DNA microarray technology (oligo and spotted microarrays) has become widely accepted for gene expression profiling (1
). There is a growing interest in applying such technologies to investigate the transcription profiles of infectious agents on a genome-wide level to develop new vaccines and drugs to combat infectious disease. A leading candidate for this approach is Mycobacterium tuberculosis
, the causative agent of human tuberculosis, responsible for 3 million annual mortalities (3
). However, microarray analysis has a number of problems, including spotting efficiency, sample labeling efficiency, transcript representation and hybridization reproducibility (4
), which are amplified with the analysis of mixed RNA samples from infected tissues. We propose an alternative procedure for array hybridization that may circumvent some problems resulting from variables associated with DNA microarrays.
Microarrays consist of in situ
/pre-synthesized oligonucleotides or spotted cDNA representing all or a portion of expressed genes in an organism arrayed onto chemically treated glass slides or any other solid surface (5
). Typically, transcripts from a variety of states are labeled with one of two dyes and pair-wise comparisons of relative changes in gene expression are estimated after co-hybridization to the same set of spotted arrays. There are a number of methods used to normalize these pair-wise comparisons. Current protocols for microarray data normalization use a ‘control’ RNA sample from a particular tissue or time point (RNA normalization), a pool of ‘grouped’ RNA samples from different tissues or different time points (6
), or a subset of control ‘reference’ genes (8
) of known transcription profile. There are several problems with these approaches. For example, only genes with hybridization signals from both RNA samples can be used to generate relative expression levels. Signals observed from only one RNA sample are discarded. Under some growth conditions, the transcription levels of some genes will be undetectable (or very low), resulting in unmeasurable relative expression levels. Furthermore, for microbial systems, the ‘grouped RNA normalization’ procedure may require pooling RNA from 20 or 30 experimental conditions at different growth phases. Comparisons of results to any new experimental condition would require a new control pool or a new set of hybridizations. Alternatively, using ‘control genes’ for microarray data normalization is subject to the problem of choosing the right control genes, especially when even ‘housekeeping genes’ can fluctuate under some experimental conditions (9
). Even when a set of reference genes or an RNA pool is agreed upon for array analysis, the production of such control samples may vary from one experiment to another and from one laboratory to another.
In response to these problems, we have explored an alternative procedure for array hybridization. In this procedure, hybridization signals from cDNA (prepared from total RNA) are normalized to signals generated from genomic DNA (gDNA) from the same organism. The proposed normalization protocol was applied to cultures of M.tuberculosis grown to either logarithmic or stationary phase. We found that a higher reproducibility and wider dynamic range are achievable using genomic normalization compared to an RNA normalization protocol.