Tag: multi-omics

Multi-omics identifies lipid accumulation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cell lines: a case-control study

Abstract:

Background: In recent years, evidence has indicated a metabolic shift towards increased demand for lipids in various lymphoid cell populations from people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS). We previously screened the mitochondrial function and gene expression of B cell-derived lymphoblastoid cell lines (LCLs) generated from the blood of people with ME/CFS to characterise a model for hypothesis discovery and testing, observing elevated expression of gene products facilitating amino acid and fatty acid degradation for energy.

Method: In this follow-up study we have expanded this characterisation by profiling the polar metabolomes and non-polar lipidomes of an all-female cohort of 17 healthy control and 15 ME/CFS LCLs, and we integrated this new data with the previously generated proteomic and transcriptomic data.

Results: In the polar metabolome we detected no significantly altered individual features, while integrated multi-omic analysis by MetaboAnalyst indicated 15 dysregulated pathways. Next, in the non-polar lipidome, we identified that PC(O-38:4) had significantly reduced levels in ME/CFS LCLs and was almost entirely discriminative of ME/CFS status. Among all detected classes of lipids we found that triradylglycerolipids (“triglycerides”), diradylglycerolipids and fatty acids were the most significantly affected and were elevated, and that most lipids exhibited average levels higher than in healthy controls. BioPAN pathway analysis of the lipidomic data predicted a more-active gene product that we confirmed to be significantly elevated in both our proteomic and transcriptomic data, this being phosphatidylserine synthase 1 (PTDSS1), plus 7 other gene products that were concordantly altered in expression in the transcriptomic data. We also found that ME/CFS LCLs exhibited a significant tendency towards more saturated lipid content.

Conclusions: LCLs generated from circulating B cells from people with ME/CFS show accumulation of lipids, skewed lipid profiles and altered activity of related metabolic enzymes such as PTDSS1. These findings will inform future hypothesis-driven studies of primary lymphoid cell populations from people with ME/CFS to dissect specific immunometabolic mechanisms that may be involved in the syndrome, particularly relating to intersections between lipid abnormalities and potential effects on immune cell effector functions.

Source: Missailidis D, Armstrong CW, Anderson D, Allan CY, Sanislav O, Smith PK, Esmaili T, Creek DJ, Annesley SJ, Fisher PR. Multi-omics identifies lipid accumulation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cell lines: a case-control study. J Transl Med. 2026 Jan 8. doi: 10.1186/s12967-025-07620-x. Epub ahead of print. PMID: 41508032. https://link.springer.com/article/10.1186/s12967-025-07620-x (Full text available as PDF file)

Exploring a genetic basis for the metabolic perturbations in ME/CFS using UK Biobank

Highlights:

  • ME/CFS shows distinct genetic influences on metabolic regulation.
  • Lipid and hormone-related pathways emerge as key areas of interest.
  • Many small genetic effects may collectively disrupt metabolic resilience in ME/CFS.

Summary:

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a clinically heterogeneous disease lacking approved therapies. To assess genetic susceptibility towards a specific metabolic phenotype, we performed a genome-wide association study on plasma biomarker levels (mGWAS) in ME/CFS patients (n=875) and healthy controls (HCs) (n=36,033).
We identified 112 significant SNP–biomarker associations in ME/CFS, compared with 4,114 in HCs. Two SNPs specific to ME/CFS, mapping to HSD11B1 and SCGN, were associated to phospholipids in extra-large very low-density lipoproteins (VLDL) and total fatty acids respectively. Genetic effects of VLDL associations were among the least correlated between ME/CFS and HCs. Heterogeneity tests found differential effects for several lipid traits at ADAP1, NR1H3 and CD40, which are involved in immune regulation.
ME/CFS mGWAS summary statistics were decomposed to uncover shared genetic-metabolic patterns, where enrichment analysis highlighted pathways in lipid metabolism, neurotransmitter transport, and inflammation. These findings provide a genetic and molecular rationale for patient heterogeneity and suggest a polygenic predisposition in which many small-effect variants may jointly perturb metabolic mechanisms.
Source: Katherine Huang, Muhammad Muneeb, Natalie Thomas, Elena K. Schneider-Futschik, Paul R. Gooley, David B. Ascher, Christopher W. Armstrong. Exploring a genetic basis for the metabolic perturbations in ME/CFS using UK Biobank. iScience, 2025, 114316 ISSN 2589-0042, https://doi.org/10.1016/j.isci.2025.114316. https://www.sciencedirect.com/science/article/pii/S2589004225025775 (Full text available as PDF file)

Urinary Peptidomic Profiling In Post-Acute Sequelae of SARS-CoV-2 Infection: A Case-Control Study

Abstract:

