Biology subjects

Liu, W.

Publications and source records attributed to Liu, W..

At least 307 records · Page 17Linked to original sources

bioRxiv2023

Illumination of the complement receptors C3aR and C5aR signaling by anaphylatoxins

The complement receptors C3aR and C5aR, whose signaling are selectively activated by anaphylatoxins C3a and C5a, are important regulators of both innate and adaptive immune responses. Dysregulations of C3aR and C5aR signaling lead to multiple inflammatory disorders, including sepsis, asthma, and acute respiratory distress syndrome (ARDS). The mechanism underlying endogenous anaphylatoxin recognition and activation of C3aR and C5aR remains elusive. Here we reported the structures of C3a-bound C3aR and C5a-bound C5aR1 as well as an apo C3aR structure. These structures, combined with mutagenesis analysis, reveal a conserved recognition pattern of anaphylatoxins to the complement receptors that is different from chemokine receptors, unique pocket topologies of C3aR and C5aR1 that mediate ligand selectivity, and a common mechanism of receptor activation. These results provide crucial insights into the molecular understandings of C3aR and C5aR1 signaling and structural templates for rational drug design for treating inflammation disorders.

biochemistry↗

bioRxiv2023

A deep learning-based stripe self-correction method for stitched microscopic images

The stitched fluorescence microscope images inevitably exist in various types of stripes or artifacts caused by uncertain factors such as optical devices or specimens, which severely affects the image quality and downstream quantitative analysis. In this paper, we present a deep learning-based Stripe Self-Correction method, so-called SSCOR. Specifically, we propose a proximity sampling scheme and adversarial reciprocal self-training paradigm that enable SSCOR to utilize stripe-free patches sampled from the stitched microscope image itself to correct their adjacent stripe patches. Comparing to off-the-shelf approaches, SSCOR can not only adaptively correct non-uniform, oblique, and grid stripes, but also remove scanning, bubble, and out-of-focus artifacts, achieving the state-of-the-art performance across different imaging conditions and modalities. Moreover, SSCOR does not require any physical parameter estimation, patch-wise manual annotation, and raw stitched information in the correction process, which provides an intelligent image quality optimization solution for microscopist or even microscope company, and facilitates more precise biomedical applications for researchers.

bioinformatics↗

bioRxiv2023

Evolutionary transcriptomics reveals longevity mostly driven by polygenic and indirect selection in mammals

The maximum lifespan varies more than 100-fold in mammals. This experiment of nature may uncover of the evolutionary forces and molecular features that define longevity. To understand the relationship between gene expression variation and maximum lifespan, we carried out a comparative transcriptomics analysis of liver, kidney, and brain tissues of 106 mammalian species. We found that expression is largely conserved and very limited genes exhibit common expression patterns with longevity in all the three organs analyzed. However, many pathways, e.g., "Insulin signaling pathway", and "FoxO signaling pathway", show accumulated correlations with maximum lifespan across mammals. Analyses of selection features further reveal that methionine restriction related genes whose expressions associated with longevity, are under strong selection in long-lived mammals, suggesting that a common approach could be utilized by natural selection and artificial intervention to control lifespan. These results suggest that natural lifespan regulation via gene expression is likely to be driven through polygenic model and indirect selection.

evolutionary biology↗

bioRxiv2023

DPHL v2: An updated and comprehensive DIA pan-human assay library for quantifying more than 14,000 proteins.

A comprehensive pan-human spectral library is critical for biomarker discovery using mass spectrometry (MS)-based proteomics. DPHL v1, a previous pan-human library built from 1096 data-dependent acquisition (DDA) MS data of 16 human tissue types, allows quantifying 10,943 proteins. However, a major limitation of DPHL v1 is the lack of semi-tryptic peptides and protein isoforms, which are abundant in clinical specimens. Here, we generated DPHL v2 from 1608 DDA-MS data acquired using Orbitrap mass spectrometers. The data included 586 DDA-MS newly acquired from 17 tissue types, while 1022 files were derived from DPHL v1. DPHL v2 thus comprises data from 24 sample types, including several cancer types (lung, breast, kidney, and prostate cancer, among others). We generated four variants of DPHL v2 to include semi-tryptic peptides and protein isoforms. DPHL v2 was then applied to a publicly available colorectal cancer dataset with 286 DIA-MS files. The numbers of identified and significantly dysregulated proteins increased by at least 21.7% and 14.2%, respectively, compared with DPHL v1. Our findings show that the increased human proteome coverage of DPHL v2 provides larger pools of potential protein biomarkers.

