Biology subjects

Wan, S.

Publications and source records attributed to Wan, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Synchronized Latency Reversal and Immune Clearance by a Multifunctional Fusion Protein Enables HIV-1 Reservoir Reduction

Strategies that concurrently reactivate latent reservoirs and enhance immune-mediated clearance hold significant promise for achieving an HIV cure. Here, we developed hyperIL-15xsCD4-Fc (15xsCD4-Fc), a fusion protein that integrates latency reactivation, targeted immune engagement, and effector-mediated killing into a single molecule. This agent not only potently reverses HIV-1 latency in CD4+ T cells from people living with HIV-1 (PLWH) through coordinated IL-15 receptor signaling and sCD4-mediated HIV-1 envelope (Env) engagement, but also enhances antigen-specific CD8+ T cell response in PBMCs derived from PLWH. Furthermore, 15xsCD4-Fc enables Env-specific elimination of reactivated latently infected cells by NK cells while preventing off-target cytotoxicity. In PBMCs from PLWH, 15xsCD4-Fc reduced replication-competent HIV-1 DNA by 93.8%. In antiretroviral treated HIV-1-infected humanized mice, the molecule demonstrated both safety and efficacy in diminishing viral reservoir in lymphoid organs. This spatiotemporally coupled approach to reservoir exposure and immune recognition establishes a clinically viable strategy for clearing the HIV-1 reservoir. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/693434v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@8f8f0org.highwire.dtl.DTLVardef@10f63f7org.highwire.dtl.DTLVardef@16c1de2org.highwire.dtl.DTLVardef@11e1787_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

An Interpretable Deep Learning Framework for Biomarker Discovery in Complex Disease Survival Outcomes

Identification of important biomarkers associated with complex disease survival outcomes is fundamental for gaining an in-depth understanding of disease mechanisms and advancing precision medicine in conditions such as cancer and cardiovascular disorders. However, these tasks are complicated by the unique nature of time-to-event data, which captures both the occurrence and timing of clinical events. Notably, complex associations such as the non-linear and non-additive biomarker interactions and the high-dimensionality challenge conventional survival data modeling approaches. To address these difficulties, we propose SurvDNN, an enhanced deep neural network framework specifically designed for survival outcomes modeling. SurvDNN incorporates a bootstrapping-based regularization strategy to mitigate overfitting and a novel stability-driven filtering algorithm to improve model robustness. To enable interpretable biomarker discovery, we extend the Permutation-based Feature Importance Test (PermFIT) to survival settings, allowing rigorous quantification of individual biomarker contributions under complex biomarker-outcome associations. Through extensive simulations and applications to real-world datasets, SurvDNN consistently outperforms existing machine learning approaches in both biomarker identification and predictive accuracy. Our results demonstrate the potential of SurvDNN coupled with PermFIT as an interpretable, robust, and powerful tool for biomarker-driven survival modeling in complex diseases. An open-source R package implementing SurvDNN is publicly available on GitHub (https://github.com/BZou-lab/SurvDNN).

bioinformatics↗

bioRxiv2025

MFPLI: A Computational Framework for Assessing Biological Authenticity of Protein-Ligand Interactions Using Molecular Fingerprints and Structural Features

Traditional computational drug discovery approaches struggle to accurately evaluate the biological authenticity of protein-ligand binding conformations due to inherent limitations in empirical scoring functions and force field approximations. This study proposes MFPLI - a deep learning framework integrating multimodal physicochemical features to systematically assess the biological authenticity alignment between molecular docking poses and true co-crystal structures. By establishing a continuous surface characterization system for protein-ligand interfaces, we concurrently incorporate geometric curvature features (radius, shape index) and chemical interaction fields (electrostatic potential, hydrogen-bond networks, hydrophobicity gradients). A contrastive learning architecture based on Siamese equivariant graph neural networks was developed to enable discriminative analysis between co-crystal conformations and parameter-perturbed pseudo-conformations generated through inverse docking. The five-channel fusion model demonstrates robust performance on the time-split PoseBuster validation set (AUC=0.91), with predicted Euclidean distance deviation ({Delta}E) effectively distinguishing native co-crystal conformations from aberrant docking poses in 80% of samples. Notably, 71% of {Delta}E-negative samples concentrate within the [-0.3, 0] interval, reflecting physical consistency between model predictions and conformational transition processes. This framework establishes a novel paradigm for biological authenticity assessment in virtual screening for computer-aided drug discovery through synergistic modeling of surface topology and interaction chemistry.

