Biology subjects

Hu, H.

Publications and source records attributed to Hu, H..

At least 271 records · Page 15Linked to original sources

bioRxiv2019

Principles Governing the Dynamics of GABAergic Interneurons in the Barrel Cortex

Information processing in the neocortex is performed by GABAergic interneurons that are integrated with excitatory neurons into precisely structured circuits. To reveal how each neuron type shapes sensory representations, we measured spikes and membrane potential of specific types of neurons in the barrel cortex while mice performed an active, whisker-dependent object localization task. Whiskers were tracked with millisecond precision. Fast-spiking (FS) neurons were activated by touch with short latency and by whisking. FS neurons track thalamic input and provide feedforward inhibition. Somatostatin (SOM)-expressing neurons were also excited by touch, but with a delay (5 ms) compared to excitatory (E) and FS neurons. SOM neurons monitor local excitation and provide feedback inhibition. Vasoactive intestinal polypeptide (VIP)-expressing neurons were not driven by touch but elevated their spike rate during whisking, disinhibiting E and FS neurons. Our data reveal rules of recruitment for specific interneuron types, providing foundations for understanding cortical computations.

neuroscience↗

bioRxiv2019

A novel QTL conferring Fusarium crown rot resistance located on chromosome arm 6HL in barley

Fusarium crown rot (FCR), caused primarily by Fusarium pseudograminearum, is a devastating disease for cereal production in semi-arid regions worldwide. To identify and characterize loci conferring FCR resistance, we assessed a landrace AWCS799 which is among the top lines identified from a screening. Genetic control of its resistance was investigated by generating and analysing two populations of recombinant inbred lines. One of the populations was used for QTL detection and the other for validation. A novel QTL, located on the long arm of chromosome 6H (designated as Qcrs.caf-6H), was consistently detected in each of the four trials conducted against the mapping population. The QTL explained up to 29.1% of the phenotypic variance and its effect was confirmed in the validation population. Significant interactions between this resistance locus and either plant height or heading date were not detected, further facilitating its manipulation in breeding programs. Key MessageThis study identified and validated a novel and large-effect QTL conferring Fusarium crown rot resistance on the long arm of chromosome 6HL in barley.

plant biology↗

bioRxiv2019

A novel blaSIM-1-carrying megaplasmid pSIM-1-BJ01 isolated from clinical Klebsiella pneumonia

A rare carbapenem-resistant gene blaSIM-1 was found in a 316-kb megaplasmid designated pSIM-1-BJ01 isolated from a clinical strain Klebsiella pneumonia 13624. The plasmid pSIM-1-BJ01 was fully sequenced and analyzed. Its length is 316,557 bp and it has 342 putative open reading frames with two multidrug-resistant regions and a total of 19 resistant genes. Its backbone was highly homologous to the newly reported plasmid pRJA166a, which was isolated from a clinical third-generation cephalosporin-resistant hypervirulen strain K. pneumonia ST23. The plasmid pSIM-1-BJ01 was verified to be able to transfer to Escherichia coli. The emergency of the transferable blaSIM-1-carrying multidrug-resistant plasmid pSIM-1-BJ01 suggests the spread of blaSIM among Enterobacteriaceae is possible. Therefore, the data presented herein provided insights into the genomic diversity and evolution of blaSIM-carrying plasmids, as well as the dissemination and epidemiology of blaSIM among Enterobacteriaceae in public health system.

