RNA-sequencing analysis of the effect of luteolin on methamphetamine-induced hepatotoxicity in rats: a preliminary study

Reagents

Methamphetamine was purchased from the National Institute for the Control of Pharmaceutical and Biological Products (Beijing, China, purity >99%). Luteolin was purchased form Push Bio-Technology Co., Ltd. (Chengdu, China, purity >96%).

Experimental animals and treatments

Adult male Sprague Dawley (SD) rats weighing between 200 and 250 g, obtained from the Laboratory Animal Center of Southern Medical University, were randomly divided into three groups of six animals each and used for experiments. The rats were housed on a 12 h light-dark cycle (temperature 22 ± 2 °C) and habituated in tub cages for 7 days before use. All the animal procedures were conducted in accordance with Institutional Guidelines for the Care and Use of Laboratory Animals of Southern Medical University (SCKY (Guangdong) 44002100016156).

The acute hepatotoxicity animal model was induced by METH. The detailed procedure based on previous studies (Wang et al., 2017a; Xie et al., 2018b) was as follows: 15 mg/mL/kg, eight injections (i.p.) at 12 h intervals. Randomized grouping, three groups (six rats, each group; 18 rats in total): (1) control group: the rats were administered the solvents that were incorporated as experiment groups. They were administrated with 0.5% sodium dodecyl sulfate (SDS) by oral gavage for 3 days (once daily) and followed with normal saline injections for 4 days (i.p.). (2) METH group: the rats were administrated with 0.5% SDS for 3 days, then were given METH injections for 4 days (i.p.). (3) luteolin pre-treated group: luteolin (100 mg/kg, was administered by oral gavage for 3 days (once daily) (Tai et al., 2015)) before METH treatment. Luteolin was dissolved in 0.5% SDS water solution. After 12 h, all the rats were sacrificed under deep anesthesia with pentobarbital sodium (60 mg/kg i.p.). Blood samples and livers were taken from the rats immediately and placed into trace element free tubes. All the samples were kept at −80 °C.

Animal welfare

Rats were housed in SPF-class animal laboratory with air-conditioning and 12 h dark-light cycle, ambient temperature at 23 °C (±2 °C), and relative humidity of 50%. Rats were feed food and tap water. The rats were acclimatized for at least one week. The procedures used were as humane as possible. All studies involving animals are reported in accordance with the ARRIVE and US NIH guidelines for reporting experiments involving animals.

Histopathology

Liver tissue was removed from the rats and fixed by 10% formalin (v/v) solution for 24–48 h. The liver tissue was embedded in paraffin, and was then stained with hematoxylin and eosin (H&E). The pathological change area of the livers was observed under light microscope (DP-73; Olympus, Tokyo, Japan).

Biochemical analysis

Blood samples were centrifuges at 800 g for 10 min at room temperature. Plasma was then used to evaluate the extent of hepatic injury by measuring serum levels of aspartate transaminase (AST) and alanine transaminases (ALT) via detected by Enzyme-Linked Immunosorbent Assay (CUSABIO Biotechnology, Wuhan, China ) (Wang et al., 2017a).

RNA extraction and quality control

Trizol Reagent was used to extract total RNA from the liver tissue, and DNase I was used to remove gDNA from the total RNA. NanoDrop, 2100 Bioanalyser, agarose gel electrophoresis of RNA, and Qubi were utilized to analyze the quality and the quantity of the total RNA extracted from the liver tissue (Botling & Micke, 2011). The quality and quantity of the total RNA that was used to construct the transcriptome RNA-seq library met the following standards: OD260/280 = 1.8~2.2, OD260/230 ≥ 2.0, RIN ≥ 6.5, 28S:18S ≥ 1.0 and >10 μg.

Library preparation and Illumina Hiseq xten Sequencing

A total of five μg of total RNA was pretreated for construction of the sequencing library according to the manufacturer’s instructions of the RNA preparation kit (Illumina, San Diego, CA, USA). Next the mRNA was isolated and enriched by oligo (dT) beads by combining with the polyA tail of mRNA. The isolated mRNA was fragmented into tiny fragments, and reverse transcripted to the complementary DNA (cDNA) in the form of double strands by using fragmentation buffer and a cDNA synthesis kit. Then the cDNA was pretreated according to the manufacturer’s instructions of the sequencing library construction. The cDNA of 200–300 bp in the cDNA pool was extracted by using agarose gel electrophoresis of DNA and amplified by PCR (Kumar et al., 2012). Lastly, the amplification product was sequenced by using the Illumina HiSeq xten (Major Biotechnology Company, Shanghai, China) according to the manufacturer’s instructions.

