Plasma Metabolomics and Machine Learning-Driven Novel Diagnostic Signature for Non-Alcoholic Steatohepatitis

Serum metabolic signatures and MetalnFF diagnostic score for mild and moderate metabolic dysfunction-associated steatotic liver disease

To explore serum metabolic changes in metabolic dysfunction-associated steatotic liver disease (MASLD) with mild or moderate steatosis and develop a diagnostic index based on liver fat content to differentiate these stages. A total of 149 participants were enrolled from the Nutrition Health Atlas Project in 2019 (Stage 1, n = 92) and 2022 (Stage 2, n = 57). Serum levels of amino acids, free fatty acids (FFAs) and other organic acids were quantified using liquid or gas chromatography-mass spectrometry. The relationships between serum metabolites and magnetic resonance imaging proton density hepatic fat fraction were analyzed and a predictive model fitting fat fraction was constructed in Stage 1 and validated in Stage 2. Patients with moderate MASLD had significantly higher pyruvic acid, 2-ketoglutaric acid, malic acid, 2-hydroxyisocaproic acid and FFA(C14:0) than mild MASLD. Pathway analysis indicated that liver fat accumulation is associated with alterations in amino acid, FFA metabolism and tricarboxylic acid cycle (TCA). The MetalnFF score was developed to discriminate among three groups, achieving an area under the curve (AUC) of 0.956 (95 %CI:0.905, 1.00) for MASLD and 0.857 (95 %CI:0.745, 0.968) for moderate MASLD in Stage 1, and was further validated in Stage 2 with an AUC of 0.986 (95 %CI: 0.951, 1.00) and 0.759 (95 %CI:0.607, 0.921), respectively. In the early stages of MASLD, disrupted amino acid, FFAs metabolism and TCA cycle have occurred. As the disease progresses, metabolic disturbances in pyruvate metabolism become more severe. These findings enhance a deeper understanding of pathogenesis and propose MetalnFF score as a potential diagnostic tool.

Integrative multi-omics analysis for identifying novel therapeutic targets and predicting immunotherapy efficacy in lung adenocarcinoma

Aim: Lung adenocarcinoma (LUAD), the most prevalent subtype of non-small cell lung cancer (NSCLC), presents significant clinical challenges due to its high mortality and limited therapeutic options. The molecular heterogeneity and the development of therapeutic resistance further complicate treatment, underscoring the need for a more comprehensive understanding of its cellular and molecular characteristics. This study sought to delineate novel cellular subpopulations and molecular subtypes of LUAD, identify critical biomarkers, and explore potential therapeutic targets to enhance treatment efficacy and patient prognosis. Methods: An integrative multi-omics approach was employed to incorporate single-cell RNA sequencing (scRNA-seq), bulk transcriptomic analysis, and genome-wide association study (GWAS) data from multiple LUAD patient cohorts. Advanced computational approaches, including Bayesian deconvolution and machine learning algorithms, were used to comprehensively characterize the tumor microenvironment, classify LUAD subtypes, and develop a robust prognostic model. Results: Our analysis identified eleven distinct cellular subpopulations within LUAD, with epithelial cells predominating and exhibiting high mutation frequencies in Tumor Protein 53 (TP53) and Titin (TTN) genes. Two molecular subtypes of LUAD [consensus subtype (CS)1 and CS2] were identified, each showing distinct immune landscapes and clinical outcomes. The CS2 subtype, characterized by increased immune cell infiltration, demonstrated a more favorable prognosis and higher sensitivity to immunotherapy. Furthermore, a multi-omics-driven machine learning signature (MOMLS) identified ribonucleotide reductase M1 (RRM1) as a critical biomarker associated with chemotherapy response. Based on this model, several potential therapeutic agents targeting different subtypes were proposed. Conclusion: This study presents a comprehensive multi-omics framework for understanding the molecular complexity of LUAD, providing insights into cellular heterogeneity, molecular subtypes, and potential therapeutic targets. Differential sensitivity to immunotherapy among various cellular subpopulations was identified, paving the way for future immunotherapy-focused research.

Emodin disrupts the KITENIN oncogenic complex by binding ErbB4 and suppresses colorectal cancer progression in dual blockade with KSRP-binding compound

BACKGROUND

The KITENIN/ErbB4 complex has been reported to participate in metastasis, which is the principal reason of death in most colorectal cancer patients.

