Biology subjects

Shen, Y.

Publications and source records attributed to Shen, Y..

At least 289 records · Page 16Linked to original sources

bioRxiv2022

Biosensor optimization using a FRET pair based on mScarlet red fluorescent protein and an mScarlet-derived green fluorescent protein

Genetically encoded biosensors based on Forster resonance energy transfer (FRET) are indispensable tools for monitoring biochemical changes in cells. Green and red fluorescent protein-based FRET pairs offer advantages over the classically employed cyan and yellow fluorescent protein pairs, such as better spectral separation, lower phototoxicity, and less autofluorescence. Here, we describe the development of an mScarlet-derived green fluorescent protein (designated as mWatermelon) and its use as a FRET donor to the red fluorescent protein mScarlet-I as a FRET acceptor. We tested the functionality of this FRET pair by engineering biosensors for the detection of protease activity, Ca2+, and K+. Furthermore, we described a strategy to enhance the FRET efficiency of these biosensors by modulating the intramolecular association between mWatermelon and mScarlet-I.

bioengineering↗

bioRxiv2022

cAMP-EPAC-PKCε-RIM1α signaling regulates presynaptic long-term potentiation and motor learning

The cerebellum is involved in learning of fine motor skills, yet whether presynaptic plasticity contributes to such learning remains elusive. Here we report that the EPAC-PKC{varepsilon} module has a critical role in a presynaptic form of long-term potentiation in the cerebellum and motor behavior. Presynaptic cAMP-EPAC-PKC{varepsilon} signaling cascade induces a previously unidentified threonine phosphorylation of RIM1, and thereby initiates the assembly of the Rab3A-RIM1-Munc13-1 tripartite complex that facilitates docking and release of synaptic vesicles. Granule cell-specific blocking of EPAC-PKC{varepsilon} signaling abolishes presynaptic long-term potentiation at the parallel fiber to Purkinje cell synapses and impairs basic performance and learning of cerebellar motor behavior. These results unveil a functional relevance of presynaptic plasticity that is regulated through a novel signaling cascade, thereby enriching the spectrum of cerebellar learning mechanisms.

neuroscience↗

bioRxiv2022

Signal Transformations and New Timing Rules of Hippocampal CA3 to CA1 Synapses

The synapse is the fundamental unit of communication in the nervous system. Determining how information is transferred across the synaptic interface is one of the most complex endeavors in neuroscience, owing to the large number of contributing factors and events. An approach to solving this problem involves collapsing across these complexities to derive concise mathematical formulas that fully capture the governing dynamics of synaptic transmission. We investigated the feasibility of deriving such a formula - an input-output transformation function for the CA3 to CA1 node of the hippocampus -- using the Volterra expansion technique for nonlinear system identification. The entirety of the field EPSP in the apical dendrites of mouse brain slices was described with >94% accuracy by a 2nd order equation that captured the linear and nonlinear influence of past inputs on current outputs. This function generalized to cases not included in its derivation and uncovered previously undetected timing rules. The basal dendrites expressed a substantially different transfer function and evidence was obtained that, unlike the apical system, a 3rd order system or higher will be needed for complete characterization. Collectively, these results describe a readily implemented and unusually sensitive means for evaluating the effects of pharmacological treatments and disease related conditions on synaptic dynamics. At scale, the approach will also provide information needed for the construction of biologically realistic models of brain networks.

neuroscience↗

bioRxiv2022

The 5-hydroxytryptaminergic Neurons Locating in the Dorsal Raphe Nucleus and Different Receptors of 5-HT Were Implicated in Mediating the Arousal from Sevoflurane Anesthesia