epidemiology↗

bioRxiv2018

ACME: Pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks

Prediction of peptide binding to MHC molecules plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Although numerous computational methods have been developed to this end, several challenges still remain in predicting peptide-MHC interactions. Many previous methods are allele-specific, training separate models for individual alleles and are thus unable to yield accurate predictions for those alleles with limited training data. Despite that there exist several pan-specific algorithms that train a common model for different alleles, they only adopt simple model structures that generally have limited performance in capturing the complex underlying patterns of peptide-MHC interactions. Here we present ACME (Attention-based Convolutional neural networks for MHC Epitope binding prediction), a new pan-specific algorithm to accurately predict the binding affinities between peptides and MHC class I molecules, even for those new alleles that are not seen in the training data. Extensive tests have demonstrated that ACME can significantly outperform other state-of-the-art prediction methods with an increase of the Pearson Correlation Coefficient by up to 23 percent. In addition, its ability to identify strong-binding peptides has been experimentally validated. Moreover, by integrating the convolutional neural network with attention mechanism, ACME is able to extract interpretable patterns that can provide useful and detailed insights into the binding preferences between peptides and their MHC partners. All these results have demonstrated that ACME can provide a powerful and practically useful tool for the studies of peptide-MHC class I interactions.

bioinformatics↗

bioRxiv2018

Rationalizing Translation Elongation by Reinforcement Learning

Translation elongation plays a crucial role in multiple aspects of protein biogenesis. In this study, we develop a novel deep reinforcement learning based framework, named RiboRL, to model the distributions of ribosomes on transcripts. In particular, RiboRL employs a policy network (PolicyNet) to perform a context-dependent feature selection to facilitate the prediction of ribosome density. Extensive tests demonstrate that RiboRL can outperform other state-of-the-art methods in predicting ribosome densities. We also show that the reinforcement learning based strategy can generate more informative features for the prediction task when compared to other commonly used attribution methods in deep learning. Moreover, the in-depth analyses and a case study also indicate the potential applications of the RiboRL framework in generating meaningful biological insights regarding translation elongation dynamics. These results have established RiboRL as a useful computational tool to facilitate the studies of the underlying mechanisms of translational regulation.

bioinformatics↗

bioRxiv2018

Noninvasive prenatal detection of fetal trisomy and single gene disease by shotgun sequencing of placenta originated exosome DNA: a proof-of-concept validation

BackgroundDuring human pregnancy, Placental trophectoderm cells can release exosomes into maternal circulation. Trophoblast cells also give rise to cell-free DNA (cfDNA) and has been used for noninvasive prenatal screening for chromosomal aneuploidy. We intended to prove the existence of exosomal DNA (exoDNA) in the exosomes of maternal blood and compared exoDNA with plasma cfDNA in terms of genome distribution, fragment length, and the possibility of detecting genetic diseases.\n\nMethodsMaternal blood from 20 euploid pregnancies, 9 T21 pregnancies, 3 T18 pregnancies, 1 T13 pregnancy and 2 pregnancies with FGFR3 mutations were obtained. Exosomes enriched from maternal plasma were confirmed by transmission electronic microscopy (TEM), western blotting and flow cytometry. ExoDNA was extracted and its fetal origin was confirmed by realtime fluorescence quantitative PCR(Q-PCR). Besides, exoDNA content was uncovered by Q-PCR. To characterize exoDNA and compare with cfDNA, pair-end whole genome sequencing was performed. Lastly, the fetal risk of genetic diseases was analyzed using the exoDNA sequencing data.\n\nResultsExoDNA span on all 23 pairs of chromosomes and mitochondria, sharing a similar distribution pattern and higher GC content comparing with cfDNA. ExoDNA showed shorter fragments yet lower fetal fraction than cfDNA. ExoDNA could be used to determine fetal gender correctly, and all trisomies as well as de novo FGFR3 mutations.\n\nConclusionsWe proved that fetal exoDNA could be identified in the exosomes extracted from maternal plasma. ExoDNA shared some similar features to cfDNA and could potentially be used to detect genetic diseases in fetus.

