Biology subjects

Sun, Q.

Publications and source records attributed to Sun, Q..

At least 163 records · Page 9Linked to original sources

bioRxiv2021

Broad neutralizing nanobody against SARS-CoV-2 engineered from pre-designed synthetic library

SARS-CoV-2 infection is initiated with Spike glycoprotein binding to the receptor of human angiotensin converting enzyme 2 via its receptor binding domain. Blocking this interaction is considered as an effective approach to inhibit virus infection. Here we report the discovery of a neutralizing nanobody, VHH60, directly produced from a humanized synthetic nanobody library. VHH60 competes with human ACE2 to bind the receptor binding domain of the Spike protein with a KD of 2.56 nM, inhibits infections of both live SARS-CoV-2 and pseudotyped viruses harboring wildtype, escape mutations and prevailing variants at nanomolar level. VHH60 also suppresses SARS-CoV-2 infection and propagation 50-fold better and protects mice from death two times longer than that of control group after live virus inoculation on mice. VHH60 therefore is a powerful synthetic nanobody with a promising profile for disease control against COVID19.

microbiology↗

bioRxiv2021

Transcriptome-wide association study in UK Biobank Europeans identifies associations with blood cell traits.

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10,000 distinct trait-specific risk loci, but the underlying causal mechanisms at these loci remain incompletely characterized. We performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399,835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 TWAS signals associated with hematological traits distinct from previously discovered GWAS variants, including 10 completely novel gene-trait pairs corresponding to 9 unique genes. Among the 557 associations, 301 were available for replication in a cohort of 141,286 participants of European ancestry from the Million Veteran Program (MVP). Of these 301 associations, 199 replicated at a nominal threshold ( = 0.05) and 108 replicated at a strict Bonferroni adjusted threshold ( = 0.05/301). Using our TWAS results, we systematically assigned 4,261 out of 16,900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity to assign variants to target genes.

genetics↗

bioRxiv2021

Chloride oscillation in pacemaker neurons regulates circadian rhythms through a chloride-sensing WNK kinase signaling cascade

Central pacemaker neurons regulate circadian rhythms and undergo diurnal variation in electrical activity in mammals and flies. In mammals, circadian variation in the intracellular chloride concentration of pacemaker neurons has been proposed to influence the response to GABAergic neurotransmission through GABAA receptor chloride channels. However, results have been contradictory, and a recent study demonstrated circadian variation in pacemaker neuron chloride without an effect on GABA response. Therefore, whether and how intracellular chloride regulates circadian rhythms remains controversial. Here, we demonstrate a signaling role for intracellular chloride in the Drosophila ventral lateral (LNv) pacemaker neurons. In control flies, intracellular chloride increases in LNv neurons over the course of the morning. Chloride transport through the sodium-potassium-2-chloride (NKCC) and potassium-chloride (KCC) cotransporters is a major determinant of intracellular chloride concentrations. Drosophila melanogaster with loss-of-function mutations in the NKCC encoded by Ncc69 have abnormally low intracellular chloride six hours after lights on, and a lengthened circadian period. Loss of kcc, which is expected to increase intracellular chloride, suppresses the long-period phenotype of Ncc69 mutant flies. Activation of a chloride-inhibited kinase cascade, consisting of the WNK (With No Lysine (K)) kinase and its downstream substrate, Fray, is necessary and sufficient to prolong period length. Fray activation of an inwardly rectifying potassium channel, Irk1, is also required for the long-period phenotype. These results indicate that the NKCC-dependent rise in intracellular chloride in Drosophila LNv pacemaker neurons restrains WNK-Fray signaling and overactivation of an inwardly rectifying potassium channel to maintain normal circadian period length.

neuroscience↗

bioRxiv2021

Mining bacterial NGS data vastly expands the complete genomes of temperate phages

