Biology subjects

Bao, Y.

Publications and source records attributed to Bao, Y..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Sequencing-based Spatial Transcriptomics with scRNA-seq Sensitivity

The advent of spatial transcriptomics has dramatically expanded our ability to study the vast network of cell-cell interactions at the molecular level in tissue. Among current methods, sequencing-based approaches have great potential in discovering because of its unbiased capture. In the last couple of years, the spatial resolution for the capture addresses has been significantly improved from 100 m to <1 m, well below the size of a mammalian cell. However, the capture efficiency has always been a pain point, [~]one order of magnitude lower than that of single cell RNA sequencing (scRNA-seq). The low capture efficiency limits the depth and breadth of its applications in the study of complex biological systems and diseases. Here, we introduce Salus Spatial transcriptomic system (Salus-STS), which provides [~]1 m capture resolution and a capture efficiency [~]1 order of magnitude higher than other current methods. Analysis with sub-cellular resolution becomes practical for sequencing-based spatial transcriptomics.

molecular biology↗

bioRxiv2025

Benchmarking the coding strategies of non-coding mutations on sequence-based downstream tasks with machine learning

Non-coding single nucleotide polymorphisms (SNPs) are key modulators of gene regulation and have been implicated in diverse complex traits and diseases. With the growing demand for accurate functional interpretation of non-coding variants, the choice of encoding strategies becomes critical in downstream predictive modeling. Despite recent advances, a systematic evaluation of encoding approaches tailored for non-coding SNPs remains lacking. To address this gap, we present a comprehensive benchmark that evaluates six representative encoding strategies, including categorical, semantic, and functional embeddings, across three quantitative trait loci (QTL)-related prediction tasks. The study encompasses nine machine learning and deep learning models and incorporates experimental controls and repeated trials to ensure robustness and reproducibility. We assess each strategy along multiple dimensions, such as interpretability, representation abundance, and computational efficiency. Rather than ranking individual methods, our analysis emphasizes the interaction between encoding strategies, model types, and preprocessing protocols, and highlights their collective influence on predictive performance. This work establishes a standardized framework for evaluating non-coding SNP representations and offers actionable guidance for selecting and optimizing prediction pipelines in regulatory genomics.

bioinformatics↗

bioRxiv2024

Predicting the effect of non-coding mutations on single-cell DNA methylation using deep learning

Predicting the effects of non-coding mutations on DNA methylation is crucial for advancing our understanding of gene expression, epigenetic inheritance, and its role in disease mechanisms. Current methods lack the capability to predict the impact of non-coding mutations on DNA methylation at single-cell resolution and long range, while remain challenges in tracking SNP influences throughout disease progression. Here, we introduce Methven, a deep learning-based framework designed to predict the effects of non-coding mutations on DNA methylation at single-cell resolution, to overcome the challenges. Methven integrates DNA sequences and ATAC-seq data, employing a divide-and-conquer approach to handle varying scales of SNP-CpG interactions. By leveraging a pretrained DNA language model, Methven accurately predicts both the direction and magnitude of methylation changes across a 100kbp range with a lightweight architecture. The evaluation results demonstrate the superior performance of Methven in prioritizing functional non-coding mutation, model interpretability, and its potential for revealing personalized mutation-disease associations.

bioinformatics↗

bioRxiv2024

Urine Proteomics in Methamphetamine Addicted Patients

Drug addiction is a serious chronic relapsing brain disease, and methamphetamine dependence has a complex course and is difficult to treat, causing a serious public health burden. In this study, we compared and analyzed the urine proteome of methamphetamine-using patients, methamphetamine-withdrawing patients, healthy individuals. The urine proteome of methamphetamine-using patients and methamphetamine-withdrawing patients was significantly different from that of healthy individuals, and some differential proteins and their enriched biological functions showed that they were associated with addiction or neurotoxicity of methamphetamine. The fact, that the urine proteome of patients who withdrew methamphetamine for more than 3 months were significantly different from that of healthy patients, may reveal the reasons for the high rate of methamphetamine relapse and potential intervention targets. This study proved that urine proteome can reflect the effects of addiction on the body comprehensively, and has the potential to provide clues for addiction.

biochemistry↗

bioRxiv2024

Protective effect of P2Y receptors antagonism on stress-induced retinal degeneration

