Metabonomic and transcriptomic analyses of Tripterygium glycosides tablet-induced hepatotoxicity in rats

Abstract We aimed to explore novel biomarkers involved in alterations of metabolism and gene expression related to the hepatotoxic effects of Tripterygium glycosides tablet (TGT) in rats. Rats were randomly divided into groups based on oral administration of TGTs for 6 weeks: control, low-dose (9.5 mg/kg), and high-dose (18.9 mg/kg). Serum samples and total liver RNA were subjected to metabonomic and transcriptomic analyses. Thirteen metabolites were significantly up-regulated by liver injury induced by Tripterygium glycosides. Five potential biomarkers were more sensitive than Alanine aminotransferase (ALT) for accurate and timely prediction of hepatic damage. The four metabolic pathways most obviously regulated by hepatotoxicity were D-glutamine and D-glutamate metabolism, alanine, aspartate and glutamate metabolism, ether lipid metabolism, and tryptophan metabolism. Transcriptomics revealed significant differences in 1792 mRNAs and 400 long non-coding (lnc) RNAs. Dysregulated lncRNAs in the TGT-induced hepatotoxicity group were associated with genes involved in amino acid metabolism using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Up-regulated expression of Ehhadh, Gpt, and Got1, and down-regulated expression of dopa decarboxylase (Ddc), Cyp1a2, Ido2, Aldh1b1, and asparagine synthetase (Asns) was validated by quantitative real-time PCR. This multiomics study has elucidated the relationship between amino metabolism and liver injury, revealing potential biomarkers.


Introduction
Drug-induced liver injury (DILI) remains a significant adverse drug reaction and is a common cause of acute liver failure and death worldwide (Haque et al. 2016). The usefulness of liver biopsy in diagnosing DILI is limited by the complexity of the surgery and confusing histopathological findings that are common to multiple liver disorders. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) are currently used to detect DILI, but changes in these biomarkers are not sufficiently specific to predict an individual's subsequent clinical course. The Roussel Uclaf Causality Assessment Method can be used to confirm or assess the suspicion of DILI, but is has poor inter-rater reliability and an arbitrary scoring system (Danan et al. 2015). The existing diagnostic parameters and approaches for DILI make it challenging to offer accurate and sufficient advice regarding medications being used outside of the clinical trial setting (Ortega-Alonso et al. 2016).
Prescription tablet preparations of Tripterygium glycosides are mainly used for anti-inflammatory and immunomodulatory therapy. Tripterygium glycosides tablets (TGTs) mainly contain wilforlide A, triptolide, triptonide, wilforine, triptophenolide, tripterine, etc. (Wang et al. 2019). The levels of the evidence and the strength of the recommendations of TGT and Tripterygium wilfordii tablets (TWTs) against rheumatoid arthritis systematically and comprehensively had been explored (Lin et al. 2020). The potential toxicity mechanism of acute liver injury induced by TGT and TWT had been determined in mice (Peng et al. 2021, Dai et al. 2022. Peroxisome proliferator-activated receptor (PPAR) signaling pathway and cellular stress are two of the pathways involved in Tripterygium wilfordii multiglycoside resulted subchronic toxicity in Wistar rats (Zhang et al. 2012). CYP2E1 and miRNA-378a-3p might contribute to Tripterygium glycosidesinduced hepatotoxicity (Chen et al. 2020). However, the exploration of TGTs-induced non-acute liver injury in the rat model has not been reported.
A series of metabolites including cytosine, 5-methyluridine, deoxyuridine, 5-methylcytidine, deoxycytidine triphosphate, etc., were shown to be better than traditional blood indicators of liver function in distinguishing between Tripterygium glycosides-treated rats and control rats (Hu et al. 2020). Triptolide, the bioactive component of Tripterygium glycosides, disrupts fatty acids and PPAR levels in the testes of male mice, which leads to testicular injury (Ma et al. 2015). So far there is a lack of systematic and exploratory research on the molecular characteristics and gene regulation involved in TGT-related hepatotoxicity. To date, potential novel biomarkers for the early detection of liver injury induced by TGTs have not been identified. The integration of metabonomics and transcriptomics is an appealing approach for a systematic biological description of drug-induced toxicity.
In this study, the utility of metabonomics and transcriptomics was explored for predicting the response to liver injury induced by TGTs in rats. A robust metabonomics profiling method using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed to detect metabolic phenotype variation in plasma samples. Microarray technology was applied to simultaneously analyze the expression profiles of long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) in the rat liver. The most obviously disturbed metabolic pathways were Dglutamine and D-glutamate metabolism, alanine, aspartate and glutamate metabolism, ether lipid metabolism, and tryptophan metabolism (Figure 1).