Temperate phages (active prophages induced from bacteria) help control pathogenicity, modulate community structure, and maintain gut homeostasis1. Complete phage genome sequences are indispensable for understanding phage biology. Traditional plaque techniques are inapplicable to temperate phages due to the lysogenicity of these phages, which curb the identification and characterization of temperate phages. Existing in silico tools for prophage prediction usually fail to detect accurate and complete temperate phage genomes2-5. In this study, by a novel computational method mining both the integrated active prophages and their spontaneously induced forms (temperate phages), we obtained 192,326 complete temperate phage genomes from bacterial next-generation sequencing (NGS) data, hence expanded the existing number of complete temperate phage genomes by more than 100-fold. The reliability of our method was validated by wet-lab experiments. The experiments demonstrated that our method can accurately determine the complete genome sequences of the temperate phages, with exact flanking sites (attP and attB sites), outperforming other state-of-the-art prophage prediction methods. Our analysis indicates that temperate phages are likely to function in the evolution of microbes by 1) cross-infecting different bacterial host species; 2) transferring antibiotic resistance and virulence genes; and 3) interacting with hosts through restriction-modification and CRISPR/anti-CRISPR systems. This work provides a comprehensive complete temperate phage genome database and relevant information, which can serve as a valuable resource for phage research.

bioinformatics↗

bioRxiv2021

Multi-Dimensional Reduction Clustering of Complex Carbohydrates Reveal Tissue Metabolism, Heterogeneity and Histopathology

Formalin-fixed paraffin-embedded (FFPE) human tissues represent the world's largest collection of accessible clinical specimens with matched, well-annotated clinical course for disease progression. Currently, FFPE sections are limited to low throughput histo- and immunological assessments. Extracting largescale molecular information remains a major technological barrier to uncover the vast potential within FFPE specimens for translation and clinical research. Two critical but understudied facets of glucose metabolism are anabolic pathways for glycogen and N-linked glycan biosynthesis. Together, these complex carbohydrates represent bioenergetics, protein-structure function, and tissue architecture in human biology. Herein, we report the high-dimensional Metabolomics-Assisted Digital pathology Imaging (Madi) workflow that combines matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) with machine learning for the comprehensive assessment of tissue heterogeneity, histopathology, and metabolism in human FFPE sections. In normal human tissue sections, Madi accurately identifies anatomical regions within liver and the brain. In human lung diseases, Madi accurately predicts major lung pathologies such as honeycomb change, late-stage fibrosis, diffuse alveolar damage (DAD), and acute fibrinous and organizing pneumonia (AFOP) from idiopathic pulmonary fibrosis (IPF) and COVID-19 pneumonia specimens with precision. In depth pathway enrichment analyses reveal unique metabolic pathways are associated with distinct pathological regions, which highlight aberrant complex carbohydrate metabolism as a previously unknown molecular event associated with disease progression that could hold key to future therapeutic interventions.

systems biology↗

bioRxiv2021

STENCIL: A web templating engine for visualizing and sharing life science datasets

The ability to aggregate experimental data analysis and results into a concise and interpretable format is a key step in evaluating the success of an experiment. This critical step determines baselines for reproducibility and is a key requirement for data dissemination. However, in practice it can be difficult to consolidate data analyses that encapsulates the broad range of datatypes available in the life sciences. We present STENCIL, a web templating engine designed to organize, visualize, and enable the sharing of interactive data visualizations. STENCIL leverages a flexible web framework for creating templates to render highly customizable visual front ends. This flexibility enables researchers to render small or large sets of experimental outcomes, producing high-quality downloadable and editable figures that retain their original relationship to the source data. REST API based back ends provide programmatic data access and supports easy data sharing. STENCIL is a lightweight tool that can stream data from Galaxy, a popular bioinformatic analysis web platform. STENCIL has been used to support the analysis and dissemination of two large scale genomic projects containing the complete data analysis for over 2,400 distinct datasets. Code and implementation details are available on GitHub: https://github.com/CEGRcode/stencil