molecular biology↗

bioRxiv2025

Secondary structure transitions and dual PIP2 binding define cardiac KCNQ1-KCNE1 channel gating

The KCNQ1+KCNE1 potassium channel complex forms the slow delayed rectifier current (IKs) critical for cardiac repolarization. Loss-of-function variants in KCNQ1 and KCNE1 cause long QT syndrome types 1 and 5 (LQT1/LQT5), accounting for over one-third of clinical LQTS cases. Despite prior structural work on KCNQ1 and KCNQ1+KCNE3, the structural basis of KCNQ1+KCNE1 remains unresolved. Using cryo-EM and electrophysiology, we determined high-resolution (2.5-3.4 [A]) structures of human KCNQ1+KCNE1 in both closed and open states. KCNE1 occupies a pivotal position at the interface of three KCNQ1 subunits, inducing seven "helix-to-loop" transitions in KCNQ1 transmembrane segments. These structural rearrangements: 1) stabilize the closed pore and the conformation of the intermediate voltage-sensing domain, thereby determining channel gating, ion permeation, and single channel conductance; 2) enable a dual-PIP2 modulation mechanism, where one PIP2 occupies the canonical site, while the second PIP2 bridges the S4-S5 linker, KCNE1, and the adjacent S6, stabilizing channel opening; 3) create a fenestration capable of binding compounds specific for KCNQ1+KCNE1 (e.g., AC-1). Together, these findings reveal a previously unrecognized large-scale secondary structural transition during ion channel gating that fine-tunes IKs function and provides a foundation for targeted LQTS therapy development.

biophysics↗

bioRxiv2025

AttentionAML: An Attention-based Deep Learning Framework for Accurate Molecular Categorization of Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive hematopoietic malignancy defined by aberrant clonal expansion of abnormal myeloid progenitor cells. Characterized by morphological, molecular, and genetic alterations, AML encompasses multiple distinct subtypes that would exhibit subtype-specific responses to treatment and prognosis, underscoring the critical need of accurately identifying AML subtypes for effective clinical management and tailored therapeutic approaches. Traditional wet lab approaches such as immunophenotyping, cytogenetic analysis, morphological analysis, or molecular profiling to identify AML subtypes are labor-intensive, costly, and time-consuming. To address these challenges, we propose AttentionAML, a novel attention-based deep learning framework for accurately categorizing AML subtypes based on transcriptomic profiling only. Benchmarking tests based on 1,661 AML patients suggested that AttentionAML outperformed state-of-the-art methods across all evaluated metrics (accuracy: 0.96, precision: 0.96, recall of 0.96, F1-score: 0.96, and Matthews correlation coefficient: 0.96). Furthermore, we also demonstrated the superiority of AttentionAML over conventional approaches in terms of AML patient clustering visualization and subtype-specific gene marker characterization. We believe AttentionAML will bring remarkable positive impacts on downstream AML risk stratification and personalized treatment design. To enhance its impact, a user-friendly Python package implementing AttentionAML is publicly available at https://github.com/wan-mlab/AttentionAML.

bioinformatics↗

bioRxiv2025

ECCFP: a consecutive full pass based bioinformatic analysis for eccDNA identification using Nanopore sequencing data

