- Research article
- Open Access
Selection and evaluation of reference genes for improved interrogation of microbial transcriptomes: case study with the extremophile Acidithiobacillus ferrooxidans
© Nieto et al; licensee BioMed Central Ltd. 2009
- Received: 30 October 2008
- Accepted: 25 June 2009
- Published: 25 June 2009
Normalization is a prerequisite for accurate real time PCR (qPCR) expression analysis and for the validation of microarray profiling data in microbial systems. The choice and use of reference genes that are stably expressed across samples, experimental conditions and designs is a key consideration for the accurate interpretation of gene expression data.
Here, we evaluate a carefully selected set of reference genes derived from previous microarray-based transcriptional profiling experiments performed on Acidithiobacillus ferrooxidans and identify a set of genes with minimal variability under five different experimental conditions that are frequently used in Acidithiobacilli research. Suitability of these and other previously reported reference genes to monitor the expression of four selected target genes from A. ferrooxidans grown with different energy sources was investigated. Utilization of reference genes map, rpoC, alaS and era results in improved interpretation of gene expression profiles in A. ferrooxidans.
This investigation provides a validated set of reference genes for studying A. ferrooxidans gene expression under typical biological conditions and an initial point of departure for exploring new experimental setups in this microorganism and eventually in other closely related Acidithiobacilli. The information could also be of value for future transcriptomic experiments in other bacterial systems.
- Reference Gene
- Candidate Reference Gene
- Pairwise Variation
- Stable Reference Gene
- Basal Salt Medium
Gene expression interrogation, both at single-gene (classical gene expression analysis) and genome-wide (global transcriptome analysis) levels, are prominent fields of study. By providing quantitative measures of mRNA changes, it has the extraordinary potential of identifying the functional consequences of genetic variability and environmental influence. Accurate quantification of gene expression is providing great insight into the physiology and metabolic complexity of microbes and their consortia and is thus contributing to our understanding of both fundamental and applied issues of major interest.
Microarray transcript profiling is the most widely used technique to evaluate global gene expression in microbial systems. However, due to methodological uncertainties inherent to the technique, it is imperative to validate the expression of key genes by an alternative procedure and quantitative real-time PCR (qPCR) has become the method of choice. qPCR is accurate, exhibits a broad dynamic range, and is sensitive and reproducible [1–6]. However, when performing qPCR, several parameters need to be controlled in order to obtain accurate and reliable expression measurements; these include variations in the amounts of starting material between samples, RNA extraction efficiency, RNA integrity/quality, efficiency of cDNA synthesis, and differences in the overall transcriptional activity of the cells analyzed .
The most frequently used strategy to control for such variations is relative normalization, where the expression of a target gene is measured with respect to total RNA, rRNA or a stably expressed internal reference gene. The use of rRNA or other reference gene has the advantage that their expression permits normalization against the cumulative errors of the entire process . Ideally, the reference gene should be universally valid, expressed stably and at a similar level across all samples, cells, experimental treatments, and designs. Unfortunately, no such reference gene has been identified [9–12] and even widely used control genes have proven unsuitable in certain situations [6, 13, 14].
Housekeeping genes are usually chosen as reference genes both in eukaryotes  and prokaryotes  because they are assumed: a) to be essential, b) to be ubiquitous, c) not to be regulated or influenced by the experimental procedure and d) to be expressed at similar levels in different types of cells. However, it is becoming increasingly clear that some commonly chosen housekeeping genes vary considerably across cell types, with time, or due to experimental treatment . This may be explained partially by the fact that some housekeeping proteins participate in other functions as well as in basal cell metabolism [17–19].
Therefore, for an accurate comparison of mRNA transcription in different samples, either validated reference genes are required for normalization or new ones should be determined empirically for each experimental system and condition studied [20, 21]. Here, we evaluate a carefully selected set of reference genes for improving the interpretation of gene expression profiles in a model microorganism, Acidithiobacillus ferrooxidans. The implementation of high-throughput microarray analyses of A. ferrooxidans [22, 23] and PCR techniques for expression profiling [24–37], have greatly enhanced our understanding of the genetic and physiological potential of this bioleaching bacterium. However, suitable reference genes to evaluate gene expression have not previously been identified in A. ferrooxidans.
