Biology subjects

Xia, Y.

Publications and source records attributed to Xia, Y..

At least 109 records · Page 6Linked to original sources

bioRxiv2024

MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants

SummaryMassively parallel reporter assay (MPRA) is an important technology to evaluate the impact of genetic variants on gene regulation. Here, we present MPRAVarDB, an online database and web server, for exploring regulatory effects of genetic variants. MPRAVarDB harbors 18 MPRA experiments designed to assess the regulatory effects of genetic variants associated with GWAS loci, eQTLs and various genomic features, resulting in a total of 242,818 variants tested across more than 30 cell lines and 30 human diseases or traits. MPRAVarDB empowers the query of MPRA variants by genomic region, disease and cell line or by any combination of these query terms. Notably, MPRAVarDB offers a suite of pretrained machine learning models tailored to the specific disease and cell line, facilitating the genome-wide prediction of regulatory variants. MPRAVarDB is friendly to use, and users only need a few clicks to receive query and prediction results. Availabilityhttps://mpravardb.rc.ufl.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2024

Hepatitis E virus infection upregulates ING5 expression in vitro and vivo

Hepatitis E virus (HEV) is the major pathogen of viral hepatitis. Immunocompromised individual infected by HEV is prone to chronic hepatitis and increases the risk of hepato-cellular carcinoma (HCC). Inhibitor of growth family member 5 (ING5) is a tumor suppressor gene, is low expressed in cancer tumor or cells. However, the underlying relationship between ING5 and HEV infection is unclear. In the present study acute and chronic HEV animal models were employed to explore the interaction between ING5 and HEV. Notably, the expression of ING5 was significantly increased both in the liver of acute HEV infected BALB/c mice and chronic HEV infected rhesus macaques. In addition, the relationship between HEV infection and ING5 expression was further identified in human hepatoma (HepG-2) cells. In conclusion, HEV infection strongly upregulated ING5 expression in vivo and in vitro, bearing significant implications in further understanding the pathogenic mechanism of HEV infection.

microbiology↗

bioRxiv2024

Crosstalk of the MAP3K1 and EGFR pathways mediates gene-environment interactions that disrupt developmental tissue closure

Aberrant signal transduction pathways can adversely derail developmental processes. One such process is embryonic eyelid closure that requires MAP3K1. Map3k1 knockout mice have defective eyelid closure and an autosomal recessive eye-open at birth phenotype. In utero exposure to dioxin, a persistent environmental toxicant, causes the same eye defect in Map3k1+/- hemizygous but not wild type pups. Here we explore the mechanisms of Map3k1 (gene) and dioxin (environment) interactions (GxE) in the tissue closure defect. We show that, acting through the AHR, dioxin activates EGFR signaling, which in turn depresses MAP3K1-dependent JNK activity. This effect of dioxin is exacerbated by Map3k1 heterozygosity. Therefore, dioxin exposed Map3k1+/- embryonic eyelids have a marked reduction of JNK activity, accelerated differentiation and impeded polarization in the epithelial cells. Knocking out Ahr or Egfr in eyelid epithelium attenuates the open-eye defects in dioxin-treated Map3k1+/- pups, whereas knockout of Jnk1 and S1pr, encoding the S1P receptors upstream of the MAP3K1-JNK pathway, potentiates dioxin toxicity. Our novel findings suggest that dioxin and genes of the AHR, EGFR and S1P-MAP3K1-JNK pathways constitute a multifactorial mechanism underlying tissue closure abnormalities. Summary statementThe crosstalk between a global environmental pollutant and the pre-existing genetic conditions is mediated through interactive signaling pathways, resulting in anatomical tissue closure abnormalities in development.