PURPOSE

New therapeutics need to be developed to suppress the malignant effects of the KITENIN/ErbB4 complex, which is related to drug resistance. The present study aimed to evaluate changes in cancer cell invasion capacity, transcriptional regulators, and cellular bioenergetics after targeting the KITENIN/ErbB4 complex with emodin. Moreover, we aimed to reveal the mechanistic effects of emodin and observe the dual blockade effects of ErbB4-targeted therapy with KH-type splicing regulatory protein (KSRP) and search for new alternative blockade pathways.

METHODS

Using in vitro, in vivo, molecular-docking, and metabolomics studies, we evaluated the anticancer effect of emodin alone or in combination with DKCC14S.

RESULTS

Emodin treatment decreased KITENIN and ErbB4 protein levels. The dysfunctional KITENIN/ErbB4 complex suppressed KITENIN-mediated cell invasion and downregulated AP-1 activity, aerobic glycolysis, and the levels of transcriptional regulators associated with cell metabolism. We conclude that emodin targets the KITENIN/ErbB4 complex and offering a novel mechanism by which it disrupts KITENIN-mediated signaling. Furthermore, we were demonstrated that the dual blocking effect of emodin and DKC-C14S on the KITENIN complex showed synergistic effects in suppressing colorectal cancer progression under in cell-based and animal assay.

CONCLUSION

The results suggest that co-treatment with ErbB4 and KSRP-binding compounds could constitute a potential strategy for controlling colorectal cancer progression by disrupting the KITENIN complex.

Ablating the glutaredoxin-2 (Glrx2) gene protects male mice against non-alcoholic fatty liver disease (NAFLD) by limiting oxidative distress

In the present study, we investigated the consequences of deleting the glutaredoxin-2 gene (Glrx2-/-) on the development of non-alcoholic fatty liver disease (NAFLD) in male and female C57BL6N mice fed a control (CD) or high-fat diet (HFD). We report that the HFD induced a significant increase in body mass in the wild-type (Wt) and Glrx2-/- male, but not female, mice, which was associated with the hypertrophying of the abdominal fat. Interestingly, while the Wt male mice fed the HFD developed NAFLD, the deletion of the Glrx2 gene mitigated vesicle formation, intrahepatic lipid accumulation, and fibrosis in the males. The protective effect associated with ablating the Glrx2 gene in male mice was due to enhancement of mitochondrial redox buffering capacity. Specifically, liver mitochondria from male Glrx2-/- fed a CD or HFD produced significantly less hydrogen peroxide (mtH2O2), had lower malondialdehyde levels, greater activities for glutathione peroxidase and thioredoxin reductase, and less protein glutathione mixed disulfides (PSSG) when compared to the Wt male mice fed the HFD. These effects correlated with the S-glutathionylation of α-ketoglutarate dehydrogenase (KGDH), a potent mtH2O2 source and key redox sensor in hepatic mitochondria. In comparison to the male mice, both Wt and Glrx2-/- female mice displayed almost complete resistance to HFD-induced body mass increases and the development of NAFLD, which was attributed to the superior redox buffering capacity of the liver mitochondria. Together, our findings show that modulation of mitochondrial S-glutathionylation signaling through Glrx2 augments resistance of male mice towards the development of NAFLD through preservation of mitochondrial redox buffering capacity. Additionally, our findings demonstrate the sex dimorphisms associated with the manifestation of NAFLD is related to the superior redox buffering capacity and modulation of the S-glutathionylome in hepatic mitochondria from female mice.

Metabolomic profiles of ovariectomized mice and their associations with body composition and frailty-related parameters in postmenopausal women

BACKGROUND

Menopause, a dramatical estrogen-deficient condition, is considered the most significant milestone in women's health.

PURPOSE

To investigate the metabolite changes attributed to estrogen deficiency using random forest (RF)-based machine learning (ML) modeling strategy in ovariectomized (OVX) mice as well as determine the clinical relevance of selected metabolites in older women.

