Alternative splicing generates multiple transcript and protein isoforms from the same gene and thus is important in gene expression regulation. To date, RNA-sequencing (RNA-seq) is the standard method for quantifying changes in alternative splicing on a genome-wide scale. Understanding the current limitations of RNA-seq is crucial for reliable analysis and the lack of high quality, comprehensive transcriptomes for most species, including model organisms such as Arabidopsis, is a major constraint in accurate quantification of transcript isoforms. To address this, we designed a novel pipeline with stringent filters and assembled a comprehensive Reference Transcript Dataset for Arabidopsis (AtRTD2) containing 82,190 non-redundant transcripts from 34 212 genes. Extensive experimental validation showed that AtRTD2 and its modified version, AtRTD2-QUASI, for use in Quantification of Alternatively Spliced Isoforms, outperform other available transcriptomes in RNA-seq analysis. This strategy can be implemented in other species to build a pipeline for transcript-level expression and alternative splicing analyses.

Plants have adapted to tolerate and survive constantly changing environmental conditions by reprogramming gene expression The dynamics of the contribution of alternative splicing (AS) to stress responses are unknown. RNA-sequencing of a time-series of plants exposed to cold determines the timing of significant AS changes. This shows a massive and rapid AS response with coincident waves of transcriptional and AS activity occurring in the first few hours of temperature reduction and further AS throughout the cold. In particular, hundreds of genes showed changes in expression due to rapidly occurring AS in response to cold ("early AS" genes); these included numerous novel cold-responsive transcription factors and splicing factors/RNA binding proteins regulated only by AS. The speed and sensitivity to small temperature changes of AS of some of these genes suggest that fine-tuning expression via AS pathways contributes to the thermo-plasticity of expression. Four early AS splicing regulatory genes have been shown previously to be required for freezing tolerance and acclimation; we provide evidence of a fifth gene, Such factors likely drive cascades of AS of downstream genes that, alongside transcription, modulate transcriptome reprogramming that together govern the physiological and survival responses of plants to low temperature.

Although significant work has been undertaken regarding the response of model and crop plants to heat shock during the acclimatory phase, few studies have examined the steadystate response to the mild heat stress encountered in temperate agriculture. In the present work, we therefore exposed tuberizing potato plants to mildly elevated temperatures (30/ 20°C, day/night) for up to 5 weeks and compared tuber yield, physiological and biochemical responses, and leaf and tuber metabolomes and transcriptomes with plants grown under optimal conditions (22/16°C). Growth at elevated temperature reduced tuber yield despite an increase in net foliar photosynthesis. This was associated with major shifts in leaf and tuber metabolite profiles, a significant decrease in leaf glutathione redox state and decreased starch synthesis in tubers. Furthermore, growth at elevated temperature had a profound impact on leaf and tuber transcript expression with large numbers of transcripts displaying a rhythmic oscillation at the higher growth temperature. RT-PCR revealed perturbation in the expression of circadian clock transcripts including StSP6A, previously identified as a tuberization signal. Our data indicate that potato plants grown at moderately elevated temperatures do not exhibit classic symptoms of abiotic stress but that tuber development responds via a diversity of biochemical and molecular signals.

Pollen, the male gametophyte of flowering plants, represents an ideal biological system to study developmental processes, such as cell polarity, tip growth, and morphogenesis. Upon hydration, the metabolically quiescent pollen rapidly switches to an active state, exhibiting extremely fast growth. This rapid switch requires relevant proteins to be stored in the mature pollen, where they have to retain functionality in a desiccated environment. Using a shotgun proteomics approach, we unambiguously identified ∼3500 proteins in Arabidopsis pollen, including 537 proteins that were not identified in genetic or transcriptomic studies. To generate this comprehensive reference data set, which extends the previously reported pollen proteome by a factor of 13, we developed a novel deterministic peptide classification scheme for protein inference. This generally applicable approach considers the gene model–protein sequence–protein accession relationships. It allowed us to classify and eliminate ambiguities inherently associated with any shotgun proteomics data set, to report a conservative list of protein identifications, and to seamlessly integrate data from previous transcriptomics studies. Manual validation of proteins unambiguously identified by a single, information-rich peptide enabled us to significantly reduce the false discovery rate, while keeping valuable identifications of shorter and lower abundant proteins. Bioinformatic analyses revealed a higher stability of pollen proteins compared to those of other tissues and implied a protein family of previously unknown function in vesicle trafficking. Interestingly, the pollen proteome is most similar to that of seeds, indicating physiological similarities between these developmentally distinct tissues.

