Soil application of a known activator of Systemic Acquired Resistance (SAR), benzo(1,2,3)thiadiazole-7-carbothioic acid-S-methyl ester (BTH), and Harmonizer, a polychlorinated copper (II) phthalocyanine pigment, reduced severity of Colletotrichum orbiculare in Nicotiana benthamiana by 99% and 38%, respectively. BTH induced expression of nine SAR/progammed cell death-related genes and primed expression of two Induced Systemic Resistance (ISR)-related genes, while Harmonizer induced expression of only one SAR-related gene. Soil application of Harmonizer also reduced severity of Sclerotinia homoeocarpa in Agrostis stolonifera up to 39%, whereas BTH was ineffective. Next generation sequencing identified over 1000 genes in A. stolonifera with two-fold or higher increased expression following Harmonizer treatment relative to a water control, and induced expression of three defense-related genes was confirmed by relative RT-PCR. These results demonstrate that Harmonizer can activate systemic resistance in a dicot and a monocot, but changes in expression of genes indicated that it differed from BTH-activated SAR. / Petro-Canada, Natural Sciences and Engineering Research Council of Canada, Ontario Turfgrass Research Foundation

Both microRNAs (miRNAs) and RNA-binding proteins (RBPs) regulate post- transcriptional events, but the post-transcriptional contribution to the global mammalian transcriptomes is still not well understood. In this study we study the synergistic interaction between microRNAs that inhibit gene production, and a special RBP, HuR, that positively regulates mRNA stability. We examined their relationship in terms of spatial, conservational and expressional perspective. We show comprehensive mapping of HuR binding sites by combination of its structural and sequential preferences; and cross-platform normalization method within a process of refining miRNA and HuR binding site mapping. Finally, we observed co-evolution of miRNA and HuR binding sites by looking at their proximity and conservation levels. Collectively, our data suggest that mammalian microRNAs and HuR, with seemingly opposing regulatory effects, cooperatively regulate their mutual targets.

Both microRNAs (miRNAs) and RNA-binding proteins (RBPs) regulate post- transcriptional events, but the post-transcriptional contribution to the global mammalian transcriptomes is still not well understood. In this study we study the synergistic interaction between microRNAs that inhibit gene production, and a special RBP, HuR, that positively regulates mRNA stability. We examined their relationship in terms of spatial, conservational and expressional perspective. We show comprehensive mapping of HuR binding sites by combination of its structural and sequential preferences; and cross-platform normalization method within a process of refining miRNA and HuR binding site mapping. Finally, we observed co-evolution of miRNA and HuR binding sites by looking at their proximity and conservation levels. Collectively, our data suggest that mammalian microRNAs and HuR, with seemingly opposing regulatory effects, cooperatively regulate their mutual targets.

Gene finding in eukaryotic genomes is an essential part of a comprehensive approach to modern systems biology. Most methods developed in the past rely on a combination of computational prediction and external information about gene structures from transcript sequences and comparative genomics. In the past, external sequence information consisted of a combination of full-length cDNA and expressed sequence tag (EST) sequences. Much improvement in prediction of genes and gene isoforms is promised by availability of RNA-seq data. However, productive use of RNA-seq for gene prediction has been difficult due to challenges associated with mapping RNA-seq reads which span splice junctions to prevalent splicing noise in the cell. This work addresses this difficulty with the development of methods and implementation of two new pipelines: 1/ a novel pipeline for accurate mapping of RNA-seq reads to compact genomes and 2/ a pipeline for prediction of genes using the RNA-seq spliced alignments in eukaryotic genomes. Machine learning methods are employed in order to overcome errors associated with the process of mapping short RNA-seq reads across introns and using them for determining sequence model parameters for gene prediction. In addition to the development of these new methods, genome annotation work was performed on several plant genome projects.

No description available.

The advance of high-throughput sequencing technologies and their application on mRNA transcriptome sequencing (RNA-seq) have enabled comprehensive and unbiased profiling of the landscape of transcription in a cell. In order to address the current limitation of analyzing accuracy and scalability in transcriptome analysis, a novel computational framework has been developed on large-scale RNA-seq datasets with no dependence on transcript annotations. Directly from raw reads, a probabilistic approach is first applied to infer the best transcript fragment alignments from paired-end reads. Empowered by the identification of alternative splicing modules, this framework then performs precise and efficient differential analysis at automatically detected alternative splicing variants, which circumvents the need of full transcript reconstruction and quantification. Beyond the scope of classical group-wise analysis, a clustering scheme is further described for mining prominent consistency among samples in transcription, breaking the restriction of presumed grouping. The performance of the framework has been demonstrated by a series of simulation studies and real datasets, including the Cancer Genome Atlas (TCGA) breast cancer analysis. The successful applications have suggested the unprecedented opportunity in using differential transcription analysis to reveal variations in the mRNA transcriptome in response to cellular differentiation or effects of diseases.

The advent of RNA-seq technologies provides an unprecedented opportunity to precisely profile the mRNA transcriptome of a specific cell population. It helps reveal the characteristics of the cell under the particular condition such as a disease. It is now possible to discover mRNA transcripts not cataloged in existing database, in addition to assessing the identities and quantities of the known transcripts in a given sample or cell. However, the sequence reads obtained from an RNA-seq experiment is only a short fragment of the original transcript. How to recapitulate the mRNA transcriptome from short RNA-seq reads remains a challenging problem. We have proposed two methods directly addressing this challenge. First, we developed a novel method MultiSplice to accurately estimate the abundance of the well-annotated transcripts. Driven by the desire of detecting novel isoforms, a max-flow-min-cost algorithm named Astroid is designed for simultaneously discovering the presence and quantities of all possible transcripts in the transcriptome. We further extend an \emph{ab initio} pipeline of transcriptome analysis to large-scale dataset which may contain hundreds of samples. The effectiveness of proposed methods has been supported by a series of simulation studies, and their application on real datasets suggesting a promising opportunity in reconstructing mRNA transcriptome which is critical for revealing variations among cells (e.g. disease vs. normal).

No description available.

This project concerns an exploration of the presence and interactions of long non-coding RNA transcripts in an experimental atherosclerosis mouse model with relevance for human abdominal aortic aneurysm development. 187 long noncoding RNAs, two of them entirely novel, were found to be differentially expressed between angiotensin II treated (developing abdominal aortic aneurysms) and non-treated apolipoprotein E deficient mice (not developing aneurysms) harvested after the same period of time. These transcripts were also studied with regards to co-expression network connections. Eleven previously annotated and two novel long non-coding RNAs were present in two significantly disease correlated co-expression groups that were further profiled with respect to network properties, Gene Ontology terms and MetaCore© connections.

Malaria is a pernicious disease that has greatly impacted and continues to affect the human population. While much research has been performed to understand the underlying nature of this disease, gaps in the knowledge-base persist. In order to address these deficiencies, a multi-disciplinary, multi-institutional project has been funded to study the systems biology of the host pathogen interaction during malaria infection in both humans and non-human primates. In the course of investigating the transcriptome during two 100-day experiments in Macaca mulatta, this work elucidated many of the underlying molecular pathways of the host and parasite that are affected by antimalarial drugs, as well as through host-pathogen interactions. The malaria-disease-related host pathways are related to, not surprisingly, immune-associated signalling and hematopoesis, and the altered parasite pathways demonstrate an association between disease severity and parasite life stage abundance. Continuing integration of this research with other data-types collected during the course of these experiments will improve our understanding of malaria systems biology and improve targeted malaria therapies.