Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases

Exploratory profiling of metabolites in cerebrospinal fluid using a commercially available targeted LC-MS based metabolomics kit to discriminate leptomeningeal metastasis

BACKGROUND

Leptomeningeal metastasis (LM) is a devastating complication of cancer that is difficult to treat. Thus, early diagnosis is essential for LM patients. However, cerebrospinal fluid (CSF) cytology has low sensitivity, and imaging approaches are ineffective. We explored targeted CSF metabolic profiling to discriminate among LM and other conditions affecting the central nervous system (CNS).

METHODS

We quantitatively measured amino acids, biogenic amines, hexoses, acylcarnitines (AC), cholesteryl esters (CE), glycerides, phosphatidylcholines (PC), lysophosphatidylcholines (LPC), sphingomyelins (SM), and ceramides (Cer) in 117 CSF samples from various groups of healthy controls (HC, n = 10), patients with LM (LM, n = 47), parenchymal brain tumor (PBT, n = 45), and inflammatory disease (ID, n = 13) with internal standards using the Absolute IDQ- p400® targeted mass spectrometry kit. Metabolites detected in > 90% of samples or showing a difference in proportional level between groups ≥ 75% were used in logistic regression models when there was no single metabolite with AUC = 1 for the groups of comparison.

RESULTS

PC and SM had higher levels in LM than in PBT or HC, whereas LPC had lower level in PBT than the other groups. Glycerides and Cer levels were higher in PBT and LM than in HC. Long-chain AC level in PBT was lower than in LM or HC. A regression model including Ala, PC (42:7), PC (30:3), PC (37:0), and Tyr achieved complete discrimination (AUC = 1.0) between LM and HC. In comparison of PBT and HC, twenty-six individual metabolites allowed complete discrimination between two groups, and between ID and HC fourty-six individual lipid metabolites allowed complete discrimination. Twenty-one individual metabolites (18 ACs and 3 PCs) allowed complete discrimination between LM and PBT.

CONCLUSIONS

Using a commercial targeted liquid chromatography-mass spectrometry (LC-MS) metabolomics kit, we were able to differentiate LM from HC and PBT. Most of the discriminative metabolites among different diseases were lipid metabolites, for which their CNS distribution and quantification in different cell types are largely unknown, whereas amino acids, biogenic amines, and hexoses failed to show significant differences. Future validation studies with larger, controlled cohorts should be performed, and hopefully, the kit may expand its metabolite coverage for unique cancer cell glucose metabolism.

CD36 in liver diseases

Cluster of differentiation 36 (CD36) is a transmembrane glycoprotein with the ability to bind to multiple ligands and perform diverse functions. Through the recognition of long-chain fatty acids, proteins containing thrombospondin structural homology repeat domains such as thrombospondin-1, and molecules with molecular structures consistent with danger- or pathogen-associated molecular patterns, CD36 participates in various physiological and pathological processes of the body. CD36 is widely expressed in various cell types, including hepatocytes and KCs in the liver, where it plays a pivotal role in lipid metabolism, inflammation, and oxidative stress. Accumulating evidence suggests that CD36 plays a complex role in the development of nonalcoholic simple fatty liver disease and NASH and contributes to the pathogenesis of inflammatory liver injury, hepatitis B/hepatitis C, liver fibrosis, and liver cancer. This review summarizes the current understanding of the structural properties, expression patterns, and functional mechanisms of CD36 in the context of liver pathophysiology. Furthermore, the potential of CD36 as a therapeutic target for the prevention and treatment of liver diseases is highlighted.

Long-term arsenic exposure decreases mice body weight and liver lipid droplets

Arsenic (As) is a widespread global pollutant, and there is significant controversy surrounding its complex relationship with obesity, primarily focused on short-term exposure. Recognizing the prolonged nature of dietary arsenic exposure, this study involved feeding mice with arsenic-contained food for 14 months. The results showed that mice exposed to arsenic developed a non-alcoholic fatty liver condition, characterized by a light-yellow hue on the liver surface and various pathological alterations in the liver cells, including enlarged nuclei, cellular necrosis, inflammatory infiltration, dysfunctional mitochondria, and endoplasmic reticulum disorganization. There were also disruptions in biochemistry indices, with a significant increase in total cholesterol (TC) level and a decrease in high-density lipoprotein (HDL) level. However, some contradictory observations occurred, such as a significant decrease in body weight, triglyceride (TG) level, and the numbers of lipid droplets. Several genes related to lipid metabolism were tested, and a model was used to explain these discrepancies. Besides, examinations of the colon revealed compromised intestinal barrier function and signs of inflammation. Fecal 16S rRNA sequencing and pseudo-targeted metabolomics revealed disruptions in internal homeostasis, such as modules, nodes, connections, and lipid-related KEGG pathways. Fecal targeted metabolomics analyses of short-chain fatty acids (SCFAs) and bile acids (BAs) demonstrated a significant upregulation in three primary bile acids (CA, CDCA, TCDCA), four secondary bile acids (TUDCA, DCA, LCA, GUDCA), and total SCFAs level. Oxidative stress and inflammation were also evident. Additionally, based on correlation analysis and mediation analysis, it was assumed that changes in the microbiota (e.g., Dubosiella) can impact the liver metabolites (e.g., TGs) through alterations in fecal metabolites (e.g., LPCs). These findings provide a theoretical reference for the long-term effect of arsenic exposure on liver lipid metabolism.

Altered Metabolites in Hepatocellular Carcinoma (HCC) Paving the Road for Metabolomics Signature and Biomarkers for Early Diagnosis of HCC

Globally, hepatocellular carcinoma (HCC) is one of the most commonly encountered cancers. Because the current early diagnostic tests for HCC are not very sensitive, most cases of the disease are discovered late when it is in its terminal stage. Cellular metabolism changes during carcinogenesis to enable cancer cells to adapt to the hypoxic milieu, boost anabolic synthesis, promote survival, and evade apoptotic death signals. Omic techniques represent a breakthrough in the field of diagnostic technology. For example, Metabolomics analysis could be used to identify these metabolite alterations. Understanding the metabolic alterations linked to HCC is crucial for improving high-risk patients' surveillance and understanding the illness's biology. This review highlights the metabolic alterations linked to energy production in cancer cells, as well as the significantly altered metabolites and pathways associated with hepatocarcinogenesis, including acylcarnitines (ACs), amino acids, proteins, lipids, carbohydrates, glucose, and lactate, which reflect the anabolic and catabolic changes occurring in these cells. Additionally, it discusses the clinical implications of recent metabolomics that may serve as potential biomarkers for early diagnosis and monitoring of the progression of HCC.

Metabolic phenotyping combined with transcriptomics metadata fortifies the diagnosis of early-stage Hepatocellular carcinoma

INTRODUCTION

The low sensitivity of alpha-fetoprotein (AFP) renders it unsuitable as a stand-alone marker for early hepatocellular carcinoma (eHCC) surveillance. Therefore, additional blood-based biomarkers with enhanced sensitivities are required.

OBJECTIVES

In light of the metabolic changes that are distinctive to eHCC development, the current study presents a panel of serum metabolites that may serve as noninvasive diagnostic indicators for patients with eHCC.

METHODS

Serum samples obtained from normal control (NC), cirrhosis, and eHCC patients were analyzed by four different metabolomic platforms. A meta-analysis of very early-stage HCC transcriptomic datasets retrieved from public sources supports the integrated interpretation with metabolic changes.

RESULTS

A total of 94 metabolites were significantly correlated with a progressive disease status. Integrated analysis of the significant metabolites and differentially expressed genes from meta-analysis emphasized metabolic pathways including bile acid biosynthesis, phenylalanine and tyrosine metabolism, and butanoate metabolism. The 11 metabolites associated with these pathways were compiled into a metabolite panel for use as diagnostic signatures. With an accuracy of 81.8%, compared with 45.4% for a model trained solely on AFP, the model enhanced its ability to differentiate between the three groups by incorporating a metabolite panel and AFP. Upon examining the trained models using receiver operating characteristic curves, the AFP and metabolite panel combined model exhibited greater area under the curve values in comparisons between NC and eHCC (1.000 versus 0.810) and cirrhosis and eHCC (0.926 versus 0.556). The result was consistent in an independent validation cohort.

