Biology subjects

Bateman, L.

Publications and source records attributed to Bateman, L..

4 recordsLinked to original sources

bioRxiv2024

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

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 four-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 robustly classifies ME/CFS in both held-out and independent external cohorts. Using an explainable AI approach, we construct the first 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 disrupted associations between microbial metabolism (e.g., 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, {gamma}{delta}T) secreting IFN{gamma} and GzA. Overall, BioMapAI provides unprecedented systems-level insights into ME/CFS, refining existing hypotheses and hypothesizing new pathways associated to the diseases heterogeneous symptoms.

bioinformatics↗

bioRxiv2021

Multi-omics of host-microbiome interactions in short- and long-term Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multi-system, debilitating disability manifesting as severe fatigue and post-exertional malaise. The chronic dysfunctions in ME/CFS are increasingly recognized as significant health factors with potential parallels with long COVID. However, the etiology of ME/CFS remains elusive with limited high-resolution human studies. In addition, reliable biomarker-based diagnostics have not been well-established, but may assist in disease classification, particularly during different temporal phases of the disease. Here, we performed deep multi- omics (shotgun metagenomics of gut microbiota and plasma metabolomics) and clinical phenotyping of healthy controls (n=79) vs. two cohorts of ME/CFS patients - those with short-term disease (<4 years, n=75), and patients with long-term disease (>10y, n=79). Overall, ME/CFS was characterized by reduced gut microbiome diversity and richness with high heterogeneity, and depletion of sphingomyelins and short-chain fatty acids in the plasma. We found significant differences when stratifying by cohort; short-term ME/CFS was associated with more microbial dysbiosis, but long-term ME/CFS was associated with markedly more severe phenotypic and metabolic abnormalities. We identified a reduction in the gene-coding capacity (and relative abundance of butyrate producers) of microbial butyrate biosynthesis together with a reduction in the plasma concentration of butyrate, especially in the short-term group. Global co-association and detailed gene pathway correlation analyses linking the microbiome and metabolome identified additional potential biological mechanisms underlying host-microbiome interactions in ME/CFS, including bile acids and benzoate pathways. Finally, we built multiple state-of-the-art classifiers to identify microbes, microbial gene pathways, metabolites, and clinical features that individually or together, were most able to differentiate short or long-term MECFS, or MECFS vs. healthy controls. Taken together, our study presents the highest resolution, multi-cohort and multi- omics analysis to date, providing an important resource to facilitate mechanistic hypotheses of host-microbiome interactions in ME/CFS.

microbiology↗

bioRxiv2021

Distinct signatures of loss of consciousness during Focal Impaired Awareness (FIA) versus Focal to Bilateral Tonic-Clonic (FBTC) seizures

Loss of consciousness (LOC) is a hallmark of many epileptic seizures and carries risks of serious injury and sudden death. While cortical sleep-like activities accompany LOC during focal impaired awareness (FIA) seizures, the mechanisms of LOC during focal to bilateral tonic-clonic (FBTC) seizures remain unclear. Quantifying differences in markers of cortical activation and ictal recruitment between FIA and FBTC seizures may also help to understand their different consequences for clinical outcomes and to optimize neuromodulation therapies. We quantified clinical signs of LOC and intracranial EEG (iEEG) activity during 129 FIA and 50 FBTC from 41 patients. We characterized iEEG changes both in the seizure onset zone (SOZ) and in areas remote from SOZ with a total of 3386 electrodes distributed across brain areas. First, we compared the dynamics of iEEG sleep-like activities: slow-wave activity (SWA; 1-4 Hz) and beta/delta ratio (B/D; a validated marker of cortical activation) during FIA vs. FBTC. Second, we quantified differences between FBTC and FIA for a marker validated to detect ictal cross-frequency coupling: phase-locked high-gamma (PLHG; high gamma phased locked to low frequencies) and a marker of ictal recruitment: the epileptogenicity index (i.e. the number of channels crossing an energy ratio threshold for high vs. low frequency power). Third, we assessed changes in iEEG activity preceding and accompanying behavioral generalization onset and their correlation with electromyogram (EMG) channels. In addition, we analyzed human cortical multi-unit activity recorded with Utah arrays during three FBTC. Compared to FIA, FBTC seizures were characterized by deeper LOC and by stronger increases in SWA in parieto-occipital cortex. FBTC also displayed more widespread increases in cortical activation (B/D), ictal cross-frequency coupling (PLHG) and ictal recruitment (epileptogenicity index). Even before generalization, FBTC displayed deeper LOC; this early LOC was accompanied by a paradoxical increase in B/D in fronto-parietal cortex. Behavioral generalization coincided with complete loss of responsiveness and a subsequent increase in high-gamma in the whole brain, which was especially synchronous in deep sources and could not be explained by EMG. Similarly, multi-unit activity analysis of FBTC revealed sustained increases in cortical firing rates during and after generalization onset in areas remote from the SOZ. Unlike during FIA, LOC during FBTC is characterized by a paradoxical increase in cortical activation and neuronal firing. These findings suggest differences in the mechanisms of ictal LOC between FIA and FBTC and may account for the more negative prognostic consequences of FBTC.

neuroscience↗

bioRxiv2019

Perturbation of effector and regulatory T cell subsets in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disorder of unknown etiology, and diagnosis of the disease is largely based on clinical symptoms. We hypothesized that immunological disruption is the major driver of this disease and analyzed a large cohort of ME/CFS patient or control blood samples for differences in T cell subset frequencies and functions. We found that the ratio of CD4+ to CD8+ T cells and the proportion of CD8+ effector memory T cells were increased, whereas NK cells were reduced in ME/CFS patients younger than 50 years old compared to a healthy control group. Remarkably, major differences were observed in Th1, Th2, Th17 and mucosal-associated invariant T (MAIT) T cell subset functions across all ages of patients compared to healthy subjects. While CCR6+ Th17 cells in ME/CFS secreted less IL-17 compared to controls, their overall frequency was higher. Similarly, MAIT cells from patients secreted lower IFN{gamma}, GranzymeA and IL-17 upon activation. Together, these findings suggest chronic stimulation of these T cell populations in ME/CFS patients. In contrast, the frequency of regulatory T cells (Tregs), which control excessive immune activation, was higher in ME/CFS patients. Finally, using a machine learning algorithm called random forest, we determined that the set of T cell parameters analyzed could identify more than 90% of the subjects in the ME/CFS cohort as patients (93% true positive rate or sensitivity). In conclusion, these multiple and major perturbations or dysfunctions in T cell subsets in ME/CFS patients suggest potential chronic infections or microbiome dysbiosis. These findings also have implications for development of ME/CFS specific immune biomarkers and reveal potential targets for novel therapeutic interventions.

immunology↗