Biology subjects

Han, Y.

Publications and source records attributed to Han, Y..

At least 289 records · Page 16Linked to original sources

bioRxiv2020

StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

DNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs.

bioinformatics↗

bioRxiv2020

Fertility-LightGBM: A fertility-related protein prediction model by multi-information fusion and light gradient boosting machine

The identification of fertility-related proteins plays an essential part in understanding the embryogenesis of germ cell development. Since the traditional experimental methods are expensive and time-consuming to identify fertility-related proteins, the purposes of predicting protein functions from amino acid sequences appeared. In this paper, we propose a fertility-related protein prediction model. Firstly, the model combines protein physicochemical property information, evolutionary information and sequence information to construct the initial feature space ALL. Then, the least absolute shrinkage and selection operator (LASSO) is used to remove redundant features. Finally, light gradient boosting machine (LightGBM) is used as a classifier to predict. The 5-fold cross-validation accuracy of the training dataset is 88.5%, and the independent accuracy of the training dataset is 91.5%. The results show that our model is more competitive for the prediction of fertility-related proteins, which is helpful for the study of fertility diseases and related drug targets.

bioinformatics↗

bioRxiv2020

TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors, but also posed computational challenges on how to integrate and utilize the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly two million cells from 76 high-quality tumor datasets across 28 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, malignant cell classification, cell clustering, cell-type annotation, differential expression analysis, and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching, and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

bioinformatics↗

bioRxiv2020

DNN-DTIs: improved drug-target interactions prediction using XGBoost feature selection and deep neural network

Research, analysis, and prediction of drug-target interactions (DTIs) play an important role in understanding drug mechanisms, drug repositioning and design. Machine learning (ML)-based methods for DTIs prediction can mitigate the shortcomings of time-consuming and labor-intensive experimental approaches, providing new ideas and insights for drug design. We propose a novel pipeline for predicting drug-target interactions, called DNN-DTIs. First, the target information is characterized by pseudo-amino acid composition, pseudo position-specific scoring matrix, conjoint triad, composition, transition and distribution, Moreau-Broto autocorrelation, and structure feature. Then, the drug compounds are encoded using substructure fingerprint. Next, we utilize XGBoost to determine nonredundant and important feature subset, then the optimized and balanced sample vectors could be obtained through SMOTE. Finally, a DTIs predictor, DNN-DTIs, is developed based on deep neural network (DNN) via layer-by-layer learning. Experimental results indicate that DNN-DTIs achieves outstanding performance than other predictors with the ACC values of 98.78%, 98.60%, 97.98%, 98.24% and 98.00% on Enzyme, Ion Channels (IC), GPCR, Nuclear Receptors (NR) and Kuang's dataset. Therefore, DNN-DTIs's accurate prediction performance on Network1 and Network2 make it logical choice for contributing to the study of DTIs, especially, the drug repositioning and new usage of old drugs.

bioinformatics↗

bioRxiv2020

Neuropeptidome regulation after baculovirus infection. A focus on proctolin and its relevance in locomotion and digestion

Baculoviruses constitute a large group of invertebrate DNA viruses, predominantly infecting larvae of the insect order Lepidoptera. During a baculovirus infection, the virus spreads throughout the insect body producing a systemic infection in multiple larval tissues. Some behavioral and physiological changes in lepidopteran larvae have been described following a baculovirus infection and those changes could be connected with alterations in the hosts central nervous system (CNS). As a main component of the CNS, neuropeptides are small protein-like molecules functioning as neurohormones, neurotransmitters or neuromodulators. These peptides are involved in regulating animal physiology and behaviour and could also be targeted by baculoviruses in order to achieve host behavioural manipulations leading to increased viral fitness. In this study, we have investigated the effect of a Spodoptera exigua multiple nucleopolyhedrovirus (SeMNPV) infection on the neuropeptidome gene expression of Spodoptera exigua larval heads and brains. Expression of the gene encoding the neuropeptide proctolin was severely downregulated following infection and was chosen for further analysis. A recombinant Autographa californica multiple nucleopolyhedrovirus (AcMNPV) overexpressing the S. exigua proctolin gene was generated and used in bioassays using S. exigua larvae to study its influence on the viral infection. AcMNPV-proctolin infected larvae showed less locomotion activity and suffered a loss of weight compared to larvae infected with wild type AcMNPV or mock-infected larvae. These results provide additional information on the role of proctolin during a baculovirus infection, and offers a novel hypothesis for the molecular bases for the behavioral changes associated with a baculovirus infection.