CONCLUSION

This study emphasizes the role of circulating metabolite markers in the diagnosis of eHCC.

Overview of Bovine Mastitis: Application of Metabolomics in Screening Its Predictive and Diagnostic Biomarkers

Bovine mastitis is an inflammatory disease of the mammary glands, and its pathogenesis and diagnosis are complicated. Through qualitative and quantitative analysis of small-molecule metabolites, the metabolomics technique plays an important role in finding biomarkers and studying the metabolic mechanism of bovine mastitis. Therefore, this paper reviews the predictive and diagnostic biomarkers of bovine mastitis that have been identified using metabolomics techniques and that are present in samples such as milk, blood, urine, rumen fluid, feces, and mammary tissue. In addition, the metabolic pathways of mastitis-related biomarkers in milk and blood were analyzed; it was found that the tricarboxylic acid (TCA) cycle was the most significant (FDR = 0.0015767) pathway in milk fluid, and glyoxylate and dicarboxylate metabolism was the most significant (FDR = 0.0081994) pathway in blood. The purpose of this review is to provide useful information for the prediction and early diagnosis of bovine mastitis.

Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

Metabolomic Analysis of Human Cirrhosis and Hepatocellular Carcinoma: A Pilot Study

BACKGROUND

Molecular changes in HCC development are largely unknown. As the liver plays a fundamental role in the body's metabolism, metabolic changes are to be expected.

AIMS

We aimed to identify metabolomic changes in HCC in comparison to liver cirrhosis (LC) patients, which could potentially serve as novel biomarkers for HCC diagnosis and prognosis.

METHODS

Metabolite expression from 38 HCC from the SORAMIC trial and 32 LC patients were analyzed by mass spectrometry. Metabolites with significant differences between LC and HCC at baseline were analyzed regarding expression over follow-up. In addition, association with overall survival was tested using univariate Cox proportional-hazard analysis.

RESULTS

41 metabolites showed differential expression between LC and HCC patients. 14 metabolites demonstrated significant changes in HCC patients during follow-up. Campesterol, lysophosphatidylcholine, octadecenoic and octadecadienoic acid, and furoylglycine showed a differential expression in the local ablation vs. palliative care group. High expression of eight metabolites (octadecenoic acid, 2-hydroxybutyrate, myo-inositol, isocitrate, erythronic acid, creatinine, pseudouridine, and erythrol) were associated with poor overall survival. The association between poor OS and octadecenoic acid and creatinine remained statistically significant even after adjusting for tumor burden and LC severity.

CONCLUSION

Our findings give promising insides into the metabolic changes during HCC carcinogenesis and provide candidate biomarkers for future studies. Campesterol and furoylglycine in particular were identified as possible biomarkers for HCC progression. Moreover, eight metabolites were detected as predictors for poor overall survival.

Alterations in serum metabolic profiles of early-stage hepatocellular carcinoma patients after radiofrequency ablation therapy

OBJECTIVE

To investigate the alterations in serum metabolic profiles and early-stage hepatocellular carcinoma (HCC) patient characteristics after radiofrequency ablation (RFA) therapy. This evaluation aimed to assess treatment effectiveness and identify potential novel approaches and targets for HCC treatment and prognosis monitoring.

METHODS

Untargeted metabolomics technology was employed to analyze serum metabolic profiles in healthy volunteer controls (NCs) and early stage HCC patients before and after RFA therapy. Additionally, Human Metabolome Database and Kyoto Encyclopedia of Genes and Genomes database were used to identify the differential metabolites (DMs) and metabolic pathways. Cystoscape was utilized to construct DM gene networks. Amino acid analyses were performed to validate our findings.

RESULTS

We identified 11, 14, and six DMs between the NC and HCC groups, HCC patients before and after RFA therapy, and post-RFA HCC and NC groups, respectively. The expression levels of these DMs, particularly those of amino acids and lipids, significantly changed. Compared with the NC group, higher levels of L-tyrosine, aspartate, and 18-oxo-oleate were observed in HCC patients, which were significantly reduced in patients after RFA therapy. Meanwhile, HCC patients after RFA therapy had increased levels of L-arginine, phosphatidic acid (20:3), and lysophosphatidyl choline (LPC) (20:4) compared to those before therapy, while their levels before therapy were lower than those of NC. Moreover, most metabolites in the post-RFA and NC groups showed no significant changes in expression, except for L-tyrosine and LPC (16:0). These metabolites could potentially serve as characteristic factors of early-stage HCC patients after RFA therapy. Joint pathway analysis revealed striking changes, mainly in phenylalanine, tyrosine, and tryptophan biosynthesis; alanine, aspartate, and glutamate metabolism; and arginine and aminoacyl-tRNA biosynthesis. Bioinformatics analysis of publicly available data preliminarily identified 187 DM-related metabolic enzymes.

CONCLUSION

Our study proposed novel targets for early-stage HCC treatment, laying the groundwork for improving treatment efficacy and prognosis of early-stage HCC patients.

Circulating fatty acids and risk of hepatocellular carcinoma and chronic liver disease mortality in the UK Biobank

Nuclear magnetic resonance (NMR)-based plasma fatty acids are objective biomarkers of many diseases. Herein, we aim to explore the associations of NMR-based plasma fatty acids with the risk of hepatocellular carcinoma (HCC) and chronic liver disease (CLD) mortality in 252,398 UK Biobank participants. Here we show plasma levels of n-3 poly-unsaturated fatty acids (PUFA) and n-6 PUFA are negatively associated with the risk of incident HCC [HRQ4vsQ1: 0.48 (95% CI: 0.33-0.69) and 0.48 (95% CI: 0.28-0.81), respectively] and CLD mortality [HRQ4vsQ1: 0.21 (95% CI: 0.13-0.33) and 0.15 (95% CI: 0.08-0.30), respectively], whereas plasma levels of saturated fatty acids are positively associated with these outcomes [HRQ4vsQ1: 3.55 (95% CI: 2.25-5.61) for HCC and 6.34 (95% CI: 3.68-10.92) for CLD mortality]. Furthermore, fibrosis stage significantly modifies the associations between PUFA and CLD mortality. This study contributes to the limited prospective evidence on the associations between plasma-specific fatty acids and end-stage liver outcomes.

Predicting the risk of subclinical atherosclerosis based on interpretable machine models in a Chinese T2DM population

Background

Cardiovascular disease (CVD) has emerged as a global public health concern. Identifying and preventing subclinical atherosclerosis (SCAS), an early indicator of CVD, is critical for improving cardiovascular outcomes. This study aimed to construct interpretable machine learning models for predicting SCAS risk in type 2 diabetes mellitus (T2DM) patients.

Methods

This study included 3084 T2DM individuals who received health care at Zhenhai Lianhua Hospital, Ningbo, China, from January 2018 to December 2022. The least absolute shrinkage and selection operator combined with random forest-recursive feature elimination were used to screen for characteristic variables. Linear discriminant analysis, logistic regression, Naive Bayes, random forest, support vector machine, and extreme gradient boosting were employed in constructing risk prediction models for SCAS in T2DM patients. The area under the receiver operating characteristic curve (AUC) was employed to assess the predictive capacity of the model through 10-fold cross-validation. Additionally, the SHapley Additive exPlanations were utilized to interpret the best-performing model.

Results

The percentage of SCAS was 38.46% (n=1186) in the study population. Fourteen variables, including age, white blood cell count, and basophil count, were identified as independent risk factors for SCAS. Nine predictors, including age, albumin, and total protein, were screened for the construction of risk prediction models. After validation, the random forest model exhibited the best clinical predictive value in the training set with an AUC of 0.729 (95% CI: 0.709-0.749), and it also demonstrated good predictive value in the internal validation set [AUC: 0.715 (95% CI: 0.688-0.742)]. The model interpretation revealed that age, albumin, total protein, total cholesterol, and serum creatinine were the top five variables contributing to the prediction model.

