Comprehensive ceRNA Network Analysis Reveals Regulatory Mechanisms of mRNA, miRNA, circRNA, and lncRNA in Methcathinone-Induced Neurotoxicity

Comprehensive ceRNA Network Analysis Reveals Regulatory Mechanisms of mRNA, miRNA, circRNA, and lncRNA in Methcathinone-Induced Neurotoxicity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive ceRNA Network Analysis Reveals Regulatory Mechanisms of mRNA, miRNA, circRNA, and lncRNA in Methcathinone-Induced Neurotoxicity Rukui Zhou, yingwen xu, chunming Xu, zhe Chen, Jieping Lv, Keming Yun, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7737272/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Methcathinone, a synthetic cathinone derivative similar to amphetamine, has raised public health concerns due to its addictive properties and health risks associated with neurotoxicity. Low-dose, medium-dose, and high-dose methcathinone-induced neurotoxicity models were established. Learning and memory functions were assessed using the Morris water maze, and changes in hippocampal synaptic morphology and structure were examined using electron microscopy and Golgi staining. Whole-transcriptome sequencing was then used to characterize the expression of mRNAs, miRNAs, circRNAs, and lncRNAs. Differentially expressed mRNAs(DEmRNAs), miRNAs(DEmiRNAs), circRNAs (DEcircRNAs), and lncRNAs (DElncRNAs) were identified, and circRNA-miRNA-mRNA and lncRNA-miRNA-mRNA networks were constructed. Compared with the control group, 784 DEmRNAs, 32 DEmiRNAs, 391 DElncRNAs and 749 DEcircRNAs were identified in the low-concentration group, 607 DEmRNAs, 28 DEmiRNAs, 369 DElncRNAs and 728 DEcircRNAs were identified in the medium-concentration group, and 501 DEmRNAs, 23 DEmiRNAs, 371 DElncRNAs and 753 DEcircRNAs were identified in the high-concentration group. Multiple genes in the immediate-early gene system and the IGF system were affected by methcathinone concentrations, including c-Fos, Nr4a1, Arc, Egr1, Egr2, Npas4, Igf1, and Igf2. These genes were regulated by rno-miR-92a-3p, rno-miR-211-5p, rno-miR-378a-3p, rno-miR-182, rno-miR-336-5p, rno-miR-21-3p, rno-miR-96-5p, rno-miR-183-5p, and rno-miR-143-3p. These miRNAs were competitively bound by 95 lncRNAs and 146 circRNAs participating in the ceRNA network by regulating these nine miRNAs. This result provides new insights into the regulatory mechanisms of mRNA, miRNA, lncRNA, and circRNA in methcathinone-induced neurotoxicity. Methcathinone Neurotoxicity Whole transcriptomics ceRNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Methcathinone (MCAT), also known as ephedrine, is a synthetic derivative of cathinone, Its chemical structure and pharmacological effects are similar to those of amphetamine [ 1 , 2 ] . MCAT use has been associated with a wide range of toxic effects, including neurological and psychopathological symptoms such as psychomotor agitation, hallucinations, delusions, hyperthermia, and even death [ 3 , 4 ] . Studies have shown that exposure to MCAT can lead to changes in neurotransmitter systems, particularly those involving dopamine and serotonin, which are crucial for mood regulation and cognitive function [ 5 – 7 ] . Disruptions in these systems during critical periods of brain development can lead to behavioral abnormalities and cognitive deficits [ 8 , 9 ] . Prenatal and lactational exposure to MCAT leads to delayed physiological and neurological reflex development and impaired learning and memory abilities in rat offspring [ 10 ] . The neurotoxic effects of MCAT have been attributed to alterations in neurodevelopmental processes, including neurogenesis and synaptic plasticity, which are crucial for cognitive function [ 10 , 11 ] . Although there are many clinical cases of MCAT abuse, little is known about the neurotoxic mechanism of its effects. Therefore, our research group established a MCAT neurotoxicity rat model to explore the neurotoxic mechanism caused by MCAT. RNA sequencing is a high-throughput screening method that can identify the expression of coding and noncoding RNAs in tissues or cells. It is crucial for studying RNA biology from multiple perspectives, including structural and gene expression perspectives [ 12 ] . A significant portion of the existing literature on the molecular mechanisms of neurotoxic injury focuses on drug-induced dysregulation of mRNA and protein species, including transcription factors and epigenetic modifiers that regulate downstream gene expression. RNA sequencing, microarray, and proteomic analyses of postmortem human samples and discrete brain regions or blood samples following drug exposure in rodent models of clinical studies have identified mRNA and protein species regulated in drug-induced neurotoxicity. However, mRNAs and proteins represent only a fraction of the cellular regulatory genetic machinery; drug exposure can also induce regulation of other noncoding RNA species. Three of the most important noncoding RNA families—lncRNAs, circRNAs, and miRNAs—are identified [ 13 ] . Competing endogenous RNAs (ceRNAs) are the most recognized molecular mechanisms underlying lncRNA/circRNA regulation. lncRNAs/circRNAs act as "sponges" for miRNAs, altering miRNA expression and thereby modulating target gene stability or translational activity, thus achieving post-transcriptional gene regulation [ 14 , 15 ] . The ceRNA network has been shown to play multiple roles in transcriptional regulation and is currently a hot topic in neurotoxicology research. Researchers use transcriptomic analysis to study the mechanisms of metal toxins such as lead, mercury, cadmium, and aluminum [ 16 – 19 ] , so it can also be used to explore the neurotoxic mechanism of MCAT. This study constructed a MCAT neurotoxicity model, obtained lncRNA, circRNA, miRNA, and mRNA whole transcriptome sequencing data, discovered key miRNAs and mRNAs, and constructed a ceRNA regulatory network to better help researchers understand the molecular mechanism of methcathinone neurotoxicity, thereby more effectively improving and treating methcathinone-induced nerve damage. Materials and methods Methcathinone exposure rat model Forty-eight 8-week-old male Sprague Dawley (SD) rats were obtained from Beijing Xingwang Experimental Animal Center and housed under standard conditions (22 ± 2°C, 45%–55% humidity, noise less than 60 dB and 12-h light-dark cycle), with free access to clean water and standard food. The rats were randomly divided into 4 groups (12 rats in each group): control group, low-dose group (0.25 mg/kg MACT), medium-dose group (5 mg/kg MACT) and high-dose group (20 mg/kg MACT). After 1 week of adaptive feeding, rats were treated with MCAT by intraperitoneal (ip) injection every other day for 2 weeks. All animal experiments were approved by the Institutional Animal Care and Use Committee of Shanxi Medical University(2021 − 338). Morris water maze(MWM) The spatial learning and memory abilities were assessed using the MWM. The maze is a circular pool, 60 cm high and 130 cm in diameter. The pool is divided into four quadrants, and the platform was placed 1–2 cm below the water surface in one quadrant. On the day before the test, each rat was allowed to swim for 2 mins to adapt to the environment. Subsequently, a navigation test was conducted for 5 consecutive days. The rats entered the pool from the four directions of S, W, NE, and SE in different orders. If the rat finds the platform and remains stable for 10 seconds, the system will stop timing, which is the escape latency of the rat this time. If the rat does not find the platform within 120 seconds, it will be manually guided to the platform and stay for 10 seconds. The daily escape latency of the rat is the average of the four directions on that day. On the sixth day, a spatial exploration experiment was conducted. We removed the platform and the rat entered the pool from the SW quadrant. The system automatically records the number of times the rat crossed the platform and the time it stayed in the NE quadrant within 120 s. A camera placed above the maze was used to record the swimming path of each rat, and the data was analyzed using Smart v3.0 software. Transmission electron microscopy (TEM) Rats were anesthetized with 5% isoflurane inhalation and then sacrificed by decapitation. And perfused with 4% paraformaldehyde, the brains were quickly removed and the hippocampus was isolated. The hippocampus region was cut into 1 mm 3 tissue slices and fixed in 2% glutaraldehyde at 4°C for 2 h. After washing the tissue blocks 4 times with buffer solution, they were fixed in 1% osmium acid for 90 min and then dehydrated with acetone gradients for 15 min each time. The tissue was embedded and then cut into 50 nm sections using an ultramicrotome (LKB, Sweden). The sections were stained with uranyl acetate and lead citrate for 30 min and then observed with TEM (JEM-100CXII, Japan). Golgi staining and counting The procedure was performed according to the instructions of the FD Fast Golgi Staining Kit (FD Neuro Technologies, Columbia, MD, USA). hippocampus of rats were collected and immersed in a mixture of solution A and solution B (prepared 24 h in advance). The mixture was changed the next day and stored in the dark at room temperature for two weeks. The hippocampus was removed, immersed in solution C, and stored in the dark for 24 h. The solution was then changed for another 4 days. The tissue was removed and cooled into blocks in precooled isopentane, and the surface isopentane was wiped off. The tissue was cut into slices of approximately 100 µm using a cryostat microtome (Leica, Wetzlar, Hessen, Germany) and attached to a slide. Then, the slide was placed in a mixture of solution D and solution E. After the reaction, the slide was washed with distilled water and dehydrated in graded ethanol for 60 s each time. The tissue was cleared with a xylene solution for 2 min. Finally, resin gum was used to seal the slices, and the images were observed and collected under a microscope (Nikon Corporation, Minato-ku, Tokyo, Japan) after avoiding light from drying. dendrites were randomly selected from each group, and the number of dendritic spines per 20µm of the same branch was counted using Image-J software (National Institutes of Health, Bethesda, Maryland, USA). RNA extraction, library construction, and sequencing RNA-seq was performed on 12 rats in the control group, low-, medium-, and high-concentration groups, with 3 rats in each group. Three libraries (mRNA + IncRNA library, circRNA library, and miRNA library) were established during the whole transcriptome sequencing process. Total RNA was extracted from the entire hippocampus using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). The purity, concentration, and integrity of the extracted RNA were assessed using Nanodrop (Thermo Fisher, Shanghai, China) and Agilent LabChip GX 2100 (Beijing, China). For mRNA, lncRNA, and circRNA sequencing, ribosomal RNA (rRNA) was removed from the extracted RNA using the rRNA Depletion Kit (Vazyme, Nanjing, China). After rRNA removal, approximately 1.5 µg of input RNA was fragmented with divalent cations at high temperature. First-strand cDNA synthesis was performed using random hexamer primers and reverse transcriptase. DNA polymerase I and RNase H were used for second-strand cDNA synthesis. For small RNA sequencing, a Small RNA Library Prep Kit (KAITAI-Bio, AT4208, Hangzhou, China) was used, input RNA and Small RNA 3 ADT were denatured under 70℃ for 2min, then RNA Ligation Buffer 1, RNase Inhibitor and Small RNA Ligase 1 was added to the reaction tube, Small RNA RT primer is then added to the reaction system, which were next used for 5' adaptor ligation with RNA Ligation Buffer 2, RNase Inhibitor, Small RNA5ADT and Small RNALigase 2. Twelve small RNA libraries were constructed from 3 µg of total RNA. After library preparation, template concentration and insert size were determined using the Qubit 3.0 and Agilent LabChip GX 2100, respectively. Finally, all validated libraries were sequenced using the Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA). Library construction and sequencing of miRNA, lncRNA, mRNA, and circRNA were performed by Tgene Biotech (shanghai,China). Read mapping and transcriptome assembly Fastp software(v0.23.4, https://github.com/OpenGene/fastp ) was used to filter the raw data to generate Clean Data.HISAT2 (v2.2.1, http://daehwankimlab.github.io/hisat2/ ) was used to map the Clean Data to the reference genome, obtaining read position information on the reference genome and sample characteristics. SAMtools (v1.16.1, http://github.com/samtools/ ) was used to generate bam files. Transcripts from the generated bam files were spliced ​​using StringTie software (v2.2.1, https://ccb.jhu.edu/software/stringtie/ ). The spliced ​​files were then integrated into annotation files in a format consistent with the reference genome file. Transcripts from different samples were merged using a reference-based approach. FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) was used to quantify lncRNA, mRNA,expression levels, SRPBM [spliced ​​reads per billion maps, defined as circular reads/(map reads × read length)] was used as a normalization method to quantify circRNA expression and RPM (Reads Per Million) was used to quantify miRNAs. Differentially Expressed mRNA and Gene Functional Enrichment Analysis Transcriptome data were quantified at the transcript level using the ballgown package in StringTie software to obtain sample read counts. Following quantitative analysis, differential mRNA expression analysis was performed on the expression matrix of all samples using the R package edgeR (v3.36.0, https://bioconductor.org/packages/release/bioc/html/edgeR.html ). Differential mRNAs (DEmRNAs)analysis was performed using edgeR with thresholds set at |log2(fold change)| >1 and P value < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on DE mRNAs using clusterProfiler( https:/bioconductor.org/packages/release/bioc/html/cluster Profiler. html). Identification and analysis of miRNA Raw sequencing data were processed using fastp to remove adapters, trim low-quality bases (Phred score < 20), and filter reads shorter than 18 nt or longer than 36 nt. Quality assessment of read length distribution and nucleotide composition bias was then performed. Clean reads were aligned to the reference genome using BWA (v0.7.17-r1188), and sRNAs were annotated using species-specific ncRNA databases. Known miRNAs were identified by aligning reads to miRBase( https://www.mirbase.org/ftp.shtml ), and novel miRNAs were predicted using miRDeep2 ( https://www.mdc-berlin.de/8551903/en/ ), incorporating pre-miRNA secondary structure analysis. Differential miRNAs(DEmiRNAs) analysis was performed using edgeR, with thresholds set at |log2(fold change)| >1 and P value < 0.05. miRanda ༈ http://www.microrna.org/microrna/getDownloads.do/༉ and TargetScan ༈ http://www.targetscan.org/༉was used to predict the target genes of differentially expressed miRNAs. To understand the functions of miRNA targets, GO and KEGG enrichment analyses were performed on the predicted target gene candidates. Identification and analysis of circRNA For each sample, CIRIquant software (version 2.0.6) was used to predict the start and end positions of circRNAs and the annotation of their source genes. CircRNA expression levels were then measured using CIRIquant software, with the number of detected back-splicing junctions (BSJs) used as the count. The following table shows the expression levels of circRNAs in different samples. We used the SRPBM method (spliced reads per billion mapping) to normalize circRNA expression. CIRI_DE_replicate software was used for differential analysis of circRNAs༈DEcircRNAs༉, with thresholds set at |log2(fold change)| >1 and a P value < 0.05. To understand the functions of circRNA source genes, GO and KEGG enrichment analyses were performed on the predicted target gene candidates. Identification and analysis of lncRNA The assembled transcripts were compared with database-annotated coding gene transcripts using gffcompare software. To identify transcripts that did not match known coding gene transcripts, five categories of transcripts were selected: "potential novel isoforms," "transcripts located entirely within introns of reference genes," "transcripts overlapping exons of reference transcripts," "transcripts in unknown, intergenic regions," and "transcripts overlapping exons of reference transcripts on the complementary strand." These transcripts were then analyzed using CPC2 [ 20 ] , CNCI [ 21 ] , Pfam( http://pfam.xfam.org/ ), and Eggnog༈ http://eggnog-mapper.embl.de/༉ to remove portions of the remaining transcripts with coding potential. Transcripts with > = 2 exons and a length > 200 nt were identified as novel lncRNAs. Differential lncRNAs ༈DElncRNAs༉analysis was performed using edgeR with thresholds set at |log2(fold