Chemicals
TGTs were purchased from Hui-Tian Biological Pharmaceutical Co., Ltd (150302, Fujian, China). Acetonitrile was from Merck (Darmstadt, Germany). Formic acid was from Dikma Technologies Inc. (Foothill Ranch, CA). Purified water was from Hangzhou Wa-Ha-Ha Group Co., Ltd. (Hangzhou, China). All other standards used were of analytical or higher grade.

Animal study and sample collection
Male Wistar rats (n ¼ 108; 6-8 weeks old) were from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). The study was approved by the Animal Ethics Committee of Beijing Chao-Yang Hospital affiliated with Capital Medical University (Beijing, China).
An environmentally controlled room at 24 ± 2 C with a relative humidity of 50 ± 5% was used to house and raise the rats. Rats were randomly divided into three groups based on oral administration of TGTs daily for 6 weeks: control (no treatment), low-dose (9.5 mg/kg, maximum clinical dose of 1.5 mg/kg for adults), and high-dose (18.9 mg/kg, based on preliminary experiments) and each dose group had six rats.
After 7 d of adaptive feeding, TGTs were administered orally according to body weight. Rats in three different dose groups were sacrificed on the 7th, 14th, 21st, 28th, 35th, and 42nd days (Figure 2). Blood was taken from the inferior vena cava, and serum was collected by centrifugation at 5000 Â g at 4 C for 5 min for biochemical analysis. Livers were collected after blood collection.

Sample preparation
A 40-lL aliquot of serum was added to 300 lL cold acetonitrile, vortexed for 5 min, and spun by centrifugation at 10 000 r/min at 4 C for 10 min; 200 lL of the supernatant was placed in a centrifugal concentrator (SAVANT SPD 121 P SpeedVac, Thermo, Waltham, MA) for 60-90 min to dry. Dry residues were reconstituted in 200 lL water containing 2% acetonitrile, vortexed for 3 min, and spun at 10 000 r/min at 4 C for 10 min. The supernatant was taken and transferred to a 96-well filtration plate (Agilent, Santa Clara, CA) for UPLC-MS analysis. Quality control (QC) samples were prepared by pooling equal volumes (30 lL) of serum from each rat to monitor the reproducibility and stability of the UPLC-MS system.
MS spectra were acquired on a Q-Exactive Orbitrap MS system (Thermo, Waltham, MA) equipped with an electrospray ion (ESI) source and Xcalibur data acquisition and processing system. The ionization method was heated ESI using the following parameters: spray voltage, 3500 V(þ)/3300 V(-); sheath gas flow rate, 46 psi(þ)/46 psi(-); aux gas flow rate, 11 psi(þ)/11 psi(-); capillary temperature, 320 C. The instrument operated at a 70 000 resolution with a full-scan acquisition ranging from 100 to 1000 m/z. For confirmation purposes, a targeted MS-MS analysis was performed using the mass inclusion list, with a 30 s time window, with the Orbitrap spectrometer was operating both in positive and negative modes at 17 500 FWHM. The AGC target was set to 1 Â 10 6 , with the maximum injection time of 100 ms. NCE: 25, 35, and 45. The precursor ions were filtered by the quadrupole, which operated at an isolation window of m/z 1. The mass tolerance window was set to 5 ppm for the two modes.

Multivariate data analysis
UPLC-MS raw data were converted to the m/z format using MassMatrix MS Data File Conversion software. The twodimensional data matrix was exported into SIMCA-P software version 13.0 (Umetrics AB, Umeå, Sweden) for multivariate analyses including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and permutation tests.

Evaluation of identical and unique metabolites
In combination with the variable importance in projection (VIP) values of variables in the OPLS-DA model, an S-plot load diagram, the load diagram with jackknifed confidence interval, the original variable contour diagram, and screening and reliability verification of the difference variables were carried out. Metabolites were identified by high-resolution MS, MS/MS spectrum analysis, isotope abundance ratio, and mass spectrum cracking characteristics, and combined with searches for exact molecular weight entities in free databases. 2.6. Transcriptomics 2.6.1. RNA extraction and purification Total RNA in liver tissues was quantified using NanoDrop ND2000 (Thermo, Waltham, MA), and the RNA integrity and purity were evaluated using an Agilent 2100 Bioanalyzer and 1% agarose electrophoresis. An RNA integrity number (RIN) > 7.0 and 28S rRNA/18S rRNA ratio > 0.7 were considered to indicate good RNA integrity. The absorbance values of RNA at 260 and 280 nm were also determined; the OD260/280 value was around 2.0. Liver total RNA extraction and gene chip expression profiling were performed by Shanghai OE Biotechnology Co., Ltd. (Shanghai, China).