It is commonly known that extrachromosomal circular DNA (eccDNA) has the potential as a molecular marker because of its close relationship with cancer progress and its prevalent existence in eukaryotic organisms. The mainstream technique of eccDNA detection is using high-throughput sequencing supported by bioinformatics analysis. Although these have various analysis pipelines for sequencing data, they are restricted by sequencing platforms or have shortcomings in accuracy and efficiency. To address these limitations, we design ECCFP, a bioinformatic analysis pipeline that detects eccDNAs amplified by rolling circle amplification (RCA) from long-read sequencing data and outputs eccDNA genomic coordinates and consensus sequences. This pipeline proposes a rigorous algorithm to retain all consecutive full passes derived from individual reads to obtain candidate eccDNAs, followed by systematic consolidation of candidate eccDNAs to detect unique eccDNAs. Using simulated datasets and experimental eccDNA sequencing datasets, we estimated ECCFP in several aspects and compared it with other existing pipelines. It exhibits a marked reduction in false positive rates compared with eccDNA_RCA_nanopore and superior sensitivity relative to CReSIL and FLED. Besides, inverse PCR and Sanger sequencing further validated the existence and accuracy of the position of detected eccDNAs by ECCFP. Collectively, ECCFP provides a more efficient choice for eccDNA detection from long-read sequencing data.

bioinformatics↗

bioRxiv2025

RPSLearner: A novel approach based on random projection and deep stacking learning for categorizing NSCLC

BackgroundLung cancer is the leading cause of cancer death, and non-small cell lung cancer (NSCLC) comprises the largest subtype with most cases. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are two NSCLC subtypes that pose challenges for accurate diagnosis using conventional methods. Existing methods are histological examination and imaging which lacks definitive histologic features and requires intense time. MethodsTo address these concerns, we propose RPSLearner, which combines Random Projection (RP) for dimensionality reduction and stacking ensemble learning to accurately predict lung cancer subtypes. Specifically, multiple independent RP matrices were first generated to project the high-dimensional RNA-seq data into lower-dimensional space, whose features were subsequently concatenated. After that, we fed the fused features into a stack of diverse base classifiers and integrated the predictions from base models via a deep linear layer network. ResultsBenchmarking tests on 1,333 NSCLC patients demonstrated that RPSLearner outperformed state-of-the-art approaches for lung cancer subtype classification. Specifically, RPSLearner efficiently preserved sample-to-sample distances even after significant dimension reduction, and the meta-model in RPSLearner yielded consistently higher accuracy, F1 and AUC scores than individual base models and state-of-the-art approaches for lung cancer subtyping. Besides, the feature fusion method applied in RPSLearner shown better performance than conventional scores ensemble methods. ConclusionWe developed a novel stacking learning method called RPSLearner which combines RP and stacking learning, enabling efficient and accurate identification of NSCLC subtypes. RPSLearner is a promising lung cancer subtyping model for downstream lung cancer clinical diagnosis and personalized treatment, and the framework holds the potentiality to be extended to subtyping of other types of cancer.

bioinformatics↗

bioRxiv2025

A Telomere-to-Telomere Diploid Reference Genome and Centromere Structure of the Chinese Quartet

Recent advances in sequencing technologies have enabled the complete assembly of human genomes from telomere to telomere (T2T), resolving previously inaccessible regions such as centromeres and segmental duplications. Here, we present an updated, higher-quality, haplotype-phased T2T assembly of the Chinese Quartet (T2T-CQ), a family cohort comprising monozygotic twins and their parents, generated using high-coverage ONT ultralong and PacBio HiFi sequencing. The T2T-CQ assembly serves as a crucial reference genome for integrating publicly available multi-omics data and advances the utility of the Quartet reference materials. The T2T-CQ assembly scores highly on multiple metrics of continuity and completeness, with Genome Continuity Inspector (GCI) scores of 77.76 (maternal) and 76.41 (paternal), quality values (QV) > 70, and Clipping Reveals Assembly Quality (CRAQ) scores > 99.6 for both haplotypes, enabling complete annotation of centromeric regions. Within these regions, we identified novel 13-mer higher-order repeat patterns on chromosome 17 which exhibited a monophyletic origin and emerged approximately 230 thousand years ago. Overall, this work establishes an essential genomic resource for the Han Chinese population and advances the development of a T2T pan-Chinese reference genome, which will significantly enable future investigations both into population-specific structural variants and the evolutionary dynamics of centromeres.

