SUMMARYBrassinosteroids (BRs) are important regulators for plant growth and development. BRs signal to control the activities of the BES1 and BZR1 family transcription factors. The transcriptional network through which BES1 and BZR regulate large number of target genes is mostly unknown. By combining chromatin immunoprecipitation coupled with Arabidopsis tiling arrays (ChIP-chip) and gene expression studies, we have identified 1609 putative BES1 target genes, 404 of which are regulated by BRs and/or in gain-of-function bes1-D mutant. BES1 targets contribute to BR responses and interactions with other hormonal or light signaling pathways. Computational modeling of gene expression data using Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) reveals that BES1-targeted transcriptional factors form a gene regulatory network (GRN). Mutants of many genes in the network displayed defects in BR responses. Moreover, we found that BES1 functions to inhibit chloroplast development by repressing the expression of GLK1 and GLK2 transcription factors, confirming a hypothesis generated from the GRN. Our results thus provide a global view of BR regulated gene expression and a GRN that guides future studies in understanding BR-regulated plant growth.

Arabidopsis (Arabidopsis thaliana) immutans (im) has green and white sectoring due to the action of a nuclear recessive gene, IMMUTANS. The green sectors contain normal-appearing chloroplasts, whereas the white sectors contain abnormal chloroplasts that lack colored carotenoids due to a defect in phytoene desaturase activity. Previous biochemical and molecular characterizations of the green leaf sectors revealed alterations suggestive of a source-sink relationship between the green and white sectors of im. In this study, we use an Affymetrix ATH1 oligoarray to further explore the nature of sink metabolism in im white tissues. We show that lack of colored carotenoids in the im white tissues elicits a differential response from a large number of genes involved in various cellular processes and stress responses. Gene expression patterns correlate with the repression of photosynthesis and photosynthesis-related processes in im white tissues, with an induction of Suc catabolism and transport, and with mitochondrial electron transport and fermentation. These results suggest that energy is derived via aerobic and anaerobic metabolism of imported sugar in im white tissues for growth and development. We also show that oxidative stress responses are largely induced in im white tissues; however, im green sectors develop additional energy-dissipating mechanisms that perhaps allow for the formation of green sectors. Furthermore, a comparison of the transcriptomes of im white and norflurazon-treated white leaf tissues reveals global as well as tissue-specific responses to photooxidation. We conclude that the differences in the mechanism of phytoene desaturase inhibition play an important role in differentiating these two white tissues.

Phylogenetic inference is a grand challenge in Bioinformatics due to immense computational requirements. The increasing popularity of multi-gene alignments in biological studies, which typically provide a stable topological signal due to a more favorable ratio of the number of base pairs to the number of sequences, coupled with rapid accumulation of sequence data in general, poses new challenges for high performance computing. In this paper, we demonstrate how stateof-the-art Maximum Likelihood (ML) programs can be efficiently scaled to the IBM BlueGene/L (BG/L) architecture, by porting RAxML, which is currently among the fastest and most accurate programs for phylogenetic inference under the ML criterion. We simultaneously exploit coarse-grained and fine-grained parallelism that is inherent in every ML-based biological analysis. Performance is assessed using datasets consisting of 212 sequences and 566,470 base pairs, and 2,182 sequences and 51,089 base pairs, respectively. To the best of our knowledge, these are the largest datasets analyzed under ML to date. The capability to analyze such datasets will help to address novel biological questions via phylogenetic analyses. Our experimental results indicate that the fine-grained parallelization scales well up to 1,024 processors. Moreover, a larger number of processors can be efficiently exploited by a combination of coarse-grained and fine-grained parallelism. Finally, we demonstrate that our parallelization scales equally well on an AMD Opteron cluster with a less favorable network latency to processor speed ratio. We recorded super-linear speedups in several cases due to increased cache efficiency.

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Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web.