Tag: multi-omics

A multi-omics recovery factor predicts long COVID in the IMPACC study

Abstract:

Background. Following SARS-CoV-2 infection, ~10-35% of COVID-19 patients experience long COVID (LC), in which debilitating symptoms persist for at least three months. Elucidating biologic underpinnings of LC could identify therapeutic opportunities.

Methods. We utilized machine learning methods on biologic analytes provided over 12-months after hospital discharge from >500 COVID-19 patients in the IMPACC cohort to identify a multi-omics “recovery factor”, trained on patient-reported physical function survey scores. Immune profiling data included PBMC transcriptomics, serum O-link and plasma proteomics, plasma metabolomics, and blood CyTOF protein levels. Recovery factor scores were tested for association with LC, disease severity, clinical parameters, and immune subset frequencies. Enrichment analyses identified biologic pathways associated with recovery factor scores.

Results. LC participants had lower recovery factor scores compared to recovered participants. Recovery factor scores predicted LC as early as hospital admission, irrespective of acute COVID-19 severity. Biologic characterization revealed increased inflammatory mediators, elevated signatures of heme metabolism, and decreased androgenic steroids as predictive and ongoing biomarkers of LC. Lower recovery factor scores were associated with reduced lymphocyte and increased myeloid cell frequencies. The observed signatures are consistent with persistent inflammation driving anemia and stress erythropoiesis as major biologic underpinnings of LC.

Conclusion. The multi-omics recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.

Trial Registration. ClinicalTrials.gov NCT04378777.

Funding. This study was funded by NIH, NIAID and NSF.

Source: Gisela Gabernet, Leying Guan, Lauren I.R. Ehrlich, et al. A multi-omics recovery factor predicts long COVID in the IMPACC study. J Clin Invest. September 9, 2025. https://doi.org/10.1172/JCI193698. https://www.jci.org/articles/view/193698/ (Full study available as PDF file)

Heightened innate immunity may trigger chronic inflammation, fatigue and post-exertional malaise in ME/CFS

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by unexplained fatigue, post-exertional malaise (PEM), and cognitive dysfunction. ME/CFS patients often report a prodrome consistent with infection. We present a multi-omics analysis based on plasma metabolomic and proteomic profiling, and immune responses to microbial stimulation, before and after exercise.

We report evidence of an exaggerated innate immune response after exposures to microbial antigens; impaired energy production involving the citric acid cycle, beta-oxidation of fatty acids, and urea cycle energy production from amino acids; systemic inflammation linked with lipid abnormalities; disrupted extracellular matrix homeostasis with release of endogenous ligands that promote inflammation; reduced cell-cell adhesion and associated gut dysbiosis; complement activation; redox imbalance reflected by disturbances in copper-dependent antioxidant pathways and dysregulation of the tryptophan-serotonin-kynurenine pathways.

Many of these underlying abnormalities worsened following exercise in ME/CFS patients, but not in healthy subjects; many abnormalities reinforced each other and several were correlated with the intensity of symptoms. Our findings may inform targeted therapeutic interventions for ME/CFS and PEM.

Source: Che X, Ranjan A, Guo C, Zhang K, Goldsmith R, Levine S, Moneghetti KJ, Zhai Y, Ge L, Mishra N, Hornig M, Bateman L, Klimas NG, Montoya JG, Peterson DL, Klein SL, Fiehn O, Komaroff AL, Lipkin WI. Heightened innate immunity may trigger chronic inflammation, fatigue and post-exertional malaise in ME/CFS. medRxiv [Preprint]. 2025 Jul 24:2025.07.23.25332049. doi: 10.1101/2025.07.23.25332049. PMID: 40778181; PMCID: PMC12330418. https://pmc.ncbi.nlm.nih.gov/articles/PMC12330418/ (Full text available as PDF file)

AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic illness with a multifactorial etiology and heterogeneous symptomatology, posing major challenges for diagnosis and treatment. Here we present BioMapAI, a supervised deep neural network trained on a 4-year, longitudinal, multi-omics dataset from 249 participants, which integrates gut metagenomics, plasma metabolomics, immune cell profiling, blood laboratory data and detailed clinical symptoms.

By simultaneously modeling these diverse data types to predict clinical severity, BioMapAI identifies disease- and symptom-specific biomarkers and classifies ME/CFS in both held-out and independent external cohorts. Using an explainable AI approach, we construct a unique connectivity map spanning the microbiome, immune system and plasma metabolome in health and ME/CFS adjusted for age, gender and additional clinical factors.

This map uncovers altered associations between microbial metabolism (for example, short-chain fatty acids, branched-chain amino acids, tryptophan, benzoate), plasma lipids and bile acids, and heightened inflammatory responses in mucosal and inflammatory T cell subsets (MAIT, γδT) secreting IFN-γ and GzA.

Overall, BioMapAI provides unprecedented systems-level insights into ME/CFS, refining existing hypotheses and hypothesizing unique mechanisms—specifically, how multi-omics dynamics are associated to the disease’s heterogeneous symptoms.

