecular contacts between the cholesterol ligand and protein were largely preserved for both native AQP1 and its QTY analog. Finally, all AlphaFold3-predicted structures for AQPs have high confidence values (pLDDT > 90; pTM [~]0.83), supporting the reliability of the predicted structures. The findings demonstrate that membrane protein hydrophobicity can be edited and reduced without compromising fold integrity or functional architecture. Integration of the QTY code with AlphaFold 3 affords a high-throughput platform for designing water-soluble, structurally faithful analogs of challenging membrane proteins. Such a strategy can provide a potent platform for detergent-free biochemical studies and water-soluble analogs for therapeutic monoclonal antibody discoveries, thus advancing research of this pharmacologically important protein family.

bioinformatics↗

bioRxiv2026

LDB1-dependent enhancer connectivity defines T-cell leukemia identities and masks metabolic vulnerabilities

Spatial enhancer connectivity is fundamental to proper gene regulation. Enhancer dysregulation has emerged as a hallmark of cancers, including T-cell acute lymphoblastic leukemias (T-ALL). T-ALL are aggressive malignancies characterized by marked transcriptional heterogeneity driven by distinct stages of developmental arrest and diverse noncoding alterations. How these cancers co-opt nuclear architecture to rewire enhancer connectivity remains poorly understood. Here, we report that the LDB1 chromatin architectural complex is an essential mediator of enhancer-oncogene looping that sustains oncogenic transcriptional programs across multiple T-ALL subtypes. Integrating bulk and single-cell transcriptomic data from patients with T-ALL and healthy hematopoietic controls, we show that the LDB1-dependent regulatory circuitry defines the molecular identities of distinct T-ALL subtypes while restricting plasticity toward alternative cell states. LDB1 loss dismantles chromatin looping among cell state-defining enhancers liberating them to form promiscuous interactions with nearby genes. This enhancer rewiring stimulates expression of key metabolic genes, creating a mevalonate pathway dependency exploitable with statin treatment. Our study establishes LDB1 as a central executor of T-ALL regulatory circuitry and more broadly illustrates chromatin rewiring as a source of targetable dependencies in cancer.

Cancer Biology↗

bioRxiv2026

The contribution of recent and historical demographic histories to genomic diversity and conservation status in plant species

Demographic factors are intrinsically crucial to evaluate species extinction risk. However, measuring them remains difficult and time-consuming and the use of genomic summary statistics has been advocated to assess the conservation status of a species. In the present study, we estimated (i) the census number (Nc), (ii) effective population size (Ne) over three different time periods, recent, historical and ancient, (iii) neutral genetic diversity ({pi}4), and (iv) a measure of the efficacy of purifying selection ({pi}0/{pi}4) for 101 plant species using population genomic sequencing data. Twenty-one species are from the Plant Species with Extremely Small Populations (PSESP) program of SW China. Threatened species exhibited significantly lower Ne, Nc,{pi} 4, and weaker purifying selection, but had a higher Ne/Nc ratio than non-threatened ones. Nc was the main determinant in identifying conservation status, and contemporary neutral genetic diversity was predominantly influenced by historical Ne. In the absence of demographic information, genetic parameters are a good proxy of conservation status, likely because currently threatened species also had a low historical population size. In summary, our findings suggest that direct estimates of Nc are more useful than{pi} 4, although the latter remains a valuable conservation indicator. Hence, efforts such as the PSESP should be extended. Significance StatementExtinction is essentially a demographic process. However, evaluating the risk of extinction of species based only on demographic parameters remains a difficult task, and today only 18% of plant species are included in the International Union for Conservation of Nature (IUCN) Red List of Threatened Species. Here, we use demographic data and whole genomic data from 101 plant species to show that while census number remains the best predictor of extinction risk, genomic based parameters provide good alternatives. More specifically, current neutral nucleotide diversity and historical effective population size are good predictors of the extinction risk of plant species.