In order to address this lacuna, we screened high-density oligonucleotide array-based expression profiles available for A. ferrooxidans and identified a set of nine genes with minimal variability under different experimental setups. Quantitative real-time PCR was then used to determine the mRNA levels of these genes, comparing their transcription in five different experimental conditions. Finally, we evaluated the suitability of these and other previously reported reference genes to monitor the expression of four selected target genes from A. ferrooxidans grown with different energy sources. This study defines reference genes for normalization of gene expression for future research, sparing other researchers in this and related fields from cumbersome and time-consuming screenings for an ideal reference gene, provided that they verify the stability of these candidates under their conditions of study.
Selection of candidate reference genes
1) A genome-wide bioinformatic identification of candidate reference genes
An initial set of candidate reference genes was compiled from a gene list that includes "housekeeping genes" of wide phylogenetic distribution . This set was used to textmine the A. ferrooxidans ATCC 23270 genome (GenBank/EMBL/DDBJ accession number CP001219) . Due to difference in ontological descriptions of gene function, not all genes could be recovered by textmining and additional candidates were identified in the A. ferrooxidans genome by BLASTP and TBLASTX searches using the housekeeping genes as queries. The combined set of candidate reference genes was then used to formulate bidirectional BLASTP and TBLASTX searches of the genomes of A. thiooxidans and A. caldus. This search for well conserved orthologs across Acidithiobacilli was performed in order to better define the set of essential genes for this bacterial genus. Such an approach will promote the ability to carry out future comparative gene expression studies within the Acidithiobacilli. Only genes present in all three genomes were accepted and provided the initial bioinformatic compilation of candidate reference genes (Additional File 1).
2) The selection of stably expressed candidate reference genes for A. ferrooxidans
The expression profiles for the set of candidate reference genes was then evaluated in three different growth conditions of A. ferrooxidans (iron, pH 1.6 vs. sulfur, pH 3.5, iron-sulfur mixture, pH 1.6 vs. sulfur, pH 1.6 and high iron, pH 1.6 vs. low iron, pH 1.6; see Methods for more details). Candidate reference genes that exhibited non-differential expression (log ratio expression |M| < 1.5) and had the most similar level of expression (log ratio expression M~0) between every pair of conditions in all three experiments were further selected (Additional file 2).
3) Removal of redundant candidate reference genes
The genetic context of these candidate reference genes in the genome of A. ferrooxidans was evaluated using the DNA sequence viewer and annotation tool Artemis v.10 . In the case where more than one candidate gene belonged to the same operon or gene cluster, only one gene was selected for further experimental validation. Also, only one candidate was chosen from genes belonging to the same functional category as defined by TIGRfams . This reduced redundancy in the set.
4) Evaluation of the expression profiles of the candidate reference genes by real time PCR
The expression of these selected candidate reference genes was analyzed by quantitative PCR in order to evaluate if the stability of expression observed in microarray experiments was supported by more sensitive and rigorous evaluation methods. Transcriptional levels were compared by assessing Ct values of each gene for two of the former experimental conditions (iron pH 1.6 and sulfur pH 3.5) and three new ones (sulfur pH 2.5, sulfur pH 4.5 and thiosulfate pH 4.5, see Methods for further detail). These conditions are frequently used in the laboratory to study the biology of Acidthiobacilli because they simulate environmental conditions. Expression values (Ct) of the selected reference genes and their dispersion are plotted in Additional file 3.
The combined bioinformatics and experimental strategy identified nine candidate reference genes (coaE, era, gmk, gyrA, map, nth, rplI, rpoC, trpS).
Expression stability of candidate reference genes
Expression stability ranking of candidate reference genes.
Expression stability of reference genes previously used in studies of A. ferrooxidans
Three genes, recA, alaS and rrs (16S rRNA) have been used in prior studies as internal controls for experimental investigations in A. ferrooxidans [28, 30, 44, 45], but there has been no formal report showing that they are reliable references. Conversely, there is evidence showing differential expression of recA and rrs under cellular stress  and starvation [45, 47]. In addition, use of rrs as a reference gene has been challenged because it is a very abundant species of RNA present at concentrations outside most calibration ranges .