developmental biology↗

bioRxiv2024

Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics

Self-supervised learning (SSL) has emerged as a powerful method for extracting meaningful representations from vast, unlabeled datasets, already transforming computer vision and natural language processing. Similarly, in single-cell genomics (SCG), representation learning is well-recognized for offering insights into complex biological data, even more so by the advent of early foundation model approaches. However, despite these advancements, identifying scenarios in SCG where SSL outperforms traditional supervised or unsupervised learning methods remains a nuanced challenge. Furthermore, selecting the most effective pretext tasks within the SSL framework for SCG is a critical yet unresolved question. Here, we address this gap by adapting and benchmarking SSL techniques in SCG, including masked autoencoders with multiple masking strategies and contrastive learning approaches. Trained on over 20 million cells, this study rigorously examines multiple downstream tasks, including cell type prediction, gene expression reconstruction, cross-modality prediction, and data integration. Our empirical analyses underscore the nuanced role of SSL, namely in transfer learning scenarios leveraging auxiliary data or analyzing novel datasets. Masked autoencoders excel over contrastive methods in SCG, diverging from computer vision trends. Moreover, our findings reveal notable capabilities of SSL in zero-shot cell type prediction and offer insights into its potential benefits in cross-modality prediction and data integration. In summary, we study the application of SSL in SCG, minimizing model bias through simple, fully connected networks, and benchmark SSLs utility across key representation learning scenarios.

bioinformatics↗

bioRxiv2024

Chromosome segment scanning for gain- or loss-of-function screening (CHASING) and its application in metabolic engineering

Constructing a library of thousands of single-gene knockout or interference strains is a powerful tool to understand the relation between genotype and phenotype, but it is labor and cost intensive. Powered by the computer-aided gene annotation and functional grouping of non-essential genes, we showed that targeting a single gene directly to a specific observed phenotype could be quickly achieved for a specific microorganism via constructing a library of strains containing single chromosome-segment-deletion per strain. As a proof-of-concept, a genome-scale library consisting of 70 chromosome-segment-deletion strains for B. subtilis was constructed by CRISPR-based methods and strains with six loss-of- and gain-of-function phenotypes were screened out. To facilitate the rapid genotyping, we developed a web tool to visualize the potential targets of each chromosome segment associated with a particular function, successfully identifying the genes for valuable representative phenotypes. To apply the library to metabolic engineering, the hosts with improved production capacity of acetoin and lycopene were screened in the presence of pathway genes. This work demonstrated the significance of our strategy of chromosome segment scanning for gain- or loss-of-function screening (CHASING) on functional genomics investigation, robust chassis engineering, and chemical overproduction. O_FIG O_LINKSMALLFIG WIDTH=159 HEIGHT=200 SRC="FIGDIR/small/578163v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@19b4938org.highwire.dtl.DTLVardef@1e41d2eorg.highwire.dtl.DTLVardef@137d4b3org.highwire.dtl.DTLVardef@6d4bfc_HPS_FORMAT_FIGEXP M_FIG TOC C_FIG

bioengineering↗

bioRxiv2024

Harnessing the regenerative potential of interleukin11 to enhance heart repair

Balancing between regenerative processes and fibrosis is crucial for heart repair, yet strategies regulating this balance remain a barrier to developing therapies. While Interleukin11 (IL11) is known as a fibrotic factor, its contribution to heart regeneration is poorly understood. We uncovered that il11a, an Il11 homolog in zebrafish, can trigger robust regenerative programs in zebrafish hearts, including cardiomyocytes proliferation and coronary expansion, even in the absence of injury. However, prolonged il11a induction in uninjured hearts causes persistent fibroblast emergence, resulting in fibrosis. While deciphering the regenerative and fibrotic effects of il11a, we found that il11-dependent fibrosis, but not regeneration, is mediated through ERK activity, suggesting to potentially uncouple il11a dual effects on regeneration and fibrosis. To harness the il11as regenerative ability, we devised a combinatorial treatment through il11a induction with ERK inhibition. This approach enhances cardiomyocyte proliferation with mitigated fibrosis, achieving a balance between regenerative processes and fibrosis. Thus, we unveil the mechanistic insights into regenerative il11 roles, offering therapeutic avenues to foster cardiac repair without exacerbating fibrosis.

