Metformin induces tolerogenicity of dendritic cells by promoting metabolic reprogramming.

Dendritic cells (DCs) can mediate immune responses or immune tolerance depending on their immunophenotype and functional status. Remodeling of DCs' immune functions can develop proper therapeutic regimens for different immune-mediated diseases. In the immunopathology of autoimmune diseases (ADs), activated DCs notably promote effector T-cell polarization and exacerbate the disease. Recent evidence indicates that metformin can attenuate the clinical symptoms of ADs due to its anti-inflammatory properties. Whether and how the therapeutic effects of metformin on ADs are associated with DCs remain unknown. In this study, metformin was added to a culture system of LPS-induced DC maturation. The results revealed that metformin shifted DC into a tolerant phenotype, resulting in reduced surface expression of MHC-II, costimulatory molecules and CCR7, decreased levels of proinflammatory cytokines (TNF-α and IFN-γ), increased level of IL-10, upregulated immunomodulatory molecules (ICOSL and PD-L) and an enhanced capacity to promote regulatory T-cell (Treg) differentiation. Further results demonstrated that the anti-inflammatory effects of metformin in vivo were closely related to remodeling the immunophenotype of DCs. Mechanistically, metformin could mediate the metabolic reprogramming of DCs through FoxO3a signaling pathways, including disturbing the balance of fatty acid synthesis (FAS) and fatty acid oxidation (FAO), increasing glycolysis but inhibiting the tricarboxylic acid cycle (TAC) and pentose phosphate pathway (PPP), which resulted in the accumulation of fatty acids (FAs) and lactic acid, as well as low anabolism in DCs. Our findings indicated that metformin could induce tolerance in DCs by reprogramming their metabolic patterns and play anti-inflammatory roles in vitro and in vivo.

Vascular endothelial growth factor (VEGF) impairs the motility and immune function of human mature dendritic cells through the VEGF receptor 2-RhoA-cofilin1 pathway.

Dendritic cells (DCs) are potent and specialized antigen presenting cells, which play a crucial role in initiating and amplifying both the innate and adaptive immune responses against cancer. Tumor cells can escape from immune attack by secreting suppressive cytokines that solely or cooperatively impair the immune function of DCs. However, the underlying mechanisms are not fully defined. Vascular endothelial growth factor (VEGF) has been identified as a major cytokine in the tumor microenvironment. To elucidate the effects of VEGF on the motility and immune function of mature DCs (mDCs), the cells were treated with 50 ng/mL VEGF and investigated by proteomics and molecular biological technologies. The results showed that VEGF can impair the migration capacity and immune function of mDCs through the RhoA-cofilin1 pathway mediated by the VEGF receptor 2, suggesting impaired motility of mDCs by VEGF is one of the aspects of immune escape mechanisms of tumors. It is clinically important to understand the biological behavior of DCs and the immune escape mechanisms of tumor as well as how to improve the efficiency of antitumor therapy based on DCs.

ACFIS: a web server for fragment-based drug discovery.

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/.

Laribacter hongkongensis anaerobic adaptation mediated by arginine metabolism is controlled by the cooperation of FNR and ArgR.

Laribacter hongkongensis is a fish-borne pathogen associated with invasive infections and gastroenteritis. Its adaptive mechanisms to oxygen-limiting conditions in various environmental niches remain unclear. In this study, we compared the transcriptional profiles of L. hongkongensis under aerobic and anaerobic conditions using RNA-sequencing. Expression of genes involved in arginine metabolism significantly increased under anoxic conditions. Arginine was exploited as the sole energy source in L. hongkongensis for anaerobic respiration via the arginine catabolism pathway: specifically via the arginine deiminase (ADI) pathway. A transcriptional regulator FNR was identified to coordinate anaerobic metabolism by tightly regulating the expression of arginine metabolism genes. FNR executed its regulatory function by binding to FNR boxes in arc operons promoters. Survival of isogenic fnr mutant in macrophages decreased significantly when compared with wild-type; and expression level of fnr increased 8 h post-infection. Remarkably, FNR directly interacted with ArgR, another regulator that influences the biological fitness and intracellular survival of L. hongkongensis by regulating arginine metabolism genes. Our results demonstrated that FNR and ArgR work in coordination to respond to oxygen changes in both extracellular and intracellular environments, by finely regulating the ADI pathway and arginine anabolism pathways, thereby optimizing bacterial fitness in various environmental niches.

