In silico Transcriptional Regulatory Networks Involved in Tomato Fruit Ripening.

Tomato fruit ripening is a complex developmental programme partly mediated by transcriptional regulatory networks. Several transcription factors (TFs) which are members of gene families such as MADS-box and ERF were shown to play a significant role in ripening through interconnections into an intricate network. The accumulation of large datasets of expression profiles corresponding to different stages of tomato fruit ripening and the availability of bioinformatics tools for their analysis provide an opportunity to identify TFs which might regulate gene clusters with similar co-expression patterns. We identified two TFs, a SlWRKY22-like and a SlER24 transcriptional activator which were shown to regulate modules by using the LeMoNe algorithm for the analysis of our microarray datasets representing four stages of fruit ripening, breaker, turning, pink and red ripe. The WRKY22-like module comprised a subgroup of six various calcium sensing transcripts with similar to the TF expression patterns according to real time PCR validation. A promoter motif search identified a cis acting element, the W-box, recognized by WRKY TFs that was present in the promoter region of all six calcium sensing genes. Moreover, publicly available microarray datasets of similar ripening stages were also analyzed with LeMoNe resulting in TFs such as SlERF.E1, SlERF.C1, SlERF.B2, SLERF.A2, SlWRKY24, SLWRKY37, and MADS-box/TM29 which might also play an important role in regulation of ripening. These results suggest that the SlWRKY22-like might be involved in the coordinated regulation of expression of the six calcium sensing genes. Conclusively the LeMoNe tool might lead to the identification of putative TF targets for further physiological analysis as regulators of tomato fruit ripening.

454 Pyrosequencing of Olive (Olea europaea L.) Transcriptome in Response to Salinity.

Olive (Olea europaea L.) is one of the most important crops in the Mediterranean region. The expansion of cultivation in areas irrigated with low quality and saline water has negative effects on growth and productivity however the investigation of the molecular basis of salt tolerance in olive trees has been only recently initiated. To this end, we investigated the molecular response of cultivar Kalamon to salinity stress using next-generation sequencing technology to explore the transcriptome profile of olive leaves and roots and identify differentially expressed genes that are related to salt tolerance response. Out of 291,958 obtained trimmed reads, 28,270 unique transcripts were identified of which 35% are annotated, a percentage that is comparable to similar reports on non-model plants. Among the 1,624 clusters in roots that comprise more than one read, 24 were differentially expressed comprising 9 down- and 15 up-regulated genes. Respectively, inleaves, among the 2,642 clusters, 70 were identified as differentially expressed, with 14 down- and 56 up-regulated genes. Using next-generation sequencing technology we were able to identify salt-response-related transcripts. Furthermore we provide an annotated transcriptome of olive as well as expression data, which are both significant tools for further molecular studies in olive.

Transcriptional profiling unravels potential metabolic activities of the olive leaf non-glandular trichome.

The olive leaf trichomes are multicellular peltate hairs densely distributed mainly at the lower leaf epidermis. Although, non-glandular, they have gained much attention since they significantly contribute to abiotic and biotic stress tolerance of olive leaves. The exact mechanisms by which olive trichomes achieve these goals are not fully understood. They could act as mechanical barrier but they also accumulate high amounts of flavonoids among other secondary metabolites. However, little is currently known about the exact compounds they produce and the respective metabolic pathways. Here we present the first EST analysis from olive leaf trichomes by using 454-pyrosequencing. A total of 5368 unigenes were identified out of 7258 high quality reads with an average length of 262 bp. Blast search revealed that 27.5% of them had high homologies to known proteins. By using Blast2GO, 1079 unigenes (20.1%) were assigned at least one Gene Ontology (GO) term. Most of the genes were involved in cellular and metabolic processes and in binding functions followed by catalytic activity. A total of 521 transcripts were mapped to 67 KEGG pathways. Olive trichomes represent a tissue of highly unique transcriptome as per the genes involved in developmental processes and the secondary metabolism. The results indicate that mature olive trichomes are trancriptionally active, mainly through the potential production of enzymes that contribute to phenolic compounds with important roles in biotic and abiotic stress responses.

De novo transcriptome analysis of petal senescence in Gardenia jasminoides Ellis.

