Biology subjects

Xiao, W.

Publications and source records attributed to Xiao, W..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

Control of Leaf Width by the APC/CTAD1-WL1-NAL1 Pathway in Rice

Leaf morphology is one of the most important features of the ideal plant architecture. However, the genetic and molecular mechanisms controlling leaf morphology in crops remain largely unknown, despite their central importance. Here we demonstrate that the APC/CTAD1-WL1-NAL1 pathway regulates leaf width in rice, and mutation of WL1 leads to width leaf variation. WL1 interacts with TAD1 and is degraded by APC/CTAD1, with the loss of TAD1 function resulting in narrow leaves. The WL1 protein directly binds to the regulatory region of NAL1 and recruits the corepressor TOPLESS-RELATED PROTEIN to inhibit NAL1 expression by down-regulating the level of histone acetylation of chromatin. Furthermore, biochemical and genetic analyses revealed that TAD1, WL1, and NAL1 function in a common pathway to control leaf width. Our study establishes an important framework for the APC/CTAD1-WL1-NAL1 pathway-mediated control of leaf width in rice and introduces novel perspectives for using this regulatory pathway for improving crop plant architecture.

plant biology↗

bioRxiv2021

CXCL8/CXCR2 signaling mediates bone marrow fibrosis and represents a therapeutic target in myelofibrosis

Pro-inflammatory signaling is a hallmark feature of human cancer, including in myeloproliferative neoplasms (MPNs), most notably myelofibrosis (MF). Dysregulated inflammatory signaling contributes to fibrotic progression in MF; however, the individual cytokine mediators elicited by malignant MPN cells to promote collagen-producing fibrosis and disease evolution remain yet to be fully elucidated. Previously we identified a critical role for combined constitutive JAK/STAT and aberrant NF-{kappa}B pro-inflammatory signaling in myelofibrosis development. Using single-cell transcriptional and cytokine-secretion studies of primary MF patient cells and two separate murine models of myelofibrosis, we extend this previous work and delineate the role of CXCL8/CXCR2 signaling in MF pathogenesis and bone marrow fibrosis progression. MF patient hematopoietic stem/progenitor cells are enriched in a CXCL8/CXCR2 gene signature and display dose-dependent proliferation and fitness in response to exogenous CXCL8 ligand in vitro. Genetic deletion of Cxcr2 in the hMPLW515L adoptive transfer model abrogates fibrosis and extends overall survival, and pharmacologic inhibition of the CXCR1/2 pathway improves hematologic parameters, attenuates bone marrow fibrosis, and synergizes with JAK inhibitor therapy. Our mechanistic insights provide a rationale for therapeutic targeting of the CXCL8/CXCR2 pathway in MF patients at risk for continued fibrotic progression.

cancer biology↗

bioRxiv2021

Diverse tumorigenic consequences of human papillomavirus integration in primary oropharyngeal cancers

Human papillomavirus (HPV) causes 5% of all cancers and frequently integrates into host chromosomes, but the impacts of integration in tumorigenesis remain unclear. Analysis of 105 HPV-positive oropharyngeal cancers by whole genome sequencing detects viral integration in 77%, revealing five statistically significant integration hotspots near genes that regulate epithelial stem cell maintenance (i.e. SOX2, TP63, FGFR, MYC) and immune evasion (i.e. CD274). Somatic hyperamplification is enriched 16-fold near HPV integrants, and the extent of focal host genomic instability increases with local density of HPV integrants. Genes expressed at extreme outlier levels are increased 86-fold within +/- 150 kb of integrants. Across 95% of tumors with integration, host gene transcription is disrupted via intragenic integrants, chimeric transcription, outlier expression, gene breaking and/or de novo expression of noncoding or imprinted genes. We conclude that HPV integration contributes substantively to cancer development by causing extensive disruption of host genome structure and gene expression.

genomics↗

bioRxiv2021

Personalized genome assembly for accurate cancer somatic mutation discovery using cancer-normal paired reference samples