METHODS AND RESULTS

Untargeted and targeted metabolomic analyses revealed that metabolites related to TCA cycle, sphingolipids, phospholipids, fatty acids, and amino acids, were significantly changed in the plasma and/or muscle of OVX mice. Subsequent ML classifiers based on RF algorithm selected alpha-ketoglutarate (AKG), arginine, carnosine, ceramide C24, phosphatidylcholine (PC) aa C36:6, and PC ae C42:3 in plasma as well as PC aa 34:1, PC aa C34:3, PC aa C36:5, PC aa C32:1, PC aa C36:2, and sphingosine in muscle as top featured metabolites that differentiate the OVX mice from the sham-operated group. When circulating levels of AKG, arginine, and carnosine, which showed the most significant changes in OVX mice blood, were measured in postmenopausal women, higher plasma AKG levels were associated with lower bone mass, weak grip strength, poor physical performance, and increased frailty risk.

CONCLUSIONS

Metabolomics- and ML-based methods identified the key metabolites of blood and muscle that were significantly changed after ovariectomy in mice, and the clinical implication of several metabolites was investigated by looking at their correlation with body composition and frailty-related parameters in postmenopausal women. These findings provide crucial context for understanding the diverse physiological alterations caused by estrogen deficiency in women.

Chrysophanol inhibits of colorectal cancer cell motility and energy metabolism by targeting the KITENIN/ErbB4 oncogenic complex

BACKGROUND

Expression of the KITENIN/ErbB4 oncogenic complex is associated with metastasis of colorectal cancer to distant organs and lymph nodes and is linked with poor prognosis and poor survival.

METHODS

Here, we used in vitro and in silico methods to test the ability of chrysophanol, a molecule of natural origin, to suppress the progression of colorectal cancer by targeting the KITENIN/ErbB4 complex.

RESULTS

Chrysophanol binds to ErbB4, disrupting the ErbB4/KITENIN complex and causing autophagic degradation of KITENIN. We demonstrated that chrysophanol binds to ErbB4 according to a molecular docking model. Chrysophanol reversed KITENIN-mediated effects on cell motility, aerobic glycolysis, and expression of downstream effector genes. Moreover, under conditions of KITENIN overexpression, chrysophanol suppressed the production of onco-metabolites.

CONCLUSION

Chrysophanol suppresses oncogenic activities by targeting the KITENIN/ErbB4 complex.

Unique metabolomics characteristics for distinguishing cirrhosis related to different liver diseases: A systematic review and meta-analysis

BACKGROUND AND AIM

Clinical evidence for early identification and diagnosis of liver cirrhosis (LC) caused by different types of liver disease is limited. We investigated this topic through a meta-analysis of quantitative metabolomics.

METHODS

Four databases were searched until October 31, 2022 for studies comparing metabolite levels between patients with different types of liver disease and control individuals. A random-effects model was applied for the meta-analysis.

RESULTS

This study included 55 studies with 8266 clinical participants, covering 348 metabolites. In LC related to drug-induced liver injury (DILI), hepatitis B virus (HBV) infection, and non-alcoholic fatty liver disease (NAFLD), the primary bile acid biosynthesis (taurocholic acid: SMD, 1.08[0.81, 1.35]; P < 0.00001; glycocholic acid: SMD, 1.35[1.07, 1.62]; P < 0.00001; taurochenodeoxycholic acid: SMD, 1.36[0.94, 1.78]; P < 0.00001; glycochenodeoxycholic acid: SMD, 1.49[0.93, 2.06]; P < 0.00001), proline and arginine (l-proline: SMD, 1.06[0.53, 1.58]; P < 0.0001; hydroxyproline: SMD, 0.81[0.30, 1.33]; P = 0.002), and fatty acid biosynthesis (palmitic acid: SMD, 0.44[0.21, 0.67]; P = 0.0002; oleic acid: SMD, 0.46[0.19, 0.73]; P = 0.0008; stearic acid: SMD, 0.37[0.07, 0.68]; P = 0.02) metabolic pathways were significantly altered.

CONCLUSION

We identified key biomarkers and metabolic characteristics for distinguishing and identifying LC related to different types of liver disease, providing a new perspective for early diagnosis, disease monitoring, and precise treatment.

Amino acid metabolomics and machine learning for assessment of post-hepatectomy liver regeneration

Objective

Amino acid (AA) metabolism plays a vital role in liver regeneration. However, its measuring utility for post-hepatectomy liver regeneration under different conditions remains unclear. We aimed to combine machine learning (ML) models with AA metabolomics to assess liver regeneration in health and non-alcoholic steatohepatitis (NASH).