Conclusion

The construction of SCAS risk models based on the Chinese T2DM population contributes to its early prevention and intervention, which would reduce the incidence of adverse cardiovascular prognostic events.

Rising Lysophosphatidylcholine Levels Post-Hepatitis C Clearance

Hepatitis C virus (HCV) infection alters lysophosphatidylcholine (LPC) metabolism, enhancing viral infectivity and replication. Direct-acting antivirals (DAAs) effectively treat HCV and rapidly normalize serum cholesterol. In serum, LPC species are primarily albumin-bound but are also present in lipoprotein particles. This study aims to assess the impact of HCV eradication on serum LPC species levels in patients infected with HCV. Therefore, 12 different LPC species were measured by electrospray ionization tandem mass spectrometry (ESI-MS/MS) in the sera of 178 patients with chronic HCV infections at baseline, and in 176 of these patients after therapy with DAAs. All LPC species increased at 4 and 12 weeks post-initiation of DAA therapy. The serum profiles of the LPC species were similar before and after the viral cure. Patients with HCV and liver cirrhosis exhibited lower serum levels of all LPC species, except LPC 16:1, both before and after DAA treatment. Percentages of LPC 18:1 (relative to the total LPC level) were higher, and % LPC 22:5 and 22:6 were lower in cirrhotic compared to non-cirrhotic patients at baseline and at the end of therapy. LPC species levels inversely correlated with the model of end-stage liver disease score and directly with baseline and post-therapy albumin levels. Receiver operating characteristic curve analysis indicated an area under the curve of 0.773 and 0.720 for % LPC 18:1 (relative to total LPC levels) for classifying fibrosis at baseline and post-therapy, respectively. In summary, HCV elimination was found to increase all LPC species and elevated LPC 18:1 relative to total LPC levels may have pathological significance in HCV-related liver cirrhosis.

SREBP Regulation of Lipid Metabolism in Liver Disease, and Therapeutic Strategies

Sterol regulatory element-binding proteins (SREBPs) are master transcription factors that play a crucial role in regulating genes involved in the biogenesis of cholesterol, fatty acids, and triglycerides. As such, they are implicated in several serious liver diseases, including non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), fibrosis, and hepatocellular carcinoma (HCC). SREBPs are subject to regulation by multiple cofactors and critical signaling pathways, making them an important target for therapeutic interventions. In this review, we first introduce the structure and activation of SREBPs, before focusing on their function in liver disease. We examine the mechanisms by which SREBPs regulate lipogenesis, explore how alterations in these processes are associated with liver disease, and evaluate potential therapeutic strategies using small molecules, natural products, or herb extracts that target these pathways. Through this analysis, we provide new insights into the versatility and multitargets of SREBPs as factors in the modulation of different physiological stages of liver disease, highlighting their potential targets for therapeutic treatment.

MALDI-MSI of lipids in a model of breast cancer brain metastasis provides a surrogate measure of ischemia/hypoxia

Breast cancer brain metastasis (BCBM) has an incidence of 10-30%. It is incurable and the biological mechanisms that promote its progression remain largely undefined. Consequently, to gain insights into BCBM processes, we have developed a spontaneous mouse model of BCBM and in this study found a 20% penetrance of macro-metastatic brain lesion formation. Considering that lipid metabolism is indispensable to metastatic progression, our goal was the mapping of lipid distributions throughout the metastatic regions of the brain. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) of lipids revealed that, relative to surrounding brain tissue, seven long-chain (13-21 carbons long) fatty acylcarnitines, as well as two phosphatidylcholines, two phosphatidylinositols two diacylglycerols, a long-chain phosphatidylethanolamine, and a long-chain sphingomyelin were highly concentrated in the metastatic brain lesion In broad terms, lipids known to be enriched in brain tissues, such as very long-chain (≥ 22 carbons in length) polyunsaturated fatty acid of phosphatidylcholines, phosphatidylethanolamine, sphingomyelins, sulfatides, phosphatidylinositol phosphates, and galactosylceramides, were not found or only found in trace amounts in the metastatic lesion and instead consistently detected in surrounding brain tissues. The data, from this mouse model, highlights an accumulation of fatty acylcarnitines as possible biological makers of a chaotic inefficient vasculature within the metastasis, resulting in relatively inadequate blood flow and disruption of fatty acid β-oxidation due to ischemia/hypoxia.

Molecular and Metabolic Phenotyping of Hepatocellular Carcinoma for Biomarker Discovery: A Meta-Analysis

Identifying and translating hepatocellular carcinoma (HCC) biomarkers from bench to bedside using mass spectrometry-based metabolomics and lipidomics is hampered by inconsistent findings. Here, we investigated HCC at systemic and metabolism-centric multiomics levels by conducting a meta-analysis of quantitative evidence from 68 cohorts. Blood transcript biomarkers linked to the HCC metabolic phenotype were externally validated and prioritized. In the studies under investigation, about 600 metabolites were reported as putative HCC-associated biomarkers; 39, 20, and 10 metabolites and 52, 12, and 12 lipids were reported in three or more studies in HCC vs. Control, HCC vs. liver cirrhosis (LC), and LC vs. Control groups, respectively. Amino acids, fatty acids (increased 18:1), bile acids, and lysophosphatidylcholine were the most frequently reported biomarkers in HCC. BAX and RAC1 showed a good correlation and were associated with poor prognosis. Our study proposes robust HCC biomarkers across diverse cohorts using a data-driven knowledge-based approach that is versatile and affordable for studying other diseases.

Metabolomics acts as a powerful tool for comprehensively evaluating vaccines approved under emergency: a CoronaVac retrospective study

Introduction

To control the COVID-19 pandemic, great efforts have been made to realize herd immunity by vaccination since 2020. Unfortunately, most of the vaccines against COVID-19 were approved in emergency without a full-cycle and comprehensive evaluation process as recommended to the previous vaccines. Metabolome has a close tie with the phenotype and can sensitively reflect the responses to stimuli, rendering metabolomic analysis have the potential to appraise and monitor vaccine effects authentically.

Methods

In this study, a retrospective study was carried out for 330 Chinese volunteers receiving recommended two-dose CoronaVac, a vaccine approved in emergency in 2020. Venous blood was sampled before and after vaccination at 5 separate time points for all the recipients. Routine clinical laboratory analysis, metabolomic and lipidomic analysis data were collected.

Results and discussion

It was found that the serum antibody-positive rate of this population was around 81.82%. Most of the laboratory parameters were slightly perturbated within the relevant reference intervals after vaccination. The metabolomic and lipidomic analyses showed that the metabolic shift after inoculation was mainly in the glycolysis, tricarboxylic acid cycle, amino acid metabolism, urea cycle, as well as microbe-related metabolism (bile acid metabolism, tryptophan metabolism and phenylalanine metabolism). Time-course metabolome changes were found in parallel with the progress of immunity establishment and peripheral immune cell counting fluctuation, proving metabolomics analysis was an applicable solution to evaluate immune effects complementary to traditional antibody detection. Taurocholic acid, lysophosphatidylcholine 16:0 sn-1, glutamic acid, and phenylalanine were defined as valuable metabolite markers to indicate the establishment of immunity after vaccination. Integrated with the traditional laboratory analysis, this study provided a feasible metabolomics-based solution to relatively comprehensively evaluate vaccines approved under emergency.

Reprogramming of lipid metabolism in hepatocellular carcinoma resulting in downregulation of phosphatidylcholines used as potential markers for diagnosis and prediction

BACKGROUND

Aberrant methylation and metabolic perturbations may deepen our understanding of hepatocarcinogenesis and help identify novel biomarkers for diagnosing hepatocellular carcinoma (HCC). We aimed to develop an HCC model based on a multi-omics.

RESEARCH DESIGN AND METHODS

Four hundred patient samples (200 with HCC and 200 with hepatitis B virus-related liver disease (HBVLD)) were subjected to liquid chromatography-mass spectrometry and multiplex bisulfite sequencing. Integrative analysis of clinical data, CpG data, and metabolome for the 20 complete imputation datasets within a for-loopwas used to identify biomarker.

