Discovering a critical transition state from nonalcoholic hepatosteatosis to nonalcoholic steatohepatitis by lipidomics and dynamical network biomarkers

The potential pathophysiological role of altered lipid metabolism and electronegative low-density lipoprotein (LDL) in non-alcoholic fatty liver disease and cardiovascular diseases

Non-alcoholic fatty liver disease (NAFLD) is an umbrella term for a range of conditions caused by a build-up of fat in the liver. It is usually seen in people who are overweight or obese. Increasingly common around the world, this disease is the most common chronic liver disease in the United States, affecting about a quarter of the population. Recently, the designation of NAFLD as 'metabolic dysfunction-associated fatty liver disease' (MAFLD) has been a subject of current debate. In this context, 'insulin resistance' is the underlying common and basic pathophysiological mechanism of metabolic dysfunction due to its association with obesity, type 2 diabetes mellitus (T2DM), metabolic syndrome, dyslipidemia and NAFLD. All these pathological conditions are among the metabolic risk factors for cardiovascular diseases, too. Also, due to the bidirectional causality between NAFLD and cardiovascular diseases, a liver-heart axis is suggested. Therefore, various factors such as insulin resistance as well as systemic inflammation, cytokines, oxidative stress, adipokines, hepatokines, genes and intestinal microbiota have been identified as possible pathogenic factors that play a role in the explanation of the complex NAFLD and cardiovascular risk relationship. Recent data and cumulative evidence show that electronegative low-density lipoprotein [LDL (-)/L5] cholesterol is a promising biomarker for complex organ interactions and diseases associated with liver-heart axis. In this mini review, we focus not only on recent data on NAFLD mechanisms, but also on the potential of the lipid mediator LDL (-)/L5 as a diagnostic and therapeutic target for liver-heart line diseases.

LC-MS-based lipidomic analysis in distinguishing patients with nonalcoholic steatohepatitis from nonalcoholic fatty liver

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is one of the main liver diseases, and its pathologic profile includes nonalcoholic fatty liver (NAFL) and nonalcoholic steatohepatitis (NASH). However, there is no reliable non-invasive parameter in distinguishing NASH from NAFL in clinical practice. The present study was to find a non-invasive way to differentiate these two categories of NAFLD via lipidomic analysis.

METHODS

Lipidomic analysis was used to determine the changes of lipid moieties in blood from 20 NAFL and 10 NASH patients with liver biopsy. Liver histology was evaluated after hematoxylin and eosin staining and Masson's trichrome staining. The profile of lipid metabolites in correlation with steatosis, inflammation, hepatocellular necroptosis, fibrosis, and NAFLD activity score (NAS) was analyzed.

RESULTS

Compared with NAFL patients, NASH patients had higher degree of steatosis, ballooning degeneration, lobular inflammation. A total of 434 different lipid molecules were identified, which were mainly composed of various phospholipids and triacylglycerols. Many lipids, such as phosphatidylcholine (PC) (P-22:0/18:1), sphingomyelin (SM) (d14:0/18:0), SM (d14:0/24:0), SM (d14:0/22:0), phosphatidylethanolamine (PE) (18:0/22:5), PC (O-22:2/12:0), and PC (26:1/11:0) were elevated in the NASH group compared to those in the NAFL group. Specific analysis revealed an overall lipidomic profile shift from NAFL to NASH, and identified valuable lipid moieties, such as PCs [PC (14:0/18:2), PE (18:0/22:5) and PC (26:1/11:0)] or plasmalogens [PC (O-22:0/0:0), PC (O-18:0/0:0), PC (O-16:0/0:0)], which were significantly altered in NASH patients. In addition, PC (14:0/18:2), phosphatidic acid (18:2/24:4) were positively correlated with NAS; whereas PC (18:0/0:0) was correlated positively with fibrosis score.

CONCLUSIONS

The present study revealed overall lipidomic profile shift from NAFL to NASH, identified valuable lipid moieties which may be non-invasive biomarkers in the categorization of NAFLD. The correlations between lipid moieties and NAS and fibrosis scores indicate that these lipid biomarkers may be used to predict the severity of the disease.

