Biology subjects

Zhang, L.

Publications and source records attributed to Zhang, L..

At least 469 records · Page 26Linked to original sources

bioRxiv2025

Deciphering the molecular mechanisms of FET fusion oncoprotein-DNA hollow co-condensates

Biomolecules such as nucleic acids and proteins can undergo liquid-liquid phase separation to form biomolecular condensates with diverse architectures. However, fundamental questions surrounding the biophysical principles that govern condensate architecture and their potential biotechnological applications remain unresolved. Here, we report that the FUS/EWS/TAF15 family fusion oncoprotein FUS-ERG forms hollow co-condensates with double-stranded DNA containing GGAA microsatellites. Through a combination of biochemical assays, super-resolution imaging, and mathematical modeling, we reveal that the interior surface of hollow co-condensates exhibits properties distinct from those of the external surface, a phenomenon we term nested asymmetric phase separation. Furthermore, we demonstrate that the self-organization and morphology control of FUS-ERG hollow co-condensates can be harnessed for DNA-based information manipulation, enabling targeted DNA deletion within dsDNA libraries and facilitating dynamic, hierarchical data selection. These findings provide critical insights into the biophysical mechanisms underlying multicomponent phase-separated cellular bodies and establish a foundation for leveraging condensate morphology in biotechnology.

biophysics↗

bioRxiv2025

Distinct Connectivity Signatures of Emotions Enhance Precision of Network Biomarkers in Mood Disorders

Mood disorders, including Major Depressive (MDD) and Bipolar (BD) Disorder, are highly prevalent and debilitating conditions that contribute significantly to the global disease burden. These disorders are characterized by persistent emotional dysregulations, such as pervasive sadness and anhedonia, resulting in substantial functional impairments. Although neuroimaging studies have identified differences in brain activity and connectivity between individuals with MDD (MDDs) or BD (BDs) and healthy controls (HCs), reliable and reproducible neurofunctional markers for clinical diagnosis and treatment remain elusive. This study seeks to address this gap by introducing a novel approach that utilizes Divergent Emotional Functional Networks (DEFN), derived from functional magnetic resonance imaging (fMRI) during dynamic emotional processing in naturalistic contexts. Using a combination of naturalistic induction of sustained emotional experience with dynamic functional connectivity (dFC) and machine learning techniques, we decoded emotion-specific patterns of happiness and sadness in healthy individuals. Based on the dynamic functional connectivity signatures, we identify the DEFN and applied it to large clinical mood disorder datasets, including MDD (n=63) and BD patients (n=61). The model with DEFN demonstrated significant improvements in classification accuracy compared to conventional baseline models, achieving up to 10.75% and 9.92% performance increases in MDD and BD datasets, respectively. Additionally, DEFN were found to be highly reproducible across age, gender and models from emotion dataset, supporting the robustness of this model in distinguishing mood disorders from healthy controls. In conclusion, the DEFN approach presents a promising, reproducible, and clinically relevant neural marker for diagnosing and understanding emotional dysfunction in mood disorders, offering potential for more effective and timely interventions.

neuroscience↗

bioRxiv2025

Single-cell copy number alteration signature analysis reveals masked patterns and potential biomarkers for cancer

Copy number alteration (CNA) is a major type of cancer genome alteration that drives cancer progression. CNA signature analysis can reveal underlying etiology and provide biomarkers for cancer treatment, and existing CNA signature analyzes are all performed using bulk tissue samples. However CNA usually affect large proportion of genome, and the CNA profile of bulk sample does not reflect the actual CNA profiles of the individual cancer cells of the sample, especially in tumors with high heterogeneity, such as hepatocellular carcinoma (HCC). Furthermore, the evolutionary trajectory of CNA mutational processes still remain elusive. Here we build a method to comprehensively analyze the CNA signatures of HCC from single-cell and bulk sample perspective, revealing patterns and potential noise signals from the usually performed bulk tissue CNA signature analysis. Single-cell signature analysis delineated the evolutionary trajectory of HCC CNA signatures, and different CNA signatures consistently emerge in different HCC evolution stages. Single-cell CNA signatures show robust performance in patient prognosis and drug sensitivity prediction. This work not only reveals specific considerations in analyzing CNA signature derived from bulk tissue but also depicts CNA evolution process and provides potential biomarkers for the prognosis and treatment of HCC patients. HighlightSingle-cell analysis reveals CNA signatures masked in bulk tissue. Single-cell analysis delineates the evolutionary trajectory of CNA signature. Small CNAs occur early and large CNAs happens late in HCC evolution. Single-cell CNA signatures show robust performance in guiding cancer clinical treatment.

