Integrating multi-omics data: Modeling biomarkers and mechanisms to reduce bacterial vaginosis recurrence

整合多组学数据：建模生物标志物和机制以减少细菌性阴道病复发

• 批准号: 10282850
• 负责人: Johanna B. Holm
• 金额: $ 9.29万
• 依托单位: UNIVERSITY OF MARYLAND BALTIMORE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10282850
• 关键词: Affect; African; Age; American; Anaerobic Bacteria; Antibiotic Therapy; Archives; Atopobium vaginae; Award; Bacteria; Bacterial Vaginosis; Basic Science; Behavioral; Big Data; Bioinformatics; Biological Markers; Biometry; Case-Control Studies; Categories; Clinical; Clinical Research; Communities; Complement; Data; Diagnosis; Ecology; Epidemiologic Methods; Epidemiology; Ethnic Origin; Evaluation; Foundations; Funding; Future; Genes; Goals; Grant; HIV; Health; Hygiene; Immune Targeting; Immune response; Immunologic Markers; Infection; Inflammatory; Institutes; Intervention; Irrigation; K-Series Research Career Programs; Lactobacillus; Lead; Learning; Life Style; Longitudinal Studies; Maryland; Mentors; Metagenomics; Methods; Mission; Modeling; Molecular; Multiomic Data; Outcome; Participant; Pelvic Inflammatory Disease; Pelvis; Predisposition; Premature Birth; Prognostic Factor; Prognostic Marker; Quality of life; Race; Recurrence; Reporting; Reproductive Health; Research; Research Design; Research Personnel; Research Support; Resources; Risk; Sampling; Scientist; Sex Behavior; Sexually Transmitted Diseases; Source; Statistical Models; Symptoms; Training; Translating; United States National Institutes of Health; Universities; Vagina; Visit; Woman; Women' s Health; Work; aged; candidate marker; career development; certificate program; cervicovaginal; clinical Diagnosis; clinical translation; complex data; data integration; data modeling; data reduction; disorder risk; epidemiology study; genome sciences; improved; insight; machine learning method; medical schools; metabolome; metabolomics; metagenome; microbial; microbiome; model development; multiple omics; novel; personalized medicine; programs; reproductive; research study; screening; supervised learning; targeted treatment; therapeutic target; tool; vaginal microbiome; vaginal microbiota

Bacterial vaginosis (BV) is characterized by a vaginal microbiota with a low abundance of Lactobacillus spp. and higher abundances of anaerobic bacteria and affects nearly 30% of reproductive-age North American women and closer to 50% of sub-Saharan African women. BV is diagnosed by clinical observation of Amsel’s criteria (Amsel-BV), but treatment is only recommended when symptoms are reported, leaving a significant proportion of women untreated. Even with treatment, BV recurrence rates range from 50-70% within 6 months, increasing a woman’s risk of negative sequelae. Regardless of symptoms, BV is associated with serious adverse health outcomes, including preterm birth and HIV, and can seriously impact a woman’s quality of life. Ideal BV treatment would eliminate recurrence. The vaginal microbiome and microenvironment together provide a detailed evaluation of BV states, and hold key functional insights to predict and understand Amsel-BV recurrence. The goal of this proposal is to integrate and operationalize microbiome (metagenomes) and microenvironment (metabolomes and immune markers) data to develop a prognostic indicator of recurrent BV, and identify candidate biomarkers and causal mechanisms which reduce recurrence. Recent work by the PI functionally categorized the vaginal microbiome for use in large clinical research studies (vaginal metagenomic community state types, mgCSTs). The broad hypothesis in this proposal is that not all microbiomes associated with bacterial vaginosis have the same potential for recurrence. Preliminary data suggest that BV recurrence is more frequently observed in only two of the nine mgCSTs containing BV-associated bacteria. This study proposes to utilize archived cervicovaginal samples from the NIH 1999 Longitudinal Study of Vaginal Flora in which participants were followed quarterly for one year. Multi-omic analyses of baseline samples will be assessed to identify microbial (metagenomic and metabolomic) and host (metabolomic and targeted immune markers) signatures of susceptibility to recurrent BV. Specific aims of this proposal are to: (1) conduct an epidemiological analysis to evaluate the demographic and lifestyle correlates of mgCSTs, and (2) employ supervised machine learning and causal inference modeling to identify prognostic factors and drivers of the vaginal microbiome and microenvironment which lead to recurrent BV. Cases are defined as women with Amsel-BV at baseline, then clearance 3 months later, followed by recurrence at six months. Controls are women with Amsel-BV that do not experience recurrence within 9 months. This grant will support the PI’s training in epidemiology and biostatistics with the completion of a Certificate Program in Clinical Research. The PI’s long- term goal is to create an independent research program translating the basic science of the vaginal microbiome to improve women’s reproductive health outcomes. The Institute for Genome Sciences and the University of Maryland School of Medicine uniquely provide the resources and support required for successful completion of this proposal and the PI’s transition to an independent investigator.

细菌性阴道病（BV）的特点是阴道微生物群具有低丰度的乳酸菌