evolutionary biology↗

bioRxiv2026

Causally measuring aging and rejuvenation through transcriptomic damage

Aging is caused, fully in large part, by the progressive accumulation of damage, yet quantifying age-related damage across tissues and conditions remains a challenge. Here, we present a computational framework to quantify damage from standard RNA-sequencing data. It captures four classes of aberrant transcript structures, including premature termination upon intron retention, domain-disrupting splice variants, repeat elements, and gene fusion events, each reflecting distinct forms of RNA integrity loss. Using this method, we revealed a robust age-associated increase in transcriptomic damage across tissues. To integrate these measurements into a unified biomarker, we constructed a transcriptomic damage-based aging (tDamAge) clock using machine learning models trained across mouse tissues or human peripheral blood. It could predict age and detect transcriptomic shifts under both pro-aging and anti-aging conditions. Progeroid models exhibited accelerated tDamAge, whereas interventions such as caloric restriction, rapamycin, and methionine restriction lowered tDamAge. Cross-dataset analysis showed that diverse anti-aging interventions converge on shared transcriptomic signatures, particularly RNA processing and chromatin organization pathways, and these age-associated patterns could be reversed by interventions. We further identified elevated damage age acceleration in Alzheimers disease and observed rejuvenation-like reductions during embryonic development. Together, our findings establish transcriptomic damage as a causal, quantifiable and biologically interpretable feature of aging and demonstrate that tDamAge could detect age progression, acceleration, deceleration, and reversal.

bioinformatics↗

bioRxiv2026

Mammalian aging involves genome-wide splicing degeneration leading to functional decline

Alternative splicing exhibits significant changes during development and aging, affecting the composition and variance in the transcriptome. However, it is unclear whether and how age-associated splicing dysregulation leads to functional consequences. Here, an integrative analysis of transcriptome data across mouse and human tissues revealed that aging is characterized by systematic deterioration of the fidelity of RNA splicing, here termed splicing degeneration, a measure of functional alteration of reading frame and domain configuration of protein products. Genes with higher aging-associated splicing degeneration were more conserved and enriched for processes such as RNA metabolism and antigen presentation. By assessing alternative splicing events associated with functional deterioration, we quantified the degree of splicing degeneration. Its level increased with age but was alleviated following calorie restriction or rapamycin treatment, indicating that it can serve as a new molecular hallmark of aging. Mechanistically, through a comprehensive meta-data analysis, we discovered that splicing degeneration is associated with age-associated changes in specific splicing factors, which in turn showed a strong association with age-related transcriptome changes. Overall, our study demonstrates the intricate relationship between aging and genome-wide splicing degeneration, revealing a promising target for aging interventions acting to reverse splicing degeneration.

Systems Biology↗

bioRxiv2026

Brain Structure Shapes Function through higher-order Functional Interactions

Brain function is deeply embedded within multiscale structural architecture. Conventional studies predominantly utilize pairwise connectivity networks to investigate structure-function relationships. However, this low-dimensional perspective overlooks multi-region collaborations for complex cognition. Consequently, whether and how anatomy constrains such higher-order functional networks remains unresolved. To address this pivotal question, we utilize an information-theoretic O-information approach to characterize higher-order functional interactions (HOIs). By reconstructing individual-level HOIs from multimodal structural networks, we directly validate the structural constraint on HOIs. The resulting reconstruction coefficients are defined as structural-functional constraint strength (SFCS), serving as a quantitative vehicle to decipher how anatomy shapes these higher-order networks. SFCS uncovers a highly heterogeneous structural constraint landscape across data modalities, spatial regions, and informational interaction modes. Crucially, individualized SFCS robustly predicts multi-domain cognitive phenotypes, showing higher sensitivity for informant-reported than patient-reported assessments. Finally, we show that this landscape undergoes pathological, mode-specific reorganization in Alzheimers disease. Cross-scale alignment with spatial transcriptomics further demonstrates that this macroscale network remodeling is coupled with microscale metabolic and regulatory gene pathways. Collectively, our findings not only validate the structural constraint on higher-order functional networks but also decipher its precise underlying mechanisms. This constraint paradigm plays a pivotal role in shaping diverse cognitive capabilities, while its pathological disruption in Alzheimers disease highlights the potential of SFCS as a biomarker for tracking neurodegenerative network impairments.