genetics↗

bioRxiv2018

The Dynamic Conformational Landscapes of the Protein Methyltransferase SETD8

Elucidating conformational heterogeneity of proteins is essential for understanding protein functions and developing exogenous ligands for chemical perturbation. While structural biology methods can provide atomic details of static protein structures, these approaches cannot in general resolve less populated, functionally relevant conformations and uncover conformational kinetics. Here we demonstrate a new paradigm for illuminating dynamic conformational landscapes of target proteins. SETD8 (Pr-SET7/SET8/KMT5A) is a biologically relevant protein lysine methyltransferase for in vivo monomethylation of histone H4 lysine 20 and nonhistone targets. Utilizing covalent chemical inhibitors and depleting native ligands to trap hidden high-energy conformational states, we obtained diverse novel X-ray structures of SETD8. These structures were used to seed massively distributed molecular simulations that generated six milliseconds of trajectory data of SETD8 in the presence or absence of its cofactor. We used an automated machine learning approach to reveal slow conformational motions and thus distinct conformational states of SETD8, and validated the resulting dynamic conformational landscapes with multiple biophysical methods. The resulting models provide unprecedented mechanistic insight into how protein dynamics plays a role in SAM binding and thus catalysis, and how this function can be modulated by diverse cancer-associated mutants. These findings set up the foundation for revealing enzymatic mechanisms and developing inhibitors in the context of conformational landscapes of target proteins.

biophysics↗

bioRxiv2018

The origin of fungi-culture in termites was associated with a shift to a mycolytic gut bacteria community

Termites forage on a range of substrates, and it has been suggested that diet shapes the composition and function of termite gut bacterial communities. Through comparative analyses of gut metagenomes in nine termite species with distinct diets, we characterise bacterial community compositions and identify biomass-degrading enzymes and the bacterial taxa that encode them. We find that fungus-growing termite guts are enriched in fungal cell wall-degrading and proteolytic enzymes, while wood-feeding termite gut communities are enriched for plant cell wall-degrading enzymes. Interestingly, wood-feeding termite gut bacteria code for abundant chitinolytic enzymes, suggesting that fungal biomass within the decaying wood likely contributes to gut bacteria or termite host nutrition. Across diets, the dominant biomass-degrading enzymes are predominantly coded for by the most abundant bacterial taxa, suggesting tight links between diet and gut community composition, with the most marked shift being the communities coding for the mycolytic capacity of the fungus-growing termite gut.

ecology↗

bioRxiv2018

Foxp1 controls neural stem cell competence and bias towards deep layer cortical fate.

SUMMARYThe laminar architecture of the mammalian neocortex depends on the orderly generation of distinct neuronal subtypes by apical radial glia (aRG) during embryogenesis. We identify critical roles for Foxp1 in maintaining RG identity and gating the temporal competency for early neurogenesis. High levels of Foxp1 are associated with early aRG and are required to promote proliferation and influence cell division symmetry, favoring aRG expansion and production of early born neurons. The potent pro-progenitor functions of Foxp1 are revealed through its ability to preserve a population of cells with aRG identity throughout development and extend the early neurogenic period into postnatal life. Foxp1 further promotes the formation of cells resembling basal RG (bRG), a progenitor group implicated in the increased size and complexity of the human cortex. Consistent with this role, we show that FOXP1 is associated with the initial formation and expansion of bRG during human corticogenesis.\n\nHIGHLIGHTSO_LIFoxp1 is transiently expressed by aRG during the early phase of corticogenesis\nC_LIO_LIFoxp1 promotes self-renewing vertical cell divisions and aRG maintenance\nC_LIO_LIFoxp1 gates the time window of deep layer neurogenesis\nC_LIO_LIEctopic Foxp1 expression can elicit bRG formation\nC_LI