developmental biology↗

bioRxiv2024

Target-aware Molecule Generation for Drug Design Using a Chemical Language Model

Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 7 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC50) of 1.9 M. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field.

biochemistry↗

bioRxiv2023

Species-specific design of artificial promoters by transfer-learning based generative deep-learning model

Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed PromoGen, a collection of nucleotide language models to generate species-specific functional promoters, across dozens of species in a data and parameter efficient way. Twenty-seven species-specific models in this collection were finetuned from the pretrained model which was trained on multi-species promoters. When systematically compared with native promoters, the Escherichia coli- and Bacillus subtilis-specific artificial PromoGen-generated promoters (PGPs) were demonstrated to hold all distribution patterns of native promoters. A regression model was developed to score generated either by PromoGen or by another competitive neural network, and the overall score of PGPs is higher. Encouraged by in silico analysis, we further experimentally characterized twenty-two B. subtilis PGPs, results showed that four of tested PGPs reached the strong promoter level while all were active. Furthermore, we developed a user-friendly website to generate species-specific promoters for 27 different species by PromoGen. This work presented an efficient deep-learning strategy for de novo species-specific promoter generation even with limited datasets, providing valuable promoter toolboxes especially for the metabolic engineering of understudied microorganisms.

synthetic biology↗

bioRxiv2023

Predicting protein folding pathway using a novel folding force field model derived from known protein universe

The protein folding problem has emerged as a new challenge with the significant advances in deep learning driven protein structure prediction methods. While the structures of almost all known proteins have been successfully predicted, the process by which they fold remains an enigma. Understanding the intricate folding mechanism is of paramount importance, as it directly impacts the stable expression and biological function of proteins. Here, we propose FoldPAthreader, a protein folding pathway prediction method that designs a novel folding force field model by exploring the intrinsic relationship between protein evolutionary history and folding mechanisms from the known protein universe. Further, the folding force field is used to guide Monte Carlo conformational sampling, driving the protein chain fold into its native state by exploring a series of transition states and potential intermediates. On the 30 targets we collected, FoldPAthreader can successfully predict 70% of the proteins whose folding pathway is consistent with wet-lab experimental data. The results show that the folding force field can capture key dynamic features of hydrogen bonding and hydrophobic interactions. Importantly, for the widely studied BPTI and TIM proteins, the folding pathway predicted by FoldPAthreader have the same microscopic dynamic properties as those simulated by molecular dynamics. Significance StatementProtein folding is the process by which a protein acquires its functional conformations by gradually transforming from random coils into a specific three-dimensional structure. In the post-Alphafold2 era, functional analysis of protein macromolecules should not only rely on the final state structure, but should pay more attention to the structural folding process, that is, the various intermediate states formed during the folding process. At present, there is no folding force field specifically used for protein folding pathway prediction in computational biology. Here we extracted folding information from 100-million-level structure database and designed a new folding force field for folding pathway prediction, proving a hypothesis that the protein evolutionary history implicitly contains folding information of individual protein. This study may provide new insights into the understanding of protein folding mechanisms, which is expected to advance drug discovery.