Expression stability ranking of previously reported control genes.
It can be concluded that, among the previously used reference genes in A. ferrooxidans, only alaS is suitable and can be used with confidence as a normalizer. Given the variability observed in the present study, use of recA as a normalizer is not recommended as it would introduce noise to the analysis and eventually produce misleading results. In addition, use of rrs as a reference gene is not recommended despite its stable expression in the five conditions analyzed because its abundance prejudices the analysis of lowly abundant transcripts.
Use of multiple reference genes for improved normalization
Use of selected reference genes to normalize expression of differentially expressed genes in Fe-S cells
To assess the value of our study, the relative expression levels of selected target genes was analyzed using the following normalization strategies: a) the three best reference genes selected by geNorm and Normfinder rpoC, era and alaS were used individually, b) a NF derived from the combination of the three genes selected by geNorm, rpoC, map and alaS (NF1), c) a NF derived from the combination of the top ranking genes selected by geNorm and Normfinder method rpoC, map and era (NF2) or d) the frequently cited reference genes rrs and recA. For this purpose, four target genes were selected that are known to be differentially expressed in A. ferrooxidans cultures: a) sdrAI (AFE0007) is 95 fold induced in iron , b) cyoB (AFE2407) is 12 fold induced in sulfur , c) cbbOIa (AFE1408) is 3 fold induced in iron  and d) mntH (AFE2920) is 24 fold induced in sulfur (unpublished results).
These results demonstrate how the interpretation of bacterial gene expression levels can be affected by the choice of the reference genes in quantitative real-time RT-PCR analysis. If a single gene is to be used, e.g. in studies where only one or a few target genes are being evaluated, the reference gene should be one of the three validated stable reference genes rpoC, era or alaS. In investigations where a larger number of target genes are to be evaluated or a higher degree of confidence is desired, use of the NF derived from the pool of the genes rpoC, map and alaS or era is advisable.
Normalization is a prerequisite for accurate real time PCR expression profiling. Significant random fluctuations or, even worse, directional changes in the expression of chosen reference genes between samples, can lead to the lack of detection of small differences between genes of interest or to erroneous results. Therefore, it is extremely important to find appropriate reference genes with minimal variability. This cumbersome task is often avoided and frequently priorly used reference genes are assumed to be good normalizers without further evaluation in unexplored experimental setups.
The geometric mean of few carefully selected genes, rpoC, map, alaS and/or era, is demonstrated to be the best normalizer for A. ferrooxidans in the diverse experimental conditions used in this study. Use of a single gene for normalization, instead, may result in relatively large variations in target gene expression and significant errors depending on the gene in question and the experimental setup, as showed to be the case when using the recA gene. Conversely, it is suggested that rpoC, era or alaS could be used as normalizers if only one reference gene is strictly necessary. Since ribosomal RNA is much more abundant than most target mRNA transcripts and its quantification falls outside most calibration ranges, the use of rrs is not recommended especially for the measurements of low abundance transcripts.
Whatever strategy is used to normalize for differences in quality and quantity of input RNA it must be validated for a particular experimental model on an individual basis. This investigation provides a validated set of reference genes for those studying A. ferrooxidans gene expression under typical biological conditions and an initial point of departure for those exploring new experimental setups in this microorganism or other closely related Acidithiobacilli or possibly also in other bacterial models.
Bioinformatic selection of candidate reference genes
An initial set of candidate reference genes was compiled from a gene list that includes "housekeeping genes" of wide phylogenetic distribution . This set was used to textmine the A. ferrooxidans ATCC 23270 genome (GenBank/EMBL/DDBJ accession number CP001219) . Additional candidates were identified in the A. ferrooxidans genome by BLASTP and TBLASTX searches. The combined set of candidate genes was then used to formulate bidirectional BLASTP and TBLASTX searches of the genomes of A. thiooxidans and A. caldus. Genomic context for genes present in all three genomes was analyzed using the DNA sequence viewer and annotation tool Artemis v.10 . Candidate genes belonging to the same predicted operon or gene cluster were excluded from further analysis. Reference genes were classified by function using TIGRfams  and one gene per functional category was selected. These last two steps were included to reduce redundancy in the gene set.
