Complete genome sequence and pathogenic genes analysis of Pectobacterium atroseptica JG10-08

Zhang, Dai; Zhou, Yuan; Zhao, Dongmei; Zhu, Jun‐Jie; Yang, Zhihui; Zhu, Mingming

doi:10.1007/s13258-017-0559-y

Cited by 6 publications

(8 citation statements)

References 52 publications

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“…Pel, which degrades plant cell walls, has profound effects on bacterial virulence. The combinations of pel gene clusters differ in various bacterial species; i.e., the pel gene cluster of P. atrosepticum consists of pelA, pelB, and pelC; that of P. carotovorum comprises pelA, pelB, pelC, and pelD (Zhang et al 2017), whereas that of Dickeya dadantii consists of pelA, pelB, pelC, pelD, and pelE followed by four secondary genes, including pelI, pelL, pelZ, and pelX (Zhang et al 2017). The Peh-encoding gene, pehA, affects bacterial virulence.…”

Section: Discussionmentioning

confidence: 99%

Manuka Honey Reduces the Virulence of Pectobacterium brasiliense by Suppressing Genes That Encode Plant Cell Wall-Degrading Enzymes

Ava

Subandiyah

Rohman

et al. 2022

AJSTD

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The primary virulence of soft rot pectobacteria, including Pectobacterium brasiliense, is mainly determined by the massive production of plant cell wall-degrading enzymes (PCWDEs), which promote plant tissue maceration in many crops. The antibiotic treatment to treat bacterial soft rot causes environmental problems and potentially affects resistance. Antibiotic resistance is driving interest in antimicrobial treatments, and no organism has been reported to have acquired resistance to honey. However, the use of honey as a therapeutic agent for plant bacterial pathogens has rarely been studied. Therefore, this study was undertaken to determine the in vitro effect of Manuka honey against P. brasiliense at the phenotypic and genotypic levels. A sublethal concentration of honey was determined by a growth inhibition assay in broth medium containing different concentrations of Manuka honey. A macerating assay was performed on orchid leaves, and the activities of the PCWDEs were examined in plate assays. The expression of PCWDE-associated genes was investigated using semi-quantitative reverse transcription-polymerase chain reaction analysis. The exposure of P. brasiliense to a sublethal concentration of Manuka honey significantly decreased the maceration ability of the orchid and the synthesis of PCWDEs, i.e., pectate lyase, polygalacturonase, and protease. Moreover, five PCWDEs-encoding genes, such as pelA, pelB, pelC, pehA, and prtW, had lower expression levels after the honey treatment compared with recA as the internal standard. The honey treatment decreased the virulence of P. brasiliense associated with the synthesis of PCWDEs. Therefore, Manuka honey reduced virulence by suppressing the expression of the PCWDE genes.

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Section: Discussionmentioning

confidence: 99%

Manuka Honey Reduces the Virulence of Pectobacterium brasiliense by Suppressing Genes That Encode Plant Cell Wall-Degrading Enzymes

Ava

Subandiyah

Rohman

et al. 2022

AJSTD

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“…It is also very possible to develop new control methods. By screening pathogenic genes based on whole-genome sequences of Pectobacterium species and analyzing the pathogenic mechanism at the molecular level, Zhang et al (2017) discovered a total of 168 genes related to pathogenesis including nine specific genes encoding toxins on the genome of P. atroseptica JG10-08. Huang et al (2019) selected five putative effectors from the genome of P. carotovorum subsp.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, PCWDEs are crucial in three distinct pathogenic functions: degradation, nutrition, and feedback regulation (Franza et al, 2002;Yang et al, 2007). The pathogens benefit from the nutrients produced after degradation; these degradation products accumulate in the host and can induce bacterium to generate more enzymes (Zhang et al, 2017). Therefore, the production of PCWDEs is characteristic of infection by Pectobacterium species.…”

Section: Discussionmentioning

confidence: 99%

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Analysis of the Taxonomy and Pathogenic Factors of Pectobacterium aroidearum L6 Using Whole-Genome Sequencing and Comparative Genomics

Wang

Qin

et al. 2021

Front. Microbiol.