Source: Xiong, R., Aiken, E., Caldwell, R. et al. AI-driven multi-omics modeling of myalgic encephalomyelitis/chronic fatigue syndrome. Nat Med (2025). https://doi.org/10.1038/s41591-025-03788-3  https://www.nature.com/articles/s41591-025-03788-3

Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis

Abstract:

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogeneous, and systemic disease defined by a suite of symptoms, including unexplained persistent fatigue, post-exertional malaise (PEM), cognitive impairment, myalgia, orthostatic intolerance, and unrefreshing sleep. The disease mechanism of ME/CFS is unknown, with no effective curative treatments.

In this study, we present a multi-site ME/CFS whole-genome analysis, which is powered by a novel deep learning framework, HEAL2. We show that HEAL2 not only has predictive value for ME/CFS based on personal rare variants, but also links genetic risk to various ME/CFS-associated symptoms. Model interpretation of HEAL2 identifies 115 ME/CFS-risk genes that exhibit significant intolerance to loss-of-function (LoF) mutations. Transcriptome and network analyses highlight the functional importance of these genes across a wide range of tissues and cell types, including the central nervous system (CNS) and immune cells.

Patient-derived multi-omics data implicate reduced expression of ME/CFS risk genes within ME/CFS patients, including in the plasma proteome, and the transcriptomes of B and T cells, especially cytotoxic CD4 T cells, supporting their disease relevance. Pan-phenotype analysis of ME/CFS genes further reveals the genetic correlation between ME/CFS and other complex diseases and traits, including depression and long COVID-19.

Overall, HEAL2 provides a candidate genetic-based diagnostic tool for ME/CFS, and our findings contribute to a comprehensive understanding of the genetic, molecular, and cellular basis of ME/CFS, yielding novel insights into therapeutic targets. Our deep learning model also offers a potent, broadly applicable framework for parallel rare variant analysis and genetic prediction for other complex diseases and traits.

Source: Zhang S, Jahanbani F, Chander V, Kjellberg M, Liu M, Glass KA, Iu DS, Ahmed F, Li H, Maynard RD, Chou T, Cooper-Knock J, Zhang MJ, Thota D, Zeineh M, Grenier JK, Grimson A, Hanson MR, Snyder MP. Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis. medRxiv [Preprint]. 2025 Apr 16:2025.04.15.25325899. doi: 10.1101/2025.04.15.25325899. PMID: 40321247; PMCID: PMC12047926. https://pmc.ncbi.nlm.nih.gov/articles/PMC12047926/ (Full text available as PDF file)

Machine learning and multi-omics in precision medicine for ME/CFS

Abstract:

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and multifaceted disorder that defies simplistic characterisation. Traditional approaches to diagnosing and treating ME/CFS have often fallen short due to the condition’s heterogeneity and the lack of validated biomarkers. The growing field of precision medicine offers a promising approach which focuses on the genetic and molecular underpinnings of individual patients.

In this review, we explore how machine learning and multi-omics (genomics, transcriptomics, proteomics, and metabolomics) can transform precision medicine in ME/CFS research and healthcare. We provide an overview on machine learning concepts for analysing large-scale biological data, highlight key advancements in multi-omics biomarker discovery, data quality and integration strategies, while reflecting on ME/CFS case study examples. We also highlight several priorities, including the critical need for applying robust computational tools and collaborative data-sharing initiatives in the endeavour to unravel the biological intricacies of ME/CFS.

Source: Huang K, Lidbury BA, Thomas N, Gooley PR, Armstrong CW. Machine learning and multi-omics in precision medicine for ME/CFS. J Transl Med. 2025 Jan 14;23(1):68. doi: 10.1186/s12967-024-05915-z. PMID: 39810236. Huang K, Lidbury BA, Thomas N, Gooley PR, Armstrong CW. Machine learning and multi-omics in precision medicine for ME/CFS. J Transl Med. 2025 Jan 14;23(1):68. doi: 10.1186/s12967-024-05915-z. PMID: 39810236. https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-024-05915-z (Full text)

Serial Paediatrics Omics Tracking in Myalgic Encephalomyelitis (SPOT-ME): protocol paper for a multidisciplinary, observational study of clinical and biological markers of paediatric myalgic encephalomyelitis/chronic fatigue syndrome in Australian adolescents aged 12-19 years

Abstract:

Introduction: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disabling condition that can affect adolescents during a vulnerable period of development. The underlying biological mechanisms for ME/CFS remain unclear and have rarely been investigated in the adolescent population, despite this period representing an age peak in the overall incidence. The primary objective of this is to provide a foundational set of biological data on adolescent ME/CFS patients. Data generated will be compared with controls and over several time points within each patient to potentially develop a biomarker signature of the disease, identify subsets or clusters of patients, and to unveil the pathomechanisms of the disease.