Methods

The liver index (liver weight/body weight) was calculated following 70% hepatectomy in healthy and NASH mice. The serum levels of 39 amino acids were measured using ultra-high performance liquid chromatography-tandem mass spectrometry analysis. We used orthogonal partial least squares discriminant analysis to determine differential AAs and disturbed metabolic pathways during liver regeneration. The SHapley Additive exPlanations algorithm was performed to identify potential AA signatures, and five ML models including least absolute shrinkage and selection operator, random forest, K-nearest neighbor (KNN), support vector regression, and extreme gradient boosting were utilized to assess the liver index.

Results

Eleven and twenty-two differential AAs were identified in the healthy and NASH groups, respectively. Among these metabolites, arginine and proline metabolism were commonly disturbed metabolic pathways related to liver regeneration in both groups. Five AA signatures were identified, including hydroxylysine, L-serine, 3-methylhistidine, L-tyrosine, and homocitrulline in healthy group, and L-arginine, 2-aminobutyric acid, sarcosine, beta-alanine, and L-cysteine in NASH group. The KNN model demonstrated the best evaluation performance with mean absolute error, root mean square error, and coefficient of determination values of 0.0037, 0.0047, 0.79 and 0.0028, 0.0034, 0.71 for the healthy and NASH groups, respectively.

Conclusion

The KNN model based on five AA signatures performed best, which suggests that it may be a valuable tool for assessing post-hepatectomy liver regeneration in health and NASH.

Harnessing Metabolites as Serum Biomarkers for Liver Graft Pathology Prediction Using Machine Learning

Graft injury affects over 50% of liver transplant (LT) recipients, but non-invasive biomarkers to diagnose and guide treatment are currently limited. We aimed to develop a biomarker of graft injury by integrating serum metabolomic profiles with clinical variables. Serum from 55 LT recipients with biopsy confirmed metabolic dysfunction-associated steatohepatitis (MASH), T-cell mediated rejection (TCMR) and biliary complications was collected and processed using a combination of LC-MS/MS assay. The metabolomic profiles were integrated with clinical information using a multi-class Machine Learning (ML) classifier. The model's efficacy was assessed through the Out-of-Bag (OOB) error estimate evaluation. Our ML model yielded an overall accuracy of 79.66% with an OOB estimate of the error rate at 19.75%. The model exhibited a maximum ability to distinguish MASH, with an OOB error estimate of 7.4% compared to 22.2% for biliary and 29.6% for TCMR. The metabolites serine and serotonin emerged as the topmost predictors. When predicting binary outcomes using three models: Biliary (biliary vs. rest), MASH (MASH vs. rest) and TCMR (TCMR vs. rest); the AUCs were 0.882, 0.972 and 0.896, respectively. Our ML tool integrating serum metabolites with clinical variables shows promise as a non-invasive, multi-class serum biomarker of graft pathology.

The comprehensive mechanism underlying Schisandra polysaccharide in AD-like symptoms of Aβ25-35-induced rats based on hippocampal metabolomics and serum lipidomics techniques

As is well documented, Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. Meanwhile, Schisandra polysaccharide (SCP) has been reported to exert a protective effect on the nervous system and can regulate metabolic disorders in AD-like symptoms of amyloid β-peptide (Aβ) 25-35-induced rats. Nevertheless, the underlying mechanisms and metabolic markers for the diagnosis of AD are yet to be determined. This study aimed to explore the neuroprotective effect and potential mechanism of action of SCP in AD-like symptoms of Aβ25-35-induced rats by combining pharmacodynamics, metabolomics, and lipidomics. The pharmacodynamic results revealed that SCP significantly improved the spatial learning and long-term memory function and the morphology of neurons in the hippocampal CA1 region, alleviated inflammatory damage and oxidative stress, inhibited the activation of microglia and astrocytes, and increased the proportion of mature neurons of AD-like symptoms of Aβ25-35-induced rats. The results of hippocampal metabolomics and serum lipidomics showed 46 and 48 potential biomarkers were identified for the SCP treatment of AD, respectively. The involved pathways principally comprised lipid metabolism, amino acid metabolism, and energy metabolism. This study elucidates the neuroprotective effect of SCP in AD and its mechanism from the perspective of metabolomics and lipidomics and provides a theoretical basis for the therapeutic effect of SCP in AD.