developmental biology↗

bioRxiv2018

Comprehensive analysis of immune evasion in breast cancer by single-cell RNA-seq

The tumor microenvironment is composed of numerous cell types, including tumor, immune and stromal cells. Cancer cells interact with the tumor microenvironment to suppress anticancer immunity. In this study, we molecularly dissected the tumor microenvironment of breast cancer by single-cell RNA-seq. We profiled the breast cancer tumor microenvironment by analyzing the single-cell transcriptomes of 52,163 cells from the tumor tissues of 15 breast cancer patients. The tumor cells and immune cells from individual patients were analyzed simultaneously at the single-cell level. This study explores the diversity of the cell types in the tumor microenvironment and provides information on the mechanisms of escape from clearance by immune cells in breast cancer.\n\nOne Sentence SummaryLandscape of tumor cells and immune cells in breast cancer by single cell RNA-seq

cancer biology↗

bioRxiv2018

Comparative genomic analysis revealed rapid differentiation in the pathogenicity-related gene repertoires between Pyricularia oryzae and Pyricularia penniseti isolated from a Pennisetum grass

BackgroundsPyricularia is a multispecies complex that could infect and cause severe blast disease on diverse hosts, including rice, wheat and many other grasses. Although the genome size of this fungal complex is small [~40 Mbp for Pyricularia oryzae (syn. Magnaporthe oryzae), and ~45 Mbp for P. grisea], the genome plasticity allows the fungus to jump and adapt to new hosts. Therefore, deciphering the genome basis of individual species could facilitate the evolutionary and genetic study of this fungus. However, except for the P. oryzae subgroup, many other species isolated from diverse hosts, such as the Pennisetum grasses, remain largely uncovered genetically.\n\nResultsHere, we report the genome sequence of a pyriform-shaped fungal strain P. penniseti P1609 isolated from a Pennisetum grass (JUJUNCAO) using PacBio SMRT sequencing technology. We performed a phylogenomic analysis of 28 Magnaporthales species and 5 non-Magnaporthales species and addressed P1609 into a Pyricularia subclade that is distant from P. oryzae. Comparative genomic analysis revealed that the pathogenicity-related gene repertoires were fairly different between P1609 and the P. oryzae strain 70-15, including the cloned avirulence genes, other putative secreted proteins, as well as some other predicted Pathogen-Host Interaction (PHI) genes. Genomic sequence comparison also identified many genomic rearrangements.\n\nConclusionTaken together, our results suggested that the genomic sequence of the P. penniseti P1609 could be a useful resource for the genetic study of the Pennisetum-infecting Pyricularia species.

genomics↗

bioRxiv2018

Next-Generation Sequencing Could be a Promising Diagnostic Approach for Pathogen Detection: Pathogenic Analysis of Pediatric Bacterial Meningitis by Next-Generation Sequencing Technology Directly from Cerebrospinal Fluid Specimens

BackgroundBacterial meningitis remains one of the major challenges in infectious diseases, leading to sequel in many cases. A prompt diagnosis of the causative microorganism is critical to significantly improve outcome of bacterial meningitis. Although various targeted tests for cerebrospinal fluid (CSF) samples are available, it is a big problem for the identification of etiology of bacterial meningitis.\n\nMethodsHere we describe the use of unbiased sequence analyses by next-generation sequencing (NGS) technology for the identification of infectious microorganisms from CSF samples of pediatric bacterial meningitis patients in the Department of Infectious Diseases from Beijing Childrens Hospital.\n\nResultsIn total, we had 99 bacterial meningitis patients in our study, 55 (55.6%) of these were etiologically confirmed by clinical microbiology methods. Combined with NGS, 68 cases (68.7%) were etiologically confirmed. The main pathogens identified in this study were Streptococcus pneumoniae (n=29), group B streptococcus (n=15), Staphylococcus aureus (n=7), Escherichia coli (n=7). In addition, two cases with cytomegalovirus infection and one with Taenia saginata asiatica were confirmed by NGS.\n\nConclusionsNGS could be a promising alternative diagnostic approach for critically ill patients suffering from bacterial meningitis in pediatric population.\n\nSummaryWe conducted the study for the identification of microorganisms by next-generation sequencing directly from CSF samples of pediatric bacterial meningitis patients. And the study showed that NGS could be a promising alternative diagnostic approach for bacterial meningitis in pediatric population.

