Relative abundances of different bacterial genera in the salivary glands of (A) <i>I. ovatus</i>, (B) <i>I. persulcatus</i> and (C) <i>H. flava</i>.

<p>All genera with less than 1.0% contribution were pooled into one group and labelled “others”.</p

Phylogenetic analysis of the 16S rDNA sequences of unclassified bacteria from IPf2, IPf3, IPf4, and IPf5 using maximum likelihood method.

<p>The tree is rooted with the <i>Escherichia coli</i>. All bootstrap values from 1000 replications are shown on interior branch nodes.</p

Microbial Population Analysis of the Salivary Glands of Ticks; A Possible Strategy for the Surveillance of Bacterial Pathogens

<div><p>Ticks are one of the most important blood-sucking vectors for infectious microorganisms in humans and animals. When feeding they inject saliva, containing microbes, into the host to facilitate the uptake of blood. An understanding of the microbial populations within their salivary glands would provide a valuable insight when evaluating the vectorial capacity of ticks. Three tick species (<i>Ixodes ovatus</i>, <i>I. persulcatus</i> and <i>Haemaphysalis flava</i>) were collected in Shizuoka Prefecture of Japan between 2008 and 2011. Each tick was dissected and the salivary glands removed. Bacterial communities in each salivary gland were characterized by 16S amplicon pyrosequencing using a 454 GS-Junior Next Generation Sequencer. The Ribosomal Database Project (RDP) Classifier was used to classify sequence reads at the genus level. The composition of the microbial populations of each tick species were assessed by principal component analysis (PCA) using the Metagenomics RAST (MG-RAST) metagenomic analysis tool. <i>Rickettsia-</i>specific PCR was used for the characterization of rickettsial species. Almost full length of 16S rDNA was amplified in order to characterize unclassified bacterial sequences obtained in <i>I. persulcatus</i> female samples. The numbers of bacterial genera identified for the tick species were 71 (<i>I. ovatus</i>), 127 (<i>I. persulcatus</i>) and 59 (<i>H. flava</i>). Eighteen bacterial genera were commonly detected in all tick species. The predominant bacterial genus observed in all tick species was <i>Coxiella</i>. <i>Spiroplasma</i> was detected in <i>Ixodes</i>, and not in <i>H. flava</i>. PCA revealed that microbial populations in tick salivary glands were different between tick species, indicating that host specificities may play an important role in determining the microbial complement. Four female <i>I. persulcatus</i> samples contained a high abundance of several sequences belonging to Alphaproteobacteria symbionts. This study revealed the microbial populations within the salivary glands of three species of ticks, and the results will contribute to the knowledge and prediction of emerging tick-borne diseases.</p></div

Sequence results and number of detected genera.

<p>Sequence results and number of detected genera.</p

Alpha diversity calculated for each tick sample.

<p>The alpha diversity of each tick sample was calculated using the MG-RAST server. The mean value obtained for each tick group is represented by the horizontal line. Mean alpha diversity values: IOf (5.75), IOm (5.33), IPf (4.97), IPm (3.11), and HFf (2.14).</p

Comparison of the relative abundance of rickettsial sequences estimated by 16S amplicon analysis and the results of <i>gltA</i> PCR.

<p>Vertical axis represents the relative abundance of rickettsial sequences calculated from the data obtained from 16S amplicon analysis. Blue dots represent samples in which <i>Rickettsia</i> was detected by both 16S amplicon analysis and <i>gltA</i> PCR. Red dots represent samples in which <i>Rickettsia</i> was detected by 16S amplicon analysis but not by <i>gltA</i> PCR. The plots with relative abundance values between 0% and 5% are shown in the magnified graph provided in the right column.</p

Principal component analysis of the bacterial composition in each tick sample.

<p>The plots were generated using the MG-RAST server. Each tick sample is shown in a different color depending on the species and sex of the tick; IOf, IOm, IPf, IPm, and HFf are respectively, shown in red, green, blue, purple, and yellow. The plots derived from the same tick species are highlighted in circles; <i>I. ovatus</i> (IO), <i>I. persulcatus</i> (IP), and <i>H. flava</i> (HF) are, respectively, highlighted in red, blue, and yellow circles.</p

Venn diagram of all 163 identified genera distributed across the tick species and sex.

<p>Venn diagram of all 163 identified genera distributed across the tick species and sex.</p

Summary of <i>gltA</i> sequencing.

<p>Summary of <i>gltA</i> sequencing.</p

Image_2_Comparison of Database Search Methods for the Detection of Legionella pneumophila in Water Samples Using Metagenomic Analysis.PDF

<p>Metagenomic analysis has become a powerful tool to analyze bacterial communities in environmental samples. However, the detection of a specific bacterial species using metagenomic analysis remains difficult due to false positive detections of sequences shared between different bacterial species. In this study, 16S rRNA amplicon and shotgun metagenomic analyses were conducted on samples collected along a stream and ponds in the campus of Hokkaido University. We compared different database search methods for bacterial detection by focusing on Legionella pneumophila. In this study, we used L. pneumophila-specific nested PCR as a gold standard to evaluate the results of the metagenomic analysis. Comparison with the results from L. pneumophila-specific nested PCR indicated that a blastn search of shotgun reads against the NCBI-NT database led to false positive results and had problems with specificity. We also found that a blastn search of shotgun reads against a database of the catalase-peroxidase (katB) gene detected L. pneumophila with the highest area under the receiver operating characteristic curve among the tested search methods; indicating that a blastn search against the katB gene database had better diagnostic ability than searches against other databases. Our results suggest that sequence searches targeting long genes specifically associated with the bacterial species of interest is a prerequisite to detecting the bacterial species in environmental samples using metagenomic analyses.</p