Biology subjects

Wan, C.

Publications and source records attributed to Wan, C..

At least 19 recordsLinked to original sources

bioRxiv2026

A kinetic-aware approach to infer metabolic variations and flux using transcriptomics and metabolomics data

Assessing metabolic variations and flux quantities enable systematic understandings of metabolic shifts, reprogramming, adaptation and interactions in human diseases. However, omics-based estimation of metabolic flux and its variation remains challenging due to several fundamental limitations: the need for disease and tissue context specific metabolic model; nonlinear enzyme kinetic model that links enzyme and substrate changes to reaction flux; partial, unpaired, and snap-shot measurements across omics modalities; and uncertainty in computational prediction. Here, we present Michaelis-Menten model-based Flux Estimation Analysis (mmFEA), a Monte Carlo framework for estimating condition-specific flux changes by integrating paired or unpaired metabolomics and transcriptomics (or proteomics) data. mmFEA separates each reaction-rate change into enzyme- and substrate-associated components and assess reaction rate using Michaelis-Menten kinetics equation. A baseline metabolite saturation rate is introduced by integrating protein language model predicted kinetic parameters and human baseline level metabolic concentration to enable kinetic-aware integration of unpaired substrate and enzyme level measurements. Distribution of metabolic flux and variations between conditions are further computed using MCMC sampling by treating Michaelis-Menten-derived marginal flux distribution as prior and coherency in flux balance as likelihood. To benchmark mmFEA, we generated an in-house multi-omics data set including transcriptomics, metabolomics, metabolic activity functional assay, and CRISPR screening data using pancreatic cancer cell line system treated by APEX1 inhibitors. We demonstrated that mmFEA could accurately capture experimentally observed metabolic changes and achieved a better performance than all baseline methods. Our analysis revealed the necessity in using both substrate and enzyme modality and kinetic aware model in metabolic flux assessment. Further analysis using independent pancreatic cancer cohorts further validated the robustness of mmFEA, supporting integration of condition-linked unpaired data. Pan-cancer and spatial multi-omic applications demonstrated the use of mmFEA for resolving context-dependent metabolic variation when direct flux measurements are unavailable. Together, mmFEA provides a mechanistically grounded framework for estimating relative metabolic flux changes and their uncertainty from heterogeneous omics data.

systems biology↗

bioRxiv2026

MetaKnogic-Alpha: A Hyper-Relational Knowledge Base for Grounded Metabolic Reasoning

The exponential trajectory of biomedical literature has precipitated a fundamental "synthesis gap" in metabolic research, where critical mechanistic insights remain fragmented across hundreds of thousands of disjointed full-text articles, preventing the consolidation of a global mechanistic view. Here, we present MetaKnogic-Alpha, a foundational mechanistic knowledge substrate designed to bridge this gap by transforming unstructured literature into a navigable, logic-based resource. MetaKnogic-Alpha synthesizes over 100K full-text articles into a hyper-relational hypergraph structure, preserving the n-ary relational logic inherent in complex metabolic pathways. To ensure biological rigor, we implemented a hierarchical discovery protocol: an autonomous reasoning agent first enriches query nomenclature for domain-specific precision, followed by a multi-hop topological expansion within the hypergraph to surface functional neighbors, such as enzymatic co-factors and distal regulators, often lost in traditional search paradigms. Crucially, the system subjects all literature-derived insights to a deterministic biochemical grounding against a curated metabolic reaction network, significantly mitigating the risk of probabilistic hallucinations common in standalone generative models. In rigorous benchmarking, MetaKnogic-Alpha achieved a mechanistic accuracy of 0.98 in scenarios where supporting evidence was present, providing a robustly attributable audit trail back to the primary literature via PubMed Central Identifiers. We designate this primary release as "alpha" to establish the foundational architectural logic for a burgeoning million-scale resource. By compressing the synthesis of thousands of papers from a multi-month manual effort into several hours of automated discovery, MetaKnogic-Alpha serves as a high-fidelity research companion that augments the human experts ability to resolve complex metabolic interactions and identify novel therapeutic drivers in precision oncology.

bioinformatics↗

bioRxiv2025

Improving cell-type identification with generative adversarial networks-enhanced augmentation-free single-cell RNA-Seq contrastive learning

Cell-type identification plays a fundamental role in single-cell RNA-Seq analytics. Thanks to the recent success of the contrastive learning paradigm, the accuracy of automatic cell-type identification has also been improved. In this work, we propose a novel contrastive learning-based cell-type identification method, namely GAN-RCL, which conducts hard positive sample selection on real and conditional generative adversarial networks-created synthetic instances to improve the performance of augmentation-free contrastive learning. Experimental results confirm that GAN-RCL successfully outperforms other recently-proposed contrastive learning-based methods and shows the state-of-the-art predictive performance on multiple single-cell RNA-Seq cell-type identification tasks.