SMAL: A Resource of Spontaneous Mutation Accumulation Lines.

Mutation is the ultimate source of genetic variation and evolution. Mutation accumulation (MA) experiments are an alternative approach to study de novo mutation events directly. We have constructed a resource of Spontaneous Mutation Accumulation Lines (SMAL; http://cefg.uestc.edu.cn/smal), which contains all the current publicly available MA lines identified by high-throughput sequencing. We have relocated and mapped the mutations based on the most recent genome annotations. A total of 5,608 single base mutations and 540 other mutations were obtained and are recorded in the current version of the SMAL database. The integrated data in SMAL provide detailed information that can be used in new theoretical analyses. We believe that the SMAL resource will help researchers better understand the processes of genetic variation and the incidence of disease.

Multiple Factors Drive Replicating Strand Composition Bias in Bacterial Genomes.

Composition bias from Chargaff's second parity rule (PR2) has long been found in sequenced genomes, and is believed to relate strongly with the replication process in microbial genomes. However, some disagreement on the underlying reason for strand composition bias remains. We performed an integrative analysis of various genomic features that might influence composition bias using a large-scale dataset of 1111 genomes. Our results indicate (1) the bias was stronger in obligate intracellular bacteria than in other free-living species (p-value=0.0305); (2) Fusobacteria and Firmicutes had the highest average bias among the 24 microbial phyla analyzed; (3) the strength of selected codon usage bias and generation times were not observably related to strand composition bias (p-value=0.3247); (4) significant negative relationships were found between GC content, genome size, rearrangement frequency, Clusters of Orthologous Groups (COG) functional subcategories A, C, I, Q, and composition bias (p-values<1.0×10(-8)); (5) gene density and COG functional subcategories D, F, J, L, and V were positively related with composition bias (p-value<2.2×10(-16)); and (6) gene density made the most important contribution to composition bias, indicating transcriptional bias was associated strongly with strand composition bias. Therefore, strand composition bias was found to be influenced by multiple factors with varying weights.

CEG: a database of essential gene clusters.

BACKGROUND: Essential genes are indispensable for the survival of living entities. They are the cornerstones of synthetic biology, and are potential candidate targets for antimicrobial and vaccine design. DESCRIPTION: Here we describe the Cluster of Essential Genes (CEG) database, which contains clusters of orthologous essential genes. Based on the size of a cluster, users can easily decide whether an essential gene is conserved in multiple bacterial species or is species-specific. It contains the similarity value of every essential gene cluster against human proteins or genes. The CEG_Match tool is based on the CEG database, and was developed for prediction of essential genes according to function. The database is available at http://cefg.uestc.edu.cn/ceg. CONCLUSIONS: Properties contained in the CEG database, such as cluster size, and the similarity of essential gene clusters against human proteins or genes, are very important for evolutionary research and drug design. An advantage of CEG is that it clusters essential genes based on function, and therefore decreases false positive results when predicting essential genes in comparison with using the similarity alignment method.

IGTCM: An integrative genome database of traditional Chinese medicine plants.

Fully understanding traditional Chinese medicines (TCMs) is still challenging because of the extreme complexity of their chemical components and mechanisms of action. The TCM Plant Genome Project aimed to obtain genetic information, determine gene functions, discover regulatory networks of herbal species, and elucidate the molecular mechanisms involved in the disease prevention and treatment, thereby accelerating the modernization of TCMs. A comprehensive database that contains TCM-related information will provide a vital resource. Here, we present an integrative genome database of TCM plants (IGTCM) that contains 14,711,220 records of 83 annotated TCM-related herb genomes, including 3,610,350 genes, 3,534,314 proteins and corresponding coding sequences, and 4,032,242 RNAs, as well as 1033 non-redundant component records for 68 herbs, downloaded and integrated from the GenBank and RefSeq databases. For minimal interconnectivity, each gene, protein, and component was annotated using the eggNOG-mapper tool and Kyoto Encyclopedia of Genes and Genomes database to acquire pathway information and enzyme classifications. These features can be linked across several species and different components. The IGTCM database also provides visualization and sequence similarity search tools for data analyses. These annotated herb genome sequences in IGTCM database are a necessary resource for systematically exploring genes related to the biosynthesis of compounds that have significant medicinal activities and excellent agronomic traits that can be used to improve TCM-related varieties through molecular breeding. It also provides valuable data and tools for future research on drug discovery and the protection and rational use of TCM plant resources. The IGTCM database is freely available at http://yeyn.group:96/.