bioinformatics↗

bioRxiv2021

Structural Variation and Polysaccharide Profiling of Intervessel Pit Membranes

Functional roles of intervessel pit membrane (PM) depend on its structure and polysaccharide composition, which are mostly unknown or lack of accurate information. This study uses grapevine as a model plant and an immunogold-scanning electron microscopy technique to simultaneously analyze structures and polysaccharide compositions of intervessel PMs in relation to their functions. Intervessel PMs with different structural integrity were found in functional xylem with about 90 % of them being intact with a smooth or relatively smooth surface and the rest 10 % with progressively degraded structures. The results also elucidated details of the removal process of wall materials from surface toward its depth during the natural intervessel PM degradation. Four groups of pectic and hemicellulosic polysaccharides were present in intervessel PMs but displayed differential spatial distributions and quantities: weakly methyl-esterified homogalacturonans abundant in the surficial layers, heavily methyl-esterified homogalacturonans and xylans mostly in deep layers, and fucosylated xyloglucans relatively uniform in presence at different depths of an intervessel PM. This information is crucial to reveal the polysaccharide profiling of primary cell wall and to understand intervessel PMs roles in the safety and regulation of water transport as well as the plant susceptibility to vascular diseases.

plant biology↗

bioRxiv2021

Phylogenomic insights into biogeographic diversification and character evolution of the Sino-Japanese disjunctive Diabelia (Caprifoliaceae)

Understanding biological diversity and the mechanisms of the Sino-Japanese disjunctions are major challenges in eastern Asia biogeography. The Sino-Japanese flora has been broadly studied as an ideal model for plant phylogeography. Diabelia (Caprifoliaceae) is an East Asian genus, with a disjunctive distribution across the Sino-Japanese region. However, relationships within Diabelia remain elusive. In this study, we reconstructed the phylogeny of Diabelia and inferred historical biogeography and evolutionary patterns based on nuclear and plastid sequences from target enrichment and genome skimming approaches, respectively. We found that the main clades within Diabelia were discordant between nuclear and plastid trees. Both nuclear and plastid phylogenetic analyses supported five main clades: D. serrata, D. tetrasepala, D. sanguinea, D. spathulata var. stenophylla and D. spathulata var. spathulata. Species network analyses revealed that Diabelia tetrasepala is likely the result of a hybridization event. Divergence time estimation and ancestral area reconstructions showed that Diabelia originated in Japan during the early Miocene, with subsequent vicariance and dispersal events between Japan and Korea, and between Japan and China. Overall, our results support the division of Diabelia into five main clades and the recognition of five species in the genus. This research provides new insights in the species delimitation and speciation processes of taxonomically complex lineages such as Diabelia.

evolutionary biology↗

bioRxiv2021

A Reference-free Approach for Cell Type Classification with scRNA-seq

The single-cell RNA sequencing (scRNA-seq) has become a revolutionary technology to detect and characterize distinct cell populations under different biological conditions. Unlike bulk RNA-seq, the expression of genes from scRNA-seq is highly sparse due to limited sequencing depth per cell. This is worsened by tossing away a significant portion of reads that cannot be mapped during gene quantification. To overcome data sparsity and fully utilize original sequences, we propose scSimClassify, a reference-free and alignment-free approach to classify cell types with k-mer level features derived from raw reads in a scRNA-seq experiment. The major contribution of scSimClassify is the simhash method compressing k-mers with similar abundance profiles into groups. The compressed k-mer groups (CKGs) serve as the aggregated k-mer level features for cell type classification. We evaluate the performance of CKG features for predicting cell types in four scRNA-seq datasets comparing four state-of-the-art classification methods as well as two scRNA-seq specific algorithms. Our experiments demonstrate that the CKG features lend themselves to better performance than traditional gene expression features in scRNA-seq classification accuracy in the majority of cases. Because CKG features can be efficiently derived from raw reads without a resource-intensive alignment process, scSimClassify offers an efficient alternative to help scientists rapidly classify cell types without relying on reference sequences. The current version of scSimClassify is implemented in python and can be found at https://github.com/digi2002/scSimClassify.

bioinformatics↗

bioRxiv2021

A new type of ERGIC-ERES membrane contact mediated by TMED9 and SEC12 is required for autophagosome biogenesis

