Eigenvector metabolite analysis reveals dietary effects on the association among metabolite correlation patterns, gene expression, and phenotypes

Metabolomic profiling for drug-induced liver injury with autoantibodies

BACKGROUNDS

Drug induced liver injury (DILI) is sometimes similar to autoimmune hepatitis (AIH) in serology and histology. Clinicians empirically screened DILI with significant autoimmune characteristics to implement clinical intervention. We tried to characterize DILI with autoantibodies by metabolomics.

METHODS

Untargeted metabolomics coupled with pattern recognition approaches were performed on sera samples including AIH (n = 59), DILI with autoantibodies (DILI^Ab+, n = 68), and DILI without autoantibodies (DILI^Ab-, n = 75). The differential metabolites and fingerprint metabolites between AIH and DILI^Ab- were screened by orthogonal partial least squares-discriminant analysis and hierarchical clustering respectively.

RESULTS

Of the 388 annotated differential metabolites between AIH and DILI^Ab-, 74 fingerprint metabolites were screened. The eigenmetabolite compressed from the fingerprint possessed high discrimination efficacy (AUC:0.891; 95 %CI, 0.838-0.944). In the fingerprint-based PCA model, AIH and DILI^Ab- were separated into three regions: the "pure region" of AIH (Region 1), the "pure region" of DILI^Ab- (Region 3), the mixture region of AIH and DILI^Ab- (Region 2). After incorporated into the PCA model, DILI^Ab+ samples were distributed into the three regions, indicating that DILI^Ab+ samples had different etiological tendencies. Moreover, the fingerprint-based radar model verified the results of PCA model characterizing DILI^Ab+. Notably, the antibody titers of DILI^Ab+ in the three regions did not differ significantly, while the response rates for glucocorticoids were obviously different. The metabolic difference among DILI^Ab+ in different regions mainly lies in energy metabolism.

CONCLUSIONS

In terms of metabolic signature, DILI^Ab+ may not be a community of same pathogenesis, including AIH-inclined parts. Which deserves further study.

Metabolomic Analysis Uncovers Lipid and Amino Acid Metabolism Disturbance During the Development of Ascites in Alcoholic Liver Disease

Ascites is one of the most common complications of cirrhosis, and there is a dearth of knowledge about ascites-related pathologic metabolism. In this study, 122 alcoholic liver disease (ALD) patients, including 49 cases without ascites, 18 cases with mild-ascites, and 55 cases with large-ascites (1) were established according to the International Ascites Club (2), and untargeted metabolomics coupled with pattern recognition approaches were performed to profile and extract metabolite signatures. A total of 553 metabolites were uniquely discovered in patients with ascites, of which 136 metabolites had been annotated in the human metabolome database. Principal component analysis (PCA) analysis was used to further identify 21 ascites-related fingerprints. The eigenmetabolite calculated by reducing the dimensions of the 21 metabolites could be used to effectively identify those ALD patients with or without ascites. The eigenmetabolite showed a decreasing trend during ascites production and accumulation and was negatively related to the disease progress. These metabolic fingerprints mainly belong to the metabolites in lipid metabolism and the amino acid pathway. The results imply that lipid and amino acid metabolism disturbance may play a critical role in the development of ascites in ALD patients and could be a potent prognosis marker.

Cytokine signature clusters as a tool to compare changes associated with tobacco product use in upper and lower airway samples

Inhalation exposure to cigarette smoke and e-cigarette aerosol is known to alter the respiratory immune system, particularly cytokine signaling. In assessments of health impacts of tobacco product use, cytokines are often measured using a variety of sample types, from serum to airway mucosa. However, it is currently unclear whether and how well cytokine levels from different sample types and the airway locations they represent are correlated, making comparing studies that utilize differing sample types challenging. To address this challenge, we compared baseline cytokine signatures in upper and lower airways and systemic samples and evaluated how groups of coexpressed cytokines change with tobacco product use. Matched nasal lavage fluid (NLF), nasal epithelial lining fluid (NELF), sputum, and circulating serum samples were collected from 14 nonsmokers, 13 cigarette smokers, and 17 e-cigarette users and analyzed for levels of 22 cytokines. Individual cytokine signatures were first compared across each sample type, followed by identification of cytokine clusters within each sample type. Identified clusters were then evaluated for potential alterations following tobacco product use using eigenvector analyses. Individual cytokine signatures in the respiratory tract were significantly correlated (NLF, NELF, and sputum) compared with randomly permutated signatures, whereas serum was not significantly different from random permutations. Cytokine clusters that were similar across airway sample types were modified by tobacco product use, particularly e-cigarettes, indicating a degree of uniformity in terms of how cytokine host defense and immune cell recruitment responses cooperate in the upper and lower airways. Overall, cluster-based analyses were found to be especially useful in small cohort assessments, providing higher sensitivity than individual signatures to detect biologically meaningful differences between tobacco use groups. This novel cluster analysis approach revealed that eigencytokine patterns in noninvasive upper airway samples simulate cytokine patterns in lower airways.