Using Fully Convolutional Network to Locate Transcription Factor Binding Sites Based on DNA Sequence and Conservation Information.

Transcription factors (TFs) play a part in gene expression. TFs can form complex gene expression regulation system by combining with DNA. Thereby, identifying the binding regions has become an indispensable step for understanding the regulatory mechanism of gene expression. Due to the great achievements of applying deep learning (DL) to computer vision and language processing in recent years, many scholars are inspired to use these methods to predict TF binding sites (TFBSs), achieving extraordinary results. However, these methods mainly focus on whether DNA sequences include TFBSs. In this paper, we propose a fully convolutional network (FCN) coupled with refinement residual block (RRB) and global average pooling layer (GAPL), namely FCNARRB. Our model could classify binding sequences at nucleotide level by outputting dense label for input data. Experimental results on human ChIP-seq datasets show that the RRB and GAPL structures are very useful for improving model performance. Adding GAPL improves the performance by 9.32% and 7.61% in terms of IoU (Intersection of Union) and PRAUC (Area Under Curve of Precision and Recall), and adding RRB improves the performance by 7.40% and 4.64%, respectively. In addition, we find that conservation information can help locate TFBSs.

[Current status of theoretical studies on essential genes in microbes].

Essential genes are indispensable for the survival of an organism in optimal conditions. Recently, study on essential gene is becoming a hot topic of microbiology, genomics, and bioinformatics. This paper described the experiments that determined essential genes in some microbes and the theoretical researches on essential genes were reviewed. The major content contained comparison of essential genes and non-essential genes based on information on evolutionary conservation and sequence composition, and in silico prediction of essential genes, and analysis of the chromosomal distributions of essential genes. Finally, related progresses were concluded and the open problems were pointed out.

Differences Between Cancer-Specific Survival of Patients With Anaplastic and Primary Squamous Cell Thyroid Carcinoma and Factors Influencing Prognosis: A SEER Database Analysis.

Purpose: Anaplastic thyroid carcinoma (ATC) and primary squamous cell carcinoma of the thyroid (PSCCTh) have similar histological findings and are currently treated using the same approaches; however, the characteristics and prognosis of these cancers are poorly researched. The objective of this study was to determine the differences in characteristics between ATC and PSCCTh and establish prognostic models. Patients and Methods: All variables of patients with ATC and PSCCTh, diagnosed from 2004-2015, were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) database. Percentage differences for categorical data were compared using the Chi-square test. Kaplan-Meier curves, log-rank test, and Cox-regression for survival analysis, and C-index value was used to evaluate the performance of the prognostic models. Results: After application of the inclusion and exclusion criteria, a total of 1164 ATC and 124 PSCCTh patients, diagnosed from 2004 to 2015, were included in the study. There were no differences in sex, ethnicity, age, marital status, or percentage of proximal metastases between the two cancers; however, radiotherapy, chemotherapy, incidence of surgical treatment, and presence of multiple primary tumors were higher in patients with ATC than those with PSCCTh. Further cancer-specific survival (CSS) of patients with PSCCTh was better than that of patients with ATC. Prognostic factors were not identical for the two cancers. Multivariate Cox model analysis indicated that age, sex, radiotherapy, chemotherapy, surgery, multiple primary tumors, marital status, and distant metastasis status are independent prognostic factors for CSS in patients with ATC, while for patients with PSCCTh, the corresponding factors are age, radiotherapy, multiple primary tumors, and surgery. The C-index values of the two models were both > 0.8, indicating that the models exhibited good discriminative ability. Conclusion: Prognostic factors influencing CSS were not identical in patients with ATC and PSCCTh. These findings indicate that different clinical treatment and management plans are required for patients with these two types of thyroid cancer.

