Biology subjects

Shen, Y.

Publications and source records attributed to Shen, Y..

At least 379 records · Page 21Linked to original sources

bioRxiv2020

Light-mediated discovery of surfaceome nanoscale organization and intercellular receptor interaction networks

Delineating the molecular nanoscale organization of the surfaceome is pre-requisite for understanding cellular signaling. Technologies for mapping the spatial relationships of cell surface receptors and their extracellular signaling synapses would open up theranostic opportunities and the possibility to engineer extracellular signaling. Here, we developed an optoproteomic technology termed LUX-MS that exploits singlet oxygen generators (SOG) for the light-triggered identification of acute protein interactions on living cells. Using SOG-coupled antibodies, small molecule-drugs, biologics and intact viral particles, we show that not only ligand-receptor interactions can be decoded across organisms, but also the surfaceome receptor nanoscale organization ligands engage in with direct implications for drug action. Furthermore, investigation of functional immunosynapses revealed that intercellular signaling inbetween APCs and CD8+ T cells can be mapped now providing insights into T cell activation with spatiotemporal resolution. LUX-MS based decoding of surfaceome signaling architectures provides unprecedented molecular insights for the rational development of theranostic strategies.

systems biology↗

bioRxiv2020

Spatially Explicit Modeling of Community Occupancy using Markov Random Field Models with Imperfect Observation: Mesocarnivores in Apostle Islands National Lakeshore

How species organize spatially is one of ecologys most motivating questions. Multiple theories have been advanced and various models developed to account for the environment, interactions among species, and spatial drivers. However, relative importance comparisons of explanatory phenomena generally are neglected in these analyses. We developed a spatially explicit community occupancy model based on Markov random fields that accounts for spatial auto-correlation and interspecific interactions in occupancy while also accounting for interspecific interaction in detection. Simulations demonstrated that the model can distinguish different mechanisms of environmental sorting, such as competition and spatial-autocorrelation. We applied our model to camera trap data from a fisher (Pekania pennanti)-marten (Martes americana) and coyote (Canis latrans)-fox (Vulpes vulpes) system in Apostle Island National Lakeshore (Wisconsin, USA). Model results indicated that the observed partitioning pattern between marten and fisher distributions could be explained best by a flipped mainland-island source-sink pattern rather than by competition. For the coyote-fox system, we determined that, in addition to a mainland-island source-sink pattern, there was a positive association between fox and coyote that deserved further study. Our model could be readily applied to other landscapes (island and non-island systems), enhancing our understanding of species coexistence patterns.

ecology↗

bioRxiv2020

Interleukin-22 mediated renal metabolic reprogramming via PFKFB3 to treat kidney injury

Kidney damage initiates the deteriorating metabolic states in tubule cells that lead to the development of end-stage renal disease (ESTD). Interleukin 22 (IL-22) is an effective therapeutic antidote for kidney injury via promoting kidney recovery, but little is known about the underlying molecular mechanisms. Here we first provide evidence that IL-22 attenuates kidney injury via metabolic reprogramming of renal tubular epithelial cells (TECs). Specifically, our data suggest that IL-22 regulates mitochondrial function and glycolysis in damaged TECs. Further observations indicate that IL-22 alleviates the accumulation of mitochondrial reactive oxygen species (ROS) and dysfunctional mitochondria via the induction of AMPK/AKT signaling and PFBFK3 activities. In mice, amelioration of kidney injury and necrosis and improvement of kidney functions via regulation of these metabolism relevant signaling and mitochondrial fitness of recombinant IL-22 are certificated in cisplatin induced kidney damage and diabetic nephropathy (DN) animal models. Taken together, our findings unravel new mechanistic insights into protective effects of IL-22 on kidney and highlight the therapeutic opportunities of IL-22 and the involved metabolic regulators in various kidney diseases.

pharmacology and toxicology↗

bioRxiv2020

BDdb: a comprehensive platform for exploration and utilization of birth defect multi-omics data