genomics↗

bioRxiv2025

Cytokine-driven cellular polarization in preeclampsia revealed through the "Dictionary of immune responses"

BackgroundPreeclampsia (PE) is a pregnancy-related hypertensive disorder and a leading cause of maternal and perinatal mortality. Current treatments focus primarily on symptom management, as delivery remains the only definitive cure. This underscores the urgent need for innovative therapeutic strategies. Cytokines released by placental immune cells may contribute to the progression of PE and represent promising therapeutic targets. MethodsWe conducted single-cell sequencing on placental tissues obtained via cesarean section from patients with severe PE and cases of non-infectious preterm birth. Machine learning was applied to identify critical immune cells. Immune Response Enrichment Analysis was performed on these key cells to evaluate their polarization states and cytokine responses associated with PE. Additionally, we investigated cell-cell communications, key genes, and their related functions ResultsWe identified cell type-specific alterations in PE, including an increased proportion of CD4+ T cells polarized towards an IL-1- and IL-1{beta} enriched T4-c state. Altered SPP1 signaling between macrophages and CD4+ T cells was observed, indicating immune response dysregulation. Additionally, machine learning algorithms identified four hub genes, and six small-molecule drugs were predicted to hold therapeutic potential for PE. ConclusionsThis study emphasizes the importance of further understanding CD4+ T cell dynamics in preeclampsia, providing potential insights into the principles of placental immunity and contributing to the exploration of immunotherapeutic strategies

bioinformatics↗

bioRxiv2025

Accurate identification of medulloblastoma subtypes from diverse data sources with severe batch effects by RaMBat

As the most common pediatric brain malignancy, medulloblastoma (MB) includes multiple distinct molecular subtypes characterized by clinical heterogeneity and genetic alterations. Accurate identification of MB subtypes is essential for downstream risk stratification and tailored therapeutic design. Existing MB subtyping approaches perform poorly due to limited cohorts and severe batch effects when integrating various MB data sources. To address these concerns, we propose a novel approach called RaMBat for accurate MB subtyping from diverse data sources with severe batch effects. Benchmarking tests based on 13 datasets with severe batch effects suggested that RaMBat achieved a median accuracy of 99%, significantly outperforming state-of-the-art MB subtyping approaches and conventional machine learning classifiers. RaMBat could efficiently deal with the batch effects and clearly separate subtypes of MB samples from diverse data sources. We believe RaMBat will bring direct positive impacts on downstream MB risk stratification and tailored treatment design.

bioinformatics↗

bioRxiv2025

A distinctive subset of microglia positioned at the paraventricular zone are dedicated to cleaning the cerebrospinal fluid

The interconnected ventricles in the central nervous system are filled with cerebrospinal fluid (CSF) which serves multifaceted roles including bring away metabolic wastes. However, whether there is an inherent "dislodging" mechanism, in addition to CSF drainage and reabsorption, at play for the removal of microparticles in the CSF is unclear. In this study, we identified a subset of microglia distributed in close proximity to the ependymal wall of ventricles. They differed from parenchymal microglia in morphology, behaviors and transcriptomes. In particular, the paraventricular microglia extended transependymal dendrites into the lumen of ventricles and were proficient in sequestering and phagocytosing intraventricular exogenous particles when present. A specific removal of the paraventricular microglia led to an acute ventriculomegaly due to an increased colloid osmotic pressure in the CSF resulting from protein accumulation. Thus, we identified the paraventricular microglia as specific guardians in monitoring and cleaning the CSF.

immunology↗

bioRxiv2025

Variation analysis of fruit phenotypic traits of different provenances of ancient Cinnamomum camphora in Jiangxi Province of China