neuroscience↗

bioRxiv2026

Btbd11 regulates glutamatergic synapse organization in GABAergic inhibitory interneurons

The mechanisms that underlie glutamatergic synapse organization and function in GABAergic inhibitory interneurons (INs) are not well described, despite evidence that impaired glutamatergic excitation of INs is implicated in psychiatric disorders such as schizophrenia and anxiety. Glutamatergic synapses received by INs have unique basal transmission properties and exhibit distinct synaptic plasticity compared to those received by excitatory neurons, likely due to cell-type specific differences in postsynaptic density (PSD) composition and maintenance mechanisms. In the present study, we show that the interneuron-specific protein Btbd11 regulates excitatory synapse transmission in hippocampal interneurons through promotion of phase separation and support of postsynaptic nanoarchitecture. Btbd11 forms a phase separated protein complex with Psd-95 and TARP{gamma}2 and impacts the stability of TARP{gamma}2 and GluA1 within glutamatergic IN synapses in an expression- and phase separation-dependent manner. Using super resolution imaging, we show that Btbd11 displays nanoscale clustering properties within IN synapses that correlate with Psd-95 nanostructure. Furthermore, genetic deletion of Btbd11 decreases PSD protein expression, reduces synapse size, and disrupts Psd-95 nanocluster organization. These effects manifest as a drastic reduction in glutamatergic synaptic transmission onto INs when Btbd11 is deleted. Together, these data provide insights into a novel cell type-specific synaptic regulatory mechanism in an understudied synapse population.

neuroscience↗

bioRxiv2026

Early Tracheal and Salivary miRNAs in Extremely Preterm Infants Predict BPD-related Pulmonary Hypertension

Pulmonary hypertension (BPD-PH) associated with bronchopulmonary dysplasia (BPD) in preterm infants associates with high morbidity and mortality within the first two years of life. In a previous unbiased study, we identified a panel miRNAs in tracheal aspirates (TA) that were differentially expressed in extremely low gestational age newborns (ELGANs) with BPD-PH compared to those with BPD but no PH. To explore the predictive potential of these miRNAs, we studied TA exosomes from 7 days old ELGANs and analysed a curated panel of 16 miRNAs through logistic regression and calculated the predictive AUROC to diagnose BPD-PH at 36 weeks PMA. AUROC of TA miRNAs was 0.76 with sensitivity and specificity of 53% and 93%, respectively. Adding sex and gestational age to the variables improved the AUROC to 0.78 with sensitivity and specificity of 61 and 87% respectively. Due to challenges of obtaining TA in non-invasively ventilated infants, we collected saliva samples from ELGANs at 7 days of age and compared the log expression of these 16 miRNAs in both biofluids and found significant correlation in their expression (pearson r=0.92, p<0.001). We calculated the predictive AUROC of the same miRNAs to diagnose BPD-PH at 36 weeks PMA. AUROC of these miRNAs in saliva was = 0.85 with sensitivity and specificity of 82% and 72%, respectively; addition of biological sex and gestational age improved AUROC to 0.86 with sensitivity and specificity of 79% and 76% respectively. Leave-one-sample-out sensitivity analysis demonstrated stable training performance with reduced performance in testing samples, supporting the need for validation in larger independent cohorts. In conclusion, early salivary miRNAs have great potential for risk stratification of ELGANs to develop BPD-PH, while also providing the opportunity to identify target molecules and mechanisms that modulate molecular function.

bioinformatics↗

bioRxiv2026

Deciphering shared and divergent tissue architectures from cross-species spatial transcriptomics

