DNA N6-methyladenine Research Articles

Structural Optimization and Structure-Activity Relationship of 1H-Pyrazole-4-carboxylic Acid Derivatives as DNA 6mA Demethylase ALKBH1 Inhibitors and Their Antigastric Cancer Activity.

DNA N6-methyladenine (6mA) demethylase ALKBH1 plays an important role in various cellular processes. Dysregulation of ALKBH1 is associated with the development of some cancer types, including gastric cancer, implicating a potential therapeutic target. However, there is still a lack of potent ALKBH1 inhibitors. Herein, we report the discovery of a highly potent ALKBH1 inhibitor, 1H-pyrazole-4-carboxylic acid derivative 29. The structure-activity relationship of this series of compounds was also discussed. Because of the poor cell membrane permeability of 29, we prepared a prodrug of 29 (29E), which showed excellent cellular activities. In gastric cancer cell lines HGC27 and AGS, 29E treatment significantly increased the abundance of 6mA, inhibited cell viability, and upregulated the AMP-activated protein kinase (AMPK) signaling pathway. In addition, the hydrolysis product 29 showed high exposure in mice after administration of 29E. Collectively, this research provides a new potent ALKBH1 inhibitor, which could serve as a lead compound for subsequent drug development.

Adenosine Deaminase-Like Gene-Carried Lentivirus Toolkit for Identification of DNA N6-Methyladenine Origins.

Post-replicative DNA N6-methyladenine (pr6mdA) can form via bona fide methylase-catalyzed adenine methylation, playing a pivotal role in embryonic development and other biological processes. Surprisingly, pre-methylated adenine can be erroneously incorporated into DNA as misincorporated N6-methyladenine (i6mdA) via DNA polymerase-mediated replication. Despite pr6mdA and i6mdA sharing identical chemical structures, their biological functions diverge significantly, presenting a substantial challenge in distinguishing between the two. Here, for the first-time, it is exploited that the adenosine deaminase-like (Adal) protein and a corresponding activity-null mutant to construct an Adal lentivirus toolkit. With this newly designed toolkit, both pr6mdA and i6mdA can be identified and quantified simultaneously. The presence of 6mdA in the bone marrow cells of mice is shown, with its levels serving as indicators for growth with age, probably reflecting the cellular stress-caused changes in RNA decay, nucleotide pool sanitation, and transcription. Collectively, a powerful toolkit to advance understanding of both pr6mdA and i6mdA is demonstrated.

Deep learning based method for predicting DNA N6-methyladenosine sites

DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intensive and traditional machine learning methods also seem to be somewhat insufficient as the database of 6mA methylation groups becomes progressively larger, so we propose a deep learning-based method called multi-scale convolutional model based on global response normalization (CG6mA) to solve the prediction problem of 6mA site. This method is tested with other methods on three different kinds of benchmark datasets, and the results show that our model can get more excellent prediction results.

Detection of DNA N6-Methyladenine Modification through SMRT-seq Features and Machine Learning Model

Introduction: N6-methyldeoxyadenine (6mA) is the most prevalent DNA modification in both prokaryotes and eukaryotes. While single-molecule real-time sequencing (SMRT-seq) can detect 6mA events at the individual nucleotide level, its practical application is hindered by a high rate of false positives. Methods: We propose a computational model for identifying DNA 6mA that incorporates comprehensive site features from SMRT-seq and employs machine learning classifiers. Results: The results demonstrate that 99.54% and 96.55% of the identified DNA 6mA instances in C.reinhardtii correspond with motifs and peak regions identified by methylated DNA immunoprecipitation sequencing (MeDIP-seq), respectively. Compared to SMRT-seq, the proportion of predicted DNA 6mA instances within MeDIP-seq peak regions increases by 2% to 70% across the six bacterial strains Conclusion: Our proposed method effectively reduces the false-positive rate in DNA 6mA prediction.

DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools.

Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation as well as the identification of exogenously placed DNA N6-methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as coprocessing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce fibertools, a state-of-the-art toolkit that features a semisupervised convolutional neural network for fast and accurate identification of m6A-marked bases using PacBio single-molecule long-read sequencing, as well as the coprocessing of long-read genetic and epigenetic data produced using either PacBio or Oxford Nanopore sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kilobase long DNA molecules with a ~1,000-fold improvement in speed. In addition, we demonstrate that fibertools can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.