The death of retinal pigment epithelial (RPE) cells and photoreceptors (PR) is a hallmark of the progression of several degenerative ocular disorders. The precise molecular driver(s) behind RPE and PR cell death, however, remains unknown. Recent studies have suggested the involvement of ATP and purinergic signaling in the progression of age-related macular degeneration (AMD) and retinal degeneration. We have discovered that RPE cells release ATP when subjected to stress, which in turn exacerbates stress-related signaling via purinergic receptors that ultimately results in degeneration. Our findings demonstrate that blocking P2Y purinergic receptors using suramin can effectively prevent toxin-induced RPE cell death and dysfunction in vitro. Furthermore, we show efficacy of suramin in preventing photoreceptor degeneration in vivo using the RHO-P23H zebrafish model. This study reinforces the involvement of ATP and purinergic signaling in maintaining retinal health, and highlights the potential of purinergic receptor antagonism as a therapeutic strategy for retinal degeneration.

cell biology↗

bioRxiv2024

Cyclic di-GMP as an Antitoxin Regulates Bacterial Genome Stability and Antibiotic Persistence in Biofilms

Biofilms are complex bacterial communities characterized by a high persister prevalence, which contributes to chronic and relapsing infections. Historically, persister formation in biofilms has been linked to constraints imposed by their dense structures. However, we observed an elevated persister frequency accompanying the stage of cell adhesion, marking the onset of biofilm development. Subsequent mechanistic studies uncovered a comparable type of toxin-antitoxin (TA) module (TA-like system) triggered by cell adhesion, which is responsible for this elevation. In this module, the toxin HipH acts as a genotoxic deoxyribonuclease, inducing DNA double strand breaks and genome instability. While the second messenger c-di-GMP functions as the antitoxin, exerting control over HipH expression and activity. The dynamic interplay between c-di-GMP and HipH levels emerges as a crucial determinant governing genome stability and persister generation within biofilms. These findings unveil a unique TA system, where small molecules act as the antitoxin, outlining a biofilm-specific molecular mechanism influencing genome stability and antibiotic persistence, with potential implications for treating biofilm infections.

microbiology↗

bioRxiv2024

CDK12 Loss Promotes Prostate Cancer Development While Exposing Vulnerabilities to Paralog-Based Synthetic Lethality

Biallelic loss of cyclin-dependent kinase 12 (CDK12) defines a unique molecular subtype of metastatic castration-resistant prostate cancer (mCRPC). It remains unclear, however, whether CDK12 loss per se is sufficient to drive prostate cancer development--either alone, or in the context of other genetic alterations--and whether CDK12-mutant tumors exhibit sensitivity to specific pharmacotherapies. Here, we demonstrate that tissue-specific Cdk12 ablation is sufficient to induce preneoplastic lesions and robust T cell infiltration in the mouse prostate. Allograft-based CRISPR screening demonstrated that Cdk12 loss is positively associated with Trp53 inactivation but negatively associated with Pten inactivation--akin to what is observed in human mCRPC. Consistent with this, ablation of Cdk12 in prostate organoids with concurrent Trp53 loss promotes their proliferation and ability to form tumors in mice, while Cdk12 knockout in the Pten-null prostate cancer mouse model abrogates tumor growth. Bigenic Cdk12 and Trp53 loss allografts represent a new syngeneic model for the study of androgen receptor (AR)-positive, luminal prostate cancer. Notably, Cdk12/Trp53 loss prostate tumors are sensitive to immune checkpoint blockade. Cdk12-null organoids (either with or without Trp53 co-ablation) and patient-derived xenografts from tumors with CDK12 inactivation are highly sensitive to inhibition or degradation of its paralog kinase, CDK13. Together, these data identify CDK12 as a bona fide tumor suppressor gene with impact on tumor progression and lends support to paralog-based synthetic lethality as a promising strategy for treating CDK12-mutant mCRPC.

cancer biology↗

bioRxiv2024

Targeting PIKfyve-driven lipid homeostasis as a metabolic vulnerability in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) subsists in a nutrient-deregulated microenvironment, making it particularly susceptible to treatments that interfere with cancer metabolism1 2. For example, PDAC utilizes and is dependent on high levels of autophagy and other lysosomal processes3-5. Although targeting these pathways has shown potential in preclinical studies, progress has been hampered by the challenge of identifying and characterizing favorable targets for drug development6. Here, we characterize PIKfyve, a lipid kinase integral to lysosomal functioning7, as a novel and targetable vulnerability in PDAC. In human patient and murine PDAC samples, we discovered that PIKFYVE is overexpressed in PDAC cells compared to adjacent normal cells. Employing a genetically engineered mouse model, we established the essential role of PIKfyve in PDAC progression. Further, through comprehensive metabolic analyses, we found that PIKfyve inhibition obligated PDAC to upregulate de novo lipid synthesis, a relationship previously undescribed. PIKfyve inhibition triggered a distinct lipogenic gene expression and metabolic program, creating a dependency on de novo lipid metabolism pathways, by upregulating genes such as FASN and ACACA. In PDAC, the KRAS-MAPK signaling pathway is a primary driver of de novo lipid synthesis, specifically enhancing FASN and ACACA levels. Accordingly, the simultaneous targeting of PIKfyve and KRAS-MAPK resulted in the elimination of tumor burden in a syngeneic orthotopic model and tumor regression in a xenograft model of PDAC. Taken together, these studies suggest that disrupting lipid metabolism through PIKfyve inhibition induces synthetic lethality in conjunction with KRAS-MAPK-directed therapies for PDAC.