The integration of spatial transcriptomics (ST) data across species is essential for cross-species and translational studies, but remains challenging due to molecular divergence and anatomical differences between organisms. We present STACAME, a graph attention autoencoder-based framework to decipher shared and divergent tissue architectures from cross-species ST data by explicitly modeling both orthologous and species-specific genes. STACAME aligns ST slices in a spatially aware manner, identifies homologous and species-specific domains, and enables a suite of downstream comparative analyses. We demonstrate its utility by integrating ST datasets from diverse tissues, including hippocampus, isocortex, embryo, breast, liver, and cerebellum, across multiple species such as human, macaque, marmoset, mouse, and zebrafish. STACAME supports cross-species spatial domain alignment, the detection of shared and divergent spatially variable genes, development alignment and comparison, and the 3D integration of tissue architecture. This flexible approach facilitates the translation of findings from model organisms to humans, providing a unified computational platform for cross-species spatial transcriptomics.

bioinformatics↗

bioRxiv2026

Structure Bioinformatics of Eight Human ATP Synthase Fo Subunits and Their AlphaFold3-Predicted Water-Soluble QTY Analogs

Human mitochondrial ATP synthase is an essential rotary motor enzyme that produces most of the cellular ATP through oxidative phosphorylation. Its membrane-embedded Fo sector contains highly hydrophobic transmembrane subunits that are challenging to study in aqueous environments without detergents. This study explores whether applying the QTY code can reduce the hydrophobicity of selected ATP synthase Fo subunits while preserving their overall molecular structures. We applied the QTY code to eight human ATP synthase Fo subunits: ATP6, ATP8, ATPK, ATP68, ATPMK, AT5G1, AT5G2, and AT5G3. Hydrophobic amino acids leucine (L), isoleucine (I), valine (V), and phenylalanine (F) in transmembrane regions were systematically replaced with hydrophilic glutamine (Q), threonine (T), and tyrosine (Y). Four native subunits with available CryoEM structures from human ATP synthase (PDB: 8H9S) were superposed with their AlphaFold3-predicted QTY analogs. The native ATP synthase Fo subunits superposed well with their respective QTY analogs. For the CryoEM-native comparisons, RMSD values ranged from 0.565[A] to 2.546[A]. For the AlphaFold3-native comparisons of subunits without CryoEM structures, RMSD values ranged from 0.204[A] to 0.297[A]. Despite substantial QTY substitutions in the transmembrane regions, ranging from 38.89% to 50.79%, the QTY analogs retained similar overall folds, molecular weights, and isoelectric points. Hydrophobic surface analysis showed that the QTY analogs had reduced hydrophobic patches compared with their native counterparts, with average hydrophobicity decreasing from 0.2959 in native proteins to -1.1023 in QTY analogs. These structural bioinformatics studies suggest that the QTY code can be applied to ATP synthase Fo subunits to generate more hydrophilic, potentially water-soluble analogs while preserving overall structural similarity. These results extend the application of the QTY code to the membrane-embedded Fo sector of ATP synthase and provide a foundation for future experimental studies testing whether these QTY analogs can be expressed, purified, and evaluated for assembly or proton-transfer-related functions.

bioinformatics↗

bioRxiv2026

Intra-nanoparticle Drug-protein Interactions Mediate Sequential Therapeutic Release

Controlled drug delivery systems have important implications in many therapeutic applications to improve patient health, but it remains challenging to deliver therapeutics to specific target sites while being released at controlled rates to minimize the off-target accumulation. Here, we report a drug (e.g., vancomycin)-modulating other drug release strategies in human serum albumin (HSA)-based nanoparticles (NPs) to achieve multiple drug encapsulation and controlled release of drugs. Without external stimuli but only with water, the release kinetics of drugs such as sulfasalazine and epidermal growth factor can be modulated by adding vancomycin. The mechanistic study suggests that the release kinetics are related to the secondary structure of albumin through the interactions between drugs and HSA. Our strategy achieved controlled drug delivery through modulating the secondary structure of albumin with a water-soluble drug as a modulator, which provides a promising way to simultaneously deliver multiple drugs while releasing in a predicted and sustained manner.

