Fungal gene expression levels do not display a common mode of distribution

BMC Research Notes

Minou Nowrousian¹ 

[1]Lehrstuhl für Allgemeine und Molekulare Botanik, Ruhr-Universität Bochum, 44780 Bochum, Germany

关键词: Zipf’s law;    Bimodal distribution;    Gene expression distribution;    Fungi;    RNA-seq;   

Others  :  1135080
DOI  :  10.1186/1756-0500-6-559

 received in 2013-09-20, accepted in 2013-12-23,  发布年份 2013

【 摘 要 】

Background

RNA-seq studies in metazoa have revealed a distinct, double-peaked (bimodal) distribution of gene expression independent of species and cell type. However, two studies in filamentous fungi yielded conflicting results, with a bimodal distribution in Pyronema confluens and varying distributions in Sordaria macrospora. To obtain a broader overview of global gene expression distributions in fungi, an additional 60 publicly available RNA-seq data sets from six ascomycetes and one basidiomycete were analyzed with respect to gene expression distributions.

Results

Clustering of normalized, log₂-transformed gene expression levels for each RNA-seq data set yielded distributions with one to five peaks. When only major peaks comprising at least 15% of all analyzed genes were considered, distributions ranged from one to three major peaks, suggesting that fungal gene expression is not generally bimodal. The number of peaks was not correlated with the phylogenetic position of a species; however, higher filamentous asco- and basidiomycetes showed up to three major peaks, whereas gene expression levels in the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe had only one to two major peaks, with one predominant peak containing at least 70% of all expressed genes. In several species, the number of peaks varied even within a single species, e.g. depending on the growth conditions as evidenced in the one to three major peaks in different samples from Neurospora crassa. Earlier studies based on microarray and SAGE data revealed distributions of gene expression level that followed Zipf’s law, i.e. log-transformed gene expression levels were inversely proportional to the log-transformed expression rank of a gene. However, analyses of the fungal RNA-seq data sets could not identify any that confirmed to Zipf’s law.

Conclusions

Fungal gene expression patterns cannot generally be described by a single type of distribution (bimodal or Zipf’s law). One hypothesis to explain this finding might be that gene expression in fungi is highly dynamic, and fine-tuned at the level of transcription not only for individual genes, but also at a global level.

【 授权许可】

   
2013 Nowrousian; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]Furusawa C, Kaneko K: Zipf’s law in gene expression. Phys Rev Lett 2003, 90:088102.
• [2]Hoyle DC, Rattray M, Jupp R, Brass A: Making sense of microarray data distributions. Bioinf 2002, 4:576-584.
• [3]Ueda HR, Hayashi S, Matsuyama S, Yomo T, Hashimoto S, Kay SA, Hogenesch JB, Iino M: Universality and flexibility in gene expression from bacteria to human. Proc Natl Acad Sci U S A 2004, 101:3765-3769.
• [4]Lu C, King RD: An investigation into the population abundance distribution of mRNAs, proteins and metabolites in biological systems. Bioinf 2009, 25:2020-2027.
• [5]Lu T, Costello CM, Croucher PJP, Häsler R, Deuschl G, Schreiber S: Can Zipf’s law be adapted to normalize microarrays? BMC Bioinf 2005, 5:37.
• [6]Ramsköld D, Wang ET, Burge CB, Sandberg R: An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data. PLoS Comp Biol 2009, 5:e1000598.
• [7]Nowrousian M: Next-generation sequencing techniques for eukaryotic microorganisms: sequencing-based solutions to biological problems. Eukaryot Cell 2010, 9:1300-1310.
• [8]Garber M, Grabherr MG, Guttman M, Trapnell C: Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods 2011, 8:469-477.
• [9]Hebenstreit D, Fang M, Gu M, Charoensawan V, van Oudenaarden A, Teichmann SA: RNA sequencing reveals two major classes of gene expression levels in metazoan cells. Mol Syst Biol 2011, 7:497.
• [10]Frenkel-Morgenstern M, Lacroix V, Ezkurdia I, Levin Y, Gabashvili A, Prilusky J, del Pozo A, Tress M, Johnson R, Guigo R, Valencia A: Chimeras taking shape: potential functions of proteins encoded by chimeric RNA transcripts. Genome Res 2012, 22:1231-1242.
• [11]Teichert I, Wolff G, Kück U, Nowrousian M: Combining laser microdissection and RNA-seq to chart the transcriptional landscape of fungal development. BMC Genomics 2012, 13:511. BioMed Central Full Text
• [12]Traeger S, Altegoer F, Freitag M, Gabaldon T, Kempken F, Kumar A, Marcet-Houben M, Pöggeler S, Stajich JE, Nowrousian M: The genome and development-dependent transcriptomes of Pyronema confluens: a window into fungal evolution. PLoS Genet 2013, 9:e1003820.
• [13]Coradetti ST, Craig JP, Xiong Y, Shock T, Tian C, Glass NL: Conserved and essential transcription factors for cellulase gene expression in ascomycete fungi. Proc Natl Acad Sci U S A 2012, 109:7397-7402.
• [14]Nookaew I, Papini M, Pornputtapong N, Scalcinati G, Fagerberg L, Uhlén M, Nielsen J: A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucl Acids Res 2012, 40:10084-10097.
• [15]Ohm RA, de Jong JF, de Bekker C, Wösten HAB, Lugones LG: Transcription factor genes of Schizophyllum commune involved in regulation of mushroom formation. Mol Microbiol 2011, 81:1433-1445.
• [16]Tisserant E, Da Silva C, Kohler A, Morin E, Wincker P, Martin F: Deep RNA sequencing improved the structural annotation of the Tuber melanosporum transcriptome. New Phytol 2011, 189:883-891.
• [17]Wang B, Guo G, Wang C, Lin Y, Wang X, Zhao M, Guo Y, He M, Zhang Y, Pan L: Survey of the transcriptome of Aspergillus oryzae via massively parallel mRNA sequencing. Nucl Acids Res 2010, 38:5075-5087.
• [18]Wilhelm BT, Marguerat S, Watt S, Schubert F, Wood V, Goodhead I, Penkett CJ, Rogers J, Bähler J: Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 2008, 453:1239-1243.
• [19]Yu J, Fedorova ND, Montalbano BG, Bhatnagar D, Cleveland TE, Bennett JW, Nierman WC: Tight control of mycotoxin biosynthesis gene expression in Aspergillus flavus by temperature as revealed by RNA-Seq. FEMS Microbiol Lett 2011, 322:145-149.
• [20]Hebenstreit D, Teichmann SA: Analysis and simulation of gene expression profiles in pure and mixed cell populations. Phys Biol 2011, 8:035013.
• [21]Hodgins-Davis A, Townsend JP: Evolving gene expression: from G to E to G x E. Trends Ecol Evol 2009, 24:649-658.
• [22]Stajich JE, Block D, Boulez K, Brenner SE, Chervitz SA, Dagdigian C, Fuellen G, Gilbert JGR, Korf I, Lapp H, Lehväslaiho H, Matsalla C, Mungall CJ, Osborne BI, Pocock MR, Schattner P, Senger M, Stein LD, Stupka E, Wilkinson MD, Birney E: The Bioperl Toolkit: Perl modules for the life sciences. Genome Res 2002, 12(10):1611-1618.
• [23]Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinf 2009, 25:1105-1111.
• [24]Langmead B, Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods 2012, 9:357-359.
• [25]Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup: The sequence alignment/map format and SAMtools. Bioinf 2009, 25(16):2078-2079.
• [26]Fraley C, Raftery AE: Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc 2002, 97:611-631.