Under stress, the endomembrane system undergoes reorganization to support autophagosome biogenesis, which is a central step in autophagy. How the endomembrane system remodels has been poorly understood. Here we identify a new type of membrane contact formed between the ER-Golgi intermediate compartment (ERGIC) and the ER-exit site (ERES) in the ER-Golgi system, which is essential for promoting autophagosome biogenesis induced by different stress stimuli. The ERGIC-ERES contact is established by the interaction between TMED9 and SEC12 which generates a short distance opposition (as close as 2-5 nm) between the two compartments. The tight membrane contact allows the ERES-located SEC12 to transactivate COPII assembly on the ERGIC. In addition, a portion of SEC12 also relocates to the ERGIC. Through both mechanisms, the ERGIC-ERES contact promotes formation of the ERGIC-derived COPII vesicle, a membrane precursor of the autophagosome. The ERGIC-ERES contact is physically and functionally different from the TFG-mediated ERGIC-ERES adjunction involved in secretory protein transport, and therefore defines a unique endomembrane structure generated upon stress conditions for autophagic membrane formation.

cell biology↗

bioRxiv2021

Somatic genetics analysis of sleep in adult mice

Classical forward and reverse mouse genetics approaches require germline mutations and, thus, are unwieldy to study sleep functions of essential genes or redundant pathways. It is also time-consuming to conduct electroencephalogram/electromyogram-based mouse sleep screening owning to labor-intensive surgeries and genetic crosses. Here, we describe a highly accurate SleepV (video) system and adeno-associated virus (AAV)-based adult brain chimeric (ABC)- expression/knockout (KO) platform for somatic genetics analysis of sleep in adult mice. A pilot ABC-expression screen identifies CREB and CRTC1, of which constitutive or inducible expression significantly reduces quantity and quality of non-rapid eye movement sleep. Whereas ABC-KO of exon 13 of Sik3 by AAV-Cre injection in Sik3-E13flox/flox adult mice phenocopies Sleepy (Sik3Slp/+) mice, ABC-CRISPR of Slp/Sik3 reverses hypersomnia of Sleepy mice, indicating a direct role of SLP/SIK3 kinase in sleep regulation. Multiplex ABC-CRISPR of both orexin/hypocretin receptors causes narcolepsy-like episodes, enabling one-step analysis of redundant genes in adult mice. Finally, ABC-expression/KO screen identifies Ankrd63 and NR1 as two potentially new sleep regulators. Therefore, this somatic genetics approach should facilitate high-throughput analysis of sleep regulatory genes, especially for essential or redundant genes, in adult mice by skipping the mouse development and genetic crosses.

neuroscience↗

bioRxiv2021

The Arabidopsis thaliana PeptideAtlas; harnessing world-wide proteomics data for a comprehensive community proteomics resource

We developed a new resource, the Arabidopsis PeptideAtlas (www.peptideatlas.org/builds/arabidopsis/), to solve central questions about the Arabidopsis proteome, such as the significance of protein splice forms, post-translational modifications (PTMs), or simply obtain reliable information about specific proteins. PeptideAtlas is based on published mass spectrometry (MS) analyses collected through ProteomeXchange and reanalyzed through a uniform processing and metadata annotation pipeline. All matched MS-derived peptide data are linked to spectral, technical and biological metadata. Nearly 40 million out of [~]143 million MSMS spectra were matched to the reference genome Araport11, identifying [~]0.5 million unique peptides and 17858 uniquely identified proteins (only isoform per gene) at the highest confidence level (FDR 0.0004; 2 non-nested peptides [≥] 9 aa each), assigned canonical proteins, and 3543 lower confidence proteins. Physicochemical protein properties were evaluated for targeted identification of unobserved proteins. Additional proteins and isoforms currently not in Araport11 were identified, generated from pseudogenes, alternative start, stops and/or splice variants and sORFs; these features should be considered for updates to the Arabidopsis genome. Phosphorylation can be inspected through a sophisticated PTM viewer. This new PeptideAtlas is integrated with community resources including TAIR, tracks in JBrowse, PPDB and UniProtKB. Subsequent PeptideAtlas builds will incorporate millions more MS data. One sentence summaryA new web resource providing the global community with mass spectrometry-based Arabidopsis proteome information and its spectral, technical and biological metadata integrated with TAIR and JBrowse

plant biology↗

bioRxiv2021

Phylogenomic insights into the divergent history and hybridization within the East Asian endemic Abelia (Caprifoliaceae)

