Systematic Analysis of Long Noncoding RNA and mRNA in Granulosa Cells during the Hen Ovulatory Cycle

Long non-coding RNAs (lncRNAs) and mRNAs are temporally expressed during chicken follicle development. However, follicle transcriptome studies in chickens with timepoints relating to changes in luteinizing hormone (LH) levels are rare. In this study, gene expression in Rohman layers was investigated at three distinct stages of the ovulatory cycle: zeitgeber time 0 (ZT0, 9:00 a.m.), zeitgeber time 12 (ZT12, 9:00 p.m.), and zeitgeber time 20 (ZT20, 5:00 a.m.) representing the early, middle, and LH surge stages, respectively, of the ovulatory cycle. Gene expression profiles were explored during follicle development at ZT0, ZT12, and ZT20 using Ribo-Zero RNA sequencing. The three stages were separated into two major stages, including the pre-LH surge and the LH surge stages. A total of 12,479 mRNAs and 7528 lncRNAs were identified among the three stages, and 4531, 523 differentially expressed genes (DEGs) and 2367, 211 differentially expressed lncRNAs (DELs) were identified in the ZT20 vs. ZT12, and ZT12 vs. ZT0, comparisons. Functional enrichment analysis revealed that genes involved in cell proliferation and metabolism processes (lipid-related) were mainly enriched in the ZT0 and ZT12 stages, respectively, and genes related to oxidative stress, steroids regulation, and inflammatory process were enriched in the ZT20 stage. These findings provide the basis for further investigation of the specific genetic and molecular functions of follicle development in chickens.

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LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer

Journal of Personalized Medicine ◽

10.3390/jpm11111177 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1177

Author(s):

Shao-Hua Yu ◽

Jia-Hua Cai ◽

De-Lun Chen ◽

Szu-Han Liao ◽

Yi-Zhen Lin ◽

...

Keyword(s):

Gene Expression ◽

Endometrial Cancer ◽

Pathway Analysis ◽

Bioinformatics Analysis ◽

Expression Profiles ◽

Gynecologic Cancer ◽

Functional Enrichment ◽

Lasso Regression ◽

Potential Biomarker ◽

Study Gene Expression

The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium–calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment.

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Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽

10.7717/peerj.7821 ◽

2019 ◽

Vol 7 ◽

pp. e7821 ◽

Cited By ~ 7

Author(s):

Xiaoming Zhang ◽

Jing Zhuang ◽

Lijuan Liu ◽

Zhengguo He ◽

Cun Liu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Prognostic Value ◽

Early Stage ◽

Expression Profiles ◽

Diagnostic Value ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

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Classification of Colorectal Carcinoma Based on Ferroptosis-related Gene Signature

10.21203/rs.3.rs-229945/v1 ◽

2021 ◽

Author(s):

QingFang Yue ◽

Yuan Zhang ◽

Fei Wang ◽

Fei Cao ◽

Xianglong Duan ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Carcinoma ◽

Drug Sensitivity ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Mutations ◽

Functional Enrichment ◽

Differentially Expressed ◽

Related Gene ◽

Molecular Features

Abstract Background: Ferroptosis is an iron-dependent form of programmed cell death, involves in the development of many cancers. However, systematic analysis of ferroptosis related-genes in colorectal carcinoma (CRC) remains to be clarified. We herein analyzed the public databases to identify the molecular features of CRC by the development of a classification based on the gene expression profile of ferroptosis-related genes.Methods: We collected the gene expression data and clinical information from The Cancer Atlas and Gene Expression Omnibus to explore the correlation of ferroptosis-related gene expression of CRC. Consensus clustering was performed to determine the clusters of colorectal cancer patients, then we analyzed the prognostic value, transcriptome features, immune microenvironment, drug sensitivity, gene mutations differences of the subclasses. Results: Four subclasses of CRC (C1, C2, C3 and C4) were identified. There were significant differences in the prognosis of patients between the four subtypes. Functional enrichment suggested that these ferroptosis related-genes were associated with biological processes such as metabolic processes and oxidative stress, and the KEGG pathway suggested that it was closely related to ferroptosis and glutathione metabolic pathway. There were significant differences in immune cell infiltrations, immune score, stromal score and tumor purity among four subclasses. The expression of PD-L1 was differentially expressed in four subclasses and PD-L1 was significantly correlated with the expression of several ferroptosis-related genes in the differentially expressed subtypes. There were significant differences in stemness indices and drug sensitivity in four subclasses, and gene mutations analysis showed that ferroptosis-related genes such as TP53 had high mutations in different subtypes, and there were significant differences in mutation frequencies in the subclasses.Conclusion: This study established a new CRC classification based on ferroptosis-related genes expression profiles, and different subgroups may have their unique gene expression patterns, indicating that the heterogeneity within CRC and the classification might provide valuable stratification for the design of future treatment.