The use of personalized genome assembly as a reference for detecting the full spectrum of somatic events from cancers has long been advocated but never been systematically investigated. Here we address the critical need of assessing the accuracy of somatic mutation detection using personalized genome assembly versus the standard human reference assembly (i.e. GRCh38). We first obtained massive whole genome sequencing data using multiple sequencing technologies, and then performed de novo assembly of the first tumor-normal paired genomes, both nuclear and mitochondrial, derived from the same donor with triple negative breast cancer. Compared to standard human reference assembly, the haplotype phased chromosomal-scale personalized genome was best demonstrated with individual specific haplotypes for some complex regions and medical relevant genes. We then used this well-assembled personalized genome as a reference for read mapping and somatic variant discovery. We showed that the personalized genome assembly results in better alignments of sequencing reads and more accurate somatic mutation calls. Direct comparison of mitochondrial genomes led to discovery of unreported nonsynonymous somatic mutations. Our findings provided a unique resource and proved the necessity of personalized genome assembly as a reference in improving somatic mutation detection at personal genome level not only for breast cancer reference samples, but also potentially for other cancers.

bioinformatics↗

bioRxiv2021

Deep oncopanel sequencing reveals fixation time- and within block position-dependent quality degradation in FFPE processed samples

Clinical laboratories routinely use formalin-fixed paraffin-embedded (FFPE) tissue or cell block cytology samples in oncology panel sequencing to identify mutations that can predict patient response to targeted therapy. To understand the technical error due to FFPE processing, a robustly characterized normal cell line was used to create FFPE samples with four different pre-tissue processing formalin fixation times. A total of 96 FFPE sections were then distributed to different laboratories for targeted sequencing analysis by four oncopanels, and variants resulting from technical error were identified. Tissue sections that failed more frequently showed low cellularity, lower than recommended library preparation DNA input, or target sequencing depth. Importantly, sections from block surfaces were more likely to show FFPE-specific errors, akin to "edge effects" seen in histology, and the depth of formalin damage was related to fixation time. To assure reliable results, we recommend avoiding the block surface portion and restricting mutation detection to genomic regions of high confidence.

cancer biology↗

bioRxiv2021

piRNA pathway is essential for generating functional oocytes in golden hamster

Piwi-interacting RNAs (piRNAs) are small RNAs predominantly expressed in germ cells that are critical for gametogenesis in various species. However, PIWI-deficient female mice are fertile and mouse oocytes express a panel of small RNAs that do not appear widely representative of mammals, and piRNA function in the oogenesis of other mammals has therefore remained elusive. Recent studies revealed the small RNA and PIWI transcriptional profiles in golden hamster oocytes more closely resemble that of humans than mice. Herein, we generated PIWIL1-, PLD6- and MOV10L1-deficient golden hamsters and found that all female mutants were sterile, with embryos arrested at the two-cell stage. In PIWIL1 mutant oocytes, we observed transposon accumulation and broad transcriptomic dysregulation, while zygotic gene activation was impaired in early embryos. Intriguingly, PIWIL1-piRNAs exhibited a unique, preferential silencing of endogenous retroviruses (ERVs), whereas silencing LINE1s depended on both PIWIL1- and PIWIL3-piRNAs. Moreover, we showed that piRNAs participate in the degradation of maternal mRNAs in MII oocytes and embryos via partially complementary targets. Together, our findings demonstrate that piRNAs are indispensable for generating functional oocytes in golden hamster, and show the informative value of this model for functional and mechanistic investigations of piRNAs, especially those related to female infertility.

developmental biology↗

bioRxiv2021

Multiomics Interrogation into HBV (Hepatitis B Virus)-Host Interaction Reveals Novel Coding potential in Human Genome, and Identifies Canonical and Non-canonical Proteins as Host Restriction Factors against HBV

Hepatitis B Virus constitutes a major threat to global public health. Current understanding of HBV-host interaction is yet limited. Here, ribosome profiling, quantitative mass spectrometry and RNA-sequencing are conducted on a recently established HBV replication system. We have identified multiomic DEGs (differentially expressed genes) that HBV orchestrated to remodel host proteostasis networks. Our multiomics interrogation revealed that HBV induced significant changes in both transcription and translation of 35 canonical genes including PPP1R15A, PGAM5 and SIRT6, as well as the expression of at least 15 non-canonical ORFs including ncPON2 and ncGRWD1, thus revealing an extra coding potential of human genome. Overexpression of these five genes but not the enzymatically deficient SIRT6 mutants suppressed HBV replication while knockdown SIRT6 had opposite effect. Furthermore, the expression of SIRT6 was down-regulated in patients, cells or animal models of HBV infection. Mechanistic study further indicated that SIRT6 directly binds to mini-chromosome and deacetylates histone H3 lysine 9 (H3K9ac) and histone H3 lysine 56 (H3K56ac), and chemical activation of endogenous SIRT6 with MDL800 suppressed HBV infection in vitro and in vivo. By generating the first multiomics landscape of host-HBV interaction, our work is thus opening a new avenue to facilitate therapeutic development against HBV infection.