change)| >1 and P value < 0.05. To understand the functions of lncRNA targets, GO and KEGG enrichment analyses were performed on the predicted target gene candidates. Construction and analysis of ceRNAs regulatory network To discover the interactions between DEmRNAs, DElncRNAs, DEcircRNAs, and DEmiRNAs, we constructed a circRNA/lncRNA-miRNA-mRNA regulatory network based on the ceRNA hypothesis. Miranda and Targetscan were used to predict miRNA-lncRNA, miRNA-mRNA, and miRNA-circRNA pairs. Cytoscape software [ 22 ] was used to visualize the interaction network. Quantitative Real-Time PCR Validation To validate the sequencing results, 10 differentially expressed RNAs were selected for qRT-PCR validation, including six mRNAs (c-Fos, Nr4a1, Npas4, Egr1, Igf-2, and Arc), three miRNAs (rno-miR-143-3p, rno-miR-92a-3p, and rno-miR-206-3p), one circRNA (7:94029713|94030339), and one lncRNA (MSTRG.3860). GAPDH (for mRNA, lncRNA, and circRNA) and U6 (for miRNA) served as internal reference genes. For qRT-PCR of mRNA, lncRNA, and circRNA, the HiScript 1st Strand cDNA Synthesis Kit (Vazyme, China) was used. For miRNA qRT-PCR, miRNA was reverse transcribed using the stem-loop method, replacing the Oligo(dT)18 and random hexamers with miRNA-specific stem-loops and U6 downstream primers. RNA sequencing results were verified by RT-PCR. Total RNA was extracted from rat hippocampus using TRIzol (TaKaRa, Japan) according to the manufacturer's instructions, and RNA purity and concentration were measured using an Eppendorf µCuvette G1.0 (Eppendorf, Germany). Total RNA was reverse transcribed into two cDNAs: one for PCR of mRNA, lncRNA, and circRNA (RR047, TaKaRa, Japan) and the other for PCR of miRNA (638313, TaKaRa, Japan). RT-PCR was performed using SYBR Green Mix (RR820, Takara, Japan) on an Applied Biosystems 7500 system (ABI, USA). A 20-µL PCR reaction volume contained 6.4 µL of H₂O, 0.8 µL of primer F, 0.8 µL of primer R, 2 µL of cDNA, and 10 µL of SYBR Green mix. PCR reaction conditions were 95°C for 10 min, followed by 40 cycles of 95°C for 10 s, 60°C for 20 s, and 72°C for 10 s. The glyceraldehyde-3-phosphate dehydrogenase gene (Gapdh) served as an endogenous control for mRNA, lncRNA, and circRNA, and U6 served as an endogenous control for miRNA. Relative RNA expression levels were calculated using the 2 − ΔΔCt method. Specific primers for qRT-PCR were designed using Primer Premier 5.0 based on the gene sequences. All primers used in the analysis are shown in Table 1. Table.1The primers information in this study Type Gene Forward sequence(5'-3') Reverse sequence(5'-3') mRNA c-Fos GAGGGAGCTGACAGATACGC TCCAGGGAGGTCACAGACAT Igf2 TTGGCCCTCCTGGAGACATA GTATCTGGGGAAGTCGTCCG Egr1 TCCAGGTTCCCATGATCCCT TGAGTGGCGAAGGCTTTGAT Npas4 GACCCTGCTGACCATCTCAC TGGGTGAGCATGGAATCGAC Nr4a1 GCACAGCTTGGGTGTTGATG ACAGCTAGCAATGCGGTTCT Arc TAAGCGGGACCTGTACCAGA CGCAGAAAGCGCTTGAACTT circRNA 7:94029713|94030339 CAGTCACAGAGTGCTTGCAGA TTCCTCCCTGTGGTCACCTTA LncRNA MSTRG.3860 CTTTTTCCGTTGTTCGGTGCT ATTTTCCGAGGCATGGCGA miRNA rno-miR-143-3p CGCGTGAGATGAAGCACTGT AGTGCAGGGTCCGAGGTATT rno-miR-92a-3p CGCGTATTGCACTTGTCCC AGTGCAGGGTCCGAGGTATT rno-miR-206-3p GCGCGTGGAATGTAAGGAAGT AGTGCAGGGTCCGAGGTATT qRT-PCR of control GAPDH CTCAGTTGCTGAGGAGTCCC ATTCGAGAGAAGGGAGGGCT U6 CTCGCTTCGGCAGCACATATACT ACGCTTCACGAATTTGCGTGTC Statistical analysis Statistical analyses were performed using GraphPad Prism 8 (GraphPad Software Inc., La Jolla, CA, USA) and SPSS 26.0 (IBM Corp., Armonk, NY, USA); all data are presented as mean ± SD. The Shapiro-Wilk test was used to assess normal distribution of the data. If the variables were normally distributed, one-way analysis of variance (ANOVA) followed by the Bonferroni test was used. If the variables were not normally distributed, the nonparametric Kruskal-Wallis test followed by the Dunn test was performed. For all analyses, p value < 0.05 was considered statistically significant. Result MCAT irreversibly impairs the learning and memory function of rats The Morris water maze test results are shown (Fig. 1 ). In this experiment, there was no statistical difference in the swimming speed of each group of rats (Fig. 1 A), indicating that MCAT exposure did not impair the motor ability of rats. In the positioning navigation experiment, the results are shown (Fig. 1 B). With the extension of the training time, the escape latency of rats in the blank control group(C) gradually shortened, while the escape latency of rats in the low concentration group (L), medium concentration group (M) and high concentration groups(H)did not change very significantly. Compared with the control group on the same day, the escape latency of rats in the low, medium and high concentration groups was extended on days 2–5; in the spatial exploration experiment, the results were shown (Fig. 1 C). Compared with the blank control group, the number of rats in the low, medium and high concentration groups crossed the platform decreased, and the difference was statistically significant (p < 0.05). As the number of training days increased, the swimming trajectories of the rats in the control group became clearer and clearer, and they arrived at the platform purposefully. The swimming trajectories of the rats in the low, medium and high concentration groups were chaotic and disorderly, as shown (Fig. 1 D). Results from the water maze show that chronic MCAT exposure impairs learning and memory function in rats. Ultrastructural damage in hippocampal neurons in rats exposed to MCAT Synaptic ultrastructure underlies structural synaptic plasticity and is crucial for establishing recognition memory [ 23 ] . Transmission electron microscopy of the CA1 region of the rat hippocampus was performed (Fig. 2 A). The width of the synaptic cleft and the thickness of the postsynaptic density were quantified and compared (Fig. 2 B-C). In the control group, synaptic morphology was well-developed, with abundant synaptic vesicles in the presynaptic membrane and a thick postsynaptic density, suggesting good synaptic function. Synaptic structure was altered in neurons exposed to low, medium, and high concentrations, with widened synaptic clefts and thinner postsynaptic densities, impairing both synaptic structure and function. Therefore, exposure to low, medium, and high concentrations of MCAT impaired the synaptic ultrastructure of neurons in the CA1 region of the rat hippocampus, leading to learning and memory impairment. Effects of MCAT on Dendritic Spines in the Rat Hippocampus Dendritic spines are the initiation sites of excitatory synaptic transmission in neurons. Their morphology and structure change dynamically, both under normal in vivo conditions and during conditions of synaptic plasticity. Maintaining the growth and stability of dendritic spines is essential for long-term memory [ 24 ] . Golgi staining results of the rat hippocampal CA1 region (Fig. 3 A). Dendritic spine density in hippocampal neurons in the low, medium, and high concentration groups was significantly lower than that in the control group, with no difference between the low, medium, and high concentration groups (Fig. 3 B). These results suggest that exposure to low, medium, and high concentrations of MCAT affects the growth and development of dendritic spines in the hippocampus, leading to changes in synaptic structure and thus impairing learning and memory in rats. Hippocampal transcriptome profiles of Sprague-Dawley rats. For mRNA, lncRNA, and circRNA sequencing,Transcriptomes from the hippocampus of control and methcathinone-treated Sprague-Dawley rats generated a total of 167.97 GB of clean data, with over 11.37 GB per sample and the percentages of Q30 bases above 95.169993%. Clean reads from each sample were mapped to the designated reference genome, and these reads were compared to the reference genome, with an average comparison rate exceeding 96% for each sample. Small RNA sequencing generated 121.94 million clean reads, with over 4.79 million per sample and the percentage of Q30 bases higher than 81.223621%. These reads were compared to the reference genome, with an average comparison rate exceeding 99% for each sample. A total of 45,826 mRNAs, 1,226 miRNAs, 89,452 circRNAs, and 7,424 lncRNAs were identified in the 12 samples(Table S1 ). Differential mRNA expression and analysis under low, medium, and high MCAT exposure In the hippocampus of rats exposed to methcathinone compared to controls, 784 (326 upregulated, 458 downregulated), 607 (411 upregulated, 196 downregulated), and 501 (249 upregulated, 252 downregulated) mRNAs were differentially expressed at low, medium, and high concentrations, respectively (Fig. 4 A-C and Table S2). Furthermore, 230 differentially expressed mRNAs were identified across the three concentrations (Fig. 4 D). To further explore the functions of DEmRNAs, GO and KEGG analyses were performed. GO terms were enriched in the low, medium, and high concentration groups by 70, 67, and 106 terms, respectively as shown in Fig. 4 E-G (p.adjust < 0.05), 26 terms were enriched across the three concentrations, as shown in Fig. 4 H. These included several cognition-related terms, such as axon (GO:0030424), glutamatergic synapse (GO:0098978), neuronal cell body (GO:0043025), dendrite (GO:0030425), postsynaptic density (GO:0014069), and neuronal projection (GO:0043005), indicating that the cellular responses to the three concentrations were largely similar. However, it's worth noting that the enrichment of GO terms in the different concentration groups was not identical, suggesting that different concentrations can lead to different changes in disease progression. For example, low-concentration exposure enriched 21 pathways, medium-concentration exposure enriched 16 pathways, and high-concentration exposure enriched 52 pathways, suggesting that high concentrations of MCAT may have more significant effects on the biological functions of rats. KEGG pathway analysis was performed to identify signaling cascades associated with DEmRNAs. The low, medium, and high concentration groups enriched 70, 57, and 68 pathways as shown in Fig. 4 I-K, respectively (p < 0.05). There were 28 overlaps in the gene ontology terms among the three concentrations, as shown in Fig. 4 L. Among the enriched pathways were pathways of neurodegeneration - multiple diseases (rno05022), MAPK signaling pathway (rno04010), endocytosis (rno04144), cAMP signaling pathway (rno04024), axon guidance (rno04360), tight junction (rno04530), calcium signaling pathway (rno04020), insulin signaling pathway (rno04910), and dopaminergic synapse (rno04728). These pathways often lead to cognitive impairment and are associated with the development of neurodegenerative diseases. The detailed information of the enrichment analysis was shown in supplementary materials (Table S3). The insulin signaling pathway (rno04910) was enriched at low, medium, and high concentrations. The expression levels of Igf1, Igf1r, Igf2, Igf2r, Igfbp2, Igfbp4, Igfbp5and Igfbp6 in the IGF system decreased compared to the blank control group as shown in Table 2 . In addition, immediate early genes can induce plastic changes in neuronal synapses [ 25 ] . Compared with the blank control group, the expression levels of the immediate early gene system including c-Fos, Nr4a1, Npas4, Egr1, Egr2, and Arc decreased in the low, medium, and high concentration groups, as shown in Table 3 . Table 2 mRNA expression levels in the IGF system ID Gene name C L M H ENSRNOT00000118307 Igf1 85.96486 55.5368 65.95205 68.02915 ENSRNOT00000019267 Igf1r 73.17422 63.63347 44.18126 58.93663 ENSRNOT00000080246 Igf2 978.6545 597.2941 369.6975 362.8413 ENSRNOT00000021840 Igf2r 102.7249 86.90546 70.01692 95.47085 ENSRNOT00000023068 Igfbp2 4106.913 3679.802 2338.387 2081.243 ENSRNOT00000014153 Igfbp4 143.5315 111.62 88.36709 105.4235 ENSRNOT00000023530 Igfbp5 629.6954 454.6541 334.5893 551.7671 ENSRNOT00000014807 Igfbp6 366.5771 204.4865 150.0264 255.7914 Table 3 mRNA expression levels in the immediate early gene system ID Gene name C L M H ENSRNOT00000010712 c-Fos 227.9184 97.16049 84.00357 65.66666 ENSRNOT00000010171 Nr4a1 98.3925 34.09494 49.65531 49.16843 ENSRNOT00000027119 Npas4 86.44879 39.08049 23.59107 46.02276 ENSRNOT00000026303 Egr1 165.4891 73.92903 75.00771 63.96981 ENSRNOT00000000792 Egr2 27.59439 7.315629 12.37618 9.598105 ENSRNOT00000076998 Arc 964.4291 580.3086 628.478 550.1627 Differential expression and analysis of miRNAs under low, medium, and high MCAT exposure The length of most miRNA clean reads in the blank control group, low concentration group, medium concentration group, and high concentration group ranged from 21 to 23 bp (Figs. 5 A-D), which was consistent with the miRNA characteristics and demonstrated the reliability of the dataset. In the hippocampus of rats exposed to MCAT compared to control, 32 (7 upregulated, 25 downregulated), 28 (15 upregulated, 13 downregulated), and 23 (5 upregulated, 18 downregulated) miRNAs were differentially expressed at low, medium, and high concentrations, respectively (Figs. 5 E-G and Table S4). Furthermore, 8 differentially expressed miRNAs were identified across the three concentrations (Fig. 5 H). GO and KEGG enrichment analysis was performed on DEmiRNA-associated target genes. Many GO terms related to cognition were found (Fig. 5 I-K), such as axon (GO:0030424), synapse (GO:0045202), glutamatergic synapse (GO:0098978), neuronal cell body (GO:0043025), dendrite (GO:0030425), postsynaptic density (GO:0014069), dendritic spine (GO:0043197), intracellular signaling (GO:0035556), and neuronal projection (GO:0043005). KEGG pathway analysis was performed to identify signaling cascades associated with DEmiRNA-associated target genes (Fig. 5 L-N). DEmiRNA-related target genes are in the PI3K-Akt signaling pathway (rno05022), MAPK signaling pathway (rno04010), cAMP signaling pathway (rno04024), Axon guidance (rno04360), Insulin signaling pathway (rno04910), and Neurotrophin signaling pathway (rno04722). These pathways often lead to cognitive impairment and are associated with the emergence of neurodegenerative diseases. The detailed information of the enrichment analysis was shown in supplementary materials (Table S5). Differential expression and analysis of circRNAs under low, medium, and high MCAT exposure In this study, A total of 89,452 potential circRNAs were identified, of which approximately 83.42%, 12.02%, and 3.51% were of the exon, intron, and intergenic types, respectively (Fig. 6 A). The SRPBM value was used to calculate the expression level of the circRNA. The SRPBM distribution of circRNAs in the blank group, low, medium, and high-concentration groups was compared (Fig. 6 B). In the hippocampus of rats exposed to MCAT compared to controls, 749 (334 upregulated, 415 downregulated), 728 (160 upregulated, 568 downregulated), and 753 (189 upregulated, 564 downregulated) circRNAs were differentially expressed at low, medium, and high concentrations, respectively (Fig. 6 D-F and Table S6). Furthermore, 53 common DEcircRNAs were identified across the three concentrations (Fig. 6 C). To further elucidate the functions of DEcircRNAs, we performed GO and KEGG analyses on the genes from which DEcircRNAs originated. Many GO terms related to cognition were found (Fig. 6 G-I), such as axon (GO:0030424), synapse (GO:0045202), glutamatergic synapse (GO:0098978), neuronal cell body (GO:0043025), dendrite (GO:0030425), postsynaptic density (GO:0014069), dendritic spine (GO:0043197), brain development (GO:0007420), MAPK cascade (GO:0000165), intracellular signal transduction (GO:0035556), and neuronal projection (GO:0043005). KEGG pathway analysis was performed to confirm the signaling cascades associated with the source genes of DEcircRNAs (Fig. 6 J-L), including MAPK signaling pathway (rno04010), Calcium signaling pathway (rno04020), Axon guidance (rno04360), Long-term potentiation (rno04720), Neurotrophin signaling pathway (rno04722), Endocrine resistance (rno01522), and VEGF signaling pathway (rno04370). These pathways often lead to cognitive impairment and are associated with the emergence of neurodegenerative diseases. The detailed information of the enrichment analysis was shown in supplementary materials (Table S7). Differential expression of lncRNAs under low, medium, and high MCAT exposure. Analysis using CPC2, CNCI, Pfam, and EGGN, using the intersection of these four tools as an example, identified 7,424 potential lncRNAs (Fig. 7 A). lncRNA classification revealed five categories of transcripts: "potential novel isoforms" (j), "transcripts located entirely within introns of the reference gene" (i), "overlapping exons of the reference transcript" (o), "transcripts in unknown, intergenic regions" (u), and "overlapping exons of the reference transcript on the complementary strand" (x) (Fig. 7 B). In the hippocampus of rats exposed to MCAT, 391 (209 upregulated, 182 