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Soft rot pectobacteria are devastating plant pathogens with a global distribution and a broad host range. Pectobacterium aroidearum L6, previously isolated from leaves of Syngonium podophyllum, is a pectolytic bacterial pathogen that causes typical soft rot on S. podophyllum. There is a shortage for genome data of P. aroidearum, which seriously hinders research on classification and pathogenesis of Pectobacterium. We present here the complete genome sequence of P. aroidearum L6. The L6 strain carries a single 4,995,896-bp chromosome with 53.10% G + C content and harbors 4,306 predicted protein-coding genes. We estimated in silico DNA–DNA hybridization and average nucleotide identity values in combination with the whole-genome-based phylogeny from 19 Pectobacterium strains including P. aroidearum L6. The results showed that L6 and PC1 formed a population distinct from other populations of the Pectobacterium genus. Phylogenetic analysis based on 16S rRNA and genome sequences showed a close evolutionary relationship among Pectobacterium species. Overall, evolutionary analysis showed that L6 was in the same branch with PC1. In comparison with 18 Pectobacterium spp. reference pathogens, strain L6 had 2,712 gene families, among which 1,632 gene families were identified as orthologous to those strains, as well as 1 putative unique gene family. We discovered 478 genes, 10.4% of the total of predicted genes, that were potentially related to pathogenesis using the Virulence Factors of Pathogenic Bacteria database. A total of 25 genes were related to toxins, 35 encoded plant cell-wall degrading enzymes, and 122 were involved in secretion systems. This study provides a foundation for a better understanding of the genomic structure of P. aroidearum and particularly offers information for the discovery of potential pathogenic factors and the development of more effective strategies against this pathogen.

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“…The signed random walk restart algorithm is a model for personalized ranking of nodes in a signed network. Signed random walk restart is different from the traditional random walk-based method, because the traditional random walkbased method is only applicable to the network that is assumed to be a positive edge, and cannot effectively rank the nodes in the signed network [48], and Lack of the ability to consider complex edge relationships, the signed random walk restart algorithm makes up for this. The traditional walk mechanism based on the random walk method and the signed random walk restart method is shown in Figure 1.…”

Section: A Gene Screening With Symbolic Random Walk Restart Methodsmentioning

confidence: 99%

Screening of Pathogenic Genes for Colorectal Cancer and Deep Learning in the Diagnosis of Colorectal Cancer

Zhang

Xing

2020

IEEE Access

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Based on complex networks and machine learning methods, this paper studies the mining of colorectal cancer treatment genes, and innovatively combines a variety of feature extraction and comparative analysis methods, from gene network features, gene attribute features, network and attribute integration The three aspects of characteristics comprehensively excavate the genetic characteristics, and demonstrate the feasibility of the study through comparative analysis from different perspectives. Constructing a colorectal cancer gene network, analyzing the changes in the network structure during the development of colorectal cancer, and mining the network characteristics of genes are the first issues to be studied in this paper. The analysis of the network structure compares the changes in the network structure of the driver genes in the Normal network and the Tumor network and the edge mechanism of the driver genes, and the distribution of the eigenvalues of the driver genes and non-driver genes in the Tumor network. During the development of colorectal cancer, the network structure of the gene has changed significantly, and the prediction results based on the network structure show better prediction results than the non-network structure. These findings are feasible for the research direction of this paper. The argument is carried out, and the relevant analysis results are also given in the article. However, the research method of the thesis is based on network research, so comparing structural features with non-structural features only shows that structural features have a good classification ability, and cannot directly explain that modeling using gene networks is better than not using gene networks. Finally, based on the random forest, the optimized classification is improved to reveal the important factors affecting the diagnosis of colorectal cancer, and then to identify the true potential colorectal cancer driver genes, providing guidance for the clinical research of colorectal cancer and driving gene mining. INDEX TERMS colorectal cancer; gene screening; diagnosis; deep learning.

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Complete genome sequence and pathogenic genes analysis of Pectobacterium atroseptica JG10-08

Cited by 6 publications

References 52 publications

Manuka Honey Reduces the Virulence of Pectobacterium brasiliense by Suppressing Genes That Encode Plant Cell Wall-Degrading Enzymes

Manuka Honey Reduces the Virulence of Pectobacterium brasiliense by Suppressing Genes That Encode Plant Cell Wall-Degrading Enzymes

Analysis of the Taxonomy and Pathogenic Factors of Pectobacterium aroidearum L6 Using Whole-Genome Sequencing and Comparative Genomics

Screening of Pathogenic Genes for Colorectal Cancer and Deep Learning in the Diagnosis of Colorectal Cancer

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