Biology subjects

Hu, J.

Publications and source records attributed to Hu, J..

At least 361 records · Page 20Linked to original sources

bioRxiv2022

Trophoblast and blood vessel organoid cultures recapitulate the role of WNT2B in promoting intravillous vascularization in human intrauterine and ectopic pregnancy

Tubal ectopic pregnancy (TEP), a pregnancy complication caused by aberrant implantation in fallopian tubes, accounts for 9-13% pregnancy-related deaths. The lack of models for human TEP hampers the understanding of its pathological mechanisms. Here, we employed multiple models to investigate the crosstalk between human trophoblast development and intravillous vascularization. We found that the severity of TEP, the size of placental villi, and the depth of trophoblast invasion are correlated with the extent of intravillous vascularization. We identified a key pro-angiogenic factor secreted by trophoblasts, WNT2B, that promotes villous vasculogenesis, angiogenesis, and vascular network expansion. In an organoid coculture model consisting of trophoblast organoids and blood vessel organoids, knockdown of WNT2B in trophoblast organoids compromises their pro-angiogenic effect on the development of blood vessel organoids. These organoid-based models reveal an important role for WNT-mediated angiogenesis in pregnancies and could be employed to investigate the commutations between trophoblasts and endothelial/endothelial progenitor cells.

cell biology↗

bioRxiv2022

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

Drug combination therapies are superior to monotherapy for cancer treatment in many ways when addressing tumor heterogeneity issue. For wet-lab experiment, screening out novel synergistic drug pairs is challenging due to the enormous searching space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic function. Notwithstanding the success of current models, the power of generalization to other datasets as wells as understanding of mechanism for chemical-chemical interaction or chemical-sample interaction are lack of study, hindering current algorithms from real application. In this paper, we proposed a deep neural model termed DTSyn (Dual Transformer model for drug pair Synergy prediction) based on multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer for capturing chemical substructure-gene and gene-gene associations and a coarse-granularity transformer for extracting chemical-chemical and chemical-cell line interactions. DTSyn achieves highest Receiver operating characteristic area under curve (ROC AUC) of 0.73, 0.78. 0.82 and 0.81 on four different cross validation tasks, outperforming all competing methods. Further, DTSyn achieved best True Positive Rate (TPR) over five independent datasets. The ablation study showed that both transformer blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, which may represent the mechanisms of drug action. Thus, we envision our model a valuable tool to prioritize synergistic drug pairs by utilizing chemicals and transcriptome data.

bioinformatics↗

bioRxiv2022

A toolkit of highly selective and sensitive genetically encoded neuropeptide sensors

Neuropeptides are key signaling molecules in the endocrine and nervous systems that regulate many critical physiological processes, including energy balance, sleep and circadian rhythms, stress, and social behaviors. Understanding the functions of neuropeptides in vivo requires the ability to monitor their dynamics with high specificity, sensitivity, and spatiotemporal resolution; however, this has been hindered by the lack of direct, sensitive and non-invasive tools. Here, we developed a series of GRAB (G protein-coupled receptor activation-based) sensors for detecting somatostatin (SST), cholecystokinin (CCK), corticotropin-releasing factor (CRF), neuropeptide Y (NPY), neurotensin (NTS), and vasoactive intestinal peptide (VIP). These fluorescent sensors utilize the corresponding GPCRs as the neuropeptide-sensing module with the insertion of a circular-permutated GFP as the optical reporter. This design detects the binding of specific neuropeptides at nanomolar concentration with a robust increase in fluorescence. We used these GRAB neuropeptide sensors to measure the spatiotemporal dynamics of endogenous SST release in isolated pancreatic islets and to detect the release of both CCK and CRF in acute brain slices. Moreover, we detect endogenous CRF release induced by stressful experiences in vivo using fiber photometry and 2-photon imaging in mice. Together, these new sensors establish a robust toolkit for studying the release, function, and regulation of neuropeptides under both physiological and pathophysiological conditions.