downregulated), 369 (188 upregulated, 181 downregulated), and 371 (196 upregulated, 175 downregulated) lncRNAs were differentially expressed at low, medium, and high concentrations, respectively (Fig. 7 D-F and Table S8). Furthermore, 113 common DElncRNAs were identified across the three concentrations (Fig. 7 C). GO analyses were performed for enrichment analysis of differentially expressed lncRNA target genes (Fig. 7 G-I). In GO terms, there are many cognitive-related terms, including glutamatergic synapse (GO:0098978), synapse (GO:0045202), nervous system development (GO:0007399), axon guidance (GO:0007411), neuron projection development (GO:0031175), chemical synaptic transmission (GO:0007268), receptor complex(GO:0043235),integral component of postsynaptic density membrane(GO:0099061),axonogenesis(GO:0007409),postsynaptic membrane(GO:0045211),synapse assembly(GO:0007416),voltage-gated potassium channel complex(GO:0008076),positive regulation of synapse assembly(GO:0051965)、GABA-ergic synapse(GO:0098982), regulation of presynapse assembly(GO:1905606), positive regulation of kinase activity(GO:0033674). KEGG pathway analysis was performed to identify signaling cascades associated with the target genes of DElncRNAs (Fig. 7 J-L), including Alzheimer's disease (rno05010), Huntington disease (rno04910), Insulin signaling pathway (rno04910), Cholinergic synapse (rno04725), Longevity regulating pathway - multiple species (rno04213), Long-term potentiation (rno04720), Pathways of neurodegeneration - multiple diseases (rno05022), Regulation of actin cytoskeleton (rno04810), MAPK signaling pathway (rno04010), PI3K-Akt signaling pathway (rno04151), and Long-term depression (rno04730). These pathways often lead to cognitive impairment and are associated with the emergence of neurodegenerative diseases. The detailed information of the enrichment analysis was shown in supplementary materials (Table S9). Construction and analysis of ceRNA regulatory networks under low, medium, and high MCAT concentrations Based on the regulatory relationships of DEmiRNA-DEmRNA, DEmiRNA-DElncRNA, and DEmiRNA-DEcircRNA, the interactions between differentially expressed miRNAs and circRNA/lncRNA/mRNA with opposite expression trends were screened and determined. Ultimately, in the low concentration group, 497 DEcircRNAs were identified, which were involved in the regulation of 547 mRNAs through interactions with 27 miRNAs; 128 DElncRNAs were identified, which were involved in the regulation of 546 mRNAs through interactions with 23 miRNAs (Fig. 8 ). In the medium concentration group, 351 DEcircRNAs were identified, which regulated 403 DEmRNAs by interacting with 24 DEmiRNAs; 123 DElncRNAs were identified, which regulated 403 DEmRNAs by interacting with 24 DEmiRNAs (Fig. 9 ). In the high concentration, 426 DEcircRNAs were identified, which regulated 518 DEmRNAs by interacting with 19 DEmiRNAs; 59 DElncRNAs were identified, which regulated 518 DEmRNAs by interacting with 20 DEmiRNAs (Fig. 10 ). To further identify candidate ceRNA transcript-interaction relationships, we ultimately extracted eight mRNAs from the IGF and immediate early gene systems, including Igf-1, Igf-2, c-Fos, Nr4a1, Arc, Egr1, Egr2, and Npas4. We further explored the candidate ceRNA subnetwork involved in methcathinone-induced hippocampal damage and established a ceRNA network model encompassing 8 mRNAs, 9 miRNAs, 95 lncRNAs, and 146 circRNAs (Fig. 11 ). qRT-PCR verification of key mRNA, miRNA, circRNA and lncRNA expression following MCAT treatment qRT-PCR was performed to confirm the whole-transcriptome RNA-seq results and verify the expression of these key mRNA, miRNA, circRNA and lncRNA. The qRT-PCR results closely matched the RNA-seq data on expression trends (Fig. 12 ). Discussion This study used a series of neurobiologically relevant research methods to evaluate the neurotoxic effects of MCAT in rats. In the Morris water maze test, compared with the control group, rats in the low, medium, and high concentration groups had prolonged escape latencies and decreased platform crossings. These Morris water maze results indicate that exposure to low, medium, and high concentrations of MCAT impaired learning and memory in rats. Electron microscopy revealed that, compared with the control group, low, medium, and high concentrations of MCAT disrupted synaptic structure in neurons in the CA1 region of the hippocampus and impaired synaptic plasticity. Golgi staining revealed a decrease in dendritic spines in the CA1 region of the hippocampus after exposure to low, medium, and high concentrations of MCAT compared with the control group. Therefore, behavioral and morphological results confirm that we have successfully established a rat model in which chronic MCAT exposure impairs learning and memory and hippocampal neurons. Recent developments in high-throughput sequencing have enabled the assessment of the entire transcriptome across mRNA, miRNA, lncRNA, and circRNA, potentially revealing the biological processes driving complex phenotypes [ 26 ] . Whole-transcriptome sequencing technology has enabled a deeper understanding of the mechanisms by which the hippocampus is affected by MCAT. By applying whole transcriptome sequencing, this study comprehensively integrated the data of mRNA, miRNA, lncRNA and circRNA in the hippocampus of rats in low, medium and high MCAT concentration groups for the first time. Compared with the control group, 784 DEmRNAs, 32 DEmiRNAs, 391 DElncRNAs and 749 DEcircRNAs were identified in the low-concentration group, 607 DEmRNAs, 28 DEmiRNAs, 369 DElncRNAs and 728 DEcircRNAs were identified in the medium-concentration group, and 501 DEmRNAs, 23 DEmiRNAs, 371 DElncRNAs and 753 DEcircRNAs were identified in the high-concentration group. In addition, there were differences in the DEmRNAs, DEmiRNAs, DEcircRNAs, and DElncRNAs between the low, medium, and high concentration groups, indicating that different concentrations of MCAT may cause neurotoxicity through different biological processes. To gain deeper insights into the functional significance of differentially expressed RNAs in methcathinone-induced neurotoxicity, we performed GO enrichment and KEGG pathway analyses. GO analysis showed that differentially expressed RNAs in the low, medium, and high concentration groups were enriched in terms related to learning and memory, such as axon (GO:0030424), glutamatergic synapse (GO:0098978), neuronal cell body (GO:0043025), dendrite (GO:0030425), postsynaptic density (GO:0014069), neuronal projection (GO:0043005), and GABAergic synapse (GO:0098982). KEGG pathway analysis showed that the differentially expressed RNAs also targeted several specific signaling pathways, including Pathways of neurodegeneration - multiple diseases (rno05022), MAPK signaling pathway (rno04010), Endocytosis (rno04144), cAMP signaling pathway (rno04024), Axon guidance (rno04360), Tight junction (rno04530), Calcium signaling pathway (rno04020), Insulin signaling pathway (rno04910), and Dopaminergic synapse (rno04728). Alterations in these signaling pathways are associated with nervous system-related diseases. This suggests that differentially expressed mRNAs, miRNAs, circRNAs, and lncRNAs are likely involved in the process of methcathinone-induced neurotoxicity. Among the differentially expressed mRNAs, immediate-early genes, including c-Fos, Nr4a1, Arc, Egr1, Egr2, and Npas4, showed varying degrees of decrease in the low, medium, and high concentration groups. Immediate-early genes primarily encode transcription factors that regulate cellular homeostasis and neuronal plasticity [ 27 ] . They are rapidly activated after sensory and behavioral experiences and play a crucial role in the experience-dependent regulation of synapses to encode memories. They are rapidly induced at the transcriptional and translational levels in response to learning, and their induction is essential for memory formation [ 28 ] . Currently, immediate-early genes are considered the "signature" of memory engrams: for example, hippocampal-mediated learning leads to rapid expression of immediate-early genes in cortical and hippocampal neurons. Therefore, immediate-early genes are often used as markers of neuronal activity to detect engram cells, which are believed to store memories in the brain [ 29 ] . The decrease in immediate-early genes suggests that MCAT may affect synaptic plasticity by affecting the expression of immediate-early genes, leading to learning and memory impairments. c-Fos is widely distributed throughout the brain, and increased neuronal activity induces upregulation of this protein [ 30 ] . Compared to rats exposed to familiar objects, exposure to novel objects leads to significantly increased c-Fos levels in certain brain structures [ 31 ] . Blocking c-Fos activation in the hippocampus impairs long-term memory related to water maze learning, radial arm maze performance, contextual fear learning, and socially transmitted food preferences [ 32 – 34 ] . Several studies have shown that high c-Fos expression in stimulated neurons, which activates them in a coordinated manner, is crucial for stable, long-lasting plasticity in the hippocampus [ 34 ] . Nr4a1 plays an important role in synaptic plasticity, long-term memory formation, and neuroprotection [ 35 ] . In humans, Nr4a1 mRNA levels in peripheral blood mononuclear cells (PBMCs) decrease with age, particularly in those with cognitive impairment. Similarly, Nr4a1 levels are reduced in the PBMCs and brains of aged mice [ 36 ] . Nr4a1 levels in the CA1 region of the mouse hippocampus are positively linearly correlated with cognitive ability. Both systemic and CA1 pyramidal neuron-specific knockout of Nr4a1 impaired excitatory synaptic function and cognitive ability in young mice, whereas Nr4a1 overexpression improved cognition in aged mice [ 37 ] . Arc is involved in various neuronal signaling pathways as an effector molecule and is considered an essential player in the molecular mechanisms necessary for learning and memory [ 38 ] . It is rapidly transcribed during neuronal activity and precisely targets activated synapses in neuronal dendrites [ 39 ] . Studies have shown that reduction of Arc in the dorsal CA1 region is sufficient to impair spatial memory ability in the Morris water maze task [ 40 ] . Knockout of Arc leads to consolidation defects in different types of long-term memory, as well as alterations in long-term plasticity [ 41 ] . Egr1 is involved in brain development, learning, and long-term neuronal plasticity [ 42 , 43 ] . Egr1 deficiency leads to impairments in multiple behavioral tasks related to different brain regions. Typically, Egr1 mutant mice exhibit severe impairments in long-term memory, including spatial memory, object recognition memory, and object location recognition memory [ 44 , 45 ] . Egr2 is essential for brain development and is important for myelination and synaptic plasticity in the central nervous system [ 46 , 47 ] . Npas4 is a brain-specific transcription factor whose expression is regulated by neuronal activity [ 48 ] . Npas4 mRNA levels are reduced in the hippocampus of aged mice with impaired memory, suggesting that Npas4 may contribute to the preservation of hippocampal-related cognition [ 49 ] . Npas4 deficiency is characterized by impaired spatial recognition memory, decreased anxiety-like behaviors, and increased depression-like behaviors [ 50 ] . Npas4 controls the balance of excitatory and inhibitory synapses to maintain neural circuit homeostasis, which is crucial for information processing and memory formation [ 51 ] . The insulin-like growth factor (IGF) signaling pathway is crucial for central nervous system development, metabolism, repair, cognition, and mood regulation and includes Igf-1, Igf-2, Igf-1r, Igf-2r, insulin receptor (Ir), and 6 insulin-like growth factor binding proteins (Igfbps) [ 52 ] . It was found that the expression levels of Igf-1, Igf1r, Igf2, Igf2r, Igfbp2, Igfbp4, Igfbp5, and Igfbp6 in the IGF system decreased to varying degrees in the low, medium, and high concentration groups. Studies have shown that Igf-1, Igf-2, and insulin are associated with cognitive function, and many studies have shown that they have potential effects on cognitive enhancement and as compounds that may improve cognitive impairment and even neurodegeneration. They play an important role in brain development and homeostasis, including neurogenesis, neuronal differentiation, and angiogenesis [ 53 ] . Igf1 plays a crucial role in the development and maturation of the central nervous system. Igf1 potently influences neuroplasticity in the central nervous system through signaling via Igf-1r and canonical signaling pathways such as PI3K-Akt [ 54 , 55 ] . Unlike Igf1, the role of Igf2 in central nervous system function is less well-researched. However, studies have shown that Igf2 can cross the blood-brain barrier, facilitate synaptic formation and maturation, and effectively enhance memory and cognitive abilities in mice [ 56 , 57 ] . miRNAs play key regulatory roles in central nervous system development, dendritic spine formation, neurite outgrowth, and neuronal differentiation and maintenance. Dysregulation of miRNAs has been linked to neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease [ 58 ] . In this study, differentially expressed miRNAs, including miRNA-218b, miR-206-3p, miR-378a-3p, miR-137, miR-182-5p, miR-96-5p, and miR-143-3p, have been implicated in several cognitive-related neurodegenerative and neuropsychiatric disorders. Studies have shown that miR-206-3p exerts neuroprotective effects in Alzheimer's disease mice by regulating brain-derived neurotrophic factor [ 59 ] . In lead (Pb) exposure, miR-378a-3p is implicated in Pb-induced neuronal damage by regulating ferroptosis [ 60 ] . miR-137, a miRNA abundantly expressed in the cerebral cortex, has been reported to be downregulated in the hippocampus following ketamine overexposure, and its overexpression has been shown to protect against ketamine-induced hippocampal neurodegeneration and memory loss [ 61 ] . miR-182-5p also plays a key role in the nervous system. Exosome-delivered miR-182-5p promotes sevoflurane-induced neuroinflammation and cognitive impairment in elderly rats with postoperative cognitive impairment by targeting brain-derived neurotrophic factor and activating the NF-κB pathway [ 62 ] . Studies have shown that blocking miR-96-5p can increase the expression of the cysteine transporter excitatory amino acid transporter 1 and glutathione, protecting neurons from oxidative stress in brain tissue [ 63 ] . miR-143-3p is upregulated in the serum of patients with mild cognitive impairment associated with Alzheimer's disease, acute ischemic stroke, and a mouse model of stroke [ 64 , 65 ] . Inhibiting the expression of miR-143-3p affects the occurrence of depressive-like behavior in mice by regulating synaptic structure [ 66 ] . LncRNAs and circRNAs possess miRNA binding sites and can act as miRNA sponges to inhibit the negative regulation of miRNAs on target genes, thereby indirectly regulating gene expression [ 14 ] . Similar GO and KEGG annotations for DElncRNAs, DEcircRNAs, DEmiRNAs, and DEmRNAs suggest potential regulatory relationships among these groups. We constructed ceRNA networks for low, medium, and high concentrations, ultimately identifying 497 DEcircRNAs in the low concentration group, which regulated 547 mRNAs through interactions with 27 miRNAs. We also identified 128 DElncRNAs, which regulated 546 mRNAs through interactions with 23 miRNAs. At medium concentrations, 351 DEcircRNAs were identified, and these DEcircRNAs participated in the regulation of 403 mRNAs through interactions with 24 miRNAs; 123 DElncRNAs were identified, and these DElncRNAs participated in the regulation of 403 mRNAs through interactions with 24 miRNAs. At high concentrations, 426 DEcircRNAs were identified, and these DEcircRNAs participated in the regulation of 518 mRNAs through interactions with 19 miRNAs; 59 DElncRNAs were identified, and these DElncRNAs participated in the regulation of 518 mRNAs through interactions with 20 miRNAs. Finally, based on c-Fos, Nr4a1, Arc, Egr1, Egr2, Npas4 in the immediate early gene system and Igf1 and Igf2 in the IGF system, a ceRNA network consisting of 8 mRNAs, 9 miRNAs, 95 lncRNAs, and 146 circRNAs was established. Conclusions Morris water maze, Golgi staining, and TEM demonstrated that we successfully established a rat model of methcathinone-induced neurotoxicity. Whole-transcriptome sequencing identified differentially expressed mRNAs, miRNAs, circRNAs, and lncRNAs in response to low, medium, and high methcathinone concentrations, and constructed ceRNA networks for each of these circRNAs/lncRNAs, miRNAs, and mRNAs. The study found that c-Fos, Nr4a1, Arc, Egr1, Egr2, Npas4, Igf1, and Igf2 are important candidate genes for