cancer biology↗

bioRxiv2024

Double-stranded RNA induces retinal pigment epithelium cell degeneration and inflammation

RIG-I signaling has been previously implicated as a driver of inflammation to the retinal pigment epithelium (RPE) during age-related macular degeneration (AMD). Double-stranded RNA (dsRNA) is known to initiate RIG-I signaling and lead to a type I interferon response. We show through shRNA knockdown that RIG-I is essential for initiating an interferon response in iPS-RPE in response to both synthetic dsRNA-mimetic 3p-hpRNA and the double-stranded retrotransposable element Alu. Analysis of human tissue from patients suffering from AMD show accumulation of dsRNA, peaking at the geographic atrophy (GA) stage. Using a new murine model of 3p-hpRNA subretinal challenge to RPE cells, we confirmed that accumulation of dsRNA initiates a type I interferon response, as well as RPE and photoreceptor degeneration. Although RPE response to synthetic dsRNA was acute, extensive leukocyte migration was observed. The results from this study verify the importance of RIG-I signaling in regulating inflammation in the subretinal space and implicates dsRNA accumulation as a driver of AMD pathogenesis.

cell biology↗

bioRxiv2024

PIKfyve controls dendritic cell function and tumor immunity

The modern armamentarium for cancer treatment includes immunotherapy and targeted therapy, such as protein kinase inhibitors. However, the mechanisms that allow cancer-targeting drugs to effectively mobilize dendritic cells (DCs) and affect immunotherapy are poorly understood. Here, we report that among shared gene targets of clinically relevant protein kinase inhibitors, high PIKFYVE expression was least predictive of complete response in patients who received immune checkpoint blockade (ICB). In immune cells, high PIKFYVE expression in DCs was associated with worse response to ICB. Genetic and pharmacological studies demonstrated that PIKfyve ablation enhanced DC function via selectively altering the alternate/non-canonical NF-{kappa}B pathway. Both loss of Pikfyve in DCs and treatment with apilimod, a potent and specific PIKfyve inhibitor, restrained tumor growth, enhanced DC-dependent T cell immunity, and potentiated ICB efficacy in tumor-bearing mouse models. Furthermore, the combination of a vaccine adjuvant and apilimod reduced tumor progression in vivo. Thus, PIKfyve negatively controls DCs, and PIKfyve inhibition has promise for cancer immunotherapy and vaccine treatment strategies.

cancer biology↗

bioRxiv2024

Genomic insights into photosymbiotic evolution in Tridacna squamosa

Photosymbiosis is fundamental driving force for ecological success of benthic coral reef ecosystems, and contributing to their biodiversity and resilience. As a benchmark organism indicative of reef health, the fluted giant clam (Tridacna squamosa) forms an exemplary photosymbiotic relationship with the symbiont Symbiodiniaceae dinoflagellates, whose initiation and maturation require finely coordinated interactions. However, much of the origin and dynamics of this reciprocal interplay remains unclarified. Here, we report the first complete whole genome of T. squamosa, in conjunction with integrated multi-omics data, to illuminate the key evolutionary innovations and molecular events supporting the establishment and maintenance of photosymbiotic lifestyle in the giant clam. Programmed regulation of symbiont recognition, host immune system and GPCRs signaling activation co-contributed to dinoflagellates acquisition in T. squamosa larvae. Adaptive metabolic remodeling in the host siphonal mantle, a photosymbiotic niche, is critical to maintain the robustness of phtosymbiosis. T. squamosa has expanded light sensing gene family and evolved sophisticated signaling pathways to protect against UV photo-damage. Evidence also supports significant contribution of positive selection to host DNA-repair. Overall, our study here offers fresh mechanistic insights into the parallel evolution and molecular machinery of photosymbiosis in the giant clam-dinoflagellates duet, with implications for devising solutions to sustainable conservation.

