Biology subjects

Fan, X.

Publications and source records attributed to Fan, X..

At least 163 records · Page 9Linked to original sources

bioRxiv2022

Top-down modulation and cortical-AMG/HPC interaction in familiar face processing

Humans can accurately recognize familiar faces in only a few hundred milliseconds, but the underlying neural mechanism remains unclear. Here we recorded intracranial electrophysiological signals from ventral temporal cortex (VTC), superior/middle temporal cortex (STC/MTC), medial parietal cortex (MPC) and amygdala/hippocampus (AMG/HPC) in 20 epilepsy patients while they viewed faces of famous people and strangers as well as common objects. In posterior VTC and MPC, familiarity-sensitive responses emerged significantly later than initial face-selective responses, suggesting that familiarity enhances face representations after they are first being extracted. Moreover, viewing famous faces increased the coupling between cortical areas and AMG/HPC in multiple frequency bands. These findings imply that the top-down modulation in local face-selective response and interactions between cortical face areas and AMG/HPC contribute to the superior recognition of familiar faces. TeaserTop-down modulation and cortical-AMG/HPC interactions contribute to the superior processing of familiar faces.

neuroscience↗

bioRxiv2022

Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk

Spatially resolved transcriptomics (ST) provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications from ST data, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells, decomposed from ST data through a non-negative linear model and spatial mapping between single-cell RNA-sequencing and ST data. The performance of SpaTalk benchmarked on public single-cell ST datasets was superior to that of existing cell-cell communication inference methods. SpaTalk was then applied to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based ST data universally, providing new insights into spatial inter-cellular dynamics.

bioinformatics↗

bioRxiv2022

Piezo mechanosensory channels regulate centrosome integrity

Piezo1 and 2 are evolutionarily conserved mechanosensory cation channels known to function on the cell surface by responding to external pressure and transducing a mechanically activated Ca2+ current. Here we show that both Piezo1 and 2 also exhibit concentrated intracellular localization at centrosomes. Both Piezo1 and 2 loss-of-function and Piezo1 activation by the small molecule Yoda1 produced supernumerary centrosomes due to inappropriate centriole disengagement. Using a centrosome-localized GCaMP Ca2+-sensitive reporter, we show that perturbations in Piezo modulate Ca2+ local concentration at centrosomes. We designed a photoactivable Yoda1 analog (caged-Yoda1) and revealed that its photoactivation specifically at centrosomes leads to rapid premature centriole disengagement within minutes. We identified the sorting nexin Snx5, which is involved in endocytic uptake and trafficking, as a protein-interactor with the conserved Piezo C-terminal domain, and Snx5 also co-localizes with Piezo1 and 2 at centrosomes. Moreover, inhibition of Polo-like-kinase 1 (PLK1) abolishes Yoda1-induced centriole disengagement. Collectively, these data suggest that Piezo1 and 2 in pericentrosomal endosomes control centrosome integrity, likely by maintaining local Ca2+ within a defined range through mechanotransduction of cell intrinsic forces from microtubules.

cell biology↗

bioRxiv2022

Ralstonia solanacearum effector RipAF1 ADP-ribosylates host FBN1 to induce resistance against bacterial wilt

Ralstonia solanacearum (Rso) causes destructive bacterial wilt across a broad range of host plants by inducing jasmonic acid (JA) signaling while suppressing salicylic acid (SA) signaling pathways during disease development. In the present study, we show that Rso type III effector RipAF1 exerts a negative effect on bacterial virulence by subverting disease signaling in association with bacterial wilt. The ADP-ribosylation activity of RipAF1 was verified both in vivo and in vitro. Host fibrillin FBN1 was identified as a RipAF1-interacting protein that acted as a susceptible factor for bacterial wilt. In particular, RipAF1 directly ADP-ribosylates FBN1 at the E175/K207 residues, thus interfering with the mediation of disease signaling by FBN1. Together, these results suggest that RipAF1 exerts a role in defense induction by ADP-ribosylation of the susceptible factor FBN1 in the host plant.