bioengineering↗

bioRxiv2026

Experiment-free learning of exoskeleton assistance is not an unsolved problem

We present three quantitative methods: 1) estimation of exoskeleton mechanical power and energy ratio from published data, 2) a systematic review of the exoskeleton literature on reported energy ratios, and 3) timing correction analysis of the replication experiment, to address concerns raised by Collins et al. (2026) about Luo et al. (2024). Together, these analyses support the reported metabolic reductions and the validity of exoskeleton control via learning in simulation. The critique rests on an unsupported premise: that exoskeleton energy ratios above 4 are physiologically implausible. This premise of Collins et al. (2026) is not supported by the cited evidence, and the error originates in their own cited source. Sawicki and Ferris (2009), the paper they invoke as authority for the limit of 4, state explicitly that "reported values of the muscular efficiency range from 0.10 to 0.34, with many sources assuming an average of [~]0.25." The value of 4 corresponds to this average, it is not a physiological ceiling. Treating an average as a physiological upper limit is a fundamental error. The published exoskeleton literature further contradicts the claim, including work by the authors of the critique themselves (Collins et al., 2015: 4.3; Young et al., 2017: 5.0) and independent work (Malcolm et al., 2013: 4.8; Seo et al., 2017: 6.7). In contrast, our walking energy ratio is 2.4, calculated directly from Fig. 4 of our paper. Our device delivers higher peak torque (14.1 Nm vs. 10.9 Nm, Lim et al., 2019) and achieves a slightly larger metabolic reduction (24.3% vs. 21%). Independent groups have since demonstrated meaningful metabolic reductions using learning-in-simulation frameworks, including Barati et al. (2026, 15.2% mean and 22.5% maximum) and Zhou et al. (2025, [~]20% during running). The claim of Collins et al. (2026) that this problem "remains unsolved" is directly contradicted by these independent results. The experiment in the critique is not a valid replication of our method. Our controller is a neural network with [~]10,000 parameters learned through deep reinforcement learning in musculoskeletal simulation; the critique instead applies a pre-programmed fixed torque curve with no learnable parameters. Beyond this, the replication contains three methodological errors: 1) a heel-strike timing assumption producing offsets up to 30% of the gait cycle; 2) an averaged torque profile that discards subject-specific control; and 3) a device [~]50% heavier than ours (4.8 kg vs. 3.2 kg) without measuring the metabolic penalty of the added weight. The critique also misreports Samsung data, with reported values approximately double those in the original publication, errors that directly underpin their physiological limit argument.

bioengineering↗

bioRxiv2026

The Logic of Thalamic Inputs onto the Molecular Taxonomy of Cortical Neurons Reveals a Visual Hierarchy

The hierarchical organization of sensory cortices and the rich molecular taxonomy of their cell types are defining features of the mammalian cortex. Cortical areas along this hierarchy are reciprocally connected via the thalamus through bottom-up and top-down projections. The logic through which these projections map onto the cellular taxonomy of the cortex is, however, poorly understood. Here we combine an anterograde transsynaptic tracer with spatial transcriptomics to reveal the molecular and spatial identity of mouse visual cortical neurons downstream of thalamic starter neurons across visual cortical areas. Distinct thalamic inputs target characteristic sets of molecularly defined cortical neurons, forming bottom-up or top-down "signatures" defined by cell-type composition and the ratio of GABAergic to glutamatergic neurons. These signatures reveal a hierarchy spanning thalamic nuclei and visual cortical areas, independently predicted by the molecular and cellular similarities between cortical areas. This work uncovers basic principles of how bottom-up and top-down thalamic inputs map onto the cellular taxonomy in the visual cortex and establishes a cellular framework for the cortical hierarchy.

neuroscience↗

bioRxiv2026

Deciphering cross-omics complexity of tissues via diagonal integration of unpaired spatial multi-omics data