evolutionary biology↗

bioRxiv2024

Associations between common genetic variants and income provide insights about the socioeconomic health gradient

We conducted a genome-wide association study (GWAS) on income among individuals of European descent and leveraged the results to investigate the socio-economic health gradient (N=668,288). We found 162 genomic loci associated with a common genetic factor underlying various income measures, all with small effect sizes. Our GWAS-derived polygenic index captures 1 - 4% of income variance, with only one-fourth attributed to direct genetic effects. A phenome-wide association study using this polygenic index showed reduced risks for a broad spectrum of diseases, including hypertension, obesity, type 2 diabetes, coronary atherosclerosis, depression, asthma, and back pain. The income factor showed a substantial genetic correlation (0.92, s.e. = .006) with educational attainment (EA). Accounting for EAs genetic overlap with income revealed that the remaining genetic signal for higher income related to better mental health but reduced physical health benefits and increased participation in risky behaviours such as drinking and smoking.

genetics↗

bioRxiv2024

Deep Learning Analysis on Images of iPSC-derived Motor Neurons Carrying fALS-genetics Reveals Disease-Relevant Phenotypes

Amyotrophic lateral sclerosis (ALS) is a devastating condition with very limited treatment options. It is a heterogeneous disease with complex genetics and unclear etiology, making the discovery of disease-modifying interventions very challenging. To discover novel mechanisms underlying ALS, we leverage a unique platform that combines isogenic, induced pluripotent stem cell (iPSC)-derived models of disease-causing mutations with rich phenotyping via high-content imaging and deep learning models. We introduced eight mutations that cause familial ALS (fALS) into multiple donor iPSC lines, and differentiated them into motor neurons to create multiple isogenic pairs of healthy (wild-type) and sick (mutant) motor neurons. We collected extensive high-content imaging data and used machine learning (ML) to process the images, segment the cells, and learn phenotypes. Self-supervised ML was used to create a concise embedding that captured significant, ALS-relevant biological information in these images. We demonstrate that ML models trained on core cell morphology alone can accurately predict TDP-43 mislocalization, a known phenotypic feature related to ALS. In addition, we were able to impute RNA expression from these image embeddings, in a way that elucidates molecular differences between mutants and wild-type cells. Finally, predictors leveraging these embeddings are able to distinguish between mutant and wild-type both within and across donors, defining cellular, ML-derived disease models for diverse fALS mutations. These disease models are the foundation for a novel screening approach to discover disease-modifying targets for familial ALS.

neuroscience↗

bioRxiv2023

EryDB: a transcriptomic profile database for erythropoiesis and erythroid-related diseases

Erythropoiesis is a finely regulated and complex process that involves multiple transformations from hematopoietic stem cells to mature red blood cells at hematopoietic sites from the embryonic to the adult stages. Investigations into its molecular mechanisms have generated a wealth of expression data, including bulk and single-cell RNA sequencing data. A comprehensively integrated and well-curated erythropoiesis-specific database will greatly facilitate the mining of gene expression data and enable large-scale research of erythropoiesis and erythroid related diseases. Here, we present EryDB, an open-access and comprehensive database dedicated to the collection, integration, analysis, and visualization of transcriptomic data for erythropoiesis and erythroid-related diseases. Currently, the database includes expertly curated quality-assured data of 3,803 samples and 1,187,119 single cells derived from 107 public studies of three species (Homo sapiens, Mus musculus, and Danio rerio), nine tissue types, and five diseases. EryDB provides users with the ability to not only browse the molecular features of erythropoiesis between tissues and species, but also perform computational analyses of single-cell and bulk RNA sequencing data, thus serving as a convenient platform for customized queries and analyses. EryDB v1.0 is freely accessible at https://ngdc.cncb.ac.cn/EryDB/home.

bioinformatics↗

bioRxiv2023

Heme biosynthesis regulates BCAA catabolism and thermogenesis in brown adipose tissue

With age, people tend to accumulate body fat and reduce energy expenditure1. Brown (BAT) and beige adipose tissue dissipate heat and increase energy expenditure via the activity of the uncoupling protein UCP1 and other thermogenic futile cycles2,3. The activity of brown and beige depots inversely correlates with BMI and age4-11, suggesting that promoting thermogenesis may be an effective approach for combating age-related metabolic disease12-15. Heme is an enzyme cofactor and signaling molecule that we recently showed to regulate BAT function16. Here, we show that heme biosynthesis is the primary contributor to intracellular heme levels in brown adipocytes. Inhibition of heme biosynthesis leads to mitochondrial dysfunction and reduction in UCP1. Although supplementing heme can restore mitochondrial function in heme-synthesis-deficient cells, the downregulation of UCP1 persists due to the accumulation of the heme precursors, particularly propionyl-CoA, which is a product of branched-chain amino acids (BCAA) catabolism. Cold exposure promotes BCAA uptake in BAT, and defects in BCAA catabolism in this tissue hinder thermogenesis17. However, BCAAs contribution to the TCA cycle in BAT and WAT never exceeds 2% of total TCA flux18. Our work offers a way to integrate current literature by describing heme biosynthesis as an important metabolic sink for BCAAs.

