Search bioRxiv⌕ Search

Biology subjects

Chen, X.

Publications and source records attributed to Chen, X..

At least 1,477 records · Page 82Linked to original sources

Co-Stimulation-Induced AP-1 Activity is Required for Chromatin Opening During T Cell Activation

Activation of T cells is dependent on organized and timely opening and closing of chromatin. Herein, we identify AP-1 as the transcription factor that directs most of this remodeling. Chromatin accessibility profiling showed quick opening of closed chromatin in naive T cells within 5 hours of activation. These newly open regions were strongly enriched for the AP-1 motif, and indeed, ChIP-seq demonstrated AP-1 binding at more than 70% of them. Broad inhibition of AP-1 activity prevented chromatin opening at AP-1 sites and reduced expression of nearby genes. Similarly, induction of anergy in the absence of co-stimulation during activation, was associated with reduced induction of AP-1 and a failure of proper chromatin remodeling. The translational relevance of these findings was highlighted by the substantial overlap of AP-1-dependent elements with risk loci for multiple immune diseases, including multiple sclerosis, inflammatory bowel disease and allergic disease. Our findings define AP-1 as the key link between T cell activation and chromatin remodeling.

immunology↗

Bayogenin 3-O-Cellobioside is a novel non-cultivar specific anti-blast metabolite produced in rice in response to Pyricularia oryzae infection

Rice cultivars from japonica and indica lineage possess differential resistance against blast fungus on an account genetic divergence. Whether different rice cultivars also show distinct metabolomic changes in response to P. oryzae, and their role in host resistance, are poorly understood. Here, we examine the responses of six different rice cultivars from japonica and indica lineage challenged with P. oryzae. Both susceptible and resistant rice cultivars expressed several metabolites exclusively during P. oryzae infection, including the saponin Bayogenin 3-O-cellobioside. Bayogenin 3-O-cellobioside level in infected rice directly correlated with their resistant attributes. These findings reveal, for the first time to our knowledge that besides oat, other grass plants including rice produces protective saponins. Our study provides insight into the role of pathogen-mediated metabolomics-reprogramming in host immunity. The correlation between Bayogenin 3-O-Cellobioside levels and blast resistance suggests that engineering saponin expression in cereal crops represents an attractive and sustainable disease control strategy.

biochemistry↗

Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform

We present KnowEnG, a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis and expression signature analysis. The system offers knowledge-guided data-mining and machine learning algorithms, where user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge-bases and encoded in a massive Knowledge Network. KnowEnG adheres to FAIR principles: its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution of compute-intensive and data-intensive algorithms, and are interoperable with other computing platforms. They are made available through multiple access modes including a web-portal, and include specialized visualization modules. We present use cases and re-analysis of published cancer data sets using KnowEnG tools and demonstrate its potential value in democratization of advanced tools for the modern genomics era.

cancer biology↗

Bimodal Regulation of the PRC2 Complex by USP7 Underlies Melanomagenesis

Although overexpression of EZH2, a catalytic subunit of the polycomb repressive complex 2 (PRC2), is an eminent feature of various cancers, the regulation of its abundance and function remains insufficiently understood. We report here that the PRC2 complex is physically associated with ubiquitin-specific protease USP7 in melanoma cells where USP7 acts to deubiquitinate and stabilize EZH2. Interestingly, we found that USP7-catalyzed H2BK120 deubiquitination is a prerequisite for chromatin loading of PRC2 thus H3K27 trimethylation. Genome-wide analysis of the transcriptional targets of the USP7/PRC2 complex identified a cohort of genes including FOXO1 that are involved in cell growth and proliferation. We demonstrated that the USP7/PRC2 complex drives melanoma cell proliferation and tumorigenesis in vitro and in vivo. We showed that the expression of both USP7 and EZH2 elevates during melanoma progression, corresponding to a diminished FOXO1 expression, and the level of the expression of USP7 and EZH2 strongly correlates with histological grades and prognosis of melanoma patients. These results reveal a dual role for USP7 in the regulation of the abundance and function of EZH2, supporting the pursuit of USP7 as a therapeutic target for melanoma.

cell biology↗

Bayesian inference of differentially expressed transcripts and their abundance from multi-condition RNA-seq data