In order to explore the mechanism of general anesthesia emergence, based on the common clinical phenomenon-delayed emergence, we explore the role of 5-hydroxytryptamine (5-HT) neurons in the dorsal raphe nucleus in promoting awakening from sevoflurane anesthesia in mice model. In this study, C57BL/6J male mice were selected to specifically activate or inhibit 5-HT neurons in the dorsal raphe nucleus (DRN) and different 5-HT receptors by intraperitoneal, lateral ventricle, intranuclear or DRN injection of agonists/antagonists and optogenetics during the sevoflurane anesthesia to record and observe the anesthesia induction and emergence time of mice. Through intraventricular infusion and intranuclear microinjection of 5-HT and the agonists or antagonists of different 5-HT receptors, our data showed that 5-HT and 5-HT1A and 2A/C receptors, especially 5-HT1A receptor, are involved in the regulation of delayed awakening mediated by DRN 5-HT neurons. This can provide a reliable theoretical basis as well as potential targets for clinical intervention to prevent delayed emergence and some postoperative risks. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/492958v2_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@ff4f7forg.highwire.dtl.DTLVardef@1bc56dborg.highwire.dtl.DTLVardef@1ea5737org.highwire.dtl.DTLVardef@41548_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Integration of gene expression data in Bayesian association analysis of rare variants

We present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts.

bioinformatics↗

bioRxiv2022

Spatiotemporal atlas of the fetal brain depicts cortical developmental gradient in Chinese population

The fetal brains experience rapid and complex development in utero during the second and third trimesters. MRI atlas of the fetal brain in this period enables us to quantify the normal fetal brain development in the spatiotemporal domain. In this study, we constructed a high quality spatiotemporal atlas between 23-38 weeks gestational age (GA) from 90 healthy Chinese fetuses using a pairwise and groupwise registration-based pipeline. We quantified the fetal cortical morphology and characterized the developmental trajectories of each morphological index. The cortical thickness exhibited a biphasic growth pattern; the developmental trend of curvature fitted well into the Gompertz model; sulcal depth increased linearly while surface area expanded exponentially. The cortical thickness and curvature trajectories consistently pointed to a characteristic time-point during development around 31 weeks of GA. The characteristic GA and growth rate obtained from individual cortical regions suggested a central-to-peripheral developmental gradient, with the earliest development in the parietal lobe, and we also observed within-lobe regional orders, which may be linked to biophysical events such as dendritic arborization and thalamocortical fibers ingrowth. The proposed atlas was also compared with an existing fetal atlas from a Caucasian/mixed population. Finally, we examined the structural asymmetry of the fetal brains and found extensive asymmetry that dynamically changed with development. The current study depicted a comprehensive profile of fetal cortical development and the established atlas could be used as a normative reference for neurodevelopmental and diagnostic purposes, especially in the Chinese population.

bioengineering↗

bioRxiv2022

Debugging and consolidating multiple synthetic chromosomes reveals combinatorial genetic interactions

The Sc2.0 project is building a eukaryotic synthetic genome from scratch, incorporating thousands of designer features. A major milestone has been achieved with the assembly of all individual Sc2.0 chromosomes. Here, we describe the consolidation of multiple synthetic chromosomes using endoreduplication intercross to generate a strain with 6.5 synthetic chromosomes. Genome-wide chromosome conformation capture and long-read direct RNA sequencing were performed on this strain to evaluate the effects of designer modifications, such as loxPsym site insertion, tRNA relocation, and intron deletion, on 3D chromosome organization and transcript isoform profiles. To precisely map "bugs", we developed a method, CRISPR Directed Biallelic URA3-assisted Genome Scan, or "CRISPR D-BUGS", exploiting directed mitotic recombination in heterozygous diploids. Using this method, we first fine-mapped a synII defect resulting from two loxPsym sites in the 3 UTR of SHM1. This approach was also used to map a combinatorial bug associated with synIII and synX, revealing a highly unexpected genetic interaction that links transcriptional regulation, inositol metabolism and tRNASerCGA abundance. "Starvation" for tRNASerCGA leads to insufficient levels of the key positive inositol biosynthesis regulator, Swi3, which contains tandem UCG codons. Finally, to expedite consolidation, we employed a new method, chromosome swapping, to incorporate the largest chromosome (synIV), thereby consolidating more than half of the Sc2.0 genome in a single strain.