bioinformatics↗

bioRxiv2023

Dual long-axis reorganization of hippocampus in youth

The reorganization of human hippocampus, especially its interaction with cortex, remains largely undefined in youth. The organization of a single hippocampal long-axis has been predominantly characterized as monotonic1-6, despite recent indications of nonmonotonic features in neuron density7 and geometric eigenmodes8. While the human cortical hierarchy has been well recognized for significant developmental and evolutionary advantages9-12, hippocampus has been typically considered an evolutionarily conserved brain structure1,13,14, and overlooked regarding its integrative role of cortical hierarchical processing during development. Here, we corroborated the presence and significance of a dual long-axis representation of the hippocampal connectome and geometry including both linear and quadratic gradients along its long-axis in youth. This finding was robust across two independent large-scale developmental cohorts. Charting development of the dual long-axis gradients underscored their specific contributions to the cortical hierarchy maturation from the frontoparietal and salience/ventral attention networks. The observed developmental variability in spontaneous brain activities in youth parallels the gradients of myelin content. During childhood through adolescence to early adulthood, the hippocampus reorganized the dual long-axis by gradually relaxing its geometric constraints on the intrinsic network organization of cortical spontaneous activity for refined executive functions. Molecular processes underlying such reorganization of the dual long-axis in hippocampus are linked to neural growth, stress hormone regulation, and neuroactive signaling. Our findings enrich the understanding of hippocampal-cortical reorganizational principles across structural, functional, and molecular dimensions as well as its maturation, and define the plasticity distribution within the human hippocampus at systems level, holding potentials to enhance and translate neurodevelopment and neuropsychiatric healthcare.

neuroscience↗

bioRxiv2023

Pan-sphingolipid profiling reveals differential functions of sphingolipid biosynthesis isozymes of C. elegans

Multiple isozymes are encoded in the C. elegans genome for the various sphingolipid biosynthesis reactions, but the contributions of individual isozymes are characterized only in part. We developed a simple but effective reversed-phase liquid chromatography-tandem mass spectrometry (RPLC-MS/MS) method that enables simultaneous identification and quantification of ceramides (Cer), glucosylceramides (GlcCer), and sphingomyelins (SM), three important classes of sphingolipids from the same MS run. Validating this pan-sphingolipid profiling method, we show that nearly all 47 quantifiable sphingolipid species found in young adult worms were reduced upon RNA interference (RNAi) of sptl-1 or elo-5, which are required for synthesis of the id17:1 sphingoid base. We also confirm that HYL-1 and HYL-2, but not LAGR-1, constitute the major ceramide synthase activity with different preference for fatty acid substrates, and that CGT-3 plays a greater role than CGT-1 does in producing glucosylceramides. Intriguingly, lagr-1 RNAi lowers the abundance of all sphingomyelin species and that of several glucosylceramide species, which suggests that LAGR-1 may have functions beyond what is predicted. Additionally, RNAi of sms-1, -2, and -3 all lower the abundance of sphingomyelins with an odd number of carbon atoms (mostly C21 and C23, with or without hydroxylation) in the N-acyl chain, and only sms-1 RNAi does not elevate sphingomyelins containing even-numbered N-acyl chains. This suggests that sphingolipids containing even-numbered N-acyl chains could be regulated separately, sometimes in opposite directions, with those containing odd-numbered N-acyls, presumably monomethyl branched chain fatty acyls. We also find that ceramide levels are kept in balance with those of glucosylceramides and sphingomyelins.

genetics↗

bioRxiv2023

Metagenomic Analysis Reveals the Transfer of Antibiotic Resistance from Manure-Fertilized Soil to Carrot Microbiome

Crops grown in manure-fertilized soil are more likely to be contaminated with antibiotic resistance genes (ARGs) than when grown in unmanured soil, and therefore more likely to represent a route of ARG exposure to consumers. Existing studies scarcely focused on the residual ARGs on the carrot peel growing in the manured soil after the careful washing. In the present study, residual microbiome and resistome on the carrots surface (Daucus carota subsp. sativus) at harvest after careful washing was investigated to reveal the impact of manure fertilization. Residual surface bacteria were recovered from the peel, and total bacterial DNA was extracted for the high-throughput sequencing-based metagenomic analysis. Despite the overall unaltered level of alpha diversity in both soil and carrot peel samples, manuring increased the resistome in soil significantly, but not on the carrot peel. The largely overlapped resistome detected on carrots grown with and without manuring, plus the pattern revealed by source tracking analysis indicated a soil-source of ARGs on carrots, whereas the beta-lactamases CTX-M-84, OXY-4, and CTX-M-122 only detected in the manured soil and on the carrot peel harvested from manured soil indicated that beta-lactamases appear to be transferred from manure to the carrot. The evident impact of soil resistome and community on carrot peel microbiome, plus the limited level of plasmid and integron mediated ARGs transfer suggested the main ARGs transfer pathway from manured soil to carrot peel was via the colonization of rhizosphere soil microbes. To further elucidate the ARG propagation within the soil-carrot system, a network was constructed to explore the pattern of nine types of concurrent ARGs genotypes carried by ten different host populations, emphasizing that the residual antibiotic resistances transfer via raw carrot represents a risk to human health even after extensive washing.