Insufficient sleep and weekend recovery sleep: classification by a metabolomics-based machine learning ensemble

Although weekend recovery sleep is common, the physiological responses to weekend recovery sleep are not fully elucidated. Identifying molecular biomarkers that represent adequate versus insufficient sleep could help advance our understanding of weekend recovery sleep. Here, we identified potential molecular biomarkers of insufficient sleep and defined the impact of weekend recovery sleep on these biomarkers using metabolomics in a randomized controlled trial. Healthy adults (n = 34) were randomized into three groups: control (CON: 9-h sleep opportunities); sleep restriction (SR: 5-h sleep opportunities); or weekend recovery (WR: simulated workweek of 5-h sleep opportunities followed by ad libitum weekend recovery sleep and then 2 days with 5-h sleep opportunities). Blood for metabolomics was collected on the simulated Monday immediately following the weekend. Nine machine learning models, including a machine learning ensemble, were built to classify samples from SR versus CON. Notably, SR showed decreased glycerophospholipids and sphingolipids versus CON. The machine learning ensemble showed the highest G-mean performance and classified 50% of the WR samples as insufficient sleep. Our findings show insufficient sleep and recovery sleep influence the plasma metabolome and suggest more than one weekend of recovery sleep may be necessary for the identified biomarkers to return to healthy adequate sleep levels.

Characterization of Salivary and Plasma Metabolites as Biomarkers for HCC: A Pilot Study

(1) Background: The incidence of hepatocellular carcinoma (HCC) is rising, and current screening methods lack sensitivity. This study aimed to identify distinct and overlapping metabolites in saliva and plasma that are significantly associated with HCC. (2) Methods: Saliva samples were collected from 42 individuals (HCC = 16, cirrhosis = 12, healthy = 14), with plasma samples from 22 (HCC = 14, cirrhosis = 2, healthy = 6). We performed untargeted mass spectrometry on blood and plasma, tested metabolites for associations with HCC or cirrhosis using a logistic regression, and identified enriched pathways with Metaboanalyst. Pearson's correlation was employed to test for correlations between salivary and plasma metabolites. (3) Results: Six salivary metabolites (1-hexadecanol, isooctanol, malonic acid, N-acetyl-valine, octadecanol, and succinic acid) and ten plasma metabolites (glycine, 3-(4-hydroxyphenyl)propionic acid, aconitic acid, isocitric acid, tagatose, cellobiose, fucose, glyceric acid, isocitric acid, isothreonic acid, and phenylacetic acid) were associated with HCC. Malonic acid was correlated between the paired saliva and plasma samples. Pathway analysis highlighted deregulation of the 'The Citric Acid Cycle' in both biospecimens. (4) Conclusions: Our study suggests that salivary and plasma metabolites may serve as independent sources for HCC detection. Despite the lack of correlation between individual metabolites, they converge on 'The Citric Acid Cycle' pathway, implicated in HCC pathogenesis.

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

The liver is a multifaceted organ that is responsible for many critical functions encompassing amino acid, carbohydrate, and lipid metabolism, all of which make a healthy liver essential for the human body. Contemporary imaging methodologies have remarkable diagnostic accuracy in discerning focal liver lesions; however, a comprehensive understanding of diffuse liver diseases is a requisite for radiologists to accurately diagnose or predict the progression of such lesions within clinical contexts. Nonetheless, the conventional attributes of radiological features, including morphology, size, margin, density, signal intensity, and echoes, limit their clinical utility. Radiomics is a widely used approach that is characterized by the extraction of copious image features from radiographic depictions, which gives it considerable potential in addressing this limitation. It is worth noting that functional or molecular alterations occur significantly prior to the morphological shifts discernible by imaging modalities. Consequently, the explication of potential mechanisms by multiomics analyses (encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics) is essential for investigating putative signal pathway regulations from a radiological viewpoint. In this review, we elaborate on the principal pathological categorizations of diffuse liver diseases, the evaluation of multiomics approaches pertaining to diffuse liver diseases, and the prospective value of predictive models. Accordingly, the overarching objective of this review is to scrutinize the interrelations between radiological features and bioinformatics as well as to consider the development of prediction models predicated on radiobioinformatics as integral components of clinical decision support systems for diffuse liver diseases.