methcathinone-induced neurotoxicity. These genes are regulated by rno-miR-92a-3p, rno-miR-211-5p, rno-miR-378a-3p, rno-miR-182, rno-miR-336-5p, rno-miR-21-3p, rno-miR-96-5p, rno-miR-183-5p, and rno-miR-143-3p. Furthermore, 95 lncRNAs and 146 circRNAs participate in the ceRNA network by regulating these nine miRNAs. This study enriches the neurotoxic gene database from a toxicological perspective and provides a new strategy for studying methcathinone neurotoxicity. Declarations Ethics approval and consent to participate All animal experiments were approved by the Institutional Animal Care and Use Committee of Shanxi Medical University（2021-338）. Consent for publication Not applicable. Data availability The raw sequencing data of circRNA, miRNA, lncRNA and mRNA were uploaded and deposited in NCBI Sequence Read Archive (SRA, PRJNA1333463, PRJNA1333391 and PRJNA1333576). circRNA data (PRJNA1333463) are available at the following website: (https://dataview.ncbi.nlm.nih.gov/object/PRJNA1333463), miRNA data (PRJNA1333391) are available at the following website:( https://dataview.ncbi.nlm.nih.gov/object/PRJNA1333391), and lncRNA and mRNA data (PRJNA1333576) are available at the following website: (https://dataview.ncbi.nlm.nih.gov/object/PRJNA1333576). Competing interests The authors declare no competing interests. Funding This work is financially supported by the National Key Research and Development Program of China (No. 2022YFC3300903), National Natural Science Foundation of China (No. 82130056), Shanxi Province Science Foundation (No. 202103021224233), Chinese Medicine Research Project of Shanxi Provincial Administration of Chinese Medicine((No.2024ZYYC102). Contributions Conceptualization:LV JP. Data curation:ZHOU RK. Formal analysis: XU CM. Funding acquisition:YUN KM, WEI ZW. Investigation:ZHOU RK. Resource: WEI ZW. Supervision:CHEN Z, YUN KM. Software:XU YW. Writing – original draft:ZHOU RK. Writing – review & editing:WEI ZW. Visualization:XU YW. All authors read and approved the final manuscript. Author information Authors and Affiliations School of Forensic Medicine, Shanxi Medical University ， Jinzhong, Shanxi Province, People’s Republic of China Rukui Zhou Email: [email protected] ； Yingwen Xu Email: [email protected] ； Chunming Xu Email: [email protected] ； Zhe Chen Email: [email protected] ; Keming Yun Email: [email protected] ； Zhiwen Wei Email: [email protected] School of Basic Medical Sciences ， Shanxi University of Chinese Medicine ， Jinzhong, Shanxi Province, People’s Republic of China Rukui Zhou Email: [email protected] Department of Anesthesiology,The first hospital of Shanxi medical university ， taiyuan, Shanxi Province, People’s Republic of China Jieping Lv Email : [email protected] Corresponding author Jieping Lv; Keming Yun；Zhiwen Wei Acknowledgements Not applicable. References Capriola M. Synthetic cathinone abuse. Clin Pharmacol. 2013;5:109–15. Silva B, Soares J, Rocha-Pereira C, Mladěnka P, Remião F, Researchers OBOTO. 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14:41:00","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":194204,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/067e0faae0c5bbb30c76a22e.html"},{"id":94452494,"identity":"72d601ec-0175-4ee8-8ec1-c19df83552f8","added_by":"auto","created_at":"2025-10-27 14:41:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137029,"visible":true,"origin":"","legend":"\u003cp\u003eMorris water maze experiment results of rats in different groups. \u003cstrong\u003e(A)\u003c/strong\u003e Swimming speed of rats in each group. \u003cstrong\u003e(B)\u003c/strong\u003eEscape latency in the navigation test. \u003cstrong\u003e(C) \u003c/strong\u003eNumber of platform crossings in the spatial exploration test. \u003cstrong\u003e(D)\u003c/strong\u003e Representative swimming trajectories of each group on days 5 and 6. \u003cstrong\u003e**\u003c/strong\u003ep \u0026lt; 0.01,\u003cstrong\u003e*** \u003c/strong\u003ep \u0026lt; 0.001 compared with the control group. N = 12. C, L, M, and H represent blank control group, low-dose group, medium-dose group, and high-dose group, respectively, The following charts also apply.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/9d348f5e0fc84827d26ce8ea.png"},{"id":94451695,"identity":"642c5cb8-cfe6-4508-a137-2d783ae78d9e","added_by":"auto","created_at":"2025-10-27 14:40:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":513205,"visible":true,"origin":"","legend":"\u003cp\u003eUltrastructure change in the hippocampal CA1 region of rats in each group (A)TEM results of rat hippocampal in control, low, medium, and high concentration groups. Magnification ×60,000. Blue arrows: synaptic structure. (B) Quantification of average PSD thickness. (C)Quantification of average Synaptic cleft width. N = 3.\u003cstrong\u003e *\u003c/strong\u003ep \u0026lt; 0.05,\u003cstrong\u003e**\u003c/strong\u003ep \u0026lt; 0.01，\u003cstrong\u003e*** \u003c/strong\u003ep \u0026lt; 0.001,\u003cstrong\u003e**** \u003c/strong\u003ep \u0026lt; 0.0001 compared with the control group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/e2753cbb4b7e12f4db6a2915.png"},{"id":94452740,"identity":"6762935e-a9cb-4576-a142-42917a0ab871","added_by":"auto","created_at":"2025-10-27 14:41:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":797782,"visible":true,"origin":"","legend":"\u003cp\u003eGolgi staining results and ultramicroscopic images of the hippocampus CA1 region of rats in each group .(A)Images of Golgi-Cox-stained neuron and synapse in each group. \u0026nbsp;B.\u003cstrong\u003e \u003c/strong\u003eDendritic spine number of neurons per 20 μm in the hippocampus of rats in each group. N = 3. ∗p \u0026lt; 0.05,**p \u0026lt; 0.01 ,***p \u0026lt; 0.001 compared with the control group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/c4ba8f4133217b0391bdb826.png"},{"id":94452741,"identity":"d50ac4c3-a0b6-4714-8c7b-78e6cc4832e8","added_by":"auto","created_at":"2025-10-27 14:41:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":217942,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and analysis of differentially expressed mRNAs in the hippocampus following low, medium, and high MCAT concentrations. (A)-(C) Volcano plots showing differentially expressed mRNAs identified in the hippocampus following low, medium, and high MCAT concentrations, respectively. Significantly differentially expressed genes are indicated by red and green dots, while genes without significant differential expression are indicated by gray dots. (D) Venn diagram showing the number of differentially expressed mRNAs identified in the low, medium, and high MCAT concentration groups. (E)-(G) Bar charts showing GO annotation analysis of differentially expressed mRNAs in the low, medium, and high MCAT concentration groups. (H) Venn diagram showing a comparison of GO enrichment analysis of differentially expressed mRNAs in the low, medium, and high MCAT concentration groups. (I)-(K) Bar charts showing KEGG pathway enrichment analysis of differentially expressed mRNAs in the low, medium, and high MCAT concentration groups. (L) Venn diagram showing a comparison of KEGG enrichment analysis of differentially expressed mRNAs in the low, medium, and high MCAT concentration groups.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/55690e599c72b5afe12b82a9.png"},{"id":94452504,"identity":"9b12bfc5-73ba-439b-a065-8267882d355e","added_by":"auto","created_at":"2025-10-27 14:41:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":241482,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and analysis of differentially expressed miRNAs in response to low, medium, and high MCAT concentrations. (A)-(D) Sequence lengths of identified miRNAs in the blank, low, medium, and high MCAT concentration groups, respectively. (E)-(G) Volcano plots of differentially expressed miRNAs in the hippocampus following low, medium, and high MCAT concentrations, respectively. Significantly differentially expressed genes are indicated by red and green dots, while genes with no significant differential expression are indicated by gray dots. (H) Venn diagram showing the number of differentially expressed miRNAs identified in the low, medium, and high MCAT concentration groups. (I)-(K) Bar charts of GO annotation analysis of differentially expressed miRNA target genes in the low, medium, and high MCAT concentration groups, respectively (L)-(N) Bar charts of KEGG pathway enrichment analysis of differentially expressed miRNA target genes in the low, medium, and high MCAT concentration groups, respectively.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/4b557721f5962cf1411bfee3.png"},{"id":94452538,"identity":"14af72b8-10f1-4779-ad2e-9921bdbe6ad8","added_by":"auto","created_at":"2025-10-27 14:41:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":214228,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and analysis of differentially expressed circRNAs in response to low, medium, and high MCAT concentrations. (A) circRNA type distribution. (B) SRPBM density: distribution density of circRNA expression in the sample; SRPBM value: normalized count value of the circRNA, calculated by the formula [SRPBM = (SR × 10\u003csup\u003e9\u003c/sup\u003e)/N], where SR is the number of spliced reads and N is the total number of mapped reads in the sample. (C) Venn diagram showing the number of differentially expressed circRNAs identified in the low, medium, and high MCAT concentration groups. (D)-(F) Volcano plots of differentially expressed circRNAs in the hippocampus following low, medium, and high MCAT concentrations, respectively. Significantly differentially expressed genes are indicated by red and green dots, while genes with no significant differential expression are indicated by gray dots. (G)-(I) Bar chart of GO annotation analysis of differentially expressed circRNA source genes in low, medium, and high concentration groups, respectively. (J)-(L) Bar charts of KEGG pathway enrichment analysis of differentially expressed circRNA source genes in the low, medium, and high MCAT concentration groups, respectively.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/71de36cbacb1e5dcb0fd37a7.png"},{"id":94452527,"identity":"efdc7bdf-c475-41e8-84b3-ce81fce5de20","added_by":"auto","created_at":"2025-10-27 14:41:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":197796,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and analysis of differentially expressed lncRNAs in response to low, medium, and high MCAT concentrations. (A) Number of lncRNAs detected by CPC2, CNCI, Pfam, and eggnoI software. (B) Identification of lncRNA types. (C) Venn diagram showing the number of differentially expressed lncRNAs identified in the low, medium, and high MCAT concentration groups. (D)-(F) Volcano plots showing differentially expressed lncRNAs in the hippocampus exposed to low, medium, and high methcathinone concentrations, respectively. Significantly differentially expressed lncRNAs are indicated by red and green dots, while non-significantly differentially expressed lncRNAs are indicated by gray dots. (G)-(I) Bar charts of GO annotation analysis of target genes of differentially expressed lncRNAs in the low, medium, and high MCAT concentration groups. (J)-(L) Bar charts of KEGG pathway enrichment analysis of target genes of differentially expressed lncRNAs in the low, medium, and high MCAT concentration groups.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/cb8aa24399ed94969ea16463.png"},{"id":94452585,"identity":"340d36d4-88aa-45eb-9632-5f32a308bc76","added_by":"auto","created_at":"2025-10-27 14:41:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":378653,"visible":true,"origin":"","legend":"\u003cp\u003eCeRNA networks constructed by DEmRNA, DEmiRNA, DEcircRNA, and DElncRNA in the low-concentration group. (A) mRNA-miRNA-circRNA ceRNA network (B) mRNA-miRNA-lncRNA ceRNA network.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/375c71e70a1a17590fe6c491.png"},{"id":94452151,"identity":"59912a66-9f72-48e8-a304-8f099f0fb028","added_by":"auto","created_at":"2025-10-27 14:40:52","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":441299,"visible":true,"origin":"","legend":"\u003cp\u003eCeRNA networks constructed by DEmRNA, DEmiRNA, DEcircRNA, and DElncRNA in the medium-concentration group. (A) mRNA-miRNA-circRNA ceRNA network (B) mRNA-miRNA-lncRNA ceRNA network.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/6331ec551e7ad70fc15b10a9.png"},{"id":94452492,"identity":"e718a4a2-8126-4d46-a1fc-2681fa59f5c7","added_by":"auto","created_at":"2025-10-27 14:41:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":383449,"visible":true,"origin":"","legend":"\u003cp\u003eCeRNA networks constructed by DEmRNA, DEmiRNA, DEcircRNA, and DElncRNA in the high-concentration group. (A) mRNA-miRNA-circRNA ceRNA network (B) mRNA-miRNA-lncRNA ceRNA network.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/3fb36e1cea37295b1fb1b37b.png"},{"id":94452339,"identity":"2418fe66-e582-4e46-a412-1a4be77768dc","added_by":"auto","created_at":"2025-10-27 14:41:00","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":574572,"visible":true,"origin":"","legend":"\u003cp\u003eceRNA network of Igf-1, Igf-2, c-Fos, Nr4a1, Arc, Egr1, Egr2, and Npas4. (A) mRNA-miRNA-circRNA ceRNA network (B) mRNA-miRNA-lncRNA ceRNA network.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/868c0cefb6dac072920dd146.png"},{"id":94452582,"identity":"757b92a8-4212-40a4-8db2-66b8abee8800","added_by":"auto","created_at":"2025-10-27 14:41:16","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":169588,"visible":true,"origin":"","legend":"\u003cp\u003e(A-F) RT-PCR validation of expression of c-Fos, Nr4a1, Arc, Egr1, Npas4, Igf-1 (G-I) RT-PCR validation of expression of rno-miR-143-3p, rno-miR-92a-3p, rno-miR-206 (J) RT-PCR validation of expression of circ7:94029713|94030339 (K). RT-PCR validation of expression of lncMSTRG:3860. N = 6. ∗p \u0026lt; 0.05,∗∗p \u0026lt; 0.01 compared with the control group.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/d4848a558ec687cb22369f47.png"},{"id":98778858,"identity":"7e87a072-131c-4e91-b1e0-e654d7b28464","added_by":"auto","created_at":"2025-12-22 12:29:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5462929,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/735d119f-c5e2-4257-a82e-67532f3ef675.pdf"},{"id":94452496,"identity":"1433c709-8996-4e8c-a85c-dbfaf653a52c","added_by":"auto","created_at":"2025-10-27 14:41:08","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10745656,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/0b02d8849110305299ca7d35.zip"},{"id":94452824,"identity":"9bdea4d0-0046-48e3-9fc0-07f8aea81da6","added_by":"auto","created_at":"2025-10-27 14:41:55","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":163963,"visible":true,"origin":"","legend":"","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7737272/v1/2bb432e9c87c39a6446da570.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive ceRNA Network Analysis Reveals Regulatory Mechanisms of mRNA, miRNA, circRNA, and lncRNA in Methcathinone-Induced Neurotoxicity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMethcathinone (MCAT), also known as ephedrine, is a synthetic derivative of cathinone, Its chemical structure and pharmacological effects are similar to those of amphetamine\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. MCAT use has been associated with a wide range of toxic effects, including neurological and psychopathological symptoms such as psychomotor agitation, hallucinations, delusions, hyperthermia, and even death\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that exposure to MCAT can lead to changes in neurotransmitter systems, particularly those involving dopamine and serotonin, which are crucial for mood regulation and cognitive function\u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Disruptions in these systems during critical periods of brain development can lead to behavioral abnormalities and cognitive deficits\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Prenatal and lactational exposure to MCAT leads to delayed physiological and neurological reflex development and impaired learning and memory abilities in rat offspring\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The neurotoxic effects of MCAT have been attributed to alterations in neurodevelopmental processes, including neurogenesis and synaptic plasticity, which are crucial for cognitive function\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Although there are many clinical cases of MCAT abuse, little is known about the neurotoxic mechanism of its effects. Therefore, our research group established a MCAT neurotoxicity rat model to explore the neurotoxic mechanism caused by MCAT.