Birth defect, not only poses a major challenge for infant health but also attracts the attention of countless people in the world. Chromosome abnormality directly results in diverse birth defects which are generally deleterious and even lethal. Therefore, gaining molecular regulatory insights into these diseases is important and necessary for effective prenatal screening. Recently, with the advance of next-generation sequencing (NGS) techniques, a myriad of treatises and data associated with these diseases are now constantly produced from different laboratories across the world. To meet the increasing requirements for birth-related data resources, we developed a birth defect multi-omics database (BDdb), freely accessible at http://t21omics.cngb.org and consisting of multi-omics data, circulating free DNA (cfDNA) data, as well as diseases biomarkers. Omics data sets from 138 GSE samples, 5271 GSM samples and 328 entries, and more than 2000 biomarkers of 22 birth-defect diseases in 5 different species were integrated into BDdb, which provides a user-friendly interface for searching, browsing and downloading selected data. Additionally, we re-analyzed and normalized the raw data so that users can also customize the analysis using the data generated from different sources or different High-Throughput Sequencing (HTS) methods. To our knowledge, BDdb is the first comprehensive database associated with birth-defect-related diseases. which would benefit the diagnosis and prevention of birth defects.

bioinformatics↗

bioRxiv2020

A correspondence between normalization strategies in artificial and biological neural networks

A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods, such as batch normalization, weight normalization, and their many variants, help to stabilize hidden unit activity and accelerate network training, and these methods have been called one of the most important recent innovations for optimizing deep networks. In the brain, homeostatic plasticity represents a set of mechanisms that also stabilize and normalize network activity to lie within certain ranges, and these mechanisms are critical for maintaining normal brain function. In this survey, we discuss parallels between artificial and biological normalization methods at four spatial scales: normalization of a single neurons activity, normalization of synaptic weights of a neuron, normalization of a layer of neurons, and normalization of a network of neurons. We argue that both types of methods are functionally equivalent -- i.e., they both push activation patterns of hidden units towards a homeostatic state, where all neurons are equally used -- and that such representations can increase coding capacity, discrimination, and regularization. As a proof of concept, we develop a neural normalization algorithm, inspired by a phenomena called synaptic scaling, and show that this algorithm performs competitively against existing normalization methods on several datasets. Overall, we hope this connection will inspire machine learners in three ways: to uncover new normalization algorithms based on established neurobiological principles; to help quantify the trade-offs of different homeostatic plasticity mechanisms used in the brain; and to offer insights about how stability may not hinder, but may actually promote, plasticity.

neuroscience↗

bioRxiv2020

A high-quality genome assembly and annotation of the humpback grouper Cromileptes altivelas

Cromileptes altivelas that belongs to Serranidae in the order Perciformes, is widely distributed throughout the tropical waters of the Indo-West Pacific regions. Due to their excellent food quality and abundant nutrients, it has become a popular marine food fish with high market values. Here, we reported a chromosome-level genome assembly and annotation of the humpback grouper genome using more than 103X PacBio long-reads and high-throughput chromosome conformation capture (Hi-C) technologies. The N50 contig length of the assembly is as large as 4.14 Mbp, the final assembly is 1.07 Gb with N50 of scaffold 44.78 Mb, and 99.24% of the scaffold sequences were anchored into 24 chromosomes. The high-quality genome assembly also showed high gene completeness with 27,067 protein coding genes and 3,710 ncRNAs. This high accurate genome assembly and annotation will not only provide an essential genome resource for C. altivelas breeding and restocking, but will also serve as a key resource for studying fish genomics and genetics.

genomics↗

bioRxiv2020

Pathogenicity, tissue tropism and potential vertical transmission of SARSr-CoV-2 in Malayan pangolins