Overview of Lipidomic Analysis of Triglyceride Molecular Species in Biological Lipid Extracts

Triglyceride (TG) is a class of neutral lipids, which functions as an energy storage depot and is important for cellular growth, metabolism, and function. The composition and content of TG molecular species are crucial factors for nutritional aspects in food chemistry and are directly associated with several diseases, including atherosclerosis, diabetes, obesity, stroke, etc. As a result of the complexities of aliphatic moieties and their different connections/locations to the glycerol backbone in TG molecules, accurate identification of individual TG molecular species and quantitative assessment of TG composition and content are particularly challenging, even at the current stage of lipidomics development. Herein, methods developed for analysis of TG species, such as liquid chromatography-mass spectrometry with a variety of columns and different mass spectrometric techniques, shotgun lipidomics approaches, and ion-mobility-based analysis, are reviewed. Moreover, the potential limitations of the methods are discussed. It is our sincere hope that the overviews and discussions can provide some insights for researchers to select an appropriate approach for TG analysis and can serve as the basis for those who would like to establish a methodology for TG analysis or develop a new method when novel tools become available. Biologically accurate analysis of TG species with an enabling method should lead us toward improving the nutritional quality, revealing the effects of TG on diseases, and uncovering the underlying biochemical mechanisms related to these diseases.

Dynamic Chronological Changes in Serum Triglycerides Are Associated With the Time Point for Non-alcoholic Fatty Liver Disease Development in the Nationwide Korean Population Cohort

Background: We investigated the effects of anthropometric, laboratory, and lifestyle factors on the development of non-alcoholic fatty liver disease (NAFLD) in a nationwide, population-based, 4-year retrospective cohort.

Methods: The propensity score-matched study and control groups contained 1,474 subjects who had data in the Korean National Health Insurance Service-National Sample Cohort in 2009, 2011, and 2013. NAFLD was defined using medical records of a diagnosis confirmed by primary clinicians and meeting two previously validated fatty liver prediction models. Chronological changes in anthropometric variables, laboratory results, and lifestyle factors during two periods were compared between patient and control groups in order to find out parameters with consistent dynamics in pre-NAFLD stage which was defined as period just before the NAFLD development.

Results: Among the 5 anthropometric, 10 laboratory, and 3 lifestyle factors, prominent chronological decremental changes in serum triglycerides were consistently observed during the pre-NAFLD stage, although the degrees of changes were more predominant in men (−9.46 mg/dL) than women (−5.98 mg/dL). Furthermore, weight and waist circumference changes during the pre-NAFLD stage were noticeable only in women (+0.36 kg and +0.9 cm for weight and waist circumference, respectively), which suggest gender difference in NAFLD.

Conclusion: Early screening strategies for people with abrupt chronological changes in serum triglycerides to predict NAFLD development before the progression is recommended.

Tgfb3 and Mmp13 regulated the initiation of liver fibrosis progression as dynamic network biomarkers

Liver fibrogenesis is a complex scar-forming process in the liver. We suggested that the liver first responded to chronic injuries with gradual changes, then reached the critical state and ultimately resulted in cirrhosis rapidly. This study aimed to identify the tipping point and key molecules driving liver fibrosis progression. Mice model of liver fibrosis was induced by thioacetamide (TAA), and liver tissues were collected at different time-points post-TAA administration. By dynamic network biomarker (DNB) analysis on the time series of liver transcriptomes, the week 9 post-TAA treatment (pathologically relevant to bridging fibrosis) was identified as the tipping point just before the significant fibrosis transition, with 153 DNB genes as key driving factors. The DNB genes were functionally enriched in fibrosis-associated pathways, in particular, in the top-ranked DNB genes, Tgfb3 negatively regulated Mmp13 in the interaction path and they formed a bistable switching system from a dynamical perspective. In the in vitro study, Tgfb3 promoted fibrogenic genes and down-regulate Mmp13 gene transcription in an immortalized mouse HSC line JS1 and a human HSC line LX-2. The presence of a tipping point during liver fibrogenesis driven by DNB genes marks not only the initiation of significant fibrogenesis but also the repression of the scar resolution.

