Biology subjects

Wang, B.

Publications and source records attributed to Wang, B..

At least 235 records · Page 13Linked to original sources

bioRxiv2024

ApoDock: Ligand-Conditioned Sidechain Packing for Flexible Molecular Docking

Molecular docking is a crucial technique for elucidating protein-ligand interactions. Machine learning-based docking methods offer promising advantages over traditional approaches, with significant potential for further development. However, many current machine learning-based methods face challenges in ensuring the physical plausibility of generated docking poses. Additionally, accommodating protein flexibility remains difficult for existing methods, limiting their effectiveness in real-world scenarios. Diffusion based models has already show a good solution of those problems, such as Alphafold3(AF3). Herein, we present ApoDock, a modular docking paradigm that combines machine learning-driven conditional sidechain packing based on protein backbone and ligand information with traditional sampling methods to ensure physically realistic poses. With accurate sidechain packing and physical based pose sampling, ApoDock demonstrates competitive performance across diverse applications, highlighting its potential as a valuable tool for protein-ligand binding studies and related applications.

bioinformatics↗

bioRxiv2024

Development of a tick-borne encephalitis virus nanoparticle vaccine utilizing envelope dimer

Tick-borne encephalitis virus (TBEV) is primarily transmitted to humans through tick bites, leading to symptoms of encephalitis with a fatality rate ranging from 1% to 30%, depending on the virus subtype. Currently, only inactivated virus vaccines are available for human use, though break-through infections can still occur. Therefore, developing new vaccines against TBEV is crucial. In this study, we designed and characterized a novel nanoparticle-based TBEV envelope (E) dimer vaccine. We successfully expressed and purified the TBEV E dimer by engineering disulfide bond mutants, and animal experiments demonstrated that the E dimer protein elicited a stronger immunogenic responses compared to the E monomer protein. Further, antibody depletion experiment confirmed that the E dimer protein effectively mimics the virion surface structure, inducing robust humoral immunity targeted at neutralizing epitopes. We also presented the TBEV E dimer on the surface of the nanoparticle Mi3 using the SpyCatcher-SpyTag system, with animal experiment showing that this TBEV E dimer nanoparticle vaccine elicited a potent humoral immune response. These findings offer new insights into the immunogenicity of the TBEV E dimer and suggest that the nanoparticle-based TBEV E dimer vaccine represents a promising and highly effective candidate for TBEV immunization.

microbiology↗

bioRxiv2024

IMMClock reveals immune aging and T cell function at single-cell resolution

The aging of the immune system substantially impacts individual immune responses, yet accurately quantifying immune age remains a complex challenge. Here we developed IMMClock, a novel immune aging clock that uses gene expression data to predict the biological age of individual CD8 T cells, CD4 T cells, and NK cells. The accuracy of IMMClock is first validated across multiple independent datasets, demonstrating its robustness. Second, utilizing the IMMClock, we find that intrinsic cellular aging processes are more strongly altered during immune aging than differentiation processes. Thirdly, our analysis confirms the strong associations between immune aging and established processes such as cellular senescence, exhaustion, and telomere length at the single cell level. Furthermore, immune aging is accelerated under several disease conditions such as type 2 diabetes, heart disease, and cancer. Finally, we apply IMMClock to analyze a perturb-seq gene activation screen of T cell functionality. We find that the post-perturbation immune age of individual T cells is strongly correlated with their pre-perturbation immune age. Furthermore, the immune age at resting state of individual T cells is strongly predictive of their post-stimulation activation state. Overall, IMMClock advances our understanding of immune aging by providing precise, single-cell level age estimations. Its future applications hold promise for identifying interventions that concomitantly rejuvenate and activate T cells, potentially enhancing efforts to counteract age-related immune decline.

bioinformatics↗

bioRxiv2024

Language model-guided anticipation and discovery of unknown metabolites

Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks within human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains a low-throughput endeavor. Biochemical large language models have transformed the interpretation of DNA, RNA, and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns the latent biosynthetic logic embedded within the structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective chemical synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We harness DeepMet to discover several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to accelerate the mapping of the metabolome.