Background and AimsAbelia (Caprifoliaceae) is a small genus with five species, including one man-made hybrid and several natural hybrids. The genus has a discontinuous distribution in mainland China, Taiwan Island, and the Ryukyu islands, providing a model system to explore mechanisms of species dispersal in the East Asian flora. However, the current phylogenetic relationships within Abelia remain uncertain. MethodsIn this study, we reconstructed phylogenetic relationships within Abelia using nuclear loci generated by target enrichment and plastomes from genome skimming. Divergence time estimation, ancestral area reconstruction, and ecological niche modelling (ENM) were used to examine the diversification history of Abelia. Key ResultsWe found extensive cytonuclear discordance across the genus. By integrating lines of evidence from molecular phylogenies, divergence times, and morphology, we propose to merge A. macrotera var. zabelioides into A. uniflora. Network analyses suggested that there have been widespread and multiple hybridization events among Abelia species. These hybridization events may have contributed to the speciation mechanism and resulted in a high observed morphological diversity. The diversification of Abelia began in the early Eocene, followed by A. chinensis var. ionandra colonizing the island of Taiwan in the Middle Miocene. The ENM results suggested an expansion of climatically suitable areas during the Last Glacial Maximum and range contraction during the Last Interglacial. Disjunction between the Himalayan-Hengduan Mountain region (HHM) and the island of Taiwan is most likely the consequence of topographic isolation and postglacial contraction. ConclusionsWe used genomic data to reconstruct the phylogeny of Abelia and found a clear pattern of reticulate evolution in the group. In addition, our results support shrinkage of postglacial range and the heterogeneity of the terrain have led to the disjunction of the mainland China-Taiwan island. This study provides important new insights into the speciation process and taxonomy of Abelia.

evolutionary biology↗

bioRxiv2021

AIM-CICs: automatic identification method for Cell-in-cell structures based on convolutional neural network

Whereas biochemical markers are available for most types of cell death, current studies on non-autonomous cell death by entosis relays strictly on the identification of cell-in-cell structure (CICs), a unique morphological readout that can only be quantified manually at present. Moreover, the manual CICs quantification is generally over-simplified as CICs counts, which represents a major hurdle against profound mechanistic investigations. In this study, we take advantage of artificial intelligence (AI) technology to develop an automatic identification method for CICs (AIM-CICs), which performs comprehensive CICs analysis in an automated and efficient way. The AIM-CICs, developed on the algorithm of convolutional neural network (CNN), can not only differentiate between CICs and non-CICs (AUC > 0.99), but also accurately categorize CICs into five subclasses based on CICs stages and cell number involved (AUC > 0.97 for all subclasses). The application of AIM-CICs would systemically fuel researches on CICs-mediated cell death such as high-throughput screening.

cell biology↗

bioRxiv2021

VAMPIRE: A model blood cell trait focused annotation tool for interpretation and prioritization of complex trait genome-wide association study results

Genome-wide association studies (GWAS) have identified hundreds of thousands of genetic variants associated with complex diseases and traits. However, most variants are noncoding and not clearly linked to genes, making it challenging to interpret these GWAS signals. We present a systematic variant-to-function study, prioritizing the most likely functional elements of the genome for experimental follow-up, for >148,000 variants identified for hematological traits. Specifically, we developed VAMPIRE: Variant Annotation Method Pointing to Interesting Regulatory Effects, an interactive web application implemented in R Shiny (http://shiny.bios.unc.edu/vampire/). This tool efficiently integrates and displays information from multiple complementary sources, including epigenomic signatures from blood cell relevant tissues or cells, functional and conservation summary scores, variant impact on protein and gene expression, chromatin conformation information, as well as publicly available GWAS and phenome-wide association study (PheWAS) results. Leveraging data generated from independently performed functional validation experiments, we demonstrate that our prioritized variants, genes, or variant-gene links are significantly more likely to be experimentally validated. This study not only has important implications for systematic and efficient revelation of functional mechanisms underlying GWAS variants for hematological traits, but also provides a prototype that can be adapted to many other complex traits, paving the path for efficient variant to function (V2F) analyses.