genomics↗

bioRxiv2025

Predicting PROTAC-targeted Degradation and Designing Androgen Receptor Degraders with AiPROTAC

Proteolysis-targeting chimeras (PROTACs), a pioneering class of heterobifunctional ligands, have emerged as transformative tools in combating cancer and immune-related diseases due to their ability to target previously undruggable proteins and overcome drug resistance. Accurate assessment of the degradation potential of PROTACs is essential for advancing their therapeutic applications. However, the development of robust predictive models is hindered by the scarcity of large-scale datasets and domain-specific tools, as well as the underutilization of existing unlabeled data. To address these challenges, we developed an innovative method called AiPROTAC to predict the degradation capacity of PROTACs. In particular, AiPROTAC integrates graph augmentation, message-passing enhanced encoders and cross-attention mechanisms within a contrastive learning framework. Moreover, AiPROTAC leverages a curated specialized dataset combined with the PROTAC-DB dataset to enhance prediction accuracy. Experimental results across two datasets and six evaluation metrics demonstrate that AiPROTAC consistently outperforms state-of-the-art models. Further case studies underline its superior sensitivity and reliability. Notably, AiPROTAC facilitated the design of a novel androgen receptor (AR) degrader, PROTAC GT19, which achieved enhanced in vitro degradation compared to Bavdegalutamide (ARV-110). This advancement highlights our AiPROTACs potential to accelerate PROTAC-based therapeutic development and optimization.

bioinformatics↗

bioRxiv2025

Evaluation of deep learning approaches for high-resolution chromatin accessibility prediction from genomic sequence

Fine-grained prediction of chromatin accessibility from DNA sequence is a foundational step in modeling gene expression changes resulting from sequence variants. Yet, few methods operate at the resolution necessary to capture subtle effects of single-nucleotide changes. Furthermore, it remains unclear which architectural components--such as residual connections, normalization strategies, or attention mechanisms--drive performance in these high-resolution predictions. To address these knowledge gaps, we systematically evaluate classic architectural choices and introduce ConvNeXt V2 blocks, originally developed for computer vision, as high-resolution feature extractors in deep learning models for genomic data. Integrated into diverse architectures--CNNs, LSTMs, dilated CNNs, and transformers--ConvNeXt V2 blocks consistently improve performance, leading to similar prediction accuracy across these different model types. This reveals that early feature extraction, rather than downstream architecture, is the primary determinant of prediction accuracy. A comprehensive evaluation of these models on ATAC-seq signal prediction at 4 bp resolution in a cell type-specific manner identifies the ConvNeXtbased dilated CNN as the most robust performer, better preserving the signals shape. Our codebase and benchmarks provide practical tools for high-resolution chromatin modeling.

genomics↗

bioRxiv2025

Gut Microbiome Signatures Reflect Within-Host Subtype Diversity of Blastocystis in Non-Human Primates

Blastocystis is a prevalent gut eukaryote intricately associated with the gut microbiota. This genetically diverse protozoan exhibits significant intra-host subtype heterogeneity, yet the implications of this diversity for the host gut microbiome remain poorly understood. Here, we investigated the interactions between Blastocystis and gut microbiota in non-human primates at the level of subtypes, using a comprehensive investigation of gut microbiota for Blastocystis carriers of captive Macaca fascicularis (discovery cohort, n = 100) and Macaca mulatta (validation cohort, n = 26). We identified highly prevalent intra-host co-occurrence patterns of Blastocystis SSU rRNA-based subtypes, primarily dominated by Subtype 1(ST1) or ST3. These patterns were associated with compositional and structural variations in the gut microbiome but were not significantly influenced by host covariates such as sex, age, or BMI. Specifically, Ruminococcaceae-enterotype was enriched in the patterns dominated by ST1, whereas Limosilactobacillus-enterotype was predominantly identified in the patterns dominated by ST3. We discovered that the absolute abundance of Blastocystis was a critical determinant in elucidating this association across concurrent patterns. In vivo experiments in a new cohort (n = 11) demonstrated that lactic acid bacteria, enriched in the Limosilactobacillus-enterotype, were sufficient to reduce Blastocystis load. We validated the strong association between gut microbiome composition and Blastocystis load in M. mulatta, confirming that specific microbial features could quantitatively predict Blastocystis status in both species. Our findings establish a direct link between gut microbial variations, intra-host genetic heterogeneity, and absolute abundance in Blastocystis for non-human primates.

