Applying Bioinformatics to Research in Immune, Muscle, and Bone Diseases

将生物信息学应用于免疫、肌肉和骨骼疾病的研究

• 批准号: 9155661
• 负责人: Massimo Gadina
• 金额: $ 88.71万
• 依托单位: NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9155661
• 关键词: ATAC-seq; Achievement; Adolescent; Ankylosing spondylitis; Arthritis; Binding; Binding Sites; Bioinformatics; Biological; Biomedical Research; Blood Vessels; Bone Diseases; CD4 Positive T Lymphocytes; Cell Line; Cell Lineage; Cells; ChIP-seq; Chronic; Chronic Childhood Arthritis; Code; Complex; Computing Methodologies; Data; Data Analyses; Data Quality; Dermatomyositis; Development; Diagnostic; Disease; Dose; Enhancers; Enzymes; Evaluation; Excision; Fibroblasts; Frequencies; Gene Expression; Gene Expression Profile; Genes; Genetic; Genome; Genome Mappings; Genomics; Goals; HLA-B27 Antigen; Health; Helper-Inducer T-Lymphocyte; Hemoglobin; IL6 gene; Immune; Immune System Diseases; Inflammatory; Inflammatory Bowel Diseases; Interferon Type I; Interferons; Investigation; Java; Lipoproteins; Lung; Macrophage Activation; Mammals; Maps; Measures; Mediating; Melorheostosis; Methods; MicroRNAs; Modeling; Mutation; Mutation Detection; Myopathy; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Neonatal; Neurons; Pathogenesis; Patients; Process; RNA-Binding Proteins; Rattus; Reading; Research; Research Project Grants; Ribonucleoproteins; Role; Root Resorption; STAT1 gene; STAT3 gene; Sampling; Semantics; Signal Transduction; Site; Small RNA; Solutions; Source; Stat5 protein; Stem cells; Syndrome; T cell regulation; T-Lymphocyte; Techniques; Time; Transfer RNA; Treatment Efficacy; Undifferentiated; Vascular Diseases; Whole Blood; attenuation; autoimmune lymphoproliferative syndrome; base; biomedical ontology; bone mass; cell type; computerized data processing; density; design; disease-causing mutation; early onset; exome sequencing; genome sequencing; improved; induced pluripotent stem cell; infancy; macrophage; method development; microbiome; nerve stem cell; next generation sequencing; paralogous gene; programs; remediation; research study; response; statistics; tRNA Precursor; targeted sequencing; text searching; tool; transcriptome sequencing

The Biodata Mining and Discovery section has been actively involved in a large number of NIAMS research projects, in particular the following: - Investigation of the role of HLA-B27 in a rat model of Inflammatory Bowel Disease, integrating transcriptome, microbiome and disease score to understand IBD pathogenesis - Investigating sources of bias in whole blood RNA-Seq and their remediation - Characterization of the transcriptome of iPSC cells - Validating an interferon score based on gene expression as a diagnostic tool for Type I interferon-mediated diseases and as a tool for measuring treatment efficacy - Activated STING in a Vascular and Pulmonary Syndrome - Investigation of genetic causes for Juvenile-onset Ankylosing Spondylitis by WES - Investigation of genetic causes for Juvenile-onset Dermatomyositis by Whole Exome Sequencing (WES) - Modulation of macrophage responses by aberrant lipoproteins in chronic inflammatory diseases - the effect of shared information on semantic calculations in biomedical ontologies - Immune dysregulation in patients with TRNT1 deficiency - An active enhancer signature defines regulatory identity in the absence of Foxp3 - Investigation on the role of STAT1 and STAT3 in IL6 and IL-27 signaling in helper T cells - Regulation of bone mass by DLX3 - Role of BRD4 in elongation of both coding and enhancer RNAs - Function of T-bet in restriction of type II IFN attenuation of a glycolytic program - STAT5 paralog dose governs T cell effector and regulatory function - Analysis of the developmental time course of gene expression in differentiation of iPSC-derived neural stem cells (NSCs) to neurons Major computational accomplishments and achievements are highlighted below. Further Development of PAPST (Peak Assignment and Profile Search Tool) More advanced features have been developed into this Java ChIP-Seq data analysis tool, including those for peak-centric data analysis and direct result-to-input conversion within the tool for efficient exploratory research. Microbiome Analysis and Integration with Transcriptome Data The use of NGS to analyze the microbiome of mammals has been recently developed. Analysis pipelines have been developed for analyzing the data and presenting