RESULTS

Totally, 1,140 metabolites were annotated, of which 125 were differentially expressed. Lipid metabolism reprogramming in HCC, resulting in phosphatidylcholines (PC) significantly downregulated, partly due to the altered mitochondrial beta-oxidation of fatty acids with diverse chain lengths. Age, sex, serum-fetoprotein levels, cg05166871,cg14171514, cg18772205, PC (O-16:0/20:3(8Z, 11Z, 14Z)), and PC (16:1(9Z)/P-18:0) were used to develop the HCC model. The model presented a good diagnostic and an acceptable predictive performance. The cumulative incidence of HCC in low- and high-risk groups of HBVLD patients were 1.19% and 21.40%, respectively (p = 0.0039).

CONCLUSIONS

PCs serve as potential plasma biomarkers and help identify patients with HBVLD at risk of HCC who should be screened for early diagnosis and intervention.

LC/MS-Based Untargeted Metabolomics Study in Women with Nonalcoholic Steatohepatitis Associated with Morbid Obesity

This study investigated the importance of a metabolomic analysis in a complex disease such as nonalcoholic steatohepatitis (NASH) associated with obesity. Using an untargeted metabolomics technique, we studied blood metabolites in 216 morbidly obese women with liver histological diagnosis. A total of 172 patients were diagnosed with nonalcoholic fatty liver disease (NAFLD), and 44 were diagnosed with normal liver (NL). Patients with NAFLD were classified into simple steatosis (n = 66) and NASH (n = 106) categories. A comparative analysis of metabolites levels between NASH and NL demonstrated significant differences in lipid metabolites and derivatives, mainly from the phospholipid group. In NASH, there were increased levels of several phosphatidylinositols and phosphatidylethanolamines, as well as isolated metabolites such as diacylglycerol 34:1, lyso-phosphatidylethanolamine 20:3 and sphingomyelin 38:1. By contrast, there were decreased levels of acylcarnitines, sphingomyelins and linoleic acid. These findings may facilitate identification studies of the main pathogenic metabolic pathways related to NASH and may also have a possible applicability in a panel of metabolites to be used as biomarkers in future algorithms of the disease diagnosis and its follow-up. Further confirmatory studies in groups with different ages and sexes are necessary.

Coordinated response of milk bacterial and metabolic profiles to subacute ruminal acidosis in lactating dairy cows

BACKGROUND

Bovine milk is an important source of nutrition for human consumption, and its quality is closely associated with the microbiota and metabolites in it. But there is limited knowledge about the milk microbiome and metabolome in cows with subacute ruminal acidosis.

METHODS

Eight ruminally cannulated Holstein cows in mid lactation were selected for a 3-week experiment. The cows were randomly allocated into 2 groups, fed either a conventional diet (CON; 40% concentrate; dry matter basis) or a high-concentrate diet (HC; 60% concentrate; dry matter basis).

RESULTS

The results showed that there was a decreased milk fat percentage in the HC group compared to the CON group. The amplicon sequencing results indicated that the alpha diversity indices were not affected by the HC feeding. At the phylum level, the milk bacteria were dominated by Proteobacteria, Actinobacteria, Bacteroidetes, and Firmicutes both in the CON and HC groups. At the genus level, the HC cows displayed an improved proportion of Labrys (P = 0.015) compared with the CON cows. Results of both the principal components analysis and partial least squares of discriminant analysis of milk metabolome revealed that samples of the CON and HC groups clustered separately. A total of 31 differential metabolites were identified between the two groups. Of these, the levels of 11 metabolites decreased (α-linolenic acid, prostaglandin E2, L-lactic acid, L-malic acid, 3-hydroxysebacic acid, succinyladenosine, guanosine, pyridoxal, L-glutamic acid, hippuric acid, and trigonelline), whereas the levels of the other 20 metabolites increased in the HC group with respect to the CON group (P < 0.05).

CONCLUSION

These results suggested that subacute ruminal acidosis less impacted the diversity and composition of milk microbiota, but altered the milk metabolic profiles, which led to the decline of the milk quality.

Human serum lipidomics analysis revealed glyphosate may lead to lipid metabolism disorders and health risks

Glyphosate-based herbicides (GBH) are one of the most widely used pesticides worldwide. Industrial workers in glyphosate-based herbicides manufacture are the populations who experience long-term exposure to high glyphosate levels. The impacts of glyphosate on human health are the important public health problem of great concern. Up to date, the potential adverse effects of glyphosate on humans or other mammals have been reported in multiple studies. However, limited research is available on lipid alternations related to human exposure to glyphosate. In fact, the perturbations in some lipid metabolisms have been found in industrial workers in previous work. This study aims to explore the serum lipidomic characterization and to understand the underlying mechanisms of health risks associated with glyphosate exposure. A nontargeted lipidomics study was conducted to investigate the 391 serum samples from the general population and chemical factory workers. It was demonstrated that glyphosate caused significant perturbations of 115 differentially expressed lipids. The main manifestations were the elevation of circulating diacylglycerols (DG), cholesteryl esters (CE), ceramides (Cer), sphingomyelins (SM), lysophosphatidylethanolamines (LPE) and phosphatidylcholines (PC), and the decrease of ysophosphatidylcholines (LPC), triacylglycerols (TG), fatty acids (FA) and phosphatidylethanolamines (PE). A total of 88 lipids were further screened as potential lipid biomarkers associated closely with glyphosate using partial correlation analysis, and five of which (including PC 16:0/18:2; O, PC 18:0/18:2; O, PC 18:0/20:4; O, PC O-40:9 and CE 18:3) showed excellent superior performance (AUC = 1) to evaluate and monitor health risks due to glyphosate exposure. The present work discovered glyphosate-induced potential health risks, including chronic hepatic and renal dysfunction, atherosclerosis, cardiovascular disease and neurodegenerative diseases from a lipidomic perspective, and could inform the identification of early indicators and interpretation of biological mechanisms to detect health risks of the glyphosate-exposed populations as early as possible.

Optimized Systematic Review Tool: Application to Candidate Biomarkers for the Diagnosis of Hepatocellular Carcinoma

This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analyzed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, precirrhotic liver disease, cirrhosis) were eligible. Tumor tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke "risk of bias" tool for metabolomic studies was developed adjusting for analytic quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine, and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines, and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardization of reporting of metabolomic data. Validation studies, standardized study designs, and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.

Metabolic Profiling Analysis of the Effect and Mechanism of Gushiling Capsule in Rabbits With Glucocorticoid-Induced Osteonecrosis of the Femoral Head

Gushiling capsule (GSLC) is an effective traditional Chinese medicine for the treatment of glucocorticoid-induced osteonecrosis of the femoral head (GIONFH). This study established the serum metabolite profiles of GSLC in rabbits and explored the metabolic mechanism and effect of GSLC on GIONFH. Seventy-five Japanese white rabbits were randomly divided into the control, model, and GSLC groups. The rabbits in the model group and the GSLC group received injection of prednisolone acetate. Meanwhile, rabbits in the GSLC group were treated by gavage at a therapeutic dose of GSLC once a day. The control group and the model group received the same volume of normal saline gavage. Three groups of serum samples were collected at different time points, and the changes in the metabolic spectrum were analyzed by ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The resulting data set was analyzed using multivariate statistical analysis to identify potential biomarkers related to GSLC treatment. The metabolic pathway was analyzed by MetaboAnalyst 4.0 and a heatmap was constructed using the HEML1.0.3.7 software package. In addition, histopathological and radiography studies were carried out to verify the anti-GIONFH effects of GSLC. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) score plots revealed a significant separation trend between the control group and the model group and the GSLC group (1-3 weeks), but there were no significant differences in the GSLC group (4-6 weeks). Orthogonal PLS-DA (OPLS-DA) score plots also revealed an obvious difference between the model and the GSLC groups (4-6 weeks). Ten potential metabolite biomarkers, mainly phospholipids, were identified in rabbit serum samples and demonstrated to be associated with GIONFH. Hematoxylin and eosin staining and magnetic resonance imaging indicated that the pathological changes in femoral head necrosis in the GSLC group were less than in the model group, which was consistent with the improved serum metabolite spectrum. GSLC regulated the metabolic disorder of endogenous lipid components in GIONFH rabbits. GSLC may prevent and treat GIONFH mainly by regulating phospholipid metabolism in vivo.