Post-acute sequelae of severe acute respiratory syndrome coronavirus 2-infection (PASC) is challenging to diagnose and treat, and its molecular pathophysiology remains unclear. Urinary peptidomics can provide valuable information on urine peptides that may enable improved and specified PASC diagnosis.
Using standardized capillary electrophoresis-MS, we examined the urinary peptidomes of 50 patients with PASC 10 months after COVID-19 and 50 controls, including healthy individuals (n = 42) and patients with non-COVID-19-associated myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) (n = 8).Based on peptide abundance differences between cases and controls, we developed a diagnostic model using a support vector machine. The abundance of 195 urine peptides among PASC patients significantly differed from that in controls, with a predominant abundance of collagen alpha chains. This molecular signature (PASC195) effectively distinguished PASC cases from controls in the training set (AUC of 0.949 [95% CI 0.900–0.998; p < 0.0001]) and independent validation set (AUC of 0.962 [95% CI 0.897–1.00]; p < 0.0001]). In silico assessment suggested exercise, GLP-1RAs and mineralocorticoid receptor antagonists (MRAs) as potentially efficacious interventions. We present a novel and non-invasive diagnostic model for PASC. Reflecting its molecular pathophysiology, PASC195 has the potential to advance diagnostics and inform therapeutic interventions.

Statement of Significance of the Study

Despite the recent emergence of omics-derived candidates for post-acute sequelae of SARS-CoV-2 infection (PASC), the pending validation of proposed markers and lack of consensus result in the continuous reliance on symptom-based criteria, being subject to diagnostic uncertainties and potential recall bias. Building upon prior findings of renal involvement in acute COVID-19 pathophysiology and PASC-associated alterations, we hypothesized that the use of urinary peptides for PASC-specific biomarker discovery, unlike conventional specimens that have been utilized thus far, may offer complementary information on putative disease mechanisms.

In the present study, 195 significantly expressed peptides were used to form a classifier termed PASC195, which effectively discriminated PASC from non-PASC (p < 0.0001), including healthy individuals and non-COVID-19-associated myalgic encephalomyelitis/chronic fatigue syndrome, in both the derivation (n = 60) and an independent validation set (n = 40). The peptidome profile associated with PASC was consistent with a shift in collagen turnover, with most PASC195 peptides derived from alpha chains. Ongoing inflammatory responses, hemostatic imbalances, and endothelial damage were indicated by cross-sectional variations in endogenous peptide excretion.

Source: Gülmez D, Siwy J, Kurz K, Wendt R, Banasik M, Peters B, Dudoignon E, Depret F, Salgueira M, Nowacki E, Kurnikowski A, Mussnig S, Krenn S, Gonos S, Löffler-Ragg J, Weiss G, Mischak H, Hecking M, Schernhammer E, Beige J; UriCoV Working Group. Urinary Peptidomic Profiling In Post-Acute Sequelae of SARS-CoV-2 Infection: A Case-Control Study. Proteomics. 2025 Nov 21:e70074. doi: 10.1002/pmic.70074. Epub ahead of print. PMID: 41273049. https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/pmic.70074 (Full text)

The Role of Nuclear and Mitochondrial DNA in Myalgic Encephalomyelitis: Molecular Insights into Susceptibility and Dysfunction

Abstract:

Myalgic Encephalomyelitis (ME), also known as chronic fatigue syndrome (CFS), is a debilitating and heterogeneous disorder marked by persistent fatigue, post-exertional malaise, cognitive impairment, and multisystem dysfunction. Despite its prevalence and impact, the molecular mechanisms underlying ME remain poorly understood.
This review synthesizes current evidence on the role of DNA, both nuclear and mitochondrial, in the susceptibility and pathophysiology of ME. We examined genetic predispositions, including familial clustering and candidate gene associations, and highlighted emerging insights from genome-wide and multi-omics studies.
Mitochondrial DNA variants and oxidative stress-related damage are discussed in relation to impaired bioenergetics and symptom severity. Epigenetic modifications, particularly DNA methylation dynamics and transposable element activation, are explored as mediators of gene–environment interactions and immune dysregulation.
Finally, we explored the translational potential of DNA-based biomarkers and therapeutic targets, emphasizing the need for integrative molecular approaches to advance diagnosis and treatment. Understanding the DNA-associated mechanisms in ME offers a promising path toward precision medicine in post-viral chronic diseases.
Source: Elremaly W, Elbakry M, Vahdani Y, Franco A, Moreau A. The Role of Nuclear and Mitochondrial DNA in Myalgic Encephalomyelitis: Molecular Insights into Susceptibility and Dysfunction. DNA. 2025; 5(4):53. https://doi.org/10.3390/dna5040053 https://www.mdpi.com/2673-8856/5/4/53 (Full text)

Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence

Abstract:

We studied a generalized question: 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 a symptom 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.

Source: Xiong R. Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence. ArXiv [Preprint]. 2025 Jun 18:arXiv:2506.15761v1. PMID: 40980765; PMCID: PMC12447721. https://pmc.ncbi.nlm.nih.gov/articles/PMC12447721/ (Full text available as PDF file)

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)