BackgroundPlant-microbe interactions feature complex signal interplay between pathogens and their hosts. Phytophthora species comprise a destructive group of fungus-like plant pathogens, collectively affecting a wide range of plants important to agriculture and natural ecosystems. Despite the availability of genome sequences of both hosts and microbes, little is known about the signal interplay between them during infection. In particular, accurate descriptions of coordinate relationships between host and microbe transcriptional programs are lacking.ResultsHere, we explore the molecular interaction between the hemi-biotrophic broad host range pathogen Phytophthora capsici and tomato. Infection assays and use of a composite microarray allowed us to unveil distinct changes in both P. capsici and tomato transcriptomes, associated with biotrophy and the subsequent switch to necrotrophy. These included two distinct transcriptional changes associated with early infection and the biotrophy to necrotrophy transition that may contribute to infection and completion of the P. capsici lifecycleConclusionsOur results suggest dynamic but highly regulated transcriptional programming in both host and pathogen that underpin P. capsici disease and hemi-biotrophy. Dynamic expression changes of both effector-coding genes and host factors involved in immunity, suggests modulation of host immune signaling by both host and pathogen. With new unprecedented detail on transcriptional reprogramming, we can now explore the coordinate relationships that drive host-microbe interactions and the basic processes that underpin pathogen lifestyles. Deliberate alteration of lifestyle-associated transcriptional changes may allow prevention or perhaps disruption of hemi-biotrophic disease cycles and limit damage caused by epidemics.

24Background: Plants have adapted to tolerate and survive constantly changing environmental 25

Micro RNAs (miRNAs) represent a class of short, non-coding, endogenous RNAs which play important roles in post-transcriptional regulation of gene expression. While the diverse functions of miRNAs in model plants have been well studied, the impact of miRNAs in crop plant biology is poorly understood. Here we used high-throughput sequencing and bioinformatics analysis to analyze miRNAs in the tuber bearing crop potato (Solanum tuberosum). Small RNAs were analysed from leaf and stolon tissues. 28 conserved miRNA families were found and potato-specific miRNAs were identified and validated by RNA gel blot hybridization. The size, origin and predicted targets of conserved and potato specific miRNAs are described. The large number of miRNAs and complex population of small RNAs in potato suggest important roles for these non-coding RNAs in diverse physiological and metabolic pathways.

Background The human transcriptome annotation is regarded as one of the most complete of any eukaryotic species. However, limitations in sequencing technologies have biased the annotation toward multi-exonic protein coding genes. Accurate high-throughput long read transcript sequencing can now provide additional evidence for rare transcripts and genes such as mono-exonic and non-coding genes that were previously either undetectable or impossible to differentiate from sequencing noise. Results We developed the Transcriptome Annotation by Modular Algorithms (TAMA) software to leverage the power of long read transcript sequencing and address the issues with current data processing pipelines. TAMA achieved high sensitivity and precision for gene and transcript model predictions in both reference guided and unguided approaches in our benchmark tests using simulated Pacific Biosciences (PacBio) and Nanopore sequencing data and real PacBio datasets. By analyzing PacBio Sequel II Iso-Seq sequencing data of the Universal Human Reference RNA (UHRR) using TAMA and other commonly used tools, we found that the convention of using alignment identity to measure error correction performance does not reflect actual gain in accuracy of predicted transcript models. In addition, inter-read error correction can cause major changes to read mapping, resulting in potentially over 6 K erroneous gene model predictions in the Iso-Seq based human genome annotation. Using TAMA’s genome assembly based error correction and gene feature evidence, we predicted 2566 putative novel non-coding genes and 1557 putative novel protein coding gene models. Conclusions Long read transcript sequencing data has the power to identify novel genes within the highly annotated human genome. The use of parameter tuning and extensive output information of the TAMA software package allows for in depth exploration of eukaryotic transcriptomes. We have found long read data based evidence for thousands of unannotated genes within the human genome. More development in sequencing library preparation and data processing are required for differentiating sequencing noise from real genes in long read RNA sequencing data.