Atmospheric drivers of storage water use in Scots pine

International audienceIn this study we determined the microclimatic drivers of storage water use in Scots pine (Pinus sylvestris L.) growing in a temperate climate. The storage water use was modeled using the ANAFORE model, integrating a dynamic water flow and ? storage model with a process-based transpiration model. The model was calibrated and validated with sap flow measurements for the growing season of 2000 (26 May?18 October). Because there was no severe soil drought during the study period, we were able to study atmospheric effects. Incoming radiation was the main driver of storage water use. The general trends of sap flow and storage water use are similar, and follow more or less the pattern of incoming radiation. Nevertheless, considerable differences in the day-to-day pattern of sap flow and storage water use were observed, mainly driven by vapour pressure deficit (VPD). During dry atmospheric conditions (high VPD) storage water use was reduced. This reduction was disproportionally higher than the reduction in measured sap flow. Our results suggest that the trees did not rely more on storage water during periods of atmospheric drought, without severe soil drought. A third important factor was the tree water deficit. When storage compartments were depleted beyond a threshold, storage water use was limited due to the low water potential in the storage compartments. The maximum relative contribution of storage water to daily transpiration was also constrained by an increasing tree water deficit

Non PCR-amplified Transcripts and AFLP fragments as reduced representations of the quail genome for 454 Titanium sequencing

<p>Abstract</p> <p>Background</p> <p>SNP (Single Nucleotide Polymorphism) discovery is now routinely performed using high-throughput sequencing of reduced representation libraries. Our objective was to adapt 454 GS FLX based sequencing methodologies in order to obtain the largest possible dataset from two reduced representations libraries, produced by AFLP (Amplified Fragment Length Polymorphism) for genomic DNA, and EST (Expressed Sequence Tag) for the transcribed fraction of the genome.</p> <p>Findings</p> <p>The expressed fraction was obtained by preparing cDNA libraries without PCR amplification from quail embryo and brain. To optimize the information content for SNP analyses, libraries were prepared from individuals selected in three quail lines and each individual in the AFLP library was tagged. Sequencing runs produced 399,189 sequence reads from cDNA and 373,484 from genomic fragments, covering close to 250 Mb of sequence in total.</p> <p>Conclusions</p> <p>Both methods used to obtain reduced representations for high-throughput sequencing were successful after several improvements.</p> <p>The protocols may be used for several sequencing applications, such as <it>de novo </it>sequencing, tagged PCR fragments or long fragment sequencing of cDNA.</p

Knowledge based identification of essential signaling from genome-scale siRNA experiments

<p>Abstract</p> <p>Background</p> <p>A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.</p> <p>Results</p> <p>We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK <it>cytoskeletal remodeling pathway</it>. Different branches of the <it>FAK </it>pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction.</p> <p>Conclusion</p> <p>PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets. </p