genetics↗

bioRxiv2020

Epigenetic age-predictions in mice using pyrosequencing, droplet digital PCR or barcoded bisulfite amplicon sequencing

Age-associated DNA methylation reflects aspects of biological aging - therefore epigenetic clocks for mice can help to elucidate the impact of treatments or genetic background on the aging process in this model organism. Initially, age-predictors for mice were trained on genome-wide DNA methylation profiles, whereas we have recently described a targeted assay based on pyrosequencing of DNA methylation at only three CG dinucleotides (CpGs). Here, we have re-evaluated pyrosequencing approaches in comparison to droplet digital PCR (ddPCR) and barcoded bisulfite amplicon sequencing (BBA-seq). At individual CpGs the correlation of DNA methylation with chronological age was slightly higher for pyrosequencing and ddPCR as compared to BBA-seq. On the other hand, BBA-seq revealed that neighboring CpGs tend to be stochastically modified in murine age-associated regions. Furthermore, the binary sequel of methylated and non-methylated CpGs in individual reads can be used for single-read predictions, which may reflect heterogeneity in epigenetic aging. In comparison to C57BL/6 mice the epigenetic age-predictions using BBA-seq were also accelerated in the shorter-lived DBA/2 mice, and in C57BL/6 mice with a lifespan quantitative trait locus of DBA/2 mice. Taken together, we describe further optimized and alternative targeted methods to determine epigenetic clocks in mice.

genomics↗

bioRxiv2020

The landscape of micro-inversions provides clues for population genetic analysis of humans

BackgroundVariations in the human genome have been studied extensively. However, little is known about the role of micro-inversions (MIs), generally defined as small (<100 bp) inversions, in human evolution, diversity, and health. Depicting the pattern of MIs among diverse populations is critical for interpreting human evolutionary history and obtaining insight into genetic diseases. ResultsIn this paper, we explored the distribution of MIs in genomes from 26 human populations and 7 nonhuman primate genomes and analyzed the phylogenetic structure of the 26 human populations based on the MIs. We further investigated the functions of the MIs located within genes associated with human health. With hg19 as the reference genome, we detected 6,968 MIs among the 1,937 human samples and 24,476 MIs among the 7 nonhuman primate genomes. The analyses of MIs in human genomes showed that the MIs were rarely located in exonic regions. Nonhuman primates and human populations shared only 82 inverted alleles, and Africans had the most inverted alleles in common with nonhuman primates, which was consistent with the "Out of Africa" hypothesis. The clustering of MIs among the human populations also coincided with human migration history and ancestral lineages. ConclusionsWe propose that MIs are potential evolutionary markers for investigating population dynamics. Our results revealed the diversity of MIs in human populations and showed that they are essential to constructing human population relationships and have a potential effect on human health.

genomics↗

bioRxiv2020

Crystal structure of steroid reductase SRD5A reveals conserved steroid reduction mechanism

Steroid hormones are essential in stress response, immune system regulation, and reproduction in mammals. Steroids with 3-oxo-{Delta}4 structure, such as testosterone, androstenedione and progesterone, could be catalyzed by steroid 5-reductases (SRD5As) to generate their corresponding 3-oxo-5 steroids, which are essential for multiple physiological and pathological processes. Abnormal activities of SRD5As will lead to benign prostatic hyperplasia, alopecia, prostatic cancer or infertility due to the poor quality of sperms. However, the detailed reduction mechanisms of SRD5As remain elusive. Here we report the crystal structure of PbSRD5A, which shares 60.6% and 51.5% sequence similarities with human SRD5A1 and -2 respectively, from Proteobacteria bacterium in complex with the cofactor NADPH at 2.0 [A] resolution. PbSRD5A exists as a monomer comprised of seven transmembrane segments (TMs). The TM1-4 enclose a hydrophobic cavity for steroids substrates binding, whereas TM5-7 coordinate with cofactor NADPH through extensive hydrogen bonds network. Homology-based structural models of HsSRD5A1 and -2, together with extensive biochemical characterizations, for the first time unveiled the substrate recognition of SRD5As and provide an important framework for further understanding of the mechanism of NADPH mediated steroids 3-oxo-{Delta}4 reduction. Based on these analyses, the design of therapeutic molecules targeting SRD5As with improved specificity and therapeutic efficacy would be possible. One Sentence SummaryStructural and biochemical characterizations decipher the evolutionarily conserved mechanism in steroid 5-reductases catalyzing NADPH mediated steroids reduction.

