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acid along with L. xyli, Mycoplasma genitalium, and Hungateiclostridium thermocellum segregated PC-S nonsurvivors and superseded the liver disease severity indices with high accuracy, sensitivity, and specificity for mortality prediction using random forest machine-learning algorithm.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.

Comparative metabolome analysis reveals higher potential of haemoperfusion adsorption in providing favourable outcome in ACLF patients.

BACKGROUND AND AIMS: Acute-on-chronic liver failure (ACLF) is a serious illness associated with altered metabolome, organ failure and high mortality. Need for therapies to improve the metabolic milieu and support liver regeneration are urgently needed. METHODS: We investigated the ability of haemoperfusion adsorption (HA) and therapeutic plasma exchange (TPE) in improving the metabolic profile and survival in ACLF patients. Altogether, 45 ACLF patients were randomized into three groups: standard medical therapy (SMT), HA and TPE groups. Plasma metabolomics was performed at baseline, post-HA and TPE sessions on days 7 and 14 using high-resolution mass spectrometry. RESULTS: The baseline clinical/metabolic profiles of study groups were comparable. We identified 477 metabolites. Of these, 256 metabolites were significantly altered post 7 days of HA therapy (p < .05, FC > 1.5) and significantly reduced metabolites linked to purine (12 metabolites), tryptophan (7 metabolites), primary bile acid (6 metabolites) and arginine-proline metabolism (6 metabolites) and microbial metabolism respectively (p < .05). Metabolites linked to taurine-hypotaurine and histidine metabolism were reduced and temporal increase in metabolites linked to phenylalanine and tryptophan metabolism was observed post-TPE therapy (p < .05). Finally, weighted metabolite correlation network analysis (WMCNA) along with inter/intragroup analysis confirmed significant reduction in inflammatory (tryptophan, arachidonic acid and bile acid metabolism) and secondary energy metabolic pathways post-HA therapy compared to TPE and SMT (p < .05). Higher baseline plasma level of 11-deoxycorticosterone (C03205; AUROC > 0.90, HR > 3.2) correlated with severity (r2 > 0.5, p < .05) and mortality (log-rank-p < .05). Notably, 51 of the 64 metabolite signatures (ACLF non-survivor) were reversed post-HA treatment compared to TPE and SMT(p < .05). CONCLUSION: HA more potentially (~80%) improves plasma milieu compared to TPE and SMT. High baseline plasma 11-deoxycorticosterone level correlates with early mortality in ACLF patients.

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.

Bile multi-omics analysis classifies lipid species and microbial peptides predictive of carcinoma of gallbladder.

BACKGROUND AND AIMS: Histopathological examination is the gold standard for detection of gallstone (GS) or gallbladder carcinoma (CAGB). Bile concentrated in the gallbladder (GB) is expected to recapitulate metagenomics and molecular changes associated with development of CAGB. APPROACH AND RESULTS: Bile samples were screened for lipidomics and metaproteome (metagenomics) signatures capable of early detection of cancer in GB anomalies. Analysis of the training cohort (n = 87) showed that metastability of bile was reduced in CAGB (p < 0.05). CAGB bile showed significant alteration of lipidome and microbiome as indicated by multivariate partial least squares regression analysis and alpha-diversity and beta-diversity indexes (p < 0.05). Significant reduction of lipid species and increase in bacterial taxa were found to be associated with patients with CAGB, CAGB with GS, and GS (p < 0.05, log fold change >1.5). A multimodular correlation network created using weighted lipid/metaproteomic correlation network analysis showed striking associations between lipid and metaproteomic modules and functionality. CAGB-linked metaproteomic modules/functionality directly correlated with lipid modules, species, clinical parameters, and bile acid profile (p < 0.05). Increased bacterial taxa (Leptospira, Salmonella enterica, Mycoplasma gallisepticum) and their functionality showed a direct correlation with lipid classes such as lysophosphatidylinositol, ceramide 1-phosphate, and lysophosphatidylethanolamine and development of CAGB (r2  > 0.85). Lipid/metaproteomic signature-based probability of detection for CAGB was > 90%, whereas that for GS was > 80% (p < 0.05). Validation of eight lipid species using four machine learning algorithms in two separate cohorts (n = 38; bile [test cohort 1] and paired plasma [test cohort 2]) showed accuracy (99%) and sensitivity/specificity (>98%) for CAGB detection. CONCLUSIONS: Bile samples of patients with CAGB showed significant reduction in lipid species and increase in bacterial taxa. Our study identifies a core set of bile lipidome and metaproteome signatures which may offer universal utility for early diagnosis of CAGB.

Protocol for global proteome, virome, and metaproteome profiling of respiratory specimen (VTM) in COVID-19 patient by LC-MS/MS-based analysis.

In this protocol, we describe global proteome profiling for the respiratory specimen of COVID-19 patients, patients suspected with COVID-19, and H1N1 patients. In this protocol, details for identifying host, viral, or bacterial proteome (Meta-proteome) are provided. Major steps of the protocol include virus inactivation, protein quantification and digestion, desalting of peptides, high-resolution mass spectrometry (HRMS)-based analysis, and downstream bioinformatics analysis. For complete details on the use and execution of this profile, please refer to Maras et al. (2021).

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).

Baseline Plasma Metabotype Correlates With Direct-Acting Antiviral Therapy Nonresponse for HCV in HIV-HCV Coinfected Patients.

Introduction: With the advent of direct-acting antiviral (DAA) therapy for HCV, the cure is achieved at similar rates among HIV-HCV coinfected patients as in HCV mono-infected patients. The present study evaluates host plasma metabolites as putative indicators in predicting the treatment response in baseline HIV-HCV patients. Methods: Non-cirrhotic HIV-HCV (N = 43) coinfected patients were treated with sofosbuvir and daclatasvir for 12 weeks. Plasma metabolite profiling of pre- and post-therapy was analyzed in 20/43 patients. Of the 20 selected, 10 (50%) attained the sustained viral response [(SVR) (responders)] as defined by the absence of HCV RNA at 12 weeks after the treatment, and 10 (50%) did not attain the cure for HCV (nonresponders). Results: A total of 563 features were annotated (metabolomic/spectral databases). Before therapy, 39 metabolites differentiated (FC ±1.5, p < 0.05) nonresponders from responders. Of these, 20 upregulated and 19 downregulated were associated with tryptophan metabolism, nicotinamide metabolism, and others. Post therapy, 62 plasma metabolites (12 upregulated and 50 downregulated, FC±1.5, p < 0.05) differentiated nonresponders from responders and highlighted a significant increase in the steroid and histidine metabolism and significant decrease in tryptophan metabolism and ascorbate and pyruvate metabolism in the nonresponders. Based on random forest and multivariate linear regression analysis, the baseline level of N-acetylspermidine (FC > 2, AUC = 0.940, Bfactor = -0.267) and 2-acetolactate (FC > 2, AUC = 0.880, Bfactor = -0.713) significantly differentiated between nonresponders from responders in HIV-HCV coinfected patients and was able to predict the failure of treatment response. Conclusion: Increased baseline levels of N-acetylspermidine and 2-acetolactate levels are associated with the likeliness of failure to attain the cure for HCV in HIV-HCV coinfected patients.