\u003c/p\u003e\u003cp\u003eRNA sequencing is a high-throughput screening method that can identify the expression of coding and noncoding RNAs in tissues or cells. It is crucial for studying RNA biology from multiple perspectives, including structural and gene expression perspectives\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. A significant portion of the existing literature on the molecular mechanisms of neurotoxic injury focuses on drug-induced dysregulation of mRNA and protein species, including transcription factors and epigenetic modifiers that regulate downstream gene expression. RNA sequencing, microarray, and proteomic analyses of postmortem human samples and discrete brain regions or blood samples following drug exposure in rodent models of clinical studies have identified mRNA and protein species regulated in drug-induced neurotoxicity. However, mRNAs and proteins represent only a fraction of the cellular regulatory genetic machinery; drug exposure can also induce regulation of other noncoding RNA species. Three of the most important noncoding RNA families\u0026mdash;lncRNAs, circRNAs, and miRNAs\u0026mdash;are identified\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Competing endogenous RNAs (ceRNAs) are the most recognized molecular mechanisms underlying lncRNA/circRNA regulation. lncRNAs/circRNAs act as \"sponges\" for miRNAs, altering miRNA expression and thereby modulating target gene stability or translational activity, thus achieving post-transcriptional gene regulation\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. The ceRNA network has been shown to play multiple roles in transcriptional regulation and is currently a hot topic in neurotoxicology research. Researchers use transcriptomic analysis to study the mechanisms of metal toxins such as lead, mercury, cadmium, and aluminum\u003csup\u003e[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, so it can also be used to explore the neurotoxic mechanism of MCAT.\u003c/p\u003e\u003cp\u003eThis study constructed a MCAT neurotoxicity model, obtained lncRNA, circRNA, miRNA, and mRNA whole transcriptome sequencing data, discovered key miRNAs and mRNAs, and constructed a ceRNA regulatory network to better help researchers understand the molecular mechanism of methcathinone neurotoxicity, thereby more effectively improving and treating methcathinone-induced nerve damage.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMethcathinone exposure rat model\u003c/h2\u003e\u003cp\u003eForty-eight 8-week-old male Sprague Dawley (SD) rats were obtained from Beijing Xingwang Experimental Animal Center and housed under standard conditions (22\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, 45%\u0026ndash;55% humidity, noise less than 60 dB and 12-h light-dark cycle), with free access to clean water and standard food. The rats were randomly divided into 4 groups (12 rats in each group): control group, low-dose group (0.25 mg/kg MACT), medium-dose group (5 mg/kg MACT) and high-dose group (20 mg/kg MACT). After 1 week of adaptive feeding, rats were treated with MCAT by intraperitoneal (ip) injection every other day for 2 weeks. All animal experiments were approved by the Institutional Animal Care and Use Committee of Shanxi Medical University(2021\u0026thinsp;\u0026minus;\u0026thinsp;338).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMorris water maze(MWM)\u003c/h3\u003e\n\u003cp\u003eThe spatial learning and memory abilities were assessed using the MWM. The maze is a circular pool, 60 cm high and 130 cm in diameter. The pool is divided into four quadrants, and the platform was placed 1\u0026ndash;2 cm below the water surface in one quadrant. On the day before the test, each rat was allowed to swim for 2 mins to adapt to the environment. Subsequently, a navigation test was conducted for 5 consecutive days. The rats entered the pool from the four directions of S, W, NE, and SE in different orders. If the rat finds the platform and remains stable for 10 seconds, the system will stop timing, which is the escape latency of the rat this time. If the rat does not find the platform within 120 seconds, it will be manually guided to the platform and stay for 10 seconds. The daily escape latency of the rat is the average of the four directions on that day. On the sixth day, a spatial exploration experiment was conducted. We removed the platform and the rat entered the pool from the SW quadrant. The system automatically records the number of times the rat crossed the platform and the time it stayed in the NE quadrant within 120 s. A camera placed above the maze was used to record the swimming path of each rat, and the data was analyzed using Smart v3.0 software.\u003c/p\u003e\n\u003ch3\u003eTransmission electron microscopy (TEM)\u003c/h3\u003e\n\u003cp\u003eRats were anesthetized with 5% isoflurane inhalation and then sacrificed by decapitation. And perfused with 4% paraformaldehyde, the brains were quickly removed and the hippocampus was isolated. The hippocampus region was cut into 1 mm\u003csup\u003e3\u003c/sup\u003e tissue slices and fixed in 2% glutaraldehyde at 4\u0026deg;C for 2 h. After washing the tissue blocks 4 times with buffer solution, they were fixed in 1% osmium acid for 90 min and then dehydrated with acetone gradients for 15 min each time. The tissue was embedded and then cut into 50 nm sections using an ultramicrotome (LKB, Sweden). The sections were stained with uranyl acetate and lead citrate for 30 min and then observed with TEM (JEM-100CXII, Japan).\u003c/p\u003e\n\u003ch3\u003eGolgi staining and counting\u003c/h3\u003e\n\u003cp\u003eThe procedure was performed according to the instructions of the FD Fast Golgi Staining Kit (FD Neuro Technologies, Columbia, MD, USA). hippocampus of rats were collected and immersed in a mixture of solution A and solution B (prepared 24 h in advance). The mixture was changed the next day and stored in the dark at room temperature for two weeks. The hippocampus was removed, immersed in solution C, and stored in the dark for 24 h. The solution was then changed for another 4 days. The tissue was removed and cooled into blocks in precooled isopentane, and the surface isopentane was wiped off. The tissue was cut into slices of approximately 100 \u0026micro;m using a cryostat microtome (Leica, Wetzlar, Hessen, Germany) and attached to a slide. Then, the slide was placed in a mixture of solution D and solution E. After the reaction, the slide was washed with distilled water and dehydrated in graded ethanol for 60 s each time. The tissue was cleared with a xylene solution for 2 min. Finally, resin gum was used to seal the slices, and the images were observed and collected under a microscope (Nikon Corporation, Minato-ku, Tokyo, Japan) after avoiding light from drying. dendrites were randomly selected from each group, and the number of dendritic spines per 20\u0026micro;m of the same branch was counted using Image-J software (National Institutes of Health, Bethesda, Maryland, USA).\u003c/p\u003e\n\u003ch3\u003eRNA extraction, library construction, and sequencing\u003c/h3\u003e\n\u003cp\u003eRNA-seq was performed on 12 rats in the control group, low-, medium-, and high-concentration groups, with 3 rats in each group. Three libraries (mRNA\u0026thinsp;+\u0026thinsp;IncRNA library, circRNA library, and miRNA library) were established during the whole transcriptome sequencing process. Total RNA was extracted from the entire hippocampus using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). The purity, concentration, and integrity of the extracted RNA were assessed using Nanodrop (Thermo Fisher, Shanghai, China) and Agilent LabChip GX 2100 (Beijing, China).\u003c/p\u003e\u003cp\u003eFor mRNA, lncRNA, and circRNA sequencing, ribosomal RNA (rRNA) was removed from the extracted RNA using the rRNA Depletion Kit (Vazyme, Nanjing, China). After rRNA removal, approximately 1.5 \u0026micro;g of input RNA was fragmented with divalent cations at high temperature. First-strand cDNA synthesis was performed using random hexamer primers and reverse transcriptase. DNA polymerase I and RNase H were used for second-strand cDNA synthesis. For small RNA sequencing, a Small RNA Library Prep Kit (KAITAI-Bio, AT4208, Hangzhou, China) was used, input RNA and Small RNA 3 ADT were denatured under 70℃ for 2min, then RNA Ligation Buffer 1, RNase Inhibitor and Small RNA Ligase 1 was added to the reaction tube, Small RNA RT primer is then added to the reaction system, which were next used for 5' adaptor ligation with RNA Ligation Buffer 2, RNase Inhibitor, Small RNA5ADT and Small RNALigase 2. Twelve small RNA libraries were constructed from 3 \u0026micro;g of total RNA. After library preparation, template concentration and insert size were determined using the Qubit 3.0 and Agilent LabChip GX 2100, respectively. Finally, all validated libraries were sequenced using the Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA). Library construction and sequencing of miRNA, lncRNA, mRNA, and circRNA were performed by Tgene Biotech (shanghai,China).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRead mapping and transcriptome assembly\u003c/h2\u003e\u003cp\u003eFastp software(v0.23.4, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/OpenGene/fastp\u003c/span\u003e\u003cspan address=\"https://github.com/OpenGene/fastp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to filter the raw data to generate Clean Data.HISAT2 (v2.2.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://daehwankimlab.github.io/hisat2/\u003c/span\u003e\u003cspan address=\"http://daehwankimlab.github.io/hisat2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) was used to map the Clean Data to the reference genome, obtaining read position information on the reference genome and sample characteristics. SAMtools (v1.16.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://github.com/samtools/\u003c/span\u003e\u003cspan address=\"http://github.com/samtools/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) was used to generate bam files. Transcripts from the generated bam files were spliced ​​using StringTie software (v2.2.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ccb.jhu.edu/software/stringtie/\u003c/span\u003e\u003cspan address=\"https://ccb.jhu.edu/software/stringtie/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). The spliced ​​files were then integrated into annotation files in a format consistent with the reference genome file. Transcripts from different samples were merged using a reference-based approach. FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) was used to quantify lncRNA, mRNA,expression levels, SRPBM [spliced ​​reads per billion maps, defined as circular reads/(map reads \u0026times; read length)] was used as a normalization method to quantify circRNA expression and RPM (Reads Per Million) was used to quantify miRNAs.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDifferentially Expressed mRNA and Gene Functional Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eTranscriptome data were quantified at the transcript level using the ballgown package in StringTie software to obtain sample read counts. Following quantitative analysis, differential mRNA expression analysis was performed on the expression matrix of all samples using the R package edgeR (v3.36.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioconductor.org/packages/release/bioc/html/edgeR.html\u003c/span\u003e\u003cspan address=\"https://bioconductor.org/packages/release/bioc/html/edgeR.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Differential mRNAs (DEmRNAs)analysis was performed using edgeR with thresholds set at |log2(fold change)| \u0026gt;1 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on DE mRNAs using clusterProfiler(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps:/bioconductor.org/packages/release/bioc/html/cluster\u003c/span\u003e\u003cspan address=\"https://bioconductor.org/packages/release/bioc/html/cluster\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Profiler. html).\u003c/p\u003e\n\u003ch3\u003eIdentification and analysis of miRNA\u003c/h3\u003e\n\u003cp\u003eRaw sequencing data were processed using fastp to remove adapters, trim low-quality bases (Phred score\u0026thinsp;\u0026lt;\u0026thinsp;20), and filter reads shorter than 18 nt or longer than 36 nt. Quality assessment of read length distribution and nucleotide composition bias was then performed. Clean reads were aligned to the reference genome using BWA (v0.7.17-r1188), and sRNAs were annotated using species-specific ncRNA databases. Known miRNAs were identified by aligning reads to miRBase(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mirbase.org/ftp.shtml\u003c/span\u003e\u003cspan address=\"https://www.mirbase.org/ftp.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and novel miRNAs were predicted using miRDeep2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mdc-berlin.de/8551903/en/\u003c/span\u003e\u003cspan address=\"https://www.mdc-berlin.de/8551903/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), incorporating pre-miRNA secondary structure analysis. Differential miRNAs(DEmiRNAs) analysis was performed using edgeR, with thresholds set at |log2(fold change)| \u0026gt;1 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. miRanda ༈\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.microrna.org/microrna/getDownloads.do/༉\u003c/span\u003e\u003cspan address=\"http://www.microrna.org/microrna/getDownloads.do/༉\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and TargetScan ༈\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.targetscan.org/༉was\u003c/span\u003e\u003cspan address=\"http://www.targetscan.org/༉was\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e used to predict the target genes of differentially expressed miRNAs. To understand the functions of miRNA targets, GO and KEGG enrichment analyses were performed on the predicted target gene candidates.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIdentification and analysis of circRNA\u003c/h2\u003e\u003cp\u003eFor each sample, CIRIquant software (version 2.0.6) was used to predict the start and end positions of circRNAs and the annotation of their source genes. CircRNA expression levels were then measured using CIRIquant software, with the number of detected back-splicing junctions (BSJs) used as the count. The following table shows the expression levels of circRNAs in different samples. We used the SRPBM method (spliced reads per billion mapping) to normalize circRNA expression. CIRI_DE_replicate software was used for differential analysis of circRNAs༈DEcircRNAs༉, with thresholds set at |log2(fold change)| \u0026gt;1 and a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To understand the functions of circRNA source genes, GO and KEGG enrichment analyses were performed on the predicted target gene candidates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eIdentification and analysis of lncRNA\u003c/h2\u003e\u003cp\u003eThe assembled transcripts were compared with database-annotated coding gene transcripts using gffcompare software. To identify transcripts that did not match known coding gene transcripts, five categories of transcripts were selected: \"potential novel isoforms,\" \"transcripts located entirely within introns of reference genes,\" \"transcripts overlapping exons of reference transcripts,\" \"transcripts in unknown, intergenic regions,\" and \"transcripts overlapping exons of reference transcripts on the complementary strand.\" These transcripts were then analyzed using CPC2\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, CNCI\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, Pfam(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pfam.xfam.org/\u003c/span\u003e\u003cspan address=\"http://pfam.xfam.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Eggnog༈\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://eggnog-mapper.embl.de/༉\u003c/span\u003e\u003cspan address=\"http://eggnog-mapper.embl.de/༉\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e to remove portions of the remaining transcripts with coding potential. Transcripts with \u0026gt;\u0026thinsp;=\u0026thinsp;2 exons and a length\u0026thinsp;\u0026gt;\u0026thinsp;200 nt were identified as novel lncRNAs. Differential lncRNAs ༈DElncRNAs༉analysis was performed using edgeR with thresholds set at |log2(fold change)| \u0026gt;1 and P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. To understand the functions of lncRNA targets, GO and KEGG enrichment analyses were performed on the predicted target gene candidates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eConstruction and analysis of ceRNAs regulatory network\u003c/h2\u003e\u003cp\u003eTo discover the interactions between DEmRNAs, DElncRNAs, DEcircRNAs, and DEmiRNAs, we constructed a circRNA/lncRNA-miRNA-mRNA regulatory network based on the ceRNA hypothesis. Miranda and Targetscan were used to predict miRNA-lncRNA, miRNA-mRNA, and miRNA-circRNA pairs. Cytoscape software \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003ewas used to visualize the interaction network.