Microarray assay
Total liver RNA was reverse transcribed into double-stranded cDNA, and used for synthesis of cRNA labeled with biotin cyanine-3-ctp (Cy3). The labeled cRNA was hybridized with an Agilent rat lncRNA Array chip, and the original probe signal was obtained using an Agilent Scanner G2505C scanning chip after elution. Feature Extraction software version 10.7.1.1 (Agilent, Santa Clara, CA) was used to process the original image and extract the original data, which were quantile standardized by GeneSpring software version 13.1 (Agilent, Santa Clara, CA).

Bioinformatics analysis of microarray data
Fold change (FC) values and p values of t-tests were used to screen differentially expressed genes. The screening criteria were set at FC > 3.0 and p < 0.05. Genes were further biologically interpreted on the basis of the GO Consortium (http://www.geneontology.org/GO.doc.html) and the KEGG public pathway resource (http://www.genome.jp/kegg).

QRT-PCR validation
Total RNA was reverse transcribed into cDNA using the GeneAmp PCR System 9700 (Applied Biosystems, Waltham, MA). RT-PCR was performed using a LightCycler 480II Realtime PCR Instrument (Roche, Basel, Switzerland). Reactions were carried out at 95 C for 5 min, followed by 40 cycles of 95 C for 10 s and 60 C for 30 s. Melting curve analysis was performed to validate the specific generation of the expected PCR product. Primer sequences were designed in the laboratory and synthesized by Generay Biotech (Generay, Shanghai, China) based on mRNA sequences obtained from the NCBI database. The expression levels of mRNAs and lncRNAs were calculated using the 2 ÀDDCt method (Livak and Schmittgen 2001). The specific primers for each gene are listed in Table S1.

Histopathological examination
The main pathological changes in liver tissues were steatosis, lobular inflammation, and balloon-like degeneration of hepatocytes ( Figure 3). DILI may trigger the transition of simple fatty liver to NAFLD, or worsen hepatic lipid accumulation, necroinflammation, and fibrosis (Allard et al. 2019). A histological scoring system for NAFLD designed by the Pathology Committee of NASH CRN was applied to evaluate and summarize the pathological features of liver injury induced by TGTs. The injury scores showed that high-dose TGT-treated rats had suffered from steatosis, lobular inflammation, and balloon-like degeneration; even fibrosis occurred in the final 2 weeks (Table 1).

Biochemical analysis
Biochemical changes in serum are shown in Table S2. Treatment with TGTs induced a significant increase in the activity of serum AST in both the high-dose and low-dose groups from weeks 1-4 ( Figure 2(a)). Compared with the control group, ALT and ALP levels showed a statistically significant increase in the first week, but were not significantly different thereafter (Figure 2(b,e)). TBIL increased significantly in the high-dose group at week 3 (Figure 2(c)). GGT and TG levels were not significantly different between the treatment and control groups ( Figure 2(d,f)). To a certain extent, these results indicated that the clinical liver injury index was synchronous with the degree of histopathological liver damage.

Assessment of data quality
PCA was employed for the analysis of QC samples, which were all in the 95% confidence interval ( Figure S1). The peak area deviation of QC samples was within two-fold of the standard deviation visualized in the line plots of the PCA ( Figure S2). The retention time deviation of each serum sample generated by XCMS software presented ±10 s and ±6 s of fluctuation in both positive and negative ionization mode, respectively, using LC-MS/MS ( Figure S3). The typical total ion chromatograms of serum samples produced by LC with or without ESI-MS are presented in Figure S4. The above results suggested that the systematic error of the samples was small in the pretreatment and detection process, and that the analysis method was stable.