plant 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

ECD of PepT1 interacts with TM1 to facilitate substrate transport

Mammalian peptide transporters, PepT1 and PepT2, mediate uptake of a wide variety of di- and tri-peptides and are essential for the absorption of dietary peptides in the digestive tract and the recovery of peptides in renal filtrate. PepT also mediates absorption of many drugs and prodrugs to enhance their bioavailability. PepT has 12 transmembrane (TM) helices that fold into two domains, the N-terminal domain (NTD, TM1-6) and C-terminal domain (CTD, TM7-12), and a large extracellular domain (ECD) of [~]200 amino acids between TM9 and TM10. It is known that peptide transport involves large motions of the N- and C-domains, but the role of ECD remains unclear. Here we report the structure of PepT1 from Equus caballus (horse) determined by cryo-electron microscopy. The structure shows that ECD interacts with TM1 and bridges the N- and C-domains. Deletion of the ECD or mutations to the TM1-ECD interface both impair the transport activity. These results demonstrate a role of ECD in structure and function of PepT1 and enhance our understanding of the mechanism of transport in PepT1.

biochemistry↗

bioRxiv2022

An Improved Linear Mixed Model for Multivariate Genome-Wide Association Studies

Current methods of multivariate analysis require complete multivariate phenotypes from each individual and have a computational time complexity of O(n2) per SNP, where n is the sample size. We develop an efficient genomic multivariate analysis tool (GMAT) for genome-wide association studies of multiple correlated traits. The new method can handle incomplete multivariate data with missing records and reduce the time complexity to O(n) per SNP. Simulation studies based on known genotypes and phenotypes of actual populations show that GMAT has increased the statistical power with a proper control of false positivity for association studies compared to the conventional linear mixed model (LMM) that removes individuals with incomplete records. Applications to a balanced donkey data and an unbalanced yeast data show that the computational efficiency of the new method has been increased about tens of times faster than the conventional LMM analysis. The GMAT package can be downloaded at https://github.com/chaoning/GMAT.

bioinformatics↗

bioRxiv2022

Single-cell analysis reveals a pathogenic cellular module associated with early allograft dysfunction after liver transplantation

Liver transplantation (LT) is the standard therapy for patients with end-stage liver disease. Although LT technology has markedly progressed in recent decades, early allograft dysfunction (EAD) exacerbates the current organ shortage and impacts the prognosis of recipients. However, understanding of cellular characteristics and molecular events contributing to EAD is limited. Here, a large single-cell transcriptomic atlas of transplanted livers collected from four patients is constructed, including 58,243 cells, which are classified into 14 cell types and 29 corresponding subtypes with known markers, including liver parenchymal cells and non-parenchymal cells with different cell states. Compared to the pre-LT livers, graft remodeling is noted in the post-LT livers, with marked changes in several immune cells in either cell ratios or cell states. More importantly, an EAD-associated pathogenic cellular module is identified, consisting of mucosal-associated invariant T (MAIT) cells, granzyme B (GZMB)+ granzyme K (GZMK)+ natural killer (NK) cells, and S100A12+ neutrophils, all of which are elevated in EAD patient after LT. This cellular module is also verified in two independent datasets. Collectively, these results reveal the cellular characteristics of transplanted livers and the EAD-associated pathogenic cellular module at the single-cell level, offering new insights into the EAD occurrence after LT.

genomics↗

bioRxiv2022

Engineering SARS-CoV-2 cocktail antibodies into a bispecific format improves neutralizing potency and breadth

One major limitation of neutralizing antibody-based COVID-19 therapy is the requirement of costly cocktails to reduce antibody resistance. We engineered two bispecific antibodies (bsAbs) using distinct designs and compared them with parental antibodies and their cocktail. Single molecules of both bsAbs block the two epitopes targeted by parental antibodies on the receptor-binding domain (RBD). However, bsAb with the IgG-(scFv)2 design (14-H-06) but not the CrossMAb design (14-crs-06) increases antigen-binding and virus-neutralizing activities and spectrum against multiple SARS-CoV-2 variants including the Omicron, than the cocktail. X-ray crystallography and computational simulations reveal distinct neutralizing mechanisms for individual cocktail antibodies and suggest higher inter-spike crosslinking potentials by 14-H-06 than 14-crs-06. In mouse models of infections by SARS-CoV-2 and the Beta, Gamma, and Delta variants, 14-H-06 exhibits higher or equivalent therapeutic efficacy than the cocktail. Rationally engineered bsAbs represent a cost-effective alternative to antibody cocktails and a promising strategy to improve potency and breadth.