SARS-CoV-2 is having severe impact on public health at a global scale. Malayan pangolin SARS-CoV-2-related coronavirus (SARSr-CoV-2) is closely related to SARS-CoV-2. We show that CT scans of virus-positive pangolins reveal bilateral ground-glass opacities in lungs in similar manner to COVID-19 patients. The virus infected multiple organs in pangolins, with the lungs being the major target. Histological expression showed that ACE2 and TMPRSS2 are co-expressed with viral RNA. Transcriptome analysis revealed an inadequate interferon response, with different dysregulated chemokines and cytokines responses in pregnant and non-pregnant adults and fetuses. Viral RNA and protein were detected in three fetuses providing evidence for vertical virus transmission. In sum, our study identifies the biological framework of SARSr-CoV-2 in pangolins, revealing striking similarities to COVID-19 in humans.

microbiology↗

bioRxiv2020

The genome assembly and annotation of Magnolia biondii Pamp., a phylogenetically, economically, and medicinally important ornamental tree species

Magnolia biondii Pamp. (Magnoliaceae, magnoliids) is a phylogenetically, economically, and medicinally important ornamental tree species widely grown and cultivated in the north-temperate regions of China. Contributing a genome sequence for M. biondii will help resolve phylogenetic uncertainty of magnoliids and further understand individual trait evolution in Magnolia. We assembled a chromosome-level reference genome of M. biondii using ~67, ~175, and ~154 Gb of raw DNA sequences generated by Pacific Biosciences Single-molecule Real-time sequencing, 10X genomics Chromium, and Hi-C scaffolding strategies, respectively. The final genome assembly was 2.22 Gb with a contig N50 of 269.11 Kb and a BUSCO complete gene ratio of 91.90%. About 89.17% of the genome length was organized to 19 chromosomes, resulting in a scaffold N50 of 92.86 Mb. The genome contained 48,319 protein-coding genes, accounting for 22.97% of the genome length, in contrast to 66.48% of the genome length for the repetitive elements. We confirmed a Magnoliaceae specific WGD event that might have probably occurred shortly after the split of Magnoliaceae and Annonaceae. Functional enrichment of the Magnolia specific and expanded gene families highlighted genes involved in biosynthesis of secondary metabolites, plant-pathogen interaction, and response to stimulus, which may improve ecological fitness and biological adaptability of the lineage. Phylogenomic analyses recovered a sister relationship of magnoliids and Chloranthaceae, which are sister to a clade comprising monocots and eudicots. The genome sequence of M. biondii could empower trait improvement, germplasm conservation, and evolutionary studies on rapid radiation of early angiosperms.

plant biology↗

bioRxiv2020

Dissecting Autism Genetic Risk Using Single-cell RNA-seq Data

Autism spectrum disorder (autism) is a condition with strong but heterogenous genetic contribution. Recent exome and genome sequencing studies have uncovered many new risk genes through de novo variants. However, a large fraction of enrichment of de novo variants observed in cases are not accounted for by known or candidate risk genes, suggesting that the majority of risk genes are still unknown. Here we hypothesize that autism risk genes share a few common cell-type specific gene expression patterns during brain development, and such information can be quantified to improve statistical power of detecting new risk genes. We obtained large-scale single-cell RNA-seq data from human fetal brain collected through a range of developmental stages, and developed a supervised machine-learning approach "A-risk" (Autism risk), to predict the plausibility of autism risk genes across the genome. Using data from recent exome sequencing studies of autism, A-risk achieves better performance in prioritizing de novo variants than other methods, especially for genes that are less intolerant of loss of function variants. We stratified genes based on A-risk and mutation intolerance metrics to improve estimation of priors in extTADA and identified 71 candidate risk genes. In particular, CLCN4, PRKAR1B, and NR2F1 are potentially new risk genes with further support from neurodevelopmental disorders. Expression patterns of both known and candidate risk genes reveals the important role of deep-layer excitatory neurons from adult human cortex in autism etiology. With the unprecedented revolution of single-cell transcriptomics and expanding autism cohorts with exome or genome sequencing, our method will facilitate systematic discovery of novel risk genes and understanding of biological pathogenesis in autism.