Multivariate statistical analysis
The LC-ESI-MS datasets obtained in positive and negative ion modes contained 4019 and 2064 peaks. To observe the metabolic profiles of rats before and after administration of TGTs and subsequent liver injury, the control group, and high-dose group serum samples were selected for multivariate analysis. The degree of liver dysfunction showed only a minor difference between the fifth and sixth weeks, thus changes in the first 5 weeks are shown in Figure S5. The obvious separation trend between each group indicated that the pathological damage was gradually aggravated in parallel with the occurrence of liver injury.
OPLS-DA and PLS-DA were carried out for multivariate data analysis and permutation tests. Graphical representations of the score plots showed clear discrimination between Table 1. Nonalcoholic fatty liver disease activity scores of liver injury in rats treated with Tripterygium glycosides tablets.

Characterization of potential biomarkers
Receiver-operating characteristic (ROC) curve analysis was conducted to further characterize the utility of these potential biomarkers for the prediction of liver injury. We used 0.7 < AUC < 0.9 to indicate a relatively good diagnostic value. SPSS version 16.0 software (SPSS Inc., Chicago, IL) was used for ROC curve analysis. Figure S6(A) shows the ROC curve of four potential biomarkers up-regulated in the serum of rats with liver injury; the AUCs of 2-methyl-3-ketovaleric acid and vinylacetylglycine were 0.727 and 0.840, respectively. Figure S6(B) shows the ROC curve of nine potential biomarkers down-regulated in the serum of rats with liver injury; the AUCs of 3-indolebutyric acid, LPC(20:2), and LPC(22:6) were 0.828, 0.735, and 0.715, respectively.
We compared the changes in the levels of ALT and five potential biomarkers with good diagnostic value in the group of rats with mild pathological changes. The levels of 2-methyl-3-ketovaleric acid and vinylacetylglycine were significantly increased, and those of 3-indolebutyric acid, LPC(20:2), and LPC(22:6) levels were significantly decreased, but there was no apparent change in ALT levels ( Figure 6). These results proved that these five potential biomarkers were more sensitive than ALT for accurate and timely prediction of hepatic damage.

Metabolic pathway analysis
Thirteen metabolic pathways were shown to be related to the pathogenesis of DILI by the pathway analysis module of MetaboAnalyst version 3.0 (www.metaboanalyst.ca) ( Table 3). There were four pathways with a pathway impact (PI) > 0.1 (D-glutamine and D-glutamate metabolism, alanine, aspartate and glutamate metabolism, ether lipid metabolism, and tryptophan metabolism), which implied important interactions in TGT-induced liver injury in rats (Figure 7).

Transcriptomics
Based on the biochemical levels and pathology results, samples from the control group and high-dose TGTs-treated subgroups (high-dose group weeks 1, weeks 2, and weeks 4) were selected for differential gene expression analysis. PCA was performed on all probes on the chip to investigate the distribution of samples. Three samples were randomly selected from six samples taken each week and used for detection ( Figure S7). The high-dose group weeks 4 was ultimately chosen to screen differentially expressed genes compared with control group.

mRNA and lncRNA expression profiles in DILI
There were 1792 mRNAs (800 up-regulated and 992 downregulated) and 400 lncRNAs (194 up-regulated and 206 down-regulated) that showed significant differences (FC ! 2.0, p < 0.05). The top 50 most significantly differentially expressed genes are shown in Table 4 (mRNA) and Table 5 (lncRNA). Hierarchical clustering analysis was performed to show the variation in mRNA and lncRNA expression between the TGT-induced liver injury group and control group ( Figure  8). Of those mRNAs, the expression of Adh6 apparently decreased with a FC of 986.24786, while Hspa1b increased by a FC of 14.64421. Of the dysregulated lncRNAs, TCONS_00106261 and ENSRNOT00000021529 were downregulated and up-regulated by FCs of 23.72614 and 17.91367, respectively.

Correlation pathway analysis of differentially expressed genes
We computed the hypergeometric cumulative distribution to calculate the enrichment of terms with functionality in annotation of co-expressed mRNAs. As shown in Figure S8(A), the GO analysis revealed six metabolic pathways associated with amino acids. Among them, negative regulation of glutamate secretion (GO: 0014050), glutamine metabolic process (GO: 0006541), and tryptophan catabolic process to kynurenine (GO: 0019441) were consistent with the metabolic pathways found in the serum metabonomics described in Section 3.2. Simultaneously, KEGG pathway analysis also revealed matches with the serum metabolic pathways: arginine biosynthesis (Ko00220), tryptophan metabolism (Ko00380), alanine, aspartate, and glutamate metabolism (Ko00250), tyrosine metabolism (Ko00350), nitrogen metabolism (Ko00910), and histidine metabolism (Ko00340). The top 40 pathways of our differentially expressed genes as identified in KEGG are listed in Figure S8(B).

