Transcriptional changes in powdery mildew infected wheat and Arabidopsis leaves undergoing syringolin-triggered hypersensitive cell death at infection sites

Blumeria graminis f.sp. tritici, the causal agent of powdery mildew in wheat, is an obligate biotrophic fungus that exclusively invades epidermal cells. As previously shown, spraying of a solution of syringolin A, a circular peptide derivative secreted by the phytopathogenic bacterium Pseudomonas syringae pv. syringae, triggers hypersensitive cell death at infection sites in powdery mildew infected wheat. Thus, the fungus is essentially eradicated. Here we show that syringolin A also triggers hypersensitive cell death in Arabidopsis infected with the powdery mildew fungus Erysiphe cichoracearum. To monitor transcriptional changes associated with this effect, we cloned 307 cDNA clones representing 158 unigenes from powdery mildew infected, syringolin A sprayed wheat leaves by a suppression subtractive hybridization cloning procedure. These cDNAs were microarrayed onto glass slides together with 1088 cDNA-AFLP clones from powdery mildew-infected wheat. Microarray hybridization experiments were performed with probes derived from leaves, epidermal tissue, and mesophyll preparations of mildewed or uninfected wheat plants after syringolin A or control treatment. Similar experiments were performed in Arabidopsis using the Affymetrix ATH1 whole genome GeneChip. The results indicate a conserved mode of action of syringolin A as similar gene groups are induced in both species. Prominent groups include genes associated with the proteasomal degradation pathway, mitochondrial and other heat shock genes, genes involved in mitochondrial alternative electron pathways, and genes encoding glycolytic and fermentative enzymes. Surprisingly, in both species the observed transcriptional response to syringolin A was considerably weaker in infected plants as compared to uninfected plants. The results lead to the working hypothesis that cell death observed at infection sites may result from a parasite-induced suppression of the transcriptional response and thus to insufficient production of protective proteins necessary for the recovery of these cells from whatever insult is imposed by syringolin

Analysis of epidermis- and mesophyll-specific transcript accumulation in powdery mildew-inoculated wheat leaves

Powdery mildew is an important disease of wheat caused by the obligate biotrophic fungus Blumeria graminis f. sp. tritici. This pathogen invades exclusively epidermal cells after penetrating directly through the cell wall. Because powdery mildew colonizes exclusively epidermal cells, it is of importance not only to identify genes which are activated, but also to monitor tissue specificity of gene activation. Acquired resistance of wheat to powdery mildew can be induced by a previous inoculation with the non-host pathogen B. graminis f. sp. hordei, the causal agent of barley powdery mildew. The establishment of the resistant state is accompanied by the activation of genes. Here we report the tissue-specific cDNA-AFLP analysis and cloning of transcripts accumulating 6 and 24 h after the resistance-inducing inoculation with B. graminis f. sp. hordei. A total of 25 000 fragments estimated to represent about 17 000 transcripts were displayed. Out of these, 141 transcripts, were found to accumulate after Bgh inoculation using microarray hybridization analysis. Forty-four accumulated predominantly in the epidermis whereas 76 transcripts accumulated mostly in mesophyll tissu

Mitochondrial genome evolution in species belonging to the Phialocephala fortinii s.l. - Acephala applanata species complex

BACKGROUND: Mitochondrial (mt) markers are successfully applied in evolutionary biology and systematics because mt genomes often evolve faster than the nuclear genomes. In addition, they allow robust phylogenetic analysis based on conserved proteins of the oxidative phosphorylation system. In the present study we sequenced and annotated the complete mt genome of P. subalpina, a member of the Phialocephala fortinii s.l. – Acephala applanata species complex (PAC). PAC belongs to the Helotiales, which is one of the most diverse groups of ascomycetes including more than 2,000 species. The gene order was compared to deduce the mt genome evolution in the Pezizomycotina. Genetic variation in coding and intergenic regions of the mtDNA was studied for PAC to assess the usefulness of mt DNA for species diagnosis. RESULTS: The mt genome of P. subalpina is 43,742 bp long and codes for 14 mt genes associated with the oxidative phosphorylation. In addition, a GIY-YIG endonuclease, the ribosomal protein S3 (Rps3) and a putative N-acetyl-transferase were recognized. A complete set of tRNA genes as well as the large and small rRNA genes but no introns were found. All protein-coding genes were confirmed by EST sequences. The gene order in P. subalpina deviated from the gene order in Sclerotinia sclerotiorum, the only other helotialean species with a fully sequenced and annotated mt genome. Gene order analysis within Pezizomycotina suggests that the evolution of gene orders is mostly driven by transpositions. Furthermore, sequence diversity in coding and non-coding mtDNA regions in seven additional PAC species was pronounced and allowed for unequivocal species diagnosis in PAC. CONCLUSIONS: The combination of non-interrupted ORFs and EST sequences resulted in a high quality annotation of the mt genome of P. subalpina, which can be used as a reference for the annotation of other mt genomes in the Helotiales. In addition, our analyses show that mtDNA loci will be the marker of choice for future analysis of PAC communities