genomics↗

bioRxiv2022

Ancestral origins are associated with SARS-CoV-2 susceptibility and protection in a Florida patient population

COVID-19 is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The severity of COVID-19 is highly variable and related to known (e.g., age, obesity, immune deficiency) and unknown risk factors. The widespread clinical symptoms encompass a large group of asymptomatic COVID-19 patients, raising a crucial question regarding genetic susceptibility, e.g., whether individual differences in immunity play a role in patient symptomatology and how much human leukocyte antigen (HLA) contributes to this. To reveal genetic determinants of susceptibility to COVID-19 severity in the population and further explore potential immune-related factors, we performed a genome-wide association study on 284 confirmed COVID-19 patients (cases) and 95 healthy individuals (controls). We compared cases and controls of European (EUR) ancestry and African American (AFR) ancestry separately. We identified two loci on chromosomes 5q32 and 11p12, which reach the significance threshold of suggestive association (p<1x10-5 threshold adjusted for multiple trait testing) and are associated with the COVID-19 susceptibility in the European ancestry (index rs17448496: odds ratio [OR] = 0.173; 95% confidence interval [CI], 0.08-0.36 for G allele; p=5.15x 10-5 and index rs768632395: OR = 0.166; 95% CI, 0.07-0.35 for A allele; p= 4.25x10-6, respectively), which were associated with two genes, PPP2R2B at 5q32, and LRRC4C at 11p12, respectively. To explore the linkage between HLA and COVID-19 severity, we applied fine-mapping analysis to dissect the HLA association with mild and severe cases. Using In-silico binding predictions to map the binding of risk/protective HLA to the viral structural proteins, we found the differential presentation of viral peptides in both ancestries. Lastly, extrapolation of the identified HLA from the cohort to the worldwide population revealed notable correlations. The study uncovers possible differences in susceptibility to COVID-19 in different ancestral origins in the genetic background, which may provide new insights into the pathogenesis and clinical treatment of the disease.

genetics↗

bioRxiv2022

The Structural and Biochemical Basis for FER Receptor Kinase Early Signaling Initiation in Arabidopsis

Accumulating evidence has indicated that receptor-like kinase (RLK) autophosphorylation and substrate phosphorylation triggered by RLK are early and essential events for RLK function. However, the structural and biochemical basis for these early events is largely unclear. Herein, we used RLK FERONIA (FER) as a model and crystallized its core kinase domain (FER-KD) in the dephosphorylated state. We found that FER-KD adopts an active conformation in its crystal structure. Moreover, FER-KD mutants with reduced or no catalytic activity also adopt an active conformation before phosphorylation. Collectively, these observations suggest that FER employs a phosphorylation-independent active state before ligand-induced phosphorylation and full activation. We further demonstrated that FER is a dual-specificity kinase and that autophosphorylation on Tyr residues lags somewhat behind Ser/Thr phosphorylation. More importantly, Tyr phosphorylation is essential for FER-KD to initiate substrate GRP7 phosphorylation. Our work provides a paradigm to study the mechanisms of the early steps of RLK signaling initiation and highlights its "active" form and Tyr phosphorylation-gated roles in response to signaling stimuli.

biochemistry↗

bioRxiv2022

Hiplot: a comprehensive and easy-to-use web service boosting publication-ready biomedical data visualization