biochemistry↗

bioRxiv2020

Modulating the transcriptional landscape of SARS-CoV-2 as an effective method for developing antiviral compounds

To interfere with the biology of SARS-CoV-2, the virus responsible for the COVID-19 pandemic, we focused on restoring the transcriptional response induced by infection. Utilizing expression patterns of SARS-CoV-2-infected cells, we identified a region in gene expression space that was unique to virus infection and inversely proportional to the transcriptional footprint of known compounds characterized in the Library of Integrated Network-based Cellular Signatures. Here we demonstrate the successful identification of compounds that display efficacy in blocking SARS-CoV-2 replication based on their ability to counteract the virus-induced transcriptional landscape. These compounds were found to potently reduce viral load despite having no impact on viral entry or modulation of the host antiviral response in the absence of virus. RNA-Seq profiling implicated the induction of the cholesterol biosynthesis pathway as the underlying mechanism of inhibition and suggested that targeting this aspect of host biology may significantly reduce SARS-CoV-2 viral load.

microbiology↗

bioRxiv2020

Structural insights into the Ca 2+-dependent gating of the human mitochondrial calcium uniporter

Mitochondrial Ca2+ uptake plays an important role in cellular physiology such as modulating ATP production, regulating cytoplasmic Ca2+ dynamics, and triggering cell death, and is mediated by the mitochondrial calcium uniporter, a highly selective calcium channel localized to the inner mitochondrial membrane. In humans, the uniporter functions as a holocomplex consisting of MCU, EMRE, MICU1 and MICU2, among which MCU and EMRE form a subcomplex and function as the conductive channel while MICU1 and MICU2 are EF-hand proteins that regulate the channel activity in a Ca2+ dependent manner. Here we present the EM structures of the human mitochondrial calcium uniporter holocomplex (uniplex) in the presence and absence of Ca2+, revealing distinct Ca2+ dependent assembly of the uniplex. In the presence of Ca2+, MICU1 and MICU2 form a heterotetramer of MICU1-(MICU2)2-MICU1 and bridge the dimeric form of the MCU-EMRE subcomplex through electrostatic interactions between MICU1 and EMRE, leaving the MCU channel pore unblocked. In the absence of Ca2+, multiple uniplex assemblies are observed but is predominantly occupied by the MICU1 subunit from a MICU1-MICU2 heterodimer blocking the MCU channel pore. Our structural observations suggest that Ca2+ changes the dimerization interaction between MICU1 and MICU2, which in turn determines how the MICU1-MICU2 subcomplex interacts with the MCU-EMRE channel and, consequently, changes the distribution of the uniplex assemblies between the blocked and unblocked states.View Full Text

biochemistry↗

bioRxiv2020

Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

We have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data of mouse brain. BIRDS features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full pipeline including image pre-processing, bi-channel registration, automatic annotation, creation of 3D digital frame, high-resolution visualization, and expandable quantitative analysis (via link with Imaris). The new bi-channel registration algorithm is adaptive to various types of whole brain data from different microscopy platforms and shows obviously improved registration accuracy. Also, the attraction of combing registration with neural network lies in that the registration procedure can readily provide training data for network, while the network can efficiently segment incomplete/defective brain data that are otherwise difficult for registration. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality whole-brain datasets, or real-time inference-based image segmentation for various brain region of interests. Jobs can be easily implemented on Fiji plugin that can be adapted for most computing environments.

neuroscience↗

bioRxiv2020

Microglial activation results in neuron-type-specific increase in mPFC GABAergic transmission and abnormal behavior in mice