Lipid alterations in chronic liver disease and liver cancer

Lipids are a complex and diverse group of molecules with crucial roles in many physiological processes, as well as in the onset, progression, and maintenance of cancers. Fatty acids and cholesterol are the building blocks of lipids, orchestrating these crucial metabolic processes. In the liver, lipid alterations are prevalent as a cause and consequence of chronic hepatitis B and C virus infections, alcoholic hepatitis, and non-alcoholic fatty liver disease and steatohepatitis. Recent developments in lipidomics have also revealed that dynamic changes in triacylglycerols, phospholipids, sphingolipids, ceramides, fatty acids, and cholesterol are involved in the development and progression of primary liver cancer. Accordingly, the transcriptional landscape of lipid metabolism suggests a carcinogenic role of increasing fatty acids and sterol synthesis. However, limited mechanistic insights into the complex nature of the hepatic lipidome have so far hindered the development of effective therapies.

Impact of Exogenous Treatment with Histidine on Hepatocellular Carcinoma Cells

Simple Summary

Sorafenib (Nexavar@) is the only currently approved anti-cancer drug for patients with advanced hepatocellular carcinoma (HCC). However, despite the development of strategies combining sorafenib with other cytotoxic chemotherapeutic agents to overcome sorafenib resistance, clinical trial results are still disappointing. In this study, we examined the enhancement of tumor responses to sorafenib by exogenous histidine treatment.

Abstract

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Sorafenib, a multi-kinase inhibitor, is the first-line therapy for advanced HCC. However, long-term exposure to sorafenib often results in reduced sensitivity and the development of resistance. Although various amino acids have been shown to contribute to cancer initiation and progression, little is known about the effects of histidine, a dietary essential amino acid that is partially taken up via histidine/large neutral amino acid transporter (LAT1), on cancer cells. In this study, we evaluated the effects of histidine on HCC cells and sensitivity to sorafenib. Remarkably, we found that exogenous histidine treatment induced a reduction in the expression of tumor markers related to glycolysis (GLUT1 and HK2), inflammation (STAT3), angiogenesis (VEGFB and VEGFC), and stem cells (CD133). In addition, LAT1 expression was downregulated in HCC tumor regions with high expression of GLUT1, CD133, and pSTAT3, which are known to induce sorafenib resistance. Finally, we demonstrated that combined treatment with sorafenib and histidine could be a novel therapeutic strategy to enhance the sensitivity to sorafenib, thereby improving long-term survival in HCC.

Metabolomic biomarkers for hepatocellular carcinoma: A systematic review

Hepatocellular carcinoma (HCC) is a highly malignant cancer which lack of effective diagnosis and prognosis biomarkers, therefore surging studies focused on the metabolite candidates for HCC. The current study was designed to systematically review the metabolic studies for HCC, summarize the current available evidence and provide implication for further studies within this area. By systematically screening Pubmed and Embase, and eligibility assessment, we eventually included 55 pieces of studies. After summarized their characteristics, we reviewed them by 3 parts, regarding to the different biofluid they carried out the experiments. By collecting the candidates from all the included studies, we carried out pathway enrichment to see the representative of the reported candidates, as expected the pathway consistent with the current knowledge of HCC. Next, we conduct quality assessment on the included studies. Only 36% of the current evidence grouped as high quality, indicating the quality of metabolic studies needs further improvement.

Study on the Hepatoprotection of Schisandra chinensis Caulis Polysaccharides in Nonalcoholic Fatty Liver Disease in Rats Based on Metabolomics

The aim of this study was to investigate the hepatoprotection of Schisandra chinensis Caulis polysaccharides (SCPs) in the nonalcoholic fatty liver disease (NAFLD) induced by high-fat diet (HFD) in rats. A total of 30 Wistar rats were randomly divided into the control group (CON), model group (MOD), and Schisandra chinensis caulis polysaccharide (SCP) group. Except for those in the CON group, the other rats were fed with high-fat diet for 4 weeks to establish an NAFLD model. From the 5th week, rats in the SCP group were given SCP solution (100 mg kg^-1) by gavage for 6 weeks, and those in the CON and MOD groups were given an equal volume of distilled water in the same way. Aspartate aminotransferase (AST), alanine aminotransferase (ALT), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) levels in serum, the malondialdehyde (MDA) level, glutathione peroxidase (GSH-Px), and superoxide dismutase (SOD) activities in the liver tissue were detected. The small molecular metabolites in the blood of rats were determined by the metabolomics method of ultra-high-performance liquid chromatography-quadrupole/electrostatic field orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap-MS/MS) combined with multivariate analysis. The enrichment analysis and pathway analysis of the different metabolites were carried out. The therapeutic mechanism of SCP in NAFLD rats was verified by western blot. The results showed that the levels of AST, ALT, TG, TC, and LDL-C in the serum of rats in the SCP group were significantly lower, and the levels of HDL-C were significantly higher than those in the MOD group. The screening and analysis of the metabolic pathways showed that SCP could alleviate the development of NAFLD by regulating the expression of UDP-glucose pyrophosphorylase (UGP2), UDP-glucose 6-dehydrogenase (UGDH), acetyl CoA carboxylase (ACC), and fatty acid synthase (FAS) in the liver of NAFLD rats. This study may provide a theoretical basis for the development and utilization of SCP.

Lipidomics of children and adolescents exposed to multiple industrial pollutants

BACKGROUND

There are limited studies on the lipidomics of children and adolescents exposed to multiple industrial pollutants.

OBJECTIVES

In this study, we aimed to investigate lipid profile perturbations in 99 children and adolescents (aged 9-15) who lived in a polluted area surrounding the largest petrochemical complex in Taiwan. Previous studies have reported increased risks of acute and chronic diseases including liver dysfunctions and chronic kidney disease (CKD) in residents living in this area.

METHODS

We measured urinary concentrations of 11 metals and metalloids and polycyclic aromatic hydrocarbons (PAHs) metabolite 1-hydroxypyrene (1-OHP) as exposure biomarkers, and urinary oxidative stress biomarkers and serum acylcarnitines as early health effect biomarkers. The association between individual exposure biomarkers and early health effect biomarkers were analyzed using linear regression, while association of combined exposure biomarkers with four oxidative stress biomarkers and acylcarnitines were analyzed using weighted quantile sum (WQS) regression. Lipid profiles were analyzed using an untargeted liquid chromatography mass spectrometry-based technique. "Meet-in-the-middle" approach was applied to identify potential lipid features that linked multiple industrial pollutants exposure with early health effects.

RESULTS

We identified 15 potential lipid features that linked elevated multiple industrial pollutants exposure with three increased oxidative stress biomarkers and eight deregulated serum acylcarnitines, including one lysophosphatidylcholines (LPCs), four phosphatidylcholines (PCs), and two sphingomyelins (SMs) that were up-regulated in high exposure group compared to low exposure group, and two LPCs, four PCs, and two phosphatidylinositols (PIs) down-regulated in high exposure group compared to low exposure group.

CONCLUSION

Our findings could provide information for understanding the health effects, including early indicators and biological mechanism identification, of children and adolescents exposed to multiple industrial pollutants during critical stages of development.

Characterization of metabolic landscape in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide, accounting for approximately 75%-85% of primary liver cancers. Metabolic alterations have been labeled as an emerging hallmark of tumors. Specially, the last decades have registered a significant improvement in our understanding of the role of metabolism in driving the carcinogenesis and progression of HCC. In this paper, we provide a review of recent studies that investigated the metabolic traits of HCC with a specific focus on three common metabolic alterations involving glycolysis, lipid metabolism, and glutamine addiction which have been gaining much attention in the field of HCC. Next, we describe some representative diagnostic markers or tools, and promising treatment agents that are proposed on the basis of the aforementioned metabolic alterations for HCC. Finally, we present some challenges and directions that may promisingly speed up the process of developing objective diagnostic markers and therapeutic options underlying HCC. Specifically, we recommend future investigations to carefully take into account the influence of heterogeneity, control for study-specific confounds, and invite the validation of existing biomarkers.

