Tissue-specific mtDNA abundance from exome data and its correlation with mitochondrial transcription, mass and respiratory activity.

Eukaryotic cells contain a population of mitochondria, variable in number and shape, which in turn contain multiple copies of a tiny compact genome (mtDNA) whose expression and function is strictly coordinated with the nuclear one. mtDNA copy number varies between different cell or tissues types, both in response to overall metabolic and bioenergetics demands and as a consequence or cause of specific pathological conditions. Here we present a novel and reliable methodology to assess the effective mtDNA copy number per diploid genome by investigating off-target reads obtained by whole-exome sequencing (WES) experiments. We also investigate whether and how mtDNA copy number correlates with mitochondrial mass, respiratory activity and expression levels. Analyzing six different tissues from three age- and sex-matched human individuals, we found a highly significant linear correlation between mtDNA copy number estimated by qPCR and the frequency of mtDNA off target WES reads. Furthermore, mtDNA copy number showed highly significant correlation with mitochondrial gene expression levels as measured by RNA-Seq as well as with mitochondrial mass and respiratory activity. Our methodology makes thus feasible, at a large scale, the investigation of mtDNA copy number in diverse cell-types, tissues and pathological conditions or in response to specific treatments.

Identification of pathways directly regulated by SHORT VEGETATIVE PHASE during vegetative and reproductive development in Arabidopsis.

BACKGROUND: MADS-domain transcription factors play important roles during plant development. The Arabidopsis MADS-box gene SHORT VEGETATIVE PHASE (SVP) is a key regulator of two developmental phases. It functions as a repressor of the floral transition during the vegetative phase and later it contributes to the specification of floral meristems. How these distinct activities are conferred by a single transcription factor is unclear, but interactions with other MADS domain proteins which specify binding to different genomic regions is likely one mechanism. RESULTS: To compare the genome-wide DNA binding profile of SVP during vegetative and reproductive development we performed ChIP-seq analyses. These ChIP-seq data were combined with tiling array expression analysis, induction experiments and qRT-PCR to identify biologically relevant binding sites. In addition, we compared genome-wide target genes of SVP with those published for the MADS domain transcription factors FLC and AP1, which interact with SVP during the vegetative and reproductive phases, respectively. CONCLUSIONS: Our analyses resulted in the identification of pathways that are regulated by SVP including those controlling meristem development during vegetative growth and flower development whereas floral transition pathways and hormonal signaling were regulated predominantly during the vegetative phase. Thus, SVP regulates many developmental pathways, some of which are common to both of its developmental roles whereas others are specific to only one of them.

Cscan: finding common regulators of a set of genes by using a collection of genome-wide ChIP-seq datasets.

The regulation of transcription of eukaryotic genes is a very complex process, which involves interactions between transcription factors (TFs) and DNA, as well as other epigenetic factors like histone modifications, DNA methylation, and so on, which nowadays can be studied and characterized with techniques like ChIP-Seq. Cscan is a web resource that includes a large collection of genome-wide ChIP-Seq experiments performed on TFs, histone modifications, RNA polymerases and others. Enriched peak regions from the ChIP-Seq experiments are crossed with the genomic coordinates of a set of input genes, to identify which of the experiments present a statistically significant number of peaks within the input genes' loci. The input can be a cluster of co-expressed genes, or any other set of genes sharing a common regulatory profile. Users can thus single out which TFs are likely to be common regulators of the genes, and their respective correlations. Also, by examining results on promoter activation, transcription, histone modifications, polymerase binding and so on, users can investigate the effect of the TFs (activation or repression of transcription) as well as of the cell or tissue specificity of the genes' regulation and expression. The web interface is free for use, and there is no login requirement. Available at: http://www.beaconlab.it/cscan.