A multi-omics strategy to understand PASC through the RECOVER cohorts: A paradigm for a systems biology approach to the study of chronic conditions
Sun, J; Aikawa, M; Ashktorab, H; et al., Frontiers in Systems Biology
Published
January 2025
Journal
Frontiers in Systems Biology
Abstract
Post-Acute Sequelae of SARS-CoV-2 infection (PASC or "Long COVID"), includes numerous chronic conditions associated with widespread morbidity and rising healthcare costs. PASC has highly variable clinical presentations, and likely includes multiple molecular subtypes, but it remains poorly understood from a molecular and mechanistic standpoint. This hampers the development of rationally targeted therapeutic strategies. The NIH-sponsored "Researching COVID to Enhance Recovery" (RECOVER) initiative includes several retrospective/prospective observational cohort studies enrolling adult, pregnant adult and pediatric patients respectively. RECOVER formed an "OMICS" multidisciplinary task force, including clinicians, pathologists, laboratory scientists and data scientists, charged with developing recommendations to apply cutting-edge system biology technologies to achieve the goals of RECOVER. The task force met biweekly over 14 months, to evaluate published evidence, examine the possible contribution of each "omics" technique to the study of PASC and develop study design recommendations. The OMICS task force recommended an integrated, longitudinal, simultaneous systems biology study of participant biospecimens on the entire RECOVER cohorts through centralized laboratories, as opposed to multiple smaller studies using one or few analytical techniques. The resulting multi-dimensional molecular dataset should be correlated with the deep clinical phenotyping performed through RECOVER, as well as with information on demographics, comorbidities, social determinants of health, the exposome and lifestyle factors that may contribute to the clinical presentations of PASC. This approach will minimize lab-to-lab technical variability, maximize sample size for class discovery, and enable the incorporation of as many relevant variables as possible into statistical models. Many of our recommendations have already been considered by the NIH through the peer-review process, resulting in the creation of a systems biology panel that is currently designing the studies we proposed. This system biology strategy, coupled with modern data science approaches, will dramatically improve our prospects for accurate disease subtype identification, biomarker discovery and therapeutic target identification for precision treatment. The resulting dataset should be made available to the scientific community for secondary analyses. Analogous system biology approaches should be built into the study designs of large observational studies whenever possible.
Authors
Jun Sun, Masanori Aikawa, Hassan Ashktorab, Noam D Beckmann, Michael L Enger, Joaquin M Espinosa, Xiaowu Gai, Benjamin D Horne, Paul Keim, Jessica Lasky-Su, Rebecca Letts, Cheryl L Maier, Meisha Mandal, Lauren Nichols, Nadia R Roan, Mark W Russell, Jacqueline Rutter, George R Saade, Kumar Sharma, Stephanie Shiau, Stephen N Thibodeau, Samuel Yang, Lucio Miele, NIH Researching COVID to Enhance Recovery (RECOVER) Consortium
Keywords
COVID-19; PASC; RECOVER; multi-omics; systems biology
Short Summary
This RECOVER study focused on what causes Long COVID and why it affects people in different ways. Researchers created an "omics" task force made up of doctors, scientists, and data experts. For 14 months, they worked together to figure out the best way to study Long COVID. They recommended using "multi-omics," which means studying many layers of health information at the same time, like genes, proteins, and chemicals that show how the body is working. To do this, RECOVER researchers collected blood, urine, and saliva samples from more than 34,000 people including adults, pregnant adults, and children, at least twice since joining the study. Looking at all these layers together gives a clearer picture than studying them one by one. It also avoids differences between laboratories and makes it easier to find patterns that could tell us more about Long COVID. The research and recommendations are important because they can help researchers find out what causes Long COVID, predict who might get it, and find treatments. The data collected in this study will also be shared with other researchers so they can continue to learn more.