microbiology↗

bioRxiv2022

Spatially resolved single-cell deconvolution of bulk RNA-seq using Bulk2Space

Uncovering the tissue molecular architecture at single-cell resolution could help better understand organisms biological and pathological processes. However, bulk RNA-seq can only measure gene expression in cell mixtures, without revealing the transcriptional heterogeneity and spatial patterns of single cells. Herein, we introduce Bulk2Space (https://github.com/ZJUFanLab/bulk2space), a deep learning framework-based spatial deconvolution algorithm that can simultaneously disclose the spatial and cellular heterogeneity of bulk RNA-seq data using existing single-cell and spatial transcriptomics references. The use of bulk transcriptomics to validate Bulk2Space unveils, in particular, the spatial variance of immune cells in different tumor regions, the molecular and spatial heterogeneity of tissues during inflammation-induced tumorigenesis, and spatial patterns of novel genes in different cell types. Moreover, Bulk2Space is utilized to perform spatial deconvolution analysis on bulk transcriptome data from two different mouse brain regions derived from our in-house developed sequencing approach termed Spatial-seq. We have not only reconstructed the hierarchical structure of the mouse isocortex but also further annotated cell types that were not identified by original methods in the mouse hypothalamus.

bioinformatics↗

bioRxiv2022

Fluorescence visualization of deep-buried hollow organs

High-definition fluorescence imaging of deep-buried organs is still challenging. Here, we develop bright fluorophores emitting to 1700 nm by enhancing electron donating ability and reducing donor-acceptor distance. In parallel, the heavy water functions as the solvent of the delicately designed fluorophores, effectively reducing the fluorescent signal loss caused by the absorption by water. The near-infrared-II (NIR-II, 900-1880 nm) emission is eventually recovered and extended beyond 1400 nm. Compared with the spectral range beyond 1500 nm, the one beyond 1400 nm gives a more accurate fluorescence visualization of the hollow organs, owing to the absorption-induced scattering suppression. In addition, the intraluminal lesions containing much water are simultaneously negatively stained, leading to a stark contrast for precise diagnosis. Eventually, the intraluminally perfused fluorescent probes are excreted from mice and thus no obvious side effects emerge. This general method can provide new avenues for future biomedical imaging of deep and highly scattering tissues.

bioengineering↗

bioRxiv2022

Impact of Nosema ceranae invasion on sucrose solution consumption, midgut epithelial cell structure, and lifespan of Apis cerana cerana workers

Nosema ceranae is an intracellular fungal parasite for honeybees, leading to chronic disease named bee nosemosis with worldwide distribution. Asian honeybee (Apis cerana) is the original host for N. ceranae, but the impact of N. ceranae infection on A. cerana physiology is largely unknown. In this current work, workers of Apis cerana cerana, a subspecies of Asian honeybee, were artificially inoculated with N. ceranae spores and reared under lab conditions, followed by detection of fungal spore load as well as host sucrose solution consumption, midgut epithelial cell structure, and lifespan. The result of spore counting suggested that the spore load in the host midgut decreased significantly during 1 dpi-2 dpi, whereas that displayed an elevated trend among 2 dpi-13 dpi. The sucrose solution consumption of workers in N. ceranae-inoculated groups among 1 dpi-20 dpi was always higher than that of workers in un-inoculated groups; additionally, the difference of sucrose solution consumption between these two groups at 4 dpi, 5 dpi, and 13 dpi was of significance. Based on microscopic observation of paraffin sections, darkly stained parasites were clearly detected in the midgut epithelial cells of N. ceranae-inoculated workers at 7 dpi-10 dpi, whereas no parasite was observed in those of un-inoculated workers. In addition, the boundaries of un-inoculated host epithelial cells were intact and the darkly stained nucleus were clear, while the boundaries of midgut epithelial cells of N. ceranae-inoculated workers were blurred, the nucleus were almost disappeared, and the nucleic acid substances were diffused. Moreover, the survival rates of workers in both N. ceranae-inoculated groups and un-inoculated groups at 1 dpi-5 dpi were pretty high and then started to decrease at 5 dpi; the survival rate of workers in N. ceranae-inoculated groups was always lower than that in un-inoculated groups, with significant difference between these two groups during 11 dpi-20 dpi. These results together indicate that the quantity of fungal spores continuously elevated with the microsporidian multiplication, causing energetic stress for workers and host cell structure damage, which further negatively affected the host lifespan. Our findings offer a solid basis not only for exploring the molecular mechanism underlying N. ceranae infection but also for investigating the interaction between N. ceranae and eastern honeybee.

