Biology subjects

Hu, M.

Publications and source records attributed to Hu, M..

At least 181 records · Page 10Linked to original sources

bioRxiv2021

Polycomb suppresses a female gene regulatory network in Sertoli cells

Gonadal sex determination is controlled by the support cells of testes and ovaries. In testes, the epigenetic mechanism that maintains cellular memory to suppress female sexual differentiation remains unknown. Here, we show that Polycomb suppresses a female gene regulatory network in Sertoli cells, the specific support cells for postnatal testes. Through genetic ablation, we removed Polycomb repressive complex 1 (PRC1) from embryonic Sertoli cells after sex determination. PRC1-depleted postnatal Sertoli cells exhibited defective proliferation and cell death, leading to the degeneration of adult testes. In adult Sertoli cells, PRC1 suppressed the specific, critical genes required for granulosa cells, the support cells of ovaries, thereby inactivating the female gene regulatory network. The underlying chromatin of female genes was coated with Polycomb-mediated repressive modifications: PRC1-mediated H2AK119ub and PRC2-mediated H3K27me3. Taken together, we identify a critical mechanism centered on Polycomb that maintains the male fate in adult testes.

developmental biology↗

bioRxiv2020

Real-time monitoring epidemic trends and key mutations in SARS-CoV-2 evolution by an automated tool

With the global epidemic of SARS-CoV-2, it is important to monitor the variation, haplotype subgroup epidemic trends and key mutations of SARS-CoV-2 over time effectively, which is of great significance to the development of new vaccines, the update of therapeutic drugs, and the improvement of detection reagents. The AutoVEM tool developed in the present study could complete all mutations detections, haplotypes classification, haplotype subgroup epidemic trends and key mutations analysis for 131,576 SARS-CoV-2 genome sequences in 18 hours on a 1 core CPU and 2G internal storage computer. Through haplotype subgroup epidemic trends analysis of 131,576 genome sequences, the great significance of the previous 4 specific sites (C241T, C3037T, C14408T and A23403G) was further revealed, and 6 new mutation sites of highly linked (T445C, C6286T, C22227T, G25563T, C26801G and G29645T) were discovered for the first time that might be related to the infectivity, pathogenicity or host adaptability of SARS-CoV-2. In brief, we proposed an integrative method and developed an efficient automated tool to monitor haplotype subgroup epidemic trends and screen out the key mutations in the evolution of SARS-CoV-2 over time for the first time, and all data could be updated quickly to track the prevalence of previous key mutations and new key mutations because of high efficiency of the tool. In addition, the idea of combinatorial analysis in the present study can also provide a reference for the mutation monitoring of other viruses.

genomics↗

bioRxiv2020

SnapHiC: a computational pipeline to map chromatin contacts from single cell Hi-C data

Single cell Hi-C (scHi-C) analysis has been increasingly used to map the chromatin architecture in diverse tissue contexts, but computational tools to define chromatin contacts at high resolution from scHi-C data are still lacking. Here, we describe SnapHiC, a method that can identify chromatin loops at high resolution and accuracy from scHi-C data. We benchmark SnapHiC against HiCCUPS, a common tool for mapping chromatin contacts in bulk Hi-C data, using scHi-C data from 742 mouse embryonic stem cells. We further demonstrate its utility by analyzing single-nucleus methyl-3C-seq data from 2,869 human prefrontal cortical cells. We uncover cell-type-specific chromatin loops and predict putative target genes for non-coding sequence variants associated with neuropsychiatric disorders. Our results suggest that SnapHiC could facilitate the analysis of cell-type-specific chromatin architecture and gene regulatory programs in complex tissues.