genomics↗

bioRxiv2020

Immune Selection Pressure Contributes to Pancreatic Cancer Immune Evasion

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer death in the United States. Pancreatic tumors are minimally infiltrated by T cells and are largely refractory to immunotherapy. Accordingly, the role of T cell immunity in pancreatic cancer has been somewhat overlooked. Here, we hypothesized that immune evasion in pancreatic cancer is induced in response to T cell-based immune selection pressure, and that understanding how pancreatic tumors respond to immune attack may facilitate the development of more effective therapeutic strategies. We now provide the first evidence that T cell-dependent host immune responses induce a PDAC-derived myeloid mimicry phenomenon and stimulate immune evasion. mT3-2D cells derived from a Kras+/LSL-G12D; Trp53+/LSL-R172H; Pdx1-Cre (KPC) mouse model of pancreatic cancer were grown in immunocompetent and immunodeficient C57BL/6 mice, and analyzed to determine the impacts of adaptive immunity specifically on malignant epithelial cells as well as on whole tumors. We found that immune selection pressure, via signal transducer and activator of transcription 1 (STAT1), stimulates malignant epithelial pancreatic cells to induce the expression of genes typically expressed by myeloid cells and alters intratumoral immunosuppressive myeloid cell profiles. Targeting the Janus Kinase (JAK)/STAT signaling pathway using the FDA approved drug, ruxolitinib, overcomes these tumor-protective responses and improves anti-PD1 antibody therapeutic efficacy. These findings provide future directions for treatments that specifically disable this mechanism of resistance in PDAC.

cancer biology↗

bioRxiv2020

Direct RNA sequencing reveals structural differences between transcript isoforms

The ability to correctly assign structure information to an individual transcript in a continuous and phased manner is critical to understanding RNA function. RNA structure play important roles in every step of an RNAs lifecycle, however current short-read high throughput RNA structure mapping strategies are long, complex and cannot assign unique structures to individual gene-linked isoforms in shared sequences. To address these limitations, we present an approach that combines structure probing with SHAPE-like compound NAI-N3, nanopore direct RNA sequencing, and one-class support vector machines to detect secondary structures on near full-length RNAs (PORE-cupine). PORE-cupine provides rapid, direct, accurate and robust structure information along known RNAs and recapitulates global structural features in human embryonic stem cells. The majority of gene-linked isoforms showed structural differences in shared sequences both local and distal to the alternative splice site, highlighting the importance of long-read sequencing for phasing of structures. Structural differences between gene-linked isoforms are associated with differential translation efficiencies globally, highlighting the role of structure as a pervasive mechanism for regulating isoform-specific gene expression inside cells.

biochemistry↗

bioRxiv2020

Molecular basis for recognition of Listeria cell wall teichoic acid by the pseudo-symmetric SH3b-like repeats of a bacteriophage endolysin

Endolysins are bacteriophage-encoded peptidoglycan hydrolases targeting the cell wall of host bacteria via their cell wall-binding domains (CBDs). The molecular basis for selective recognition of surface carbohydrate ligands by CBDs remains elusive. Here, we describe, in atomic detail, the interaction between the Listeria phage endolysin domain CBD500 and its cell wall teichoic acid (WTA) ligands. We show that 3 O-acetylated GlcNAc residues integrated into the WTA polymer chain are the key epitope recognized by a CBD binding cavity located at the interface of tandem copies of beta-barrel, pseudo-symmetric SH3b-like repeats. This cavity consists of multiple aromatic residues making extensive interactions with two GlcNAc acetyl groups via hydrogen bonds and van der Waals contacts, while permitting the docking of the diastereomorphic ligands. The multidisciplinary approach described here delineates a previously unknown recognition mechanism by which a phage endolysin specifically recognizes and targets WTA, suggesting an adaptable model for regulation of endolysin specificity.