microbiology↗

bioRxiv2018

Identification of pathogens in culture-negative infective endocarditis with metagenomic analysis

Pathogens identification is critical for the proper diagnosis and precise treatment of infective endocarditis. Although blood and valve cultures are the gold standard for IE pathogens detection, many cases are culture-negative, especially in patients who had received long-term antibiotic treatment, and precise diagnosis has therefore become a major challenge in the clinic. Metagenomic sequencing can provide both information on the pathogenic strain and the antibiotic susceptibility profile of patient samples without culturing, offering a powerful method to deal with culture-negative cases. In this work, we assessed the feasibility of a metagenomic approach to detect the causative pathogens in resected valves from IE patients.\n\nUsing our in-house developed bioinformatics pipeline, we analyzed the sequencing results generated from both next-generation sequencing and Oxford Nanopore Technologies MinION nanopore sequencing for the direct identification of pathogens from the resected valves of seven clinically culture-negative IE patients according to the modified Duke criteria. Moreover, we were able to simultaneously characterize respective antimicrobial resistance features. This provides clinicians with valuable information to diagnose and treat IE patients after valve replacement surgery.

microbiology↗

bioRxiv2018

Autophagy Decreases Alveolar Epithelial Cell Injury by Suppressing the NF-κB Signaling Pathway and Regulating the Release of Inflammatory Mediators

To research the impact of autophagy on alveolar epithelial cell inflammation and its possible mechanism in early stages of hypoxia, we established a cell hypoxia-reoxygenation model and orthotopic left lung ischemia-reperfusion model. Rat alveolar epithelial cells stably expressing GFP-LC3 were treated with an autophagy inhibitor (3-methyladenine, 3-MA) or autophagy promoter (rapamycin), followed by hypoxia-reoxygenation treatment at 2, 4 and 6h in vitro. In vivo, twenty-four male Sprague-Dawley rats were randomly divided into four groups (model group: no blocking of hilum in the left lung; control group: blocking of hilum in the left lung for 1h with DMSO lavage; 3-MA group: blocking of hilum in the left lung for 1h with 100ml/kg of 3-MA (5mol/L) solution lavage; rapamycin group: blocking of hilum in the left lung for 1h with 100ml/kg of rapamycin (250nmol/L) solution lavage) to establish an orthotopic left lung ischemia model. This study demonstrated that rapamycin significantly suppressed the NF-{kappa}B signaling pathway, restrained the expression of pro-inflammatory factors. A contrary result was confirmed by 3-MA pretreatment. These findings indicate that autophagy reduces ischemia-reperfusion injury by repressing inflammatory signaling pathways in the early stage of hypoxia in vitro and in vivo. This could be a new protective method for lung ischemia-reperfusion injury.

cell biology↗

bioRxiv2018

DeepHINT: Understanding HIV-1 integration via deep learning with attention

MotivationHuman immunodeficiency virus type 1 (HIV-1) genome integration is closely related to clinical latency and viral rebound. In addition to human DNA sequences that directly interact with the integration machinery, the selection of HIV integration sites has also been shown to depend on the heterogeneous genomic context around a large region, which greatly hinders the prediction and mechanistic studies of HIV integration.\n\nResultsWe have developed an attention-based deep learning framework, named DeepHINT, to simultaneously provide accurate prediction of HIV integration sites and mechanistic explanations of the detected sites. Extensive tests on a high-density HIV integration site dataset showed that DeepHINT can outperform conventional modeling strategies by automatically learning the genomic context of HIV integration solely from primary DNA sequence information. Systematic analyses on diverse known factors of HIV integration further validated the biological relevance of the prediction result. More importantly, in-depth analyses of the attention values output by DeepHINT revealed intriguing mechanistic implications in the selection of HIV integration sites, including potential roles of several basic helix-loop-helix (bHLH) transcription factors and zinc-finger proteins. These results established DeepHINT as an effective and explainable deep learning framework for the prediction and mechanistic study of HIV integration.\n\nAvailabilityDeepHINT is available as an open-source software and can be downloaded from https://github.com/nonnerdling/DeepHINT\n\nContactlzhang20@mail.tsinghua.edu.cn and zengjy321@tsinghua.edu.cn

bioinformatics↗

bioRxiv2017

Identification of Cancer-associated Metabolic Vulnerabilities by Modeling Multi-objective Optimality in Metabolism

