Copy number variants (CNVs) are genomic rearrangements resulting from gains or losses of DNA segments. Typically, the term refers to rearrangements of sequences larger than 1 kb. This type of polymorphism has recently been shown to be a key contributor to intra-species genetic variation, along with single-nucleotide polymorphisms and short insertion-deletion polymorphisms. Over the last decade, a growing number of studies have highlighted the importance of copy number variation (CNV) as a factor affecting human phenotype and individual CNVs have been linked to risks for severe diseases. In plants, the exploration of the extent and role of CNV is still just beginning. Initial genomic analyses indicate that CNVs are prevalent in plants and have greatly affected plant genome evolution. Many CNV events have been observed in outcrossing and autogamous species. CNVs are usually found on all chromosomes, with CNV hotspots interspersed with regions of very low genetic variation. Although CNV is mainly associated with intergenic regions, many CNVs encompass protein-coding genes. The collected data suggest that CNV mainly affects the members of large families of functionally redundant genes. Thus, the effects of individual CNV events on phenotype are usually modest. Nevertheless, there are many cases in which CNVs for specific genes have been linked to important traits such as flowering time, plant height and resistance to biotic and abiotic stress. Recent reports suggest that CNVs may form rapidly in response to stress.

There is increasing evidence indicating that the production of small regulatory RNAs is not the only process in which ribonuclease Dicer can participate. For example, it has been demonstrated that this enzyme is also involved in chromatin structure remodelling, inflammation and apoptotic DNA degradation. Moreover, it has become increasingly clear that cellular transcript and protein levels of Dicer must be strictly controlled because even small changes in their accumulation can initiate various pathological processes, including carcinogenesis. Accordingly, in recent years, a number of studies have been performed to identify the factors regulating Dicer gene expression and protein activity. As a result, a large amount of complex and often contradictory data has been generated. None of these data have been subjected to an exhaustive review or critical discussion. This review attempts to fill this gap by summarizing the current knowledge of factors that regulate Dicer gene transcription, primary transcript processing, mRNA translation and enzyme activity. Because of the high complexity of this topic, this review mainly concentrates on human Dicer. This review also focuses on an additional regulatory layer of Dicer activity involving the interactions of protein and RNA factors with Dicer substrates.

Leaf senescence is a tightly regulated developmental or stress-induced process. It is accompanied by dramatic changes in cell metabolism and structure, eventually leading to the disintegration of chloroplasts, the breakdown of leaf proteins, internucleosomal fragmentation of nuclear DNA and ultimately cell death. In light of the global and intense reorganization of the senescing leaf transcriptome, measuring time-course gene expression patterns in this model is challenging due to the evident problems associated with selecting stable reference genes. We have used oligonucleotide microarray data to identify 181 genes with stable expression in the course of dark-induced senescence of barley leaf. From those genes, we selected 5 candidates and confirmed their invariant expression by both reverse transcription quantitative PCR and droplet digital PCR (ddPCR). We used the selected reference genes to normalize the level of the expression of the following senescence-responsive genes in ddPCR assays: SAG12, ICL, AGXT, CS and RbcS. We were thereby able to achieve a substantial reduction in the data variability. Although the use of reference genes is not considered mandatory in ddPCR assays, our results show that it is advisable in special cases, specifically those that involve the following conditions: i) a low number of repeats, ii) the detection of low-fold changes in gene expression or iii) series data comparisons (such as time-course experiments) in which large sample variation greatly affects the overall gene expression profile and biological interpretation of the data.

Copy number variations (CNVs) greatly contribute to intraspecies genetic polymorphism and phenotypic diversity. Recent analyses of sequencing data for >1000 Arabidopsis (Arabidopsis thaliana) accessions focused on small variations and did not include CNVs. Here, we performed genome-wide analysis and identified large indels (50 to 499 bp) and CNVs (500 bp and larger) in these accessions. The CNVs fully overlap with 18.3% of protein-coding genes, with enrichment for evolutionarily young genes and genes involved in stress and defense. By combining analysis of both genes and transposable elements (TEs) affected by CNVs, we revealed that the variation statuses of genes and TEs are tightly linked and jointly contribute to the unequal distribution of these elements in the genome. We also determined the gene copy numbers in a set of 1060 accessions and experimentally validated the accuracy of our predictions by multiplex ligation-dependent probe amplification assays. We then successfully used the CNVs as markers to analyze population structure and migration patterns. Finally, we examined the impact of gene dosage variation triggered by a CNV spanning the SEC10 gene on SEC10 expression at both the transcript and protein levels. The catalog of CNVs, CNV-overlapping genes, and their genotypes in a top model dicot will stimulate the exploration of the genetic basis of phenotypic variation.