Discussion
The multiple clinical presentations and deficiency of specific biomarkers create challenges for the clinical diagnosis and treatment of DILI. Our integration of the results of metabonomic and transcriptomic analyses to investigate metabolic changes and related pathways could provide new insights into the understanding of TGT-induced liver injury. Thirteen potential biomarkers were identified from metabonomic analysis of serum from liver-injured rats. Macrophage activation is considered to be one of the characteristics of chronic liver disease. Picolinic acid, a macrophage secondary signal related to the activation of interferon-c, primes macrophages and triggers cytokine-driven inflammatory reactions. Elevated picolinic acid assessed in patients with chronic hepatitis C has the opposite effect in rats with DILI (Zuwała-Jagiello et al. 2012). Increased L-histidinol levels have been found in long-term environmental exposure to cadmium (Gao et al. 2014). As a common subordinate metabolite of fatty acids, urinary vinylacetylglycine is statistically increased in acrylamide-treated rats (Shi et al. 2017). In this study, the increased vinylacetylglycine level in the TGTinduced liver injury group indicated that fatty acid metabolism might be affected and disturbed. Glutamic acid is considered to be involved in multiple biological and pathological functions in peripheral tissues, including the lung, kidney, liver, heart, stomach, and immune system (Du et al. 2016). Glutamic acid participates in D-glutamine and D-glutamate metabolism, and alanine, aspartate, and glutamate metabolism. Gentisic acid is an active metabolite of salicylic acid degradation and efficiently scavenges hydroxyl radicals. The upregulation of serum gentisic acid confirmed the occurrence of liver damage in our study. The metabolite 7-methylguanine is a product of DNA methylation and is correlative with DNA damage. The formation of 7-methylguanine was demonstrated in the liver of rats administered with hydrazine in a previous study (Becker et al. 1981). Additionally, gentisic acid and 7-methylguanine are thought to have a clear, distinctive effect on liver and renal dysfunction induced by pesticides (Qi et al. 2017). Serotonin was reported to relieve acetaminophen (APAP)-induced liver injury by promoting liver regeneration and inhibiting endoplasmic reticulum stress in hepatocytes undergoing apoptosis (Zhang et al. 2015). Therefore, decreased serotonin levels might be an important indicator related to liver injury. Serotonin is also an important metabolite in tryptophan metabolism. Other potential biomarkers identified in the serum of rats were LPCs. Decreased levels of LPCs represent an immune-suppressive function to prevent recovery of the damaged liver (Liu et al. 2013). Similar LPC trends were also observed in NAFLD, cirrhosis, valproate sodium-induced hepatotoxicity, and D-galactosamine-induced liver injury (Huo et al. 2014).
Combining the biological databases, four highly correlated metabolic pathways were predicted based on the liver injuryspecific metabolites mentioned above. The most obviously disturbed metabolic pathways were D-glutamine and D-glutamate metabolism, alanine, aspartate, and glutamate Figure 7. Metabolic pathway analysis for potential biomarkers related to liver-injury induced by Tripterygium glycosides tablets in rats. Pathways with stronger correlation are represented by larger nodes with stronger red coloring (pathway impact > 0.1). metabolism, ether lipid metabolism, and tryptophan metabolism. Simultaneously, a functional enrichment analysis was performed using the genes encoding the significantly differentially expressed and mRNAs and lncRNAs. Based on GO analysis, the biological processes of these lncRNAs involve negative regulation of glutamate secretion, including glutamine metabolism and catabolism of tryptophan to kynurenine. Indications that the genes associated with the dysregulated lncRNAs in the TGT-induced liver injury group were involved in tryptophan metabolism and alanine, aspartate, and glutamate metabolism stemmed from KEGG pathway analysis. The results of the two omics are consistent. Notably, the up-regulation of Ehhadh, Gpt, and Got1 and down-regulation of Ddc, Cyp1a2, Ido2, Aldh1b1, and Asns between the DILI and control groups shown in microarray analysis were validated by identical results using qRT-PCR. There might be unique lncRNAs and mRNA co-expression signatures in these tissues. In a previous study, Ehhadh was one of a number of up-regulated genes observed between a standard chow diet group and a high-fat diet group, which had a lower birth weight but increased