Differential expression analysis of cold olerance adaptation in D. buzzatii by RNA seq de novo approach

MOREYRA, N; MENSCH, J; HURTADO J; HASSON, E

Bahía Blanca

Congreso; VI Conference on Bioinformatics and Computational Biology; 2015

Universidad Nac del Sur

BackgroundMany traits and biological processes can beaffected by climatic changes and genetic variation has a major role in theecological adaptation to thermal changes during development steps [1]. Adaptationto these kinds of environments appears to involve a set of traits related toreproduction, stress resistance and metabolic processes [2]. We have performed aRNA-seq analysis in Drosophila buzzatii toinvestigate profiles of gene expression changes during female reproductivediapause [1]. Materialsand methodsThe study was conducted by exposing sets of females to three differentconditions of temperature. Two conditions were controls with little differencesbetween each other (named J25 and C) and the third implied the measure of coldtolerance (named D ? diapause state). We run all treatments and replicates simultaneously.We used the Trinity package software to generate a de novo RNA-seq assemblyfrom ~100 paired-end Illumina reads. Next, we mapped reads and Trinitytranscripts to a reference genome (obtained from Flybase) guided by the Trinityprotocol [3]. To analyze expression levels of the Trinity-reconstructedtranscripts, we aligned the RNA-seq reads data against the contigs (Trinitytranscripts), and then estimated the number of RNA-seq fragments that hadmapped to each contig. Taking into account the skew generated for the differenttranscript proportions in each sample, we applied the TMM normalization methodto generate the normalized FPKM expression values separately for each sampleand to get the differences in RNA composition. Thereby, we extractedtranscripts that were at least 6.25-fold differentially expressed at asignificance of 0.001(FDR) in any of the sample comparisons. A transcriptscluster dendrogram was created based on expression profiles. From these, weselected only two clusters involving overexpressed and underexpressed genes indiapause relative to other treatments. ResultsWe found 87 transcript differentially expressed into two clusters (Figure1). All transcripts were blasted against genome assembly, annotated genes andnon-redundant databases (Flybase and NCBI). The most relevant transcripts exhibitedhigh identity to chorion protein coding genes of D. melanogaster, Vitellinemembrane cysteine-rich domains, Heat Shock Protein (HSP) coding genes and criticalrespiratory chain genes of the mitochondrial genome (D. mojavensis).   Figure 1: Twoextracted clusters from transcripts with similar expression profiles thatmostly represent the different expression levels of transcripts. The subclusterA involves underexpressed and the subcluster B overexpressed genes in thediapause state.  ConclusionAll CDS were consistent with the initial hypothesis and therefore, forthe near future, we propose a validation by the Real-Time PCR (qPCR) method. Inaddition, examine the data set to find overexpressed genes across conditionswill allow us to determine which regions of the transcriptome preferentiallyparticipate depending on the environmental temperature. Reference1. Chen J,Nolte V, SchlöttererC: Temperature related reaction norms ofgene expression: regulatory architecture and functional implications. Molecular biology and evolution 2015,msv120.2. SchmidtP, Matzkin L, Ippolito M, Eanes W: Geographicvariation in diapause incidence, life-history traits, and climatic adaptationin drosophila melanogaster. Evolution 2005, 59(8):1721-1732.3. HassB, Papnicolaou A, Yassour M, Grabherr M, D Blood P, Bowden J, Couger M, EcclesD, Li B, Lieber M et al: De novotranscript sequence reconstruction from RNA-seq using the Trinity platform forreference generation and analysis. Nature Protocols 2013, VOL.8-NO.8.