Modern web techniques provide an unprecedented opportunity for leveraging complex biomedical data generating in clinical, omics, and mechanism experiments. Currently, the functions for carrying out publication-ready biomedical data visualization represent primary technical hurdles in the state-of-art omics-based web services, whereas the demand for visualization-based interactive data mining is ever-growing. Here, we propose an easy-to-use web service, Hiplot (https://hiplot.com.cn), equipping with comprehensive and interactive biomedical data visualization functions (230+) including basic statistics, multi-omics, regression, clustering, dimensional reduction, meta-analysis, survival analysis, risk modeling, etc. We used the demo and real datasets to demonstrate the usage workflow and the core functions of Hiplot. It permits users to conveniently and interactively complete a few specialized visualization tasks that previously could only be done by senior bioinformatics or biostatistics researchers. A modern web client with efficient user interfaces and interaction methods has been implemented based on the custom components library and the extensible plugin system. The versatile output can also be produced in different environments via using the cross-platform portable command-line interface (CLI) program, Hctl. A switchable view between the editable data table and the file uploader/path selection could facilitate data importing, previewing, and exporting, while the plumber-based response strategy significantly reduced the time costs for generating basic scientific graphics. Diversified layouts, themes/styles, and color palettes in this website allow users to create high-quality and publication-ready graphics. Researchers devoted to both life and data science may benefit from the emerging web service.

bioinformatics↗

bioRxiv2022

Accurate in silico confirmation of rare copy number variant calls from exome sequencing data using transfer learning

Exome sequencing has been widely used in genetic studies of human diseases and clinical genetic diagnosis. Accurate detection of copy number variants (CNVs) is important to fully utilize exome sequencing data. However, due to the nature of noisy data, none of the existing methods can achieve high precision and high recall rate at the same time. A common practice is to perform filtration with quality metrics followed by manual inspection of read depth of candidate CNV regions. This approach does not scale in large studies. To address this issue, we present a deep transfer learning method, CNV-espresso, for confirming rare CNVs from exome sequencing data in silico. CNV-espresso encodes candidate CNV regions from exome sequencing data as images and uses convolutional neural networks to classify the image into different copy numbers. We trained and evaluated CNV-espresso on a large-scale offspring-parents trio exome sequencing dataset, using inherited CNVs in probands as positives and CNVs with mendelian errors as negatives. We further tested the performance using samples that have both exome and whole genome sequencing (WGS) data. Assuming the CNVs detected from WGS data as proxy of ground truth, CNV-espresso significantly improves precision while keeping recall almost intact, especially for CNVs that span small number of exons in exome data. We conclude that CNV-espresso is an effective method to replace most of manual inspection of CNVs in large-scale exome sequencing studies.

bioinformatics↗

bioRxiv2022

The CIpP activator, TR-57, is highly effective as a single agent and in combination with venetoclax against CLL cells in vitro.

Despite advances in treatment, a significant proportion of patients with chronic lymphocytic leukaemia (CLL) will relapse with drug-resistant disease. Recent studies demonstrate that the imipridones ONC-201 and ONC-212 and the more potent TR-compounds are effective against a range of different cancers, including acute myeloid leukaemia and tumours of the brain, breast, and prostate. These drugs induce cell death through inhibition of mitochondrial function and activation of the mitochondrial protease, caseinolytic protease (CIpP), and the unfolded protein response (UPR). Here we demonstrate that a drug in this class, TR-57, has efficacy as a single agent and is synergistic with venetoclax against CLL cells cultured under in vitro conditions that mimic the tumour microenvironment. The inhibitory effects of TR-57 on cell survival, proliferation and migration were irrespective of poor-risk features, including aberrations of TP53. Changes in protein expression suggest the mechanisms of action of TR-57 and its synergy with venetoclax involve activation of the UPR, inhibition of the AKT and ERK1/2 pathways and a pro-apoptotic shift in expression of proteins of the BCL-2 family. The study suggests TR-57, as a single agent and in combination with venetoclax, may represent an effective treatment option for CLL, including for patients with poor-risk disease.

cancer biology↗

bioRxiv2022

Omicron-specific mRNA vaccine elicits potent immune responses in mice, hamsters, and nonhuman primates