microbiology↗

bioRxiv2021

Whole Genome and Exome Sequencing Reference Datasets from A Multi-center and Cross-platform Benchmark Study

With the rapid advancement of sequencing technologies in the past decade, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from errors or artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically benchmarking sequencing platforms, library protocols, bioinformatics pipelines and for measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples, a human triple-negative breast cancer cell line and a matched normal cell line derived from B lymphocytes. We generated whole-genome (WGS) and whole-exome sequencing (WES) data using 16 NGS library preparation protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the paired reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

bioinformatics↗

bioRxiv2021

Comprehensive Assessment of Somatic Copy Number Variation Calling Using Next-Generation Sequencing Data

Copy number variation (CNV) is a common type of mutation that often drives cancer progression. With advances in next-generation sequencing (NGS), CNVs can be detected in a detailed manner via newly developed computational tools but quality of such CNV calls has not been carefully evaluated. We analyzed CNV calls reported by 6 cutting-edge callers for 91 samples which were derived from the same cancer cell line, prepared and sequenced by varying the following factors: type of tissue sample (Fresh vs. Formalin Fixed Paraffin Embedded (FFPE)), library DNA amount, tumor purity, sequencing platform (Whole-Genome Sequencing (WGS) versus Whole-Exome Sequencing (WES)), and sequencing coverage. We found that callers greatly determined the pattern of CNV calls. Calling quality was drastically impaired by low purity (<50%) and became variable when WES, FFPE, and medium purity (50%-75%) were applied. Effects of low DNA amount and low coverage were relatively minor. Our analysis demonstrates the limitation of benchmarking somatic CNV callers when the real ground truth is not available. Our comprehensive analysis has further characterized each caller with respect to confounding factors and examined the consistency of CNV calls, thereby providing guidelines for conducting somatic CNV analysis.

bioinformatics↗

bioRxiv2020

Tracking pre-mRNA maturation across subcellular compartments identifies developmental gene regulation through intron retention and nuclear anchoring.

To globally assess the distribution and processing of gene transcripts across subcellular compartments, we developed extensive RNA-seq datasets of both polyA+ and total RNA from chromatin, nucleoplasm and cytoplasm of mouse ESC, neuronal progenitors, and neurons. We identified protein-coding genes whose polyadenylated transcripts were more abundant in chromatin than cytoplasm. We defined introns exhibiting cotranscriptional splicing, complete intron retention in cytoplasmic RNA, and many introns retained in nucleoplasmic and chromatin RNA but absent from cytoplasmic RNA, including new introns controlled during neuronal development. In particular, we found that polyadenylated Gabbr1 transcripts are expressed in mESC but remain sequestered on chromatin until neuronal differentiation when they are processed and released to the cytoplasm. This developmental regulation of splicing and chromatin association demonstrates that the abundance of polyadenylated RNA is not always an indicator of functional gene expression. Our datasets provide a rich resource for analyzing many other aspects of mRNA maturation.

molecular biology↗

bioRxiv2020

Satellite subgenomic particles are key regulators of adeno-associated virus life cycle

Historically, AAV defective interfering particles (DI) were known as abnormal virions arising from natural replication and encapsidation errors. Through single virion genome analysis, we revealed that a major category of DI particles contains a double stranded DNA genome in a "snapback" configuration (SBG). The 5-SBGs include the P5 promoters and partial rep gene sequences. The 3-SBGs contains the capsid region. The molecular configuration of 5-SBGs allowed double stranded RNA transcription in their dimer configuration, which in turn regulate AAV rep expression and may improve AAV packaging. In contrast, 3-SBGs at its dimer configuration increased levels of cap protein. The generation and accumulation of 5-SBGs and 3-SBGs appears to be coordinated to balance the viral gene expression level. Therefore, the functions of 5-SBGs and 3-SBGs may help maximize the yield of AAV progenies. We postulate that AAV virus population behaved as a colony and utilizes its subgenomic particles to overcome the size limit of viral genome and encodes additional essential functions.

molecular biology↗

bioRxiv2020

Attractive internuclear force drives the collective behavior of nuclear arrays in Drosophila embryos