Development and validation of a nomogram model for cancer-specific survival of patients with poorly differentiated thyroid carcinoma: A SEER database analysis.

Background: This study aimed to establish and validate an accurate prognostic model, based on demographic and clinical parameters, for predicting the cancer-specific survival (CSS) of patients with poorly differentiated thyroid carcinoma (PDTC). Materials and methods: Patients diagnosed with PDTC between 2004 to 2015 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Randomly split the data into training and validation sets. Kaplan-Meier analysis with the log-rank test was performed to compare the survival distribution among cases. Univariate and multivariate Cox proportional hazards regression analyses were used to identify independent prognostic factors, which were subsequently utilized to construct a nomogram for predicting the 5- and 10-year cancer-specific survival of patients with PDTC. The discriminative ability and calibration of the nomogram model were assessed using the concordance index and calibration plots, respectively. In addition, we performed a decision curve analysis to assess the clinical value of the nomogram. Simultaneously, we compared the predictive performance of the nomogram model against that of the American Joint Committee on Cancer (AJCC) T-, N-, M-stage. Results: A total of 970 eligible patients were randomly assigned to either a training cohort (n = 679) or a validation cohort (n = 291). The Kaplan-Meier analysis revealed that there were no significant differences in cumulative survival based on the race, radiation, and marital status of patients. The stepwise Cox regression model showed that the model was optimal when the following five variables were included: age, tumor size, T-, N-, and M-stage. A nomogram was developed as a graphical representation of the model and exhibited good calibration and discriminative ability in the study. Compared to the T-, N-, and M-stage, the C-index of nomogram (training group: 0.807, validation group: 0.802), the areas under the receiver operating characteristic curve of the training set (5-year AUC: 0.843, 10-year AUC:0.834) and the validation set (5-year AUC:0.878, 10-year AUC:0.811), and the calibration plots of this model all exhibited better performance. At last, compared with T-, N-, and M-stage, the decision curve analysis indicated that the nomogram had excellent clinical net benefit. Conclusions: The nomogram developed by us can accurately predict the CSS of PDTC patients. It can help clinicians determine appropriate treatment strategies for poorly differentiated thyroid carcinoma patients.

Regulation of biomaterial implantation-induced fibrin deposition to immunological functions of dendritic cells.

The performance of implanted biomaterials is largely determined by their interaction with the host immune system. As a fibrous-like 3D network, fibrin matrix formed at the interfaces of tissue and material, whose effects on dendritic cells (DCs) remain unknown. Here, a bone plates implantation model was developed to evaluate the fibrin matrix deposition and DCs recruitment in vivo. The DCs responses to fibrin matrix were further analyzed by a 2D and 3D fibrin matrix model in vitro. In vivo results indicated that large amount of fibrin matrix deposited on the interface between the tissue and bone plates, where DCs were recruited. Subsequent in vitro testing denoted that DCs underwent significant shape deformation and cytoskeleton reorganization, as well as mechanical property alteration. Furthermore, the immune function of imDCs and mDCs were negatively and positively regulated, respectively. The underlying mechano-immunology coupling mechanisms involved RhoA and CDC42 signaling pathways. These results suggested that fibrin plays a key role in regulating DCs immunological behaviors, providing a valuable immunomodulatory strategy for tissue healing, regeneration and implantation.

Automatic prediction of non-coding RNA genes in prokaryotes based on compositional statistics.

Although non-coding RNA (ncRNA) genes do not encode proteins, they play vital roles in cells by producing functionally important RNAs. In this paper, we present a novel method for predicting ncRNA genes based on compositional features extracted directly from gene sequences. Our method consists of two Support Vector Machine (SVM) models--Codon model which uses codon usage features derived from ncRNA genes and protein-coding genes and Kmer model which utilizes features of nucleotide and dinucleotide frequency extracted respectively from ncRNA genes and randomly chosen genome sequences. The 10-fold cross-validation accuracy for the two models is found to be 92% and 91%, respectively. Thus, we could make an automatic prediction of ncRNA genes in one genome without manual filtration of protein-coding genes. After applying our method in Sulfolobus solfataricus genome, 25 prediction results have been generated according to 25 cut-off pairs. We have also applied the approach in E. coli and found our results comparable to those of previous studies. In general, our method enables automatic identification of ncRNA genes in newly sequenced prokaryotic genomes.

