Unknown,Transcriptomics,Genomics,Proteomics

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Detecting novel genes with sparse arrays

ABSTRACT: Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently find genes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these methods are expensive and thus are not easily applicable, e.g., to finding genes that are expressed only in very specific conditions. We demonstrate a method for finding novel genes with sparse arrays, applying it on the 33.9 Mb genome of the filamentous fungus Trichoderma reesei. Our computational method does not require normalisations between arrays and it takes into account the multiple-testing problem typical for analysis of microarray data. In contrast to tiling arrays, that use overlapping probes, only one 25mer microarray oligonucleotide probe was used for every 100 b. Thus, only relatively little space on a microarray slide was required to cover the intergenic regions of a genome. The analysis was done as a by-product of a conventional microarray experiment with no additional costs. We found at least 23 good candidates for novel transcripts that could code for proteins and all of which were expressed at high levels. Candidate genes were found to neighbour ire1 and cre1 and many other regulatory genes. Our simple, low-cost method can easily be applied to finding novel species-specific genes without prior knowledge of their sequence properties. Experiment consists of three conditions relevant for study of protein secretion, growth rate D=0.03 h-1, growth rate D=0.06 h-1 and growth rate D=0.03 h-1 with high cell density. Each condition has three biological repeats thus there are nine chemostat cultivations.

ORGANISM(S): Hypocrea jecorina

SUBMITTER: Mikko Arvas

PROVIDER: E-GEOD-12960 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Detecting novel genes with sparse arrays.

Arvas Mikko M Haiminen Niina N Smit Bart B Rautio Jari J Vitikainen Marika M Wiebe Marilyn M Martinez Diego D Chee Christine C Kunkel Joe J Sanchez Charles C Nelson Mary Anne MA Pakula Tiina T Saloheimo Markku M Penttilä Merja M Kivioja Teemu T

Gene 20100805 1-2

Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently find genes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these metho ...[more]

PMID: 20691772

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Project description:Peroxynitrite is formed in macrophages by the diffusion-limited reaction of superoxide and nitric oxide. This highly reactive species is capable of causing both oxidative and nitrosative stress in Escherichia coli. Previous studies have focused on the reactions of peroxynitrite with specific proteins or the effects of peroxynitrite on the growth and viability of whole cells. This work shows for the first time the transcriptomic response of E. coli to peroxynitrite, highlighting specific areas targeted by the stress. Upregulation of the cysteine biosynthesis pathway and subsequent identification of an increase in S-nitrosothiol levels suggests S-nitrosylation as a consequence of peroxynitrite exposure. Genes involved in the assembly / repair of iron-sulfur clusters also show enhanced transcription identifying another target of this reactive species. Unexpectedly arginine biosynthesis gene transcription levels were also elevated after treatment with peroxynitrite. Analysis of the negative regulator for these genes, ArgR, showed that the post-translational nitration of tyrosine residues within this protein is responsible for its degradation in vitro. Further upregulation is seen in oxidative stress response genes including katG and ahpCF. Probabilistic modelling of this data identified 5 altered transcription factors in response to peroxynitrite exposure including OxyR and ArgR. Hydrogen peroxide can be present as a contaminant in commercially available peroxynitrite preparations. Transcriptomic analysis of cells treated with hydrogen peroxide also showed an upregulation of oxidative stress response genes; however it did not show increased transcription of many other genes which are upregulated by peroxynitrite suggesting that cellular responses to peroxynitrite and hydrogen peroxide are distinct. Biological experiments (i.e. a comparison of control and plus peroxynitrite cells) were carried out three times, and a dye swap performed for each experiment, providing two technical repeats for each of the three biological repeats. Data from the independent experiments were combined. Genes that were differentially expressed ≥ twofold and displayed and P value of < 0.05 (as determined by a t test) were defined as being statistically significantly differentially transcribed. Additional biological experiments (i.e. a comparison of control and plus hydrogen peroxide cells) were carried out three times, and a dye swap performed for each experiment, providing two technical repeats for each of the three biological repeats. Data from the independent experiments were combined. Genes that were differentially expressed ≥ twofold and displayed and P value of < 0.05 (as determined by a t test) were defined as being statistically significantly differentially transcribed.

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Detecting novel genes with sparse arrays

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Detecting novel genes with sparse arrays.

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