Nanopore sequencing demonstrates the roles of spermatozoal DNA N6-methyladenine in mediating transgenerational lipid metabolism disorder induced by excessive folate consumpton

Increased consumption of folic acid is prevalent due to its beneficial effects, but growing evidence emphasizes the side effects pointing to excessive dietary folate intake. The effects of excessive paternal folic acid consumption on offspring and its transgenerational inheritance mechanism have not been elucidated. We hypothesize that excessive folic acid consumption will alter sperm DNA N6-methyladenine (6mA) and 5-methylcytosine (5mC) methylation and heritably influence offspring metabolic homeostasis. Here, we fed roosters either folic acid-control or folic acid-excess diet throughout life. Paternal chronic folic acid excessive supplementation increased hepatic lipogenesis and lipid accumulation but reduced lipolysis both in the roosters and their offspring, which was further confirmed to be induced by one-carbon metabolism inhibition and gene expression alteration associated with the Peroxisome proliferator-activated receptor pathway. Based on the spermatozoal genome-wide DNA methylome identified by Nanopore sequencing, multi-omics association analysis of spermatozoal and hepatic DNA methylome, transcriptome, and metabolome suggested that differential spermatozoal DNA 6mA and 5mC methylation could be involved in regulating lipid metabolism-related gene expression in offspring chickens. This model suggests that sperm DNA N6-methyladenine and 5-methylcytosine methylation were involved in epigenetic transmission and that paternal dietary excess folic acid leads to hepatic lipid accumulation in offspring.

Identification of AtALKBH1A and AtALKBH1D as DNA N6-adenine demethylases in Arabidopsis thaliana

DNA N6-methyladenine (6 mA) has recently been discovered as a novel DNA modification in animals and plants. In mammals, AlkB homolog 1 (ALKBH1) has been identified as a DNA 6 mA demethylase. ALKBH1 tightly controls the DNA 6 mA methylation level of mammalian genomes and plays important role in regulating gene expression. DNA 6 mA methylation has also been reported to exist in plant genomes, however, the plant DNA 6 mA demethylases and their function remain largely unknown. Here we identify homologs of ALKBH1 as DNA 6 mA demethylases in Arabidopsis. We discover that there are four homologs of ALKBH1, AtALKBH1A, AtALKBH1B, AtALKBH1C and AtALKBH1D, in Arabidopsis. In vitro enzymatic activity studies reveal that AtALKBH1A and 1D can efficiently erase DNA 6 mA methylation. Loss of function of AtALKBH1A and AtALKBH1D causes elevated DNA 6 mA methylation levels in vivo. atalkbh1a/1d mutant displays delayed seed gemination. Based on our RNA-seq data, we find some regulators of seed gemination are dysregulated in atalkbh1a/1d, and the dysregulation is correlated with changes of DNA 6 mA methylation levels. This study identifies plant DNA 6 mA demethylases and reports the function of DNA 6 mA methylation in regulating seed germination.

Significant differences in the degree of genomic DNA N6-methyladenine modifications in Acidithiobacillus ferrooxidans with two different culture substrates.

DNA N6-methyladenine (6mA) modification is widespread in organisms and plays an important functional role in the regulation of cellular processes. As a model organism in biohydrometallurgy, Acidithiobacillus ferrooxidans can obtain energy from the oxidation of ferrous iron (Fe2+) and various reduced inorganic sulfides (RISCs) under acidic conditions. To determine the linkage between genomic DNA methylation and the switching between the two oxidative metabolic pathways in A. ferrooxidans, the 6mA landscape in the genome of A. ferrooxidans cultured under different conditions was evaluated by using 6mA-IP-seq. A total of 214 and 47 high-confidence peaks of 6mA were identified under the Fe2+ and RISCs oxidizing conditions, respectively (P<10-5), suggesting that genomic methylation was greater under Fe2+ oxidizing conditions. 6mA experienced a decline at the transcription start site (TSS) and occurs frequently in gene bodies under both oxidizing conditions. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that 7 KEGG pathways were mapped into and most of the differentially methylated genes were enriched in oxidative phosphorylation and metabolic pathways. Fourteen genes were selected for studying the effect of differences in methylation on mRNA expression. Thirteen genes, excluding petA-1, demonstrated a decrease in mRNA expression as methylation levels increased. Overall, the 6mA methylation enrichment patterns are similar under two conditions but show differences in the enriched pathways. The phenomenon of upregulated gene methylation levels coupled with downregulated expression suggests a potential association between the regulation mechanisms of 6mA and the Fe2+ and RISCs oxidation pathways.