microbiology↗

bioRxiv2023

Reconciling ASPP-p53 Binding Mode Discrepancies through an Ensemble Binding Framework that Bridges Crystallography and NMR Data

ASPP2 and iASPP bind to p53 through their conserved ANK-SH3 domains to respectively promote and inhibit p53-dependent cell apoptosis. While crystallography has indicated that these two proteins employ distinct surfaces of their ANK-SH3 domains to bind to p53, solution NMR data has suggested similar surfaces. In this study, we employed multi-scale molecular dynamics (MD) combined with free energy calculations to reconcile the discrepancy in the binding modes. We demonstrated that the binding mode based solely on a single crystal structure does not enable iASPPs RT loop to engage with p53s C-terminal linker--a verified interaction. Instead, an ensemble of simulated iASPP-p53 complexes facilitates this interaction. We showed that the ensemble-average inter-protein contacting residues and NMR-detected interfacial residues align well with ASPP proteins, and the ensemble-average binding free energies better match experimental Kd values compared to single crystallgarphy-determined binding mode. For iASPP, the sampled ensemble complexes can be grouped into two classes, resembling the binding modes determined by crystallography and solution NMR. We thus propose that crystal packing shifts the equilibrium of binding modes towards the crystallographydetermined one. Lastly, we show that the ensemble binding complexes are sensitive to p53s intrinsically disordered regions (IDRs), attesting to experimental observations that these IDRs contribute to biological functions. Our results provide a dynamic and ensemble perspective for scrutinizing these important cancer-related protein-protein interactions (PPIs).

biophysics↗

bioRxiv2023

Exploring the metabolic profiling of A. baumannii for antimicrobial development using genome-scale modeling

With the emergence of multidrug-resistant bacteria, the World Health Organization published a catalog of microorganisms urgently needing new antibiotics, with the carbapenem-resistant Acinetobacter baumannii designated as "critical". Such isolates, frequently detected in healthcare settings, pose a global pandemic threat. One way to facilitate a systemic view of bacterial metabolism and allow the development of new therapeutics is to apply constraint-based modelling. Here, we developed a versatile workflow to build high-quality and simulation-ready genome-scale metabolic models. We applied our workflow to create a novel metabolic model for A. baumannii and validated its predictive capabilities using experimental nutrient utilization and gene essentiality data. Our analysis showed that our model i ACB23LX could recapitulate cellular metabolic phenotypes observed during in vitro experiments, while positive biomass production rates were observed and experimentally validated in various growth media. We further defined a minimal set of compounds that increase A. baumannii s cellular biomass and identified putative essential genes with no human counterparts, offering novel candidates for future antimicrobial development. Finally, we assembled and curated the first collection of reconstructions for distinct A. baumannii strains and analysed their growth characteristics. The presented models are in a standardised and well-curated format, enhancing their usability for multi-strain network reconstruction.

systems biology↗

bioRxiv2023

Simplicity: web-based visualization and analysis of high-throughput cancer cell line screens

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatic skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high-throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicitys outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.

pharmacology and toxicology↗

bioRxiv2023

Development of Sensorimotor-Visual Connectome Gradient at Birth Predicts Cognitive Outcomes at 2 Years of Age