biophysics↗

bioRxiv2020

Template-based prediction of protein structure with deep learning

Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available. We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residueresidue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56%, 13%, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13s TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16%, 9% and 8% in terms of TM-score, respectively. These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins. Availabilityhttps://github.com/ShenLab/ThreaderAI

bioinformatics↗

bioRxiv2020

Rare variant analysis of 4,241 pulmonary arterial hypertension cases from an international consortium implicate FBLN2, PDGFD and rare de novo variants in PAH

BackgroundGroup 1 pulmonary arterial hypertension (PAH) is a lethal vasculopathy characterized by pathogenic remodeling of pulmonary arterioles leading to increased pulmonary pressures, right ventricular hypertrophy and heart failure. Recent high-throughput sequencing studies have identified additional PAH risk genes and suggested differences in genetic causes by age of onset. However, known risk genes explain only 15-20% of non-familial idiopathic PAH cases. MethodsTo identify new risk genes, we utilized an international consortium of 4,241 PAH cases with 4,175 sequenced exomes (n=2,572 National Biological Sample and Data Repository for PAH; n=469 Columbia University Irving Medical Center, enriched for pediatric trios) and 1,134 sequenced genomes (UK NIHR Bioresource - Rare Diseases Study). Most of the cases were adult-onset disease (93%), and 55% idiopathic (IPAH) and 35% associated with other diseases (APAH). We identified protein-coding variants and performed rare variant association analyses in unrelated participants of European ancestry, including 2,789 cases and 18,819 controls (11,101 unaffected parents from the Simons Powering Autism Research for Knowledge study and 7,718 gnomAD individuals). We analyzed de novo variants in 124 pediatric trios. ResultsSeven genes with rare deleterious variants were significantly associated (false discovery rate <0.1) with IPAH, including three known genes (BMPR2, GDF2, and TBX4), two recently identified candidate genes (SOX17, KDR), and two new candidate genes (FBLN2, fibulin 2; PDGFD, platelet-derived growth factor D). The candidate genes exhibit expression patterns in lung and heart similar to that of known PAH risk genes, and most of the variants occur in conserved protein domains. Variants in known PAH gene, ACVRL1, showed association with APAH. Predicted deleterious de novo variants in pediatric cases exhibited a significant burden compared to the background mutation rate (2.5x, p=7.0E-6). At least eight novel candidate genes carrying de novo variants have plausible roles in lung/heart development. ConclusionsRare variant analysis of a large international consortium identifies two new candidate genes - FBLN2 and PDGFD. The new genes have known functions in vasculogenesis and remodeling but have not been previously implicated in PAH. Trio analysis predicts that ~15% of pediatric IPAH may be explained by de novo variants.

genetics↗

bioRxiv2020

Structural basis of bile acid receptor activation and Gs coupling

G protein-coupled bile acid receptor (GPBAR) is a membrane receptor that senses bile acids to regulate diverse functions through Gs activation. Here, we report the cryo-EM structures of GPBAR-Gs complexes stabilized by either high-affinity P395 or the semisynthesized bile acid derivative INT-777 at 3-[A] resolution. These structures revealed a large oval-shaped ligand pocket with several sporadic polar groups to accommodate the amphipathic cholic core of bile acids. A fingerprint of key residues recognizing diverse bile acids in the orthosteric site, a putative second bile acid binding site with allosteric properties and structural features contributing to bias property were identified through structural analysis and mutagenesis studies. Moreover, structural comparison of GPBAR with other GPCRs uncovered an atypical mode of receptor activation and G-protein- coupling, featuring a different set of key residues connecting the ligand binding pocket to the Gs coupling site, and a specific interaction motif localized in intracellular loop 3. Overall, our study not only provides unique structural features of GPBAR in bile acid recognition, allosteric effects and biased signaling, but also suggests that distinct allosteric connecting mechanisms between the ligand binding pocket and the G protein binding site exist in the GPCR superfamily.