genetics↗

bioRxiv2021

Mitochondrial Fission Regulates Transcription of Ribosomal Protein Genes in Embryonic Hearts

Mitochondrial dysfunction causes severe congenital heart diseases and prenatal/neonatal lethality. The lack of sufficient knowledge regarding how mitochondrial abnormalities affect cardiogenesis poses a major barrier for the development of clinical applications that target inborn heart defects due to mitochondrial deficiency. Mitochondrial morphology, which is regulated by fission and fusion, plays key roles in determining mitochondrial activity. Drp1 encodes a dynamin-related GTPase required for mitochondrial fission. To investigate the role of mitochondrial fission on cardiogenesis during the embryonic metabolic shift period, we specifically inactivated Drp1 in second heart field derived structures. Deletion of Drp1 in embryonic cardiomyocytes led to severe defects in mitochondrial morphology, ultrastructure, and activity. These defects caused increased cell death, decreased cell survival, disorganized cardiomyocytes, and embryonic lethality. Through characterizing this model, we reveal a novel AMPK-SIRT7-GABPB axis that relays the mitochondrial fission anomaly to reduced transcription of ribosomal protein genes in mutant cardiomyocytes. We therefore provide the first mouse genetic evidence to show that mitochondrial fission is essential for embryonic heart development. Furthermore, we uncovered a novel signaling cascade that mediates the crosstalk between mitochondrial dysfunction and protein synthesis. Our research provides further mechanistic insight regarding how mitochondrial dysfunction causes pathological molecular and cellular alterations during cardiogenesis.

developmental biology↗

bioRxiv2021

Nutritional Redundancy in Human Diet

Studying human dietary intake may help us identify effective measures to treat or prevent many chronic diseases whose natural histories are influenced by nutritional factors. Here, by examining five cohorts with dietary intake data collected on different time scales, we show that the food intake profile varies substantially across individuals and over time, while the nutritional intake profile appears fairly stable. We refer to this phenomenon as nutritional redundancy and attribute it to the nested structure of the food-nutrient network. This network enables us to quantify the level of nutritional redundancy for each diet assessment of any individual. Interestingly, this nutritional redundancy measure does not strongly correlate with any classical healthy diet scores, but its performance in predicting healthy aging shows comparable strength. Moreover, after adjusting for age, we find that a high nutritional redundancy is associated with lower risks of cardiovascular disease and type 2 diabetes.

systems biology↗

bioRxiv2021

NUCLEIC TRANSFORMER: DEEP LEARNING ONNUCLEIC ACIDS WITH SELF-ATTENTION ANDCONVOLUTIONS

AO_SCPLOWBSTRACTC_SCPLOWMuch work has been done to apply machine learning and deep learning to genomics tasks, but these applications usually require extensive domain knowledge and the resulting models provide very limited interpretability. Here we present the Nucleic Transformer, a conceptually simple but effective and interpretable model architecture that excels in a variety of DNA/RNA tasks. The Nucleic Transformer processes nucleic acid sequences with self-attention and convolutions, two deep learning techniques that have proved dominant in the fields of computer vision and natural language processing. We demonstrate that the Nucleic Transformer can be trained in both supervised and unsupervised fashion without much domain knowledge to achieve high performance with limited amounts of data in Escherichia coli promoter classification, viral genome identification, and degradation properties of COVID-19 mRNA vaccine candidates. Additionally, we showcase extraction of promoter motifs from learned attention and how direct visualization of self-attention maps assists informed decision making using deep learning models.

bioinformatics↗