Exploring fruit phenotypic diversity of different provenances of ancient Cinnamomum camphora in Jiangxi Province of China, it will provide a theoretical basis for the collection, selection and improvement of germplasm resources. Taking the naturally distributed ancient Cinnamomum camphora in 45 counties of 11 cities in Jiangxi Province as the research object, nested ANOVA, Pearson correlation analysis, principal component analysis and other methods were used to statistically analyze 10 phenotypic traits such as fruit thousand-grain weight, fruit volume, fruit side diameter, fruit horizontal diameter, fruit vertical diameter, and peel thickness in different provenances. The resuit was as follows. (1) The fruit phenotypic traits of ancient Cinnamomum camphora in Jiangxi Province were rich in variation among and within provenances, and the variation among provenances was the main source of phenotypic variation. The mean value of phenotypic differentiation coefficient among provenances was 80.1%. The coefficients of variation of fruit phenotypic traits among different provenances was ranged from 2.64% to 25.45%, with the largest coefficient of variation for peel thickness. The mean value of coefficient of variation among provenances(9.65%) was larger than that within provenances(8.52%). (2) Significant or highly significant correlations were existed among the fruit phenotypic traits of ancient Cinnamomum camphora in Jiangxi Province. Fruit thousand-grain weight was significantly positively correlated with fruit volume, fruit side diameter, fruit horizontal diameter, fruit vertical diameter, peel thickness, fruit side diameter-fruit horizontal diameter ratio, fruit side diameter-fruit vertical diameter ratio, fruit horizontal diameter-fruit vertical diameter ratio (P<0.05), while fruit horizontal diameter and fruit vertical diameter were significantly negatively correlated with fruit thousand-grain weight to fruit volume ratio(P<0.05). (3) There was a significant correlation between fruit phenotypic traits of ancient Cinnamomum camphora and latitude, longitude and climate factors in Jiangxi Province. Among them, fruit thousand-grain weight and fruit volume were highly significant negatively correlated (P<0.01) with annual average sunshine duration and mean temperature in July, and highly significant positively correlated with mean temperature in January and mean altitude factor (P<0.01). (4) The cumulative variance contribution of the first three principal components in the principal component analysis reached 78.78%, and the fruit side diameter, fruit side diameter-fruit vertical diameter ratio, and fruit side diameter-fruit horizontal diameter ratio could comprehensively reflect the information and ranking of the 10 traits. (5) In the cluster analysis, it was divided into two groups A and B at the distance coefficient of 1.2, and further divided into different subgroups at the distance coefficient of 0.9. The fruit morphology indexes from subgroup B2 were bigger. In conclusion, fruit phenotypic traits of ancient Cinnamomum camphora in Jiangxi Province were rich in variation, and the variation of among provenances was more than within provenances. At the same time, there was a certain geographic variation pattern of the phenotypic traits of Cinnamomum camphora. The results would provide an important direction for the selection and breeding of excellent germplasm resources in Jiangxi Province, China.

plant biology↗

bioRxiv2025

ECD co-operates with ERBB2 to promote tumorigenesis through upregulation of unfolded protein response and glycolysis