Facile Synthesis of Antibody-Coupled Polydopamine-Coated Magnetic Graphene Oxide Composites for Efficient Immunopurification and Metabolomics Analysis of Mitochondria

As a vital hub, a mitochondrion houses metabolic pathways that play important roles in cellular physiology. Aberrant metabolites occurring in mitochondria are closely associated with the emergence and progression of various mitochondria-related diseases. Therefore, a simple and versatile approach to efficiently purify intact mitochondria is urgently needed to precisely and comprehensively characterize the composition and abundance of the mitochondrial metabolome in different physiological and pathological states. In this work, novel immunoaffinitive magnetic composites MagG@PD@Avidin@TOM20 were prepared to achieve highly selective isolation of intact mitochondria from three different hepatocytes (LO2, HepG2, and Huh7). The prepared composites inherit combined merits, including strong magnetic responsiveness, excellent stability, and specific and high affinity between antibody TOM20 and mitochondrial outer membrane protein. These mitochondria attached on MagG@PD@Avidin@TOM20 were characterized by the western blot and fluorescence microscopy to confirm their purity and integrity, which are vital for reliable mitochondrial metabolic analysis. Subsequently, ultrahigh-performance liquid chromatography-high-resolution mass spectrometry-based untargeted metabolomics analysis was conducted to characterize the metabolomes in the immunopurified mitochondria and whole cells. Notably, the metabolite profiles of whole cells and mitochondria including itaconic acid, acetylcarnitine, malic acid, etc., were significantly different. These data underscore the importance of determining metabolites at the mitochondrial level, which would supplement us new knowledge at the subcellular level.

An Untargeted Metabolomics Investigation of Milk from Dairy Cows with Clinical Mastitis by 1H-NMR

Mastitis is one of the diseases with the highest incidence in dairy cows, causing huge economic losses to the dairy industry all over the world. The aim of the study was to characterize mastitic milk metabolome through untargeted nuclear magnetic resonance spectroscopy (¹H-NMR). Taking advantage of the high reproducibility of ¹H-NMR, we had the opportunity to provide quantitative information for all the metabolites identified. Fifty-four molecules were characterized, sorted mainly into the chemical groups, namely amino acids, peptides and analogues, carbohydrates and derivates, organic acids and derivates, nucleosides, nucleotides and analogues. Combined with serum metabolomic investigations, several pathways were addressed to explain the mechanisms of milk metabolome variation affected by clinical mastitis, such as tricarboxylic acid cycle (TCA cycle) and phenylalanine, tyrosine and tryptophan biosynthesis. These results provide a further understanding of milk metabolome altered by clinical mastitis, which can be used as a reference for the further milk metabolome investigations.

Pharmacological or genetic inhibition of iNOS prevents cachexia-mediated muscle wasting and its associated metabolism defects

Cachexia syndrome develops in patients with diseases such as cancer and sepsis and is characterized by progressive muscle wasting. While iNOS is one of the main effectors of cachexia, its mechanism of action and whether it could be targeted for therapy remains unexplored. Here, we show that iNOS knockout mice and mice treated with the clinically tested iNOS inhibitor GW274150 are protected against muscle wasting in models of both septic and cancer cachexia. We demonstrate that iNOS triggers muscle wasting by disrupting mitochondrial content, morphology, and energy production processes such as the TCA cycle and acylcarnitine transport. Notably, iNOS inhibits oxidative phosphorylation through impairment of complexes II and IV of the electron transport chain and reduces ATP production, leading to energetic stress, activation of AMPK, suppression of mTOR, and, ultimately, muscle atrophy. Importantly, all these effects were reversed by GW274150. Therefore, our data establish how iNOS induces muscle wasting under cachectic conditions and provide a proof of principle for the repurposing of iNOS inhibitors, such as GW274150 for the treatment of cachexia.

The Role of Fatty Acids in Non-Alcoholic Fatty Liver Disease Progression: An Update

Non-alcoholic fatty liver disease (NAFLD) is a major public health problem worldwide. NAFLD (both simple steatosis and steatohepatitis) is characterized by alterations in hepatic lipid metabolism, which may lead to the development of severe liver complications including cirrhosis and hepatocellular carcinoma. Thus, an exhaustive examination of lipid disorders in the liver of NAFLD patients is much needed. Mass spectrometry-based lipidomics platforms allow for in-depth analysis of lipid alterations in a number of human diseases, including NAFLD. This review summarizes the current research on lipid alterations associated with NAFLD and related complications, with special emphasis on the changes in long-chain and short-chain fatty acids levels in both serum and liver tissue, as well as in the hepatic expression of genes encoding the enzymes catalyzing lipid interconversions.

Trend of 25-hydroxycholesterol and 27-hydroxycholesterol plasma levels in patients affected by active chronic hepatitis B virus infection and inactive carriers

Hepatitis B virus (HBV) infection is a global health problem with different immunological phases and therapeutic approaches. The serological condition of inactive carrier (IC) was recently well defined as a clinical and virological stable status, in which specific treatment is usually deferred, while the active chronic hepatitis B (CHB) condition requires an immediate treatment strategy. Recently, a possible broad antiviral effect of oxysterols, in particular 25-hydroxycholesterol (25OHC) and 27-hydroxycholesterol (27OHC), was observed, as most likely linked to the positive modulation of innate immunity, but no clear evidence is available about their possible role in chronic HBV infection. Thus, we examined the relationship between the plasma levels of oxysterols and the disease condition of 40 HBV patients, without treatment at the start of the study. Of these, 33 were ICs and 7 were active CHB subjects. A marked reduction of 25OHC and 27OHC plasma levels was detectable in all active CHB recruited patients, while the plasma values observed in ICs all remained within the physiological range. No difference was observed between the two groups of patients with regard to the plasma levels of 24-hydroxycholesterol (24OHC). Further, the plasma level of 27OHC ≥ 140 μg/L was shown to be predictive of an inactive carrier status. This cohort study points to 27OHC as a good candidate biomarker to differentiate active and inactive CHB status. An increasing bulk of research reports is supporting the very likely contribution of this oxysterol to the immunological control of chronic hepatitis B.

Systematic evaluation of sample preparation strategy for GC-MS-based plasma metabolomics and its application in osteoarthritis

Sample preparation plays a crucial part in plasma metabolomics. In order to obtain an optimal sample extraction method for gas chromatography mass spectrometry (GC-MS)-based plasma metabolomics, five different extraction strategies including protein precipitation, liquid-liquid extraction and solid-phase extraction were evaluated systematically for both plasma untargeted- and targeted-metabolomics. The comprehensive evaluation revealed that the all-in-one sample preparation method, MeOH-MTBE-H₂O (1:5:1.5, v/v/v), was the optimal extraction method for both untargeted- and targeted-metabolomics. Next, the optimal sample preparation protocol was applied in plasma metabolomics of osteoarthritis (OA). A panel containing cholesterol, lactic acid, stearic acid, alpha-tocopherol and oxalic acid was selected as candidate biomarker to distinguish OA patients from healthy controls (HC) based on the support vector machine (SVM) classification model. The discriminating capability of the candidate biomarker panel was further validated successfully with logistic regression and principal components analysis (PCA) analysis. Therefore, the panel could potentially act as diagnostic biomarker for osteoarthritis.

Metabolomic alterations associated with copper stress in Cryptococcus neoformans

Background: Copper stress is an effective host strategy in resisting the opportunistic pathogenic fungus Cryptococcus neoformans. We studied metabolic changes in C. neoformans under copper stress. Materials & methods: Wild-type and metallothionein-null C. neoformans were treated with copper on agar containing glucose, glycerol or ethanol as the carbon source and their metabolites were analyzed by untarget metabolomics strategy using gas chromatography coupled with time-of-flight mass spectrometry. Results: The metabolic profile of C. neoformans varied in the presence and absence of copper. Pathway enrichment analysis showed that the differentially abundant metabolites were related to amino acid and carbohydrate metabolism. C. neoformans grown on glycerol or ethanol resisted copper toxicity better than C. neoformans grown on glucose. Conclusion: Copper stress alters the metabolic profile of C. neoformans.