Bacterial strains and growth conditions
Gene expression was evaluated under the following experimental conditions: sulfur at pH 2.5, pH 3.5 and pH 4.5; thiosulfate at pH 4.5 and ferrous iron 200 mM pH 1.6. A. ferrooxidans strain ATCC 23270 was grown in modified 9 K basal salt media (0.7 mM (NH4)2SO4, 0.2 mM K2HPO4, 1.6 mM MgSO4.7H2O) containing iron (9 K + Fe: 200 mM FeSO4; adjusted to pH 1.6 with H2SO4) or sulfur (9 K + S: 1% ethanol-sterilized powdered sulfur, adjusted to pH 2.5; 3.5 and 4.5 with H2SO4). DSMZ71 medium was used for thiosulfate growth (20 mM Na2S2O3.5H2O, 22 mM KH2PO4, 2 mM MgSO4.7H2O, 22 mM (NH4)2SO4 and 1.7 mM CaCl2.2H2O). All cultures were incubated at 30°C under aerobic conditions on a rotary shaker at 150 r.p.m.
A. ferrooxidans cultures to be used for nucleic acid purification were harvested at 8000 r.p.m. for 10 min. The cell pellet was washed in 9 K basal salt solution (adjusted at the corresponding pH). Washed cells were collected by centrifugation at 12000 r.p.m. for 10 min.
A. ferrooxidans cultures were grown for 72 h until stationary phase. DNA isolation was carried out by phenol-chloroform extraction. Briefly, cells were collected and resuspended in buffer TE (25:10) pH 8.0 with 5 mg/ml lysozyme, and incubated at 37°C for 30 minutes, followed by another hour of incubation at the same temperature with 1% SDS and 0.2 mg/ml proteinase K. Cell lysis was completed by alternate shifting of the suspension from 80°C to -80°C. DNA extraction was performed twice with a mixture of phenol:chloroform:isoamylic alcohol (25:24:1). Removal of the residual phenol was accomplished by one treatment with a mixture of chloroform:isoamylic alcohol (24:1). The DNA contained in the final aqueous phase was precipitated overnight at -20°C with absolute ethanol, washed with 70% ethanol, and finally resuspended in sterilized water. Genomic DNA quality and integrity were assessed by 1% (w/v) agarose gel electrophoresis and standard PCR, and concentration was determined by absorbance at 260 nm.
Total RNA isolation
RNA was isolated from A. ferrooxidans mid-logarithmic cultures grown in modified 9 K basal salt medium in the presence of iron 200 mM, sulfur (1%, pH 2.5, 3.5 and 4.5) or 0.5% thiosulfate. Briefly, cells were collected and resuspended in ice-cold buffer TE (25:10) pH 8.0 with 1× Extraction Buffer (per liter: 1% SDS, 50 mM Tris-HCl pH 8.0, and 2 mM EDTA). Cell lysis was accomplished by incubation at 100°C for 5 minutes. The suspension was treated with TRIzol (Invitrogen), and the recovered aqueous phase was treated with chloroform followed by two extractions with acid phenol and chloroform. RNA was precipitated with absolute ethanol overnight at -20°C, washed with 70% ethanol, and finally resuspended in sterilized water. Samples were treated with DNase and purified with the Roche High Pure RNA Isolation Kit, following the manufacturer's recommendations and checked for DNA contamination by standard PCR, including a genomic DNA positive control. RNA quality was evaluated by 1.0% agarose gel electrophoresis and its concentration was measured by absorbance at 260 nm.
cDNA was prepared from 1 μg total RNA using random hexamers and Superscript II reverse transcriptase (Invitrogen) according to manufacturer instructions. The resulting cDNA was diluted 1:10 in distilled water and stored in aliquots at -20°C until further use.
Reference genes, qPCR primers and reaction parameters.
