VALIDACIÓN DEL PLAN INSTITUCIONAL DE GESTIÓN DE RIESGOS

The DNA microarray technique is one of the most powerful techniques to study whole genome transcriptomics (study of the total RNA of an organism) (Kothapalliet al., 2002). The significant advantage of microarrays over other techniques is that transcription of all genes in an organism can be studied simultaneously in a single experiment (Hintonet al., 2004). This technique has enabled unraveling of the complexities of the host-pathogen interaction, response of an organism to particular substrate or certain type of stimuli and metabolic switch, to the comparative genomics of an organism and genomic diversity

required (Dorrellet al., 2001; Chalabiet al., 2007; Bornemanet al., 2010), therefore, determining the locations and the conditions under which a gene is transcribed allows inferences about its function. However, the disadvantage of the microarray technique is that it does not reflect any possible post-transcriptional events. Moreover, validation of the microarray results by reverse transcription quantitative PCR (RT-qPCR) is required to make the microarray findings meaningful.

A DNA microarray consists of a series of DNA targets (PCR products or oligonucleotide probes) immobilized on a solid substrate. These are then hybridized with fluorescently labelled target made from nucleic acids in the test sample, so as to allow analysis of the relative concentrations of mRNA (Schenaet al., 1996). The size of the PCR products may vary from 250 to 500 bp, whereas synthetic oligonucleotide probes are often between 25 to 75 bp in length (Rhodiuset al., 2002). In general, when comparing DNA transcription under two different conditions, RNA is prepared from the two samples to be compared (test and control), and labeled cDNA/cRNA is prepared by reverse

transcription, incorporating either Cyanine 3 (Cy3) or Cyanine 5 (Cy5) fluorescent dyes. The two labeled cDNAs are mixed and hybridized to the microarray (two colour

microarray), and the slide is washed, dried and scanned. In single or one-colour microarrays, the arrays provide intensity data for each probe or probe set, indicating a relative level of hybridization with the labeled target generated from a single condition only. With the use of image analysis software, signal intensities are determined for each dye for each spot of the array, and the logarithm of the ratio of Cy5 intensity to Cy3 intensity is calculated. Finally the data are analyzed by a variety of standard softwares such as, GeneSpring®(Agilent Technologies, USA), to determine the transcription pattern of genes under different conditions. A schematic representation of processes involved in the gene expression microarray is presented inFigure 1.8.

Figure 1.8. Schematic representation of processes involved in gene expression microarray. Adapted from: www.en.wikipedia.org/wiki/DNA_microarray_experiment

Minimum Information About a Microarray Experiment (MIAME) has been established (Brazma et al., 2001). MIAME describes the minimum information required to ensure that data generated from microarray experiments can be easily interpreted and that results derived from its analysis can be independently verified. The ultimate goal of MIAME is to establish a standard for recording and reporting microarray-based gene expression data, to facilitate the establishment of databases, public repositories and enable the development of data analysis tools.

Microarray data normalization and analysis has been an area of considerable discussion and has been one of many factors that have led to the considerable expansion of the field of bioinformatics (Fundelet al., 2008b; Fundelet al., 2008a). The very first step in microarray data analysis is background correction, where background fluorescence signals are removed from the spot fluorescence signals to have the actual signals coming from the hybridized target cDNA/cRNA. Any probes where the fluorescence signals are below the background are often removed from the analysis. However, in such a case genes expressed at low levels are missed. Data are normalized before analysis so that data obtained from different arrays in a particular experiment can be compared. Normalization compensates for technical differences between chips. There are several ways to normalize, such as using housekeeping genes, the expression of which remains quite stable under different experimental conditions (Reidet al., 2000; Quackenbush, 2002; Janduet al., 2009). If a housekeeping gene is not available, whole genomic DNA can be used for the purpose, with an assumption that in a whole genome, relatively few genes are expressed and the number of genes transcribed up and down are about equal (Padmanabhanet al., 2003). Since most of the genes remain unchanged, the mean transcript levels should be the same for each condition. Based on this theory, data are often normalized using the “total intensity-based global normalization” where the ratios

of intensity of both (fluorescent dye) channels are made equal across the array grid. In GeneSpring®the data are normalized based on a percentile distribution. In the default normalization settings in GeneSpring®, all the data are ranked according to their intensity. These data are first transformed into log2, and then a percentile shift to the 75thpercentile

for each array is carried out. This value is subtracted from all of the values on the array. Percentile normalization is based on the assumption that a certain level of expression values should be equal for all arrays. When the 75thpercentile is used, it is assumed that the expression level, below which 75% of the expression values are found, should be the same for all samples. A multiplicative factor is also applied to the data so that the chosen percentile is at the same expression value for all arrays (Fundelet al., 2008b).

Microarray gene expression studies are based on the hypothesis that gene expression changes are ‘biologically important’ (Kendallet al., 2004). What level of change in the expression of a gene will be considered as biologically important is an issue in

interpreting the findings of microarray experiments. The commonly used criterion is that the expression of a gene is considered as biologically significant if the expression ratio is 2 or more (Butcher, 2004). However, whether an expression ratio of less than 2 is

biologically insignificant remains a question for most microarray experiments, and is best addressed by statistical analysis, followed by further characterizationin vivo. Using a figures of “2” or similar as a cut-off arbitrary and is intuitively less useful than ensuring reproducibility of the data by appropriate replication and analysis.

Gene expression microarrays have been applied successfully to compare the whole genome transcriptional profiling of Azorhizobium caulinodansORS571 grown under free-living and symbiotic conditions (Tsukadaet al., 2009), the response ofBacillus

growth ofClostridium botulinum(Artinet al., 2010), the response ofListeria

monocytogenesto iron limitation (Ledalaet al., 2010) and the response ofPyrococcus furiosusgrown on carbohydrates or peptides (Schutet al., 2003). The microarray technique has also been applied successfully to study genes transcribed differentially across the whole genome of the methylotrophMethylibium petroleiphilumPM1 grown on methyl tert-butyl ether or ethanol (Hristovaet al., 2007). In addition, genes

transcribed differentially inMethylobacterium extorquensAM1 grown on methanol and succinate were also studied by the microarray technique (Okuboet al., 2007).