The ecdysoneless (ECD) mRNA and protein are overexpressed in breast cancer (BC), and its overexpression correlates with poor prognosis and short patient survival, particularly in ERBB2/HER2-positive BC. This study investigates the co-operative oncogenic mechanism of ECD and ERBB2 by deriving transgenic mice overexpressing ECD and/or ERBB2 (huHER2) in mammary epithelium under MMTV promoter, as well as human mammary immortal epithelial cell lines (hMECs) overexpressing ECD and/or ERBB2. While huHER2Tg mice developed more homogenous solid nodular carcinomas, double transgenic mice (ECD;huHER2Tg) developed heterogenous and histologically aggressive mammary tumors with basal-like phenotype and epithelial mesenchymal transition (EMT) features, like ECDTg tumors, resembling more to patient tumors. Importantly, transcriptomic profile of ECD;huHER2Tg tumors revealed upregulation of two major oncogenic pathways, unfolded protein response (UPR) and glycolysis. Similarly, hMECs expressing both ECD and ERBB2 as compared to single gene expressing cells showed increase in oncogenic traits, and RNA-seq analysis showed a significant upregulation of glycolysis and UPR pathways. ECD is an RNA binding protein, and directly associates with three key glycolytic enzymes (LDHA, PKM2 and HK2) and mRNA of a major UPR regulated gene GRP78, that results in increased mRNA stability. Lastly, we show an increase in glucose uptake and enhanced glycolytic rate in ECD+ERBB2-overexpressing cells as compared to ECD- or ERBB2-overexpressing hMECs. Taken together, our findings support a co-operative role of ECD and ERBB2 in oncogenesis by enhancing two major oncogenic pathways, UPR and glycolysis. SignificanceThis study provides mechanistic insights that overexpression of ECD in ERBB2+ breast cancer patients correlates with shorter patient survival, by identifying direct ECD binding to mRNAs for UPR and glycolysis pathways.

cancer biology↗

bioRxiv2025

Metabolic shifts in the heart of hypothyroid mice -- insights from untargeted metabolomics

ABSTRACTSO_ST_ABSIntroductionC_ST_ABSHypothyroidism is a prevalent disorder that affect all systems of body, including the cardiovascular system and control of cardiac function. However, much remains unknown about the metabolic pathways and disorders in the heart induced by hypothyroidism, as well as the metabolic mechanisms involved. ObjectivesThis work aims to investigate the impacts of hypothyroidism on the heart and explore its underlying mechanisms through untargeted metabolomics analysis. MethodsAn experimental model of hypothyroidism was established by giving water containing 0.1% methimazole and 1% potassium perchlorate to 12-week-old C57BL/6J mice for six consecutive weeks. ResultsAfter treatment, hypothyroidism significantly reduced mices heart weight and heart rate. Metabolomics analysis revealed that the different metabolites (DEMs) primarily concentrated in Phospholipids, Lipids or Lipids-like, Carbohydrates and Carbohydrate conjugates. These specific metabolites were mainly enriched in pathways related to lipid metabolism. The hearts of hypothyroid mice exhibited significantly lower levels of lipoyl-carnitine compared to controls, along with markedly higher levels of glycolysis and pentose phosphate pathway (PPP) intermediates. Furthermore, carnitine palmitoyltransferase 1B (CPT1B) and carnitine palmitoyltransferase 2 (CPT2) expressions were significantly decreased in the hypothyroid mouse heart, while lactate dehydrogenase A (LDHA) expressions was significantly increased, according to the immunoblotting assay. ConclusionsResearch indicates that hypothyroidism triggers disruptions in both cardiac energy metabolism and phospholipid metabolism in adult mice, which in turn results in myocardial atrophy. These findings could pave the way for novel therapeutic strategies aimed at treating hypothyroidism-induced myocardial atrophy.

pathology↗

bioRxiv2025

Axon Trafficking Counteracts Aberrant Protein Aggregation in Neurons

Directed axon trafficking of mRNA via ribonucleoprotein complexes (RNPs) is essential for the proper function and survival of neurons. However, the mechanisms governing RNP transport in axons remain poorly understood. Here, we identify Annexin A7 (ANXA7) as a critical adaptor facilitating the retrograde transport of T-cell intracellular antigen 1 (TIA1)-containing RNPs by linking them to the cytoplasmic dynein. Persistent axonal Ca{superscript 2} elevation disrupts ANXA7s linker role, causing the detachment of TIA1 granules from dynein, consequently impairing transport and triggering pathological TIA1 aggregation within axons. Similarly, ANXA7 knockdown decouples TIA1 granules from dynein, severely obstructing trafficking and causing pathological aggregation of TIA1 in axons, which culminates in axonopathy and neurodegeneration both in vitro and in vivo. Conversely, ANXA7 overexpression enhances trafficking and counteracts aberrant aggregation of TIA1-containing RNPs in axons. Our findings elucidate a novel mechanism underlying RNP axonal transport, highlighting its significance in the biology and pathology of central neurons.

