Comparison of usefulness of randomly amplified polymorphic DNA and amplified-fragment length polymorphism techniques in epidemiological studies on nasopharyngeal carriage of non-typable Haemophilus influenzae

Analysis of genetic relatedness of Haemophilus influenzae isolates by multilocus sequence typing

The gram-negative bacterium Haemophilus influenzae is a human-restricted commensal of the nasopharynx that can also be associated with disease. The majority of H. influenzae respiratory isolates lack the genes for capsule production and are nontypeable (NTHI). Whereas encapsulated strains are known to belong to serotype-specific phylogenetic groups, the structure of the NTHI population has not been previously described. A total of 656 H. influenzae strains, including 322 NTHI strains, have been typed by multilocus sequence typing and found to have 359 sequence types (ST). We performed maximum-parsimony analysis of the 359 sequences and calculated the majority-rule consensus of 4,545 resulting equally most parsimonious trees. Eleven clades were identified, consisting of six or more ST on a branch that was present in 100% of trees. Two additional clades were defined by branches present in 91% and 82% of trees, respectively. Of these 13 clades, 8 consisted predominantly of NTHI strains, three were serotype specific, and 2 contained distinct NTHI-specific and serotype-specific clusters of strains. Sixty percent of NTHI strains have ST within one of the 13 clades, and eBURST analysis identified an additional phylogenetic group that contained 20% of NTHI strains. There was concordant clustering of certain metabolic reactions and putative virulence loci but not of disease source or geographic origin. We conclude that well-defined phylogenetic groups of NTHI strains exist and that these groups differ in genetic content. These observations will provide a framework for further study of the effect of genetic diversity on the interaction of NTHI with the host.

GOGOT: a method for the identification of differentially expressed fragments from cDNA-AFLP data

Background

One-dimensional (1-D) electrophoretic data obtained using the cDNA-AFLP method have attracted great interest for the identification of differentially expressed transcript-derived fragments (TDFs). However, high-throughput analysis of the cDNA-AFLP data is currently limited by the need for labor-intensive visual evaluation of multiple electropherograms. We would like to have high-throughput ways of identifying such TDFs.

Results

We describe a method, GOGOT, which automatically detects the differentially expressed TDFs in a set of time-course electropherograms. Analysis by GOGOT is conducted as follows: correction of fragment lengths of TDFs, alignment of identical TDFs across different electropherograms, normalization of peak heights, and identification of differentially expressed TDFs using a special statistic. The output of the analysis is a highly reduced list of differentially expressed TDFs. Visual evaluation confirmed that the peak alignment was performed perfectly for the TDFs by virtue of the correction of peak fragment lengths before alignment in step 1. The validity of the automated ranking of TDFs by the special statistic was confirmed by the visual evaluation of a third party.

Conclusion

GOGOT is useful for the automated detection of differentially expressed TDFs from cDNA-AFLP temporal electrophoretic data. The current algorithm may be applied to other electrophoretic data and temporal microarray data.

A normalization strategy applied to HiCEP (an AFLP-based expression profiling) analysis: toward the strict alignment of valid fragments across electrophoretic patterns

Background

Gene expression analysis based on comparison of electrophoretic patterns is strongly dependent on the accuracy of DNA fragment sizing. The current normalization strategy based on molecular weight markers has limited accuracy because marker peaks are often masked by intense peaks nearby. Cumulative errors in fragment lengths cause problems in the alignment of same-length fragments across different electropherograms, especially for small fragments (< 100 bp). For accurate comparison of electrophoretic patterns, further inspection and normalization of electrophoretic data after fragment sizing by conventional strategies is needed.

Results

Here we describe a method for the normalization of a set of time-course electrophoretic data to be compared. The method uses Gaussian curves fitted to the complex peak mixtures in each electropherogram. It searches for target ranges for which patterns are dissimilar to the other patterns (called "dissimilar ranges") and for references (a kind of mean or typical pattern) in the set of resultant approximate patterns. It then constructs the optimal normalized pattern whose correlation coefficient against the reference in the range achieves the highest value among various combinations of candidates. We applied the procedure to time-course electrophoretic data produced by HiCEP, an AFLP-based expression profiling method which can detect a slight expression change in DNA fragments. We obtained dissimilar ranges whose electrophoretic patterns were obviously different from the reference and as expected, most of the fragments in the detected ranges were short (< 100 bp). The normalized electrophoretic patterns also agreed well with reference patterns.

Conclusion

The normalization strategy presented here demonstrates the importance of pre-processing before electrophoretic signal comparison, and we anticipate its usefulness especially for temporal expression analysis by the electrophoretic method.