Biology subjects

Giamarellos-Bourboulis, E. J.

Publications and source records attributed to Giamarellos-Bourboulis, E. J..

4 recordsLinked to original sources

bioRxiv2024

International multi-cohort analysis identifies novel framework for quantifying immune dysregulation in critical illness: results of the SUBSPACE consortium

Progress in the management of critical care syndromes such as sepsis, Acute Respiratory Distress Syndrome (ARDS), and trauma has slowed over the last two decades, limited by the inherent heterogeneity within syndromic illnesses. Numerous immune endotypes have been proposed in sepsis and critical care, however the overlap of the endotypes is unclear, limiting clinical translation. The SUBSPACE consortium is an international consortium that aims to advance precision medicine through the sharing of transcriptomic data. By evaluating the overlap of existing immune endotypes in sepsis across over 6,000 samples, we developed cell-type specific signatures to quantify dysregulation in these immune compartments. Myeloid and lymphoid dysregulation were associated with disease severity and mortality across all cohorts. This dysregulation was not only observed in sepsis but also in ARDS, trauma, and burn patients, indicating a conserved mechanism across various critical illness syndromes. Moreover, analysis of randomized controlled trial data revealed that myeloid and lymphoid dysregulation is linked to differential mortality in patients treated with anakinra or corticosteroids, underscoring its prognostic and therapeutic significance. In conclusion, this novel immunology-based framework for quantifying cellular compartment dysregulation offers a valuable tool for prognosis and therapeutic decision-making in critical illness.

immunology↗

bioRxiv2023

Fatty acid desaturation and lipoxygenase pathways support trained immunity

Infections and vaccinations can induce long-term enhanced responses of innate immune cells to heterologous stimuli, establishing a de facto innate immunological memory termed trained immunity. Monocytes exposed to the Bacillus Calmette-Guerin (BCG) vaccine, have a trained immunity phenotype, characterized by an increased biosynthesis of different lipid mediators (LMs) derived from long-chain polyunsaturated fatty acids (PUFAs). Pharmacological and genetic approaches showed that long-chain PUFA synthesis and lipoxygenase (LOX)-derived LMs are crucial for the BCG trained immunity responses of human monocytes. Furthermore, monocytes of healthy individuals vaccinated with BCG are enriched in 12-LOX products. The elucidation of the lipid metabolic pathways that promote innate immune memory contributes to our understanding of trained immunity and may help identify therapeutic tools and targets for the modulation of innate immune responses.

immunology↗

bioRxiv2020

Swarm Learning as a privacy-preserving machine learning approach for disease classification

Identification of patients with life-threatening diseases including leukemias or infections such as tuberculosis and COVID-19 is an important goal of precision medicine. We recently illustrated that leukemia patients are identified by machine learning (ML) based on their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed because of privacy legislation. To facilitate integration of any omics data from any data owner world-wide without violating privacy laws, we here introduce Swarm Learning (SL), a decentralized machine learning approach uniting edge computing, blockchain-based peer-to-peer networking and coordination as well as privacy protection without the need for a central coordinator thereby going beyond federated learning. Using more than 14,000 blood transcriptomes derived from over 100 individual studies with non-uniform distribution of cases and controls and significant study biases, we illustrate the feasibility of SL to develop disease classifiers based on distributed data for COVID-19, tuberculosis or leukemias that outperform those developed at individual sites. Still, SL completely protects local privacy regulations by design. We propose this approach to noticeably accelerate the introduction of precision medicine.

bioinformatics↗

bioRxiv2018

Use of IFNγ/IL10 ratio for stratification of hydrocortisone therapy in patients with septic shock

BackgroundLarge clinical trials testing hydrocortisone therapy in septic shock have produced conflicting results. Subgroups may however benefit depending on their individual immune response.\n\nMethodsWe performed an exploratory analysis of the CORTICUS trial database employing machine learning to a panel of 137 variables collected from 83 patients (60 survivors, 23 non-survivors) including demographic and clinical measures, organ failure scores, leukocyte counts and circulating cytokine levels. The identified biomarker was validated against data collected from patients enrolled into a cohort of the Hellenic Sepsis Study Group (HSSG) (n=162) and two data sets of two other clinical trials. Ex vivo studies were performed on this biomarker to assess a possible mechanistic role.\n\nResultsA low serum IFN{gamma}/IL10 ratio predicted increased survival in the hydrocortisone group whereas a high ratio predicted better survival in the placebo group. Using this ratio for a decision rule, we found significant improvement in survival in the groups of patients being in compliance with the prediction rule (discovery set: OR=3.03 [95% Cl: 1.05-8.75], P=0.031, validation set: OR=2.01 [95% CI: 1.04-3.88], P=0.026). Applying the rule to two further, smaller datasets showed the same tendency. Mechanistic studies revealed that IFN{gamma}/IL10 was negatively associated with pathogen load in spiked human blood. An in silico analysis of published IFN{gamma} and IL10 values in bacteremic and non-bacteremic SIRS patients supported this association between the ratio and pathogen burden.\n\nConclusionIf confirmed prospectively, the IFN{gamma}/IL10 ratio could be used as a rapidly available theranostic for use of hydrocortisone therapy in septic shock.

immunology↗