neuroscience↗

bioRxiv2022

Microglial Piezo1 senses Aβ fibrils stiffness to restrict Alzheimer's disease

The pathology of Alzheimers disease (AD) is associated with the extracellular amyloid-{beta} (A{beta}) plaques that perturb the mechanical properties of brain tissue. Microglia sense and integrate biochemical cues in their local microenvironment, intimate linking with AD progress. However, neither the microglial mechanosensing pathway nor its impact on AD pathogenesis is well studied. Here, we showed that the mechanosensitive ion channel Piezo1 is increased in microglia upon stiffness stimuli of A{beta} fibrils. The upregulation of Piezo1 in A{beta} plaque-associated microglia was observed in AD mouse models and human patients. Microglia lacking Piezo1 disturbed microglial clustering, phagocytosis, and compaction of A{beta} plaques, resulting in the exacerbation of A{beta} and neurodegenerative pathologies in AD. Conversely, pharmacological activation of Piezo1 ameliorated brain A{beta} burden and cognitive impairment in the 5xFAD mouse. Together, our results reveal Piezo1, as a mechanosensor of A{beta} fibrils stiffness in microglia, could represent a promising therapeutic target for AD.

neuroscience↗

bioRxiv2022

OncoPubMiner: A platform for oncology publication mining

Knowledge bases that are up-to-date and of expert quality are fundamental in biomedical research fields. A knowledge base established with human participation and subjected to multiple inspections is crucial for supporting clinical decision-making, especially in the exponentially growing field of precision oncology. The number of original publications in the field has skyrocketed with the advancement of technology and in-depth research evolved. It has become an increasingly pressing issue that researchers need to consider how to gather and mine these articles accurately and efficiently. In this paper, we present OncoPubMiner (https://oncopubminer.chosenmedinfo.com), a free and powerful system that combines text mining, data structure customization, publication search with online reading, project-centered and team-based data collection to realize a one-stop "keyword in, knowledge out" oncology publication mining platform. It was built by integrating all the open-access abstracts from PubMed and full-text articles from PubMed Central, and is updated on a daily basis. The system makes it straightforward to obtain precision oncology knowledge from scientific articles. OncoPubMiner will assist researchers in developing professional structured knowledge base systems efficiently, and bringing the oncology community closer to achieving precision oncology goals. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/483968v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@68235aorg.highwire.dtl.DTLVardef@e9256aorg.highwire.dtl.DTLVardef@9783daorg.highwire.dtl.DTLVardef@15193e2_HPS_FORMAT_FIGEXP M_FIG OncoPubMiners one-stop "keyword in, knowledge out" workflow (A) is built on key features such as text mining (B), publication search (C), form customization (D), and team-based curation (E). C_FIG

bioinformatics↗

bioRxiv2022

Regulation of Cardiomyocyte Adhesion and Mechanosignalling Through Distinct Nanoscale Behaviour of Integrin Ligands Mimicking Healthy or Fibrotic ECM

The stiffness of the cardiovascular environment changes during ageing and in disease and contributes to disease incidence and progression. Changing collagen expression and crosslinking regulate the rigidity of the cardiac ECM. Additionally, basal lamina glycoproteins, especially laminin and fibronectin regulate cardiomyocyte adhesion formation, mechanics and mechano-signalling. Laminin is abundant in the healthy heart, but fibronectin is increasingly expressed in the fibrotic heart. ECM receptors are co-regulated with the changing ECM. Due to differences in integrin dynamics, clustering, and downstream adhesion formation this is expected to ultimately influence cardiomyocyte mechanosignalling; however details remain elusive. Here we sought to investigate how different cardiomyocyte ligand/integrin combinations are affecting adhesion formation, traction forces and mechanosignalling, using a combination of uniformly coated surfaces with defined stiffness, PDMS nanopillars, micropatterning and specifically designed bionanoarrays for precise ligand presentation. Thereby we find that neonatal rat cardiomyocytes (which express both laminin and fibronectin binding integrins) adhesion nanoscale organisation, signalling and traction force generation are strongly dependent on the integrin/ligand combination. Together our data indicates that the presence of fibronectin in combination with the enhanced stiffness in fibrotic areas will strongly impact on the cardiomyocyte behaviour and influence disease progression.

