Circulating bacterial peptides and linked metabolomic signatures are indicative of early mortality in pediatric cirrhosis.

BACKGROUND: Patients with pediatric cirrhosis-sepsis (PC-S) attain early mortality. Plasma bacterial composition, the cognate metabolites, and their contribution to the deterioration of patients with PC-S to early mortality are unknown. We aimed to delineate the plasma metaproteome-metabolome landscape and identify molecular indicators capable of segregating patients with PC-S predisposed to early mortality in plasma, and we further validated the selected metabolite panel in paired 1-drop blood samples using untargeted metaproteomics-metabolomics by UHPLC-HRMS followed by validation using machine-learning algorithms. METHODS: We enrolled 160 patients with liver diseases (cirrhosis-sepsis/nonsepsis [n=110] and noncirrhosis [n=50]) and performed untargeted metaproteomics-metabolomics on a training cohort of 110 patients (Cirrhosis-Sepsis/Nonsepsis, n=70 and noncirrhosis, n=40). The candidate predictors were validated on 2 test cohorts-T1 (plasma test cohort) and T2 (1-drop blood test cohort). Both T1 and T2 had 120 patients each, of which 70 were from the training cohort. RESULTS: Increased levels of tryptophan metabolites and Salmonella enterica and Escherichia coli-associated peptides segregated patients with cirrhosis. Increased levels of deoxyribose-1-phosphate, N5-citryl-d-ornithine, and Herbinix hemicellulolytic and Leifsonia xyli segregated patients with PC-S. MMCN-based integration analysis of WMCNA-WMpCNA identified key microbial-metabolic modules linked to PC-S nonsurvivors. Increased Indican, Staphylobillin, glucose-6-phosphate, 2-octenoylcarnitine, palmitic acid, and guanidoacetic acid along with L. xyli, Mycoplasma genitalium, and Hungateiclostridium thermocellum segregated PC-S nonsurvivors and superseded the liver disease severity indices with high accuracy, sensitivity, and specificity for mortality prediction using random forest machine-learning algorithm. CONCLUSIONS: Our study reveals a novel metabolite signature panel capable of segregating patients with PC-S predisposed to early mortality using as low as 1-drop blood.

Rapid Electrochemical Detection of Bacterial Sepsis in Cirrhotic Patients: A Microscaffold-Based Approach for Early Intervention.

Sepsis is a dysregulated inflammatory response leading to multiple organ failure. Current methods of sepsis detection are time-consuming, involving nonspecific clinical signs, biomarkers, and blood cultures. Hence, efficient and rapid sepsis detection platforms are of utmost need for immediate antibiotic treatment. In the current study, a noninvasive rapid monitoring electrochemical sensing (ECS) platform was developed for the detection and classification of plasma samples of patients with liver cirrhosis by measuring the current peak shifts using the cyclic voltammetry (CV) technique. A total of 61 hospitalized cirrhotic patients with confirmed (culture-positive) or suspected (culture-negative) sepsis were enrolled. The presence of bacteria in the plasma was observed by growth kinetics, and for rapidness, the samples were co-encapsulated in microscaffolds with carbon nanodots that were sensitive enough to detect redox changes occurring due to the change in the pH of the surrounding medium, causing shifts in current peaks in the voltammograms within 2 h. The percentage area under the curve for confirmed infections was 94 and that with suspected cases was 87 in comparison to 69 and 71 with PCT, respectively. Furthermore, the charge was measured for class identification. The charge for LPS-absent bacteria ranged from -400 to -600 µC, whereas the charge for LPS-containing bacteria class ranged from -290 to -300 µC. Thus, the developed cost-effective system was sensitive enough to detect and identify bacterial sepsis.

Biomolecular map of albumin identifies signatures of severity and early mortality in acute liver failure.

BACKGROUND & AIMS: Acute liver failure (ALF) is associated with high mortality. Alterations in albumin structure and function have been shown to correlate with outcomes in cirrhosis. We undertook a biomolecular analysis of albumin to determine its correlation with hepatocellular injury and early mortality in ALF. METHODS: Altogether, 225 participants (200 patients with ALF and 25 healthy controls [HC]) were enrolled. Albumin was purified from the baseline plasma of the training cohort (ALF, n = 40; survivors, n = 8; non-survivors, n = 32; and HC, n = 5); analysed for modifications, functionality, and bound multi-omics signatures; and validated in a test cohort (ALF, n = 160; survivors, n = 53; non-survivors, n = 107; and HC, n = 20). RESULTS: In patients with ALF, albumin is more oxidised and glycosylated with a distinct multi-omics profile than that in HC, more so in non-survivors (p <0.05). In non-survivors, albumin was more often bound (p <0.05, false discovery rate <0.01) to proteins associated with inflammation, advanced glycation end product, metabolites linked to arginine, proline metabolism, bile acid, and mitochondrial breakdown products. Increased bacterial taxa (Listeria, Clostridium, etc.) correlated with lipids (triglycerides [4:0/12:0/12:0] and phosphatidylserine [39:0]) and metabolites (porphobilinogen and nicotinic acid) in non-survivors (r2 >0.7). Multi-omics signature-based probability of detection for non-survival was >90% and showed direct correlation with albumin functionality and clinical parameters (r2 >0.85). Probability-of-detection metabolites built on the top five metabolites, namely, nicotinic acid, l-acetyl carnitine, l-carnitine, pregnenolone sulfate, and N-(3-hydroxybutanoyl)-l-homoserine lactone, showed diagnostic accuracy of 98% (AUC 0.98, 95% CI 0.95-1.0) and distinguish patients with ALF predisposed to early mortality (log-rank <0.05). On validation using high-resolution mass spectrometry and five machine learning algorithms in test cohort 1 (plasma and paired one-drop blood), the metabolome panel showed >92% accuracy/sensitivity and specificity for prediction of mortality. CONCLUSIONS: In ALF, albumin is hyperoxidised and substantially dysfunctional. Our study outlines distinct 'albuminome' signatures capable of distinguishing patients with ALF predisposed to early mortality or requiring emergency liver transplantation. IMPACTS AND IMPLICATIONS: Here, we report that the biomolecular map of albumin is distinct and linked to severity and outcome in patients with acute liver failure (ALF). Detailed structural, functional, and albumin-omics analysis in patients with ALF led to the identification and classification of albumin-bound biomolecules, which could segregate patients with ALF predisposed to early mortality. More importantly, we found albumin-bound metabolites indicative of mitochondrial damage and hyperinflammation as a putative indicator of <30-day mortality in patients with ALF. This preclinical study validates the utility of albuminome analysis for understanding the pathophysiology and development of poor outcome indicators in patients with ALF.

Global metabolome profiling of COVID-19 respiratory specimen using high-resolution mass spectrometry (HRMS).

Here we describe a protocol for identifying metabolites in respiratory specimens of patients that are SARS-CoV-2 positive, SARS-CoV-2 negative, or H1N1 positive. This protocol provides step-by-step instructions on sample collection from patients, followed by metabolite extraction. We use ultra-high-pressure liquid chromatography (UHPLC) coupled with high-resolution mass spectrometry (HRMS) for data acquisition and describe the steps for data analysis. The protocol was standardized with specific customization for SARS-CoV-2-containing respiratory specimens. For complete details on the use and execution of this protocol, please refer to Maras et al. (2021).