bioinformatics↗

bioRxiv2025

Pseudotimecascade visualizes gene expression cascade in pseudotime analysis

Single-cell transcriptomic technologies enable the reconstruction of dynamic biological processes such as cell development and differentiation. While existing pseudotime methods allow the analysis of temporal expression patterns, they primarily focus on individual genes, overlooking the coordinated programs that drive cellular transitions. We introduce Pseudotimecascade, a tool for visualizing and comparing multi-gene expression cascades along pseudotime. In addition, it links these cascades to biological functions by identifying stage-specific pathways. Applied to hematopoietic stem cell differentiation, Pseudotimecascade highlights regulatory hierarchies and stage-specific processes, offering a deeper understanding of gene programs that govern cell fate decisions. The Pseudotimecascade package is freely available at https://github.com/changxinw/Pseudotimecascade.

bioinformatics↗

bioRxiv2025

An extensive evaluation of single-cell RNA-Seq contrastivelearning generative networks for intrinsic cell-typesdistribution estimation

Contrastive learning has already been widely used to handle single-cell RNA-Seq data due to its outstanding performance in transforming original data distributions into hypersphere feature spaces. In this work, we conduct a large-scale empirical evaluation to investigate the generative encoder networks that are learned by five different state-of-the-art single-cell RNA-Seq contrastive learning methods. Unlike the conventional discriminative model-based cell-type prediction studies, this work is focused on the performance of contrastive learning-based generative encoder networks in terms of their capacity to estimate the intrinsic distributions of different cell-types - a fundamental property that directly affects the performance of any downstream single-cell RNA-Seq data analytics. The experimental results confirm that supervised contrastive learning-based encoder networks lead to better performance than self-supervised contrastive learning-based encoder networks, and the recently proposed Gaussian noise augmentation-based single-cell RNA-Seq contrastive learning method shows the best performance on estimating the intrinsic distribution of different cell-types.

bioinformatics↗

bioRxiv2025

Ploidy and neuron size impact nervous system development and function in Xenopus

Neuron size varies significantly over evolution, contributing to diverse nervous systems of variable complexity, while aberrant neuron size is associated with neurodevelopmental and degenerative diseases. How do neuron cell body and neurite size and organization impact nervous system development and function? To systematically study effects of neuron size on the vertebrate nervous system, we characterized triploid Xenopus tadpoles that possess a 1.5-fold increase in genome size compared to diploids. Triploids displayed a linear increase in total neuronal volume and a superlinear increase in membrane surface area. Imaging, flow cytometry, and RNA-seq analyses revealed that triploid brains were morphologically and transcriptionally similar to diploid brains, but less proliferative, containing fewer neurons and displaying increased global activity. Interestingly, physiological differences at the neuron and nervous system levels affected swimming behavior in tadpoles. Our findings thus establish a framework to link genome size, neuron size, and nervous system development and function in vertebrates.

cell biology↗

bioRxiv2025

Lysosomal cathepsin D regulates bone turnover through distinct mode of actions of the autophagy pathways in osteoblasts and osteoclasts

Insufficiency in nutrient availability, oxidative stress and autophagy failure are fundamental factors for the decline of bone mass and strength with aging. Accumulating evidence indicates that these factors affect normal autophagosomal or lysosomal activities which are the major force in clearance of aggregated or damaged proteins. Cathepsin D (CtsD), the principal lysosomal aspartate protease and a main endopeptidase, exists in the skeleton during development or homeostasis. However, the molecular and cellular mechanisms of CtsD mediated autophagosome or lysosome function in the skeletal homeostasis remain unclear. In the present study, we showed that deletion of CtsD dramatically decreased bone mass in the 3-week old mutant mice compared with their control littermates as indicated by decreased bone volume (BV), bone volume / total volume (BV/TV), bone surface (BS), trabecular number, trabecular thickness and increase trabecular separation in the microCT analysis. Histomorphometry analysis revealed that the phenotype was characterized by decreased osteoblast numbers, osteoblast surface/bone surface and mineral apposition rate, increased osteoclast numbers, osteoclast surface/bone surface and erosion surface/bone surface. At molecular level, siRNA medicated inactivation of CtsD in MC3T3E1 cells attenuated osteoblastic differentiation and downregulated LC3B expression, which was accompanied by decreased levels of P62, p-Akt and p-GSK3beta in osteoblasts. Intriguingly, inactivation of CtsD in RAW264.7 cells increased osteoclast differentiation with decreased LC3B expression but upregulated P62 level. This was accompanied by alterations in the formation of autophagosome and differential transcription profiles associated with the autophagy pathway during the differentiation of osteoblasts and osteoclasts. The results suggest that CtsD mediated autophagy pathway plays important roles in regulation of bone mass and homeostasis through distinct mode of actions in osteoblasts and osteoclasts, and CtsD may serve as a potential therapeutic target for the maintenance of bone mass.