Neuroinflammation and synaptic dysfunction are two early symptoms of most neurological diseases. However, the mechanisms underlying microglia-associated neuroinflammation in the regulation of synaptic activity remain obscure. We report here that acute neuroinflammation induced by a single-dose proinflammatory cytokine inducer, lipopolysaccharide (LPS), results in enhanced inhibitory postsynaptic currents (IPSCs) of glutamatergic neurons, upregulated levels of GABAAR subunits, glutamine synthetase (GS) and vGAT, and downregulated BDNF and pTrkB levels, due to enhanced activation of microglia in the medial prefrontal cortex (mPFC). Blockage of microglial activation by minocycline ameliorated LPS-induced aberrant mIPSCs and associated aberrant protein expression and behavior. Exogenous application of BDNF prior to LPS challenge also ameliorated LPS-induced abnormal mIPSCs. Thus, this study elucidates a critical role for microglia in the neurobiology of GABAergic synaptic dysfunction induced by neuroinflammation, revealing a novel GABAergic signaling pathway that might be targeted therapeutically to treat neuroinflammation-induced abnormal synaptic activity and associated aberrant behavior.

neuroscience↗

bioRxiv2020

Proteomic signatures of acute oxidative stress response to paraquat in the mouse heart

The heart is sensitive to oxidative damage but a global view on how the cardiac proteome responds to oxidative stressors has yet to fully emerge. Using quantitative tandem mass spectrometry, we assessed the effects of acute exposure of the oxidative stress inducer paraquat on protein expression in mouse hearts. We observed widespread protein expression changes in the paraquat-exposed heart especially in organelle-containing subcellular fractions. During cardiac response to acute oxidative stress, proteome changes are consistent with a rapid reduction of mitochondrial metabolism, coupled with activation of multiple antioxidant proteins, reduction of protein synthesis and remediation of proteostasis. In addition to differential expression, we saw evidence of spatial reorganizations of the cardiac proteome including the translocation of hexokinase 2 to more soluble fractions. Treatment with the antioxidants Tempol and MitoTEMPO reversed many proteomic signatures of paraquat but this reversal was incomplete. We also identified a number of proteins with unknown function in the heart to be triggered by paraquat, suggesting they may have functions in oxidative stress response. Surprisingly, protein expression changes in the heart correlate poorly with those in the lung, consistent with differential sensitivity or stress response in these two organs. The results provide insights into oxidative stress responses in the heart and may avail the search for new therapeutic targets.

physiology↗

bioRxiv2020

MSCs Successfully Deliver Oncolytic Virotherapy to Diffuse Intrinsic Pontine Glioma

Diffuse intrinsic pontine glioma (DIPG) is among the deadliest of pediatric brain tumors. Radiation therapy is the standard of care treatment for DIPG, but offers only transient relief of symptoms for DIPG patients without providing significant survival benefit. Oncolytic virotherapy (OV) is an anticancer treatment that has been investigated for treating various types of brain tumors. Here, we have explored the use of mesenchymal stem cells (MSC) for OV delivery and evaluated treatment efficacy using preclinical models of DIPG. Our results show that DIPG cells and tumors exhibit robust expression of cell surface proteins that are important for OV entry, and that MSCs loaded with OV disseminate within and release OV throughout the tumor in mice bearing DIPG brainstem xenografts. When combining administration of OV-loaded MSCs with radiotherapy, mice bearing brainstem DIPG xenografts experience a significant survival benefit, relative to that conferred by either therapy alone (p<0.0001). Our results support further preclinical investigation of cell-based OV therapy with radiation for potential translation in treating DIPG patients.

cancer biology↗

bioRxiv2020

Labeling subcellular structures in living specimens using live-cell incompatible dyes with excellent optical properties

Despite the urgent needs of imaging living specimens for cutting-edge biological research, most of the existing fluorescent labeling methods suffer from either poor optical properties or complicated operations to realize cell-permeability and specificity. Here, we introduce a method to overcome this tradeoff by incubating living cells and tissues with fluorescent dyes, no matter if they are cell-permeable or not, at particular conditions (concentration and temperature) without physical cell-penetration or chemical modifications. Based on this method, the mitochondrial labeling capability of Atto dyes, especially Atto 647N with extraordinary optical properties, together with interesting interactions between organelles is revealed. These results indicate the great potential of using dyes, which are normally considered "live-cell incompatible", to capture the morphology and dynamics of subcellular structures in living specimens.

cell biology↗

bioRxiv2020

Identification of Candidate COVID-19 Therapeutics using hPSC-derived Lung Organoids