bioinformatics↗

bioRxiv2023

Allele mining of eukaryotic translation initiation factor genes in Prunus species for the identification of new sources of resistance to sharka

BackgroundMembers of the eukaryotic translation initiation complex are co-opted in viral infection, leading to susceptibility in many crop species, including stone fruit trees (Prunus spp). Therefore, modification of one of those eukaryotic translation initiation factors or changes in their gene expression may result in resistance. ObjectiveWe searched the crop and wild Prunus germplasm from the Armeniaca and Amygdalus taxonomic sections for allelic variants in the eIF4E and eIFiso4E genes, to identify alleles potentially linked to resistance to the Plum Pox Virus (PPV). Methodology and resultsOver one thousand stone fruit accessions (1,397) were screened for variation in eIF4E and eIFiso4E transcript sequences which are in single copy within the diploid Prunus genome. We identified new alleles for both genes that are not evident in haplotypes associated with PPV susceptible individuals. Overall, analyses showed that eIFiso4E is genetically more constrained since it displayed less polymorphism than eIF4E. We also demonstrated more variation at both loci in the related wild species than in crop species. As the eIFiso4E translation initiation factor was identified as indispensable for PPV infection in Prunus species, a selection of ten different eIFiso4E haplotypes along 13 individuals were tested by infection with PPV and eight of them displayed a range of reduced susceptibility to resistance, indicating new potential sources of resistance to sharka.

genetics↗

bioRxiv2023

RabbitVar: ultra-fast and accurate somatic small-variant calling on multi-core architectures

The continuous development of next-generation sequencing (NGS) technology has led to extensive and frequent use of genomic analysis in cancer research. The associated production of large-scale NGS datasets establishes the need for high-precision somatic variant calling methods that are highly optimized on commonly used hardware platforms. We present RabbitVar (https://github.com/LeiHaoa/RabbitVar), a scalable variant caller that can detect small somatic variants from paired tumor/normal NGS data on modern multi-core CPUs. Our approach combines candidate-finding and machine-learning-based filtering strategies with optimized data structures and multi-threading to achieve both high accuracy and efficiency. We have compared the performance of RabbitVar to leading state-of-the-art callers (Strelka2, Mutect2, NeuSomatic, VarDict, VarScan2) on real-world HCC1395 breast cancer datasets under different sequencing conditions and contamination rates. The evaluation results demonstrate that RabbitVar achieves highly competitive F1-scores when calling SNVs. Moreover, when calling the more challenging indel variants, it consistently achieves the highest F1-scores. RabbitVar is able to process a paired tumor and normal whole human genome sequencing datasets with 80x depth in less than 20 minutes on a 48-core workstation outperforming all other tested variant callers in terms of efficiency.

bioinformatics↗

bioRxiv2023

Multi-omics analysis reveals the regulatory mechanism of flavonol biosynthesis during the formation of petal color in Camellia nitidissima

Camellia nitidissima is a prized woody flower with golden-yellow flowers, and has high ornamental, medicinal and economic value. Previous works indicated that the content of flavonol accumulated greatly during golden petal formation. However, the molecular mechanism of golden flower formation in C. nitidissima remains largely unknown. In this study, we performed an integrative anlysis of transcriptome, proteome and metablome of petals at five developmental stages to construct the regulatory network during golden flower formation in C. nitidissima. Metablome anlysis showed that two flavonols, the quercetin and kaempferol glycosides, were highly accumulated in the golden petals. Furthermore, transcriptome and proteome sequencing suggested that the expression of flavonol biosynthesis genes or proteins was increased in golden petal stage, whereas expression of anthocyanin and proanthocyanidin genes or proteins were decreased. Six MYB and 20 bHLH genes were identified as potentially involved in flavonoid biosynthesis, and the brassinosteroid (BR) and jasmonate (JA) pathways were positively and negatively corretated with flavonol biosynthesis, respectively. Network correlation analysis suggested close relationships among BR and JA, MYB and bHLH, and the flavonoid pathway and flavonoid metabolites. Overall, this study shows a potential regulatory mechanism of flavonol biosynthesis duing golden petal formation in C. nitidissima. HighlightThe BR and JA pathway may positively and negatively regulate flavonol synthesis in Camellia nitidissima, respectively.

