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

cDNA-amplified fragment length polymorphism to study the transcriptional responses of Lactobacillus rhamnosus growing in cheese-like medium

AIMS

Lactobacillus rhamnosus is a dominant species during Parmigiano Reggiano cheese ripening and exhibits a great adaptability to unfavourable growth conditions. Gene expression of a Lact. rhamnosus, isolated from Parmigiano Reggiano cheese, grown in a rich medium (MRS) and in a cheese-like medium (CB) has been compared by a novel cDNA-amplified fragment length polymorphism (cDNA-AFLP) protocol.

METHODS AND RESULTS

Two techniques, capillary and gel electrophoresis cDNA-AFLP, were applied to generate unique transcript tags from reverse-transcribed messenger RNA using the immobilization of biotinylated 3'-terminal cDNA fragments on streptavidin-coated Dynabeads. The use of three pairs of primers allowed detecting 64 genes expressed in MRS and 96 in CB. Different transcripts were observed when Lact. rhamnosus was cultured on CB and MRS.

CONCLUSIONS

The cDNA-AFLP approach proved to be able to show that Lact. rhamnosus modifies the expression of a large part of genes when cultivated in CB compared with growth under optimal conditions (MRS). In particular, the profiles of the strain grown in CB were more complex probably because the cells activate different metabolic pathways to generate energy and to respond to the environmental changes.

SIGNIFICANCE AND IMPACT OF STUDY

This is the first research on Lact. rhamnosus isolated from cheese and represents one of the few concerning bacterial transcriptomic analysis towards cDNA-AFLP approaches.

Components of variance in transcriptomics based on electrophoretic separation of cDNA fragments (cDNA-AFLP)

The sources of variance and errors in transcriptomics based on the electrophoretic separation of amplified cDNA fragments were investigated using cDNA-amplified fragment length polymorphism (AFLP). Transcriptome profiles of the plant-pathogenic fungus Verticillium longisporum were generated by a standard cDNA-AFLP protocol followed by electrophoretic separation of amplified DNA fragments in flatbed polyacrylamide gels with fluorescence detection as well as by capillary electrophoresis (DNA sequencer). The total variance was partitioned into contributions of cDNA synthesis, adapter ligation, preamplification, amplification, and electrophoresis. Parameters of computer-aided peak recognition and matching were investigated and strategies improving matching success based on double passage with different signal intensity thresholds were developed. The overall quality of data was similar for cDNA-AFLP and microarray hybridization. Variance of cDNA-AFLP was independent of signal intensity, whereas microarray data showed higher variance for low-intensity signals. Capillary electrophoresis significantly reduced the number of wrongly matched and unmatched signals as compared with flatbed gels. These results are also likely to apply to related electrophoresis-based transcriptome analysis techniques such as mRNA differential display.

Application of a clustering-based peak alignment algorithm to analyze various DNA fingerprinting data

DNA fingerprinting analysis such as amplified ribosomal DNA restriction analysis (ARDRA), repetitive extragenic palindromic PCR (rep-PCR), ribosomal intergenic spacer analysis (RISA), and denaturing gradient gel electrophoresis (DGGE) are frequently used in various fields of microbiology. The major difficulty in DNA fingerprinting data analysis is the alignment of multiple peak sets. We report here an R program for a clustering-based peak alignment algorithm, and its application to analyze various DNA fingerprinting data, such as ARDRA, rep-PCR, RISA, and DGGE data. The results obtained by our clustering algorithm and by BioNumerics software showed high similarity. Since several R packages have been established to statistically analyze various biological data, the distance matrix obtained by our R program can be used for subsequent statistical analyses, some of which were not previously performed but are useful in DNA fingerprinting studies.

Ranking differentially expressed genes from Affymetrix gene expression data: methods with reproducibility, sensitivity, and specificity

Background

To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility.

Results

We compared eight conventional methods for ranking genes: weighted average difference (WAD), average difference (AD), fold change (FC), rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT) with six preprocessing algorithms (PLIER, VSN, FARMS, multi-mgMOS (mmgMOS), MBEI, and GCRMA). A total of 36 real experimental datasets was evaluated on the basis of the area under the receiver operating characteristic curve (AUC) as a measure for both sensitivity and specificity. We found that the RP method performed well for VSN-, FARMS-, MBEI-, and GCRMA-preprocessed data, and the WAD method performed well for mmgMOS-preprocessed data. Our analysis of the MicroArray Quality Control (MAQC) project's datasets showed that the FC-based gene ranking methods (WAD, AD, FC, and RP) had a higher level of reproducibility: The percentages of overlapping genes (POGs) across different sites for the FC-based methods were higher overall than those for the t-statistic-based methods (modT, samT, shrinkT, and ibmT). In particular, POG values for WAD were the highest overall among the FC-based methods irrespective of the choice of preprocessing algorithm.

Conclusion

Our results demonstrate that to increase sensitivity, specificity, and reproducibility in microarray analyses, we need to select suitable combinations of preprocessing algorithms and gene ranking methods. We recommend the use of FC-based methods, in particular RP or WAD.