cell biology↗

bioRxiv2022

Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing

Wool traits of rabbits are important in fiber production and model organism research on hair growth, while the genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a well-known fiber breed. Balancing genotyping cost and variant detection, we proposed low-coverage whole genome sequencing (LCS) followed by genotype imputation for genotyping. Different genotype imputation strategies, sequencing coverages and sample sizes were compared, and we found by BaseVar + STITCH, genotyping reached high accuracy (>0.97) at a depth of 1.0X and a sample size > 300. Multivariate GWAS followed by conditional GWAS and confidence interval estimation of QTLs were used to reveal the genetic architecture of wool traits. Six QTLs were detected with phenotypic variation contribution ranging from 0.42% to 7.50%. Gene-level mapping implicated FGF10 associated with fiber growth and diameter, which supported previous function research on fibroblast growth factor family in other species and provided genetic information for wool rabbit breeding. We suggest LCS as a cost-effective alternative for assessing common variants. GWAS combined with LCS can excavate QTLs and fine-map genes associated with quantitative traits. This study provides a powerful analysis mentality for investigating complex traits, which lays the foundation for genomic breeding.

genetics↗

bioRxiv2022

Extensive neutralization against SARS-CoV-2 variants elicited by Omicron-specific subunit vaccine booster

The currently dominant variant of SARS-CoV-2 Omicron, carrying a great number of mutations, has been verified its strong capacity of immune escape in COVID-19 convalescents and vaccinated individuals. An increased risk of SARS-CoV-2 reinfection or breakthrough infection should be concerned. Here we reported higher humoral immune response elicited by Delta and Omicron variants after breaking through previous infection and cross-neutralization against VOCs, compared to the ancestral wild-type (WT) virus infection. To overcome the immune escape of Omicron, Omicron-specific vaccine was considered as a novel and potential strategy. Mouse models were used to verify whether Omicron-specific RBD subunit boost immune response by immunizing Omicron-RBD recombinant proteins. Three doses of Omicron-RBD immunization elicit comparable neutralizing antibody (NAb) titers with three doses of WT-RBD immunization, but the neutralizing activity was not cross-active. By contrast, two doses of WT-RBD with an Omicron-RBD booster increased the NAb geometric mean titers against Omicron by 9 folds. Moreover, an additional boost vaccination with Omicron-RBD protein could increase humoral immune response against both WT and current VOCs. These results suggest that the Omicron-specific subunit booster shows its advantages in the immune protection from both WT and current VOCs, and that SARS-CoV-2 vaccines administration using two or more virus lineages as antigens might improve the NAb response.

microbiology↗

bioRxiv2022

TrieDedup: A fast trie-based deduplication algorithm to handle ambiguous bases in high-throughput sequencing

BackgroundHigh-throughput sequencing is a powerful tool that is extensively applied in biological studies. However, sequencers may produce low-quality bases, leading to ambiguous bases, Ns. PCR duplicates introduced in library preparation should usually be removed in genomics studies, and several deduplication tools have been developed for this purpose. However, two identical reads may appear different due to ambiguous bases and the existing tools cannot address Ns correctly or efficiently. ResultsHere we proposed and implemented TrieDedup, which uses trie (prefix tree) data structure to compare and store sequences. TrieDedup can handle ambiguous base Ns, and efficiently deduplicate at the level of raw sequences. We also reduced its memory usage by approximately 20% by implementing restrictedDict in Python. We benchmarked the performance of the algorithm and showed that TrieDedup can deduplicate reads up to 270-fold faster than pairwise comparison at a cost of 32-fold higher memory usage. ConclusionsTrieDedup algorithm may facilitate PCR deduplication, barcode or UMI assignment and repertoire diversity analysis of large scale high-throughput sequencing datasets with its ultra-fast algorithm that can account for ambiguous bases due to sequencing errors. AvailabilityTrieDedup is available at https://github.com/lolrenceH/TrieDedup

bioinformatics↗

bioRxiv2022

An integrated strain-level analytic pipeline utilizing longitudinal metagenomic data

