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Peptidome profiling for the immunological stratification in sepsis: a proof of concept study.

ABSTRACT: Sepsis has been called the graveyard of pharmaceutical companies due to the numerous failed clinical trials. The lack of tools to monitor the immunological status in sepsis constrains the development of therapies. Here, we evaluated a test based on whole plasma peptidome acquired by MALDI-TOF-mass spectrometer and machine-learning algorithms to discriminate two lipopolysaccharide-(LPS) induced murine models emulating the pro- and anti-inflammatory/immunosuppression environments that can be found during sepsis. The LPS group was inoculated with a single high dose of LPS and the IS group was subjected to increasing doses of LPS, to induce proinflammatory and anti-inflammatory/immunosuppression profiles respectively. The LPS group showed leukopenia and higher levels of cytokines and tissue damage markers, and the IS group showed neutrophilia, lymphopenia and decreased humoral response. Principal component analysis of the plasma peptidomes formed discrete clusters that mostly coincided with the experimental groups. In addition, machine-learning algorithms discriminated the different experimental groups with a sensitivity of 95.7% and specificity of 90.9%. Data reveal the potential of plasma fingerprints analysis by MALDI-TOF-mass spectrometry as a simple, speedy and readily transferrable method for sepsis patient stratification that would contribute to therapeutic decision-making based on their immunological status.

SUBMITTER: Ledesma M

PROVIDER: S-EPMC9259554 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Peptidome profiling for the immunological stratification in sepsis: a proof of concept study.

Ledesma Martín M Todero María Florencia MF Maceira Lautaro L Prieto Mónica M Vay Carlos C Galas Marcelo M López Beatriz B Yokobori Noemí N Rearte Bárbara B

Scientific reports 20220706 1

Sepsis has been called the graveyard of pharmaceutical companies due to the numerous failed clinical trials. The lack of tools to monitor the immunological status in sepsis constrains the development of therapies. Here, we evaluated a test based on whole plasma peptidome acquired by MALDI-TOF-mass spectrometer and machine-learning algorithms to discriminate two lipopolysaccharide-(LPS) induced murine models emulating the pro- and anti-inflammatory/immunosuppression environments that can be found ...[more]

PMID: 35794460

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Project description:BackgroundTranscriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models.MethodsRNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gene ontology (GO), and interferon-stimulated gene (ISG) score were also conducted.ResultsOur first classifier could differentiate pediatric rheumatic patients from controls and infection cases with high area-under-the-curve (AUC) values (AUC = 0.8 ± 0.1 and 0.7 ± 0.1, respectively). Three other classifiers could distinguish chronic recurrent multifocal osteomyelitis (CRMO), juvenile idiopathic arthritis (JIA), and interferonopathies (IFN) from control and infection cases with AUC ≥ 0.8. DEG and GO analyses reveal that the pathophysiology of CRMO, IFN, and JIA involves innate immune responses including myeloid leukocyte and granulocyte activation, neutrophil activation and degranulation. IFN is specifically mediated by antibacterial and antifungal defense responses, CRMO by cellular response to cytokine, and JIA by cellular response to chemical stimulus. IFN patients particularly had the highest mean ISG score among all disease groups.ConclusionOur data show that blood transcriptomics combined with machine learning is a promising diagnostic tool for pediatric rheumatic diseases and may assist physicians in making data-driven and patient-specific decisions in clinical practice.

Project description:BACKGROUND:Estimation of glomerular filtration rate (GFR) using estimated glomerular filtration rate creatinine (eGFRcr) is central to clinical practice but has limitations. We tested the hypothesis that serum metabolomic profiling can identify novel markers that in combination can provide more accurate GFR estimates. METHODS:We performed a cross-sectional study of 200 African American Study of Kidney Disease and Hypertension (AASK) and 265 Multi-Ethnic Study of Atherosclerosis (MESA) participants with measured GFR (mGFR). Untargeted gas chromatography/dual mass spectrometry- and liquid chromatography/dual mass spectrometry-based quantification was followed by the development of targeted assays for 15 metabolites. On the log scale, GFR was estimated from single- and multiple-metabolite panels and compared with eGFR using the Chronic Kidney Disease Epidemiology equations with creatinine and/or cystatin C using established metrics, including the proportion of errors >30% of mGFR (1-P30), before and after bias correction. RESULTS:Of untargeted metabolites in the AASK and MESA, 283 of 780 (36%) and 387 of 1447 (27%), respectively, were significantly correlated (P ≤ 0.001) with mGFR. A targeted metabolite panel eGFR developed in the AASK and validated in the MESA was more accurate (1-P30 3.7 and 1.9%, respectively) than eGFRcr [11.2 and 18.5%, respectively (P < 0.001 for both)] and estimating GFR using cystatin C (eGFRcys) [10.6% (P = 0.02) and 9.1% (P < 0.05), respectively] but was not consistently better than eGFR using both creatinine and cystatin C [3.7% (P > 0.05) and 9.1% (P < 0.05), respectively]. A panel excluding creatinine and demographics still performed well [1-P30 6.4% (P = 0.11) and 3.4% (P < 0.001) in the AASK and MESA] versus eGFRcr. CONCLUSIONS:Multimetabolite panels can enable accurate GFR estimation. Metabolomic equations, preferably excluding creatinine and demographic characteristics, should be tested for robustness and generalizability as a potential confirmatory test when eGFRcr is unreliable.

Project description:BackgroundAlbumin has potential endothelial protective effects through antioxidant and anti-inflammatory properties. However, the effect of albumin on peripheral tissue perfusion in human sepsis remains poorly known.MethodsBi-centric prospective study included patients with sepsis with or without shock and prolonged CRT > 3 s despite initial resuscitation. Clinicians in charge of the patients were free to infuse either saline 500 mL or human serum albumin 20% 100 mL over 15 min. Global hemodynamic parameters as well as peripheral tissue perfusion were analyzed after 1 (H1) and 4 h (H4). The primary endpoint was CRT normalization (< 3 s) at H1.Results62 patients were screened, and 50 patients (13 sepsis and 37 septic shock) were included, 21 in the saline group and 29 in the albumin group. SOFA score was 8 [5-11], and SAPS II was 53 [45-70]. Median age was 68 [60-76] years with a higher proportion of men (74%). The primary sources of infection were respiratory (54%) and abdominal (24%). At baseline, comorbidities, clinical and biological characteristics were similar between groups. At H1, CRT normalization (< 3 s) was more frequent in patients receiving albumin as compared to patients treated by saline (63 vs 29%, P = 0.02). The decrease in fingertip CRT was more important in the albumin group when compared to saline group (- 1.0 [- 0.3; - 1.5] vs - 0.2 [- 0.1; - 1.1] seconds, P = 0.04) as well as decrease in mottling score. At H4, beneficial effects of albumin on peripheral tissue perfusion were maintained and urinary output trended to be higher in the albumin group (1.1 [0.5-1.8] vs 0.7 [0.5-0.9] ml/kg/h, P = 0.08). Finally, arterial lactate level did not significantly change between H0 and H4 in the saline group but significantly decreased in the albumin group (P = 0.03).ConclusionIn patients with resuscitated sepsis, albumin infusion might lead to greater improvement of tissue hypoperfusion compared to saline.Clinicaltrialsgov Identifier: NCT05094856.

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Peptidome profiling for the immunological stratification in sepsis: a proof of concept study.

Publications

Peptidome profiling for the immunological stratification in sepsis: a proof of concept study.

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