Additional file 1 of Virulence, antibiotic resistance phenotypes and molecular characterisation of Vibrio furnissii isolates from patients with diarrhoea

Supplementary Material

The results of genome sequencing and assembly.

<p>The results of genome sequencing and assembly.</p

Pan-Genomic Analysis Provides Insights into the Genomic Variation and Evolution of <em>Salmonella</em> Paratyphi A

<div><p><em>Salmonella</em> Paratyphi A (<em>S</em>. Paratyphi A) is a highly adapted, human-specific pathogen that causes paratyphoid fever. Cases of paratyphoid fever have recently been increasing, and the disease is becoming a major public health concern, especially in Eastern and Southern Asia. To investigate the genomic variation and evolution of <em>S.</em> Paratyphi A, a pan-genomic analysis was performed on five newly sequenced <em>S.</em> Paratyphi A strains and two other reference strains. A whole genome comparison revealed that the seven genomes are collinear and that their organization is highly conserved. The high rate of substitutions in part of the core genome indicates that there are frequent homologous recombination events. Based on the changes in the pan-genome size and cluster number (both in the core functional genes and core pseudogenes), it can be inferred that the sharply increasing number of pseudogene clusters may have strong correlation with the inactivation of functional genes, and indicates that the <em>S.</em> Paratyphi A genome is being degraded.</p> </div

The results of genome sequencing and assembly.

<p>The results of genome sequencing and assembly.</p

The distribution of the number of SNPs per cluster.

<p>The distribution of the number of SNPs per cluster.</p

The SNPs detected in the whole genome of all seven strains.

<p>The SNPs detected in the whole genome of all seven strains.</p

A comparison of the genomic structures of all seven strains.

<p>The structures shown from top to bottom are for GXS2268, GZ9A00052, JX05-19, YN09620, ZJ98-53, AKU_1260 and ATCC_9150. The same colors represent homologous fragments identified by the Mauve program.</p

The distribution of pseudogene functions based on the COG classification.

<p>The distribution of pseudogene functions based on the COG classification.</p

The phylogenetic relationships among all seven <i>S. </i>Paratyphi A strains, with <i>S.</i> Typhi CT18 used as an outgroup.

<p>The phylogenetic tree was based on 1689 core functional genes using the maximum likelihood method. The outgroup strain is marked in blue.</p

Data_Sheet_1_Identification of foodborne pathogenic bacteria using confocal Raman microspectroscopy and chemometrics.doc

Rapid and accurate identification of foodborne pathogenic bacteria is of great importance because they are often responsible for the majority of serious foodborne illnesses. The confocal Raman microspectroscopy (CRM) is a fast and easy-to-use method known for its effectiveness in detecting and identifying microorganisms. This study demonstrates that CRM combined with chemometrics can serve as a rapid, reliable, and efficient method for the detection and identification of foodborne pathogenic bacteria without any laborious pre-treatments. Six important foodborne pathogenic bacteria including S. flexneri, L. monocytogenes, V. cholerae, S. aureus, S. typhimurium, and C. botulinum were investigated with CRM. These pathogenic bacteria can be differentiated based on several characteristic peaks and peak intensity ratio. Principal component analysis (PCA) was used for investigating the difference of various samples and reducing the dimensionality of the dataset. Performances of some classical classifiers were compared for bacterial detection and identification including decision tree (DT), artificial neural network (ANN), and Fisher’s discriminant analysis (FDA). Correct recognition ratio (CRR), area under the receiver operating characteristic curve (ROC), cumulative gains, and lift charts were used to evaluate the performance of models. The impact of different pretreatment methods on the models was explored, and pretreatment methods include Savitzky–Golay algorithm smoothing (SG), standard normal variate (SNV), multivariate scatter correction (MSC), and Savitzky–Golay algorithm 1st Derivative (SG 1st Der). In the DT, ANN, and FDA model, FDA is more robust for overfitting problem and offers the highest accuracy. Most pretreatment methods raised the performance of the models except SNV. The results revealed that CRM coupled with chemometrics offers a powerful tool for the discrimination of foodborne pathogenic bacteria.</p