BACKGROUND: The petal senescence of ethylene insensitive species has not been investigated thoroughly while little is known about the temporal and tissue specific expression patterns of transcription factors (TFs) in this developmental process. Even less is known on flower senescence of the ornamental pot plant Gardenia jasminoides, a non climacteric flower with significant commercial value. RESULTS: We initiated a de novo transcriptome study to investigate the petal senescence in four developmental stages of cut gardenia flowers considering that the visible symptoms of senescence appear within 4 days of flower opening. De novo assembly of transcriptome sequencing resulted in 102,263 contigs with mean length of 360 nucleotides that generated 57,503 unigenes. These were further clustered into 20,970 clusters and 36,533 singletons. The comparison of the consecutive developmental stages resulted in 180 common, differentially expressed unigenes. A large number of Simple Sequence Repeats were also identified comprising a large number of dinucleotides and trinucleotides. The prevailing families of differentially expressed TFs comprise the AP2/EREBP, WRKY and the bHLH. There are 81 differentially expressed TFs when the symptoms of flower senescence become visible with the most prevailing being the WRKY family with 19 unigenes. No other WRKY TFs had been identified up to now in petal senescence of ethylene insensitive species. A large number of differentially expressed genes were identified at the initiation of visible symptoms of senescence compared to the open flower stage indicating a significant shift in the expression profiles which might be coordinated by up-regulated and/or down-regulated TFs. The expression of 16 genes that belong to the TF families of WRKY, bHLH and the ethylene sensing pathway was validated using qRT--PCR. CONCLUSION: This de novo transcriptome analysis resulted in the identification of TFs with specific temporal expression patterns such as two WRKYs and one bHLH, which might play the role of senescence progression regulators. Further research is required to investigate their role in gardenia flowers in order to develop tools to delay petal senescence.

Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response.

Over the last decade, numerous computational methods have been developed in order to infer and model biological networks. Transcriptional networks in particular have attracted significant attention due to their critical role in cell survival. The majority of network inference methods use genome-wide experimental data to search for modules of genes with coherent expression profiles and common regulators, often ignoring the multi-layer structure of transcriptional cascades. Modeling methodologies on the other hand assume a given network structure and vary significantly in their algorithmic approach, ranging from over-simplified representations (e.g., Boolean networks) to detailed -but computationally expensive-network simulations (e.g., with differential equations). In this work we use Artificial Neural Networks (ANNs) to model transcriptional regulatory cascades that emerge during the stress response in Saccharomyces cerevisiae and extend in three layers. We confine the structure of the ANNs to match the structure of the biological networks as determined by gene expression, DNA-protein interaction and experimental evidence provided in publicly available databases. Trained ANNs are able to predict the expression profile of 11 target genes across multiple experimental conditions with a correlation coefficient >0.7. When time-dependent interactions between upstream transcription factors (TFs) and their indirect targets are also included in the ANNs, accurate predictions are achieved for 30/34 target genes. Moreover, heterodimer formation is taken into account. We show that ANNs can be used to (1) accurately predict the expression of downstream genes in a 3-layer transcriptional cascade based on the expression of their indirect regulators and (2) infer the condition- and time-dependent activity of various TFs as well as during heterodimer formation. We show that a three-layer regulatory cascade whose structure is determined by co-expressed gene modules and their regulators can successfully be modeled using ANNs with a similar configuration.

Comparative transcriptome analysis of two olive cultivars in response to NaCl-stress.

BACKGROUND: Olive (Olea europaea L.) cultivation is rapidly expanding and low quality saline water is often used for irrigation. The molecular basis of salt tolerance in olive, though, has not yet been investigated at a system level. In this study a comparative transcriptomics approach was used as a tool to unravel gene regulatory networks underlying salinity response in olive trees by simulating as much as possible olive growing conditions in the field. Specifically, we investigated the genotype-dependent differences in the transcriptome response of two olive cultivars, a salt-tolerant and a salt-sensitive one. METHODOLOGY/PRINCIPAL FINDINGS: A 135-day long salinity experiment was conducted using one-year old trees exposed to NaCl stress for 90 days followed by 45 days of post-stress period during the summer. A cDNA library made of olive seedling mRNAs was sequenced and an olive microarray was constructed. Total RNA was extracted from root samples after 15, 45 and 90 days of NaCl-treatment as well as after 15 and 45 days of post-treatment period and used for microarray hybridizations. SAM analysis between the NaCl-stress and the post-stress time course resulted in the identification of 209 and 36 differentially expressed transcripts in the salt-tolerant and salt-sensitive cultivar, respectively. Hierarchical clustering revealed two major, distinct clusters for each cultivar. Despite the limited number of probe sets, transcriptional regulatory networks were constructed for both cultivars while several hierarchically-clustered interacting transcription factor regulators such as JERF and bZIP homologues were identified. CONCLUSIONS/SIGNIFICANCE: A systems biology approach was used and differentially expressed transcripts as well as regulatory interactions were identified. The comparison of the interactions among transcription factors in olive with those reported for Arabidopsis might indicate similarities in the response of a tree species with Arabidopsis at the transcriptional level under salinity stress.