cell biology↗

bioRxiv2023

EMO: Predicting Non-coding Mutation-induced Up- and Down-regulation of Risk Gene Expression using Deep Learning

The challenge of understanding how alterations in non-coding DNA regulate gene expression is substantial, with far-reaching consequences for the advancement of human genetics and disease research. Accurately predicting the up- and down-regulation of gene expression quantitative trait loci (eQTLs) offers a potential avenue to accelerate the identification of associations between non-coding variants and phenotypic traits. However, current methods for predicting the impact of non-coding mutations on gene expression changes fail to predict the sign of eQTLs accurately. Additionally, the requirement for tissue-specific training models within these methods restricts their applicability, especially when extending predictive abilities to single-cell resolution. In this study, we present EMO, an innovative transformer-based pre-trained method, designed to predict the up- and down-regulation of gene expression caused by single non-coding mutations using DNA sequences and ATAC-seq data. EMO extends the effective prediction range up to 1Mbp between the non-coding mutation and the transcription start site (TSS) of the target gene. It demonstrates competitive prediction performance across various variant TSS distances and surpasses the state-of-the-art structure. To assess its effectiveness, EMO was fine-tuned using eQTLs from two brain tissues for external validation. We also evaluated EMOs transferability to single-cell resolution by fine-tuning it on eQTLs from six types of immune cells, achieving satisfactory results in each cell type (AUC > 0.860). Furthermore, EMO displayed promising potential in analyzing disease-associated eQTLs.

bioinformatics↗

bioRxiv2023

RCoV19: A One-stop Hub for SARS-CoV-2 Genome Data Integration, Variants Monitoring, and Risk Pre-warning

The Resource for Coronavirus 2019 (RCoV19, https://ngdc.cncb.ac.cn/ncov/) is an open-access information resource dedicated to providing valuable data on the genomes, mutations, and variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this updated implementation of RCoV19, we have made significant improvements and advancements over the previous version. Firstly, we have implemented a highly refined genome data curation model. This model now features an automated integration pipeline and optimized curation rules, enabling efficient daily updates of data in RCoV19. Secondly, we have developed a global and regional lineage evolution monitoring platform, alongside an outbreak risk pre-warning system. These additions provide a comprehensive understanding of SARS-CoV-2 evolution and transmission patterns, enabling better preparedness and response strategies. Thirdly, we have developed a powerful interactive mutation spectrum comparison module. This module allows users to compare and analyze mutation patterns, assisting in the detection of potential new lineages. Furthermore, we have incorporated a comprehensive knowledgebase on mutation effects. This knowledgebase serves as a valuable resource for retrieving information on the functional implications of specific mutations. In summary, RCoV19 serves as a vital scientific resource, providing access to valuable data, relevant information, and technical support in the global fight against COVID-19.

bioinformatics↗

bioRxiv2023

Multi-omics analysis reveals critical cis-regulatory roles of transposable elements in livestock genomes

As a major source of genetic and regulatory variation in their host genome, transposable elements (TEs) have gained a growing interest in research on humans and model organisms. In this species, integrative analysis of multi-omics data has shed light on the regulatory functions of TEs. However, there remains a notable gap in our understanding of TEs in domesticated animals. we annotated TEs in the genomes of pigs, cattle, and chickens, respectively, and systematically compared the genome distributions and amplification patterns of TEs across these three species. Furthermore, by integrating multi-tissue RNA-seq, ATAC-seq, and histone modification ChIP-seq data, we explored the expression atlas of TEs and their contribution to cis-regulatory elements (CREs) in different tissues of the three species. Most importantly, we developed a novel computational framework that revealed TE-mediated gene regulatory networks (TE-GRNs) underlying tissue-related biological processes. To demonstrate the power of this approach, we applied our framework to analyze liver tissues across the three different species. Overall, our research provides novel insights into the regulatory functions of TEs in livestock animals and highlights a computational framework to uncover TE-GRNs in various biological contexts.

genomics↗