the results graphically. In addition, microbiome results are being analyzed along with gut transcriptome data to provide a combined model of gene expression and gut flora changes in health and disease. Investigating and Remediating Sources of Bias in Whole Blood RNA-Seq Sources of bias in whole blood RNA-Seq have been investigated. A pipeline has been developed to remove hemoglobin and intergenic reads to improve the quality of RNA-Seq data. This newly developed technique has been applied to more than 400 patient and control samples in a study of interferonopathies. Characterization of the Transcriptome of iPSC Cells Fibroblasts from patients and controls were reprogrammed into induced pluripotent stem cells (iPSCs) to understand the disease pathogenesis of Ankylosing Spondylitis. The transcriptomes of these cells were analyzed to determine whether the iPSCs were in fact stem cell-like. These iPSCs were then differentiated into disease-relevant cell lineages. The transcriptomes of the differentiated cells were examined to try to discern gene expression changes that correlate with disease. WGS Data Analysis Analyzed whole-genome sequencing (WGS) data from patients with undifferentiated auto-inflammatory diseases. Low Frequency Mutation Detection Methods Evaluation Evaluated experimental and computational methods for detecting low frequency mutations such as duplex sequencing, circle sequencing, HaloplexHs, and ultra-deep target sequencing. Identification of Disease-causing Mutation Identified a likely disease-causing mutation in MYD88 gene in patients with early-onset severe arthritis. Pipeline Improvement for Mutational Data Analysis Improved and maintained the computational pipeline for WES data processing and quality assessment, as well as mutational data analysis workflow for WES experiments. ATAC-Seq Pipeline Development A 202-step pipeline has been developed for ATAC-Seq data analysis. It includes sequence redundancy removal at the fastq file level, genome-mapping, fragmentsize-based parsing of the mapping results, making USCS genome browser viewable files, peak calling, and relevant data manipulation. Super Enhancers and Disease Genes Using publicly available data, super enhancers have been determined for 18 cell lines of 12 unique immune cell types. These super enhancers have been assigned to genes based on linear sequence proximity, resulting in a total of 3622 unique genes that have SE assigned in at least one of the 18 cell lines. The combined SE assignment table for these 18 cell lines has been used to assess the statistical significance of certain disease genes with assigned SEs of increased binding signals. The super enhancer plots for these 18 cell lines have been similarly utilized to study SE and disease relationships. Method Development for tRNA Expression Quantification A method has been developed to specifically quantify the tRNA expression levels at the very 3 end in order to assess the activity of a tRNA processing enzyme that turns a precursor tRNA into a mature one by adding the bases CCA to the 3 end. The method involves the genomic mapping of all the original small RNA reads first, then performing a second mapping for those reads that have failed in mapping number one, after removing the CCA from the 3 ends of the failed reads. Reads density calculations are followed around the 3 ends of all the tRNAs and the results are graphically represented for each tRNA using the data from both mappings. A more detailed analysis focusing on exactly the 3 ends of tRANS is also carried out to count the reads that have mapped to the very last 3 base to calculate the differences between the levels of tRNAs with and without CCA at the 3 end (between mature and precursor tRNAs). A General Graphical Method for Gene gGroup Switching A Circos-based general method has been developed to graphically demonstrate how many genes have changed, either epigenetically or at the expression level, between two different biological conditions. An Approach to Calculate Conservative Score Statistics of PAR-CLIP Sites This approach has been developed to calculate seven species-based phyloP score statistics of the binding sites of RNA-binding proteins and micro-RNA-containing ribonucleoprotein complexes. The statistics include mean score of all binding site bases, standard deviation, minimum and maximum scores, % of conserved bases, and mean score of conserved bases.