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eQuantitative Real-Time PCR Validation\u003c/h2\u003e\u003cp\u003eTo validate the sequencing results, 10 differentially expressed RNAs were selected for qRT-PCR validation, including six mRNAs (c-Fos, Nr4a1, Npas4, Egr1, Igf-2, and Arc), three miRNAs (rno-miR-143-3p, rno-miR-92a-3p, and rno-miR-206-3p), one circRNA (7:94029713|94030339), and one lncRNA (MSTRG.3860). GAPDH (for mRNA, lncRNA, and circRNA) and U6 (for miRNA) served as internal reference genes. For qRT-PCR of mRNA, lncRNA, and circRNA, the HiScript 1st Strand cDNA Synthesis Kit (Vazyme, China) was used. For miRNA qRT-PCR, miRNA was reverse transcribed using the stem-loop method, replacing the Oligo(dT)18 and random hexamers with miRNA-specific stem-loops and U6 downstream primers. RNA sequencing results were verified by RT-PCR. Total RNA was extracted from rat hippocampus using TRIzol (TaKaRa, Japan) according to the manufacturer's instructions, and RNA purity and concentration were measured using an Eppendorf \u0026micro;Cuvette G1.0 (Eppendorf, Germany). Total RNA was reverse transcribed into two cDNAs: one for PCR of mRNA, lncRNA, and circRNA (RR047, TaKaRa, Japan) and the other for PCR of miRNA (638313, TaKaRa, Japan). RT-PCR was performed using SYBR Green Mix (RR820, Takara, Japan) on an Applied Biosystems 7500 system (ABI, USA). A 20-\u0026micro;L PCR reaction volume contained 6.4 \u0026micro;L of H₂O, 0.8 \u0026micro;L of primer F, 0.8 \u0026micro;L of primer R, 2 \u0026micro;L of cDNA, and 10 \u0026micro;L of SYBR Green mix. PCR reaction conditions were 95\u0026deg;C for 10 min, followed by 40 cycles of 95\u0026deg;C for 10 s, 60\u0026deg;C for 20 s, and 72\u0026deg;C for 10 s. The glyceraldehyde-3-phosphate dehydrogenase gene (Gapdh) served as an endogenous control for mRNA, lncRNA, and circRNA, and U6 served as an endogenous control for miRNA. Relative RNA expression levels were calculated using the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCt method. Specific primers for qRT-PCR were designed using Primer Premier 5.0 based on the gene sequences. All primers used in the analysis are shown in Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003eTable.1The primers information in this study\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eForward sequence(5'-3')\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReverse sequence(5'-3')\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec-Fos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGAGGGAGCTGACAGATACGC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTCCAGGGAGGTCACAGACAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIgf2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTTGGCCCTCCTGGAGACATA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGTATCTGGGGAAGTCGTCCG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEgr1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTCCAGGTTCCCATGATCCCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTGAGTGGCGAAGGCTTTGAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNpas4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGACCCTGCTGACCATCTCAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTGGGTGAGCATGGAATCGAC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNr4a1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCACAGCTTGGGTGTTGATG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eACAGCTAGCAATGCGGTTCT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTAAGCGGGACCTGTACCAGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCGCAGAAAGCGCTTGAACTT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecircRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7:94029713|94030339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCAGTCACAGAGTGCTTGCAGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTTCCTCCCTGTGGTCACCTTA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLncRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSTRG.3860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTTTTTCCGTTGTTCGGTGCT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eATTTTCCGAGGCATGGCGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003emiRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erno-miR-143-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCGCGTGAGATGAAGCACTGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAGTGCAGGGTCCGAGGTATT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erno-miR-92a-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCGCGTATTGCACTTGTCCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAGTGCAGGGTCCGAGGTATT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erno-miR-206-3p\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGCGCGTGGAATGTAAGGAAGT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAGTGCAGGGTCCGAGGTATT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eqRT-PCR of control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGAPDH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTCAGTTGCTGAGGAGTCCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eATTCGAGAGAAGGGAGGGCT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eU6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCTCGCTTCGGCAGCACATATACT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eACGCTTCACGAATTTGCGTGTC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using GraphPad Prism 8 (GraphPad Software Inc., La Jolla, CA, USA) and SPSS 26.0 (IBM Corp., Armonk, NY, USA); all data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. The Shapiro-Wilk test was used to assess normal distribution of the data. If the variables were normally distributed, one-way analysis of variance (ANOVA) followed by the Bonferroni test was used. If the variables were not normally distributed, the nonparametric Kruskal-Wallis test followed by the Dunn test was performed. For all analyses, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003eMCAT irreversibly impairs the learning and memory function of rats\u003c/h2\u003e\u003cp\u003eThe Morris water maze test results are shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In this experiment, there was no statistical difference in the swimming speed of each group of rats (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), indicating that MCAT exposure did not impair the motor ability of rats. In the positioning navigation experiment, the results are shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). With the extension of the training time, the escape latency of rats in the blank control group(C) gradually shortened, while the escape latency of rats in the low concentration group (L), medium concentration group (M) and high concentration groups(H)did not change very significantly. Compared with the control group on the same day, the escape latency of rats in the low, medium and high concentration groups was extended on days 2\u0026ndash;5; in the spatial exploration experiment, the results were shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Compared with the blank control group, the number of rats in the low, medium and high concentration groups crossed the platform decreased, and the difference was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). As the number of training days increased, the swimming trajectories of the rats in the control group became clearer and clearer, and they arrived at the platform purposefully. The swimming trajectories of the rats in the low, medium and high concentration groups were chaotic and disorderly, as shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Results from the water maze show that chronic MCAT exposure impairs learning and memory function in rats.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eUltrastructural damage in hippocampal neurons in rats exposed to MCAT\u003c/h2\u003e\u003cp\u003eSynaptic ultrastructure underlies structural synaptic plasticity and is crucial for establishing recognition memory\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Transmission electron microscopy of the CA1 region of the rat hippocampus was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The width of the synaptic cleft and the thickness of the postsynaptic density were quantified and compared (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). In the control group, synaptic morphology was well-developed, with abundant synaptic vesicles in the presynaptic membrane and a thick postsynaptic density, suggesting good synaptic function. Synaptic structure was altered in neurons exposed to low, medium, and high concentrations, with widened synaptic clefts and thinner postsynaptic densities, impairing both synaptic structure and function. Therefore, exposure to low, medium, and high concentrations of MCAT impaired the synaptic ultrastructure of neurons in the CA1 region of the rat hippocampus, leading to learning and memory impairment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eEffects of MCAT on Dendritic Spines in the Rat Hippocampus\u003c/h2\u003e\u003cp\u003eDendritic spines are the initiation sites of excitatory synaptic transmission in neurons. Their morphology and structure change dynamically, both under normal in vivo conditions and during conditions of synaptic plasticity. Maintaining the growth and stability of dendritic spines is essential for long-term memory\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Golgi staining results of the rat hippocampal CA1 region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Dendritic spine density in hippocampal neurons in the low, medium, and high concentration groups was significantly lower than that in the control group, with no difference between the low, medium, and high concentration groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These results suggest that exposure to low, medium, and high concentrations of MCAT affects the growth and development of dendritic spines in the hippocampus, leading to changes in synaptic structure and thus impairing learning and memory in rats.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHippocampal transcriptome profiles of Sprague-Dawley rats.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor mRNA, lncRNA, and circRNA sequencing,Transcriptomes from the hippocampus of control and methcathinone-treated Sprague-Dawley rats generated a total of 167.97 GB of clean data, with over 11.37 GB per sample and the percentages of Q30 bases above 95.169993%. Clean reads from each sample were mapped to the designated reference genome, and these reads were compared to the reference genome, with an average comparison rate exceeding 96% for each sample. Small RNA sequencing generated 121.94\u0026nbsp;million clean reads, with over 4.79\u0026nbsp;million per sample and the percentage of Q30 bases higher than 81.223621%. These reads were compared to the reference genome, with an average comparison rate exceeding 99% for each sample. A total of 45,826 mRNAs, 1,226 miRNAs, 89,452 circRNAs, and 7,424 lncRNAs were identified in the 12 samples(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eDifferential mRNA expression and analysis under low, medium, and high MCAT exposure\u003c/h2\u003e\u003cp\u003eIn the hippocampus of rats exposed to methcathinone compared to controls, 784 (326 upregulated, 458 downregulated), 607 (411 upregulated, 196 downregulated), and 501 (249 upregulated, 252 downregulated) mRNAs were differentially expressed at low, medium, and high concentrations, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C and Table S2). Furthermore, 230 differentially expressed mRNAs were identified across the three concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further explore the functions of DEmRNAs, GO and KEGG analyses were performed. GO terms were enriched in the low, medium, and high concentration groups by 70, 67, and 106 terms, respectively as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-G (p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 26 terms were enriched across the three concentrations, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH. These included several cognition-related terms, such as axon (GO:0030424), glutamatergic synapse (GO:0098978), neuronal cell body (GO:0043025), dendrite (GO:0030425), postsynaptic density (GO:0014069), and neuronal projection (GO:0043005), indicating that the cellular responses to the three concentrations were largely similar. However, it's worth noting that the enrichment of GO terms in the different concentration groups was not identical, suggesting that different concentrations can lead to different changes in disease progression. For example, low-concentration exposure enriched 21 pathways, medium-concentration exposure enriched 16 pathways, and high-concentration exposure enriched 52 pathways, suggesting that high concentrations of MCAT may have more significant effects on the biological functions of rats. KEGG pathway analysis was performed to identify signaling cascades associated with DEmRNAs. The low, medium, and high concentration groups enriched 70, 57, and 68 pathways as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI-K, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were 28 overlaps in the gene ontology terms among the three concentrations, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eL. Among the enriched pathways were pathways of neurodegeneration - multiple diseases (rno05022), MAPK signaling pathway (rno04010), endocytosis (rno04144), cAMP signaling pathway (rno04024), axon guidance (rno04360), tight junction (rno04530), calcium signaling pathway (rno04020), insulin signaling pathway (rno04910), and dopaminergic synapse (rno04728). These pathways often lead to cognitive impairment and are associated with the development of neurodegenerative diseases. The detailed information of the enrichment analysis was shown in supplementary materials (Table S3).\u003c/p\u003e\u003cp\u003eThe insulin signaling pathway (rno04910) was enriched at low, medium, and high concentrations. The expression levels of Igf1, Igf1r, Igf2, Igf2r, Igfbp2, Igfbp4, Igfbp5and Igfbp6 in the IGF system decreased compared to the blank control group as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In addition, immediate early genes can induce plastic changes in neuronal synapses\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Compared with the blank control group, the expression levels of the immediate early gene system including c-Fos, Nr4a1, Npas4, Egr1, Egr2, and Arc decreased in the low, medium, and high concentration groups, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003emRNA expression levels in the IGF system\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eH\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000118307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIgf1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.96486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55.5368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e65.95205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e68.02915\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000019267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIgf1r\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.17422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e63.63347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44.18126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e58.93663\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000080246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIgf2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e978.6545\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e597.2941\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e369.6975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e362.8413\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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colname=\"c4\"\u003e\u003cp\u003e3679.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2338.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2081.243\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000014153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIgfbp4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e143.5315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e111.