Ense-i6mA: Identification of DNA N6-methyl-adenine Sites Using XGB-RFE Feature Se-lection and Ensemble Machine Learning.

DNA N6-methyladenine (6mA) is an important epigenetic modification that plays a vital role in various cellular processes. Accurate identification of the 6mA sites is fundamental to elucidate the biological functions and mechanisms of modification. However, experimental methods for detecting 6mA sites are high-priced and time-consuming. In this study, we propose a novel computational method, called Ense-i6mA, to predict 6mA sites. Firstly, five encoding schemes, i.e., one-hot encoding, gcContent, Z-Curve, K-mer nucleotide frequency, and K-mer nucleotide frequency with gap, are employed to extract DNA sequence features. Secondly, to our knowledge, it is the first time that eXtreme gradient boosting coupled with recursive feature elimination is applied to 6mA sites prediction domain to remove noisy features for avoiding over-fitting, reducing computing time and complexity. Then, the best subset of features is fed into base-classifiers composed of Extra Trees, eXtreme Gradient Boosting, Light Gradient Boosting Machine, and Support Vector Machine. Finally, to minimize generalization errors, the prediction probabilities of the base-classifiers are aggregated by averaging for inferring the final 6mA sites results. We conduct experiments on two species, i.e., Arabidopsis thaliana and Drosophila melanogaster, to compare the performance of Ense-i6mA against the recent 6mA sites prediction methods. The experimental results demonstrate that the proposed Ense-i6mA achieves area under the receiver operating characteristic curve values of 0.967 and 0.968, accuracies of 91.4% and 92.0%, and Mathew's correlation coefficient values of 0.829 and 0.842 on two benchmark datasets, respectively, and outperforms several existing state-of-the-art methods.

SoftVoting6mA: An improved ensemble-based method for predicting DNA N6-methyladenine sites in cross-species genomes.

The DNA N6-methyladenine (6mA) is an epigenetic modification, which plays a pivotal role in biological processes encompassing gene expression, DNA replication, repair, and recombination. Therefore, the precise identification of 6mA sites is fundamental for better understanding its function, but challenging. We proposed an improved ensemble-based method for predicting DNA N6-methyladenine sites in cross-species genomes called SoftVoting6mA. The SoftVoting6mA selected four (electron-ion-interaction pseudo potential, One-hot encoding, Kmer, and pseudo dinucleotide composition) codes from 15 types of encoding to represent DNA sequences by comparing their performances. Similarly, the SoftVoting6mA combined four learning algorithms using the soft voting strategy. The 5-fold cross-validation and the independent tests showed that SoftVoting6mA reached the state-of-the-art performance. To enhance accessibility, a user-friendly web server is provided at http://www.biolscience.cn/SoftVoting6mA/.

6mA-StackingCV: an improved stacking ensemble model for predicting DNA N6-methyladenine site

DNA N6-adenine methylation (N6-methyladenine, 6mA) plays a key regulating role in the cellular processes. Precisely recognizing 6mA sites is of importance to further explore its biological functions. Although there are many developed computational methods for 6mA site prediction over the past decades, there is a large root left to improve. We presented a cross validation-based stacking ensemble model for 6mA site prediction, called 6mA-StackingCV. The 6mA-StackingCV is a type of meta-learning algorithm, which uses output of cross validation as input to the final classifier. The 6mA-StackingCV reached the state of the art performances in the Rosaceae independent test. Extensive tests demonstrated the stability and the flexibility of the 6mA-StackingCV. We implemented the 6mA-StackingCV as a user-friendly web application, which allows one to restrictively choose representations or learning algorithms. This application is freely available at http://www.biolscience.cn/6mA-stackingCV/. The source code and experimental data is available at https://github.com/Xiaohong-source/6mA-stackingCV.

