A multi-omics strategy to understand PASC through the RECOVER cohorts: a paradigm for a systems biology approach…, 2025, Sun+

[SNT Gatchaman]

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, Jun; Aikawa, Masanori; Ashktorab, Hassan; Beckmann, Noam D.; Enger, Michael L.; Espinosa, Joaquin M.; Gai, Xiaowu; Horne, Benjamin D.; Keim, Paul; Lasky-Su, Jessica; Letts, Rebecca; Maier, Cheryl L.; Mandal, Meisha; Nichols, Lauren; Roan, Nadia R.; Russell, Mark W.; Rutter, Jacqueline; Saade, George R.; Sharma, Kumar; Shiau, Stephanie; Thibodeau, Stephen N.; Yang, Samuel; Miele, Lucio; , NIH Researching COVID to Enhance Recovery (RECOVER) Consortium

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.


Link | PDF (Frontiers in Systems Biology) [Open Access]

 

[SNT Gatchaman]

Genomics is an invaluable asset to understand disease risk, mechanism and etiology, and to serve as a backbone to allow for better modeling of multi-omics profiles in patient populations. Several genome-wide association studies (GWAS) have identified reproducible associations between specific loci and risk and outcomes of acute COVID-19 […] A recent GWAS study, currently in pre-print, detected an association between a locus near the FOXP4 gene and risk of developing PASC.

FOXP4 is a broadly expressed transcription factor. Lammi et al. analyzed single-cell RNASeq data to confirm the expression of FOXP4 in surfactant-producing Type II alveolar cells and granulocytes. This correlation supports a possible mechanistic link, and demonstrates the importance of integrative multi-omics approaches.

Single-cell transcriptomics: Bulk transcriptomics measures RNA expression as an average of all cell types present in a sample. This can potentially mask the contribution of rare cell types or cellular states to the transcriptome. Single cell RNA sequencing (scRNAseq) can add further detail to immune phenotyping by measuring the transcriptomes of up to 20,000 individual cells simultaneously. This can provide highly detailed information, albeit at higher cost than bulk transcriptomics.

In the context of an integrated multi-omics strategy, proteomics data could maximize the opportunities to discover mechanisms underlying PASC pathophysiology as well as molecular subtypes, clinically actionable biomarkers and treatment targets.

Studies that have begun to use CyTOF to explore immunological differences between fully recovered vs individuals with PASC have revealed a dysregulated adaptive immune response in the latter, e.g., global differences in T-cell subsets, sex-specific differences in cytolytic T-cells, increased frequency of T-cells migrating to inflamed tissues but also exhausted T-cells, as well as increased frequency of exhausted T-cells

As a measure of the status of hundreds of metabolic pathways, the overall metabolome and the lipidome represent biologically and mechanistically informative data streams. The endogenous metabolome captures a broad range of inflammatory processes, energy production, microbial metabolites, organ-specific biomarkers, lipids, carbohydrates, steroids, and amino acids, among other relevant information on physiologic processes.

There is observational evidence of gut microbiome compositional alterations in patients with long-term complications of COVID-19 (Liu et al., 2022). However, the current studies have sample sizes varying from 8 to 130 patients and few studies followed patients beyond 6 months post-infection (reviewed in (Zhang et al., 2023)). A recent study (Xiong et al., 2023) using multi-omics of microbiome-host interactions identified phenotypic, intestinal microbial, and metabolic biomarkers for short-and longterm myalgic encephalomyelitis/chronic fatigue syndrome. Large amounts of microbiome data can be easily generated at low-cost in the RECOVER adult and pediatric cohorts.

 

[SNT Gatchaman]

3.2.3 Task force recommendations

We recommended the following strategy: germline whole genome sequencing (WGS) be performed on every RECOVER participant consented for genetic analysis to be used for GWAS studies. Epigenomics, bulk PBMC transcriptomics, plasma proteomics, plasma targeted metabolomics and stool proteomics should be performed on biospecimens from at least 2 time points per participant (baseline, 90 days and 180 days, or at a minimum baseline and 180 days) on biospecimens from as many participants as possible.

PASC joins the number of poorly understood, chronic diseases that have been the bane of patients, healthcare providers and clinical researchers. While different clinical presentations of PASC have been described, traditional molecular approaches have thus far failed to produce a deep mechanistic understanding of the etiology, pathogenesis and molecular subtypes of PASC. 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. Currently, this panel is hammering down the details of the analytical strategies. The NIH RECOVER initiative offers an ideal opportunity to understand PASC in diverse populations, and can serve as a paradigm for the study of other complex, poorly understood chronic diseases.