Cataloguing the Gene Expression Patterns of Dental Plaque Biofilms: A Reference Dental Plaque Transcriptome

The RNA-Seq method has been widely adopted as an alternative to the use of DNA microarrays. In most contexts, the RNA-Seq method is implemented when a single reference organism is being studied. Our project endeavored to establish working methods to enable the generation of cDNA libraries that were depleted of contaminating human mRNA and host/microbiome rRNA sequences that would otherwise represent over 95% of the total sequence reads obtained. We have also made significant efforts to define bioinformatics procedures that allow RNA-Seq data to be assigned to appropriate species such that global gene expression analyses can be routinely conducted by the dental research community and those involved in HMP research objectives.

We have established a catalogue of expressed genes in dental plaque by turning to the Solexa sequencing platform and applying RNA-Seq to a collection of 19 twin pairs that are either concordant for dental health (caries-free concordant twin pairs), concordant for dental caries (caries-active concordant twin pairs) or discordant for dental caries (one twin caries-free and the other member of the twin pair caries-active). Based on our analysis of the data we have established that the most abundant ten species in each sample varies significantly from subject to subject. This fact greatly complicates the mapping of reads to reference genomes. Another significant conceptual challenge we faced was how to conduct highly specific mapping of transcripts to genomes of interest. We know that genes in genomes evolve at substantially different rates; some genes may differ by 2-5% across species boundaries whereas others may differ by 25-30%. The consequence of this is that no single cut-off for mapping a transcript to a reference genome may be reliably employed. We therefore reasoned that by creating an oral cavity reference genome database we could map each transcript according to reasonable specificity criteria but impose a best-hit criteria on the data to ensure minimal mis-mapping.

Based upon the data generated (38 samples X ~32.8 million reads/sample) ~1 billion reads or over 100 Gb of sequence data, we have fulfilled the goal of establishing a robust procedure for RNA-Seq and the specific transcripts expressed in dental plaque biofilms. These sequences and the associated SOPs developed for effective microbial RNA enrichment have been made available through the DACC (http://www.hmpdacc.org/RSEQ/). In addition, we have devised a strategy for mapping reads to particular functional or biochemical pathways such as those related to acid/base production as an independent means of exploiting RNA-Seq data. In this scheme the details of which species are expressing functions is not considered of importance but rather the sum total of expressed sequences related to acid/base production is. The approach used here is similar to that described above in that a database is created pertaining to all sequence data derived from particular biochemical pathways as a means of recruiting reads of appropriate sequence identity mapping to annotated genes. Over- or under-representation of expressed genes constituting discrete pathways may then be evaluated.

The projects described above were supported by NIAID via a contract to JCVI under the Pathogen Functional genomics Resource Center (N01-AI15447)and funds from NIDCR to PFGRC in an attempt to enable the HMP research community to exploit genomic and metagenomic methods. The work pertaining to the oral cavity was done in collaboration with Dr. Walter Bretz at NYU and the efforts pertaining to the gut microbiome were done in collaboration with Dr. Cynthia Sears at JHU.

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