Comprehensive metabolomics profiling reveals common metabolic alterations underlying the four major non-communicable diseases in treated HIV infection

Background

HIV infection and normal aging share immune and inflammatory changes that result in premature aging and non-communicable diseases (NCDs), but the exact pathophysiology is not yet uncovered. We identified the common metabolic pathways underlying various NCDs in treated HIV infection.

Methods

We performed untargeted metabolomics including 87 HIV-negative (–) normal controls (NCs), 87 HIV-positive (+) NCs, and 148 HIV+ subjects with only one type of NCDs, namely, subclinical carotid atherosclerosis, neurocognitive impairment (NCI), liver fibrosis (LF) and renal impairment. All HIV+ subjects were virally suppressed.

Results

HIV+ patients presented widespread alterations in cellular metabolism compared to HIV– NCs. Glycerophospholipid (GPL) metabolism was the only one disturbed pathway presented in comparisons including HIV– NCs across age groups, HIV+ NCs across age groups, HIV+ NCs vs HIV– NCs and each of HIV+ NCDs vs HIV+ NCs. D-glutamine and D-glutamate metabolism and alanine-aspartate-glutamate metabolism were presented in comparisons between HIV+ NCs vs HIV– NCs, HIV+ LF or HIV+ NCI vs HIV+ NCs. Consistently, subsequent analysis identified a metabolomic fingerprint specific for HIV+ NCDs, containing 42 metabolites whose relative abundance showed either an upward (mainly GPL-derived lipid mediators) or a downward trend (mainly plasmalogen phosphatidylcholines, plasmalogen phosphatidylethanolamines, and glutamine) from HIV– NCs to HIV+ NCs and then HIV+ NCDs, reflecting a trend of increased oxidative stress.

Interpretation

GPL metabolism emerges as the common metabolic disturbance linking HIV to NCDs, followed by glutamine and glutamate metabolism. Together, our data point to the aforementioned metabolisms and related metabolites as potential key targets in studying pathophysiology of NCDs in HIV infection and developing therapeutic interventions.

Funding

China National Science and Technology Major Projects on Infectious Diseases, National Natural Science Foundation of China, Yi-wu Institute of Fudan University, and Shanghai Municipal Health and Family Planning Commission.

Metabolomic Analysis Uncovers Energy Supply Disturbance as an Underlying Mechanism of the Development of Alcohol-Associated Liver Cirrhosis

Alcohol-associated liver disease (ALD) is caused by alcohol metabolism's effects on the liver. The underlying mechanisms from a metabolic view in the development of alcohol-associated liver cirrhosis (ALC) are still elusive. We performed an untargeted serum metabolomic analysis in 14 controls, 16 patients with ALD without cirrhosis (NC), 27 patients with compensated cirrhosis, and 79 patients with decompensated ALC. We identified two metabolic fingerprints associated with ALC development (38 metabolites) and those associated with hepatic decompensation (64 metabolites) in ALC. The cirrhosis-associated fingerprint (eigenmetabolite) showed a better capability to differentiate ALC from NC than the aspartate aminotransferase-to-platelet ratio index score. The eigenmetabolite associated with hepatic decompensation showed an increasing trend during the disease progression and was positively correlated with the Model for End-Stage Liver Disease score. These metabolic fingerprints belong to the metabolites in lipid metabolism, amino acid pathway, and intermediary metabolites in the tricarboxylic acid cycle. Conclusion: The metabolomic fingerprints suggest the disturbance of the metabolites associated with cellular energy supply as an underlying mechanism in the development and progression of alcoholic cirrhosis.

Assessing the role of amino acids in systemic inflammation and organ failure in patients with ACLF

BACKGROUND & AIMS

Systemic inflammation and organ failure(s) are the hallmarks of acute-on-chronic liver failure (ACLF), yet their pathogenesis remains uncertain. Herein, we aimed to assess the role of amino acids in these processes in patients with ACLF.

METHODS

The blood metabolomic database of the CANONIC study (comprising 137 metabolites, with 43% related to amino acids) - obtained in 181 patients with ACLF and 650 with acute decompensation without ACLF (AD) - was reanalyzed with a focus on amino acids, in particular 9 modules of co-regulated metabolites. We also compared blood metabolite levels between ACLF and AD.

RESULTS

The main findings in ACLF were: i) Metabolite modules were increased in parallel with increased levels of markers of systemic inflammation and oxidative stress. ii) Seventy percent of proteinogenic amino acids were present and most were increased. iii) A metabolic network, comprising the amino acids aspartate, glutamate, the serine-glycine one-carbon metabolism (folate cycle), and methionine cycle, was activated, suggesting increased purine and pyrimidine nucleotide synthesis. iv) Cystathionine, L-cystine, glutamate and pyroglutamate, which are involved in the transsulfuration pathway (a methionine cycle branch) were increased, consistent with increased synthesis of the antioxidant glutathione. v) Intermediates of the catabolism of 5 out of the 6 ketogenic amino acids were increased. vi) The levels of spermidine (a polyamine inducer of autophagy with anti-inflammatory effects) were decreased.