molecular biology↗

bioRxiv2022

DeepGWAS: Enhance GWAS Signals for Neuropsychiatric Disorders via Deep Neural Network

Genetic dissection of neuropsychiatric disorders can potentially reveal novel therapeutic targets. While genome-wide association studies (GWAS) have tremendously advanced our understanding, we approach a sample size bottleneck (i.e., the number of cases needed to identify >90% of all loci is impractical). Therefore, computationally enhancing GWAS on existing samples may be particularly valuable. Here, we describe DeepGWAS, a deep neural network-based method to enhance GWAS by integrating GWAS results with linkage disequilibrium and brain-related functional annotations. DeepGWAS enhanced schizophrenia (SCZ) loci by [~]3X when applied to the largest European GWAS, and 21.3% enhanced loci were validated by the latest multi-ancestry GWAS. Importantly, DeepGWAS models can be transferred to other neuropsychiatric disorders. Transferring SCZ-trained models to Alzheimers disease and major depressive disorder, we observed 1.3-17.6X detected loci compared to standard GWAS, among which 27-40% were validated by other GWAS studies. We anticipate DeepGWAS to be a powerful tool in GWAS studies.

genetics↗

bioRxiv2022

Quantifying constraint in human mitochondrial DNA

Mitochondrial DNA (mtDNA) has an important, yet often overlooked, role in health and disease. Constraint models quantify the removal of deleterious variation from the population by selection, representing a powerful tool for identifying genetic variation underlying human phenotypes1-4. However, a constraint model for the mtDNA has not been developed, due to its unique features. Here we describe the development of a mitochondrial constraint model and its application to the Genome Aggregation Database (gnomAD), a large-scale population dataset reporting mtDNA variation across 56,434 humans5. Our results demonstrate strong depletion of expected variation, suggesting most deleterious mtDNA variants remain undiscovered. To aid their identification, we compute constraint metrics for every mitochondrial protein, tRNA, and rRNA gene, revealing a spectrum of intolerance to variation. We characterize the most constrained regions within genes via regional constraint, and positions across the entire mtDNA via local constraint, showing their enrichment in pathogenic variation and functionally critical sites, including topological clustering in 3D protein and RNA structures. Notably, we identify constraint at often overlooked sites, such as rRNAs and non-coding regions. Lastly, we demonstrate how these metrics can improve the discovery of mtDNA variation underlying rare and common human phenotypes.

genetics↗

bioRxiv2022

The pan-genome and local adaptation of Arabidopsis thaliana

Arabidopsis thaliana has been used as a model species for research in a diverse collection of plant species. However, previous studies based on single reference genomes and short-read sequencing data are restricted to detecting variable genes and large structural variation (SV) underlying local adaptation. Here we de novo assemble high-quality chromosomal genomes of 38 A. thaliana ecotypes (with 6 relict ones) using PacBio-HiFi long-read sequencing. From these newly assembled genomes, we annotate several thousand new genes through pan-genomic analysis in comparison to the previous reference genome. The identified variable genes are mainly enriched in and associated with ecological adaptation and this species substantially expands its gene repertoire for local adaptation. We construct a graph-based pan-genome and identify 62,525 SVs which overlap with 14,243 genes. These genes are enriched in multiple ecological adaptation functions, including secondary metabolic processes, enzyme regulation, and biotic/abiotic stimulus. For example, a 566 bp insertion in the promoter of the light-adaptation KNAT3 gene was specific to the high-altitude relict Tibet-0 ecotype. This SV reduces the expression level of KNAT3 and promotes A. thaliana adaptation to habitats high in light radiation. In addition, compared with the SNPs, the SVs identified in this study captured the missing heritability and we detected novel SV associations with environmental variables in their native range, highlighting the value of SVs in environmental adaptation. The genome resources presented here will help pinpoint genetic changes that include both SVs and the ecotype-specific genes for local adaptation of A. thaliana and increase our understanding of the molecular mechanisms in this model species to respond to varied habitats.

genomics↗

bioRxiv2022

Mincle-GSDMD-mediated release of IL-1β containing small extracellular vesicles contributes to ethanol-induced liver injury