bioinformatics↗

bioRxiv2020

HPRep: Quantifying reproducibility in HiChIP and PLAC-seq datasets

HiChIP and PLAC-seq are emerging technologies for studying genome-wide long-range chromatin interactions mediated by protein of interest, enabling more sensitive and cost-efficient interrogation of protein-centric chromatin conformation. However, due to the unbalanced read distribution introduced by protein immunoprecipitation, existing reproducibility measures developed for Hi-C data are not appropriate for the analysis of HiChIP and PLAC-seq data. Here, we present HPRep, a stratified and weighted correlation metric derived from normalized contact counts, to quantify reproducibility in HiChIP and PLAC-seq data. We applied HPRep to multiple real datasets and demonstrate that HPRep outperforms existing reproducibility measures developed for Hi-C data. Specifically, we applied HPRep to H3K4me3 PLAC-seq data from mouse embryonic stem cells and mouse brain tissues, as well as H3K27ac HiChIP data from human lymphoblastoid cell line GM12878 and leukemia cell line K562, showing that HPRep can more clearly separate among pseudo-replicates, real replicates, and non-replicates. Furthermore, in an H3K4me3 PLAC-seq dataset consisting of 11 samples from four human brain cell types, HPRep demonstrates expected clustering of data which could not be achieved by existing methods developed for Hi-C data, highlighting the need of a reproducibility metric tailored to HiChIP and PLAC-seq data.

genomics↗

bioRxiv2020

MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector): a statistical framework for identifying long-range chromatin interactions from multiple cell types

Chromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions. Existing approaches, such as HiCCUPS, FitHiC/FitHiC2 and FastHiC, are all designed for analyzing individual cell types or samples. None of them accounts for unbalanced sequencing depths and heterogeneity among multiple cell types or samples in a unified statistical framework. To fill in the gap, we have developed a novel statistical framework MUNIn (Multiple sample UNifying long-range chromatin Interaction detector) for identifying long-range chromatin interactions from multiple samples. MUNIn adopts a hierarchical hidden Markov random field (H-HMRF) model, in which the status (peak or background) of each interacting chromatin loci pair depends not only on the status of loci pairs in its neighborhood region, but also on the status of the same loci pair in other samples. To benchmark the performance of MUNIn, we performed comprehensive simulation studies and real data analysis, and showed that MUNIn can achieve much lower false positive rates for detecting sample-specific interactions (33.1 - 36.2%), and much enhanced statistical power for detecting shared peaks (up to 74.3%), compared to uni-sample analysis. Our data demonstrated that MUNIn is a useful tool for the integrative analysis of interactomic data from multiple samples.

genetics↗

bioRxiv2020

THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data

Hi-C data provide population averaged estimates of three-dimensional chromatin contacts across cell types and states in bulk samples. Effective analysis of Hi-C data entails controlling for the potential confounding factor of differential cell type proportions across heterogeneous bulk samples. We propose a novel unsupervised deconvolution method for inferring cell type composition from bulk Hi-C data, the Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER). We conducted extensive simulations to test THUNDER based on combining two published single-cell Hi-C (scHi-C) datasets. THUNDER more accurately estimates the underlying cell type proportions compared to supervised and unsupervised methods (e.g., MuSiC, TOAST, and NMF). We further demonstrate the practical utility of THUNDER to estimate cell type proportions and identify cell-type-specific interactions in Hi-C data from adult human cortex tissue samples. THUNDER will be a useful tool in adjusting for varying cell type composition in population samples, facilitating valid and more powerful downstream analysis such as differential chromatin organization studies. Additionally, THUNDER estimated contact profiles provide a useful exploratory framework to investigate cell-type-specificity of the chromatin interactome while experimental data is still rare.

bioinformatics↗

bioRxiv2020

HiCAR: a robust and sensitive multi-omic co-assay for simultaneous measurement of transcriptome, chromatin accessibility, and cis-regulatory chromatin contacts