Metabolomic characterisation of progression and spontaneous regression of melanoma in the melanoma-bearing Libechov minipig model

Melanoma-bearing Libechov minipig (MeLiM) represents a large animal model for melanoma research. This model shows a high incidence of complete spontaneous regression of melanoma - a phenomenon uncommon in humans. Here, we present the first metabolomic characterisation of the MeLiM model comparing animals with progressing and spontaneously regressing melanomas. Plasma samples of 19 minipigs with progression and 27 minipigs with evidence of regression were analysed by a targeted metabolomic assay based on mass spectrometry detection. Differences in plasma metabolomics patterns were investigated by univariate and multivariate statistical analyses. Overall, 185 metabolites were quantified in each plasma sample. Significantly altered metabolomic profile was found, and 42 features were differentially regulated in plasma. Besides, the machine learning approach was used to create a predictive model utilising Arg/Orn and Arg/ADMA ratios to discriminate minipigs with progressive disease development from minipigs with regression evidence. Our results suggest that progression of melanoma in the MeLiM model is associated with alteration of arginine, glycerophospholipid and acylcarnitines metabolism. Moreover, this study provides targeted metabolomics characterisation of an animal model of melanoma with progression and spontaneous regression of tumours.

Blood molecular biomarkers for chemotherapy-induced peripheral neuropathy: From preclinical models to clinical practice

Chemotherapy-induced peripheral neuropathy (CIPN) has long been recognized as a clinically significant issue in patients treated with antineoplastic drugs. This common long-term toxic side-effect which negatively impacts the outcome of the disease can lead to disability and have detrimental effects on patients' quality of life. Since axonal injury is a prominent feature of CIPN, responsible for several sensory symptoms, including pain, sensory loss and hypersensitivity to mechanical and/or cold stimuli in the hands and feet, neurophysiological assessments remain the gold standard for clinical diagnosis of CIPN. Given the large impact of CIPN on cancer patients, there is increasing emphasis on biomarkers of adverse outcomes in safety assessment and translational research, to prevent permanent neuroaxonal damage. Since the results on reliable blood molecular markers for axonal degeneration are still controversial, here we provide a brief overview of blood molecular biomarkers used for assessing and/or predicting CIPN in preclinical and clinical settings.

L-Carnitine and Acylcarnitines: Mitochondrial Biomarkers for Precision Medicine

Biomarker discovery and implementation are at the forefront of the precision medicine movement. Modern advances in the field of metabolomics afford the opportunity to readily identify new metabolite biomarkers across a wide array of disciplines. Many of the metabolites are derived from or directly reflective of mitochondrial metabolism. L-carnitine and acylcarnitines are established mitochondrial biomarkers used to screen neonates for a series of genetic disorders affecting fatty acid oxidation, known as the inborn errors of metabolism. However, L-carnitine and acylcarnitines are not routinely measured beyond this screening, despite the growing evidence that shows their clinical utility outside of these disorders. Measurements of the carnitine pool have been used to identify the disease and prognosticate mortality among disorders such as diabetes, sepsis, cancer, and heart failure, as well as identify subjects experiencing adverse drug reactions from various medications like valproic acid, clofazimine, zidovudine, cisplatin, propofol, and cyclosporine. The aim of this review is to collect and interpret the literature evidence supporting the clinical biomarker application of L-carnitine and acylcarnitines. Further study of these metabolites could ultimately provide mechanistic insights that guide therapeutic decisions and elucidate new pharmacologic targets.

Application of Graphdiyne in Surface-Assisted Laser Desorption Ionization Mass Spectrometry

Graphdiyne (GD) is a new kind of carbon nanomaterial which has carbon-carbon triple bonds to form a layered structure. Here, we report the application of GD as the matrix for small molecule analysis in laser desorption ionization mass spectrometry (LDI MS). The GD matrix displayed two advantages: little background in the low mass range and good molecular ion signal in negative ion mode for many small molecules, e.g., fatty acids, amino acids, peptides, and drugs can be obtained in negative ion mode. By comparing the signal intensity of tetraphenylborate and juglone with and without GD existing, it was found that GD can enhance both of the desorption efficiency and ionization efficiency in LDI. Through analysis of the serum samples from liver cancer patients and healthy people, the GD-assisted LDI MS results showed that fatty acids could be used as potential biomarkers for the early diagnosis of liver cancer.

Metabolic alterations and vulnerabilities in hepatocellular carcinoma

Liver cancer is a serious disease. It is ranked as the cancer with the second highest number of cancer-related deaths worldwide. Hepatocellular carcinoma (HCC), which arises from transformed hepatocytes, is the major subtype of liver cancer. It accounts for 85% of total liver-cancer cases. An important aspect of HCC that has been actively studied is its metabolism. With the liver as the primary site of numerous metabolic processes in the body, it has been shown that the metabolism of HCC cells is highly dysregulated compared to that of normal hepatocytes. It is therefore crucial to understand the metabolic alterations caused by HCC and the underlying mechanisms for these alterations. This deeper understanding will allow diagnostic and therapeutic advancements in the treatment of HCC. In this review, we will summarize the current literature in HCC metabolic alterations, induced vulnerabilities, and potential therapeutic interventions.

Remodeling Lipids in the Transition from Chronic Liver Disease to Hepatocellular Carcinoma

Simple Summary

Hepatocellular carcinoma (HCC) has poor prognosis. We studied blood lipids by comparing healthy volunteers to patients with chronic liver disease (CLD), and to patients with HCC caused by viral infections. We contrasted our findings in blood to lipid alterations in liver tumor and nontumor tissue samples from HCC patients. In blood, most lipid species were found at increased levels in CLD patients compared to healthy volunteers. This trend was mostly reversed in HCC versus CLD patients. In liver tumor tissues, levels of many lipids were decreased compared to paired nontumor liver tissues. Differences in lipid levels were further defined by alterations in the degree of saturation in the fatty acyl chains. Some lipids, including free fatty acids, saturated lysophosphatidylcholines and saturated triacylglycerides, showed a continuous trend in the transition from the blood of healthy controls to CLD and HCC patients. For HCC patients, phosphatidylglycerides showed similar alterations in both blood and tissues.

Abstract

Hepatocellular carcinoma (HCC) is a worldwide health problem. HCC patients show a 50% mortality within two years of diagnosis. To better understand the molecular pathogenesis at the level of lipid metabolism, untargeted UPLC MS—QTOF lipidomics data were acquired from resected human HCC tissues and their paired nontumor hepatic tissues (n = 46). Blood samples of the same HCC subjects (n = 23) were compared to chronic liver disease (CLD) (n = 15) and healthy control (n = 15) blood samples. The participants were recruited from the National Liver Institute in Egypt. The lipidomics data yielded 604 identified lipids that were divided into six super classes. Five-hundred and twenty-four blood lipids were found as significantly differentiated (p < 0.05 and qFDR p < 0.1) between the three study groups. In the blood of CLD patients compared to healthy control subjects, almost all lipid classes were significantly upregulated. In CLD patients, triacylglycerides were found as the most significantly upregulated lipid class at qFDR p = 1.3 × 10⁻⁵⁶, followed by phosphatidylcholines at qFDR p = 3.3 × 10⁻⁵¹ and plasmalogens at qFDR p = 1.8 × 10^-46. In contrast, almost all blood lipids were significantly downregulated in HCC patients compared to CLD patients, and in HCC tissues compared to nontumor hepatic tissues. Ceramides were found as the most significant lipid class (qFDR p = 1 × 10⁻¹⁴) followed by phosphatidylglycerols (qFDR p = 3 × 10⁻⁹), phosphatidylcholines and plasmalogens. Despite these major differences, there were also common trends in the transitions between healthy controls, CLD and HCC patients. In blood, several mostly saturated triacylglycerides showed a continued increase in the trajectory towards HCC, accompanied by reduced levels of saturated free fatty acids and saturated lysophospatidylcholines. In contrast, the largest overlaps of lipid alterations that were found in both HCC tissue and blood comparisons were decreased levels of phosphatidylglycerols and sphingolipids. This study highlights the specific impact of HCC tumors on the circulating lipids. Such data may be used to target lipid metabolism for prevention, early detection and treatment of HCC in the background of viral-related CLD etiology.