cell biology↗

bioRxiv2025

N153-linked glycans on envelope protein protect orthoflaviviruses from antibody-mediated clearance

The envelope (E) protein of dengue virus (DENV) is glycosylated at two highly conserved asparagine (N) sites (N67 and N153). The role and importance of these N-linked glycans in DENV pathogenesis has been elusive. Here, we report the critical role of N153-linked glycans on E protein in preventing antibody-mediated viral clearance. A DENV2 mutant lacking N153-linked glycans (N153Q mutant) was engineered and found to be mildly impaired in vitro but drastically attenuated in a symptomatic mouse model of severe dengue, as evidenced by accelerated viral clearance. In B cell-deficient mouse models, N153Q mutant displayed parental virulence and viremia profile. Homologous and heterologous passive transfers of purified IgM from infected B cell-proficient mice into B cell-deficient mice demonstrated the role of N153Q-specific IgM in N153Q attenuation and accelerated clearance, while WT DENV was unaffected by IgM from both WT- and N153Q-infected mice. Furthermore, in vitro neutralization assay supported that the accelerated clearance of N153Q mutant in mice was mediated by non-neutralizing IgM. Furthermore, using plasma samples from convalescent dengue patients and monoclonal antibodies, in vitro neutralization assays showed that N153Q virus was more susceptible than WT to IgG-mediated neutralization. Glycoproteomics combined with molecular dynamics (MD) simulations revealed that glycan composition on E protein influenced IgG binding. Our findings were extended to all DENV serotypes and ZIKV, hence supporting that the N153 glycans-mediated immune evasion strategy is conserved across orthoflaviviruses.

microbiology↗

bioRxiv2025

EpiFoundation: A Foundation Model for Single-Cell ATAC-seq via Peak-to-Gene Alignment

Foundation models exhibit strong capabilities for downstream tasks by learning generalized representations through self-supervised pre-training on large datasets. While several foundation models have been developed for single-cell RNA-seq (scRNA-seq) data, there is still a lack of models specifically tailored for single-cell ATAC-seq (scATAC-seq), which measures epigenetic information in individual cells. The principal challenge in developing such a model lies in the vast number of scATAC peaks and the significant sparsity of the data, which complicates the formulation of peak-to-peak correlations. To address this challenge, we introduce EpiFoundation, a foundation model for learning cell representations from the high-dimensional and sparse space of peaks. Epi-Foundation relies on an innovative cross-modality pre-training procedure with two key technical innovations. First, EpiFoundation exclusively processes the non-zero peak set, thereby enhancing the density of cell-specific information within the input data. Second, EpiFoundation utilizes dense gene expression information to supervise the pretraining process, aligning peak-to-gene correlations. EpiFoundation can handle various types of downstream tasks, including cell-type annotation, batch correction, and gene expression prediction. To train and validate EpiFoundation, we curated MiniAtlas, a dataset of 100,000+ single cells with paired scRNA-seq and scATAC-seq data, along with diverse test sets spanning various tissues and cell types for robust evaluation. EpiFoundation demonstrates state-of-the-art performance across multiple tissues and diverse downstream tasks.

genomics↗

bioRxiv2024

A Commander-independent function of COMMD3 in endosomal trafficking

Endosomal recycling is a branch of intracellular membrane trafficking that retrieves endocytosed cargo proteins from early and late endosomes to prevent their degradation in lysosomes. A key player in endosomal recycling is the Commander complex, a 16-subunit protein assembly that cooperates with other endosomal factors to recruit cargo proteins and facilitate the formation of tubulo-vesicular carriers. While the crucial role of Commander in endosomal recycling is well established, its molecular mechanism remains poorly understood. Here, we genetically dissected the Commander complex using unbiased genetic screens and comparative targeted mutations. Unexpectedly, our findings revealed a Commander-independent function for COMMD3, a subunit of the Commander complex, in endosomal recycling. COMMD3 regulates a subset of cargo proteins independently of the other Commander subunits. The Commander-independent function of COMMD3 is mediated by its N-terminal domain (NTD), which binds and stabilizes ADP-ribosylation factor 1 (ARF1), a small GTPase regulating endosomal recycling. Mutations disrupting the COMMD3-ARF1 interaction diminish ARF1 expression and impair COMMD3-dependent cargo recycling. These data provide direct evidence that Commander subunits can function outside the holo-complex and raise the intriguing possibility that components of other membrane trafficking complexes may also possess functions beyond their respective complexes.