The long-range interactions of cis-regulatory elements (cREs) play a central role in regulating the spatial-temporal gene expression program of multi-cellular organism. cREs are characterized by the presence of accessible (or open) chromatin, which can be identified at genome-wide scale with assays such as ATAC-seq, DHS-seq, and FAIRE-seq. However, it remains technically challenging to comprehensively identify the long-range physical interactions that occur between cREs, especially in a cost effective manner using low-input samples. Here, we report HiCAR (High-throughput Chromosome conformation capture on Accessible DNA with mRNA-seq co-assay), a method that enables simultaneous assessment of cis-regulatory chromatin interactions and chromatin accessibility, as well as evaluation of the transcriptome, which represents the functional output of chromatin structure and accessibility. Unlike immunoprecipitation-based methods such as HiChIP, PLAC-seq, and ChIA-PET, HiCAR does not require target-specific antibodies and thus can comprehensively capture the cis-regulatory chromatin contacts anchored at accessible regulatory DNA regions and associated with diverse epigenetic modifications and transcription factor binding. Compared to Trac-looping, another method designed to capture interactions between accessible chromatin regions, HiCAR produced a 17-fold greater yield of informative long-range cis- reads at a similar sequencing depth and required 1,000-fold fewer cells as input. Applying HiCAR to H1 human embryonic stem cells (hESCs) revealed 46,792 cis-regulatory chromatin interactions at 5kb resolution. Interestingly, we found that epigenetically poised, bivalent, and repressed cREs exhibit comparable spatial interaction activity to those transcriptionally activated cREs. Using machine learning approaches, we predicated 22 epigenome features that are potentially important for the spatial interaction activity of cREs in H1 hESC. Lastly, we also identified long-range cis-regulatory chromatin interactions in GM12878 and mouse embryonic stem cells with HiCAR. Our results demonstrate that HiCAR is a robust and cost-effective multi-omics assay, which is broadly applicable for simultaneous analysis of genome architecture, chromatin accessibility, and the transcriptome using low-input samples.

genomics↗

bioRxiv2020

Transcriptional Network Orchestrating Regional Patterning of Cortical Progenitors

We uncovered a transcription factor (TF) network that regulates cortical regional patterning. Screening the expression of hundreds of TFs in the developing mouse cortex identified 38 TFs that are expressed in gradients in the ventricular zone (VZ). We tested whether their cortical expression was altered in mutant mice with known patterning defects (Emx2, Nr2f1 and Pax6), which enabled us to define a cortical regionalization TF network (CRTFN). To identify genomic programming underlying this network, we performed TF ChIP-seq and chromatin-looping conformation to identify enhancer-gene interactions. To map enhancers involved in regional patterning of cortical progenitors, we performed assays for epigenomic marks and DNA accessibility in VZ cells purified from wild-type and patterning mutant mice. This integrated approach has identified a CRTFN and VZ enhancers involved in cortical regional patterning.

neuroscience↗

bioRxiv2020

HiC-ACT: Improved Detection of Chromatin Interactions from Hi-C Data via Aggregated Cauchy Test

Genome-wide chromatin conformation capture technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.g., 40Kb bin), but is invalid at high resolution (e.g., 5 or 10Kb bins) since spatial dependency of neighboring chromatin interactions is non-negligible at high resolution. Our previous hidden Markov random field based methods accommodate spatial dependency but are computationally intensive. It is urgent to develop approaches that can model spatial dependence, in a computationally efficient and scalable manner. Here, we develop HiC-ACT, an aggregated Cauchy test (ACT) based approach, to improve the detection of chromatin interactions by post-processing results from methods assuming independence. To benchmark the performance of HiC-ACT, we re-analyzed deeply sequenced Hi-C data from a human lymphoblastoid cell line GM12878 and mouse embryonic stem cell line (mESC). Our results demonstrate advantages of HiC-ACT in improving sensitivity with controlled type-I error. By leveraging information from neighboring chromatin interactions, HiC-ACT enhances the power to detect interactions with lower signal to noise ratio and similar (if not stronger) epigenetic signatures that suggest regulatory roles. We further demonstrate that HiC-ACT peaks show higher overlap with known enhancers than Fit-Hi-C/FitHiC2 peaks, in both GM12878 and mESC. HiC-ACT, effectively a summary statistic based approach, is computationally efficient (~6 minutes and ~2GB memory to process 25,000 pairwise interactions).

