Ethylene is the main regulator of climacteric fruit ripening, by contrast the putative role of other phytohormones in this process remains poorly understood. The present study brings auxin signaling components into the mechanism regulating tomato fruit ripening through the functional characterization of Auxin Response Factor2 (SlARF2) which encodes a downstream component of auxin signaling. Two paralogs, SlARF2A and SlARF2B, are found in the tomato genome, both displaying a marked ripening-associated expression but distinct responsiveness to ethylene and auxin. Down-regulation of either SlARF2A or SlARF2B resulted in ripening defects while simultaneous silencing of both genes led to severe ripening inhibition suggesting a functional redundancy among the two ARFs. Tomato fruits under-expressing SlARF2 produced less climacteric ethylene and exhibited a dramatic down-regulation of the key ripening regulators RIN, CNR, NOR and TAGL1. Ethylene treatment failed to reverse the non-ripening phenotype and the expression of ethylene signaling and biosynthesis genes was strongly altered in SlARF2 down-regulated fruits. Although both SlARF proteins are transcriptional repressors the data indicate they work as positive regulators of tomato fruit ripening. Altogether, the study defines SlARF2 as a new component of the regulatory network controlling the ripening process in tomato.

SummaryIn Arabidopsis, the miR171‐GRAS module has been clarified as key player in meristem maintenance. However, the knowledge about its role in fruit crops like tomato (Solanum lycopersicum) remains scarce. We previously identified tomato SlGRAS24 as a target gene of Sly‐miR171. To study the role of this probable transcription factor, we generated transgenic tomato plants underexpressing SlGRAS24, overexpressing SlGRAS24, overexpressing Sly‐miR171 and expressing β‐glucuronidase (GUS) under the SlGRAS24 promoter (proSlGRAS24‐GUS). Plants overexpressing SlGRAS24 (SlGRAS24‐OE) had pleiotropic phenotypes associated with multiple agronomical traits including plant height, flowering time, leaf architecture, lateral branch number, root length, fruit set and development. Many GA/auxin‐related genes were down‐regulated and altered responsiveness to exogenous IAA/NAA or GA 3 application was observed in SlGRAS24‐OE seedlings. Moreover, compromised fruit set and development in SlGRAS24‐OE was also observed. These newly identified phenotypes for SlGRAS24 homologs in tomato were later proved to be caused by impaired pollen sacs and fewer viable pollen grains. At anthesis, the comparative transcriptome results showed altered expression of genes involved in pollen development and hormone signalling. Taken together, our data demonstrate that SlGRAS24 participates in a series of developmental processes through modulating gibberellin and auxin signalling, which sheds new light on the involvement of hormone crosstalk in tomato development.

BackgroundFruit set is a key process for crop production in tomato which occurs after successful pollination and fertilization naturally. However, parthenocarpic fruit development can be uncoupled from fertilization triggered by exogenous auxin or gibberellins (GAs). Global transcriptome knowledge during fruit initiation would help to characterize the molecular mechanisms by which these two hormones regulate pollination-dependent and -independent fruit set.Principal FindingsIn this work, digital gene expression tag profiling (DGE) technology was applied to compare the transcriptomes from pollinated and 2, 4-D/GA3-treated ovaries. Activation of carbohydrate metabolism, cell division and expansion as well as the down-regulation of MADS-box is a comprehensive regulatory pathway during pollination-dependent and parthenocarpic fruit set. The signaling cascades of auxin and GA are significantly modulated. The feedback regulations of Aux/IAAs and DELLA genes which functioned to fine-tune auxin and GA response respectively play fundamental roles in triggering fruit initiation. In addition, auxin regulates GA synthesis via up-regulation of GA20ox1 and down-regulation of KNOX. Accordingly, the effect of auxin on fruit set is mediated by GA via ARF2 and IAA9 down-regulation, suggesting that both pollination-dependent and parthenocarpic fruit set depend on the crosstalk between auxin and GA.SignificanceThis study characterizes the transcriptomic features of ovary development and more importantly unravels the integral roles of auxin and GA on pollination-dependent and parthenocarpic fruit set.

RNA-Seq is a widely used technology that allows an efficient genome-wide quantification of gene expressions for, for example, differential expression (DE) analysis. After a brief review of the main issues, methods and tools related to the DE analysis of RNA-Seq data, this article focuses on the impact of both the replicate number and library size in such analyses. While the main drawback of previous relevant studies is the lack of generality, we conducted both an analysis of a two-condition experiment (with eight biological replicates per condition) to compare the results with previous benchmark studies, and a meta-analysis of 17 experiments with up to 18 biological conditions, eight biological replicates and 100 million (M) reads per sample. As a global trend, we concluded that the replicate number has a larger impact than the library size on the power of the DE analysis, except for low-expressed genes, for which both parameters seem to have the same impact. Our study also provides new insights for practitioners aiming to enhance their experimental designs. For instance, by analyzing both the sensitivity and specificity of the DE analysis, we showed that the optimal threshold to control the false discovery rate (FDR) is approximately 2−r, where r is the replicate number. Furthermore, we showed that the false positive rate (FPR) is rather well controlled by all three studied R packages: DESeq, DESeq2, and edgeR. We also analyzed the impact of both the replicate number and library size on gene ontology (GO) enrichment analysis. Interestingly, we concluded that increases in the replicate number and library size tend to enhance the sensitivity and specificity, respectively, of the GO analysis. Finally, we recommend to RNA-Seq practitioners the production of a pilot data set to strictly analyze the power of their experimental design, or the use of a public data set, which should be similar to the data set they will obtain. For individuals working on tomato research, on the basis of the meta-analysis, we recommend at least four biological replicates per condition and 20 M reads per sample to be almost sure of obtaining about 1000 DE genes if they exist.