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88.36709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e105.4235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000023530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIgfbp5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e629.6954\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e454.6541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e334.5893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e551.7671\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000014807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIgfbp6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e366.5771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e204.4865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e150.0264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e255.7914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003emRNA expression levels in the immediate early gene system\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGene name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eH\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000010712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ec-Fos\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e227.9184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.16049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84.00357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e65.66666\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000010171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNr4a1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.3925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.09494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49.65531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49.16843\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000027119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNpas4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e86.44879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.08049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.59107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e46.02276\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000026303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEgr1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e165.4891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.92903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e75.00771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63.96981\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000000792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEgr2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.59439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.315629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.37618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.598105\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSRNOT00000076998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e964.4291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e580.3086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e628.478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e550.1627\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eDifferential expression and analysis of miRNAs under low, medium, and high MCAT exposure\u003c/h2\u003e\u003cp\u003eThe length of most miRNA clean reads in the blank control group, low concentration group, medium concentration group, and high concentration group ranged from 21 to 23 bp (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D), which was consistent with the miRNA characteristics and demonstrated the reliability of the dataset. In the hippocampus of rats exposed to MCAT compared to control, 32 (7 upregulated, 25 downregulated), 28 (15 upregulated, 13 downregulated), and 23 (5 upregulated, 18 downregulated) miRNAs were differentially expressed at low, medium, and high concentrations, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-G and Table S4). Furthermore, 8 differentially expressed miRNAs were identified across the three concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGO and KEGG enrichment analysis was performed on DEmiRNA-associated target genes. Many GO terms related to cognition were found (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI-K), such as axon (GO:0030424), synapse (GO:0045202), glutamatergic synapse (GO:0098978), neuronal cell body (GO:0043025), dendrite (GO:0030425), postsynaptic density (GO:0014069), dendritic spine (GO:0043197), intracellular signaling (GO:0035556), and neuronal projection (GO:0043005).\u003c/p\u003e\u003cp\u003eKEGG pathway analysis was performed to identify signaling cascades associated with DEmiRNA-associated target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eL-N). DEmiRNA-related target genes are in the PI3K-Akt signaling pathway (rno05022), MAPK signaling pathway (rno04010), cAMP signaling pathway (rno04024), Axon guidance (rno04360), Insulin signaling pathway (rno04910), and Neurotrophin signaling pathway (rno04722). These pathways often lead to cognitive impairment and are associated with the emergence of neurodegenerative diseases. The detailed information of the enrichment analysis was shown in supplementary materials (Table S5).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eDifferential expression and analysis of circRNAs under low, medium, and high MCAT exposure\u003c/h2\u003e\u003cp\u003eIn this study, A total of 89,452 potential circRNAs were identified, of which approximately 83.42%, 12.02%, and 3.51% were of the exon, intron, and intergenic types, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The SRPBM value was used to calculate the expression level of the circRNA. The SRPBM distribution of circRNAs in the blank group, low, medium, and high-concentration groups was compared (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). In the hippocampus of rats exposed to MCAT compared to controls, 749 (334 upregulated, 415 downregulated), 728 (160 upregulated, 568 downregulated), and 753 (189 upregulated, 564 downregulated) circRNAs were differentially expressed at low, medium, and high concentrations, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-F and Table S6). Furthermore, 53 common DEcircRNAs were identified across the three concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eTo further elucidate the functions of DEcircRNAs, we performed GO and KEGG analyses on the genes from which DEcircRNAs originated. Many GO terms related to cognition were found (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG-I), such as axon (GO:0030424), synapse (GO:0045202), glutamatergic synapse (GO:0098978), neuronal cell body (GO:0043025), dendrite (GO:0030425), postsynaptic density (GO:0014069), dendritic spine (GO:0043197), brain development (GO:0007420), MAPK cascade (GO:0000165), intracellular signal transduction (GO:0035556), and neuronal projection (GO:0043005). KEGG pathway analysis was performed to confirm the signaling cascades associated with the source genes of DEcircRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ-L), including MAPK signaling pathway (rno04010), Calcium signaling pathway (rno04020), Axon guidance (rno04360), Long-term potentiation (rno04720), Neurotrophin signaling pathway (rno04722), Endocrine resistance (rno01522), and VEGF signaling pathway (rno04370). These pathways often lead to cognitive impairment and are associated with the emergence of neurodegenerative diseases. The detailed information of the enrichment analysis was shown in supplementary materials (Table S7).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDifferential expression of lncRNAs under low, medium, and high MCAT exposure.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnalysis using CPC2, CNCI, Pfam, and EGGN, using the intersection of these four tools as an example, identified 7,424 potential lncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). lncRNA classification revealed five categories of transcripts: \"potential novel isoforms\" (j), \"transcripts located entirely within introns of the reference gene\" (i), \"overlapping exons of the reference transcript\" (o), \"transcripts in unknown, intergenic regions\" (u), and \"overlapping exons of the reference transcript on the complementary strand\" (x) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). In the hippocampus of rats exposed to MCAT, 391 (209 upregulated, 182 downregulated), 369 (188 upregulated, 181 downregulated), and 371 (196 upregulated, 175 downregulated) lncRNAs were differentially expressed at low, medium, and high concentrations, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-F and Table S8). Furthermore, 113 common DElncRNAs were identified across the three concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eGO analyses were performed for enrichment analysis of differentially expressed lncRNA target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG-I). In GO terms, there are many cognitive-related terms, including glutamatergic synapse (GO:0098978), synapse (GO:0045202), nervous system development (GO:0007399), axon guidance (GO:0007411), neuron projection development (GO:0031175), chemical synaptic transmission (GO:0007268), receptor complex(GO:0043235),integral component of postsynaptic density membrane(GO:0099061),axonogenesis(GO:0007409),postsynaptic membrane(GO:0045211),synapse assembly(GO:0007416),voltage-gated potassium channel complex(GO:0008076),positive regulation of synapse assembly(GO:0051965)、GABA-ergic synapse(GO:0098982), regulation of presynapse assembly(GO:1905606), positive regulation of kinase activity(GO:0033674). KEGG pathway analysis was performed to identify signaling cascades associated with the target genes of DElncRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eJ-L), including Alzheimer's disease (rno05010), Huntington disease (rno04910), Insulin signaling pathway (rno04910), Cholinergic synapse (rno04725), Longevity regulating pathway - multiple species (rno04213), Long-term potentiation (rno04720), Pathways of neurodegeneration - multiple diseases (rno05022), Regulation of actin cytoskeleton (rno04810), MAPK signaling pathway (rno04010), PI3K-Akt signaling pathway (rno04151), and Long-term depression (rno04730). These pathways often lead to cognitive impairment and are associated with the emergence of neurodegenerative diseases. The detailed information of the enrichment analysis was shown in supplementary materials (Table S9).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eConstruction and analysis of ceRNA regulatory networks under low, medium, and high MCAT concentrations\u003c/h2\u003e\u003cp\u003eBased on the regulatory relationships of DEmiRNA-DEmRNA, DEmiRNA-DElncRNA, and DEmiRNA-DEcircRNA, the interactions between differentially expressed miRNAs and circRNA/lncRNA/mRNA with opposite expression trends were screened and determined. Ultimately, in the low concentration group, 497 DEcircRNAs were identified, which were involved in the regulation of 547 mRNAs through interactions with 27 miRNAs; 128 DElncRNAs were identified, which were involved in the regulation of 546 mRNAs through interactions with 23 miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In the medium concentration group, 351 DEcircRNAs were identified, which regulated 403 DEmRNAs by interacting with 24 DEmiRNAs; 123 DElncRNAs were identified, which regulated 403 DEmRNAs by interacting with 24 DEmiRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). In the high concentration, 426 DEcircRNAs were identified, which regulated 518 DEmRNAs by interacting with 19 DEmiRNAs; 59 DElncRNAs were identified, which regulated 518 DEmRNAs by interacting with 20 DEmiRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo further identify candidate ceRNA transcript-interaction relationships, we ultimately extracted eight mRNAs from the IGF and immediate early gene systems, including Igf-1, Igf-2, c-Fos, Nr4a1, Arc, Egr1, Egr2, and Npas4. We further explored the candidate ceRNA subnetwork involved in methcathinone-induced hippocampal damage and established a ceRNA network model encompassing 8 mRNAs, 9 miRNAs, 95 lncRNAs, and 146 circRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eqRT-PCR verification of key mRNA, miRNA, circRNA and lncRNA expression following MCAT treatment\u003c/h2\u003e\u003cp\u003eqRT-PCR was performed to confirm the whole-transcriptome RNA-seq results and verify the expression of these key mRNA, miRNA, circRNA and lncRNA. The qRT-PCR results closely matched the RNA-seq data on expression trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study used a series of neurobiologically relevant research methods to evaluate the neurotoxic effects of MCAT in rats. In the Morris water maze test, compared with the control group, rats in the low, medium, and high concentration groups had prolonged escape latencies and decreased platform crossings. These Morris water maze results indicate that exposure to low, medium, and high concentrations of MCAT impaired learning and memory in rats. Electron microscopy revealed that, compared with the control group, low, medium, and high concentrations of MCAT disrupted synaptic structure in neurons in the CA1 region of the hippocampus and impaired synaptic plasticity. Golgi staining revealed a decrease in dendritic spines in the CA1 region of the hippocampus after exposure to low, medium, and high concentrations of MCAT compared with the control group. Therefore, behavioral and morphological results confirm that we have successfully established a rat model in which chronic MCAT exposure impairs learning and memory and hippocampal neurons.\u003c/p\u003e\u003cp\u003eRecent developments in high-throughput sequencing have enabled the assessment of the entire transcriptome across mRNA, miRNA, lncRNA, and circRNA, potentially revealing the biological processes driving complex phenotypes \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Whole-transcriptome sequencing technology has enabled a deeper understanding of the mechanisms by which the hippocampus is affected by MCAT. By applying whole transcriptome sequencing, this study comprehensively integrated the data of mRNA, miRNA, lncRNA and circRNA in the hippocampus of rats in low, medium and high MCAT concentration groups for the first time. Compared with the control group, 784 DEmRNAs, 32 DEmiRNAs, 391 DElncRNAs and 749 DEcircRNAs were identified in the low-concentration group, 607 DEmRNAs, 28 DEmiRNAs, 369 DElncRNAs and 728 DEcircRNAs were identified in the medium-concentration group, and 501 DEmRNAs, 23 DEmiRNAs, 371 DElncRNAs and 753 DEcircRNAs were identified in the high-concentration group. In addition, there were differences in the DEmRNAs, DEmiRNAs, DEcircRNAs, and DElncRNAs between the low, medium, and high concentration groups, indicating that different concentrations of MCAT may cause neurotoxicity through different biological processes.\u003c/p\u003e\u003cp\u003eTo gain deeper insights into the functional significance of differentially expressed RNAs in methcathinone-induced neurotoxicity, we performed GO enrichment and KEGG pathway analyses. GO analysis showed that differentially expressed RNAs in the low, medium, and high concentration groups were enriched in terms related to learning and memory, such as axon (GO:0030424), glutamatergic synapse (GO:0098978), neuronal cell body (GO:0043025), dendrite (GO:0030425), postsynaptic density (GO:0014069), neuronal projection (GO:0043005), and GABAergic synapse (GO:0098982). KEGG pathway analysis showed that the differentially expressed RNAs also targeted several specific signaling pathways, including Pathways of neurodegeneration - multiple diseases (rno05022), MAPK signaling pathway (rno04010), Endocytosis (rno04144), cAMP signaling pathway (rno04024), Axon guidance (rno04360), Tight junction (rno04530), Calcium signaling pathway (rno04020), Insulin signaling pathway (rno04910), and Dopaminergic synapse (rno04728). Alterations in these signaling pathways are associated with nervous system-related diseases. This suggests that differentially expressed mRNAs, miRNAs, circRNAs, and lncRNAs are likely involved in the process of methcathinone-induced neurotoxicity.\u003c/p\u003e\u003cp\u003eAmong the differentially expressed mRNAs, immediate-early genes, including c-Fos, Nr4a1, Arc, Egr1, Egr2, and Npas4, showed varying degrees of decrease in the low, medium, and high concentration groups. Immediate-early genes primarily encode transcription factors that regulate cellular homeostasis and neuronal plasticity\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. They are rapidly activated after sensory and behavioral experiences and play a crucial role in the experience-dependent regulation of synapses to encode memories. They are rapidly induced at the transcriptional and translational levels in response to learning, and their induction is essential for memory formation\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Currently, immediate-early genes are considered the \"signature\" of memory engrams: for example, hippocampal-mediated learning leads to rapid expression of immediate-early genes in cortical and hippocampal neurons. Therefore, immediate-early genes are often used as markers of neuronal activity to detect engram cells, which are believed to store memories in the brain\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The decrease in immediate-early genes suggests that MCAT may affect synaptic plasticity by affecting the expression of immediate-early genes, leading to learning and memory impairments.