Computational modeling of the genome-wide metabolic network is essential for designing new therapeutics targeting cancer-associated metabolic disorder, which is a hallmark of human malignancies. However, previous studies generally assumed that metabolic fluxes of cancer cells are subjected to the maximization of biomass production, despite the wide existence of trade-offs among multiple metabolic objectives. To address this issue, we developed a multi-objective model of cancer metabolism with algorithms depicting approximate Pareto surfaces and incorporating multiple omics datasets. To validate this approach, we built individualized models for NCI-60 cancer cell lines, and accurately predicted cell growth rates and other biological consequences of metabolic perturbations in these cells. By analyzing the landscape of approximate Pareto surface, we identified a list of metabolic targets essential for cancer cell proliferation and the Warburg effect, and further demonstrated their close association with cancer patient survival. Finally, metabolic targets predicted to be essential for tumor progression were validated by cell-based experiments, confirming this multi-objective modelling method as a novel and effective strategy to identify cancer-associated metabolic vulnerabilities.

systems biology↗

bioRxiv2017

Functional analyses of the CIF1-CIF2 complex in Trypanosoma brucei identify the structural motifs required for complex formation and cytokinesis

Cytokinesis in trypanosome occurs uni-directionally along the longitudinal axis from the cell anterior towards the cell posterior and requires a trypanosome-specific CIF1-CIF2 protein complex. However, little is known about the contribution of the structural motifs in CIF1 and CIF2 to complex assembly and cytokinesis. Here, we demonstrated that the two zinc-finger motifs but not the coiled-coil motif in CIF1 are required for interaction with the EF-hand motifs in CIF2. We further showed that localization of CIF1 depends on the coiled-coil motif and the first zinc-finger motif and that localization of CIF2 depends on the EF-hand motifs. Deletion of the coiled-coil motif and mutation of either zinc-finger motifs in CIF1 disrupted cytokinesis. Further, mutation of either zinc-finger motif in CIF1 mis-localized CIF2 to the cytosol and destabilized CIF2, whereas deletion of the coiled-coil motif in CIF1 spread CIF2 over to the new flagellum attachment zone and stabilized CIF2. Together, these results uncovered the requirement of the coiled-coil motif and zinc-finger motifs for CIF1 function in cytokinesis and for CIF2 localization and stability, providing structural insights into the functional interplay between the two cytokinesis regulators.

cell biology↗

bioRxiv2017

Hemimetabolous genomes reveal molecular basis of termite eusociality

Around 150 million years ago, eusocial termites evolved from within the cockroaches, 50 million years before eusocial Hymenoptera, such as bees and ants, appeared. Here, we report the first, 2GB genome of a cockroach, Blattella germanica, and the 1.3GB genome of the drywood termite, Cryptotermes secundus. We show evolutionary signatures of termite eusociality by comparing the genomes and transcriptomes of three termites and the cockroach against the background of 16 other eusocial and non-eusocial insects. Dramatic adaptive changes in genes underlying the production and perception of pheromones confirm the importance of chemical communication in the termites. These are accompanied by major changes in gene regulation and the molecular evolution of caste determination. Many of these results parallel molecular mechanisms of eusocial evolution in Hymenoptera. However, the specific solutions are remarkably different, thus revealing a striking case of convergence in one of the major evolutionary transitions in biological complexity.

evolutionary biology↗