Recent spatial multi-omics technologies enable the simultaneous in situ profiling of multiple omics modalities on the same tissue section; however, they face challenges in experimental complexity and high costs. This technical limitation can be circumvented by diagonal integration methods, which integrate omics data from different modalities. However, existing single-cell diagonal integration approaches overlook spatial information, causing unreliable anchoring across omics layers. Here, we introduce STAMO, a graph attention neural network model for spatially aware integration of unpaired spatial slices from different omics. Systematic benchmarking on spatial epigenome-transcriptome slices proves that STAMO outperforms the state-of-the-art methods in generating aligned embeddings and identifying consensus spatial domains across omics. We apply STAMO to integrate unpaired data from diverse spatial omics types (transcripts, epigenetics, DNA, and proteins), including slices from spatial RNA and four different epigenomic modalities, spatial ATAC and RNA slices across embryonic stages, spatial protein and RNA slices, and spatial DNA and RNA slices. In addition, the integration capability of STAMO can be further used to achieve cross-omics generation, offering a solution for exploring spatial region-specific gene regulatory mechanisms.

bioinformatics↗

bioRxiv2026

Functional orthogonality of WRN inhibitor resistance enables alternating therapy and mutation-tolerant inhibitor design in MSI-H cancers

Werner syndrome helicase (WRN) is a synthetic-lethal vulnerability in microsatellite instability-high (MSI-H)/mismatch repair-deficient (dMMR) cancers1-5, and non-covalent and covalent WRN inhibitors are now entering clinical development6-9. A central unresolved question is whether resistance to one WRN inhibitor class inevitably compromises the target, or instead creates actionable vulnerabilities to an alternate modality. Here we generated ten stable acquired-resistance models across three MSI-H cell lines using the non-covalent inhibitor HRO761 and the covalent inhibitor VVD-214. Whole-exome sequencing, biochemical reconstitution and isogenic knock-in models identified recurrent on-target WRN missense mutations as dominant resistance drivers, but with sharply modality-specific spectra: HRO761 resistance clustered at G729/F730/I852, whereas VVD-214 resistance concentrated at E846. These mutations impaired inhibitor engagement and abolished the canonical WRN inhibitor (WRNi)-induced pharmacodynamic cascade, including WRN reduction, DNA damage response (DDR) activation and G2/M arrest. Crucially, most resistance mutations retained biochemical, cellular and in vivo sensitivity to the alternate inhibitor modality, revealing a functional orthogonality that enabled a 7-day cyclic alternating regimen to delay tumour regrowth in xenografts. We further identified F730L as an engineered cross-resistant bottleneck model and used structure-guided, artificial intelligence (AI)-enabled optimization to generate GBA-007, a proof-of-concept mutation-tolerant WRN inhibitor candidate with promising activity against F730L in vitro and in vivo. Thus, clinically relevant WRN inhibitor classes impose distinct on-target resistance trajectories that can be exploited through schedule design, while cross-resistant bottlenecks can be addressed by rapid mutation-aware inhibitor engineering.

Cancer Biology↗

bioRxiv2026

Validation of MHPC512: A Publicly Available Special-Purpose Supercomputer for Molecular Dynamics Simulations of Biomolecular Systems

MHPC512 is a massively parallel, special-purpose supercomputer designed primarily for atomic-level molecular dynamics (MD) simulations of biomolecular systems. It comprises 512 processor units interconnected by a high-speed three-dimensional torus network and employs a custom chip architecture that uses 35-bit fixed-point arithmetic to accelerate computation while controlling precision loss within an acceptable margin. The preprocessor is compatible with GROMACS and AMBER input formats and supports widely used biomolecular force fields (including CHARMM, AMBER, and OPLS/AA), the Neutral Territory method for short-range nonbonded interactions, the k-space Gaussian Split Ewald method for long-range electrostatics, and multiple thermostats, barostats, and integrators. We present a three-tier validation protocol--comparing static energy and virial components, examining ensemble distributions (NVE, NVT, NPT), and evaluating long-time statistical properties--demonstrating that MHPC512 reproduces results consistent with GROMACS and AmberTools. Application examples, including bulk water, dipeptide conformational sampling, folding of fast-folding peptides, membrane-protein systems, lipid self-assembly, and GPCR conformational transitions, further confirm its reliability. MHPC512 has been deployed at multiple supercomputing centers and is publicly accessible, representing a significant advance in high-throughput, large-scale biomolecular MD simulations.

Molecular Biology↗