\u003c/p\u003e\u003cp\u003ec-Fos is widely distributed throughout the brain, and increased neuronal activity induces upregulation of this protein\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Compared to rats exposed to familiar objects, exposure to novel objects leads to significantly increased c-Fos levels in certain brain structures\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Blocking c-Fos activation in the hippocampus impairs long-term memory related to water maze learning, radial arm maze performance, contextual fear learning, and socially transmitted food preferences\u003csup\u003e[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Several studies have shown that high c-Fos expression in stimulated neurons, which activates them in a coordinated manner, is crucial for stable, long-lasting plasticity in the hippocampus\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNr4a1 plays an important role in synaptic plasticity, long-term memory formation, and neuroprotection\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In humans, Nr4a1 mRNA levels in peripheral blood mononuclear cells (PBMCs) decrease with age, particularly in those with cognitive impairment. Similarly, Nr4a1 levels are reduced in the PBMCs and brains of aged mice\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Nr4a1 levels in the CA1 region of the mouse hippocampus are positively linearly correlated with cognitive ability. Both systemic and CA1 pyramidal neuron-specific knockout of Nr4a1 impaired excitatory synaptic function and cognitive ability in young mice, whereas Nr4a1 overexpression improved cognition in aged mice\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eArc is involved in various neuronal signaling pathways as an effector molecule and is considered an essential player in the molecular mechanisms necessary for learning and memory\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. It is rapidly transcribed during neuronal activity and precisely targets activated synapses in neuronal dendrites\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that reduction of Arc in the dorsal CA1 region is sufficient to impair spatial memory ability in the Morris water maze task\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Knockout of Arc leads to consolidation defects in different types of long-term memory, as well as alterations in long-term plasticity\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEgr1 is involved in brain development, learning, and long-term neuronal plasticity\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Egr1 deficiency leads to impairments in multiple behavioral tasks related to different brain regions. Typically, Egr1 mutant mice exhibit severe impairments in long-term memory, including spatial memory, object recognition memory, and object location recognition memory\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Egr2 is essential for brain development and is important for myelination and synaptic plasticity in the central nervous system\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eNpas4 is a brain-specific transcription factor whose expression is regulated by neuronal activity\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Npas4 mRNA levels are reduced in the hippocampus of aged mice with impaired memory, suggesting that Npas4 may contribute to the preservation of hippocampal-related cognition\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Npas4 deficiency is characterized by impaired spatial recognition memory, decreased anxiety-like behaviors, and increased depression-like behaviors\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Npas4 controls the balance of excitatory and inhibitory synapses to maintain neural circuit homeostasis, which is crucial for information processing and memory formation\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe insulin-like growth factor (IGF) signaling pathway is crucial for central nervous system development, metabolism, repair, cognition, and mood regulation and includes Igf-1, Igf-2, Igf-1r, Igf-2r, insulin receptor (Ir), and 6 insulin-like growth factor binding proteins (Igfbps)\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. It was found that the expression levels of Igf-1, Igf1r, Igf2, Igf2r, Igfbp2, Igfbp4, Igfbp5, and Igfbp6 in the IGF system decreased to varying degrees in the low, medium, and high concentration groups. Studies have shown that Igf-1, Igf-2, and insulin are associated with cognitive function, and many studies have shown that they have potential effects on cognitive enhancement and as compounds that may improve cognitive impairment and even neurodegeneration. They play an important role in brain development and homeostasis, including neurogenesis, neuronal differentiation, and angiogenesis\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. Igf1 plays a crucial role in the development and maturation of the central nervous system. Igf1 potently influences neuroplasticity in the central nervous system through signaling via Igf-1r and canonical signaling pathways such as PI3K-Akt\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Unlike Igf1, the role of Igf2 in central nervous system function is less well-researched. However, studies have shown that Igf2 can cross the blood-brain barrier, facilitate synaptic formation and maturation, and effectively enhance memory and cognitive abilities in mice\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003emiRNAs play key regulatory roles in central nervous system development, dendritic spine formation, neurite outgrowth, and neuronal differentiation and maintenance. Dysregulation of miRNAs has been linked to neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. In this study, differentially expressed miRNAs, including miRNA-218b, miR-206-3p, miR-378a-3p, miR-137, miR-182-5p, miR-96-5p, and miR-143-3p, have been implicated in several cognitive-related neurodegenerative and neuropsychiatric disorders. Studies have shown that miR-206-3p exerts neuroprotective effects in Alzheimer's disease mice by regulating brain-derived neurotrophic factor\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. In lead (Pb) exposure, miR-378a-3p is implicated in Pb-induced neuronal damage by regulating ferroptosis\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. miR-137, a miRNA abundantly expressed in the cerebral cortex, has been reported to be downregulated in the hippocampus following ketamine overexposure, and its overexpression has been shown to protect against ketamine-induced hippocampal neurodegeneration and memory loss\u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. miR-182-5p also plays a key role in the nervous system. Exosome-delivered miR-182-5p promotes sevoflurane-induced neuroinflammation and cognitive impairment in elderly rats with postoperative cognitive impairment by targeting brain-derived neurotrophic factor and activating the NF-κB pathway\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that blocking miR-96-5p can increase the expression of the cysteine transporter excitatory amino acid transporter 1 and glutathione, protecting neurons from oxidative stress in brain tissue\u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e. miR-143-3p is upregulated in the serum of patients with mild cognitive impairment associated with Alzheimer's disease, acute ischemic stroke, and a mouse model of stroke\u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]\u003c/sup\u003e. Inhibiting the expression of miR-143-3p affects the occurrence of depressive-like behavior in mice by regulating synaptic structure\u003csup\u003e[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eLncRNAs and circRNAs possess miRNA binding sites and can act as miRNA sponges to inhibit the negative regulation of miRNAs on target genes, thereby indirectly regulating gene expression\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Similar GO and KEGG annotations for DElncRNAs, DEcircRNAs, DEmiRNAs, and DEmRNAs suggest potential regulatory relationships among these groups. We constructed ceRNA networks for low, medium, and high concentrations, ultimately identifying 497 DEcircRNAs in the low concentration group, which regulated 547 mRNAs through interactions with 27 miRNAs. We also identified 128 DElncRNAs, which regulated 546 mRNAs through interactions with 23 miRNAs. At medium concentrations, 351 DEcircRNAs were identified, and these DEcircRNAs participated in the regulation of 403 mRNAs through interactions with 24 miRNAs; 123 DElncRNAs were identified, and these DElncRNAs participated in the regulation of 403 mRNAs through interactions with 24 miRNAs. At high concentrations, 426 DEcircRNAs were identified, and these DEcircRNAs participated in the regulation of 518 mRNAs through interactions with 19 miRNAs; 59 DElncRNAs were identified, and these DElncRNAs participated in the regulation of 518 mRNAs through interactions with 20 miRNAs. Finally, based on c-Fos, Nr4a1, Arc, Egr1, Egr2, Npas4 in the immediate early gene system and Igf1 and Igf2 in the IGF system, a ceRNA network consisting of 8 mRNAs, 9 miRNAs, 95 lncRNAs, and 146 circRNAs was established.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eMorris water maze, Golgi staining, and TEM demonstrated that we successfully established a rat model of methcathinone-induced neurotoxicity. Whole-transcriptome sequencing identified differentially expressed mRNAs, miRNAs, circRNAs, and lncRNAs in response to low, medium, and high methcathinone concentrations, and constructed ceRNA networks for each of these circRNAs/lncRNAs, miRNAs, and mRNAs. The study found that c-Fos, Nr4a1, Arc, Egr1, Egr2, Npas4, Igf1, and Igf2 are important candidate genes for methcathinone-induced neurotoxicity. These genes are regulated by rno-miR-92a-3p, rno-miR-211-5p, rno-miR-378a-3p, rno-miR-182, rno-miR-336-5p, rno-miR-21-3p, rno-miR-96-5p, rno-miR-183-5p, and rno-miR-143-3p. Furthermore, 95 lncRNAs and 146 circRNAs participate in the ceRNA network by regulating these nine miRNAs. This study enriches the neurotoxic gene database from a toxicological perspective and provides a new strategy for studying methcathinone neurotoxicity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal experiments were approved by the Institutional Animal Care and Use Committee of Shanxi Medical University（2021-338）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data of circRNA, miRNA, lncRNA and mRNA were uploaded and deposited in NCBI Sequence Read Archive (SRA, PRJNA1333463, PRJNA1333391 and PRJNA1333576).\u003c/p\u003e\n\u003cp\u003ecircRNA data (PRJNA1333463) are available at the following website: (https://dataview.ncbi.nlm.nih.gov/object/PRJNA1333463), miRNA data (PRJNA1333391) are available at the following website:( https://dataview.ncbi.nlm.nih.gov/object/PRJNA1333391), and lncRNA and mRNA data (PRJNA1333576) are available at the following website: (https://dataview.ncbi.nlm.nih.gov/object/PRJNA1333576).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is financially supported by the National Key Research and Development Program of China (No. 2022YFC3300903), National Natural Science Foundation of China (No. 82130056), Shanxi Province Science Foundation (No. 202103021224233),\u0026nbsp;Chinese Medicine Research Project of Shanxi Provincial Administration of Chinese Medicine((No.2024ZYYC102).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization:LV JP. \u0026nbsp;Data curation:ZHOU RK. \u0026nbsp;Formal analysis: XU CM. Funding acquisition:YUN KM, WEI ZW. Investigation:ZHOU RK.\u003c/p\u003e\n\u003cp\u003eResource: WEI ZW. Supervision:CHEN Z, YUN KM. Software:XU YW. Writing\u0026nbsp;\u0026ndash;\u0026nbsp;original draft:ZHOU RK. \u0026nbsp;Writing\u0026nbsp;\u0026ndash;\u0026nbsp;review \u0026amp; editing:WEI ZW. \u0026nbsp;Visualization:XU YW. \u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool of Forensic Medicine, Shanxi Medical University\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eJinzhong, Shanxi Province, People\u0026rsquo;s Republic of China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRukui Zhou \u0026nbsp;\u003cstrong\u003eEmail:\u003c/strong\[email protected]；\u003c/p\u003e\n\u003cp\u003eYingwen Xu \u0026nbsp;\u003cstrong\u003eEmail:\u003c/strong\[email protected]；\u003c/p\u003e\n\u003cp\u003eChunming Xu \u0026nbsp;\u003cstrong\u003eEmail:\u003c/strong\[email protected]；\u003c/p\u003e\n\u003cp\u003eZhe Chen \u0026nbsp;\u003cstrong\u003eEmail:\u003c/strong\[email protected];\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKeming Yun \u0026nbsp;\u003cstrong\u003eEmail:\u003c/strong\[email protected]；\u003c/p\u003e\n\u003cp\u003eZhiwen Wei \u0026nbsp;\u003cstrong\u003eEmail:\u003c/strong\[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSchool of Basic Medical Sciences\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eShanxi University of Chinese Medicine\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eJinzhong, Shanxi Province, People\u0026rsquo;s Republic of China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRukui Zhou \u0026nbsp;\u003cstrong\u003eEmail:\u003c/strong\u003e [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Anesthesiology,The first hospital of Shanxi medical university\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003etaiyuan, Shanxi Province, People\u0026rsquo;s Republic of China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJieping Lv \u0026nbsp; \u003cstrong\u003eEmail\u003c/strong\u003e :[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJieping Lv; Keming Yun；Zhiwen Wei\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCapriola M. 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Commun Biol. 2024;7(1):944.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Methcathinone, Neurotoxicity, Whole transcriptomics, ceRNA","lastPublishedDoi":"10.21203/rs.3.rs-7737272/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7737272/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMethcathinone, a synthetic cathinone derivative similar to amphetamine, has raised public health concerns due to its addictive properties and health risks associated with neurotoxicity. Low-dose, medium-dose, and high-dose methcathinone-induced neurotoxicity models were established. Learning and memory functions were assessed using the Morris water maze, and changes in hippocampal synaptic morphology and structure were examined using electron microscopy and Golgi staining. Whole-transcriptome sequencing was then used to characterize the expression of mRNAs, miRNAs, circRNAs, and lncRNAs. Differentially expressed mRNAs(DEmRNAs), miRNAs(DEmiRNAs), circRNAs (DEcircRNAs), and lncRNAs (DElncRNAs) were identified, and circRNA-miRNA-mRNA and lncRNA-miRNA-mRNA networks were constructed. Compared with the control group, 784 DEmRNAs, 32 DEmiRNAs, 391 DElncRNAs and 749 DEcircRNAs were identified in the low-concentration group, 607 DEmRNAs, 28 DEmiRNAs, 369 DElncRNAs and 728 DEcircRNAs were identified in the medium-concentration group, and 501 DEmRNAs, 23 DEmiRNAs, 371 DElncRNAs and 753 DEcircRNAs were identified in the high-concentration group. Multiple genes in the immediate-early gene system and the IGF system were affected by methcathinone concentrations, including c-Fos, Nr4a1, Arc, Egr1, Egr2, Npas4, Igf1, and Igf2. These genes were regulated by rno-miR-92a-3p, rno-miR-211-5p, rno-miR-378a-3p, rno-miR-182, rno-miR-336-5p, rno-miR-21-3p, rno-miR-96-5p, rno-miR-183-5p, and rno-miR-143-3p. These miRNAs were competitively bound by 95 lncRNAs and 146 circRNAs participating in the ceRNA network by regulating these nine miRNAs. This result provides new insights into the regulatory mechanisms of mRNA, miRNA, lncRNA, and circRNA in methcathinone-induced neurotoxicity.\u003c/p\u003e","manuscriptTitle":"Comprehensive ceRNA Network Analysis Reveals Regulatory Mechanisms of mRNA, miRNA, circRNA, and lncRNA in Methcathinone-Induced Neurotoxicity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:34:39","doi":"10.21203/rs.3.rs-7737272/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"feb1c5c6-1b6e-421d-a9aa-912c5bb9a597","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T07:25:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 11:34:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7737272","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7737272","identity":"rs-7737272","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}