Hepatic Lipidomics and Molecular Imaging in a Murine Non-Alcoholic Fatty Liver Disease Model: Insights into Molecular Mechanisms

An imbalance between hepatic fatty acid uptake and removal results in ectopic fat accumulation, which leads to non-alcoholic fatty liver disease (NAFLD). The amount and type of accumulated triglycerides seem to play roles in NAFLD progression; however, a complete understanding of how triglycerides contribute to NAFLD evolution is lacking. Our aim was to evaluate triglyceride accumulation in NAFLD in a murine model and its associations with molecular mechanisms involved in liver damage and adipose tissue-liver cross talk by employing lipidomic and molecular imaging techniques. C57BL/6J mice fed a high-fat diet (HFD) for 12 weeks were used as a NAFLD model. Standard-diet (STD)-fed animals were used as controls. Standard liver pathology was assessed using conventional techniques. The liver lipidome was analyzed by liquid chromatography–mass spectrometry (LC–MS) and laser desorption/ionization–mass spectrometry (LDI–MS) tissue imaging. Liver triglycerides were identified by MS/MS. The transcriptome of genes involved in intracellular lipid metabolism and inflammation was assessed by RT-PCR. Plasma leptin, resistin, adiponectin, and FABP4 levels were determined using commercial kits. HFD-fed mice displayed increased liver lipid content. LC–MS analyses identified 14 triglyceride types that were upregulated in livers from HFD-fed animals. Among these 14 types, 10 were identified in liver cross sections by LDI–MS tissue imaging. The accumulation of these triglycerides was associated with the upregulation of lipogenesis and inflammatory genes and the downregulation of β-oxidation genes. Interestingly, the levels of plasma FABP4, but not of other adipokines, were positively associated with 8 of these triglycerides in HFD-fed mice but not in STD-fed mice. Our findings suggest a putative role of FABP4 in the liver-adipose tissue cross talk in NAFLD.

Lipidomics in non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD), the most common chronic liver disorder in Western countries, comprises steatosis to nonalcoholic steatohepatitis (NASH), with the latter having the potential to progress to cirrhosis. The transition from isolated steatosis to NASH is still poorly understood, but lipidomics approach revealed that the hepatic lipidome is extensively altered in the setting of steatosis and steatohepatitis and these alterations correlate with disease progression. Recent data suggest that both quantity and quality of the accumulated lipids are involved in pathogenesis of NAFLD. Changes in glycerophospholipid, sphingolipid, and fatty acid composition have been described in both liver biopsies and plasma of patients with NAFLD, implicating that specific lipid species are involved in oxidative stress, inflammation, and cell death. In this article, we summarize the findings of main human lipidomics studies in NAFLD and delineate the currently available information on the pathogenetic role of each lipid class in lipotoxicity and disease progression.

Hunt for the tipping point during endocrine resistance process in breast cancer by dynamic network biomarkers

Acquired drug resistance is the major reason why patients fail to respond to cancer therapies. It is a challenging task to determine the tipping point of endocrine resistance and detect the associated molecules. Derived from new systems biology theory, the dynamic network biomarker (DNB) method is designed to quantitatively identify the tipping point of a drastic system transition and can theoretically identify DNB genes that play key roles in acquiring drug resistance. We analyzed time-course mRNA sequence data generated from the tamoxifen-treated estrogen receptor (ER)-positive MCF-7 cell line, and identified the tipping point of endocrine resistance with its leading molecules. The results show that there is interplay between gene mutations and DNB genes, in which the accumulated mutations eventually affect the DNB genes that subsequently cause the change of transcriptional landscape, enabling full-blown drug resistance. Survival analyses based on clinical datasets validated that the DNB genes were associated with the poor survival of breast cancer patients. The results provided the detection for the pre-resistance state or early signs of endocrine resistance. Our predictive method may greatly benefit the scheduling of treatments for complex diseases in which patients are exposed to considerably different drugs and may become drug resistant.

Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing

Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. co-expressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1, HLA-DQB1, MMP2, and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives.

Personalized Early-Warning Signals during Progression of Human Coronary Atherosclerosis by Landscape Dynamic Network Biomarker

Coronary atherosclerosis is one of the major factors causing cardiovascular diseases. However, identifying the tipping point (predisease state of disease) and detecting early-warning signals of human coronary atherosclerosis for individual patients are still great challenges. The landscape dynamic network biomarkers (l-DNB) methodology is based on the theory of dynamic network biomarkers (DNBs), and can use only one-sample omics data to identify the tipping point of complex diseases, such as coronary atherosclerosis. Based on the l-DNB methodology, by using the metabolomics data of plasma of patients with coronary atherosclerosis at different stages, we accurately detected the early-warning signals of each patient. Moreover, we also discovered a group of dynamic network biomarkers (DNBs) which play key roles in driving the progression of the disease. Our study provides a new insight into the individualized early diagnosis of coronary atherosclerosis and may contribute to the development of personalized medicine.