bioinformatics↗

bioRxiv2024

Koolen-de Vries Syndrome causal gene KANSL1 is required for motile ciliogenesis

Koolen-de Vries Syndrome (KdVS), characterized by hypersociability, intellectual disability, and seizures, is caused by pathogenic variants in the gene KANSL1, which encodes a chromatin regulator in the NSL complex that also directly functions in mitotic spindle microtubule stability. Here we explored whether KANSL1 functions at the cilium, a microtubule-rich organelle critical for brain development, neuronal excitability, and sensory integration. Leveraging the Xenopus model, we found that Kansl1 is highly expressed in developing ciliated tissues and localizes within motile cilia. Moreover, Kansl1 depletion caused ciliogenesis defects that could be partially rescued by human KANSL1. Based on these findings, we explored the prevalence of cilia-related clinical features including structural heart defects, hypogonadism, and structural respiratory defects among 99 individuals with KdVS, ages 1 month to 37 years old. To directly test if KdVS causes ciliary dysfunction in humans, we measured the well-established ciliary functional biomarker, nasal nitric oxide, in 11 affected individuals and observed a significant decrease when compared to unaffected family members. Together, this work establishes a ciliary contribution of KANSL1 mutations to KdVS. This work adds to a growing literature highlighting the relevance of the cilium to neurodevelopmental disorders, particularly to those impacting sociability. Going forward, KANSL1 provides a unique opportunity to study a monogenic mechanism of hypersociability that may be useful in elaborating the molecular underpinnings of social behavior.

developmental biology↗

bioRxiv2024

Reinforced identity confers cells with cancer hallmarks

Genetic alterations trigger tumorigenesis in a cell type-specific manner, but the biological basis for this, remains unclear. Here we define reinforced original cell identity established by interaction between genetic alterations and original cell type is a pivotal determinant of tumor development. Using AML-M5MLL-AF9 as a model system, we demonstrated that MLL-AF9 enhanced key transcriptional factors (TFs) expression of promonocyte and cooperated with them to amplify their downstream programs for AML hallmarks. Interactions between MLL-AF9 and promonocyte endowed cancer cells with a reinforced promonocyte identity. Reinforced intrinsic cell identity also sustained cancer hallmarks across cancer types. Furthermore, we identified narciclasine, as an effective disruptor of reinforced promonocyte identity, effectively treating AML-M5MLL-AF9. This study not only identified reinforced cell identity as a fundamental tumor driver beyond genetic mutations but also offers a proof-of-concept for reinforced identity- targeted cancer therapy.

cancer biology↗

bioRxiv2024

Single-cell atlas of developing mouse palates reveals cellular and molecular transitions in periderm cell fate

Cleft palate is one of the most common congenital craniofacial disorders that affects childrens appearance and oral functions. Investigating the transcriptomics during palatogenesis is crucial for comprehending the etiology of this disorder and facilitating prenatal molecular diagnosis. However, there is limited knowledge about the single-cell differentiation dynamics during mid- and late-palatogenesis, specifically regarding the subpopulations and developmental trajectories of periderm, a rare but critical cell population. Here we explore the single-cell landscape of mouse developing palates from E10.5 to E16.5. We systematically depict the single-cell transcriptomics of mesenchymal and epithelial cells during palatogenesis, including subpopulations and differentiation dynamics. Additionally, we identify four subclusters of palatal periderm and construct two distinct trajectories of cell fates for periderm cells. Our findings reveal that Claudins and Arhgap29 play a role in the non-stick function of the periderm before the palatal shelves contact, and Pitx2 mediates the adhesion of periderm during the contact of opposing palatal shelves. Furthermore, we demonstrate that epithelial-mesenchymal transition (EMT), apoptosis, and migration collectively contribute to the degeneration of periderm cells in the medial epithelial seam. Taken together, our study suggests a novel model of periderm development during palatogenesis and delineates the cellular and molecular transitions in periderm cell determination.

genomics↗

bioRxiv2024

MethylGPT: a foundation model for the DNA methylome

DNA methylation serves as a powerful biomarker for disease diagnosis and biological age assessment. However, current analytical approaches often rely on linear models that cannot capture the complex, context-dependent nature of methylation regulation. Here we present MethylGPT, a transformer-based foundation model trained on 226,555 (154,063 after QC and deduplication) human methylation profiles spanning diverse tissue types from 5,281 datasets, curated 49,156 CpG sites, and 7.6 billion training tokens. MethylGPT learns biologically meaningful representations of CpG sites, capturing both local genomic context and higher-order chromosomal features without external supervision. The model demonstrates robust methylation value prediction (Pearson R=0.929) and maintains stable performance in downstream tasks with up to 70% missing data. Applied to age prediction across multiple tissue types, MethylGPT achieves superior accuracy compared to existing methods. Analysis of the models attention patterns reveals distinct methylation signatures between young and old samples, with differential enrichment of developmental and aging-associated pathways. When finetuned to mortality and disease prediction across 60 major conditions using 18,859 samples from Generation Scotland, MethylGPT achieves robust predictive performance and enables systematic evaluation of intervention effects on disease risks, demonstrating potential for clinical applications. Our results demonstrate that transformer architectures can effectively model DNA methylation patterns while preserving biological interpretability, suggesting broad utility for epigenetic analysis and clinical applications.