Quantitative elucidation of associations between nucleotide identity and physicochemical properties of amino acids and the functional insight.

Studies on codon property would deepen our understanding of the origin of primitive life and enlighten biotechnical application. Here, we proposed a quantitative measurement of codon-amino acid association and found that seven out of 13 physicochemical properties have stronger associations with the nucleotide identity at the second codon position, indicating that protein structure and function may associate more closely with it than the other two sites. When extending the effect of codon-amino acid association to protein level, it was found that the correlation between the second codon position (measured by the relative frequencies of nucleobase T and A at this codon site) and hydrophobicity (by the form of GRAVY value) became stronger with 96% genomes having R > 0.90 and p < 1e-60. Furthermore, we revealed that informational genes encoding proteins have lower GRAVY values than operational proteins (p < 3e-37) in both prokaryotic and eukaryotic genomes. The above results reveal a complete link from codon identity (A2 versus T2) to amino acid property (hydrophilic versus hydrophobic) and then to protein functions (informational versus operational). Hence, our work may help to understand how the nucleotide sequence determines protein function.

Spectroscopic and Theoretical Investigation of ß-Lactoglobulin Interactions with Hematoporphyrin and Protoporphyrin IX.

Hematoporphyrin (HP) and protoporphyrin IX (PPIX) are useful porphyrin photosensitizers with significant application values in photodynamic therapy. Currently, many strategies have been developed to improve their clinical performance, such as incorporating them with nanoparticle (NP) carriers. In this work, we studied the possibility of using ß-lactoglobulin (BLG) as a potential NP carrier due to their hydrophobic affinity, pH sensitivity, and low cost of extraction and preservation. The interaction mechanisms of BLG with HP and PPIX were investigated using spectroscopic techniques and molecular docking methods. The molecular docking results agree well with the experimental results, which demonstrate that the formations of HP-BLG and PPIX-BLG complexes are endothermic processes and the main acting force is hydrophobic force. Furthermore, the opening-closure states of EF loop have a great influence on the HP-BLG complex formation, where the central hydrophobic cavity of ß-barrel is available for HP binding at pH 7.4 but not available at pH 6.2. However, the formation of the PPIX-BLG complex is less dependent on the states of the EF loop, and the binding sites of PPIX are both located on the external surface of BLG under both pH 7.4 and 6.2 conditions. All of our results would provide new insight into the mechanisms of noncovalent interactions between BLG and HP/PPIX. It is believed that this work indicated the potential application values of BLG in designing pH-sensitive carriers for the delivery of HP and PPIX, as well as other poorly soluble drugs.

Development of an Immunogenomic Landscape-Based Prognostic Index of Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the eighth leading cancer by incidence worldwide, with approximately 700,000 new cases in 2018 (accounting for 11% of all cancers). The occurrence and development of tumors are closely related to the immunological function of the body and sensitivity to treatment schemes as well as prognosis. It is urgent for clinicians to systematically study patients' immune gene maps to help select a treatment plan and analyze the potential to cure HNSCC. Here, the transcriptomic data of HNSCC samples were downloaded from The Cancer Genome Atlas (TCGA), and 4,793 genes differentially expressed in normal and cancer tissues of HNSCC were identified, including 1,182 downregulated and 3,611 upregulated genes. From these genes, 400 differentially expressed immune-related genes (IRGs) were extracted, including 95 downregulated genes and 305 upregulated genes. The prognostic values of IRGs were evaluated by univariate Cox analysis, and 236 genes that were significantly related to the overall survival (OS) of patients were identified. The signaling pathways that play roles in the prognosis of IRGs were investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and the expression profiles of IRGs and OS in 499 HNSCC patients based on TCGA dataset were integrated. Potential molecular mechanisms and characteristics of these HNSCC-specific IRGs were further explored with the help of a new prognostic index based on IRGs developed by least absolute shrinkage and selection operator (LASSO) Cox analysis. A total of 64 hub genes (IRGs associated with prognosis) were markedly associated with the clinical outcome of HNSCC patients. KEGG functional enrichment analysis revealed that these genes were actively involved in several pathways, e.g., cytokine-cytokine receptor interaction, T-cell receptor signaling, and natural killer cell-mediated cytotoxicity. IRG-based prognostic signatures performed moderately in prognostic predictions. Interestingly, the prognostic index based on IRGs reflected infiltration by several types of immune cells. These data screened several IRGs of clinical significance and revealed drivers of the immune repertoire, demonstrating the importance of a personalized IRG-based immune signature in the recognition, surveillance, and prognosis of HNSCC.