Oleic Acid Attenuates Ang II (Angiotensin II)-Induced Cardiac Remodeling by Inhibiting FGF23 (Fibroblast Growth Factor 23) Expression in Mice

Plasma metabolic profiles were compared between patients with hypertension with and without left ventricular hypertrophy and significantly decreased oleic acid (OA) levels were observed in the peripheral blood of patients with hypertension with left ventricular hypertrophy. We sought to determine the effect and underlying mechanisms of OA on cardiac remodeling. In vitro studies with isolated neonatal mouse cardiomyocytes and cardiac fibroblasts revealed that OA significantly attenuated Ang II (angiotensin II)-induced cardiomyocyte growth and cardiac fibroblast collagen expression. In vivo, cardiac function, hypertrophic growth of cardiomyocytes, and fibrosis were analyzed after an Ang II (1000 ng/kg/minute) pump was implanted for 14 days. We found that OA could significantly prevent Ang II-induced cardiac remodeling in mice. RNA sequencing served as a gene expression roadmap highlighting gene expression changes in the hearts of Ang II-induced mice and OA-treated mice. The results revealed that FGF23 (fibroblast growth factor 23) expression was significantly upregulated in mouse hearts in response to Ang II infusion, which was significantly suppressed in the hearts of OA-treated mice. Furthermore, overexpression of FGF23 in the heart by injection of an AAV-9 vector aggravated Ang II-induced cardiac remodeling and impaired the protective effect of OA on cardiac remodeling. Further study found that OA could suppress Ang II-induced FGF23 expression by inhibiting the translocation of Nurr1 (nuclear receptor–related 1 protein) from the cytoplasm to the nucleus. Our findings suggest a novel role of OA in preventing Ang II-induced cardiac remodeling via suppression of FGF23 expression.

Dietary citrus peel essential oil ameliorates hypercholesterolemia and hepatic steatosis by modulating lipid and cholesterol homeostasis

Integration of lipidomics and gene expression analysis provided new insights into in-depth mechanistic understanding of the effects of dietary CPEO.

Chronic hepatitis B: dynamic change in Traditional Chinese Medicine syndrome by dynamic network biomarkers

Background

In traditional Chinese medicine (TCM) clinical practice, TCM syndromes help to understand human homeostasis and guide individualized treatment. However, the TCM syndrome changes with disease progression, of which the scientific basis and mechanism remain unclear.

Methods

To demonstrate the underlying mechanism of dynamic changes in the TCM syndrome, we applied a dynamic network biomarker (DNB) algorithm to obtain the DNBs of changes in the TCM syndrome, based on the transcriptomic data of patients with chronic hepatitis B and typical TCM syndromes, including healthy controls and patients with liver-gallbladder dampness-heat syndrome (LGDHS), liver-depression spleen-deficiency syndrome (LDSDS), and liver-kidney yin-deficiency syndrome (LKYDS). The DNB model exploits collective fluctuations and correlations of the observed genes, then diagnoses the critical state.

Results

Our results showed that the DNBs of TCM syndromes were comprised of 52 genes and the tipping point occurred at the LDSDS stage. Meanwhile, there were numerous differentially expressed genes between LGDHS and LKYDS, which highlighted the drastic changes before and after the tipping point, implying the 52 DNBs could serve as early-warning signals of the upcoming change in the TCM syndrome. Next, we validated DNBs by cytokine profiling and isobaric tags for relative and absolute quantitation (iTRAQ). The results showed that PLG (plasminogen) and coagulation factor XII (F12) were significantly expressed during the progression of TCM syndrome from LGDHS to LKYDS.

Conclusions

This study provides a scientific understanding of changes in the TCM syndrome. During this process, the cytokine system was involved all the time. The DNBs PLG and F12 were confirmed to significantly change during TCM-syndrome progression and indicated a potential value of DNBs in auxiliary diagnosis of TCM syndrome in CHB.