The emerging collective behaviors during embryogenesis play an important role in precise and reproducible morphogenesis. An important question in the study of collective behavior is what rule underlies the emerging pattern. Here we use the Drosophila embryo as a test tube to study this question. We focus on the nuclear array without membrane separation on the embryo periphery from the nuclear cycle (NC) 11 to NC14. After live imaging with light sheet microscopy, we extract the nuclear trajectory, speed, and internuclear distance with an automatic nuclear tracing method. We find that the nuclear speed shows a period of standing waves along the anterior-posterior (AP) axis after each metaphase as the nuclei collectively migrate towards the embryo poles and partially move back. And the maximum nuclear speed dampens by 38% in the second half of the standing wave. Moreover, the nuclear density is 35% higher in the middle than the pole region of the embryo during the S phase of NC11-NC14. To find mechanical rules controlling the collective motion and packing patterns of the nuclear array, we use the deep neural network (DNN) to learn the force field from data. We find two potential strong nuclear-age-dependent force fields, i.e., the repulsive or attractive force field. Simulations with the particle-based model indicate that only if the net internuclear force is attractive and increases with distance, the pseudo-synchronous mitotic wave in a nuclear array with lower nuclear density in embryo poles can drive the collective motion with the damped standing wave of the nuclear speed, and the collective nuclear motion, in turn, maintains the non-uniform nuclear density.

developmental biology↗

bioRxiv2020

A Multi-center Cross-platform Single-cell RNA Sequencing Reference Dataset

Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmarking scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as a mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. Our benchmark datasets provide a resource that we believe will have great value for the single-cell community by serving as a reference dataset for evaluating various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

bioinformatics↗

bioRxiv2020

A Deep Learning Framework for Predicting Human Essential Genes by Integrating Sequence and Functional data

Essential genes are necessary to the survival or reproduction of a living organism. The prediction and analysis of gene essentiality can advance our understanding to basic life and human diseases, and further boost the development of new drugs. Wet lab methods for identifying essential genes are often costly, time consuming, and laborious. As a complement, computational methods have been proposed to predict essential genes by integrating multiple biological data sources. Most of these methods are evaluated on model organisms. However, prediction methods for human essential genes are still limited and the relationship between human gene essentiality and different biological information still needs to be explored. In addition, exploring suitable deep learning techniques to overcome the limitations of traditional machine learning methods and improve the prediction accuracy is also important and interesting. We propose a deep learning based method, DeepSF, to predict human essential genes. DeepSF integrates sequence features derived from DNA and protein sequence data with features extracted or learned from different types of functional data, such as gene ontology, protein complex, protein domain, and protein-protein interaction network. More than 200 features from these biological data are extracted/learned which are integrated together to train a cost-sensitive deep neural network by utilizing multiple deep leaning techniques. The experimental results of 10-fold cross validation show that DeepSF can accurately predict human gene essentiality with an average AUC of 95.17%, the area under precision-recall curve (auPRC) of 92.21%, the accuracy of 91.59%, and the F1 measure about 78.71%. In addition, the comparison experimental results show that DeepSF significantly outperforms several popular traditional machine learning models (SVM, Random Forest, and Adaboost), and performs slightly better than a recent deep learning model (DeepHE). We have demonstrated that the proposed method, DeepSF, is effective for predicting human essential genes. Deep learning techniques are promising at both feature learning and classification levels for the task of essential gene prediction.

bioinformatics↗

bioRxiv2020

Subgenomic satellite particle generation in recombinant AAV vectors results from DNA lesion/breakage and non-homologous end joining

Recombinant AAV (rAAV) vectors have been developed for therapeutic treatment of genetic diseases. Nevertheless, current rAAV vectors administered to patients often contain non-vector related DNA contaminants. Here, we present a thorough molecular analysis of the configuration of non-standard AAV genomes generated during rAAV production. In addition to the sub-vector genomic size particles containing incomplete AAV genomes, our results found that rAAV preparations were contaminated with multiple categories of subgenomic particles with either snapback genomes or vector genomes with deletions in the mid regions. Through CRISPR and restriction enzyme-based in vivo and in vitro modeling, we identified that the main mechanism leading to the formation of non-canonical genome particles occurred through nonhomologous end joining of fragmented vector genomes caused by genome lesions or DNA breaks that were generated by the host cell/environment. The results of this study advance our understanding of AAV vectors and provide new clues on improving vector efficiency and safety profile for use in human gene therapy.