METTL4-mediated N6-methyladenine DNA modification regulates thermotolerance in Arabidopsis thaliana

DNA N6-methyladenine (6 mA) is an evolutionarily conserved DNA modification in procaryotes and eukaryotes. The DNA 6 mA methylation is tightly controlled by 6 mA regulatory proteins. DNA N6-adenine methyltransferase 1 (DAMT-1) has been identified as a DNA 6 mA methyltransferase in animals. In plants, DNA 6 mA methylation has been found, however, the DNA 6 mA methyltransferases and their function in plants are largely unknown. In our study, we find METTL4 is a DNA 6 mA methyltransferase in Arabidopsis thaliana. Both in vitro and in vivo evidences support the DNA 6 mA methyltransferase activity of METTL4. mettl4 mutant is hypersensitive to heat stress, suggesting DNA 6 mA methylation plays important role in heat stress adaption. RNA-seq and 6 mA IP-qPCR analysis show that METTL4 participates in heat stress tolerance by regulating expression of heat responsive genes. Our study find METTL4 is a plant DNA 6 mA methyltransferase and illustrates its function in regulating heat stress response.

Novel function of the PsDMAP1 protein in regulating the growth and pathogenicity of Phytophthora sojae

The DNA methyltransferase 1-associated protein (DMAP1) was initially identified as an activator of DNA methyltransferase 1 (DNMT1), a conserved eukaryotic enzyme involved in diverse molecular processes, including histone acetylation and chromatin remodeling. However, the roles and regulatory mechanisms of DMAP1 in filamentous pathogens are still largely unknown. Here, employing bioinformatic analysis, we identified PsDMAP1 in P. sojae, which features a canonical histone tail-binding domain, as the ortholog of the human DMAP1. A phylogenetic analysis of DMAP1 protein sequences across diverse eukaryotic organisms revealed the remarkable conservation and distinctiveness of oomycete DMAP1 orthologs. Homozygous knockout of PsDMAP1 resulted in the mortality of P. sojae. Furthermore, silencing of PsDMAP1 caused a pronounced reduction in mycelial growth, production of sporangia and zoospore, cystospore germination, and virulence. PsDMAP1 also played a crucial role in the response of P. sojae to reactive oxygen species (ROS) and osmotic stresses. Moreover, PsDMAP1 interacted with DNA N6-methyladenine (6 mA) methyltransferase PsDAMT1, thereby enhancing its catalytic activity and effectively regulating 6 mA abundance in P. sojae. Our findings reveal the functional importance of PsDAMP1 in the development and infection of P. sojae, and this marks the initial exploration of the novel 6 mA regulator PsDMAP1 in plant pathogens.

SNN6mA: Improved DNA N6-methyladenine site prediction using Siamese network-based feature embedding

DNA N6-methyladenine (6mA) is one of the most common and abundant modifications, which plays essential roles in various biological processes and cellular functions. Therefore, the accurate identification of DNA 6mA sites is of great importance for a better understanding of its regulatory mechanisms and biological functions. Although significant progress has been made, there still has room for further improvement in 6mA site prediction in DNA sequences. In this study, we report a smart but accurate 6mA predictor, termed as SNN6mA, using Siamese network. To be specific, DNA segments are firstly encoded into feature vectors using the one-hot encoding scheme; then, these original feature vectors are mapped to a low-dimensional embedding space derived from Siamese network to capture more discriminative features; finally, the obtained low-dimensional features are fed to a fully connected neural network to perform final prediction. Stringent benchmarking tests on the datasets of two species demonstrated that the proposed SNN6mA is superior to the state-of-the-art 6mA predictors. Detailed data analyses show that the major advantage of SNN6mA lies in the utilization of Siamese network, which can map the original features into a low-dimensional embedding space with more discriminative capability. In summary, the proposed SNN6mA is the first attempt to use Siamese network for 6mA site prediction and could be easily extended to predict other types of modifications. The codes and datasets used in the study are freely available at https://github.com/YuXuan-Glasgow/SNN6mA for academic use.

Single-Nucleotide Resolution Mapping of N6-Methyladenine in Genomic DNA.

N6-Methyladenine (6mA) is a naturally occurring DNA modification in both prokaryotes and eukaryotes. Herein, we developed a deaminase-mediated sequencing (DM-seq) method for genome-wide mapping of 6mA at single-nucleotide resolution. The method capitalizes on the selective deamination of adenine, but not 6mA, in DNA mediated by an evolved adenine deaminase, ABE8e. By employing this method, we achieved genome-wide mapping of 6mA in Escherichia coli and in mammalian mitochondrial DNA (mtDNA) at single-nucleotide resolution. We found that the 6mA sites are mainly located in the GATC motif in the E. coli genome. We also identified 17 6mA sites in mtDNA of HepG2 cells, where all of the 6mA sites are distributed in the heavy strand of mtDNA. We envision that DM-seq will be a valuable tool for uncovering new functions of 6mA in DNA and for exploring its potential roles in mitochondria-related human diseases.