The development of sequencing technology and analytic tools have advanced our insights into the complexity of microbiome. Since different strains within species may display great phenotypic variability, studying within-species variations enhances the understanding of microbial biological processes. However, most existing methods for strain-level analysis do not allow for the simultaneous interrogation of strain proportions and genome-wide variants in longitudinal metagenomic samples. In this study, we introduce LongStrain, an integrated pipeline for the analysis of metagenomic data from individuals with longitudinal or repeated samples. Our algorithm improves the efficiency and accuracy of strain identification by jointly modeling the strain proportion and genomic variants in combined multiple samples within individuals. With simulation studies of a microbial community and single species, we show that LongStrain is superior to three extensively used methods in variant calling and proportion estimation. Furthermore, we illustrate the potential applications of LongStrain in the real data analysis of The Environmental Determinants of Diabetes in the Young (TEDDY) study and a gastric intestinal metaplasia microbiome study. We investigate the association between the dynamic change of strain proportions and early life events, such as birth delivery mode, antibiotic treatment, and weaning. By joint analysis of phylogeny and strain transition, we also identify a subspecies clade of Bifidobacterium longum which is significantly correlated with breastfeeding.

bioinformatics↗

bioRxiv2022

CRISPR-Detector: Fast and Accurate Detection, Visualization, and Annotation of Genome Wide Mutations Induced by Gene Editing Events

The leading edge of genome editing, driven by CRISPR/Cas technology, is revolutionizing biotechnologies. In pace with the burst of novel genome editing methods, bioinformatic tools to monitor the on/off-target events need to be more evolved as well. Existing tools suffer from limitations in speed and scalability, and they especially struggle with whole genome sequencing (WGS) data analysis which has the potential to detect off-target mutations at a genome-wide level via an unbiased manner. Here, we introduce our CRISPR-detector tool, which is a web-hosted or locally deployable pipeline with five key innovations: 1) optimized scalability allowing for WGS data analysis beyond BED (Browser Extensible Data) file-defined regions; 2) improved accuracy benefiting from haplotype-based variant calling to handle sequencing errors; 3) treated and control sample co-analysis to remove background variants existing prior to genome editing; 4) integrated structural variation (SV) calling; and 5) functional and clinical annotation of editing-induced mutations.

bioinformatics↗

bioRxiv2022

Generative pretraining from large-scale transcriptomes: Implications for single-cell deciphering and clinical translation

Exponential accumulation of single-cell transcriptomes poses great challenge for efficient assimilation. Here, we present an approach entitled tGPT towards integration of 22.3 million single-cell transcriptomes by modeling gene expression rankings as generative pretraining task. tGPT is conceptually simple in that it autoregressively models the ranking of a gene in the context of its preceding neighbors. We demonstrated the high performance of tGPT on a range of fundamental single-cell analysis tasks and novel applications on bulk tissues. The single-cell clusters and cell lineage trajectories derived from tGPT are highly aligned with known cell labels and states. The feature patterns of tumor bulk tissues learned by tGPT are associated with a wide range of genomic alteration events, prognosis and treatment outcome of immunotherapy. tGPT represents a new analytical paradigm for integrating and deciphering massive amount of transcriptome data and it will facilitate the interpretation and clinical translation of single-cell transcriptomes.

bioinformatics↗

bioRxiv2022

ARZIMM: A Novel Analytic Platform for the Inference of Microbial Interactions and Community Stability from Longitudinal Microbiome Study

Dynamic changes of microbiome communities may play important roles in human health and diseases. The recent rise in longitudinal microbiome studies calls for statistical methods that can model the temporal dynamic patterns and simultaneously quantify the microbial interactions and community stability. Here, we propose a novel autoregressive zero-inflated mixed-effects model (ARZIMM) to capture the sparse microbial interactions and estimate the community stability. ARZIMM employs a zero-inflated Poisson autoregressive model to model the excessive zero abundances and the non-zero abundances separately, a random effect to investigate the underlining dynamic pattern shared within the group, and a Lasso-type penalty to capture and estimate the sparse microbial interactions. Based on the estimated microbial interaction matrix, we further derive the estimate of community stability, and identify the core dynamic patterns through network inference. Through extensive simulation studies and real data analyses we evaluated ARZIMM in comparison with the other methods.