microbiology↗

bioRxiv2025

A foundation model for protein-ligand affinity prediction through jointly optimizing virtual screening and hit-to-lead optimization

The structure of the protein binding pocket governs the ligand binding affinity by providing crucial intermolecular interactions and spatial compatibility. While existing methods have leveraged these structural insights to advance affinity prediction, they often treat virtual screening and hit-to-lead optimization separately, mainly due to incompatible speed-accuracy requirements. However, these two tasks complement each other, and their integration enables broader chemical exploration while preserving focus on affinity-determining substructures. Here, we present LigUnity, a foundation model for affinity prediction that jointly embeds ligands and pockets into a shared space. In particular, LigUnity learns coarse-grained active/inactive distinction through scaffold discrimination and fine-grained pocket-specific ligand preference through pharmacophore ranking. We demonstrate the effectiveness and versatility of LigUnity on eight benchmarks across six settings. In virtual screening, LigUnity outperforms 24 methods with >50% improvement and demonstrates robust generalization to novel targets. In hit-to-lead optimization, it achieves state-of-the-art performance across split-by-time, split-by-scaffold, and split-by-unit settings, emerging as a cost-efficient alternative to free energy perturbation. We further showcase how LigUnity can be employed in an active learning framework for TYK2 to efficiently find optimal ligands. Collectively, these results establish LigUnity as a versatile foundation model for affinity prediction, offering broad applicability across the drug discovery pipeline.

bioinformatics↗

bioRxiv2025

Towards machine learning fairness in classifying multicategory causes of deaths in colorectal or lung cancer patients

Classification of patient multicategory survival outcomes is important for personalized cancer treatments. Machine Learning (ML) algorithms have increasingly been used to inform healthcare decisions, but these models are vulnerable to biases in data collection and algorithm creation. ML models have previously been shown to exhibit racial bias, but their fairness towards patients from different age and sex groups have yet to be studied. Therefore, we compared the multimetric performances of 5 ML models (random forests, multinomial logistic regression, linear support vector classifier, linear discriminant analysis, and multilayer perceptron) when classifying colorectal cancer patients (n=515) of various age, sex, and racial groups using the TCGA data. All five models exhibited biases for these sociodemographic groups. We then repeated the same process on lung adenocarcinoma (n=589) to validate our findings. Surprisingly, most models tended to perform more poorly overall for the largest sociodemographic groups. Methods to optimize model performance, including testing the model on merged age, sex, or racial groups, and creating a model trained on and used for an individual or merged sociodemographic group, show potential to reduce disparities in model performance for different groups. Notably, these methods may be used to improve ML fairness while avoiding penalizing the model for exhibiting bias and thus sacrificing overall performance.

bioinformatics↗

bioRxiv2025

Themis dominates T cell exhaustion by regulation of TCR and PD-1 signaling

T cell exhaustion is important to protect the host from immunopathology during chronic viral infection, but it also impairs T cell anti-tumor immunity1-5. A fundamental unresolved question is whether and how T cell exhaustion is determined at the onset of TCR signaling6-8. Here we report an unexpected role of Themis, a TCR-proximal signaling molecule9, in T cell exhaustion. Chronic viral infection in mice usually leads to T cell exhaustion and survival of the host. Surprisingly, Themis T-cell conditional knockout mice died from severe CD8+-dependent lung immunopathology in chronic viral infection, showing Themis importance in establishing T cell exhaustion. We found that Themis-deficient CD8+ T cells were hyperactivated at the single-cell level - producing more TNF and IFN{gamma} compared to wild-type counterparts - but defective in population-level expansion. Moreover, TCF-1 and TOX expression were inhibited in Themis-deficient CD8+ T cells, thereby impairing differentiation of exhausted T cell precursors (T-pex) and maintenance of terminally exhausted T cells (T-ex), respectively. Mechanistically, Themis initially promotes TCR signaling to induce PD-1 expression and subsequently mediates PD-1 signaling. In the latter, Themis binds to PD-1 and promotes PD-1 phosphorylation and its recruitment of SHP2, thereby acting as a negative regulator to inhibit T cell effector functions. Without Themis, the orderly regulation of TCR and PD-1 signaling, and therefore exhaustion, is disrupted. Thus, our results unequivocally demonstrate that Themis-mediated early TCR signaling plays a decisive role in T cell exhaustion and provide a novel mechanism of PD-1 signaling through Themis.

