454 raw sequences of the V4 region 16S rRNA gene from the spleens of 190 bank volesThis FASTA file contains 78,087 raw sequences produced using 454 GS-FLX pyrosequencing. The 190 multiplexed amplicons were tagged using both forward and reverse primers. The list of the 190 multiplexed samples and associated tags are provided in the following CSV file titled: Information concerning the samples multiplexed in the 454 run.454_Reads_16Sv4_Myodes.fastaInformation concerning the samples multiplexed in the 454 runThis CSV file contains the sample names, the forward and reverse tag sequences, the forward and reverse primer sequences, the gene name, the species name and the population name for each of the 190 samples multiplexed in the 454 pyrosequencing run.454_Reads_16Sv4_Myodes.csvMiSeq raw sequences of the V4 region 16S rRNA gene from the spleens of 190 bank voles (part 1)This ZIP file contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each individual using the MiSeq platform. The 190 multiplexed amplicons were indexed using both forward and reverse indexes. The list of the 190 multiplexed samples and associated indexes are provided in the following CSV file titled: Information concerning the samples multiplexed in the MiSeq run.MiSeq_Reads_16Sv4_Myodes_part1.zipMiSeq raw sequences of the V4 region 16S rRNA gene from the spleens of 190 bank voles (part 2)This ZIP file contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each individual using the MiSeq platform. The 190 multiplexed amplicons were indexed using both forward and reverse indexes. The list of the 190 multiplexed samples and associated indexes are provided in the following CSV file titled: Information concerning the samples multiplexed in the MiSeq run.MiSeq_Reads_16Sv4_Myodes_part2.zipMiSeq raw sequences of the V4 region 16S rRNA gene from the spleens of 190 bank voles (part 3)This ZIP file contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each individual using the MiSeq platform. The 190 multiplexed amplicons were indexed using both forward and reverse indexes. The list of the 190 multiplexed samples and associated indexes are provided in the following CSV file titled: Information concerning the samples multiplexed in the MiSeq run.MiSeq_Reads_16Sv4_Myodes_part3.zipInformation concerning the samples multiplexed in the MiSeq runThis CSV file contains the sample names, the forward and reverse index names and the forward and reverse index sequences for each of the 190 samples multiplexed in the Illumina MiSeq run.MiSeq_Reads_16Sv4_Myodes.csvContigs of sequences of the HiSeq RNA sequencing from the spleens of 190 bank volesThis FASTA file contains 5,338 contigs of sequences produced using HiSeq2000 RNAseq sequencing. These contigs result from the de novo assembly of HiSeq reads. They were assigned to bacteria via successive sequence alignment using the non-redundant nucleotide and protein databases from NCBI and the BLAST algorithm.HiSeq_Contigs_RNAseq_bacteria_Myodes.fasta

Background: Rodents are major reservoirs of pathogens responsible for numerous zoonotic diseases in humans and livestock. Assessing their microbial diversity at both the individual and population level is crucial for monitoring endemic infections and revealing microbial association patterns within reservoirs. Recently, NGS approaches have been employed to characterize microbial communities of different ecosystems. Yet, their relative efficacy has not been assessed. Here, we compared two NGS approaches, RNA-Sequencing (RNA-Seq) and 16S-metagenomics, assessing their ability to survey neglected zoonotic bacteria in rodent populations. Methodology/Principal Findings: We first extracted nucleic acids from the spleens of 190 voles collected in France. RNA extracts were pooled, randomly retro-transcribed, then RNA-Seq was performed using HiSeq. Assembled bacterial sequences were assigned to the closest taxon registered in GenBank. DNA extracts were analyzed via a 16S-metagenomics approach using two sequencers: the 454 GS-FLX and the MiSeq. The V4 region of the gene coding for 16S rRNA was amplified for each sample using barcoded universal primers. Amplicons were multiplexed and processed on the distinct sequencers. The resulting datasets were de-multiplexed, and each read was processed through a pipeline to be taxonomically classified using the Ribosomal Database Project. Altogether, 45 pathogenic bacterial genera were detected. The bacteria identified by RNA-Seq were comparable to those detected by 16S-metagenomics approach processed with MiSeq (16S-MiSeq). In contrast, 21 of these pathogens went unnoticed when the 16S-metagenomics approach was processed via 454-pyrosequencing (16S-454). In addition, the 16S-metagenomics approaches revealed a high level of coinfection in bank voles. Conclusions/Significance: We concluded that RNA-Seq and 16S-MiSeq are equally sensitive in detecting bacteria. Although only the 16S-MiSeq method enabled identification of bacteria in each individual reservoir, with subsequent derivation of bacterial prevalence in host populations, and generation of intra-reservoir patterns of bacterial interactions. Lastly, the number of bacterial reads obtained with the 16S-MiSeq could be a good proxy for bacterial prevalence.