neuroscience↗

bioRxiv2024

Functional Connectivity Alterations in Cocaine Use Disorder: Insights from the Triple Network Model and the Addictions Neuroclinical Assessment Framework

Cocaine use disorder (CUD) disrupts functional connectivity within key brain networks, specifically the default mode network (DMN), salience network (SN), and central executive network (CEN). While the triple network model has been proposed to explain various psychiatric disorders, its applicability to CUD requires further exploration. In the present study, we built machine learning classifiers based on different combinations of DMN/SN/CEN to distinguish cocaine-use disorder (CUD) subjects from healthy control (HC) subjects. Among them, the combination of the SN and the CEN results in a remarkably high accuracy of 73.4% (sensitivity/specificity: 69.6%/78.6%, AUC: 0.78), outperforming the model based on the full triple network. This supports the hypothesis that during the binge/intoxication stage of addiction, the SN and the CEN play a more critical role than the DMN, consistent with the Addictions Neuroclinical Assessment (ANA) framework. Functional connectivity analysis revealed decreased connectivity within the DMN and the SN and increased connectivity within the CEN in CUD patients, suggesting that alterations in these networks could serve as biomarkers for addiction severity.

neuroscience↗

bioRxiv2024

The Cryo-EM structure of TRPC1 and TRPC5 heterotetramer

Ion channel heteromers display distinct electrophysiological and ligand-binding properties, allowing precise modulation of cellular functions in specific tissues. Among the TRPC subtypes, the TRPC1/4/5 heteromers are the most abundant isoform in the human brain and represent a primary target for treating anxiety and depression. However, the structural organization of TRPC1/4/5 heteromers remain unclear, limiting the advancement of research on these channels. Here, we report the cryo-EM structure of TRPC1/5 heterotatramer at a resolution of 2.84 [A]. It consists of an asymmetric structure with three TRPC5 subunits and one TRPC1 subunit. Further structural and functional analysis revealed that the TRPC1 subunit contributes to an asymmetrical ion conduction pathway, a characteristic that differentiates it from TRPC5 homomers. The unique features of the TRPC1 pore loop and its interface interactions with TRPC5 were shown to play crucial roles in modulating the ion conduction properties and gating mechanisms of the heteromeric complex. In addition, we identified key residues within the gating regions that influence channel activation and ion permeability, elucidating the structural basis for the functional divergence between the TRPC1/C5 heterotetramer and TRPC5 homotetramer. Notably, TRPC5 residues were found to dominate gating mechanisms, underscoring the complex interplay between subunit composition and functional outcomes in TRPC channels. Our findings provide novel insights into the structural and functional dynamics of TRPC1/C5 heteromers, paving the way for targeted therapeutic strategies in TRPC5-related disorders.

biophysics↗

bioRxiv2024

WIMOAD: Weighted Integration of Multi-omics data for Alzheimer's Disease (AD) Diagnosis

INTRODUCTIONAlzheimers disease (AD), the most prevalent subtype of dementia, is characterized by a gradual decline in brain cognitive function. Early detection is critical for initiating timely interventions that may delay the severe progression of the disease. Recent advances in next-generation sequencing (NGS) offer promising, non-invasive, and cost-effective strategies for AD screening. However, most current approaches rely on single-omics data, which may fail to capture the complex biological heterogeneity among individuals. METHODSWe introduce WIMOAD, a stacking ensemble and weighted multi-omics integration for AD diagnosis. It leverages paired gene expression and methylation data from ADNI and presents a meta learning framework for multi-cognitive stage classification during AD progression. RESULTS AND DISCUSSIONWIMOAD outperforms existing integration methods in AD diagnosis, effectively capturing complex multi-omics patterns linked to clinical outcomes. Its interpretability also facilitates the detection of novel biomarkers across different omics layers.

bioinformatics↗