immunology↗

bioRxiv2025

Therapeutic assessment of a novel mitochondrial complex I inhibitor in in vitro and in vivo models of Alzheimer's disease

Despite recent approval of monoclonal antibodies that reduce amyloid (A{beta}) accumulation, the development of disease-modifying strategies targeting the underlying mechanisms of Alzheimers disease (AD) is urgently needed. We demonstrate that mitochondrial complex I (mtCI) represents a druggable target, where its weak inhibition activates neuroprotective signaling, benefiting AD mouse models with A{beta} and p-Tau pathologies. Rational design and structure-activity relationship studies yielded novel mtCI inhibitors profiled in a drug discovery funnel designed to address their safety, selectivity, and efficacy. The new lead compound C458 is highly protective against A{beta} toxicity, has favorable pharmacokinetics, and has minimal off-target effects. C458 exhibited excellent brain penetrance, activating neuroprotective pathways with a single dose. Preclinical studies in APP/PS1 mice were conducted via functional tests, metabolic assessment, in vivo 31P- NMR spectroscopy, blood cytokine panels, ex vivo electrophysiology, and Western blotting. Chronic oral administration improved long-term potentiation, reduced oxidative stress and inflammation, and enhanced mitochondrial biogenesis, antioxidant signaling, and cellular energetics. These studies provide further evidence that the restoration of mitochondrial function and brain energetics in response to mild energetic stress represents a promising disease- modifying strategy for AD.

neuroscience↗

bioRxiv2025

RAPID: Reliable and efficient Automatic generation of submission rePortIng checklists with large language moDels

ImportanceMedical reporting guidelines are significant in improving the transparency, quality, and integrity of medical research, particularly in randomized clinical trials; adherence to these guidelines supports research interpretability and has direct implications for downstream applications, such as patient treatment. However, with over 600 distinct reporting guidelines, manual assessments are often time-consuming and labor-intensive. ObjectiveTo evaluate an automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, called RAPID. Design, Setting, and ParticipantsThis study used large language models to design a retrieval augmentation generation architecture and collected published journal articles as training and validation sets to optimize prompts within the framework and comprehensively evaluate the performance of the framework. Medical reporting experiments were collected from 50 randomized controlled trials without the intervention of AI tools and 41 randomized controlled trials with the intervention of AI tools. Main Outcomes and MeasuresFor effective evaluation of the performance of this tool, a classification accuracy metric (Reported/Not Reported) defined as the number of correct judgments divided by all judgments and a content consistent score metric defined as the number of contents retrieved by the tool that are the same as those retrieved by researchers divided by the total number of judgments were calculated. ResultsThe RAPID tool uses the widely used Word document and Portable Document Format as an input file. Fifty published journal articles without the intervention of AI tools and 41 published journal articles with the intervention of AI tools were collected as CONSORT and CONSORT-AI datasets. All of the CONSORT reporting items (37) were included in the tool. RAPID achieved a high average accuracy rate of 92.11% and a content consistency score of 81.14% on the CONSORT dataset. Of the CONSORT-AI reporting items, 11 items related to the intervention of AI tools were included in the tool. RAPID achieved an average accuracy of 83.81% with a content consistency score of 72.51% on the CONSORT-AI dataset. For these two reporting guidelines, a training set of 5 articles was selected from each dataset to refine the prompts used in the tools for CONSORT and CONSORT-AI reporting checklist. The validation set of the remaining articles was used to assess the performance of the RAPID. The RAPID tool used the Word document and Portable Document Format of the articles as input files. A RAPID graphical user interface was built using JavaScript and Vue. Conclusions and RelevanceThe RAPID tool is designed to assist in the reporting of various types of trials. RAPID has strong scalability, which can be easily adapted to different medical reporting guidelines without transfer learning on a large dataset. RAPID may effectively save time and improve working efficiency for different user groups, for example, 1) simplifying the submission process and improving report quality by verifying manuscript completeness for medical authors; 2) facilitating evaluation of report quality for medical researchers; 3) expediting manuscript distribution for medical editors; and 4) identifying reporting deficiencies and providing deeper insights for review comments for reviewers. Key PointsO_ST_ABSQuestionC_ST_ABSCan large language model tools automatically generate medical reporting checklists for manuscripts of different types of clinic trials? FindingsAn automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, RAPID, was developed. RAPID was fully evaluated on all items across two separate datasets related to the Consolidated Standards of Reporting Trials (CONSORT) and CONSORT-AI reporting guidelines. In the first dataset corresponding to CONSORT, RAPID achieved an average accuracy of 92.11%, while in the second dataset associated with CONSORT - AI, it reached an average accuracy of 83.81%. Additionally, RAPID is highly scalable. It can be easily and smoothly adapted to different medical reporting guidelines without transfer learning on a large dataset. MeaningRAPID can effectively save time and improve working efficiency for different user groups such as medical authors, researchers, editors, and reviewers.