bioinformatics↗

bioRxiv2022

Diet outweighs genetics in shaping gut microbiomes in Asian honeybee

BackgroundThe gut microbiome is a crucial element that facilitates a hosts adaptation to a changing environment. Host-specificity often coincides with distinctions in gut microbes, suggesting a co-evolution of the holobionts. However, it is unclear how gut microbiota shared by a common host ancestor would co-diversify with hosts and eventually become distinct among sister hosts. In this context, understanding the evolutionary pathway of gut microbiomes of the same host species could provide insight on how holobionts co-adapt along environmental gradients. Specifically, we ask which factor, nature or nurture, i.e., genetics or diets, contributes more to the shaping of gut microbiome, along with host diversification and range expansion. ResultsWe compared and analyzed the gut microbiomes of 99 Asian honeybees, Apis cerana, from genetically diverged populations covering 13 provinces across China. Bacterial composition varied significantly across populations at phylotype, sequence-discrete population (SDP), and strain levels, but with extensive overlaps, indicating the diversity of microbial community among A. cerana populations is driven by nestedness. Taken together, genetics exhibited tangential impacts, while pollen diets were significantly correlated with both the composition and function of gut microbiome. Core bacteria, Gilliamella and Lactobacillus Firm-5, showed antagonistic turnovers and contributed to the enrichment in carbohydrate transport and metabolism. By feeding and inoculation bioassays, we confirmed that the variations in pollen polysaccharide composition contributed to the trade-off of these core bacteria. ConclusionsProgressive change, i.e., nestedness, is the foundation of gut microbiome evolution in the Asian honeybee. Such a transition during the co-diversification of gut microbiomes is shaped primarily by environmental factors, diets in general, pollen polysaccharide in particular.

microbiology↗

bioRxiv2022

Computed tomographic analysis of the dental system of three Jurassic ceratopsians: implications for the evolution of the tooth replacement pattern and diet in early-diverging ceratopsians

The dental system of ceratopsids is among the most specialized structure in Dinosauria, and includes high angled wear surfaces, split tooth roots, and multiple teeth in each tooth family. However, the early evolution of this unique dental system is generally poorly understood due to a lack of knowledge of the dental morphology and development in early-diverging ceratopsians. Here we study the dental system of three of the earliest-diverging Chinese ceratopsians: Yinlong and Hualianceratops from the early Late Jurassic of Xinjiang, and Chaoyangsaurus from the Late Jurassic of Liaoning. By using micro-computed tomographic analyses, our study has revealed significant new information regarding the dental system of these early ceratopsians, including no more than five replacement teeth in each jaw quadrant; at most one generation of replacement teeth in each alveolus; nearly full resorption of the functional tooth root during tooth replacement; and occlusion with low-angled, concave wear facets that differs significantly from the shearing occlusal system seen in ceratopsids. Yinlong displays an increase in the number of maxillary tooth alveoli and a decrease in the number of replacement teeth during ontogeny as well as the retention of remnants of functional teeth in the largest individual. Early-diverging ceratopsians thus display a relatively slow tooth replacement rate compared to late-diverging ceratopsians. Combined with paleobotany and palaeoenvironment data, Yinlong likely uses gastroliths to triturate foodstuffs, and the difference in diet strategy might have influenced the pattern of tooth replacement in later-diverging ceratopsians.

paleontology↗

bioRxiv2022

SELINA: Single-cell Assignment using Multiple-Adversarial Domain Adaptation Network with Large-scale References