Deep sequencing of bulk RNA enables the differential expression analysis at transcript level. We develop a Bayesian approach to directly identify differentially expressed transcripts from RNA-seq data, which features a novel joint model of the sample variability and the differential state of individual transcripts. For each transcript, to minimize the inaccuracy of differential state caused by transcription abundance estimation, we estimate its expression abundance together with the differential state iteratively and enable the differential analysis of weakly expressed transcripts. Simulation analysis demonstrates that the proposed approach has a superior performance over conventional methods (estimating transcription expression first and then identifying differential state), particularly for lowly expressed transcripts. We further apply the proposed approach to a breast cancer RNA-seq data of patients treated by tamoxifen and identified a set of differentially expressed transcripts, providing insights into key signaling pathways associated with breast cancer recurrence.

bioinformatics↗

Alteration of Gut Microbiome in Lung Cancer Patients

Lung cancer is the leading cause of cancer death. Better understanding of factors and pathways involved in lung cancer is needed to improve diagnose and treatment strategies. Recent studies have provided insights into the possible correlation between intestinal dysbiosis and cancer development. Although the immunological relationship between gut and lung had been suggested by many researches, however, to date, no study had investigated the characterization of gut microbiome in treatment naive lung cancer patients, whether it is distinct from that of health individuals and contribute to the onset and development of lung cancer remain unclear. In this study, we investigated whether gut microbiome of lung cancer patients (LC, n=28) is altered compare with that of matched healthy individuals (HC, n=19) by high throughout sequencing of the V3-V4 regions of 16S rDNA in their fecal samples. We also identified microbiota signatures specific for different histological types of lung cancer, including SSC, ADC, and SCLC. The gut microbiome of lung cancer patients is characterized by decreased relative abundance of Prevotella, and increased bacteria groups such as Actinomyces, and Streptococcus, etc. We also detected a mild structural shift in gut microbiome between ADC and SCLC patients. Our results showed that the gut microbiome of lung cancer patients altered significantly compared with healthy individuals. However, the association between microbial dysbiosis and lung cancer is not clearly understood, future studies involving larger cohorts and metagenomics, or metabolomics, may elucidate the correlations between gut microbiota and lung cancer development.\n\nIMPORTANCEThis is the first report to show the alteration of gut microbiome in lung cancer patients. Our results showed that the gut microbiome of lung cancer patients altered significantly compared with healthy individuals.

microbiology↗

Fast, Accurate Polarization and Polarity Imaging withPolarized Structured Illumination

The orientation and wobbling behavior of the fluorescent dipoles are of great significance in revealing the structure and state of cells. Due to the poor optical sectioning capability of wide-field microscopy, the polarization modulation signals are susceptible to the neighboring fluorophores. The missing cone of wide field optical transfer function induces vast out-of-focus background, resulting in biased polarization orientation and decrease polarization factor. Here, we apply polarized structured illumination to achieve polarization modulation imaging with optical sectioning, and simultaneously measure the lipid polarity with two-color ratiometric imaging. Our results demonstrate a significant increase in measurement accuracy of not only the dipole orientations but also the wobbling behavior of the ensemble dipole. Compared to the conventional confocal polarization imaging, our method obtains an order-of-magnitude faster imaging speed, capturing the fast dynamics of subcellular structures in live cells.

biophysics↗

MAPPING TRANSCRIPTION FACTOR NETWORKS BY COMPARING TF BINDING LOCATIONS TO TF PERTURBATION RESPONSES

BackgroundA transcription-factor (TF) network map indicates the direct, functional targets of each TF -- the genes it regulates by binding to their cis-regulatory DNA. Data on the genomic binding locations of each TF and the transcriptional responses to perturbations of its activity, such as overexpressing it, could support TF network mapping. Systematic data sets of both types exist for yeast and for human K562 and HEK293 cells.\n\nResultsIn previous data, most TF binding sites appear to be non-functional, so one cannot take the genes in whose promoters a TF binds as its direct, functional (DF) targets. Taking the genes that are both bound by a TF and responsive to a perturbation of it as its DF targets (intersection algorithm) is also not safe, as we show by deriving a new lower bound on the expected false discovery rate of the intersection algorithm. When there are many non-functional binding sites and many indirect targets, non-functional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. Dual threshold optimization, a new method for setting significance thresholds on binding and response data, improves the intersection algorithm, as does post-processing perturbation-response data with NetProphet 2.0. A comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a TF also helps, as does transposon calling cards, a new method for identifying TF binding locations.\n\nConclusionsThe combination of dual threshold optimization and NetProphet greatly expands the high-confidence TF network map in both yeast and human. In yeast, measuring the response shortly after inducing TF overexpression and measuring binding locations by using transposon calling cards improve the network synergistically.