Methods and analysis: This protocol paper outlines a comprehensive, multilevel, longitudinal, observational study in paediatric ME/CFS. ME/CFS patients aged 12-19 years and controls will donate biosamples of urine, blood, and peripheral blood mononuclear cells for an in-depth omics profiling analysis (whole-genome sequencing, metabolomics and quantitative proteomics) while being assessed by gold-standard clinical and neuropsychological measures. ME/CFS patients will then be provided with a take-home kit that enables them to collect urine and blood microsamples during an average day and during days when they are experiencing postexertional malaise. The longitudinal repeated-measures study design is optimal for studying heterogeneous chronic diseases like ME/CFS as it can detect subtle changes, control for individual differences, enhance precision and boost statistical power. The outcomes of this research have the potential to identify biomarker signatures, aid in understanding the underlying mechanisms, and ultimately, improve the lives of children with ME/CFS.

Ethics and dissemination: This project was approved by the Royal Children’s Hospital’s Human Research Ethics Committee (HREC 74175). Findings from this study will be disseminated through peer-reviewed journal publications and presentations at relevant conferences. All participants will be provided with a summary of the study’s findings once the project is completed.

Source: Thomas N, Chau T, Tantanis D, Huang K, Scheinberg A, Gooley PR, Josev EK, Knight SJ, Armstrong CW. Serial Paediatrics Omics Tracking in Myalgic Encephalomyelitis (SPOT-ME): protocol paper for a multidisciplinary, observational study of clinical and biological markers of paediatric myalgic encephalomyelitis/chronic fatigue syndrome in Australian adolescents aged 12-19 years. BMJ Open. 2024 Dec 10;14(12):e089038. doi: 10.1136/bmjopen-2024-089038. PMID: 39658280. https://bmjopen.bmj.com/content/14/12/e089038 (Full text)

BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome

Abstract:

Chronic diseases like ME/CFS and long COVID exhibit high heterogeneity with multifactorial etiology and progression, complicating diagnosis and treatment. To address this, we developed BioMapAI, an explainable Deep Learning framework using the richest longitudinal multi-‘omics dataset for ME/CFS to date.

This dataset includes gut metagenomics, plasma metabolome, immune profiling, blood labs, and clinical symptoms. By connecting multi-‘omics to asymptom matrix, BioMapAI identified both disease- and symptom-specific biomarkers, reconstructed symptoms, and achieved state-of-the-art precision in disease classification. We also created the first connectivity map of these ‘omics in both healthy and disease states and revealed how microbiome-immune-metabolome crosstalk shifted from healthy to ME/CFS.

Thus, we proposed several innovative mechanistic hypotheses for ME/CFS: Disrupted microbial functions – SCFA (butyrate), BCAA (amino acid), tryptophan, benzoate – lost connection with plasma lipids and bile acids, and activated inflammatory and mucosal immune cells (MAIT, γδT cells) with INFγ and GzA secretion. These abnormal dynamics are linked to key disease symptoms, including gastrointestinal issues, fatigue, and sleep problems.

Source: Xiong R, Fleming E, Caldwell R, Vernon SD, Kozhaya L, Gunter C, Bateman L, Unutmaz D, Oh J. BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome. bioRxiv [Preprint]. 2024 Jun 28:2024.06.24.600378. doi: 10.1101/2024.06.24.600378. PMID: 38979186; PMCID: PMC11230215. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11230215/ (Full text available as PDF file)

Sequential multi-omics analysis identifies clinical phenotypes and predictive biomarkers for long COVID

Abstract:

The post-acute sequelae of COVID-19 (PASC), also known as long COVID, is often associated with debilitating symptoms and adverse multisystem consequences. We obtain plasma samples from 117 individuals during and 6 months following their acute phase of infection to comprehensively profile and assess changes in cytokines, proteome, and metabolome.

Network analysis reveals sustained inflammatory response, platelet degranulation, and cellular activation during convalescence accompanied by dysregulation in arginine biosynthesis, methionine metabolism, taurine metabolism, and tricarboxylic acid (TCA) cycle processes.

Furthermore, we develop a prognostic model composed of 20 molecules involved in regulating T cell exhaustion and energy metabolism that can reliably predict adverse clinical outcomes following discharge from acute infection with 83% accuracy and an area under the curve (AUC) of 0.96.

Our study reveals pertinent biological processes during convalescence that differ from acute infection, and it supports the development of specific therapies and biomarkers for patients suffering from long COVID.

Source: Wang K, Khoramjoo M, Srinivasan K, Gordon PMK, Mandal R, Jackson D, Sligl W, Grant MB, Penninger JM, Borchers CH, Wishart DS, Prasad V, Oudit GY. Sequential multi-omics analysis identifies clinical phenotypes and predictive biomarkers for long COVID. Cell Rep Med. 2023 Oct 18:101254. doi: 10.1016/j.xcrm.2023.101254. Epub ahead of print. PMID: 37890487. https://www.cell.com/cell-reports-medicine/fulltext/S2666-3791(23)00431-7 (Full text)