body weight with impaired glucose tolerance, higher serum cholesterol, and hepatic steatosis at weaning (Huang et al. 2017). Ehhadh was also shown to be a significantly down-regulated, crucial gene for hepatocellular carcinoma pathogenesis by gene expression microarray technology and bioinformatics methods (Gao et al. 2018  immunohistochemical staining technique (Suto et al. 1999). Ehhadh was identified as an up-regulated gene related to toxic compounds, and was then successfully tested with structural analogs of valproic acid (Grinberg et al. 2018). Ehhadh is also regarded as a pathogenic biomarker for hepatocellular carcinoma based on RNA-seq analyses (Jiang et al. 2019). The gluconeogenesis enzyme that Gpt encodes, cytosolic alanine aminotransaminase 1, catalyzes alanine, and 2oxoglutarate to generate pyruvate and glutamate. Gpt has been used as a blood biomarker for hepatic damage (Honma et al. 2017). The expression levels of Got and Gpt were remarkably reduced in the mice administered with Ginsenoside Rg1 compared with the mice suffering from APAP-induced liver injury (Bi et al. 2021).
Ddc encodes a protein that leads multiple reactions, catalyzing L-3,4-dihydroxyphenylalanine (DOPA), L-5-hydroxytryptophan, and L-tryptophan to dopamine, serotonin, and tryptamine, respectively. Defects in Ddc cause aromatic Lamino-acid decarboxylase deficiency, which contribute to problems, such as inborn errors in neurotransmitter metabolism that lead to combined serotonin and catecholamine deficiency. Cytochrome P450 (CYP) enzymes are important phase I enzymes in the biotransformation of xenobiotics (Wrighton and Stevens 1992). Cyp1a2 is one of the major CYP enzymes in the human liver and plays an important role in the metabolism of commonly used clinical drugs Zhou 2009, Singh et al. 2010). Cyp1a2polymorphism may contribute to agomelatine-induced acute liver injury and dendrobine Table 5. Top 50 dysregulated lncRNAs in microarray analysis between the control group (BK0) and high-dose Tripterygium glycosides tablets-treated group (H4). Primary ID: long non-coding (lnc)RNA name; p values: calculated from unpaired t-test; Fold change: absolute ratio of normalized intensities between BK0 and H4. Chrom: chromosome number from which lncRNA is transcribed; Strand: the strand of chromosome from which lncRNA is transcribed (þ for sense strand and À for antisense strand).
ameliorated isoniazid (INH) and rifampicin (RIF) induced mouse liver injury by miR-295-5p-mediated Cyp1a2 expression (Wang et al. 2021a(Wang et al. , 2021b. Along with the enzymes encoded by the indoleamine-pyrrole 2,3 dioxygenase (INDO) and tryptophan 2,3-dioxygenase (TDO2) genes, Ido2 encoded by the INDOL1 gene metabolizes tryptophan in the kynurenine pathway. The action of Ido2 in the liver may prevent severe hepatocellular damage and liver fibrosis (Hoshi et al. 2020). Aldh1b1 belongs to the aldehyde dehydrogenase family, and is the second enzyme of the major oxidative pathway of alcohol metabolism. Canagliflozin treatment activated AMP-activated protein kinase (AMPK), leading to increased nuclear translocation of nuclear factor erythroid 2-related factor 2 (Nrf2) and activating transcription factor 4 (ATF4), which upregulated Asns expression in liver-injured rats (Wang et al. 2021a(Wang et al. , 2021b. Ours is the first study to show Ehhadh, Gpt, Got1 Cyp1a2, Aldh1b1, and Asns expression in relation to liver metabolism in rats with non-acute liver injury induced by TGTs. In particular, our data suggest that Ido2 and Ddc might be responsible for the regulation of amino acid metabolism in liver injury. In conclusion, this study is the first to delineate the molecular pathway activities of amino acids and genes involved in amino acid metabolism that contribute to the pathogenesis of hepatotoxicity induced by TGTs (Figure 9). These findings could provide novel insights regarding Figure 8. Heat maps of the top 50 differentially expressed mRNAs (a) and long non-coding (lnc)RNAs (b) between the high-dose group of liver injury and control groups. The relative expression of genes is displayed according to the color gradient shown at the top; red and green indicate relatively high and low expression, respectively. potential metabolites, mRNAs, and lncRNAs for early-warning diagnostic methods and mechanistic exploration of DILI. Further studies are required to fully validate these biomarkers and pathways in larger cohorts of feasible patients. Figure 9. The metabolic pathways and genetic differences induced by Tripterygium glycosides tablets in rats. The red ellipse represents up regulation and the green ellipse represents down regulation.