Uncovering the molecular underpinnings of lupus nephritis with muli-omic analysis

Active Project Grant Awarded Last action

Award ID
F31AR087780
Recipient
Awarding agency
Obligated
$87,282

Award facts

Completion date 0% complete
Originally
Place of performance
San Francisco, CA 94143, USA

Award details come from USAspending.gov.

Description

As the agency reported it.

Abstract
Systemic lupus erythematosus (SLE) is a complex autoimmune disease with highly variable clinical presentation. One manifestation, lupus nephritis (LN), is a severe kidney complication affecting up to 60% of SLE patients. Patients with LN suffer from more severe disease and worse prognosis than patients with SLE that is not complicated by LN. While IT is known that SLE is impacted by genetic, epigenetic, and environmental factors, IT is poorly understood how those factors influence each other in LN. In addition, current monitoring strategies for LN leave room for improvement. There remains a critical need to develop predictive tools to identify SLE patients at high risk for LN and enable earlier and easier intervention. This project will leverage two rich data sources: multi-omics and electronic health records (EHR), to improve disease understanding and prediction capability for LN. In recent years, the rise of multi-omics has enabled a more detailed view into the biology of disease. By incorporating measurements taken at multiple stages of the cellular response, a more complete understanding of affected pathways across biological layers can be attained. EHR contains a wealth of information about diagnoses, medications, and labs - information which can complement molecular measurements to reveal a more detailed portrait of a patient's health. Through a combined analysis of both data sources, this project will identify multi-omic signatures that distinguish LN from SLE without nephritis and develop predictive models for LN onset and stage. In addition, this project will assess the role of biological SEX/genetic ancestry and environmental impacts to detangle higher LN risk in some parts of the population. This project consists of two specific aims. Specific aim 1 will employ multi-omic data integration to generate low- dimensional integrated factors from single cell transcriptomics data, methylation data, exposome data and survey data collected from an established patient cohort. These factors will be investigated for associations with clinical manifestations of SLE and weights of molecular and environmental features across patient risk groups. The expected outcome is a reproducible signal that reveals how genetic, epigenetic, and environmental influences interact with each other in patients with LN. Specific aim 2 will integrate EHR and omics data in predictive models. Random forest models and a transfer learning model will be trained to predict LN onset and LN classification at biopsy. The expected outcome is a machine learning based tool that could be used by clinicians to guide treatment and monitoring decisions for LN patients. Once completed, this work could advance understanding of the interplay between biology and exposures in LN and develop clinically useful predictors of LN onset and severity. These may inform more targeted and effective treatments in the future.

Transactions

Action Obligated Date
Amendment $0
Amendment $43,641
Original award $43,641

Quick answers

Who received award F31AR087780?

F31AR087780 was awarded to The Regents Of The University Of California. San Francisco by National Insititue of Arthritis and Musculoskeletal and Skin Diseases (National Institutes of Health) on December 5, 2026.

How much is award F31AR087780 worth?

$87,282 has been obligated on F31AR087780.

On GovTribe

See this award's full record

  • Every transaction and subaward
  • The recipient's other awards and teaming partners
  • Similar grant awards
  • Ask GovTribe AI about this award
Try for free Login