生物数据挖掘和发现部门积极参与了 NIAMS 的大量研究项目，特别是以下项目： - 研究 HLA-B27 在炎症性肠病大鼠模型中的作用，整合转录组、微生物组和疾病评分以了解 IBD 发病机制 - 调查全血 RNA-Seq 的偏差来源及其补救措施 - iPSC 细胞转录组的表征 - 验证基于基因表达的干扰素评分作为 I 型干扰素介导疾病的诊断工具和测量治疗效果的工具 - 血管和肺部综合征中的激活 STING - 通过 WES 研究青少年发病的强直性脊柱炎的遗传原因 - 通过全外显子组测序（WES）调查青少年发病的皮肌炎的遗传原因 - 慢性炎症性疾病中异常脂蛋白对巨噬细胞反应的调节 - 共享信息对生物医学本体语义计算的影响 - TRNT1 缺乏症患者的免疫失调 - 在没有 Foxp3 的情况下，主动增强子签名定义了监管身份 - 研究 STAT1 和 STAT3 在辅助 T 细胞中 IL6 和 IL-27 信号传导中的作用 - DLX3 调节骨量 - BRD4 在编码和增强子 RNA 延伸中的作用 - T-bet 在限制糖酵解程序的 II 型 IFN 减弱中的功能 - STAT5旁系同源剂量控制T细胞效应器和调节功能 - 分析 iPSC 来源的神经干细胞 (NSC) 分化为神经元时基因表达的发育时间过程 主要的计算成就和成就如下所示。 PAPST（峰值分配和轮廓搜索工具）的进一步开发 此 Java ChIP-Seq 数据分析工具已开发出更多高级功能，包括用于以峰为中心的数据分析以及工具内直接结果到输入转换的功能，以实现高效的探索性研究。 微生物组分析以及与转录组数据的整合 最近开发了使用 NGS 来分析哺乳动物微生物组的技术。已经开发了分析管道来分析数据并以图形方式呈现结果。此外，微生物组结果与肠道转录组数据一起进行分析，以提供健康和疾病中基因表达和肠道菌群变化的组合模型。 调查和纠正全血 RNA 测序中的偏差来源 全血 RNA-Seq 的偏差来源已得到调查。已经开发出一种去除血红蛋白和基因间读数的管道，以提高 RNA-Seq 数据的质量。这项新开发的技术已应用于干扰素病研究中的 400 多名患者和对照样本。 iPSC 细胞转录组的表征 来自患者和对照的成纤维细胞被重新编程为诱导多能干细胞（iPSC），以了解强直性脊柱炎的疾病发病机制。对这些细胞的转录组进行分析，以确定 iPSC 是否实际上是干细胞样的。然后这些 iPSC 分化成与疾病相关的细胞谱系。检查分化细胞的转录组，试图辨别与疾病相关的基因表达变化。 全基因组测序数据分析 分析了未分化自身炎症性疾病患者的全基因组测序 (WGS) 数据。 低频突变检测方法评估 评估了检测低频突变的实验和计算方法，例如双链测序、环状测序、HaloplexHs 和超深靶测序。 致病突变的鉴定 在早发性严重关节炎患者中鉴定出 MYD88 基因可能致病的突变。 突变数据分析的流程改进 改进和维护了 WES 数据处理和质量评估的计算管道，以及 WES 实验的突变数据分析工作流程。 ATAC-Seq 管道开发 已开发出用于 ATAC-Seq 数据分析的 202 步骤流程。它包括 fastq 文件级别的序列冗余去除、基因组映射、基于片段大小的映射结果解析、使 USCS 基因组浏览器可查看文件、峰值调用和相关数据操作。 超级增强子和疾病基因 使用公开数据，已确定 12 种独特免疫细胞类型的 18 种细胞系的超级增强剂。这些超级增强子已根据线性序列邻近性分配给基因，从而在 18 个细胞系中的至少一种中产生了总共 3622 个具有 SE 分配的独特基因。这 18 种细胞系的组合 SE 分配表已用于评估某些疾病基因的统计显着性（分配的 SE 具有增加的结合信号）。这 18 种细胞系的超级增强子图也被类似地用于研究 SE 和疾病关系。 tRNA 表达定量方法开发 已经开发出一种方法来专门量化 3 端的 tRNA 表达水平，以评估 tRNA 加工酶的活性，该酶通过在 3 端添加碱基 CCA 将前体 tRNA 转变为成熟的 tRNA。该方法首先对所有原始小 RNA 读段进行基因组作图，然后在从失败读段的 3 端去除 CCA 后，对第一次映射失败的那些读段进行第二次映射。在所有 tRNA 的 3 端周围进行读数密度计算，并使用两个映射的数据以图形方式表示每个 tRNA 的结果。还针对 tRANS 的 3 端进行了更详细的分析，以计算映射到最后 3 个碱基的读数，以计算 3 端有和没有 CCA 的 tRNA 水平之间的差异（成熟和成熟之间）。前体 tRNA）。 基因组切换的通用图形方法 已经开发出一种基于 Circos 的通用方法，以图形方式展示两种不同生物条件之间有多少基因发生了表观遗传或表达水平的变化。 计算 PAR-CLIP 位点保守得分统计的方法 这种方法已被开发用于计算 RNA 结合蛋白和含有微 RNA 的核糖核蛋白复合物的结合位点的七个基于物种的 phyloP 评分统计数据。统计数据包括所有结合位点碱基的平均得分、标准差、最小和最大得分、保守碱基的百分比以及保守碱基的平均得分。