SARS-CoV-2 has infected more than 400 million people around the globe and caused millions of deaths. Since its identification in November 2021, Omicron, a highly transmissible variant, has become the dominant variant in most countries. Omicrons highly mutated spike protein, the main target of vaccine development, significantly compromises the immune protection from current vaccination. We develop an mRNA vaccine (SOmicron-6P) based on an Omicron-specific sequence. In mice, SOmicron-6P shows superior neutralizing antibodies inducing abilities to a clinically approved inactivated virus vaccine, a clinically approved protein subunit vaccine, and an mRNA vaccine (SWT-2P) with the same sequence of BNT162b2 RNA. Significantly, SOmicron-6P induces a 14.4[~]27.7-fold and a 28.3[~]50.3-fold increase of neutralizing activity against the pseudovirus of Omicron and authentic Omicron compared to SWT-2P, respectively. In addition, two doses SOmicron-6P significantly protects Syrian hamsters against challenge with SARS-CoV-2 Omicron variant and elicits high titers of nAbs in a dose-dependent manner in macaques. Our results suggest that SOmicron-6P offers advantages over current vaccines, and it will be helpful for those with weak immunity.

microbiology↗

bioRxiv2022

CD81 partners with CD44 in promoting exosome biogenesis, tumor cluster formation, and lung metastasis in triple negative breast cancer

Tumor-initiating cells with reprogramming plasticity are thought to be essential for cancer development and metastatic regeneration in many cancers; however, the molecular mechanisms are not fully understood. This study reports that CD81, a tetraspanin protein marker of small extracellular vesicles (exosomes), functions as a binding partner of CD44 and facilitates self-renewal of tumor initiating cells. Using machine learning-assisted protein structure modeling, co-immunoprecipitation, and mutagenesis approaches, we further demonstrate that CD81 interacts with CD44 on the cellular membrane through their extracellular regions. In-depth global and phosphoproteomic analyses of clustering tumor cells unveils endocytosis-related signature pathways of proteins and phosphorylation patterns regulated by CD81 and CD44 specifically or shared between two. Notably, CRISPR Cas9-mediated depletion of either CD44 or CD81 results in loss of both proteins in cancer cell-secreted exosomes, a state which abolishes exosome-induced self-renewal of recipient cells for mammosphere formation. CD81 is expressed in >80% of human circulating tumor cells (CTCs) and specifically enriched in clustered CTCs along with CD44 isolated from breast cancer patients. Mimicking the phenotypes of CD44 deficiency, loss of CD81 also inhibits tumor cluster aggregation, tumorigenesis, and lung metastasis of triple negative breast cancer (TNBC), supporting the clinical significance of CD81 in association with patient outcomes. Our study highlights the novel role of CD81 and its partnership with CD44 in cancer exosomes, self-renewal, CTC clustering, and metastasis initiation of TNBC.

cancer biology↗

bioRxiv2022

Single-Cell Multi-Omic Roadmap of Human Fetal Pancreatic Development

The critical cellular transitions that govern human pancreas development are largely unknown. We performed large-scale single-cell RNA-sequencing (scRNA-Seq) to interrogate human fetal pancreas development from 8-20 weeks post conception. We identified 103 distinct cell types, including four novel endocrine progenitor subtypes displaying unique transcriptional features and differentiation potency. Integration with single-nucleus Assay for Transposase Accessible Chromatin Sequencing (snATAC-Seq) identified candidate regulators of human endocrine cell fate and revealed development-specific regulatory annotation at diabetes risk loci. Comparison of in vitro stem cell-derived and endogenous endocrine cells predicted aberrant genetic programs leading to the generation of off-target cells. Finally, knock-out studies revealed that the gene FEV regulates human endocrine differentiation. This work establishes a roadmap of human pancreatic development, highlights previously unappreciated cellular diversity and lineage dynamics, and provides a blueprint for understanding pancreatic disease and physiology, as well as generating human stem cell-derived islet cells in vitro for regenerative medicine purposes.

developmental biology↗

bioRxiv2022

Cell surface receptor kinase FERONIA linked to nutrient sensor TORC1 signaling controls root hair growth at low temperature in Arabidopsis thaliana