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Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma

Cancer Cell International ◽

10.1186/s12935-019-1080-y ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 2

Author(s):

Cheng Zhang ◽

Bingye Zhang ◽

Di Meng ◽

Chunlin Ge

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Functional Enrichment ◽

Differentially Expressed ◽

Hub Genes ◽

Ppi Networks ◽

Differentially Methylated Genes ◽

Tumorigenesis And Progression

Abstract Background The incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis. Methods The gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein–protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database. Results We identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA. Conclusions Our study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.

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Integrated computational analysis revealed hub genes in lung adenocarcinoma

10.21203/rs.3.rs-294392/v1 ◽

2021 ◽

Author(s):

Muhammad Jamal ◽

Abdul Saboor Khan ◽

Hina Iqbal Bangash ◽

Tian Xie ◽

Tianbao Song ◽

...

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Survival Analysis ◽

Differentially Expressed Genes ◽

Functional Enrichment ◽

Differentially Expressed ◽

Normal Lung ◽

Hub Genes ◽

Altered Expression ◽

Study Gene Expression

Abstract Background Lung cancer (LUCA) is the leading cause of cancer-related morbidities and mortalities globally. Despite the recent advancements in lung cancer research, understanding of the molecular mechanism underlying LUCA tumorigenesis and prognosis remains suboptimal. This study aims to identify the candidate biomarkers and therapeutic genes in lung cancer. Methods In this study, gene expression profiles of GSE30219, GSE33532, GSE32863 and GSE43458 were downloaded from GEO. The differentially expressed genes (DEGs) in LUAD tissue and normal lung tissue with a p-value < 0.05 and a |log fold change (FC)| >1.0 were identified by GEO2R. For functional enrichment analysis of these DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed with KOBAS and DAVID tools. Next, the candidate hub genes were filtered out with Cytoscape using CytoHubba plugin. These hub genes were validated by (the Cancer Genome Atlas) TCGA-based gene expression analysis, protein-protein network interaction (PPI) analysis, survival analysis. Moreover, the expression of these genes in cancer and normal tissue was assessed in the Human Protein Atlas (HPA) database. In addition, miRNA network of the hub genes was constructed. Finally, DGIdb database was used to check the drug-targeting potentials of the hub genes. Results a total of 332 overlapping differentially expressed genes (DEGs) including 73 upregulated and 259 downregulated, respectively were identified. GO analysis revealed that the DEGs were principally regulating various cancer-associated functions and pathways. The module analysis revealed 55 hub genes in 4 modules. The survival analysis through Kaplan-Meier (KM) plotter indicated that the altered expression of these genes resulted in the poor overall survival (OS) of LUCA patients. Moreover, these genes show a differential expression on both protein and mRNA level in cancer patient compared to the normal. In addition, in addition, 6 potential microRNAs (miRNAs) interacting with hub genes were identified. Finally, a list of 117 therapeutic small molecules was tabulated that could facilitate LUCA treatment. Conclusions the findings of this study may help in the development of novel and reliable biomarkers for diagnosis, prognosis and therapeutic intervention for LUAD.

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DIFFERENT MORPHOLOGICAL STRUCTURES OF BREAST TUMORS DEMONSTRATE INDIVIDUAL DRUG RESISTANCE GENE EXPRESSION PROFILES

Experimental Oncology ◽

10.31768/2312-8852.2018.40(3):228-234 ◽

2018 ◽

Vol 40 (3) ◽

pp. 228-234 ◽

Cited By ~ 3

Author(s):

T S Gerashchenko ◽

E V Denisov ◽

N M Novikov ◽

L A Tashireva ◽

E V Kaigorodova ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Drug Resistance ◽

Anticancer Drugs ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Drug Uptake ◽

Functional Enrichment ◽

Differentially Expressed ◽

Luminal Breast Cancer

Aim: To identify gene expression profiles involved in drug resistance of different morphological structures (tubular, alveolar, solid, trabecular, and discrete) presented in breast cancer. Material and Methods: Ten patients with luminal breast cancer have been included. A laser microdissection-assisted microarrays and qRT-PCR were used to perform whole-transcriptome profiling of different morphological structures, to select differentially expressed drug response genes, and to validate their expression. Results: We found 27 differentially expressed genes (p < 0.05) encoding drug uptake (SLC1A3, SLC23A2, etc.) and efflux (ABCC1, ABCG1, etc.) transporters, drug targets (TOP2A, TYMS, and Tubb3), and proteins that are involved in drug detoxification (NAT1 and ALDH1B1), cell cycle progression (CCND1, AKT1, etc.), apoptosis (CASP3, TXN2, etc.), and DNA repair (BRCA1 and USP11). Each type of structures showed an individual gene expression profile related to resistance and sensitivity to anticancer drugs. However, most of the genes (19/27; p < 0.05) were expressed in alveolar structures. Functional enrichment analysis showed that drug resistance is significantly associated with alveolar structures. Other structures demonstrated the similar number (10–13 out of 27) of expressed genes; however, the spectrum of resistance and sensitivity to different anticancer drugs varied. Conclusion: Different morphological structures of breast cancer show individual expression of drug resistance genes.