molecular biology↗

bioRxiv2021

ME-ACP: Multi-view Neural Networks with Ensemble Model for Identification of Anticancer Peptides

Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.

bioinformatics↗

bioRxiv2021

HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides

Identifying the targets of an antimicrobial peptide is a fundamental step in studying the innate immune response and combating antibiotic resistance, and more broadly, precision medicine and public health. There have been extensive studies on the statistical and computational approaches to identify (i) whether a peptide is an antimicrobial peptide (AMP) or a non-AMP and (ii) which targets are these sequences effective to (Gram-positive, Gram-negative, etc.). Despite the existing deep learning methods on this problem, most of them are unable to handle the small AMP classes (anti-insect, anti-parasite, etc.). And more importantly, some AMPs can have multiple targets, which the previous methods fail to consider. In this study, we build a diverse and comprehensive multi-label protein sequence database by collecting and cleaning amino acids from various AMP databases. To generate efficient representations and features for the small classes dataset, we take advantage of a protein language model trained on 250 million protein sequences. Based on that, we develop an end-to-end hierarchical multi-label deep forest framework, HMD-AMP, to annotate AMP comprehensively. After identifying an AMP, it will further predict what targets the AMP can effectively kill from eleven available classes. Extensive experiments suggest that our framework outperforms state-of-the-art models in both the binary classification task and the multi-label classification task, especially on the minor classes. Compared with the previous deep learning methods, our method improves the performance on macro-AUROC by 11%. The model is robust against reduced features and small perturbations and produces promising results. We believe HMD-AMP will both contribute to the future wet-lab investigations of the innate structural properties of different antimicrobial peptides and build promising empirical underpinnings for precise medicine with antibiotics.

bioinformatics↗

bioRxiv2021

Nicotinic modulation insecticides act on diverse receptor subtypes with distinct subunit compositions

Insect nicotinic acetylcholine receptors (nAChRs) are ligand gated ion channels mainly expressed in the central nervous system of insects. They are the directed targets of nicotinic modulation insecticides including neonicotinoids, the most widely used insecticides in the world. However, the resistance development from pests and the negative impacts on the pollinators affect their applications and create demand for the alternatives. Thus, it is very important to understand the mode of action of these insecticides at the molecular level, which is actually unclear for more than 30 years. In this study, we systematically examined the susceptibility of ten Drosophila melanogaster nAChR subunits mutants against eleven nicotinic modulation insecticides. Our results showed that there are several subtypes of nAChRs with distinct subunits compositions that are responsible for the toxicity of different insecticides, respectively. At least three of them are the major molecular targets of seven structurally similar neonicotinoids in vivo. On the other hand, the spinosyns may exclusively act on the 6 homomeric nAChR but not any other heteromeric pentamers. Behavioral assays using thermogenetic tools further confirmed the bioassay results and support the idea that receptor activation rather than inhibition leads to the insecticidal effects of neonicotinoids. The present findings reveal native nAChR subunit interactions with various insecticides and have important implications for resistance management and the development of novel insecticides targeting this important ion channels. Author SummaryThe neonicotinoids and spinosyns make up about 27% of the insecticides by world market value. Novel insecticides like sulfoxaflor, flupyradifurone and triflumezopyrim are developed as alternatives due to the negative effects of neonicotinoids on pollinators. Although all act via insect nicotinic acetylcholine receptors, the mode of action is unclear. Our work shows that these insecticides act on diverse receptor subtypes with distinct subunit compositions. This finding could lead to the development of more selective insecticides to control pests with minimal effects on beneficial insects.