systems biology↗

bioRxiv2024

Defective Neurogenesis in Lowe Syndrome is Caused by Mitochondria Loss and Cilia-related Sonic Hedgehog Defects

Human brain development requires tight coordination of metabolic and signaling pathways. Lowe syndrome (LS) is a recessive X-linked disorder characterized by proximal tubular renal disease, congenital cataracts, glaucoma, and neurodevelopmental delays. While LS results from mutations in the OCRL gene, which encodes an inositol polyphosphate 5-phosphatase, the cellular mechanisms driving neuronal dysfunction remain poorly understood. In this study, using patient-derived iPSC neurons, an OCRL knockout mouse model, and an independent zebrafish OCRL-deficient model, we identified mitochondrial dysfunction as a conserved phenotype of OCRL loss across species. Collectively, our findings showed that OCRL deficiency leads to reduced mitochondrial activity, decreased mtDNA levels, reduced mitochondrial content (TOM20), and increased oxidative stress. We further showed that OCRL-deficient neural cells exhibited an altered balance of neuronal versus astrocytic differentiation, rather than a defect in neurogenesis. Additionally, we observed impaired Sonic Hedgehog (Shh) signaling and ciliary homeostasis. Thus, we propose that mitochondrial dysfunction-induced oxidative stress acts as a central mediator linking OCRL loss to altered cell fate and disrupted Shh signaling, providing a unifying framework for these phenotypes.

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

CpGPT: a Foundation Model for DNA Methylation

DNA methylation is a type of epigenetic modification that plays a significant role in development, aging, and disease. Despite extensive research, how genome-wide DNA methylation patterns collectively encode and influence complex phenotypes such as aging and disease remains difficult to characterize with conventional approaches. Foundation models are a class of machine learning model that leverage vast quantities of data to make sense of complex data types, such as genome sequences or single-cell transcriptomes. Here, we present the Cytosine-phosphate-Guanine Pretrained Transformer (CpGPT), a novel foundation model pretrained on CpGCorpus, a novel database with more than 2,000 DNA methylation datasets encompassing over 150,000 samples from diverse conditions. CpGPT lever-ages an improved transformer architecture to learn comprehensive representations of methylation patterns, allowing it to impute and reconstruct genome-wide methylation profiles from limited input data. By capturing sequence, positional, and epigenetic contexts, CpGPT outperforms specialized models when finetuned for aging-related tasks, including the state-of-the-art GrimAge2 and PCGrimAge for mortality and morbidity estimation. The model is highly adaptable and can impute beta values across different methylation platforms, tissue types, mammalian species, and even single-cell data. As a foundation model, CpGPT can be leveraged as a new tool for biological discovery in the field of epigenetics. The open-source code and model can be found at http://github.com/lucascamillomd/CpGPT. HighlightsO_LICpGPT is a novel foundation model for DNA methylation analysis, pretrained on over 2,000 datasets encompassing 150,000+ samples. C_LIO_LIThe model demonstrates strong performance in zero-shot tasks including imputation, array conversion, and reference mapping. C_LIO_LICpGPT achieves state-of-the-art results in mortality prediction and chronological age estimation. C_LI

systems biology↗

bioRxiv2024

Integration of protein and coding sequences enables mutual augmentation of the language model

Recent language models have significantly accelerated our understanding on the massive biological data, using protein or DNA/RNA sequences as a single-language modality. Here we present a dual-language foundation model, which integrates both protein and coding sequences (CDS) for pre-training. Compared to the benchmark models, it shows a superior performance up to [~]20% on both protein and mRNA-related discriminative tasks, and gains the capacity to de novo generate coding sequences of [~]50% increased protein yield. Moreover, the model also possesses the knowledge transferability from the pre-training data to the upstream 5 untranslated regions. These findings indicate the intrinsic correlations between protein and its CDS, as well as the coding region and beyond. It provides a new paradigm that leverages the multiple-language foundation model to interpret the hidden context of distinct corpora/biological languages, which could be further applied to mine the yet-unknown biological information/correlation beyond the Central Dogma.

bioinformatics↗

bioRxiv2024

In silico design of a multi-epitope vaccine against tick-borne encephalitis virus via immunoinformatic analysis