molecular biology↗

bioRxiv2020

A novel fragmented mitochondrial genome in the protist pathogen Toxoplasma gondii and related tissue coccidia

Mitochondrial genome content and structure vary widely across the eukaryotic tree of life with protists displaying extreme examples. Apicomplexan and dinoflagellate protists have evolved highly-reduced mitochondrial genome sequences, mtDNA, consisting of only 3 cytochrome genes and fragmented rRNA genes. Here we report the independent evolution of fragmented cytochrome genes in Toxoplasma and related tissue coccidia and evolution of a novel genome architecture consisting minimally of 21 sequence blocks (SBs) that exist as non-random concatemers. Single-molecule Nanopore reads consisting entirely of SB concatemers ranging from 1-23 kb reveal both whole and fragmented cytochrome genes. Full-length cytochrome transcripts including a divergent coxIII are detected. The topology of the mitochondrial genome remains an enigma. Analysis of a cob point mutation reveals that homoplasmy of SBs is maintained. Tissue coccidia are important pathogens of man and animals and the mitochondrion represents an important therapeutic target. Their mtDNA sequence has remained elusive until now.

genomics↗

bioRxiv2020

Plasmacytoid dendritic cell expansion defines a distinct subset of RUNX1 mutated acute myeloid leukemia

Plasmacytoid dendritic cells (pDC) are the principal natural type I interferon producing dendritic cells. Neoplastic expansion of pDCs and pDC precursors leads to blastic plasmacytoid dendritic cell neoplasm (BPDCN) and clonal expansion of mature pDCs has been described in chronic myelomonocytic leukemia (CMML). The role of pDC expansion in acute myeloid leukemia (AML) is poorly studied. Here we characterize AML patients with pDC expansion (pDC-AML), which we observe in approximately 5% of AML. pDC-AML often possess crosslineage antigen expression and have adverse risk stratification with poor outcome. RUNX1 mutations are the most common somatic alterations in pDC-AML (>70%) and are much more common than in AML without PDC expansion. We demonstrate that pDCs are clonally related to, and originate from, leukemic blasts in pDC-AML. We further demonstrate that leukemic blasts from RUNX1-mutated AML upregulate a pDC transcriptional program, poising the cells towards pDC differentiation and expansion. Finally, tagraxofusp, a targeted therapy directed to CD123, reduces leukemic burden and eliminates pDCs in a patient-derived xenograft model. In conclusion, pDC-AML is characterized by a high frequency of RUNX1 mutations and increased expression of a pDC transcriptional program. CD123 targeting represents a potential treatment approach for pDC-AML.Competing Interest StatementConflict-of-interest disclosure: W.X. has received research support from Stemline Therapeutics. S.F.C. is a consultant for Imago Biosciences and has received honoraria from DAVA Oncology. A.D.G. served on advisory boards or as a consultant for Abbvie, Aptose, Celgene, Daiichi Sanyko, Genentech, received research funding from Abbvie, ADC Therapeutics, Aprea, AROG, Daiichi Sanyko, Pfizer, received Honoraria from Dava Oncology. R.K.R has received consulting fees from: Constellation, Incyte, Celgene, Promedior, CTI, Jazz Pharmaceuticals, Blueprint, Stemline, and research funding from Incyte, Constellation, and Stemline Therapeutics. M.S.T has received research funding from AbbVie, Cellerant, Orsenix, ADC Therapeutics, Biosight, Glycomimetics, Rafael Pharmaceuticals and Amgen. He also served on advisory Boards for AbbVie, BioLineRx, Daiichi-Sankyo, Orsenix, KAHR, Rigel, Nohla, Delta Fly Pharma, Tetraphase, Oncolyze, Jazz Pharma, Roche, Biosight and Novartis. He received royalties from UpToDate. R.L.L. is on the supervisory board of Qiagen and is a scientific advisor to Loxo (until 2019), Auron, Ajax, Mission Bio, Imago, C4 Therapeutics and Isoplexis, which each include an equity interest. He receives research support from and consulted for Celgene and Roche, he has received research support from Prelude Therapeutics, and he has consulted for Incyte, Novartis and Janssen. He has received honoraria from Lilly and Amgen for invited lectures and from Gilead for grant reviews.View Full Text

pathology↗

bioRxiv2020

NOGEA: Network-Oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting new drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

systems biology↗