bioinformatics↗

bioRxiv2020

Microfluidic Antibody Affinity Profiling for In-Solution Characterisation of Alloantibody - HLA Interactions in Human Serum

The detection and characterisation of antibodies in human blood is a key for clinical diagnostics and risk assessment for autoimmunity, infectious diseases and transplanta-tion. Antibody titre derived from immunoassays is a commonly used measure for anti-body response, but this metric does not resolve readily the two fundamental properties of antibodies in solution, namely their affinity and concentration. This difficulty originates from the fact that the fundamental parameters describing the binding interaction, affinity and ligand concentration, are convoluted into the titre measurement; moreover, the difficulty of controlling the surface concentration and activity of the immobilised ligand can make it challenging to distinguish between avidity and affinity. To address these challenges, we developed microfluidic antibody affinity profiling, an assay which allows the simultaneous determination of both affinity and antibody concentration, directly in solution, without surface immobilisation or antibody purification. We demonstrate these measurements in the context of alloantibody characterisation in organ transplantation, using complex patient sera, and quantify the concentration and affinity of alloantibodies against donor Human Leukocyte Antigens (HLA), an extensively used clinical biomarker to access the risk of allograft rejection. These results outline a path towards detection and in depth profiling of antibody response in patient sera.

biophysics↗

bioRxiv2020

Statistics and classification of spatial rotations and projection directions considering molecular symmetry in 3D electron cryo-microscopy

Electron cryo-microscopy (cryoEM) three-dimensional (3D) reconstruction is based on estimations of orientations of projection images or 3D volumes. It is common that the macromolecules studied by cryoEM have molecular symmetry, which, unfortunately, has not been taken into consideration by any statistics for either spatial rotations or projection directions at this point. Meanwhile, there are growing needs to adopt advanced statistical methods, and further, modern machine learning techniques in cryoEM. Since those methodologies are built heavily upon statistical learning cornerstones, the absence of their domain-specific statistical justification limits their applications in cryoEM. In this research, based on the concept of non-unique-games (NUG), we propose two key statistical measurements, the mean and the variance, of both spatial rotations and projection directions when molecular symmetry is considered. Such methods are implemented in the open-source python package pySymStat.

biophysics↗

bioRxiv2020

Generation of nonhuman primate retinitis pigmentosa model by in situ knockout of RHO in rhesus macaque retina

Retinitis pigmentosa (RP) is a form of inherited retinal degenerative disease that ultimately involves the macula, which is present in primates but not in the rodents. Therefore, creating nonhuman primate (NHP) models of RP is of critical importance to study its mechanism of pathogenesis and to evaluate potential therapeutic options in the future. Here we applied adeno-associated virus (AAV)-delivered CRISPR/SaCas9 technology to knockout the RHO gene in the retinae of the adult rhesus macaque (Macaca mulatta) to investigate the hypothesis whether non-germline mutation of the RHO gene is sufficient to recapitulate RP. Through a series of studies, we were able to demonstrate successful somatic editing of the RHO gene and reduced RHO protein expression. More importantly, the mutant macaque retinae displayed clinical RP phenotypes, including photoreceptor degeneration, retinal thinning, abnormal rod subcellular structures, and reduced photoresponse. Therefore, we suggest somatic editing of the RHO gene is able to phenocopy RP, and the reduced time span in generating NHP mutant accelerates RP research and expands the utility of NHP model for human disease study.