Background & AimsMacrophage inducible C-type lectin (Mincle) is expressed on Kupffer cells and senses ethanol-induced danger signals released from dying hepatocytes and promotes IL-1{beta} production. However, it remains unclear what and how ethanol-induced Mincle ligands activate downstream signaling events to mediate IL-1{beta} release and contribute to alcohol-associated liver disease (ALD). In this study, we investigated the association of circulating {beta}-glucosylceramide ({beta}-GluCer), an endogenous Mincle ligand, with severity of ALD and examined the mechanism by which {beta}-GluCer engages Mincle on Kupffer cells to release IL-1{beta} in the absence of cell death and exacerbates ALD. Approach and ResultsConcentrations of {beta}-GluCer were increased in serum of patients with severe AH and correlated with disease severity. Challenge of Kupffer cells with LPS and {beta}-GluCer induced formation of a Mincle and Gsdmd-dependent secretory complex containing chaperoned full-length GSDMD (Hsp90-CDC37-NEDD4) with polyubiquitinated pro-IL-1{beta} and components of the Casp8-NLRP3 inflammasome loaded as cargo in small extracellular vesicles (sEV). Gao-binge ethanol exposure to wild-type, but not Mincle-/- and Gsdmd-/-, mice increased release of IL-1{beta} containing sEVs from liver explant cultures. Myeloid-specific deletion of Gsdmd similarly decreased the formation of sEVs by liver explant cultures and protected mice from ethanol-induced liver injury. sEVs collected from ethanol-fed wild-type, but not Gsdmd-/-, mice promoted injury of cultured hepatocytes and, when injected into wild-type mice, aggravated Gao-binge ethanol-induced liver injury. Conclusion{beta}-GluCer functions as a DAMP activating Mincle-dependent GSDMD-mediated formation and release of IL-1{beta}-containing sEVs, which in turn exacerbate hepatocyte cell death and contribute to the pathogenesis of ALD.

immunology↗

bioRxiv2022

MSdb: an integrated expression atlas of human musculoskeletal system

We introduce MSdb (https://www.msdb.org.cn), a database for visualization and integrated analysis of next-generation sequencing data from human musculoskeletal system, along with manually curated patient phenotype data. Systematic categorizing, standardized processing and freely accessible knowledge enables the reuse of public data. MSdb provides various types of analysis, including sample-level browsing of metadata information, gene/miRNA expression, and single-cell RNA-seq dataset. In addition, MSdb also allows integrated analysis for cross-samples and cross-omics analysis, including customized differentially-expressed gene/microRNA analysis, microRNA-gene network, scRNA-seq cross-sample/disease integration, and gene regulatory network analysis.

bioinformatics↗

bioRxiv2022

scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data

In droplet-based single-cell RNA-seq (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq) assays, systematic contamination of ambient RNA molecules biases the estimation of genuine transcriptional levels. To correct the contamination, several computational methods have been developed. However, these methods do not distinguish the contamination-causing genes and thus either under- or over-corrected the contamination in our in-house snRNA-seq data of virgin and lactating mammary glands. Hence, we developed scCDC as the first method that specifically detects the contamination-causing genes and only corrects the expression counts of these genes. Benchmarked against existing methods on synthetic and real scRNA-seq and snRNA-seq datasets, scCDC achieved the best contamination correction accuracy with minimal data alteration. Moreover, scCDC applies to processed scRNA-seq and snRNA-seq data with empty droplets removed. In conclusion, scCDC is a flexible, accurate decontamination method that detects the contamination-causing genes, corrects the contamination, and avoids the over-correction of other genes.

bioinformatics↗

bioRxiv2022

Novel mRNA vaccines encoding Monkeypox virus M1R and A35R protect mice from a lethal virus challenge

The outbreak of Monkeypox virus infection urgently need effective vaccines. However, the vaccines so far approved are all based on whole-virus, which raises safety concerns. MRNA vaccines has demonstrated its high efficacy and safety against SARS-Cov-2 infection. Here, we developed three mRNA vaccines encoding Monkeypox proteins M1R and A35R, including A35R-M1R fusions (VGPox1 and VGPox 2) and a combination of encapsulated full-length mRNAs for A35R and M1R (VGPox 3). All three vaccines induced anti-A35R total IgGs as early as day 7 following a single vaccination. However, only VGPox 1 and 2 produced anti-M1R total IgGs at early dates following vaccination while VGPox 3 did not show significant anti-M1R antibody till day 35. Similar results were also found in neutralizing antibodies and T cell immune response. However, all mRNA vaccine groups completely protected mice from a lethal dose virus challenge and effectively cleared virus in lungs. Collectively, our results indicate that the novel mRNA vaccines coding for a fusion protein of A35R and M1R had a better anti-virus immunity than co-expression of the two individual proteins. The mRNA vaccines are highly effective and can be an alternative to the current whole-virus vaccines to defend Monkeypox virus infection.