cell biology↗

bioRxiv2024

Structural basis of pseudoGTPase-mediated protein-protein interactions

GTPases regulate various cellular processes through conformational changes triggered by GTP or GDP binding. Recently, pseudoGTPases, the catalytically inactive counterparts of GTPases, have been identified across species from bacteria to human, although their functions and mechanisms remain unexplored. In this study, we demonstrate that the N-terminal region of the assembly chaperone AAGAB is a type i pseudoGTPase using biochemistry and X-ray crystallography. Furthermore, we discovered that the AAGAB pseudoGTPase domain (psGD) interacts with the {sigma} subunits of AP1 and AP2 adaptor complexes, heterotetrameric complexes involved in clathrin-mediated membrane trafficking. AAGAB psGD engages the {sigma} subunits via a unique interface distinct from the conventional GTPase interacting regions. Further biochemical and cell-based assays confirmed the crucial role of the newly identified interface in binding and membrane trafficking. Collectively, our results establish AAGAB pseudoGTPase domain as a critical protein-protein interaction module. These findings offer new insight into the structural basis and molecular mechanisms of pseudoGTPases. HighlightsO_LIAAGAB N-terminal region is a pseudoGTPase C_LIO_LIAAGAB pseudoGTPase domain interacts with and stabilizes AP1 and AP2 {sigma} subunits C_LIO_LIThe crystal structure of AAGAB psGD:AP1{sigma}3 is the first reported psGD complex structure, revealing a unique interface independent of guanine nucleotide regulation C_LIO_LIThe psGD:AP1{sigma}3 structure offers mechanistic insights into {sigma} subunits stabilization and protection through adaptor complex assembly C_LI

biochemistry↗

bioRxiv2024

Functional yeast promoter sequence design using temporal convolutional generative language models

Functional promoter sequence design plays a crucial role in accurately controlling gene expression processes that are one of the most fundamental mechanisms in biological systems. Thanks to the recent community effort, we are now able to elucidate the associations between yeast promoter sequences and their corresponding expression levels using advanced deep learning methods. This milestone boosts the further development of many downstream biological sequence research tasks including synthetic DNA sequence design. In this work, we propose a novel synthetic promoter sequence generation method, namely Gen-DNA-TCN, which exploits a pre-trained sequence-to-expression predictive model to facilitate its autoregressive generative model training. A large-scale evaluation confirms that Gen-DNA-TCN successfully generates a large number of unique, diverse and functional synthetic yeast promoter sequences that also encode similar transcription factor binding site distributions compared with real yeast promoter sequences.

bioinformatics↗

bioRxiv2024

Vcam1+ Fibro-adipogenic Progenitors Mark Fatty Infiltration in Chronic Limb Threatening Ischemia

Skeletal muscle health and function is a critical determinant of clinical outcomes in patients with peripheral arterial disease (PAD). Herein, we identify fatty infiltration, the ectopic deposition of adipocytes in skeletal muscle, as a histological hallmark of end-stage PAD, also known as chronic limb threatening ischemia (CLTI). Leveraging single cell transcriptome mapping in mouse models of PAD, we identify a pro-adipogenic mesenchymal stromal cell population marked by expression of Vcam1 (termed Vcam1+ FAPs) that expands in the ischemic limb. Mechanistically, we identify Sfrp1 and Nr3c1 as regulators of Vcam1+ FAP adipogenic differentiation. Loss of Sfrp1 and Nr3c1 impair Vcam1+ FAP differentiation into adipocytes in vitro. Finally, we show that Vcam1+ FAPs are enriched in human CLTI patients. Collectively, our results identify a pro-adipogenic FAP subpopulation in CLTI patients and provide a potential therapeutic target for muscle regeneration in PAD.

genomics↗

bioRxiv2024

De-glycosylated non-structural protein 1 enhances dengue virus clearance by limiting PD-L1/PD-1 mediated T cell apoptosis