Q-PCR Linear correlation
Ct ± SD
Candidate reference genes
ACT ATC GCC CAT TGC TGG AT
TTG GTA ATG ATC CAG TCG GC
25.50 ± 0.69
CAT GGA TGA GAT CAA GAG CG
GTA TCC CGA GAA TCT GAT CC
22.56 ± 0.21
ATG GCA CTA GTG AAC CTT GG
AAT GAC CGT CTC CGA ATC CT
24.49 ± 0.50
TAC CTC GAT TAC GCC ATG AG
TGT CAT AGA CAG CGG TAT CG
26.24 ± 0.45
TTA CCA CCG ATG AAC TGG AC
AGC CAT CCT TGA TAA CCG TG
20.64 ± 0.29
ATC GTC TGG GAC TGT TCA AC
TAT TGA GGA CGA CAT TGG CG
25.31 ± 0.40
GAT GCC ATG GTT ACG ATT GC
GAC CAT CAC ATC CAG TTC GA
21.68 ± 0.51
AAT GCG GTG TTG AGG TAA CC
AGG TAC TGG TCT TCG GTA AG
20.79 ± 0.23
TCT GCT CAT CGA ATG GTT GG
GAA TGT CTG CCG TCA TCA AC
23.86 ± 0.70
Reference genes used in prior studies
CTG GAA TCT GGT CTT CAT GC
GCT TGA AGA GAT CGG TGT CA
22.35 ± 0.39
CCG CCA ACA TTT CCC GGA CC
ACG CCG CGG TCC ACC AGT TC
29.41 ± 0.88
ACA CTG GGA CTG AGA CAC GG
ACC GCC TAC GCA CCC TTT AC
8.93 ± 0.27
Differentially expressed target genes
ACG GCA TTG AGC TAT ACC G
TCC GGG TCT AGT AGT GCA T
21.55 ± 0.72
CAA TTA CAT GGT CCA CAA CA
AGC GTA TAC ACC ACG ATA CC
28.11 ± 2.17
ATA TCG GTG CCG TCA TCA TG
GAG AGC TGA TGC ACC GTA TT
21.92 ± 2.02
GTT TGG GTG CTG AAG TAG TG
GCA ACA GTG GCA AAC AGG C
23.61 ± 2.82
Stability of gene expression and relative quantification
The stability of gene expression was evaluated using the Excel-based applications geNorm  and Normfinder  and the relative expression was calculated with qBase 1.3.5 . Briefly, the geNorm method is based on a pairwise comparison approach and depends on the calculation of an M value, defined as the average pairwise variation of a particular gene with all others. The NormFinder method is based on a different mathematic model that considers the intra- and inter-treatment variation in expression for gene ranking. The normalization factors were calculated following the criteria defined by Vandesompele . These include: a) to use the geometric mean (n numbers are multiplied and then the nth root of the resulting product is taken) as this controls better for possible outliers and abundance differences between genes and b) to define the minimum number of reference genes needed for a reliable calculation by evaluating the pairwise variation between sequential normalization factors including three, four, five or more stable reference genes (NFn/NFn+1).
Evaluation of reference gene expression by microarray transcript profiling
A. ferrooxidans gene expression was evaluated under three experimental conditions: (1) cells were grown in 9 K medium containing 62 mM FeSO4 at pH 1.6 versus cells grown in 9 K medium 1% elemental sulfur at pH 3.5 containing; (2) cells were grown in 9 K medium containing 62 mM FeSO4 plus 1% elemental sulfur at pH 1.6 versus cells grown in 9 K medium containing 1% elemental sulfur at pH 1.6 and (3) cells were grown in 9 K medium containing 200 mM FeSO4 at pH 1.6 versus cells grown in 9 K medium containing 62 mM FeSO4 at pH 1.6. Construction, experimental and data analysis protocols for A. ferrooxidans type strain specific oligonucleotide microarrays have been previously described  and deposited in the ArrayExpress database under the following accession numbers (A-MEXP-1478, A-MEXP-1479).
Work supported by Fondecyt 11060164, Fondecyt 1050063, Conicyt Basal CCTE PFB16, UNAB DI-3406-R, Innova 08CM01-03 and a Microsoft Sponsored Research Award.
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