immunology↗

bioRxiv2022

A novel method revealing animal evolutionary relationships based on milk Mid-infrared

Mid-infrared spectra (MIRS) can effectively reflect the chemical bonds in milk, which has been widely used in dairy herd improvement. However, the relationship between MIRS and animal evolution remains largely unclear. This study firstly found great differences in MIRS and the components of milk by analyzing MIRS information of 12 different mammal species. A five-level discriminant model of evolutionary level based on MIRS was established with a test set kappa coefficient >0.97. In addition, a regression model of genetic distance was also established to estimate the genetic distance of different animal species with a correlation coefficient of R >0.94. These results showed that this method could be used for accurate mammalian evolutionary relationship assessment. We further clarified the potential relationship between MIRS and genes, such as PPP3CA and SCD that could change MIRS by regulating specific milk components. In conclusion, we expand the application of MIRS in animal species identification and evolution research and provide new perspectives for the research on the formation mechanism of different animal milk special components.

evolutionary biology↗

bioRxiv2022

tFold-Ab: Fast and Accurate Antibody Structure Prediction without Sequence Homologs

Accurate prediction of antibody structures is critical in analyzing the function of antibodies, thus enabling the rational design of antibodies. However, existing antibody structure prediction methods often only formulate backbone atoms and rely on additional tools for side-chain conformation prediction. In this work, we propose a fully end-to-end architecture for simultaneous prediction of backbone and side-chain conformations, namely tFold-Ab. Pre-trained language models are adopted for fast structure prediction by avoiding the time-consuming search for sequence homologs. The model firstly predicts monomer structures of each chain, and then refines them into heavy-light chain complex structure prediction, which enables multi-level supervision for model training. Evaluation results verify the effectiveness of tFold-Ab for both antibody and nanobody structure prediction. In addition, we provide a public web service for antibody structure prediction at https://drug.ai.tencent.com/en.

bioinformatics↗

bioRxiv2022

An image cryptography method in highly error-prone DNA storage channel

Rapid development in synthetic technologies has boosted DNA as a potential medium for large-scale data storage. Meanwhile, how to implement data security in DNA storage system is still an unsolved problem. In this paper, we propose an image encryption method based on the modulation-based storage architecture. The key idea is to take advantage of the unpredictable modulation signals to encrypt image in highly error-prone DNA storage channel. Numerical results demonstrate that our image encryption method is feasible and effective with excellent security against various attacks (statistical, differential, noise and data loss, etc.). Compared with other methods by DNA molecules hybridization reaction, the proposed method is more reliable and feasible for large-scale applications.

synthetic biology↗

bioRxiv2022

An alternative malonyl-CoA producing pathway in nature

Malonyl-CoA is a key metabolic intermediate for biosynthesis of diverse cellular molecules and natural products. Carboxylation of acetyl-CoA is almost the unique pathway for malonly-CoA biosynthesis. Biotechnological production of numerous value-added malonyl-CoA-derived chemicals require high intracellular supply of malonyl-CoA. However, because of the central role of acetyl-CoA in primary metabolism, it is difficult to develop flexible strategies to balance malonyl-CoA supply with other cellular metabolism. Here we find that there is a natural alternative malonyl-CoA-producing pathway, in which the key reaction is catalyzed by an -keto acid dehydrogenase complex BkdFGH from Streptomyces avermitilis. This dehydrogenase complex could efficiently catalyze biosynthesis of malonyl-CoA from oxaloacetate in addition to recognizing its native substrate branched-chain -keto acid. Oxaloacetate dehydrogenase (OADH) was shown to play important physiological roles during the regulation of biosynthesis of native malonyl-CoA-derived polyketides in Streptomyces. Furthermore, the oxlaocetate dehydrogenation reaction is thermodynamically superior to acetyl-CoA carboxylation and enable efficient bioproduction of diverse malonyl-CoA-derived chemicals in engineering Escherichia coli. This novel malonyl-CoA source thus has great potential in the biotechnological field.

microbiology↗