DNA N6-methyladenine methylase N6AMT1 controls neuropathic pain through epigenetically modifying Kcnj16 in dorsal horn neurons.

Nerve injury-induced aberrant changes in gene expression in spinal dorsal horn neurons are critical for the genesis of neuropathic pain. N6-methyladenine (m 6 A) modification of DNA represents an additional layer of gene regulation. Here, we report that peripheral nerve injury significantly decreased the level of m 6 A-specific DNA methyltransferase 1 ( N6amt1 ) in dorsal horn neurons. This decrease was attributed, at least partly, to a reduction in transcription factor Nr2f6 . Rescuing the decrease in N6amt1 reversed the loss of m 6 A at the promoter for inwardly rectifying potassium channel subfamily J member 16 ( Kcnj16 ), mitigating the nerve injury-induced upregulation of Kcnj16 expression in the dorsal horn and alleviating neuropathic pain hypersensitivities. Conversely, mimicking the downregulation of N6amt1 in naive mice erased DNA m 6 A at the Kcnj16 promoter, elevated Kcnj16 expression, and led to neuropathic pain-like behaviors. Therefore, decreased N6amt1 caused by NR2F6 is required for neuropathic pain, likely through its regulation of m 6 A-controlled KCNJ16 in dorsal horn neurons, suggesting that DNA m 6 A modification may be a potential new target for analgesic and treatment strategies.

Improving DNA 6mA Site Prediction via Integrating Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Self-Attention Mechanism.

Identifying DNA N6-methyladenine (6mA) sites is significantly important to understanding the function of DNA. Many deep learning-based methods have been developed to improve the performance of 6mA site prediction. In this study, to further improve the performance of 6mA site prediction, we propose a new meta method, called Co6mA, to integrate bidirectional long short-term memory (BiLSTM), convolutional neural networks (CNNs), and self-attention mechanisms (SAM) via assembling two different deep learning-based models. The first model developed in this study is called CBi6mA, which is composed of CNN, BiLSTM, and fully connected modules. The second model is borrowed from LA6mA, which is an existing 6mA prediction method based on BiLSTM and SAM modules. Experimental results on two independent testing sets of different model organisms, i.e., Arabidopsis thaliana and Drosophila melanogaster, demonstrate that Co6mA can achieve an average accuracy of 91.8%, covering 89% of all 6mA samples while achieving an average Matthews correlation coefficient value (0.839), which is higher than the second-best method DeepM6A.

EpiSemble: A Novel Ensemble-based Machine-learning Framework for Prediction of DNA N6-methyladenine Sites Using Hybrid Features Selection Approach for Crops

Aim: The study aimed to develop a robust and more precise 6mA methylation prediction tool that assists researchers in studying the epigenetic behaviour of crop plants. Background: N6-methyladenine (6mA) is one of the predominant epigenetic modifications involved in a variety of biological processes in all three kingdoms of life. While in vitro approaches are more precise in detecting epigenetic alterations, they are resource-intensive and time-consuming. Artificial intelligence- based in silico methods have helped overcome these bottlenecks Methods: A novel machine learning framework was developed through the incorporation of four techniques: ensemble machine learning, hybrid approach for feature selection, the addition of features, such as Average Mutual Information Profile (AMIP), and bootstrap samples. In this study, four different feature sets, namely di-nucleotide frequency, GC content, AMIP, and nucleotide chemical properties were chosen for the vectorization of DNA sequences. Nine machine learning models, including support vector machine, random forest, k-nearest neighbor, artificial neural network, multiple logistic regression, decision tree, naïve Bayes, AdaBoost, and gradient boosting were employed using relevant features extracted through the feature selection module. The top three best-performing models were selected and a robust ensemble model was developed to predict sequences with 6mA sites. Results: EpiSemble, a novel ensemble model was developed for the prediction of 6mA methylation sites. Using the new model, an improvement in accuracy of 7.0%, 3.74%, and 6.65% was achieved over existing models for RiceChen, RiceLv, and Arabidopsis datasets, respectively. An R package, EpiSemble, based on the new model was developed and made available at https://cran.rproject. org/web/packages/EpiSemble/index.html. Conclusion: The EpiSemble model added AMIP as a novel feature, integrated feature selection modules, bootstrapping of samples, and ensemble technique to achieve an improved output for accurate prediction of 6mA sites in plants. To our knowledge, this is the first R package developed for predicting epigenetic sites of genomes in crop plants, which is expected to help plant researchers in their future explorations.