CEG 2.0: an updated database of clusters of essential genes including eukaryotic organisms.

Essential genes are key elements for organisms to maintain their living. Building databases that store essential genes in the form of homologous clusters, rather than storing them as a singleton, can provide more enlightening information such as the general essentiality of homologous genes in multiple organisms. In 2013, the first database to store prokaryotic essential genes in clusters, CEG (Clusters of Essential Genes), was constructed. Afterward, the amount of available data for essential genes increased by a factor >3 since the last revision. Herein, we updated CEG to version 2, including more prokaryotic essential genes (from 16 gene datasets to 29 gene datasets) and newly added eukaryotic essential genes (nine species), specifically the human essential genes of 12 cancer cell lines. For prokaryotes, information associated with drug targets, such as protein structure, ligand-protein interaction, virulence factor and matched drugs, is also provided. Finally, we provided the service of essential gene prediction for both prokaryotes and eukaryotes. We hope our updated database will benefit more researchers in drug targets and evolutionary genomics. Database URL: http://cefg.uestc.cn/ceg.

A Mathematical Modelling of Initiation of Dendritic Cells-Induced T Cell Immune Response.

Dendritic cells (DCs) are the most potent specialized antigen-presenting cells as now known, which play a crucial role in initiating and amplifying both the innate and adaptive immune responses. Immunologically, the motilities and T cell activation capabilities of DCs are closely related to the resulting immune responses. However, due to the complexity of the immune system, the dynamic changes in the number of cells during the peripheral tissue (e.g. skin and mucosa) immune response induced by DCs are still poorly understood. Therefore, this study simulated dynamic number changes of DCs and T cells in this process by constructing several ordinary differential equations and setting the initial conditions of the functions and parameters. The results showed that these equations could simulate dynamic numerical changes of DCs and T cells in peripheral tissue and lymph node, which was in accordance with the physiological conditions such as the duration of immune response, the proliferation rates and the motilities of DCs and T cells. This model provided a theoretical reference for studying the immunologic functions of DCs and practical guidance for the clinical DCs-based therapy against immune-related diseases.

Mutation Landscape of Base Substitutions, Duplications, and Deletions in the Representative Current Cholera Pandemic Strain.

Pandemic cholera is a major concern for public health because of its high mortality and morbidity. Mutation accumulation (MA) experiments were performed on a representative strain of the current cholera pandemic. Although the base-pair substitution mutation rates in Vibrio cholerae (1.24 × 10-10 per site per generation for wild-type lines and 3.29 × 10-8 for mismatch repair deficient lines) are lower than that previously reported in other bacteria using MA analysis, we discovered specific high rates (8.31 × 10-8 site/generation for wild-type lines and 1.82 × 10-6 for mismatch repair deficient lines) of base duplication or deletion driven by large-scale copy number variations (CNVs). These duplication-deletions are located in two pathogenic islands, IMEX and the large integron island. Each element of these islands has discrepant rate in rapid integration and excision, which provides clues to the pandemicity evolution of V. cholerae. These results also suggest that large-scale structural variants such as CNVs can accumulate rapidly during short-term evolution. Mismatch repair deficient lines exhibit a significantly increased mutation rate in the larger chromosome (Chr1) at specific regions, and this pattern is not observed in wild-type lines. We propose that the high frequency of GATC sites in Chr1 improves the efficiency of MMR, resulting in similar rates of mutation in the wild-type condition. In addition, different mutation rates and spectra were observed in the MA lines under distinct growth conditions, including minimal media, rich media and antibiotic treatments.