Mushroom body-specific profiling of gene expression identifies regulators of long-term memory in Drosophila

Memory formation is achieved by genetically tightly controlled molecular pathways that result in a change of synaptic strength and synapse organization. While for short- term memory traces rapidly acting biochemical pathways are in place, the formation of long-lasting memories requires changes in the transcriptional program of a cell. Although many genes involved in learning and memory formation have been identified, little is known about the genetic mechanisms required for changing the transcriptional program during different phases of long-term memory formation. With Drosophila melanogaster as a model system we profiled transcriptomic changes in the mushroom body, a memory center in the fly brain, at distinct time intervals during long- term memory formation using the targeted DamID technique. We describe the gene expression profiles during these phases and tested 33 selected candidate genes for deficits in long-term memory formation using RNAi knockdown. We identified 10 genes that enhance or decrease memory when knocked-down in the mushroom body. For vajk-1 and hacd1, the two strongest hits, we gained further support for their crucial role in learning and forgetting. These findings show that profiling gene expression changes in specific cell-types harboring memory traces provides a powerful entry point to identify new genes involved in learning and memory. The presented transcriptomic data may further be used as resource to study genes acting at different memory phases

Avoiding the pitfalls of gene set enrichment analysis with SetRank.

The purpose of gene set enrichment analysis (GSEA) is to find general trends in the huge lists of genes or proteins generated by many functional genomics techniques and bioinformatics analyses. Here we present SetRank, an advanced GSEA algorithm which is able to eliminate many false positive hits. The key principle of the algorithm is that it discards gene sets that have initially been flagged as significant, if their significance is only due to the overlap with another gene set. The algorithm is explained in detail and its performance is compared to that of other methods using objective benchmarking criteria. Furthermore, we explore how sample source bias can affect the results of a GSEA analysis. The benchmarking results show that SetRank is a highly specific tool for GSEA. Furthermore, we show that the reliability of results can be improved by taking sample source bias into account. SetRank is available as an R package and through an online web interface

Solar flare electron acceleration: comparing theories and observations

A popular scenario for electron acceleration in solar flares is transit-time damping of low-frequency MHD waves excited by reconnection and its outflows. The scenario requires several processes in sequence to yield energetic electrons of the observed large number. Until now there was very little evidence for this scenario, as it is even not clear where the flare energy is released. RHESSI measurements of bremsstrahlung by non-thermal flare electrons yield energy estimates as well as the position where the energy is deposited. Thus quantitative measurements can be put into the frame of the global magnetic field configuration as seen in coronal EUV line observations. We present RHESSI observations combined with TRACE data that suggest primary energy inputs mostly into electron acceleration and to a minor fraction into coronal heating and primary motion. The more sensitive and lower energy X-ray observations by RHESSI have found also small events (C class) at the time of the acceleration of electron beams exciting meter wave Type III bursts. However, not all RHESSI flares involve Type III radio emissions. The association of other decimeter radio emissions, such as narrowband spikes and pulsations, with X-rays is summarized in view of electron accelerationComment: COSPAR meeting Houston 2002, PASP proceedings, in pres

Streaming Support for Data Intensive Cloud-Based Sequence Analysis

Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of “resources-on-demand” and “pay-as-you-go”, scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client's site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem, where the NGS data is processed while being transferred to the cloud. Our scheme targets the wide class of NGS data analysis tasks, where the NGS sequences can be processed independently from one another. We also provide the elastream package that supports the use of this scheme with individual analysis programs or with workflow systems. Experiments presented in this paper show that our solution mitigates the effect of data transfer latency and saves both time and cost of computation