Predictive drivers of new onset, relapse, and progression of human autoimmunity in skin

人类皮肤自身免疫新发、复发和进展的预测驱动因素

• 批准号: 10658149
• 负责人: Manuel Garber
• 金额: $ 126.5万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-20 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658149
• 关键词: Affect; Age of Onset; Autoantibodies; Autoimmune Diseases; Autoimmunity; Biological Markers; Blood; Blood capillaries; Cells; Clinical; Clinical Management; Collection; Cutaneous Lupus Erythematosus; Data; Dermatologist; Development; Devices; Diagnosis; Disease; Disease Marker; Disease Progression; Engineering; Enrollment; Epithelial Cells; Exhibits; Face; Family member; Future; Gene Expression; Genetic; Genotype; Hazard Models; High Prevalence; Home; Human; Immune; Individual; Interferon Type I; Liquid substance; Longitudinal cohort; Lupus; Methods; Modeling; Molecular; Monitor; Morbidity - disease rate; Needles; Onset of illness; Organ; Participant; Patients; Physicians; Population; Populations at Risk; Prevalence; Property; Proteins; Proteomics; Public Health; Punch Biopsy; Questionnaires; Recurrent disease; Relapse; Reporting; Research Personnel; Risk; Sampling; Scientist; Scourge; Serum; Severity of illness; Skin; Statistical Models; Systemic Lupus Erythematosus; Systemic disease; Testing; Time; Tissue Sample; Tissues; UV induced; Vitiligo; Woman; biomarker identification; clinical diagnosis; cohort; design; health disparity; innovation; insight; interstitial; minimally invasive; mortality; multidisciplinary; multiple omics; pre-clinical; predictive modeling; prevent; response; sample collection; single-cell RNA sequencing; skin color; skin disorder; tool; transcriptome sequencing

ABSTRACT Autoimmune diseases affect up to 8% of the US population and their prevalence is rising, setting the stage for an impending public health crisis that we do not yet understand and are not prepared to face. Many autoimmune diseases disproportionately affect women and those with skin of color, potentially worsening existing health disparities within our population. We must define the mechanisms that drive this growing risk for autoimmunity so that we can better manage, or even prevent, a scourge of severe morbidity and mortality. Skin diseases are among the most prevalent autoimmune diseases and are simple to study due to the ability to diagnose and track the progression of disease through direct observation and sampling using minimally invasive tools. Thus, skin diseases provide unique insight into mechanisms of autoimmunity that are difficult to determine when studying other organs and tissues. We will leverage a multidisciplinary team of investigators, cutting edge tools designed for at-home, longitudinal tissue sampling, and an innovative strategy to discover how autoimmunity begins, relapses, and spreads in a large population of at-risk individuals. We and others have determined that unaffected, non-lesional skin from patients with autoimmunity exists in a disease-specific “preclinical” state, but whether this predisposes the patient to develop disease is an open question. We hypothesize that a molecular immune signature drives a preclinical state within the skin that predisposes to disease initiation and advancement of autoimmunity to other organs. To test this, we will use two ideally suited models of autoimmunity. To predict development of autoimmunity de novo as well as disease relapse, we will take advantage of the unique properties of vitiligo: a strong genetic component, early age of onset (majority <30 years old), strong association with other autoimmune diseases, high prevalence (>1%), and rapid relapse after stopping therapy. We will longitudinally monitor 200 individuals with vitiligo for disease relapse and 800 of their relatives who are “at-risk” for developing new onset disease. To predict the “progression” of autoimmunity to other organs we will monitor a cohort of patients with cutaneous lupus erythematosus (CLE): up to 20% of patients who initially exhibit skin-limited lupus eventually develop systemic disease, with a median time to progression of two years. We will monitor 50 subjects with CLE to detect disease progression to internal organs. We will use these innovative tools on a large scale through “population multiomics” to define immune drivers of autoimmunity in patients and their family members over time. To test our hypothesis, we will use computational integration of clinical, genetic, and molecular data points to define a “preclinical signature” of autoimmunity and use it to predict disease initiation and systemic progression. This approach will provide insight into autoimmunity that will help physicians better manage, or even prevent, devastating consequences of these diseases in the future.

摘要