Root hairs (RH) are excellent model systems for studying cell size and polarity since they elongate several hundred-fold their original size. Their tip growth is determined both by intrinsic and environmental signals. Although nutrient availability and temperature are key factors for a sustained plant growth, the molecular mechanisms underlying their sensing and downstream signaling pathways remain unclear. Here, we identified that low temperature (10{degrees}C) triggers a strong RH elongation response involving the cell surface receptor kinase FERONIA (FER) and the nutrient sensing TOR Complex 1 (TORC). In this study, we found that FER is required to perceive limited nutrient availability caused by low temperature. FER interacts with and activates TORC downstream components to trigger RH growth. In addition, the small GTPase Rho-related protein from plants 2 (ROP2) is also involved in this RH growth response linking FER and TOR. We also found that limited nitrogen nutrient availability can mimic the RH growth response at 10{degrees}C in a NRT1.1-dependent manner. These results uncover a molecular mechanism by which a central hub composed by FER-ROP2-TORC is involved in the control of RH elongation under low temperature and nitrogen deficiency.

plant biology↗

bioRxiv2022

Machine learning-assisted identification of factors contributing to the technical variability between bulk and single-cell RNA-seq experiments

BackgroundRecent studies in the area of transcriptomics performed on single-cell and population levels reveal noticeable variability in gene expression measurements provided by different RNA sequencing technologies. Due to increased noise and complexity of single-cell RNA-Seq (scRNA-Seq) data over the bulk experiment, there is a substantial number of variably-expressed genes and so-called dropouts, challenging the subsequent computational analysis and potentially leading to false positive discoveries. In order to investigate factors affecting technical variability between RNA sequencing experiments of different technologies, we performed a systematic assessment of single-cell and bulk RNA-Seq data, which have undergone the same pre-processing and sample preparation procedures. ResultsOur analysis indicates that variability between gene expression measurements as well as dropout events are not exclusively caused by biological variability, low expression levels, or random variation. Furthermore, we propose FAVSeq, a machine learning-assisted pipeline for detection of factors contributing to gene expression variability in matched RNA-Seq data provided by two technologies. Based on the analysis of the matched bulk and single-cell dataset, we found the 3-UTR and transcript lengths as the most relevant effectors of the observed variation between RNA-Seq experiments, while the same factors together with cellular compartments were shown to be associated with dropouts. ConclusionsHere, we investigated the sources of variation in RNA-Seq profiles of matched single-cell and bulk experiments. In addition, we proposed the FAVSeq pipeline for analyzing multimodal RNA sequencing data, which allowed to identify factors affecting quantitative difference in gene expression measurements as well as the presence of dropouts. Hereby, the derived knowledge can be employed further in order to improve the interpretation of RNA-Seq data and identify genes that can be affected by assay-based deviations. Source code is available under the MIT license at https://github.com/slipnitskaya/FAVSeq.

bioinformatics↗

bioRxiv2021

Modeling of microRNA-derived disease network repurposes methotrexate for the prevention and therapy of abdominal aortic aneurysm in mice

Abdominal aortic aneurysm (AAA) is a highly lethal vascular disease characterized by permanent dilatation of the abdominal aorta. The main purpose of the current study is to search for noninvasive medical therapies for abdominal aortic aneurysm (AAA), for which there is currently no effective drug therapy. Network medicine represents a cutting-edge technology, as analysis and modeling of disease networks can provide critical clues regarding the etiology of specific diseases and which therapeutics may be effective. Here, we proposed a novel algorithm to quantify disease relations based on a large accumulated microRNA-disease association dataset and then built a disease network that covered 15 disease classes and included 304 diseases. Analysis revealed a number of patterns for these diseases; for example, diseases tended to be clustered and coherent in the network. Surprisingly, we found that AAA showed the strongest similarity with rheumatoid arthritis and systemic lupus erythematosus, both of which are autoimmune diseases, suggesting that AAA could be one type of autoimmune disease in etiology. Based on this observation, we further hypothesized that drugs for autoimmune disease could be repurposed for the prevention and therapy of AAA. Finally, animal experiments confirmed that methotrexate, a drug for autoimmune disease, was able to prevent the formation and inhibit the development of AAA.

bioinformatics↗