Functional connectome gradients represent fundamental brain organizing principles. Here, we investigate the maturation of the macroscopic connectome gradients in a task-free functional MRI dataset consisting of 39 preterm and term babies aged 31-42 postmenstrual weeks and its connection with postnatal cognitive growth. We show that the principal sensorimotor-to-visual gradient is present in baby connectomes around 35.6 postmenstrual weeks and continuously develops towards a more term-like pattern. The gradients global topographical structure undergoes progressive maturation with age, characterized by increases in explanation ratio, gradient range and gradient variation. The development of focal gradients is mostly localized in primary regions serving sensory, motor and visual functions. Machine learning approaches revealed that the connectome gradient at birth significantly predicts individual cognitive outcomes at 2-y follow-up. These findings highlight the early emerging rules of functional connectome gradients and have implications for understanding how the connectome gradients at birth underlies cognitive growth in later life. HighlightsO_LISensorimotor-to-visual gradient is present around 35.6 postmenstrual weeks C_LIO_LIGlobal gradient structure undergoes substantial changes during the third trimester C_LIO_LIRegional gradient develops mostly in primary sensory, motor, and visual regions C_LIO_LISensorimotor-to-visual gradient at birth predicts cognitive outcomes at age 2 C_LI

neuroscience↗

bioRxiv2023

Nifuroxazide enhances radiotherapy efficacy against hepatocellular carcinoma by upregulating PD-L1 degradation via the ubiquitin-proteasome pathway

Radiation therapy is a primary treatment for hepatocellular carcinoma (HCC), but its effectiveness can be diminished by various factors. The over-expression of PD-L1 has been identified as a critical reason for radiotherapy resistance. Previous studies have demonstrated that nifuroxazide exerts antitumor activity by damaging the Stat3 pathway, but its efficacy against PD-L1 has remained unclear. In this study, we investigated whether nifuroxazide could enhance the efficacy of radiotherapy in HCC by reducing PD-L1 expression. Our results showed that nifuroxazide significantly increased the sensitivity of tumor cells to radiation therapy by inhibiting cell proliferation and migration while increasing apoptosis in vitro. Additionally, nifuroxazide attenuated the up-regulation of PD-L1 expression induced by irradiation, which may be associated with increased degradation of PD-L1 through the ubiquitination-proteasome pathway. Furthermore, nifuroxazide greatly enhanced the efficacy of radiation therapy in H22-bearing mice by inhibiting tumor growth, improving survival, boosting the activation of T lymphocytes, and decelerating the ratios of Treg cells in spleens. Importantly, nifuroxazide limited the increased expression of PD-L1 in tumor tissues induced by radiation therapy. This study confirms, for the first time, that nifuroxazide can augment PD-L1 degradation to improve the efficacy of radiation therapy in HCC-bearing mice.

immunology↗

bioRxiv2023

Cell therapy with IL-10-producing group 2 innate lymphoid cells suppresses Graft-versus-Host disease

IL-10 producing group 2 innate lymphoid cells (ILC210) have immunoregulatory functions, and limit harmful immune responses across various tissues. Despite their crucial roles in maintaining immune homeostasis, the cell therapy potential of human ILC210 has not been demonstrated, due to both limited numbers in human peripheral blood and lack of definitive markers for identification. Here, we isolate and expand circulating human ILC210, and assess their cell therapy potential in a humanized model of Graft-versus-Host Disease (GVHD). Cell therapy with human ILC210 decreased GVHD severity and prolonged survival of NOD-scid IL2R{gamma}null (NSG) mice. Adoptive transfer of ILC210 inhibited pathogenic T cell proliferation and intestinal infiltration, and suppressed CD4+ Th1 and CD8+ Tc1 cells in an IL-4 and IL-10 dependent manner. Critically, increased proportions of ILC2s did not correlate with higher rates of cancer relapse in HSCT recipients, and adoptive transfer of ILC210 did not compromise graft-versus-leukemic (GVL) effects in a humanized model. Finally, we identify CD49d and CD86 as novel markers that discriminate ILC210 from conventional ILC2s. Collectively, these findings demonstrate the potential of harnessing ILC210 in cell therapies for GVHD and other immune-driven pathologies.

immunology↗