genomics↗

Functional genomics of the stable fly, Stomoxys calcitrans, reveals mechanisms underlying reproduction, host interactions, and novel targets for pest control

BackgroundThe stable fly, Stomoxys calcitrans, is a major blood-feeding pest of livestock that has near worldwide distribution, causing an annual cost of over $2 billion for control and product loss in the United States alone. Control of these flies has been limited to increased sanitary management practices and insecticide application for suppressing larval stages. Few genetic and molecular resources are available to help in developing novel methods for controlling stable flies.\n\nResultsThis study examines stable fly biology by utilizing a combination of high-quality genome sequencing, microbiome analyses, and RNA-seq analyses targeting multiple developmental stages and tissues. In conjunction, manual curation of over 1600 genes was used to examine gene content related to stable fly reproduction, interactions with their host, host-microbe dynamics, and putative routes for control. Most notable was establishment of reproduction-associated genes and identification of expanded vision, chemosensation, immune repertoire, and metabolic detoxification pathway gene families.\n\nConclusionsThe combined sequencing, assembly, and curation of the male stable fly genome followed by RNA-seq and downstream analyses provide insights necessary to understand the biology of this important pest. These resources and knowledge will provide the groundwork for expanding the tools available to control stable fly infestations. The close relationship of Stomoxys to other blood-feeding (Glossina) and non-blood-feeding flies (medflies, Drosophila, house flies) will allow for understanding the evolution of blood feeding among Cyclorrhapha flies.

genomics↗

The Contribution of Parietal Cortex to Visual Salience

Unique stimuli stand out. In spite of an abundance of competing sensory stimuli, the detection of the most salient ones occurs without effort, and that detection contributes to the guidance of adaptive behavior. Neurons sensitive to the salience of visual stimuli are widespread throughout the primate visual system and are thought to shape the selection of visual targets. However, mechanisms underlying the representation of salience remain elusive. Among the possible candidates are areas within posterior parietal cortex, which appear to be crucial in the control of visual attention and are thought to play a unique role in representing stimulus salience. Here we show that reversible inactivation of parietal cortex not only selectively reduces the representation of visual salience within the brain, but it also diminishes the influence of salience on visually guided behavior. These results demonstrate a distinct contribution of parietal areas to vision and visual attention.

neuroscience↗

Identifying neoantigens for cancer vaccines by personalized deep learning of individual immunopeptidomes

Tumor-specific neoantigens play the main role for developing personal vaccines in cancer immunotherapy. We propose, for the first time, a personalized de novo sequencing workflow to identify HLA-I and HLA-II neoantigens directly and solely from mass spectrometry data. Our workflow trains a personal deep learning model on the immunopeptidome of an individual patient and then uses it to predict mutated neoantigens of that patient. This personalized learning and mass spectrometry-based approach enables comprehensive and accurate identification of neoantigens. We applied the workflow to datasets of five melanoma patients and substantially improved the accuracy and identification rate of de novo HLA peptides by 14.3% and 38.9%, respectively. This subsequently led to the identification of 10,440 HLA-I and 1,585 HLA-II new peptides that were not presented in existing databases. Most importantly, our workflow successfully discovered 17 neoantigens of both HLA-I and HLA-II, including those with validated T cell responses and those novel neoantigens that had not been reported in previous studies.