CONCLUSIONS

In ACLF, blood amino acids fueled protein and nucleotide synthesis required for the intense systemic inflammatory response. Ketogenic amino acids were extensively catabolized to produce energy substrates in peripheral organs, an effect that was insufficient because organs failed. Finally, the decrease in spermidine levels may cause a defect in autophagy contributing to the proinflammatory phenotype in ACLF.

LAY SUMMARY

Systemic inflammation and organ failures are hallmarks of acute-on-chronic liver failure (ACLF). Herein, we aimed to characterize the role of amino acids in these processes. The blood metabolome of patients with acutely decompensated cirrhosis, and particularly those with ACLF, reveals evidence of intense skeletal muscle catabolism. Importantly, amino acids (along with glucose), are used for intense anabolic, energy-consuming metabolism in patients with ACLF, presumably to support de novo nucleotide and protein synthesis in the activated innate immune system.

Blood metabolomics uncovers inflammation-associated mitochondrial dysfunction as a potential mechanism underlying ACLF

BACKGROUND & AIMS

Acute-on-chronic liver failure (ACLF), which develops in patients with cirrhosis, is characterized by intense systemic inflammation and organ failure(s). Because systemic inflammation is energetically expensive, its metabolic costs may result in organ dysfunction/failure. Therefore, we aimed to analyze the blood metabolome in patients with cirrhosis, with and without ACLF.

METHODS

We performed untargeted metabolomics using liquid chromatography coupled to high-resolution mass spectrometry in serum from 650 patients with AD (acute decompensation of cirrhosis, without ACLF), 181 with ACLF, 43 with compensated cirrhosis, and 29 healthy individuals.

RESULTS

Of the 137 annotated metabolites identified, 100 were increased in patients with ACLF of any grade, relative to those with AD, and 38 comprised a distinctive blood metabolite fingerprint for ACLF. Among patients with ACLF, the intensity of the fingerprint increased across ACLF grades, and was similar in patients with kidney failure and in those without, indicating that the fingerprint reflected not only decreased kidney excretion but also altered cell metabolism. The higher the ACLF-associated fingerprint intensity, the higher the plasma levels of inflammatory markers, tumor necrosis factor α, soluble CD206, and soluble CD163. ACLF was characterized by intense proteolysis and lipolysis; amino acid catabolism; extra-mitochondrial glucose metabolism through glycolysis, pentose phosphate, and D-glucuronate pathways; depressed mitochondrial ATP-producing fatty acid β-oxidation; and extra-mitochondrial amino acid metabolism giving rise to metabotoxins.

CONCLUSIONS

In ACLF, intense systemic inflammation is associated with blood metabolite accumulation and profound alterations in major metabolic pathways, in particular inhibition of mitochondrial energy production, which may contribute to the development of organ failures.

LAY SUMMARY

Acute-on-chronic liver failure (ACLF), which develops in patients with cirrhosis, is characterized by intense systemic inflammation and organ failure(s). Because systemic inflammation is energetically expensive, its metabolic costs may result in organ dysfunction/failure. We identified a 38-metabolite blood fingerprint specific for ACLF that revealed mitochondrial dysfunction in peripheral organs. This may contribute to organ failures.

Random Forest Analysis of Untargeted Metabolomics Data Suggests Increased Use of Omega Fatty Acid Oxidation Pathway in Drosophila Melanogaster Larvae Fed a Medium Chain Fatty Acid Rich High-Fat Diet

Obesity is a complex disease, shaped by both genetic and environmental factors such as diet. In this study, we use untargeted metabolomics and Drosophila melanogaster to model how diet and genotype shape the metabolome of obese phenotypes. We used 16 distinct outbred genotypes of Drosophila larvae raised on normal (ND) and high-fat (HFD) diets, to produce three distinct phenotypic classes; genotypes that stored more triglycerides on a ND relative to the HFD, genotypes that stored more triglycerides on a HFD relative to ND, and genotypes that showed no change in triglyceride storage on either of the two diets. Using untargeted metabolomics we characterized 350 metabolites: 270 with definitive chemical IDs and 80 that were chemically unidentified. Using random forests, we determined metabolites that were important in discriminating between the HFD and ND larvae as well as between the triglyceride phenotypic classes. We found that flies fed on a HFD showed evidence of an increased use of omega fatty acid oxidation pathway, an alternative to the more commonly used beta fatty acid oxidation pathway. Additionally, we observed no correlation between the triglyceride storage phenotype and free fatty acid levels (laurate, caprate, caprylate, caproate), indicating that the distinct metabolic profile of fatty acids in high-fat diet fed Drosophila larvae does not propagate into triglyceride storage differences. However, dipeptides did show moderate differences between the phenotypic classes. We fit Gaussian graphical models (GGMs) of the metabolic profiles for HFD and ND flies to characterize changes in metabolic network structure between the two diets, finding the HFD to have a greater number of edges indicating that metabolome varies more across samples on a HFD. Taken together, these results show that, in the context of obesity, metabolomic profiles under distinct dietary conditions may not be reliable predictors of phenotypic outcomes in a genetically diverse population.