genetics↗

bioRxiv2021

Revealing Free Energy Landscape from MD Data via Conditional Angle Partition Tree

Deciphering the free energy landscape of biomolecular structure space is crucial for understanding many complex molecular processes, such as protein-protein interaction, RNA folding, and protein folding. A major source of current dynamic structure data is Molecular Dynamics (MD) simulations. Several methods have been proposed to investigate the free energy landscape from MD data, but all of them rely on the assumption that kinetic similarity is associated with global geometric similarity, which may lead to unsatisfactory results. In this paper, we proposed a new method called Conditional Angle Partition Tree to reveal the hierarchical free energy landscape by correlating local geometric similarity with kinetic similarity. Its application on the benchmark alanine dipeptide MD data showed a much better performance than existing methods in exploring and understanding the free energy landscape. We also applied it to the MD data of Villin HP35. Our results are more reasonable on various aspects than those from other methods and very informative on the hierarchical structure of its energy landscape.

molecular biology↗

bioRxiv2021

DNA replication stress stratifies prognosis and enables exploitable therapeutic vulnerabilities of HBV-associated hepatocellular carcinoma: an in silico strategy towards precision oncology

BackgroundHepatitis B virus (HBV), the main risk factor for hepatocellular carcinoma (HCC) development, integrates into the host genome, causing genetic instability, which may trigger malignancies to exhibit chronic DNA replication stress, providing exploitable therapeutic vulnerabilities. Therefore, customizing prognostication approach and expanding therapeutic options are of great clinical significance to HBV-associated HCCs. MethodsA robust machine-learning framework was designed to develop a DNA replication stress-related prognostic index (PIRS) based on 606 retrospectively collected HBV-associated HCC cases. Molecular profiles and drug response of HCC cell lines were leveraged to predict therapeutic targets and agents for patients with high mortality risk. ResultsCompared with established population-based predictors, PIRS manifested superior performance for prognostic prediction in HBV-associated HCCs. Lower PIRS tightly associated with higher expression of HBV oncoproteins, activated immune/metabolism pathways and higher likelihood of responding to immunotherapy; while higher PIRS showed co-occurrence manner with elevated Ki-67 progression marker and cancer stemness, and significantly enriched in DNA replication stress, cell cycle pathways, chromatin remodeling regulons, and presented an immune-cold phenotype with unfavorable clinical outcome. Through large-scale in silico drug screening, four potential therapeutic targets (TOP2A, PRMT1, CSNK1D, and PPIH) and five agents including three topoisomerase inhibitors (teniposide, doxorubicin, and epirubicin) and two CDK inhibitors (JNJ-7706621 and PHA-793887) were identified for patients with high PIRS. ConclusionsOverall, PIRS holds potential to improve the population-based therapeutic strategies in HCC and sheds new insight to the clinical management for those HBV carriers; current analytic framework provides a roadmap for the rational clinical development of personalized treatment.

bioinformatics↗

bioRxiv2021

The two-component response regulator OrrA confers dehydration tolerance by regulating anaKa expression in the cyanobacterium Anabaena sp. strain PCC 7120

The aquatic cyanobacterium Anabaena sp. strain PCC 7120 exhibits dehydration tolerance. The regulation of gene expression in response to dehydration is crucial for the acquisition of dehydration tolerance, but the molecular mechanisms underlying dehydration responses remain unknown. In this study, the functions of the response regulator OrrA in the regulation of salt and dehydration responses were investigated. Disruption of orrA abolished or diminished the induction of hundreds of genes in response to salt stress and dehydration. Thus, OrrA is a principal regulator of both stress responses. In particular, OrrA plays a crucial role in dehydration tolerance because an orrA disruptant completely lost the ability to regrow after dehydration. Moreover, in the OrrA regulon, anaKa encoding a protein of unknown function was revealed to be indispensable for dehydration tolerance. OrrA and AnaK are conserved among the terrestrial cyanobacteria, suggesting their conserved functions in dehydration tolerance in cyanobacteria.

microbiology↗