bioinformatics↗

coMethDMR: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies

Recent technology has made it possible to measure DNA methylation profiles in a cost-effective and comprehensive genome-wide manner using array-based technology for epigenome-wide association studies. However, identifying differentially methylated regions (DMRs) remains a challenging task because of the complexities in DNA methylation data. Supervised methods typically focus on the regions that contain consecutive highly significantly differentially methylated CpGs in the genome, but may lack power for detecting small but consistent changes when few CpGs pass stringent significance threshold after multiple comparison. Unsupervised methods group CpGs based on genomic annotations first and then test them against phenotype, but may lack specificity because the regional boundaries of methylation are often not well defined. We present coMethDMR, a flexible, powerful, and accurate tool for identifying DMRs. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first. Next, coMethDMR tests association between methylation levels within the sub-region and phenotype via a random coefficient mixed effects model that models both variations between CpG sites within the region and differential methylation simultaneously. coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing of targeted genomic regions, and is available as an open-source R package.

bioinformatics↗

pathwayPCA: an R package for integrative pathway analysis with modern PCA methodology and gene selection

With the advance in high-throughput technology for molecular assays, multi-omics datasets have become increasingly available. However, most currently available pathway analysis software provide little or no functionalities for analyzing multiple types of -omics data simultaneously. In addition, most tools do not provide sample-specific estimates of pathway activities, which are important for precision medicine. To address these challenges, we present pathwayPCA, a unique R package for integrative pathway analysis that utilizes modern statistical methodology including supervised PCA and adaptive elastic-net PCA for principal component analysis. pathwayPCA can analyze continuous, binary, and survival outcomes in studies with multiple covariate and/or interaction effects. We provide three case studies to illustrate pathway analysis with gene selection, integrative analysis of multi-omics datasets to identify driver genes, estimating and visualizing sample-specific pathway activities in ovarian cancer, and identifying sex-specific pathway effects in kidney cancer. pathwayPCA is an open source R package, freely available to the research community. We expect pathwayPCA to be a useful tool for empowering the wide scientific community on the analyses and interpretation of the wealth of multiomics data recently made available by TCGA, CPTAC and other large consortiums.

bioinformatics↗

Pan-genome analysis reveals the molecular basis of niche adaptation of Staphylococcus epidermidis strains

Staphylococcus epidermidis is the most commonly isolated species from human skin and the second leading cause of bloodstream infections. Here, we performed a large-scale comparative study without any pre-assigned reference to identify genomic determinants associated with their diversity and adaptation as a \"double-side spy\", a skin dominant colonization, and a successful pathogen. The pan-genome of S. epidermidis is open with 435 core proteins and a pan-genome size of 8034 proteins. Genome-wide phylogenetic tree shows that whole genome sequence is a powerful tool to analyze the complex evolutionary process of S. epidermidis and investigate the source of infection. Comparative genome analyses demonstrate the high diversity of antimicrobial resistances, especially mobile genetic elements. The complicated relationships of host-bacterium and bacterium-bacterium help S. epidermidis to play a vital role in balancing the epithelial microflora. The highly variable and dynamic nature of the S. epidermidis genome may be the result of its success in adapting to broad habitats, which is necessary to deal with complex environments. This study gives the general landscape of S. epidermidis pan-genome and provides valuable insights into mechanisms for genome evolution and lifestyle adaptation of this ecologically flexible species.

microbiology↗

Function and clinical relevance of RHAMM isoforms in pancreatic tumor progression

The receptor for hyaluronic acid-mediated motility (RHAMM) is upregulated in various cancers. We previously screened genes upregulated in human hepatocellular carcinomas for their metastatic function in a mouse model of pancreatic neuroendocrine tumor (PNET) and identified that human RHAMMB promoted liver metastasis. It was unknown whether RHAMMB is upregulated in pancreatic cancer or contributes to its progression. In this study, we found that RHAMM protein was frequently upregulated in human PNETs. We investigated alternative splicing isoforms, RHAMMA and RHAMMB, by RNA-Seq analysis of primary PNETs and liver metastases. RHAMMB, but not RHAMMA, was significantly upregulated in liver metastases. RHAMMB was crucial for in vivo metastatic capacity of mouse and human PNETs. RHAMMA, carrying an extra 15-amino acid-stretch, did not promote metastasis in spontaneous and experimental metastasis mouse models. Moreover, RHAMMB was substantially higher than RHAMMA in pancreatic ductal adenocarcinoma (PDAC). RHAMMB, but not RHAMMA, correlated with both higher EGFR expression and poorer survival of PDAC patients. Knockdown of EGFR abolished RHAMMB-driven PNET metastasis. Altogether, our findings suggest a clinically relevant function of RHAMMB, but not RHAMMA, in promoting PNET metastasis in part through EGFR signaling. RHAMMB can thus serve as a prognostic factor for pancreatic cancer.

