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Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays.

ABSTRACT: BACKGROUND: Recent developments in high-throughput methods of analyzing transcriptomic profiles are promising for many areas of biology, including ecophysiology. However, although commercial microarrays are available for most common laboratory models, transcriptome analysis in non-traditional model species still remains a challenge. Indeed, the signal resulting from heterologous hybridization is low and difficult to interpret because of the weak complementarity between probe and target sequences, especially when no microarray dedicated to a genetically close species is available. RESULTS: We show here that transcriptome analysis in a species genetically distant from laboratory models is made possible by using MAXRS, a new method of analyzing heterologous hybridization on microarrays. This method takes advantage of the design of several commercial microarrays, with different probes targeting the same transcript. To illustrate and test this method, we analyzed the transcriptome of king penguin pectoralis muscle hybridized to Affymetrix chicken microarrays, two organisms separated by an evolutionary distance of approximately 100 million years. The differential gene expression observed between different physiological situations computed by MAXRS was confirmed by real-time PCR on 10 genes out of 11 tested. CONCLUSIONS: MAXRS appears to be an appropriate method for gene expression analysis under heterologous hybridization conditions.

SUBMITTER: Degletagne C

PROVIDER: S-EPMC2901317 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays.

Degletagne Cyril C Keime Céline C Rey Benjamin B de Dinechin Marc M Forcheron Fabien F Chuchana Paul P Jouventin Pierre P Gautier Christian C Duchamp Claude C

BMC genomics 20100531

<h4>Background</h4>Recent developments in high-throughput methods of analyzing transcriptomic profiles are promising for many areas of biology, including ecophysiology. However, although commercial microarrays are available for most common laboratory models, transcriptome analysis in non-traditional model species still remains a challenge. Indeed, the signal resulting from heterologous hybridization is low and difficult to interpret because of the weak complementarity between probe and target se ...[more]

PMID: 20509979

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Project description:BACKGROUND: Gene expression profiles of non-model mammals may provide valuable data for biomedical and evolutionary studies. However, due to lack of sequence information of other species, DNA microarrays are currently restricted to humans and a few model species. This limitation may be overcome by using arrays developed for a given species to analyse gene expression in a related one, an approach known as "cross-species analysis". In spite of its potential usefulness, the accuracy and reproducibility of the gene expression measures obtained in this way are still open to doubt. The present study examines whether or not hybridization values from cross-species analyses are as reproducible as those from same-species analyses when using Affymetrix oligonucleotide microarrays. RESULTS: The reproducibility of the probe data obtained hybridizing deer, Old-World primates, and human RNA samples to Affymetrix human GeneChip U133 Plus 2.0 was compared. The results show that cross-species hybridization affected neither the distribution of the hybridization reproducibility among different categories, nor the reproducibility values of the individual probes. Our analyses also show that a 0.5% of the probes analysed in the U133 plus 2.0 GeneChip are significantly associated to un-reproducible hybridizations. Such probes-called in the text un-reproducible probe sequences- do not increase in number in cross-species analyses. CONCLUSION: Our study demonstrates that cross-species analyses do not significantly affect hybridization reproducibility of GeneChips, at least within the range of the mammal species analysed here. The differences in reproducibility between same-species and cross-species analyses observed in previous studies were probably caused by the analytical methods used to calculate the gene expression measures. Together with previous observations on the accuracy of GeneChips for cross-species analysis, our analyses demonstrate that cross-species hybridizations may provide useful gene expression data. However, the reproducibility and accuracy of these measures largely depends on the use of appropriated algorithms to derive the gene expression data from the probe data. Also, the identification of probes associated to un-reproducible hybridizations-useless for gene expression analyses- in the studied GeneChip, stress the need of a re-evaluation of the probes' performance.

Project description:Background'Systems-wide' approaches such as microarray RNA-profiling are ideally suited to the study of the complex overlapping responses of plants to biotic and abiotic stresses. However, commercial microarrays are only available for a limited number of plant species and development costs are so substantial as to be prohibitive for most research groups. Here we evaluate the use of cross-hybridisation to Affymetrix oligonucleotide GeneChip(R) microarrays to profile the response of the banana (Musa spp.) leaf transcriptome to drought stress using a genomic DNA (gDNA)-based probe-selection strategy to improve the efficiency of detection of differentially expressed Musa transcripts.ResultsFollowing cross-hybridisation of Musa gDNA to the Rice GeneChip(R) Genome Array, ~33,700 gene-specific probe-sets had a sufficiently high degree of homology to be retained for transcriptomic analyses. In a proof-of-concept approach, pooled RNA representing a single biological replicate of control and drought stressed leaves of the Musa cultivar 'Cachaco' were hybridised to the Affymetrix Rice Genome Array. A total of 2,910 Musa gene homologues with a >2-fold difference in expression levels were subsequently identified. These drought-responsive transcripts included many functional classes associated with plant biotic and abiotic stress responses, as well as a range of regulatory genes known to be involved in coordinating abiotic stress responses. This latter group included members of the ERF, DREB, MYB, bZIP and bHLH transcription factor families. Fifty-two of these drought-sensitive Musa transcripts were homologous to genes underlying QTLs for drought and cold tolerance in rice, including in 2 instances QTLs associated with a single underlying gene. The list of drought-responsive transcripts also included genes identified in publicly-available comparative transcriptomics experiments.ConclusionOur results demonstrate that despite the general paucity of nucleotide sequence data in Musa and only distant phylogenetic relations to rice, gDNA probe-based cross-hybridisation to the Rice GeneChip(R) is a highly promising strategy to study complex biological responses and illustrates the potential of such strategies for gene discovery in non-model species.

Dataset Information

Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays.

Publications

Transcriptome analysis in non-model species: a new method for the analysis of heterologous hybridization on microarrays.

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