bioinformatics↗

bioRxiv2025

Accuracy and Scalability of Machine Learning Methods for Genotype-Phenotype Association Data

Many machine learning methods can be applied to predicting phenotypes from genetic data. Which of these methods work best remains an open question, however. To answer this question, we propose to compare a variety of approaches ability to predict a simulated non-linear complex trait. Specifically, we evaluate these methods on their accuracy and scalability with respect to the amount training data available, the noise present in the data, the complexity of the simulated (trait) functions, and their ability to provide insight into the simulated trait. We then compare the best approach to state-of-the-art models in real data, predicting gout in the UK Biobank. We find that transformer encoders outperform all other methods in simulations, and perform comparably to the state-of-the-art with real data, with a promise to scale to significantly larger datasets.

bioinformatics↗

bioRxiv2025

Conditional success of adaptive therapy: the role of treatment-pausing thresholds revealed by mathematical modeling

Adaptive therapy improves cancer treatment by controlling the competition between sensitive and resistant cells through treatment holidays. This study highlights the critical role of treatmentholiday thresholds in adaptive therapy for tumors composed of drug-sensitive and resistant cells. Using a Lotka-Volterra model, adaptive therapy outcomes are compared with maximum tolerated dose therapy and intermittent therapy outcomes, showing that adaptive therapy success depends critically on the threshold for pausing and resuming treatment and on competitive interactions between cell populations. Three comparison scenarios between adaptive therapy and other therapies emerge: uniform-decline where adaptive therapy underperforms regardless of threshold, conditional-improve where efficacy requires threshold optimization, and uniform-improve where adaptive therapy consistently outperforms alternatives. Tumor composition including initial burden and resistant cell proportion influences outcomes. Threshold adjustments enable adaptive therapy to suppress resistant subclones while preserving sensitive cells, extending progression-free survival. Crucially, this work establishes an optimal control problem for time-to-progression and mathematically proves that under biological constraints like neutral competition or low initial burden, the theoretically optimal strategy is unrealizable as it requires infinitely many treatment holidays, rendering it clinically impractical. These findings emphasize personalized treatment strategies for enhancing long-term therapeutic outcomes. Mathematics Subject Classification92C50,92C42.

systems biology↗

bioRxiv2025

SLICK: A Sandwich-LIke Culturing Kit for in situ Cryo-ET Sample Preparation

In situ cryo-electron tomography (cryo-ET) has recently been widely used in observing subcellular structures and macromolecules in their native states at high resolution. One of the reasons that it has not been more widely adopted by cell biologists and structural biologists is the difficulties in sample preparation. Here we present the Sandwich-LIke Culturing Kit (SLICK), simplifying the procedure and increasing the throughput for sample preparation for in situ cryo-ET (69 words).

biophysics↗

bioRxiv2025

CAR-T cells locally delivered in porcine decellularized matrix hydrogels enhance survival in post-resection glioblastoma