Multidimensional landscape of non-alcoholic fatty liver disease-related disease spectrum uncovered by big omics data: Profiling evidence and new perspectives

Characterized by hepatic lipid accumulation, non-alcoholic fatty liver disease (NAFLD) is a multifactorial metabolic disorder that could promote the progression of non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma (HCC). Benefiting from recent advances in omics technologies, such as high-throughput sequencing, voluminous profiling data in HCC-integrated molecular science into clinical medicine helped clinicians with rational guidance for treatments. In this review, we conclude the majority of publicly available omics data on the NAFLD-related disease spectrum and bring up new insights to inspire next-generation therapeutics against this increasingly prevalent disease spectrum in the post-genomic era.

Circulating Metabolomic and Lipidomic Signatures Identify a Type 2 Diabetes Risk Profile in Low-Birth-Weight Men with Non-Alcoholic Fatty Liver Disease

The extent to which increased liver fat content influences differences in circulating metabolites and/or lipids between low-birth-weight (LBW) individuals, at increased risk of type 2 diabetes (T2D), and normal-birth-weight (NBW) controls is unknown. The objective of the study was to perform untargeted serum metabolomics and lipidomics analyses in 26 healthy, non-obese early-middle-aged LBW men, including five men with screen-detected and previously unrecognized non-alcoholic fatty liver disease (NAFLD), compared with 22 age- and BMI-matched NBW men (controls). While four metabolites (out of 65) and fifteen lipids (out of 279) differentiated the 26 LBW men from the 22 NBW controls (p ≤ 0.05), subgroup analyses of the LBW men with and without NAFLD revealed more pronounced differences, with 11 metabolites and 56 lipids differentiating (p ≤ 0.05) the groups. The differences in the LBW men with NAFLD included increased levels of ornithine and tyrosine (PFDR ≤ 0.1), as well as of triglycerides and phosphatidylcholines with shorter carbon-chain lengths and fewer double bonds. Pathway and network analyses demonstrated downregulation of transfer RNA (tRNA) charging, altered urea cycling, insulin resistance, and an increased risk of T2D in the LBW men with NAFLD. Our findings highlight the importance of increased liver fat in the pathogenesis of T2D in LBW individuals.

Impaired Function of Solute Carrier Family 19 Leads to Low Folate Levels and Lipid Droplet Accumulation in Hepatocytes

Low serum folate levels are inversely related to metabolic associated fatty liver disease (MAFLD). The role of the folate transporter gene (SLC19A1) was assessed to clarify its involvement in lipid accumulation during the onset of MAFLD in humans and in liver cells by genomic, transcriptomic, and metabolomic techniques. Genotypes of 3 SNPs in a case-control cohort were initially correlated to clinical and serum MAFLD markers. Subsequently, the expression of 84 key genes in response to the loss of SLC19A1 was evaluated with the aid of an RT² profiler-array. After shRNA-silencing of SLC19A1 in THLE2 cells, folate and lipid levels were measured by ELISA and staining techniques, respectively. In addition, up to 482 amino acids and lipid metabolites were semi-quantified in SLC19A1-knockdown (KD) cells through ultra-high-performance liquid chromatography coupled with mass spectrometry. SNPs, rs1051266 and rs3788200, were significantly associated with the development of fatty liver for the single-marker allelic test. The minor alleles of these SNPs were associated with a 0.6/-1.67-fold decreased risk of developing MAFLD. When SLC19A1 was KD in THLE2 cells, intracellular folate content was four times lower than in wild-type cells. The lack of functional SLC19A1 provoked significant changes in the regulation of genes associated with lipid droplet accumulation within the cell and the onset of NAFLD. Metabolomic analyses showed a highly altered profile, where most of the species that accumulated in SLC19A1-KD-cells belong to the chemical groups of triacylglycerols, diacylglycerols, polyunsaturated fatty acids, and long chain, highly unsaturated cholesterol esters. In conclusion, the lack of SLC19A1 gene expression in hepatocytes affects the regulation of key genes for normal liver function, reduces intracellular folate levels, and impairs lipid metabolism, which entails lipid droplet accumulation in hepatocytes.