Application of Metabolomics in Diagnosis and Treatment of Chronic Liver Diseases

Chronic liver disease represents stepwise destruction of the liver parenchyma after chronic liver injury, which is often difficult to be diagnosed accurately. Thus, the development of specific biomarkers of chronic liver disease is important. Metabolomics is a powerful tool for biomarker exploration, which enables the exploration of disease pathogenesis or drug action mechanisms at the global metabolic level. The metabolomics workflow generally includes collection, preparation, and analysis of samples, and data processing and bioinformatics. A metabolomics study can simultaneously detect the dysfunctions in the glucose, lipid, amino-acid, and nucleotide metabolisms. Hence, it facilitates the obtaining of a better understanding of the pathogenesis of chronic liver disease and its diagnosis. Many effective drugs could reverse the change of comprehensive biochemical phenotypes induced by chronic liver disease. They can even potentially restore the normal metabolic signatures of patients. Increasingly more researchers have begun to apply metabolomics technologies to diagnose chronic liver disease and investigate the mechanism of action of effective drugs or the variations in drug responses. We are convinced that deepening the understanding of the metabolic alterations could extend their use as powerful biomarkers, promoting the more effective clinical diagnosis and treatment of chronic liver disease in the future.

Discussion on Relevance and Studies of Prescription Compatibility in Chinese Medicine

With Chinese medicine (CM) gaining popularity in recent years, researchers and clinicians have put in much interest and effort into the makings and effects of it, especially after the recent announcement of World Health Orgnitation's incorporation of CM into mainstream medical compendium. Individual herb has complex properties, coming from its pharmacological properties and the Chinese medical principles of organ-directed, taste and dynamic orientational behaviours. The use of individual herb in CM is rare, where various herbs/ingredients are mostly found in a prescribed formula. To fully reveal the effects of CM is a great challenge. The complexity of various herbs in combined effect, the absorption and utility rate by the body, uniqueness of individual physique, sub-types of pathological behaviors and time-line progression of the healing process add on to the complication of understanding the full effect of CM. Various theories such as pathophysiology guidance, pharmacokinetic-pharmacodynamic compatibility method, and Global Systems Biology for Integrative Genomics, Proteomics and Metabolomics, which interactively provide a wider scope, more details, with the consideration of development timeline, may shed more light to revealing the full picture of the effects of compatibility prescription.

Analysis of plasma metabolic profile, characteristics and enzymes in the progression from chronic hepatitis B to hepatocellular carcinoma

Hepatitis B virus (HBV) infection is an important factor causing hepatocellular carcinoma (HCC). The aim of this study was to investigate the metabolic characteristics and related metabolic enzyme changes during the progression from chronic hepatitis B (CHB) to liver cirrhosis (LC) and, ultimately, to HCC. An untargeted metabolomics assay was performed in plasma from 50 healthy volunteers, 43 CHB patients, 67 LC patients, and 39 HCC patients. A total of 24 differential metabolites (DMs) were identified. Joint pathway analysis suggested striking changes in amino acid metabolism and lipid metabolism from CHB to HCC. The panel of L-serine, creatine and glycine distinguished LC from CHB, and L-serine, cystathionine, creatine and linoleic acid distinguished HCC from LC. Bioinformatic analysis of publicly available data showed that differential metabolite profile-associated enzyme genes, including alanine-glyoxylate aminotransferase-2 (AGXT2), D-amino-acid oxidase (DAO), and cystathionine gamma-lyase (CTH), were downregulated, while bisphosphoglycerate mutase (BPGM), cystathionine-β-synthase (CBS), phosphoserine phosphatase (PSPH) and acyl-CoA thioesterase 7 (ACOT7) were upregulated, in HCC, all of which correlated with a poor prognosis for HCC patients. Our results indicated that serum metabolites and related enzymes are of considerable significance for the diagnosis and prognosis of HCC and can provide a theoretical basis and therapeutic index for future diagnosis and treatment.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

Low UGP2 Expression Is Associated with Tumour Progression and Predicts Poor Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a malignant tumour associated with a high mortality rate and poor prognosis worldwide. Uridine diphosphate-glucose pyrophosphorylase 2 (UGP2), a key enzyme in glycogen biosynthesis, has been reported to be associated with the occurrence and development of various cancer types. However, its diagnostic value and prognostic value in HCC remain unclear. The present study observed that UGP2 expression was significantly downregulated at both the mRNA and protein levels in HCC tissues. Receiver operating characteristic (ROC) curve analysis revealed that UGP2 may be an indicator for the diagnosis of HCC. In addition, Kaplan-Meier and Cox regression multivariate analyses indicated that UGP2 is an independent prognostic factor of overall survival (OS) in patients with HCC. Furthermore, gene set enrichment analysis (GSEA) suggested that gene sets negatively correlated with the survival of HCC patients were enriched in the group with low UGP2 expression levels. More importantly, a significant correlation was identified between low UGP2 expression and fatty acid metabolism. In summary, the present study demonstrates that UGP2 may contribute to the progression of HCC, indicating a potential therapeutic target for HCC patients.

Explorative study of serum biomarkers of liver failure after liver resection

Conventional biochemical markers have limited usefulness in the prediction of early liver dysfunction. We, therefore, tried to find more useful liver failure biomarkers after liver resection that are highly sensitive to internal and external challenges in the biological system with a focus on liver metabolites. Twenty pigs were divided into the following 3 groups: sham operation group (n = 6), 70% hepatectomy group (n = 7) as a safety margin of resection model, and 90% hepatectomy group (n = 7) as a liver failure model. Blood sampling was performed preoperatively and at 1, 6, 14, 30, 38, and 48 hours after surgery, and 129 primary metabolites were profiled. Orthogonal projection to latent structures-discriminant analysis revealed that, unlike in the 70% hepatectomy and sham operation groups, central carbon metabolism was the most significant factor in the 90% hepatectomy group. Binary logistic regression analysis was used to develop a predictive model for mortality risk following hepatectomy. The recommended variables were malic acid, methionine, tryptophan, glucose, and γ-aminobutyric acid. Area under the curve of the linear combination of five metabolites was 0.993 (95% confidence interval: 0.927–1.000, sensitivity: 100.0, specificity: 94.87). We proposed robust biomarker panels that can accurately predict mortality risk associated with hepatectomy.

Metabolomics markers in Neurology: current knowledge and future perspectives for therapeutic targeting

INTRODUCTION

Metabolomics is an emerging approach providing new insights into the metabolic changes and underlying mechanisms involved in the pathogenesis of neurological disorders.

AREAS COVERED

Here, the authors present an overview of the current knowledge of metabolic profiling (metabolomics) to provide critical insight on the role of biochemical markers and metabolic alterations in neurological diseases.

EXPERT OPINION

Elucidation of characteristic metabolic alterations in neurological disorders is crucial for a better understanding of their pathogenesis, and for identifying potential biomarkers and drug targets. Nevertheless, discrepancies in diagnostic criteria, sample handling protocols, and analytical methods still affect the generalizability of current study results.

Lipid Metabolism in Development and Progression of Hepatocellular Carcinoma

: Metabolic reprogramming is critically involved in the development and progression of cancer. In particular, lipid metabolism has been investigated as a source of energy, micro-environmental adaptation, and cell signalling in neoplastic cells. However, the specific role of lipid metabolism dysregulation in hepatocellular carcinoma (HCC) has not been widely described yet. Alterations in fatty acid synthesis, β-oxidation, and cellular lipidic composition contribute to initiation and progression of HCC. The aim of this review is to elucidate the mechanisms by which lipid metabolism is involved in hepatocarcinogenesis and tumour adaptation to different conditions, focusing on the transcriptional aberrations with new insights in lipidomics and lipid zonation. This will help detect new putative therapeutic approaches in the second most frequent cause of cancer-related death.