1. Introduction

Recent systematic genome searches revealed that bacteria encode a tremendous number of non-coding small RNAs (sRNAs) that control numerous cellular processes, primarily as regulators of translation and message stability. However, genome-wide annotations for sRNA-encoding genes have been conducted in only a few of the 417 bacterial genomes sequenced to date and none have been done in nine completed oral pathogen genomes. To provide high quality annotation for the oral pathogen research community, we developed a computer pipeline designed to locate potential sRNAs in bacteria by searching for highly conserved regions in the intergenic sequence (IGS) among and within genomes. Combining this approach with same-orientation, terminator-pair prediction, we conducted a genome-wide survey for putative sRNA-encoding genes in the nine oral pathogens. As more genomes are sequenced, this pipeline can be used to locate more non-coding small RNAs, which will open up an avenue to understand sRNA regulation in bacteria.

2. Materials and Methods

The nine oral pathogens that were used in this research are listed in below:

1. Actinobacillus actinomycetemcomitans
2. Fusobacterium nucleatum
3. Porphyromonas gingivalis
4. Prevotella intermedia
5. Streptococcus mutans
6. Streptococcus mitis
7. Streptococcus sanguinis
8. Tannerella forsythensis
9. Treponema denticola
   

2.1 IGS (InterGenetic Sequence) Database Preparation

1. Completed prokaryotic genome sequences are downloaded from NCBI RefSeq Database (Feb 14^th 2007).
2. Intergenic sequences are extracted from the region between two adjacent but non-overlapping coding sequences (CDS) and longer than 50bps.
3. All intergenic sequences are fomated into blast searchable database to search conserved IGS for small RNA prediction pragrom.
4. In order to predict possible regulatory mRNA target of small RNA, sequences of 140bp upstream and 60bp downstream from start condon, sequences of 60bp upstream and 90bp downstream from stop codon are extracted for mRNA target prediction.

Figure 1. Database preparation flowchart

2.2 sRNA Prediction

1. All integernic sequences are extracted from GenBank flat file.
2. Intergenic sequences that are shorter than 50bps or adjacent to insertion elements (IS), transposases or prophages are eliminated by a keyword filter.
3. The nucleotide and amino acid insertion element databases (from IS Finder) also applied to identify IGS that escape the keyword filter.
4. IGSs that pass the second step are submitted to Blast against IGS database to search for conserved IGS sequences.
5. All IGS sequences are also subject to promoter and Rho-independent terminator prediction by PromScan with RpoD/RpoN weight matrix and TransTerm HP.
6. Conserved IGS segments are submitted to Blast against complement coding sequence database to search for a possible regulatory target.
7. In order to obtain more possible sRNA candidates, terminator pairs that lie on the same strand are are extracted as well.
   

Figure 2. Small RNA prediction flowchart

3. Results

Parameters for small RNA prediction

1. IGS len >= 100bps
2. Exclude tRNA, rRNA
3. IGS Blast e-value: 1e-5
4. PromScan score > 75
5. TransTerm Hp con > 70
6. In Table 2. only Inter-species conservation number of IGS (column 17, Inter Spp. IGS) large than 1 (for S. mit, larger than 17) is considered, but all information are availabled at raw data of Table 1.
7. The transcribing strand of predicted small RNA candidate can be guessed by P-T Pair information (see Definition of table header).

Table 1. Raw data of prediction results:

Table 2. Small RNA prediction results for nine oral pathogens. For detail explanation of table header please see: Definition of table header. In Table 2, rows with light green background color are known small RNA prediction from Rfam 8.0, the information from left to right are: organism abbrevation, Rfam ID, bit score of rfam scan, sRNA type, start, and end position.