Trial registration Identifier: NCT03189992. Registered on June 4, 2017. Retrospectively registered (http://www.clinicaltrials.gov)

Dysfunction of PLA2G6 and CYP2C44-associated network signals imminent carcinogenesis from chronic inflammation to hepatocellular carcinoma

Little is known about how chronic inflammation contributes to the progression of hepatocellular carcinoma (HCC), especially the initiation of cancer. To uncover the critical transition from chronic inflammation to HCC and the molecular mechanisms at a network level, we analyzed the time-series proteomic data of woodchuck hepatitis virus/c-myc mice and age-matched wt-C57BL/6 mice using our dynamical network biomarker (DNB) model. DNB analysis indicated that the 5th month after birth of transgenic mice was the critical period of cancer initiation, just before the critical transition, which is consistent with clinical symptoms. Meanwhile, the DNB-associated network showed a drastic inversion of protein expression and coexpression levels before and after the critical transition. Two members of DNB, PLA2G6 and CYP2C44, along with their associated differentially expressed proteins, were found to induce dysfunction of arachidonic acid metabolism, further activate inflammatory responses through inflammatory mediator regulation of transient receptor potential channels, and finally lead to impairments of liver detoxification and malignant transition to cancer. As a c-Myc target, PLA2G6 positively correlated with c-Myc in expression, showing a trend from decreasing to increasing during carcinogenesis, with the minimal point at the critical transition or tipping point. Such trend of homologous PLA2G6 and c-Myc was also observed during human hepatocarcinogenesis, with the minimal point at high-grade dysplastic nodules (a stage just before the carcinogenesis). Our study implies that PLA2G6 might function as an oncogene like famous c-Myc during hepatocarcinogenesis, while downregulation of PLA2G6 and c-Myc could be a warning signal indicating imminent carcinogenesis.

Hyperlipidemias and Obesity

The organs require oxygen and other types of nutrients (amino acids, sugars, and lipids) to function, the heart consuming large amounts of fatty acids for oxidation and adenosine triphosphate (ATP) generation.

Genome-wide dynamic network analysis reveals a critical transition state of flower development in Arabidopsis

BACKGROUND

The flowering transition which is controlled by a complex and intricate gene regulatory network plays an important role in the reproduction for offspring of plants. It is a challenge to identify the critical transition state as well as the genes that control the transition of flower development. With the emergence of massively parallel sequencing, a great number of time-course transcriptome data greatly facilitate the exploration of the developmental phase transition in plants. Although some network-based bioinformatics analyses attempted to identify the genes that control the phase transition, they generally overlooked the dynamics of regulation and resulted in unreliable results. In addition, the results of these methods cannot be self-explained.

RESULTS

In this work, to reveal a critical transition state and identify the transition-specific genes of flower development, we implemented a genome-wide dynamic network analysis on temporal gene expression data in Arabidopsis by dynamic network biomarker (DNB) method. In the analysis, DNB model which can exploit collective fluctuations and correlations of different metabolites at a network level was used to detect the imminent critical transition state or the tipping point. The genes that control the phase transition can be identified by the difference of weighted correlations between the genes interested and the other genes in the global network. To construct the gene regulatory network controlling the flowering transition, we applied NARROMI algorithm which can reduce the noisy, redundant and indirect regulations on the expression data of the transition-specific genes. In the results, the critical transition state detected during the formation of flowers corresponded to the development of flowering on the 7th to 9th day in Arabidopsis. Among of 233 genes identified to be highly fluctuated at the transition state, a high percentage of genes with maximum expression in pollen was detected, and 24 genes were validated to participate in stress reaction process, as well as other floral-related pathways. Composed of three major subnetworks, a gene regulatory network with 150 nodes and 225 edges was found to be highly correlated with flowering transition. The gene ontology (GO) annotation of pathway enrichment analysis revealed that the identified genes are enriched in the catalytic activity, metabolic process and cellular process.

CONCLUSIONS

This study provides a novel insight to identify the real causality of the phase transition with genome-wide dynamic network analysis.