Improved coverage of cDNA-AFLP by sequential digestion of immobilized cDNA

BACKGROUND

cDNA-AFLP is a transcriptomics technique which does not require prior sequence information and can therefore be used as a gene discovery tool. The method is based on selective amplification of cDNA fragments generated by restriction endonucleases, electrophoretic separation of the products and comparison of the band patterns between treated samples and controls. Unequal distribution of restriction sites used to generate cDNA fragments negatively affects the performance of cDNA-AFLP. Some transcripts are represented by more than one fragment while other escape detection, causing redundancy and reducing the coverage of the analysis, respectively.

RESULTS

With the goal of improving the coverage of cDNA-AFLP without increasing its redundancy, we designed a modified cDNA-AFLP protocol. Immobilized cDNA is sequentially digested with several restriction endonucleases and the released DNA fragments are collected in mutually exclusive pools. To investigate the performance of the protocol, software tool MECS (Multiple Enzyme cDNA-AFLP Simulation) was written in Perl. cDNA-AFLP protocols described in the literature and the new sequential digestion protocol were simulated on sets of cDNA sequences from mouse, human and Arabidopsis thaliana. The redundancy and coverage, the total number of PCR reactions, and the average fragment length were calculated for each protocol and cDNA set.

CONCLUSION

Simulation revealed that sequential digestion of immobilized cDNA followed by the partitioning of released fragments into mutually exclusive pools outperformed other cDNA-AFLP protocols in terms of coverage, redundancy, fragment length, and the total number of PCRs. Primers generating 30 to 70 amplicons per PCR provided the highest fraction of electrophoretically distinguishable fragments suitable for normalization. For A. thaliana, human and mice transcriptome, the use of two marking enzymes and three sequentially applied releasing enzymes for each of the marking enzymes is recommended.

Analysis of manganese-regulated gene expression in the ligninolytic basidiomycete Ceriporiopsis subvermispora

In this work, we explore the use of the unbiased cDNA-AFLP strategy to identify genes involved in Mn(2+) homeostasis in Ceriporiopsis subvermispora. In this ligninolytic white-rot fungus, whose genome has not yet been sequenced, three Mn peroxidase genes responding to Mn(2+) have been characterized. Using cDNA-AFLP to identify transcript-derived fragments (TDFs), a total of 37 differentially expressed cDNA fragments were identified by comparing band intensities among cDNA-AFLP patterns obtained from mycelia from cultures supplemented with different concentrations of Mn(2+). Of 21 differentially expressed TDFs, nine were classified as upregulated, five as downregulated and seven as unregulated. Of these, six upregulated and two downregulated TDFs were selected for further characterization. The expected TDFs for the known Mn peroxidases were not isolated, but several genes encoding proteins related to protein sorting, storage and excretion of excess Mn(2+) were identified. Transcripts induced under Mn(2+) supplementation exhibited homologies to the elongation factor eEF3, a HDEL sequence binding protein and the ARD1 subunit of the N-acetyltransferase complex, among others. Overall, the results obtained in this study suggest a complex picture of Mn(2+) homeostasis and provide the possibility to search for common regulatory elements in the promoters of the novel putatively identified genes.

A weighted average difference method for detecting differentially expressed genes from microarray data

Background

Identification of differentially expressed genes (DEGs) under different experimental conditions is an important task in many microarray studies. However, choosing which method to use for a particular application is problematic because its performance depends on the evaluation metric, the dataset, and so on. In addition, when using the Affymetrix GeneChip^® system, researchers must select a preprocessing algorithm from a number of competing algorithms such as MAS, RMA, and DFW, for obtaining expression-level measurements. To achieve optimal performance for detecting DEGs, a suitable combination of gene selection method and preprocessing algorithm needs to be selected for a given probe-level dataset.

Results

We introduce a new fold-change (FC)-based method, the weighted average difference method (WAD), for ranking DEGs. It uses the average difference and relative average signal intensity so that highly expressed genes are highly ranked on the average for the different conditions. The idea is based on our observation that known or potential marker genes (or proteins) tend to have high expression levels. We compared WAD with seven other methods; average difference (AD), FC, rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT). The evaluation was performed using a total of 38 different binary (two-class) probe-level datasets: two artificial "spike-in" datasets and 36 real experimental datasets. The results indicate that WAD outperforms the other methods when sensitivity and specificity are considered simultaneously: the area under the receiver operating characteristic curve for WAD was the highest on average for the 38 datasets. The gene ranking for WAD was also the most consistent when subsets of top-ranked genes produced from three different preprocessed data (MAS, RMA, and DFW) were compared. Overall, WAD performed the best for MAS-preprocessed data and the FC-based methods (AD, WAD, FC, or RP) performed well for RMA and DFW-preprocessed data.

Conclusion

WAD is a promising alternative to existing methods for ranking DEGs with two classes. Its high performance should increase researchers' confidence in microarray analyses.