The rapid accumulation of single-cell RNA-seq data has provided rich resources to characterize various human cell types. Cell type annotation is the critical step in analyzing single-cell RNA-seq data. However, accurate cell type annotation based on public references is challenging due to the inconsistent annotations, batch effects, and poor characterization of rare cell types. Here, we introduce SELINA (single cELl identity NAvigator), an integrative annotation transferring framework for automatic cell type annotation. SELINA optimizes the annotation for minority cell types by synthetic minority over-sampling, removes batch effects among reference datasets using a multiple-adversarial domain adaptation network (MADA), and fits the query data with reference data using an autoencoder. Finally, SELINA affords a comprehensive and uniform reference atlas with 1.7 million cells covering 230 major human cell types. We demonstrated the robustness and superiority of SELINA in most human tissues compared to existing methods. SELINA provided a one-stop solution for human single-cell RNA-seq data annotation with the potential to extend for other species.

bioinformatics↗

bioRxiv2022

The bladder microbiome, metabolome, cytokines, and phenotypes in patients with systemic lupus erythematosus

Background and aimsEmerging studies reveal a unique bacterial community in the human bladder, with alteration of composition associated to disease states. Systemic lupus erythematosus (SLE) is a complex autoimmune disease that is characterized by frequent impairment of the kidney. Here, we explored the bladder microbiome, metabolome, and cytokine profiles in SLE patients, as well as correlations between microbiome and metabolome, cytokines, and disease profiles. Methods and materialsWe recruited a cohort of 50 SLE patients and 50 individually matched asymptomatic controls. We used transurethral catheterization to collect urine samples, 16S rRNA gene sequencing to profile bladder microbiomes, and LC-MS/MS to perform untargeted metabolomic profiling. ResultsCompared to controls, SLE patients possessed a unique bladder microbial community and increased alpha diversity. These differences were accompanied by differences in urinary metabolomes, cytokines, and patients disease profiles. The SLE-enriched genera, including Bacteroides, were positively correlated with several SLE-enriched metabolites, including olopatadine. The SLE-depleted genera, such as Pseudomonas, were negatively correlated to SLE-depleted cytokines, including IL-8. Alteration of the bladder microbiome was associated with disease profile. For example, the genera Megamonas and Phocaeicola were negatively correlated with serum complement C3, and Streptococcus was positively correlated with IgG. ConclusionsOur present study reveals associations between the bladder microbiome and the urinary metabolome, cytokines, and disease phenotypes. Our results could help identify biomarkers for SLE.

microbiology↗

bioRxiv2022

Survival and Transcriptomic Response of Clinically relevant pathogens on Surfaces in Relation to Probiotic and Traditional Cleaning Strategies

Indoor surfaces are paradoxically presumed to be both colonized by pathogens, necessitating disinfection, and "microbial wastelands." In these resource-poor, dry environments, competition and decay are thought to be important drivers of microbial community composition. However, the relative contributions of these two processes have not been specifically evaluated. To bridge this knowledge gap, we used microcosms to specifically evaluate whether interspecies interactions occur on surfaces. We combined transcriptomics and traditional microbiology techniques to investigate whether competition occurred between two clinically important pathogens, Acinetobacter baumannii and Klebsiella pneumoniae, and a probiotic cleaner containing a consortium of Bacillus species. Probiotic cleaning seeks to take advantage of ecological principles such as competitive exclusion, thus using benign microorganisms to inhibit viable pathogens, but there is limited evidence that competitive exclusion in fact occurs in environments of interest (i.e., indoor surfaces). Our results indicate that competition in this setting has a negligible impact on community composition but may influence the functions expressed by active organisms. Although Bacillus spp. remained viable on surfaces for an extended period of time after application, viable colony forming units (CFUs) of A. baumannii recovered following exposure to a chemical-based detergent with and without Bacillus spp. showed no statistical difference. Similarly, for K. pneumoniae, there were small statistical differences in CFUs between cleaning scenarios with or without Bacillus spp. in the chemical-based detergent. The transcriptome of A. baumannii with and without Bacillus spp. exposure shared a high degree of similarity in overall gene expression, but the transcriptome of K. pneumoniae differed in overall gene expression, including reduced response in genes related to antimicrobial resistance. Together, these results highlight the need to fully understand the underlying biological and ecological mechanisms for community assembly and function on indoor surfaces, as well as having practical implications for cleaning and disinfection strategies for infection prevention.

microbiology↗