biochemistry↗

bioRxiv2020

The Host Cell ViroCheckpoint: Identification and Pharmacologic Targeting of Novel Mechanistic Determinants of Coronavirus-Mediated Hijacked Cell States

Most antiviral agents are designed to target virus-specific proteins and mechanisms rather than the host cell proteins that are critically dysregulated following virus-mediated reprogramming of the host cell transcriptional state. To overcome these limitations, we propose that elucidation and pharmacologic targeting of host cell Master Regulator proteins--whose aberrant activities govern the reprogramed state of infected-coronavirus cells--presents unique opportunities to develop novel mechanism-based therapeutic approaches to antiviral therapy, either as monotherapy or as a complement to established treatments. Specifically, we propose that a small module of host cell Master Regulator proteins (ViroCheckpoint) is hijacked by the virus to support its efficient replication and release. Conventional methodologies are not well suited to elucidate these potentially targetable proteins. By using the VIPER network-based algorithm, we successfully interrogated 12h, 24h, and 48h signatures from Calu-3 lung adenocarcinoma cells infected with SARS-CoV, to elucidate the time-dependent reprogramming of host cells and associated Master Regulator proteins. We used the NYS CLIA-certified Darwin OncoTreat algorithm, with an existing database of RNASeq profiles following cell perturbation with 133 FDA-approved and 195 late-stage experimental compounds, to identify drugs capable of virtually abrogating the virus-induced Master Regulator signature. This approach to drug prioritization and repurposing can be trivially extended to other viral pathogens, including SARS-CoV-2, as soon as the relevant infection signature becomes available.

systems biology↗

bioRxiv2020

Superior Colliculus-Projected GABAergic Retinal Ganglion Cells Mediate Looming-Evoked Flight Response

The looming stimulus-evoked flight response is an experimental paradigm for studying innate defensive behaviors. However, how the visual looming stimulus is transmitted from the retina to the brain remains poorly understood. Here, we report that superior colliculus (SC)-projecting RGCs transmit the looming signal from the retina to the brain to mediate the looming-evoked flight behavior by releasing GABA. In the mouse retina, GABAergic RGCs are capable of projecting to many brain areas, including the SC. Superior colliculus (SC)-projecting GABAergic RGCs (spgRGCs) are mono-synaptically connected to the parvalbumin-positive SC neurons known to be required for the looming-evoked flight response. Optogenetic activation of spgRGCs triggers GABA-mediated inhibition in SC neurons. The ablation or silence of spgRGCs compromises looming-evoked flight response but not image-forming functions. Therefore, this study shows that spgRGCs control the looming-evoked flight response by regulating SC neurons via GABA, providing novel insight into the regulation of innate defensive behaviors.

neuroscience↗

bioRxiv2020

An automatic and efficient pulmonary nodule detection system based on multi-model ensemble

Accurate pulmonary nodule detection plays an important role in early screening of lung cancer. Although there are many presented CAD systems based on deep learning for pulmonary nodule detection, these methods still have some problems in clinical use. The improvement of false negatives rate of tiny nodules, the reduction of false alarms and the optimization of time consumption are some of them that need to be solved as soon as possible. In view of the above problems, in this paper, we first propose a novel full convolution segmentation framework for lung cavity extraction in preprocessing stage to solve the time consumption problem of the existing pulmonary nodule detection systems. Furthermore, a 2D-NestedUNet segmentation network and a 3D-RPN detection network is stacked to get the high recall and low false positive rate on nodule candidate extraction, especially the recall of tiny nodules. Finally, a false positive reduction method based on multi-model ensemble is proposed for the further classification of nodule candidates. Our methods are evaluated on several public datasets, LUNA16, LNDb and ChestCT2019, which demonstrated the superior performance of our CAD system.

bioengineering↗