Mitochondrial dysfunction-related lipid changes occur in nonalcoholic fatty liver disease progression

Nonalcoholic fatty liver disease (NAFLD) comprises fat-accumulating conditions within hepatocytes that can cause severe liver damage and metabolic comorbidities. Studies suggest that mitochondrial dysfunction contributes to its development and progression and that the hepatic lipidome changes extensively in obesity and in NAFLD. To gain insight into the relationship between lipid metabolism and disease progression through different stages of NAFLD, we performed lipidomic analysis of plasma and liver biopsy samples from obese patients with nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH) and from those without NAFLD. Congruent with earlier studies, hepatic lipid levels overall increased with NAFLD. Lipid species that differed with NAFLD severity were related to mitochondrial dysfunction; specifically, hepatic cardiolipin and ubiquinone accumulated in NAFL, and levels of acylcarnitine increased with NASH. We propose that increased levels of cardiolipin and ubiquinone may help to preserve mitochondrial function in early NAFLD, but that mitochondrial function eventually fails with progression to NASH, leading to increased acylcarnitine. We also found a negative association between hepatic odd-chain phosphatidylcholine and NAFLD, which may result from mitochondrial dysfunction-related impairment of branched-chain amino acid catabolism. Overall, these data suggest a close link between accumulation of specific hepatic lipid species, mitochondrial dysfunction, and the progression of NAFLD.

Efficient Mining Multi-mers in a Variety of Biological Sequences

Counting the occurrence frequency of each -mer in a biological sequence is a preliminary yet important step in many bioinformatics applications. However, most -mer counting algorithms rely on a given k to produce single-length -mers, which is inefficient for sequence analysis for different k. Moreover, existing -mer counters focus more on DNA and RNA sequences and less on protein ones. In practice, the analysis of -mers in protein sequences can provide substantial biological insights in structure, function and evolution. To this end, an efficient algorithm, called MulMer (Multiple-Mer mining), is proposed to mine -mers of various lengths termed multi-mers via inverted-index technique, which is orders of magnitude faster than the conventional forward-index methods. Moreover, to the best of our knowledge, MulMer is the first able to mine multi-mers in a variety of sequences, including DNARNA and protein sequences.

Spatial Systems Lipidomics Reveals Nonalcoholic Fatty Liver Disease Heterogeneity

Hepatocellular lipid accumulation characterizes nonalcoholic fatty liver disease (NAFLD). However, the types of lipids associated with disease progression are debated, as is the impact of their localization. Traditional lipidomics analysis using liver homogenates or plasma dilutes and averages lipid concentrations, and does not provide spatial information about lipid distribution. We aimed to characterize the distribution of specific lipid species related to NAFLD severity by performing label-free molecular analysis by mass spectrometry imaging (MSI). Fresh frozen liver biopsies from obese subjects undergoing bariatric surgery (n = 23) with various degrees of NAFLD were cryosectioned and analyzed by matrix-assisted laser desorption/ionization (MALDI)-MSI. Molecular identification was verified by tandem MS. Tissue sections were histopathologically stained, annotated according to the Kleiner classification, and coregistered with the MSI data set. Lipid pathway analysis was performed and linked to local proteome networks. Spatially resolved lipid profiles showed pronounced differences between nonsteatotic and steatotic tissues. Lipid identification and network analyses revealed phosphatidylinositols and arachidonic acid metabolism in nonsteatotic regions, whereas low–density lipoprotein (LDL) and very low–density lipoprotein (VLDL) metabolism was associated with steatotic tissue. Supervised and unsupervised discriminant analysis using lipid based classifiers outperformed simulated analysis of liver tissue homogenates in predicting steatosis severity. We conclude that lipid composition of steatotic and nonsteatotic tissue is highly distinct, implying that spatial context is important for understanding the mechanisms of lipid accumulation in NAFLD. MSI combined with principal component–linear discriminant analysis linking lipid and protein pathways represents a novel tool enabling detailed, comprehensive studies of the heterogeneity of NAFLD.