The non-structural protein 1 (NS1) of dengue virus (DENV) contains two highly conserved N-glycosylation sites at positions 130 and 207 (N130 and N207). Intracellular NS1 monomers and homo-dimers participate in viral RNA replication within membrane-bound replication complexes. Soluble multimeric NS1 (sNS1) is secreted into the extracellular milieu and represents an important virulence factor for DENV through its ability to interfere with the host complement activation cascade and to induce vascular leakage. The role of the two N-glycans in NS1 biological activities, however, has not been carefully examined. Here, stable DENV2 mutants that lack glycan at either N sites of NS1 were engineered. We showed that the lack of glycans at either N site of NS1 did not impair viral replication nor viral output in both mosquito and mammalian cell lines. In contrast, while N130 de-glycosylated DENV displayed parental in vivo fitness in IFNAR-/- mice, the N207 de-glycosylated mutant was significantly attenuated as evidenced by 100% survival rate, which correlated with accelerated viral clearance in circulation. sNS1-depletion, sNS1 exogenous administration and co-infection experiments supported that N207 de-glycosylated NS1 exerted a dominant attenuating effect during in vivo infection. Bulk RNAseq, inflammatory cytokine profile, immune phenotyping of neutrophils and T cells, immune cell depletion and immune checkpoint blockade approaches led us to propose that N207 de-glycosylated NS1 limited CD8+ T cell apoptosis mediated by the PD-L1/PD-1 axis, thereby improving viral clearance efficacy. This work uncovers a novel immune evasion strategy where N207 glycans on NS1 prevent the protein from exerting immune modulation activity that would be detrimental to DENV.

microbiology↗

bioRxiv2023

Determining the microbial species content in tissue from Alzheimer's and Parkinson's Disease patients

The aetiology of Alzheimers disease (AD) and Parkinsons Disease (PD) are unknown and tend to manifest at a later stage in life; even though these diseases have different pathogenic mechanisms, they are both characterized by neuroinflammation in the brain. Links between bacterial and viral infection and AD/PD has been suggested in several studies, however, few have attempted to establish a link between fungal infection and AD/PD. In this study we develop and describe a nanopore-based sequencing approach to characterise the presence or absence of fungi in both human brain tissue and cerebrospinal fluid (CSF). This approach detects fungal DNA in human brain and CSF samples even at low levels, whereas our quantitative polymerase chain reaction (qPCR) assay (FungiQuant) was unable to detect fungal DNA in the same samples. Comparison against kit-controls showed ubiquitous low-level fungal contamination that we observed in healthy human brains and CSF as well AD/PD brains and CSF. We use this technique to demonstrate the presence of fungal DNA in healthy human brains as well as AD/PD brains, with Alternaria spp., Colletotrichum graminicola, and Filobasidium floriforme as the most prominent species. In addition, antibiotic resistant Pseudomonas spp. was identified within the brain of an AD patient. Our method will be broadly applicable to investigating potential links between microbial infection and AD/PD.

microbiology↗

bioRxiv2023

Elucidating human ageing-related phenotypic abnormalities with a novel hierarchical feature selection-based knowledge discovery framework

Ageing is a complex biological process involving multiple genes that are also related to phenotypic abnormalities. However, the micro view of the associations between ageing and human phenotypic abnormalities is still under-studied. We propose a new hierarchical feature selection method namely HIP+, which selects positive hierarchical information-preserving features according to pre-defined hierarchical ontology information. The experimental results confirm that HIP+ obtained better performance than the state-of-the-art hierarchical feature selection method in predicting human phenotypic abnormalities annotations. We further propose a HIP+-based knowledge discovery framework that also successfully highlights some important associations between biological processes and human phenotypic abnormalities.

bioinformatics↗

bioRxiv2023

Integrating multiple single-cell multi-omics samples with Smmit

Multi-sample single-cell multi-omics datasets, which simultaneously measure multiple data modalities in the same cells across multiple samples, facilitate the study of gene expression, gene regulatory activities, and protein abundances on a population scale. We developed Smmit, a computational method for integrating data both across samples and modalities. Compared to existing methods, Smmit more effectively removes batch effects while preserving relevant biological information, resulting in superior integration outcomes. Additionally, Smmit is more computationally efficient and builds upon existing computational pipelines, requiring minimal effort for implementation. Smmit is an R software package that is freely available on Github: https://github.com/zji90/Smmit.

bioinformatics↗

bioRxiv2022

Improving cell-type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning

Cell-type identification is an important task for single-cell RNA-seq (scRNA-seq) data analysis. In this work, we proposed a novel Gaussian noise augmented scRNA-seq contrastive learning framework (GsRCL) to learn a type of discriminative feature representations for cell-type prediction tasks. The experimental results suggest that the feature representations learned by GsRCL successfully improved the accuracy of cell-type prediction using scRNA-seq expression profiles.

bioinformatics↗