Tag: multi-omics

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)