Predicting sepsis severity at first clinical presentation: the role of endotypes and mechanistic signatures
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE185263 | GEO | 2022/01/10
REPOSITORIES: GEO
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Project description:Predicting sepsis severity at first clinical presentation: the role of endotypes and mechanistic signatures
| PRJNA768419 | ENA
Project description:BackgroundInter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use.MethodsBlood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms.FindingsGene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77-80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89-97%) accurately predicted endotype status in both ER and ICU validation cohorts.InterpretationThe severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients.
| S-EPMC8808161 | biostudies-literature
Project description:Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1-5 days post-hospital admission) and late (6-20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.
2022-12-18 | GSE221234 | GEO
Project description:Predicting severity in COVID-19 disease using sepsis blood gene expression signatures
| PRJNA913576 | ENA
Project description:This study used nasal transcriptomic profiling of the inferior turbinate in control and pediatric ARDS subjects to identify endotypes. This data set is for amplified specimens. The study identfied three pediatric ARDS endotypes.
2020-06-23 | GSE152979 | GEO
Project description:This study used nasal transcriptomic profiling of the inferior turbinate in control and pediatric ARDS subjects to identify endotypes. This data set is for non-amplified specimens. The study identfied three pediatric ARDS endotypes.
2020-06-23 | GSE152978 | GEO
Project description:Recent transcriptomic studies describe two subgroups of adults with sepsis differentiated by a sepsis response signature. The implied biology and related clinical associations are comparable with recently reported pediatric sepsis endotypes, labeled "A" and "B." We classified adults with sepsis using the pediatric endotyping strategy and the sepsis response signature and determined how endotype assignment, sepsis response signature membership, and age interact with respect to mortality.Retrospective analysis of publically available transcriptomic data representing critically ill adults with sepsis from which the sepsis response signature groups were derived and validated.Multiple ICUs.Adults with sepsis.None.Transcriptomic data were conormalized into a single dataset yielding 549 unique cases with sepsis response signature assignments. Each subject was assigned to endotype A or B using the expression data for the 100 endotyping genes. There were 163 subjects (30%) assigned to endotype A and 386 to endotype B. There was a weak, positive correlation between endotype assignment and sepsis response signature membership. Mortality rates were similar between patients assigned endotype A and those assigned endotype B. A multivariable logistic regression model fit to endotype assignment, sepsis response signature membership, age, and the respective two-way interactions revealed that endotype A, sepsis response signature 1 membership, older age, and the interactions between them were associated with mortality. Subjects coassigned to endotype A, and sepsis response signature 1 had the highest mortality.Combining the pediatric endotyping strategy with sepsis response signature membership might provide complementary, age-dependent, biological, and prognostic information.
| S-EPMC5693699 | biostudies-literature
Project description:The SARS-CoV-2 outbreak started on December 2019 in China and rapidly spread worldwide. Clinical manifestations of Coronavirus-disease 2019 (COVID-19) vary broadly, ranging from asymptomatic infection to acute respiratory failure and death, yet the underlying mechanisms and predictive biomarkers for this high variability are still unknown. Emerging evidence has shown that circulating extracellular vesicles (EVs) and extracellular RNAs (exRNAs) are functionally involved in a number of physiologic and pathologic processes. To test the hypothesis that these extracellular components are a key determinant of severity in COVID-19, we collected 31 serum samples from mild COVID-19 patients at admission in single center. After standard therapy without corticosteroids, 9 of 31 patients became severe COVID-19. We analyzed exRNA profiles from the 31 serums and 10 healthy controls for predicting COVID-19 severity value.
2022-03-31 | GSE158877 | GEO
Project description:Objectives: This study aimed to investigate the association between immunoglobulin G (IgG) N-glycosylation patterns and the clinical endotypes of patients with idiopathic inflammatory myopathies (IIM), potentially informing disease classification and therapeutic target identification. Methods: We recruited 168 IIM patients and 52 healthy controls, and performed IgG N-glycosylation profiling using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and employed GlycoQuant for intact glycopeptide analysis. Clinical data were extracted, and statistical analyses were conducted to correlate glycosylation profiles with clinical manifestations. Unsupervised clustering analysis was applied to identify novel IIM subgroups based on differential IgG N-glycosylation. Results: Our analysis revealed significant disparities in IgG N-glycosylation between IIM patients and controls, with 13 specific glycoforms exhibiting marked alterations. The level of fucosylated glycans was significantly higher in the IIM patients than healthy controls. Three distinct IIM endotypes were identified, each characterized by unique glycosylation signatures and clinical phenotypes. These endotypes were not congruent with conventional diagnostic subgroups and demonstrated associations with various clinical outcomes, including muscle weakness, interstitial lung disease (ILD), and serological profiles.
2024-12-03 | PXD058502 |
Project description:AimsArginine metabolism via inducible nitric oxide synthase (iNOS) and arginase 2 (ARG2) is higher in asthmatics than in healthy individuals. We hypothesized that a sub-phenotype of asthma might be defined by the magnitude of arginine metabolism categorized on the basis of high and low fraction of exhaled nitric oxide (FENO).MethodsTo test this hypothesis, asthmatics (n = 52) were compared to healthy controls (n = 51) for levels of FENO, serum arginase activity, and airway epithelial expression of iNOS and ARG2 proteins, in relation to clinical parameters of asthma inflammation and airway reactivity. In parallel, bronchial epithelial cells were evaluated for metabolic effects of iNOS and ARG2 expression in vitro.ResultsAsthmatics with high FENO (≥ 35 ppb; 44% of asthmatics) had higher expression of iNOS (P = 0.04) and ARG2 (P = 0.05) in the airway, indicating FENO is a marker of the high arginine metabolic endotype. High FENO asthmatics had the lowest FEV1% (P < 0.001), FEV1/FVC (P = 0.0002) and PC20 (P < 0.001) as compared to low FENO asthmatics or healthy controls. Low FENO asthmatics had near normal iNOS and ARG2 expression (both P > 0.05), and significantly higher PC20 (P < 0.001) as compared to high FENO asthmatics. In vitro studies to evaluate metabolic effects showed that iNOS overexpression and iNOS+ARG2 co-expression in a human bronchial epithelial cell line led to greater reliance on glycolysis with higher rate of pyruvate going to lactate.ConclusionsThe high FENO phenotype represents a large portion of the asthma population, and is typified by greater arginine metabolism and more severe and reactive asthma.
| S-EPMC5552347 | biostudies-literature