Low-Grade Dysplastic Nodules Revealed as the Tipping Point during Multistep Hepatocarcinogenesis by Dynamic Network Biomarkers

Hepatocellular carcinoma (HCC) is a complex disease with a multi-step carcinogenic process from preneoplastic lesions, including cirrhosis, low-grade dysplastic nodules (LGDNs), and high-grade dysplastic nodules (HGDNs) to HCC. There is only an elemental understanding of its molecular pathogenesis, for which a key problem is to identify when and how the critical transition happens during the HCC initiation period at a molecular level. In this work, for the first time, we revealed that LGDNs is the tipping point (i.e., pre-HCC state rather than HCC state) of hepatocarcinogenesis based on a series of gene expression profiles by a new mathematical model termed dynamic network biomarkers (DNB)—a group of dominant genes or molecules for the transition. Different from the conventional biomarkers based on the differential expressions of the observed genes (or molecules) for diagnosing a disease state, the DNB model exploits collective fluctuations and correlations of the observed genes, thereby predicting the imminent disease state or diagnosing the critical state. Our results show that DNB composed of 59 genes signals the tipping point of HCC (i.e., LGDNs). On the other hand, there are a large number of differentially expressed genes between cirrhosis and HGDNs, which highlighted the stark differences or drastic changes before and after the tipping point or LGDNs, implying the 59 DNB members serving as the early-warning signals of the upcoming drastic deterioration for HCC. We further identified the biological pathways responsible for this transition, such as the type I interferon signaling pathway, Janus kinase–signal transducers and activators of transcription (JAK–STAT) signaling pathway, transforming growth factor (TGF)-β signaling pathway, retinoic acid-inducible gene I (RIG-I)-like receptor signaling pathway, cell adhesion molecules, and cell cycle. In particular, pathways related to immune system reactions and cell adhesion were downregulated, and pathways related to cell growth and death were upregulated. Furthermore, DNB was validated as an effective predictor of prognosis for HCV-induced HCC patients by survival analysis on independent data, suggesting a potential clinical application of DNB. This work provides biological insights into the dynamic regulations of the critical transitions during multistep hepatocarcinogenesis.

Serum Monounsaturated Triacylglycerol Predicts Steatohepatitis in Patients with Non-alcoholic Fatty Liver Disease and Chronic Hepatitis B

Chronic liver disease is associated with lipid metabolic disruption. We carried out a study to determine serum lipidomic features of patients with non-alcoholic fatty liver disease (NAFLD) and active chronic hepatitis B (CHB) and explored the biomarkers for non-alcoholic steatohepatitis (NASH). Serum lipidomic profiles of healthy controls (n = 23) and of biopsy–proven NAFLD (n = 42), CHB with NAFLD (n = 22) and without NAFLD (n = 17) were analyzed by ultra-performance liquid chromatography–tandem mass spectrometry. There were distinct serum lipidome between groups of NAFLD and CHB without NAFLD. Most of the neutral lipids and ceramide were elevated in the NAFLD group but were decreased in the CHB without NAFLD group. Plasmalogens were decreased in both groups. Triacylglycerols (TAGs) with lower carbon numbers and double bonds were increased in subjects with NASH. Serum monounsaturated TAG was a significant predictor of NASH (OR = 3.215; 95%CI 1.663–6.331) and positively correlated with histological activity (r = 0.501; P < 0.001). It showed good predictability for NASH in the NAFLD group [area under the receiver operating characteristic curves (AUROC) = 0.831] and was validated in the CHB group (AUROC = 0.833); this characteristic was superior to that of cytokeratin-18 and alanine transaminase. The increase in monounsaturated TAG might be a specific marker for NASH in both NAFLD and CHB patients.

Shotgun lipidomics in substantiating lipid peroxidation in redox biology: Methods and applications

Multi-dimensional mass spectrometry-based shotgun lipidomics (MDMS-SL) has made profound advances for comprehensive analysis of cellular lipids. It represents one of the most powerful tools in analyzing lipids directly from lipid extracts of biological samples. It enables the analysis of nearly 50 lipid classes and thousands of individual lipid species with high accuracy/precision. The redox imbalance causes oxidative stress, resulting in lipid peroxidation, and alterations in lipid metabolism and homeostasis. Some lipid classes such as oxidized fatty acids, 4-hydroxyalkenal species, and plasmalogen are sensitive to oxidative stress or generated corresponding to redox imbalance. Therefore, accurate assessment of these lipid classes can provide not only the redox states, but also molecular insights into the pathogenesis of diseases. This review focuses on the advances of MDMS-SL in analysis of these lipid classes and molecular species, and summarizes their recent representative applications in biomedical/biological research. We believe that MDMS-SL can make great contributions to redox biology through substantiating the aberrant lipid metabolism, signaling, trafficking, and homeostasis under oxidative stress-related condition.