cancer biology↗

Hybrid decay: a transgenerational epigenetic decline in vigor and viability triggered in backcross populations of teosinte with maize

In the course of generating populations of maize with teosinte chromosomal introgressions, an unusual sickly plant phenotype was noted in individuals from crosses with two teosinte accessions collected near Valle de Bravo, Mexico. The plants of these Bravo teosinte accessions appear phenotypically normal themselves and the F1 plants appear similar to typical maize x teosinte F1s. However, upon backcrossing to maize, the BC1 and subsequent generations display a number of detrimental characteristics including shorter stature, reduced seed set and abnormal floral structures. This phenomenon is observed in all BC individuals and there is no chromosomal segment linked to the sickly plant phenotype in advanced backcross generations. Once the sickly phenotype appears in a lineage, normal plants are never again recovered by continued backcrossing to the normal maize parent. Whole-genome shotgun sequencing reveals a small number of genomic sequences, some with homology to transposable elements, that have increased in copy number in the backcross populations. Transcriptome analysis of seedlings, which do not have striking phenotypic abnormalities, identified segments of 18 maize genes that exhibit increased expression in sickly plants. A de novo assembly of transcripts present in plants exhibiting the sickly phenotype identified a set of 59 up-regulated novel transcripts. These transcripts include some examples with sequence similarity to transposable elements and other sequences present in the recurrent maize parent (W22) genome as well as novel sequences not present in the W22 genome. Genome-wide profiles of gene expression, DNA methylation and sRNAs are similar between sickly plants and normal controls, although a few up-regulated transcripts and transposable elements are associated with altered sRNA or methylation profiles. This study documents hybrid incompatibility and genome instability triggered by the backcrossing of Bravo teosinte with maize. We name this phenomenon \"hybrid decay\" and present ideas on the mechanism that may underlie it.

genetics↗

A multi-omic cohort as a reference point for promoting a healthy human gut microbiome

More than a decade of gut microbiome studies have a common goal for human health. As most of the disease studies sample the elderly or the middle-aged, a reference cohort for young individuals has been lacking. It is also not clear what other omics data need to be measured to better understand the gut microbiome. Here we present high-depth metagenomic shotgun sequencing data for the fecal microbiome together with other omics data in a cohort of 2,183 adults, and observe a number of vitamins, hormones, amino acids and trace elements to correlate with the gut microbiome and cluster with T cell receptors. Associations with physical fitness, sleeping habits and dairy consumption are identified in this large multi-omic cohort. Many of the associations are validated in an additional cohort of 1,404 individuals. Our comprehensive data are poised to advise future study designs to better understand and manage our gut microbiome both in population and in mechanistic investigations.

microbiology↗

Computational comparison of developmental cell lineage trees by alignments

The developmental cell lineage tree, which records every cell division event and the terminal developmental state of each single cell, is one of the most important traits of multicellular organisms, as well as key to many significant unresolved questions in biology. Recent technological breakthroughs are paving the way for direct determination of cell lineage trees, yet a general framework for the computational analysis of lineage trees, in particular an algorithm to compare two lineage trees, is still lacking. Based on previous findings that the same developmental program can be invoked by different cells on the lineage tree to produce highly similar subtrees, we designed Developmental Cell Lineage Tree Alignment (DELTA), an algorithm that exhaustively searches for lineage trees with phenotypic resemblance in lineal organization of terminal cells, meanwhile resolving detailed correspondence between individual cells. Using simulated and nematode lineage trees, we demonstrated DELTAs capability of revealing similarities of developmental programs by lineal resemblances. Moreover, DELTA successfully identifies gene deletion-triggered homeotic cell fate transformations, reveals functional relationship between mutants by quantifying their lineal similarities, and finds the evolutionary correspondence between cell types defined non-uniformly for different species. DELTA establishes novel foundation for comparative study of lineage trees, much like sequence alignment algorithm for biological sequences, and along with the increase of lineage tree data, will likely bring unique insights for the myriads of important questions surrounding cell lineage trees.

bioinformatics↗