Tick-borne encephalitis virus (TBEV) is a serious pathogen that poses a significant threat to humans, causing encephalitis that can result in lifelong sequelae. In this study, we focused on the complete proteomes of the five current TBEV subtypes to identify dominant epitopes. Immunoinformatics tools were employed to screen for LBL, HTL, and CTL epitopes. These epitopes were then linked using various linkers and combined with adjuvants and histidine tag. The vaccine underwent a series of physicochemical property analyses, including secondary structure prediction, three-dimensional structure prediction, molecular docking, molecular dynamics simulation, immune simulation, and in silico cloning. The results indicate that the vaccine is highly conserved, strongly immunogenic, stable, non-allergenic, and non-toxic. Molecular docking and molecular dynamics simulation demonstrate that the vaccine can form a stable binding complex with TLR3. Immune simulation analysis shows that the vaccine effectively stimulates both cellular and humoral immune responses, accompanied by an increase in cytokine titers. Furthermore, through codon optimization and in silico cloning, the vaccine can be stably and effectively expressed in the Escherichia coli system. As an effective candidate for TBEV vaccination, the multi-epitope vaccine developed in this study has promising application prospects and provides a new approach for the research, development, and improvement of vaccines targeting TBEV.

microbiology↗

bioRxiv2024

Circular RNA AEBP2: A novel regulator in dendritic cell development

Dendritic cells (DCs) are the main antigen-presenting cells and play a central role in activating adaptive immunity. How DCs is regulated during their development remains to be further elucidated. Circular RNA (circRNA) is a new type of non-coding RNA playing a critical in various cell events. However, little is known about the role of circRNA in DCs. Using in situ RNA hybridization, we found that DCs expressed exonic circular RNA AEBP2 (circAEBP2). circAEBP2 was upregulated in mature DCs compared to immature DCs. Knockdown of circAEBP2 in DCs using small interference RNA (siRNA) arrested DCs at an immature state with lower expression of MHC class II, CD40, CD80, and CD83. circAEBP2 knockdown in DCs also decreased the phosphorylation of nuclear factor-kappa B p65 (P-p65) but increased CD200R1. CircAEBP2-silenced DCs had a lower capacity to activate allogeneic CD4+ and CD8+ T cells in vitro and induced differentiation of regulatory T cells (Tregs). RNA immunoprecipitation assays revealed that circAEBP2 interacts with heterogeneous nuclear ribonucleoprotein F (hnRNP F) which is highly expressed in DCs and subsequently prevented hnRNP F nuclear translocation. In conclusion, we demonstrate that circAEBP2 regulates the development and function of DCs and knockdown of circAEBP2 induces tolerogenic DCs (Tol-DCs) through interacting with the hnRNP F/CD200R1/P-p65 signaling pathway. circAEBP2 is a new regulator in DCs and modulating it provides a novel strategy for inducing Tol-DCs.

immunology↗

bioRxiv2024

Irx3/5 define the cochlear sensory domain and regulate vestibular and cochlear sensory patterning in the mammalian inner ear

The mammalian inner ear houses the vestibular and cochlear sensory organs dedicated to sensing balance and sound, respectively. These distinct sensory organs arise from a common prosensory region, but the mechanisms underlying their divergence remain elusive. Here, we showed that two evolutionarily conserved homeobox genes, Irx3 and Irx5, are required for the patterning and segregation of the saccular and cochlear sensory domains, as well as for the formation of auditory sensory cells. Irx3/5 were highly expressed in the cochlea, their deletion resulted in a significantly shortened cochlea with a loss of the ductus reuniens that bridged the vestibule and cochlea. Remarkably, ectopic vestibular hair cells replaced the cochlear non-sensory structure, the Greater Epithelial Ridge. Moreover, most auditory sensory cells in the cochlea were transformed into hair cells of vestibular identity, with only a residual organ of Corti remaining in the mid-apical region of Irx3/5 double knockout mice. Conditional temporal knockouts further revealed that Irx3/5 are essential for controlling cochlear sensory domain formation before embryonic day 14. Our findings demonstrate that Irx3/5 regulate the patterning of vestibular and cochlear sensory cells, providing insights into the separation of vestibular and cochlear sensory organs during mammalian inner ear development.

developmental biology↗

bioRxiv2024

Comprehensive structure-function analysis reveals gain- and loss-of-function mechanisms impacting oncogenic KRAS activity