References

1. Argaman, L., Hershberg, R., Vogel, J., Bejerano, G., Wagner, E. G., Margalit, H., and Altuvia, S. (2001). Novel small RNA-encoding genes in the intergenic regions of Escherichia coli. Curr Biol 11, 941-950.
2. Axmann, I. M., Kensche, P., Vogel, J., Kohl, S., Herzel, H., and Hess, W. R. (2005). Identification of cyanobacterial non-coding RNAs by comparative genome analysis. Genome Biol 6, R73.
3. Barrick, J. E., Sudarsan, N., Weinberg, Z., Ruzzo, W. L., and Breaker, R. R. (2005). 6S RNA is a widespread regulator of eubacterial RNA polymerase that resembles an open promoter. Rna 11, 774-784.
4. Chen, S., Lesnik, E. A., Hall, T. A., Sampath, R., Griffey, R. H., Ecker, D. J., and Blyn, L. B. (2002). A bioinformatics based approach to discover small RNA genes in the Escherichia coli genome. Biosystems 65, 157-177.
5. Eddy, S. R. (2002). A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. BMC Bioinformatics 3.
6. Ermolaeva, M. D., Khalak, H. G., White, O., Smith, H. O., and Salzberg, S. L. (2000). Prediction of transcription terminators in bacterial genomes. J Mol Biol 301, 27-33.
7. Huttenhofer, A., and Vogel, J. (2006). Experimental approaches to identify non-coding RNAs. Nucleic Acids Res 34, 635-646.
8. Kulkarni, P. R., Cui, X., Williams, J. W., Stevens, A. M., and Kulkarni, R. V. (2006). Prediction of CsrA-regulating small RNAs in bacteria and their experimental verification in Vibrio fischeri. Nucleic Acids Res 34, 3361-3369.
9. Lenz, D. H., Mok, K. C., Lilley, B. N., Kulkarni, R. V., Wingreen, N. S., and Bassler, B. L. (2004). The small RNA chaperone Hfq and multiple small RNAs control quorum sensing in Vibrio harveyi and Vibrio cholerae. Cell 118, 69-82.
10. Li, W-H., and Godzik, A. (2006). Cd-hit: a fast program fro clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658-1659.
11. Livny, J., Brencic, A., Lory, S., and Waldor, M. K. (2006). Identification of 17 Pseudomonas aeruginosa sRNAs and prediction of sRNA-encoding genes in 10 diverse pathogens using the bioinformatic tool sRNAPredict2. Nucleic Acids Res 34, 3484-3493.
12. Majdalani, N., Vanderpool, C. K., and Gottesman, S. (2005). Bacterial small RNA regulators. Crit Rev Biochem Mol Biol 40, 93-113.
13. Rivas, E., Klein, R. J., Jones, T. A., and Eddy, S. R. (2001). Computational identification of noncoding RNAs in E. coli by comparative genomics. Curr Biol 11, 1369-1373.
14. Studholme DJ, Dixon R. (2003). Domain architectures of sigma⁵⁴-dependent transcriptional activators. J Bacteriol. 185,1757-67.
15. van Helden, J. (2003). Regulatory sequence analysis tools. Nucleic Acids Res 31, 3593-3596.
16. Vogel, J., and Sharma, C. M. (2005). How to find small non-coding RNAs in bacteria. Biol Chem 386, 1219-1238.
17. Wang, C., Ding, C., Meraz, R. F., and Holbrook, S. R. (2006). PSoL: a positive sample only learning algorithm for finding non-coding RNA genes. Bioinformatics 22, 2590-2596.
18. Yachie, N., Numata, K., Saito, R., Kanai, A., and Tomita, M. (2006). Prediction of non-coding and antisense RNA genes in Escherichia coli with Gapped Markov Model. Gene 372, 171-181.
19. Zhang, A., Wassarman, K. M., Rosenow, C., Tjaden, B. C., Storz, G., and Gottesman, S. (2003). Global analysis of small RNA and mRNA targets of Hfq. Mol Microbiol 50, 1111-1124.
20. Zhang, Y., Sun, S., Wu, T., Wang, J., Liu, C., Chen, L., Zhu, X., Zhao, Y., Zhang, Z., Shi, B., et al. (2006). Identifying Hfq-binding small RNA targets in Escherichia coli. Biochem Biophys Res Commun 343, 950-955.
21. http://www-is.biotoul.fr/