Patients diagnosed with glioblastoma (GBM) receive a devastating prognosis of less than 15 months, and recurrence of GBM is most often local, suggesting that regional therapies would serve both immediate and long-term needs of patients. Here, we investigate a biomaterials-based approach for local delivery of chimeric antigen receptor (CAR) T cells using a murine model of partial GBM resection that mimics patient recurrence. We demonstrate that hydrogel delivery of CAR T cells directly into the intracranial resection cavity can stably implant cellular immunotherapies against CNS solid tumors, and significantly prolongs survival in recurrent GBM-bearing mice compared to those receiving resection alone. One Sentence SummaryDuring resection surgeries for brain tumors, delivering cell therapy at time of surgery could improve outcomes for patients.

cancer biology↗

bioRxiv2025

A versatile miniature two-photon microscope enabling multicolor deep-brain imaging

Here we present the FHIRM-TPM 3.0, a 2.6 g miniature two-photon microscope capable of multicolor deep-brain imaging in freely behaving mice. The system was integrated with a broadband anti-resonant hollow-core fiber featuring low transmission loss, minimal dispersion from 700-1060 nm, and high tolerance of laser power. By correcting chromatic and spherical aberrations and optimizing the fluorescence collection aperture, we achieved cortical neuronal imaging at depths exceeding 820 m and, using a GRIN lens, hippocampal Ca2+ imaging at single dendritic spine resolution. Moreover, we engineered three interchangeable parfocal objectives, allowing for a tenfold scalable field-of-view up to 1x0.8 mm{superscript 2}, with lateral resolutions ranging from 0.68 to 1.46 m. By multicolor imaging at excitation wavelengths of 780 nm, 920 nm and 1030 nm, we investigated mitochondrial and cytosolic Ca2+ activities relative to the deposition of amyloid plaques in the cortex of awake APP/PS1 transgenic mice. Thus, the FHIRM-TPM 3.0 provides a versatile imaging system suitable for diverse brain imaging scenarios.

neuroscience↗

bioRxiv2025

RNA-EFM : Energy based Flow Matching for Protein-conditioned RNA Sequence-Structure Co-design

Ribonucleic acids (RNAs) are essential biomolecules involved in gene regulation and molecular recognition. Designing RNA molecules that can bind specific protein targets is crucial for therapeutic applications but remains challenging due to the structural flexibility of RNA and the laborious nature of experimental techniques. We propose RNA-EFM, a novel Energy-based Flow Matching framework for protein-conditioned RNA sequence and structure co-design. RNA-EFM integrates biophysical constraints, including the Lennard-Jones potential and sequence-derived free energy, to generate low-energy and biologically plausible RNA conformations. By incorporating an idempotent refinement strategy for iterative structural correction, RNA-EFM consistently outperforms existing baselines, achieving lower RMSD, higher lDDT, and superior sequence recovery across multiple evaluation splits.

bioinformatics↗

bioRxiv2025

Multi-omic Characterization of HIV Effects at Single Cell Level across Human Brain Regions

HIV infection exerts profound and long-lasting neurodegenerative effects on the central nervous system (CNS) that can persist despite antiretroviral therapy (ART). Here, we used single-nucleus multiome sequencing to map the transcriptomic and epigenetic landscapes of postmortem human brains from 13 healthy individuals and 20 individuals with HIV who have a history of treatment with ART. Our study spanned three distinct regions--the prefrontal cortex, insular cortex, and ventral striatum--enabling a comprehensive exploration of region-specific and cross-regional perturbations. We found widespread and persistent HIV-associated transcriptional and epigenetic alterations across multiple cell types. Detailed analyses of microglia revealed state changes marked by immune activation and metabolic dysregulation, while integrative multiomic profiling of astrocytes identified multiple subpopulations, including a reactive subpopulation unique to HIV-infected brains. These findings suggest that cells from people with HIV exhibit molecular shifts that may underlie ongoing neuroinflammation and CNS dysfunction. Furthermore, cell-cell communication analyses uncovered dysregulated and pro-inflammatory interactions among glial populations, underscoring the multifaceted and enduring impact of HIV on the brain milieu. Collectively, our comprehensive atlas of HIV-associated brain changes reveals distinct glial cell states with signatures of pro-inflammatory signaling and metabolic dysregulation, providing a framework for developing targeted therapies for HIV-associated neurological dysfunction.

bioinformatics↗