HCV Ghost Sequencing Center

HCV Ghost 测序中心

基本信息

批准号：
10649195
负责人：
TODD M ALLEN
金额：
$ 16.26万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10649195
关键词：
Administrative Supplement Affect American Americas Appalachian Region Automobile Driving Award Blood COVID-19 pandemic Centers for Disease Control and Prevention (U.S.)Cessation of life Collection Communities County Data Disease Outbreaks Drug Prescriptions Drug usage Epidemic Freezing Funding Generations Genomics Genotype Geographic Locations Goals Grant HIV HIV Infections Hepatitis Hepatitis C Hepatitis C virus Incidence Indiana Individual Informatics Injecting drug user Institutes Investigation Laboratories Link Molecular Nature Overdose Parents Participant Pathway Analysis Patients Persons Pharmaceutical Preparations Phylogenetic Analysis Pilot Projects Plasma Play Population Publications Reporting Research Activity Residual state Resource-limited setting Risk Factors Role Rural Rural Community Rural Population Sampling Serum Shipping Ships Site Social Network Specimen Spottings Syphilis Serodiagnosis Technology United States Validation Viral Work Workload acute infection clinical research site cluster merger cost cost effective data management genetic linkage analysis illicit opioid injection drug use laboratory equipment next generation sequencing novel opioid epidemic opioid overdose opioid use prescription opioid prevent public health intervention rural area sample collection sequencing platform transmission process

项目摘要

PROJECT SUMMARY: The United States is in the midst of an enormous opioid epidemic. In 2015, more than 52,000 American’s died due to a drug overdose, over 60% of which were associated with prescription or illicit opioid use. Given that injection drug use remains the major risk factor for HCV transmission, concomitantly the US has observed a striking >150% increase in the incidence of HCV between 2010-2013, and a 364% increase in new HCV infections between 2006 and 2012 in four Appalachian states, most notably in rural areas. As such, there is urgent need to more strategically detect, prevent, treat and control HCV on a national level. Under the parent award we built a high-quality, high-throughput HCV next-generation sequencing (NGS) platform that enabled the generation of HCV HVR1 sequences from over 1,000 patient samples derived from 8 rural study sites, along with an in-house informatics pipeline capable of validating phylogenetic clustering identified by the CDC GHOST laboratory. Transmission network analysis of the NGS data showed that the rate of clustering (likely direct transmission) varied considerably across study sites ranging from 10% to 42%. Across all geographical sites, genotype 1a (57%) was the most common followed by genotype 3a (30%), 2b (9%), 1b (4%) and mixed genotypes represented less than 5% of cases. The implementation of the GHOST HCV molecular surveillance technology illustrates that genomic surveillance of HCV in rural communities is feasible and suggest underlying factors may be influencing the size of transmission networks across sites. Under the parent award we also examined the feasibility of using dried blood spot (DBS) collection for this application. A pilot study comprising 14 serum samples revealed that viral populations within DBS were genetically indistinguishable from viral populations derived from plasma, demonstrating that DBS can capture comparable transmission networks and viral diversity observed from the traditional collection of serum samples. Thus, DBS can augment traditional HCV surveillance approaches as a practical and cost-effective alternative to frozen plasma in resource-limited settings such as rural populations. Due to the substantial impact of the SARS-CoV-2 pandemic upon both our laboratory work at the Ragon Institute, and upon our UH3 collaborators providing specimens for our work, many of the initial goals of our application were not completed. This Administrative Supplement to our U24 application proposes to support the generation and analysis of additional HCV NGS data for the CDC’s GHOST center to identify HCV transmission links among persons who inject drugs (PWID); validate the application of dried blood spots for the collection and generation of transmission links by NGS data; manage the collection and storage of serum samples from HIV- and HCV- infected participants from the clinical research sites under RFA-DA-17-014; and ship specimens to the CDC for syphilis testing and phylogenetic mapping for HIV and HCV. We plan to use these funds to cover additional workload in Year 6 to meet the original goals of our project. This includes, the processing and analysis of several hundred new patient samples that have recently arrived from our clinical sites, identify factors driving high rates of clustering by merging NGS data with site-specific RDS data, and completing our DBS study for publication.

项目概要： 2015 年，美国正处于阿片类药物泛滥之中，超过 52,000 名美国人死亡。由于药物过量，其中超过 60% 与处方或非法使用阿片类药物有关。注射吸毒仍然是丙型肝炎病毒传播的主要危险因素，同时美国也观察到 2010 年至 2013 年间，HCV 发病率显着增加 150% 以上，新发 HCV 增加 364% 2006 年至 2012 年间，阿巴拉契亚地区的四个州都出现了感染病例，其中最引人注目的是农村地区。迫切需要在国家层面上更具战略性地检测、预防、治疗和控制丙肝病毒。在家长奖下我们建立了高质量、高通量的HCV下一代测序（NGS）平台能够从 8 项农村研究的 1,000 多个患者样本中生成 HCV HVR1 序列网站，以及能够验证系统发育聚类的内部信息学管道 CDC GHOST 实验室对 NGS 数据的传输网络分析表明，聚类率较高。（可能是直接传播）在所有研究地点之间差异很大，从 10% 到 42% 不等。地理位置方面，基因型 1a (57%) 最常见，其次是基因型 3a (30%)、2b (9%)、1b (4%) 混合基因型占不到 5% 的病例 GHOST HCV 分子的实施。监测技术表明，在农村社区对 HCV 进行基因组监测是可行的，并建议潜在因素可能会影响母公司奖励下的跨站点传输网络的规模。我们还研究了使用干血斑 (DBS) 采集进行此应用的可行性。包含 14 份血清样本的研究表明，DBS 内的病毒群体在基因上与来自血浆的病毒群体，证明 DBS 可以捕获类似的传播网络以及从传统的血清样本采集中观察到的病毒多样性，因此，DBS 可以增强传统的方法。 HCV 监测方法作为资源有限的冰冻血浆的实用且经济有效的替代方案农村人口等环境。由于 SARS-CoV-2 大流行对我们 Ragon 研究所的实验室工作产生了重大影响，在我们的 UH3 合作者为我们的工作提供样本后，我们应用程序的许多初始目标我们的 U24 申请的行政补充建议支持这一代。为 CDC GHOST 中心分析额外的 HCV NGS 数据，以确定 HCV 传播联系注射吸毒者（PWID）；验证干血点在收集和生成中的应用通过 NGS 数据分析传播环节；管理 HIV 和 HCV 血清样本的收集和存储来自 RFA-DA-17-014 下的临床研究场所的受感染参与者，并将样本运送到 CDC 进行检测； HIV 和 HCV 的梅毒检测和系统发育图谱。我们计划使用这些资金来支付第 6 年的额外工作量，以实现我们项目的最初目标。包括处理和分析最近从美国运来的数百个新患者样本我们的临床站点，通过将 NGS 数据与特定站点的 RDS 合并来确定驱动高聚类率的因素数据，并完成我们的 DBS 研究以供发表。