neuroscience↗

bioRxiv2020

GluD2- and Cbln1-mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells

The synaptotrophic hypothesis posits that synapse formation stabilizes dendritic branches, yet this hypothesis has not been causally tested in vivo in the mammalian brain. Presynaptic ligand cerebellin-1 (Cbln1) and postsynaptic receptor GluD2 mediate synaptogenesis between granule cells and Purkinje cells in the molecular layer of the cerebellar cortex. Here we show that sparse but not global knockout of GluD2 causes under-elaboration of Purkinje cell dendrites in the deep molecular layer and overelaboration in the superficial molecular layer. Developmental, overexpression, structure-function, and genetic epistasis analyses indicate that dendrite morphogenesis defects result from competitive synaptogenesis in a Cbln1/GluD2-dependent manner. A generative model of dendritic growth based on competitive synaptogenesis largely recapitulates GluD2 sparse and global knockout phenotypes. Our results support the synaptotrophic hypothesis at initial stages of dendrite development, suggest a second mode in which cumulative synapse formation inhibits further dendrite growth, and highlight the importance of competition in dendrite morphogenesis.

neuroscience↗

bioRxiv2020

A sound strategy for homology modeling-based affinity maturation of a HIF-1α single-domain intrabody

A sound strategy for computer-aided binding affinity prediction was developed for in silico nanobody affinity maturation. Venn-intersection of multi-algorithm screening (VIMAS), an iterative computer-assisted nanobody affinity maturation virtual screening procedure, was designed. Homology modeling and protein docking methods were used to substitute the need for solution of a complex crystal structure, which is expanding the application of this platform. As a test case, an anti-HIF-1 nanobody, VHH212, was screened via a native ribosome display library with a 26.6 nM of KD value was used as the parent. A mutant with a 17.5-fold enhancement in binding affinity (1.52 nM) was obtained by using the VIMAS strategy. Furthermore, the protein-protein interaction of interface residues, which is important for binding affinity, was analyzed in-depth. Targeting HIF-1 can sensitize PDAC tumors to gemcitabine, which is a potential co-treatment method for pancreatic cancer patients. Under combined treatment, the cytotoxicity of gemcitabine on pancreatic cancer cell lines increased with the enhanced-affinity of an intrabody. Thus, this study provides a platform for universal, efficient and convenient in silico affinity maturation of nanobodies.

biochemistry↗

bioRxiv2020

Sexually dimorphic roles for the type 2 diabetes-associated C2cd4b gene in murine glucose homeostasis

Variants close to the VPS13C/C2CD4A/C2CD4B locus are associated with altered risk of type 2 diabetes in genome-wide association studies. Whilst previous functional work has suggested roles for VPS13C and C2CD4A in disease development, none has explored the role of C2CD4B. Here, we show that systemic inactivation of C2cd4b in mice leads to marked, but highly sexually dimorphic, changes in body weight and glucose homeostasis. Female C2cd4b mice display unchanged body weight but abnormal glucose tolerance and defective in vivo, but not in vitro, insulin secretion, associated with a marked decrease in follicle stimulating hormone levels. In sharp contrast, male C2cd4b null mice displayed normal glucose tolerance but an increase in body weight and fasting glycemia after maintenance on high fat diet. No metabolic disturbances were observed after global inactivation of C2cd4a in mice, or in pancreatic {beta} cell function at larval stages in C2cd4ab null zebrafish. These studies suggest that C2cd4b may act centrally to influence sex-dependent circuits which control pancreatic {beta} cell function and glucose tolerance in rodents. However, the absence of sexual dimorphism in the impact of diabetes risk variants argues for additional roles for C2CD4A or VPS13C in the control of glucose homeostasis in man.