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Genome-Wide Expression Profiling in Malaria Infection Reveals Transcriptional Changes Associated with Lethal and Nonlethal Outcomes

Infection and Immunity ◽

10.1128/iai.73.9.6091-6100.2005 ◽

2005 ◽

Vol 73 (9) ◽

pp. 6091-6100 ◽

Cited By ~ 24

Author(s):

Kurt Schaecher ◽

Sanjai Kumar ◽

Anjali Yadava ◽

Maryanne Vahey ◽

Christian F. Ockenhouse

Keyword(s):

Gene Expression ◽

Immune Responses ◽

Transcriptional Activation ◽

Malaria Infection ◽

Target Genes ◽

Expression Profiles ◽

Plasmodium Yoelii ◽

Differentially Expressed ◽

Genome Wide ◽

Study Gene Expression

ABSTRACT High-density oligonucleotide microarrays are widely used to study gene expression in cells exposed to a variety of pathogens. This study addressed the global genome-wide transcriptional activation of genes in hosts infected in vivo, which result in radically different clinical outcomes. We present an analysis of the gene expression profiles that identified a set of host biomarkers which distinguish between lethal and nonlethal blood stage Plasmodium yoelii malaria infections. Multiple biological replicates sampled during the course of infection were used to establish statistically valid sets of differentially expressed genes. These genes that correlated with the intensity of infection were used to identify pathways of cellular processes related to metabolic perturbations, erythropoiesis, and B-cell immune responses and other innate and cellular immune responses. The transcriptional apparatus that controls gene expression in erythropoiesis was also differentially expressed and regulated the expression of target genes involved in the host's response to malaria anemia. The biological systems approach provides unprecedented opportunities to explore the pathophysiology of host-pathogen interactions in experimental malaria infection and to decipher functionally complex networks of gene and protein interactions.

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Transcriptomic Analyses Reveal Gene Expression Profiles and Networks in Nasopharyngeal Carcinoma

BioMed Research International ◽

10.1155/2021/8890176 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yaqi Zhou ◽

Weiqiang Yang ◽

Xueshuang Mei ◽

Hongyi Hu

Keyword(s):

Gene Expression ◽

Nasopharyngeal Carcinoma ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Principal Component ◽

Functional Enrichment ◽

Differentially Expressed ◽

Clinical Samples ◽

Tissue Samples

Background. Nasopharyngeal carcinoma (NPC) is a rare but highly aggressive tumor that is predominantly encountered in Southeast Asia and China in particular. Aside from radiotherapy, no effective therapy that specifically treats NPC is available, including targeted drugs. Finding more sensitive biomarkers is important for new drug discovery and for evaluating patient prognosis. Methods. mRNA expression datasets from the Gene Expression Omnibus database (GSE53819, GSE64634, and GSE40290) were selected. After all samples in each dataset were subjected to quality control using principal component analyses, the qualified samples were used for additional analyses. The genes that were significantly expressed in each dataset were intersected to identify the most significant of these. Gene functional enrichment analyses were performed on these genes, using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses. The protein–protein interaction network of selected genes was analyzed using the Search Tool for the Retrieval of Interacting Genes database. Significantly, differentially expressed genes were further verified with two RNA-seq datasets (GSE68799 and GSE12452), as well as in clinical samples. Results. In all, 34 (8 upregulated genes and 26 downregulated) genes were identified as significantly differentially expressed. The immune response and the regulation of cell proliferation were the most enriched biological GO terms. Using reverse transcription quantitative real-time PCR (RT-qPCR), the genes MMP1, AQP9, and TNFAIP6 were detected to be upregulated, and FAM3D, CR2, and LTF were downregulated in NPC tissue samples. Conclusion. This study provides information on the genes that may be involved in the development of NPC and suggests possible druggable targets and biomarkers for diagnosing and evaluating the prognosis of NPC.