Read mapping

In order to get clean reads, the raw reads should be filtered by the following standards: (1) remove the reads with adapters; (2) remove the unknown reads with a proportion of more than 10% in the raw reads; (3) remove the low-quality reads. After removing the mentioned three types of reads, the clean reads could be filtered. And then TopHat software was used to map the clean reads to reference the genome to get mapped reads (Trapnell, Pachter & Salzberg, 2009).

Differential expression analysis and functional enrichment

In order to screen the DEGs in the three groups, the fragments per kilobase of exon per million mapped reads was utilized to calculate the relative quantity of each gene (Li & Dewey, 2011). As for the differential expression analysis, the Empirical analysis of Digital Gene Expression in R was used to analyze the difference among the three groups (Robinson, McCarthy & Smyth, 2009). The expression of genes with a fold change ≥2 or ≤0.5 would be filtered as DEGs. The function analysis of DEGs was carried out by GO analysis and KEGG pathway enrichment analysis using Goatools and KOBAS (Xie et al., 2011).

Quantitative real-time polymerase chain reaction analysis

In order to confirm the reliability of RNA-seq, qRT-PCR was used to confirm the results. If the results of qRT-PCR were consistent with the ones of RNA-seq, this meant that the reliability of RNA-seq was quite high. We could use the results in the RNA-seq, as a basic database, to explore some potential pathways and mechanisms.

Total RNA (one μg) was reverse-transcribed into cDNA using HiScript^® II 1st Strand cDNA Synthesis Kit (Vazyme, Nanjing, China) according to the manufacturer’s protocol, and an amplification mixture was prepared using ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China). The mRNA expression was quantified by a real-time fluorescence quantitative PCR system (ABI 7500, Foster City, CA, USA) (Tables S1 and S2) (Johnson et al., 2014). The primers of selected mRNAs (Gereray Biotechnology, China) were used (Table S3). The results were normalized to β-actin expression (Guo et al., 2013). The relative expression of mRNAs was calculated by the method of 2^−ΔΔCt.

Statistical analysis

The results were presented as means ± SD. Statistical analysis was carried out by using the software SPSS 22.0. One-way analysis of variance followed by a Student–Newman–Keuls test was used for comparisons of multiple groups. Values of p < 0.05 were considered statistically significant.

Luteolin attenuated hepatotoxicity caused by METH

Histopathological changes could give a direct assessment of the protective effects of luteolin on the hepatotoxicity caused by METH. No obvious injuries were shown in the control group (Fig. 1A). In the METH-treated group, extensive morphological changes could be observed mainly including hepatocyte ballooning (Fig. 1B). In the group pretreated with luteolin, the pathological changes were obviously attenuated compared with the METH group (Fig. 1C).

In order to assess the levels of hepatotoxicity, the levels of serum ALT and AST were determined. The levels of ALT and AST were significantly increased in the group treated with METH compared with the control group. In the group with luteolin pretreatment, the levels of ALT and AST were reduced compared with the METH-treated group (Fig. 1D).

Differentially expressed genes

In order to explore the potential functions of mRNA, we detected DEGs in all the three groups. Compared with the control group, 1,859 DEGs were identified in the METH-treated group including 873 up-regulated and 986 down-regulated DEGs. Meanwhile, in the group pretreated with luteolin, 899 up-regulated and 978 down-regulated DEGs were filtered. Finally, 497 DEGs including 314 up-regulated and 183 down-regulated DEGs which might have protective effects were found. The top 60 DEGs were summarized. A Heatmap and Venn map diagram of the DEGs is shown in Figs. 2, 3A and 3B.

GO analysis

In order to further predict the biological functions of the selected DEGs, GO analysis was performed to test for cellular components, molecular functions, and biological processes. The 497 DEGs were divided into three groups including cellular components, biological processes, and molecular functions according to the functions and pathways (Fig. 3C).

KEGG pathway enrichment analysis

Eight pathways were enriched by KEGG pathway analysis (Table 1). Oxidative phosphorylation, Non-alcoholic fatty liver disease (NAFLD), Parkinson’s disease (PD), PPAR signaling pathway, AMPK signaling pathway, Alzheimer’s disease (AD), Metabolism of xenobiotics by cytochrome P450, and Drug metabolism—cytochrome P450, were the enriched pathways.

Validation of RNA-seq by qRT-PCR

To confirm the accuracy of RNA-seq results, six genes (Leap2, Fasn, Fabp5, Pnpla3, Mbp and Calm3) were randomly selected to verify the results of sequencing using quantitative real-time PCR. Leap2, Fasn, Fabp5 and Pnpla3 were up-regulated DEGs in the METH group compared with the control group, and Mbp and Calm3 were genes which did not change among the three groups. The results of qRT-PCR were consistent with the results of RNA-seq (Fig. 4).