genetics↗

bioRxiv2020

Evolution and molecular characteristics of SARS-CoV-2 genome

ObjectivesTo reveal epidemic trend and possible origins of SARS-CoV-2 by exploring its evolution and molecular characteristics based on a large number of genomes since it has infected millions of people and spread quickly all over the world. MethodsVarious evolution analysis methods were employed. ResultsThe estimated Ka/Ks ratio of SARS-CoV-2 is 1.008 or 1.094 based on 622 or 3624 SARS-CoV-2 genomes, and the time to the most recent common ancestor (tMRCA) was inferred in late September 2019. Further 9 key specific sites of highly linkage and four major haplotypes H1, H2, H3 and H4 were found. The Ka/Ks, detected population size and development trends of each major haplotype showed H3 and H4 subgroups were going through a purify evolution and almost disappeared after detection, indicating H3 and H4 might have existed for a long time, while H1 and H2 subgroups were going through a near neutral or neutral evolution and globally increased with time. Notably the frequency of H1 was generally high in Europe and correlated to death rate (r>0.37). ConclusionsIn this study, the evolution and molecular characteristics of more than 16000 genomic sequences provided a new perspective for revealing epidemiology of SARS-CoV-2.

bioinformatics↗

bioRxiv2020

Risperidone-induced changes in DNA methylation from peripheral blood in first-episode schizophrenia parallel neuroimaging and cognitive phenotype

Today, second generation anti-psychotics such as clozapine and risperidone are the favored treatment for schizophrenia. Yet, the absence of relevant biomarkers that can decode their neurobiological effect shackles our ability to accurately predict and track response to treatment. While researchers have investigated DNA methylation as a biomarker for schizophrenia risk, none have performed a systematic analysis of the effect of antipsychotics upon DNA methylation. We hypothesize that disease-related methylation changes occur before treatment, and that acute antipsychotic treatment may affect DNA methylation. We designed a longitudinal DNA methylation study to estimate risperidones effect on DNA methylation and how changes in DNA methylation might influence risperidones therapeutic effect on behavioral and neuroimaging phenotypes. Thirty-eight patients with first-episode drug-naive schizophrenia (FES) and 38 demographically-matched individuals (healthy controls) participated. We identified brain related pathways enriched in 8,204 FES-associated methylation sites. Risperidone administration altered methylation in 6,143 CpG DNA sites. Post-treatment FES associated with methylation in 6760 CpG sites. Majority of the DNA methylation changes were treatment effect in the overall CpG sites, the FES associated CpG sites, and risperidone associated CpG sites, except for the post-treatment FES associated CpG sites. There were 590 DNA methylation cites normalized by risperidone treatment. The methylation changes of these 590 CpG sites were related to alterations in symptom severity, spontaneous neurophysiological activity, and cognitive function. To our knowledge, this is the first longitudinal methylation study of drug treatment effect and side effect in psychiatric disorders to include parallel studies of neuroimaging and cognitive phenotypes. We identified FES-associated CpG sites not confounded by drug treatment as potential SCZ biomarkers. The normalization effect of risperidone monotherapy suggests that DNA methylation changes may serve as a predictive biomarker for treatment effect. The constructed methylation-phenotype network revealed a relationship between methylation and a wide range of biological and psychological variables.

pharmacology and toxicology↗

bioRxiv2020

Chromatin 3D interaction analysis of the STARD10 locus unveils FCHSD2 as a new regulator of insulin secretion

Genome-wide association studies have identified thousands of genetic variants associated with type 2 diabetes (T2D) risk. Using chromatin conformation capture we show that an enhancer cluster in the STARD10 T2D locus forms a defined 3D chromatin domain. A 4.1 Kb region within this region, carrying five disease-associated variants, physically interacts with CTCF-binding regions and with an enhancer possessing strong transcriptional activity. Analysis of human islet 3D chromatin interaction maps identifies FCHSD2 gene as an additional target of the enhancer cluster. CRISPR-Cas9-mediated deletion of the variant region, or of an associated enhancer, in human pancreatic beta cells impaired glucose-stimulated insulin secretion. Expression of both STARD10 and FCHSD2, but not ARAP1, was reduced in cells harboring CRISPR deletions, and expression of STARD10 and FCHSD2 was associated with the possession of variant alleles in human islets. Finally, CRISPR-Cas9-mediated loss of STARD10 or FCHSD2 impaired regulated insulin secretion. Thus, multiple genes at the STARD10 locus influence {beta} cell function.

cell biology↗