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Differential gene expression analysis of ankylosing spondylitis shows deregulation of the HLA-DRB, HLA-DQB, ITM2A, and CTLA4 genes

Egyptian Journal of Medical Human Genetics ◽

10.1186/s43042-021-00161-0 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Rowan AlEjielat ◽

Anas Khaleel ◽

Amneh H. Tarkhan

Keyword(s):

Gene Expression ◽

Ankylosing Spondylitis ◽

Differentially Expressed Genes ◽

Gene Network ◽

Disease Diagnosis ◽

Receptor Activation ◽

Functional Enrichment ◽

Differentially Expressed ◽

Inflammatory Disorder ◽

Data Set

Abstract Background Ankylosing spondylitis (AS) is a rare inflammatory disorder affecting the spinal joints. Although we know some of the genetic factors that are associated with the disease, the molecular basis of this illness has not yet been fully elucidated, and the genes involved in AS pathogenesis have not been entirely identified. The current study aimed at constructing a gene network that may serve as an AS gene signature and biomarker, both of which will help in disease diagnosis and the identification of therapeutic targets. Previously published gene expression profiles of 16 AS patients and 16 gender- and age-matched controls that were profiled on the Illumina HumanHT-12 V3.0 Expression BeadChip platform were mined. Patients were Portuguese, 21 to 64 years old, were diagnosed based on the modified New York criteria, and had Bath Ankylosing Spondylitis Disease Activity Index scores > 4 and Bath Ankylosing Spondylitis Functional Index scores > 4. All patients were receiving only NSAIDs and/or sulphasalazine. Functional enrichment and pathway analysis were performed to create an interaction network of differentially expressed genes. Results ITM2A, ICOS, VSIG10L, CD59, TRAC, and CTLA-4 were among the significantly differentially expressed genes in AS, but the most significantly downregulated genes were the HLA-DRB6, HLA-DRB5, HLA-DRB4, HLA-DRB3, HLA-DRB1, HLA-DQB1, ITM2A, and CTLA-4 genes. The genes in this study were mostly associated with the regulation of the immune system processes, parts of cell membrane, and signaling related to T cell receptor and antigen receptor, in addition to some overlaps related to the IL2 STAT signaling, as well as the androgen response. The most significantly over-represented pathways in the data set were associated with the “RUNX1 and FOXP3 which control the development of regulatory T lymphocytes (Tregs)” and the “GABA receptor activation” pathways. Conclusions Comprehensive gene analysis of differentially expressed genes in AS reveals a significant gene network that is involved in a multitude of important immune and inflammatory pathways. These pathways and networks might serve as biomarkers for AS and can potentially help in diagnosing the disease and identifying future targets for treatment.

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Behavioral sensitization induced by methamphetamine causes differential alterations in gene expression and histone acetylation of the prefrontal cortex in rats

BMC Neuroscience ◽

10.1186/s12868-021-00616-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Hui Li ◽

Jing-An Chen ◽

Qian-Zhi Ding ◽

Guan-Yi Lu ◽

Ning Wu ◽

...

Keyword(s):

Gene Expression ◽

Prefrontal Cortex ◽

Histone Acetylation ◽

Differentially Expressed Genes ◽

Behavioral Sensitization ◽

Functional Enrichment ◽

Differentially Expressed ◽

Adult Rats ◽

Mrna Microarray ◽

H3 Acetylation

Abstract Background Methamphetamine (METH) is one of the most widely abused illicit substances worldwide; unfortunately, its addiction mechanism remains unclear. Based on accumulating evidence, changes in gene expression and chromatin modifications might be related to the persistent effects of METH on the brain. In the present study, we took advantage of METH-induced behavioral sensitization as an animal model that reflects some aspects of drug addiction and examined the changes in gene expression and histone acetylation in the prefrontal cortex (PFC) of adult rats. Methods We conducted mRNA microarray and chromatin immunoprecipitation (ChIP) coupled to DNA microarray (ChIP-chip) analyses to screen and identify changes in transcript levels and histone acetylation patterns. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were performed to analyze the differentially expressed genes. We then further identified alterations in ANP32A (acidic leucine-rich nuclear phosphoprotein-32A) and POU3F2 (POU domain, class 3, transcription factor 2) using qPCR and ChIP-PCR assays. Results In the rat model of METH-induced behavioral sensitization, METH challenge caused 275 differentially expressed genes and a number of hyperacetylated genes (821 genes with H3 acetylation and 10 genes with H4 acetylation). Based on mRNA microarray and GO and KEGG enrichment analyses, 24 genes may be involved in METH-induced behavioral sensitization, and 7 genes were confirmed using qPCR. We further examined the alterations in the levels of the ANP32A and POU3F2 transcripts and histone acetylation at different periods of METH-induced behavioral sensitization. H4 hyperacetylation contributed to the increased levels of ANP32A mRNA and H3/H4 hyperacetylation contributed to the increased levels of POU3F2 mRNA induced by METH challenge-induced behavioral sensitization, but not by acute METH exposure. Conclusions The present results revealed alterations in transcription and histone acetylation in the rat PFC by METH exposure and provided evidence that modifications of histone acetylation contributed to the alterations in gene expression caused by METH-induced behavioral sensitization.

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