T4SP: A Novel Tool and Database for Type IV Secretion Systems in Bacterial Genomes

Secretion systems, macromolecules to pass which can mediate the across cellular membranes, are essential for virulent and genetic material exchange among bacterial species[1]. Type IV secretion system （T4SS） is one of the secretion systems and it usually consists of 12 genes： VirB1, VirB2 ...VirB11, and VirD4[2]. The structure and molecular mechanisms of these genes have been well analyzed in Gram-negative strains[3] and Gram-positive strains were once believed to be lack of T4SS. However, some recent studies revealed that one or more virB/D genes also exist in some kinds of Gram-positive bacteria and play similar role, and form a T4SS-like system[3]. The VirBl-like, VirB4, VirB6, and VirD4 genes were identified in the chromosome of Gram-positive bacterium Streptococcus suis in our previous studies and their role as important mobile elements for horizontal transfer to recipients in an 89 K pathogenicity island （PAl） was demonstrated[45]. However, their structure and molecular mechanisms in other strains, especially in Gram-positive strains, are remained unclear.

Relationship between Genomic Types of Escherichia coli and Clinical Diseases

In this study, by analysis of genome structures of E. coli, the relationships between the genomic types of E. coli and the associated diseases were investigated. Samples of sputum, urine and other excretions from patients with different infective diseases were collected. And 62 E. coli strains were isolated from these samples. Intact bacterial genomic DNA was cleaved with I-CeuI, separated by pulsed field gel electrophoresis and then typed on the basis of cleavage map. The results showed that 7 I-CeuI sites were found in all the genome structures of the 62 E. coli, indicating that there were 7 rrn operons in the genomes. The size of genome ranged from 4500 kb to 5000 kb. According to the genome structures, 62 E. coli strains were divided into 30 genome types. It was concluded that genome structures of E. coli isolated from the patients with different infective diseases varied to some extent, suggesting that some genome types of E. coli were closely related to some infective diseases.

Family Competition Pheromone Genetic Algorithm for Comparative Genome Assembly

Genome assembly is a prerequisite step for analyzing next generation sequencing data and also far from being solved. Many assembly tools have been proposed and used extensively. Majority of them aim to assemble sequencing reads into contigs; however, we focus on the assembly of contigs into scaffolds in this paper. This is called scaffolding, which estimates the relative order of the contigs as well as the size of the gaps between these contigs. Pheromone trail-based genetic algorithm （PGA） was previously proposed and had decent performance according to their paper. From our previous study, we found that family competition mechanism in genetic algorithm is able to further improve the results. Therefore, we propose family competition pheromone genetic algorithm （FCPGA） and demonstrate the improvement over PGA.

Public health implications of Yersinia enterocolitica investigation:an ecological modeling and molecular epidemiology study

Background Yersinia enterocolitica has been sporadically recovered from animals,foods,and human clinical samples in various regions of Ningxia,China.However,the ecological and molecular characteristics of Y.enterocolitica,as well as public health concerns about infection in the Ningxia Hui Autonomous Region,remain unclear.This study aims to analyze the ecological and molecular epidemiological characteristics of Y.enterocolitis in order to inform the public health intervention strategies for the contains of related diseases.Methods A total of 270 samples were collected for isolation[animals(n=208),food(n=49),and patients(n=13)],then suspect colonies were isolated and identified by the API20E biochemical identification system,serological tests,biotyping tests,and 16S rRNA-PCR.Then,we used an ecological epidemiological approach combined with machine learning algorithms(general linear model,random forest model,and eXtreme Gradient Boosting)to explore the associations between ecological factors and the pathogenicity of Y.enterocolitis.Furthermore,average nucleotide identity(ANI)estimation,single nucleotide polymorphism(SNP),and core gene multilocus sequence typing(cgMLST)were applied to characterize the molecular profile of isolates based on whole genome sequencing.The statistical test used single-factor analysis,Chi-square tests,t-tests/ANOVA-tests,Wilcoxon rank-sum tests,and Kruskal–Wallis tests.Results A total of 270 isolates of Yersinia were identified from poultry and livestock(n=191),food(n=49),diarrhoea patients(n=13),rats(n=15),and hamsters(n=2).The detection rates of samples from different hosts were statistically different(χ^(2)=22.636,P<0.001).According to the relatedness clustering results,270 isolates were divided into 12 species,and Y.enterocolitica(n=187)is a predominated species.Pathogenic isolates made up 52.4%(98/187),while non-pathogenic isolates made up 47.6%(89/187).Temperature and precipitation were strongly associated with the pathogenicity of the isolates(P<0.001).The random forest(RF)prediction model showed the best performance.The prediction result shows a high risk of pathogenicity Y.enterocolitica was located in the northern,northwestern,and southern of the Ningxia Hui Autonomous Region.The Y.enterocolitica isolates were classified into 54 sequence types(STs)and 125 cgMLST types(CTs),with 4/O:3 being the dominant bioserotype in Ningxia.The dominant STs and dominant CTs of pathogenic isolates in Ningxia were ST429 and HC100_2571,respectively.Conclusions The data indicated geographical variations in the distribution of STs and CTs of Y.enterocolitica isolates in Ningxia.Our work offered the first evidence that the pathogenicity of isolates was directly related to fluctuations in temperature and precipitation of the environment.CgMLST typing strategies showed that the isolates were transmitted to the population via pigs and food.Therefore,strengthening health surveillance on pig farms in high-risk areas and focusing on testing food of pig origin are optional strategies to prevent disease outbreaks.