To dissect variant-function relationships in the KRAS oncoprotein, we performed deep mutational scanning (DMS) screens for both wild-type and KRASG12D mutant alleles. We defined the spectrum of oncogenic potential for nearly all possible KRAS variants, identifying several novel transforming alleles and elucidating a model to describe the frequency of KRAS mutations in human cancer as a function of transforming potential, mutational probability, and tissue-specific mutational signatures. Biochemical and structural analyses of variants identified in a KRASG12D second-site suppressor DMS screen revealed that attenuation of oncogenic KRAS can be mediated by protein instability and conformational rigidity, resulting in reduced binding affinity to effector proteins, such as RAF and PI3-kinases, or reduced SOS-mediated nucleotide exchange activity. These studies define the landscape of single amino acid alterations that modulate the function of KRAS, providing a resource for the clinical interpretation of KRAS variants and elucidating mechanisms of oncogenic KRAS inactivation for therapeutic exploitation.

cancer biology↗

bioRxiv2024

Hydroxy polyethylene glycol: a solution to evade human pre-existing anti-PEG antibodies for efficient delivery

Polyethylene glycol (PEG) has been extensively utilized in food, cosmetics, and pharmaceutical fields, especially in the realm of nanomedicines, where it serves as a pivotal excipient for extending the nanoparticles circulation half-life. Contrary to its historical perception as non-immunogenic, pre-existing anti-PEG antibodies have been widely detected in human who even have never been exposed to PEGylated therapeutics, which considered to be associated with serious side effects of PEGylated nanomedicines including infusion reactions and other hypersensitive reactions. Herein, we elucidated the prevalence and distribution characteristics of pre-existing anti-PEG antibodies in 2074 human blood samples, and investigated its binding with PEG. Pre-existing anti-PEG antibodies were found to primarily recognize the PEG terminus, especially methoxy, which is the only PEG terminus contained in currently marketed PEGylated nanomedicines. While hydroxy PEG (OH-PEG) significantly evaded binding with pre-existing anti-PEG antibodies among most clinical samples. Noteworthily, substituting OH-PEG for MeO-PEG significantly mitigated complement activation of lipid nanoparticle (LNP) caused by pre-existing anti-PEG antibodies, thereby markedly enhancing stability and reducing mRNA leakage in human serum. Additionally, LNP modified with OH-PEG exhibited reduced immunogenicity, which was crucial for repeated drug administrations. The present work elucidated the crucial role of OH-PEG in evading human pre-existing anti-PEG antibodies, and discovered that the current pre-clinical studies inadequately simulated the biological effects of clinical pre-existing anti-PEG antibodies on such formulations through interspecies study, which had a profound impact on clinical translation of PEGylated nanomedicines.

pharmacology and toxicology↗

bioRxiv2024

In silico generation of synthetic cancer genomes using generative AI

Cancer originates from alterations in the genome, and understanding how these changes lead to disease is crucial for achieving the goals of precision oncology. Connecting genomic alterations to health outcomes requires extensive computational analysis using accurate algorithms. Over the years, these algorithms have become increasingly sophisticated, but a severe shortage of open access gold-standard datasets presents a fundamental challenge. Since genomic data is considered personal health information, only an extremely limited number of deeply sequenced legacy cancer genomes can be shared and redistributed. As a result, tool benchmarking is often conducted on the same small set of genomes sequenced with older technologies and uncertain ground truths. This is a major obstacle to the development of improved analytic tools. To address this issue, we have developed OncoGAN, a novel generative AI tool that uses a combination of generative adversarial networks and tabular variational autoencoders to generate realistic but entirely synthetic cancer genomes based on training sets derived from large-scale genomic projects. Our results demonstrate that this approach accurately reproduces the scale, distribution, and characteristics of somatic point mutations, copy number alterations and structural variants across multiple common cancer types, while protecting donors privacy information. OncoGAN accurately recapitulates tumor type-specific mutational signatures as well as the positional distribution of somatic mutations. To evaluate the fidelity of the simulations, we tested the synthetic genomes using DeepTumour, a software capable of identifying tumor types based on mutational patterns, and demonstrated a high level of concordance between the synthetic genome tumor type and DeepTumours prediction of the type. We also showed that augmenting real donor data with OncoGAN-generated synthetic data could be used to train a more accurate version of DeepTumour. This tool will allow the generation of an extensive and realistic set of training and testing cancer genomes whose ground truth is known exactly. This advance provides computational biologists with the ability to develop realistic cancer genome benchmarking sets and make them available to the research community for the testing, development and enhancement of cancer genome analysis tools.

bioinformatics↗