Functional Genomics of Mycobacterium tuberculosis Using DNA Microarrays
Completion of the sequence of the entire genome of strain H37Rv was a benchmark for Mycobacterium tuberculosis research (1). This achievement ushers in the era of genome-wide functional and comparative genomics for this organism. At present, the most powerful enabling technology of the postgenomic era is microarray-based hybridization. Microarrays, by whatever means they are fabricated, contain surface-bound representations of each open reading frame (ORF) of a sequenced genome. Thus, they provide a method for parallel sampling of thousands of different genes within a complex pool of nucleic acids. Microarray gene capacity readily accommodates the number of ORFs in the relatively small genomes of bacteria and yeast and, in principle, can accommodate the entire genetic repertoire of complex multicellular animals. Below, we discuss our fabrication and use of an M. tuberculosis microarray, containing representations of each of the identified 3924 ORFs of this organism. We will describe two applications of this method. In the first—microarray-based gene response, i.e., transcript profiling — we ask the question: which genes are selectively expressed under a particular condition of growth, in a particular host compartment or as a result of inhibition of a metabolic or biosynthetic pathway? In the second, comparative genomics, we use a microarray containing the ORFs of one strain or species to identify ORFs deleted or absent from a second strain or species whose genome sequence may not have been determined. In this manner, microarray-based comparative genomics seeks to learn the ORF-by-ORF relatedness of two similar, but nonidentical organisms whose biological differences are under investigation. Examples of each application have been applied to M. tuberculosis (2,3).
KeywordsLog2 Ratio Succinic Anhydride Wash Solution National Human Genome Research Institute Polymerase Chain Reaction Plate
- 1.Cole S. T., Brosch R., Parkhill J., Garnier T., Churcher C., Harris D., Gordon S. V., Figimejer K., Gas S., Barry C. E., 3rd, Tekaia F., Badeock K., Basham D., Brown D., Chillingworth T., Connor R., Davies R., Devlin K., Feitwell T., Gentles. S., Hamlin N., Hoiroyd S., Hornsby T., Jagels K., Barrell B. G., et al (1998) Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence. Nature 393, 537–544.CrossRefPubMedGoogle Scholar
- 16.Tamayo P., Slonim D., Mesirov J., Zhu Q., Kitareewan S., Dmitrovsky E., Lander E. S., and Golub T. R. (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA 96, 2907–2912.CrossRefPubMedGoogle Scholar
- 18.Steve Rozen H. J. S. (1996, 1997) Primer3. Whitehead Institute for Biomedical Research http://www-genome.wi.mit.edulgenome_software/other/primer3.html.