Summary ParagraphThe SARS-CoV-2 virus has caused already over 3.5 million COVID-19 cases and 250,000 deaths globally. There is an urgent need to create novel models to study SARS-CoV-2 using human disease-relevant cells to understand key features of virus biology and facilitate drug screening. As primary SARS-CoV-2 infection is respiratory-based, we developed a lung organoid model using human pluripotent stem cells (hPSCs) that could be adapted for drug screens. The lung organoids, particularly aveolar type II cells, express ACE2 and are permissive to SARS-CoV-2 infection. Transcriptomic analysis following SARS-CoV-2 infection revealed a robust induction of chemokines and cytokines with little type I/III interferon signaling, similar to that observed amongst human COVID-19 pulmonary infections. We performed a high throughput screen using hPSC-derived lung organoids and identified FDA-approved drug candidates, including imatinib and mycophenolic acid, as inhibitors of SARS-CoV-2 entry. Pre- or post-treatment with these drugs at physiologically relevant levels decreased SARS-CoV-2 infection of hPSC-derived lung organoids. Together, these data demonstrate that hPSC-derived lung cells infected by SARS-CoV-2 can model human COVID-19 disease and provide a valuable resource to screen for FDA-approved drugs that might be repurposed and should be considered for COVID-19 clinical trials.

microbiology↗

bioRxiv2020

Identification of Drugs Blocking SARS-CoV-2 Infection using Human Pluripotent Stem Cell-derived Colonic Organoids

Summary ParagraphThe current COVID-19 pandemic is caused by SARS-coronavirus 2 (SARS-CoV-2). There are currently no therapeutic options for mitigating this disease due to lack of a vaccine and limited knowledge of SARS-CoV-2 biology. As a result, there is an urgent need to create new disease models to study SARS-CoV-2 biology and to screen for therapeutics using human disease-relevant tissues. COVID-19 patients typically present with respiratory symptoms including cough, dyspnea, and respiratory distress, but nearly 25% of patients have gastrointestinal indications including anorexia, diarrhea, vomiting, and abdominal pain. Moreover, these symptoms are associated with worse COVID-19 outcomes1. Here, we report using human pluripotent stem cell-derived colonic organoids (hPSC-COs) to explore the permissiveness of colonic cell types to SARS-CoV-2 infection. Single cell RNA-seq and immunostaining showed that the putative viral entry receptor ACE2 is expressed in multiple hESC-derived colonic cell types, but highly enriched in enterocytes. Multiple cell types in the COs can be infected by a SARS-CoV-2 pseudo-entry virus, which was further validated in vivo using a humanized mouse model. We used hPSC-derived COs in a high throughput platform to screen 1280 FDA-approved drugs against viral infection. Mycophenolic acid and quinacrine dihydrochloride were found to block the infection of SARS-CoV-2 pseudo-entry virus in COs both in vitro and in vivo, and confirmed to block infection of SARS-CoV-2 virus. This study established both in vitro and in vivo organoid models to investigate infection of SARS-CoV-2 disease-relevant human colonic cell types and identified drugs that blocks SARS-CoV-2 infection, suitable for rapid clinical testing.

microbiology↗

bioRxiv2020

Cross-reactive neutralization of SARS-CoV-2 by serum antibodies from recovered SARS patients and immunized animals

The current COVID-19 pandemic, caused by a novel coronavirus SARS-CoV-2, poses serious threats to public health and social stability, calling for urgent need for vaccines and therapeutics. SARS-CoV-2 is genetically close to SARS-CoV, thus it is important to define the between antigenic cross-reactivity and neutralization. In this study, we firstly analyzed 20 convalescent serum samples collected from SARS-CoV infected individuals during the 2003 SARS outbreak. All patient sera reacted strongly with the S1 subunit and receptor-binding domain (RBD) of SARS-CoV, cross-reacted with the S ectodomain, S1, RBD, and S2 proteins of SARS-CoV-2, and neutralized both SARS-CoV and SARS-CoV-2 S protein-driven infections. Multiple panels of antisera from mice and rabbits immunized with a full-length S and RBD immunogens of SARS-CoV were also characterized, verifying the cross-reactive neutralization against SARS-CoV-2. Interestingly, we found that a palm civet SARS-CoV-derived RBD elicited more potent cross-neutralizing responses in immunized animals than the RBD from a human SARS-CoV strain, informing a strategy to develop a universe vaccine against emerging CoVs. SummarySerum antibodies from SARS-CoV infected patients and immunized animals cross-neutralize SARS-CoV-2 suggests strategies for universe vaccines against emerging CoVs.

microbiology↗