Functional characteristics of DNA N6-methyladenine modification based on long-read sequencing in pancreatic cancer.

Abnormalities of DNA modifications are closely related to the pathogenesis and prognosis of pancreatic cancer. The development of third-generation sequencing technology has brought opportunities for the study of new epigenetic modification in cancer. Here, we screened the N6-methyladenine (6mA) and 5-methylcytosine (5mC) modification in pancreatic cancer based on Oxford Nanopore Technologies sequencing. The 6mA levels were lower compared with 5mC and upregulated in pancreatic cancer. We developed a novel method to define differentially methylated deficient region (DMDR), which overlapped 1319 protein-coding genes in pancreatic cancer. Genes screened by DMDRs were more significantly enriched in the cancer genes compared with the traditional differential methylation method (P < 0.001 versus P = 0.21, hypergeometric test). We then identified a survival-related signature based on DMDRs (DMDRSig) that stratified patients into high- and low-risk groups. Functional enrichment analysis indicated that 891 genes were closely related to alternative splicing. Multi-omics data from the cancer genome atlas showed that these genes were frequently altered in cancer samples. Survival analysis indicated that seven genes with high expression (ADAM9, ADAM10, EPS8, FAM83A, FAM111B, LAMA3 and TES) were significantly associated with poor prognosis. In addition, the distinction for pancreatic cancer subtypes was determined using 46 subtype-specific genes and unsupervised clustering. Overall, our study is the first to explore the molecular characteristics of 6mA modifications in pancreatic cancer, indicating that 6mA has the potential to be a target for future clinical treatment.

Xiao Chai Hu Tang alleviates the pancreatic tumorigenesis via improving the mtDNA N6-Methyladenine modification mediated mitochondrial dysfunction in Syrian hamster model

BackgroundPancreatic intraepithelial neoplasia (PanIN) is the most common precursor lesion of pancreatic ductal adenocarcinoma (PDAC), which is a highly malignant tumor and lack of effective treatment. Although Xiao Chai Hu Tang (XCHT) has a good therapeutic effect on pancreatic cancer patients with advanced stage, the effect and mechanism of XCHT remains unclear in pancreatic tumorigenesis. PurposeTo assess the therapeutic effects of XCHT on the malignant transformation from PanIN to PDAC and to reveal its mechanisms of pancreatic tumorigenesis. MethodsSyrian golden hamster were induced by N-Nitrosobis (2-oxopropyl) amine (BOP) to establish the pancreatic tumorigenesis model. The morphological changes of pancreatic tissue were observed by H&E and Masson staining; the Gene ontology (GO) analysis the transcriptional profiling changes; the mitochondrial ATP generation, mitochondrial redox status, mitochondrial DNA (mtDNA) N6-methyladenine (6mA) level and relative mtDNA genes expressions were examined. In addition, immunofluorescence detect the cell localization of 6mA in human pancreatic cancer PANC1 cell. Using the TCGA database, the prognostic effect of mtDNA 6mA demethylation ALKBH1 expression on pancreatic cancer patients was analyzed. ResultsWe confirmed the mtDNA 6mA levels were gradually increased with the mitochondrial dysfunction in PanINs progression. XCHT showed the effect to inhibit the occurrence and development of pancreatic cancer in Syrian hamster pancreatic tumorigenesis model. In addition, the lack of ALKBH1 mediated mtDNA 6mA increase, mtDNA coded genes down-expression and abnormal redox status were rescued by XCHT. ConclusionsALKBH1/mtDNA 6mA mediated mitochondrial dysfunction to induce the occurrence and progression of pancreatic cancer. XCHT can improve ALKBH1 expression and mtDNA 6mA level, regulate the oxidative stress and expression of mtDNA coded genes. This study investigated a new molecular mechanism of pancreatic tumorigenesis, and revealed the therapeutic efficacy of XCHT in pancreatic tumorigenesis for the first time.