Systems-Level Modeling of Cancer-Fibroblast Interaction

Cancer cells interact with surrounding stromal fibroblasts during tumorigenesis, but the complex molecular rules that govern these interactions remain poorly understood thus hindering the development of therapeutic strategies to target cancer stroma. We have taken a mathematical approach to begin defining these rules by performing the first large-scale quantitative analysis of fibroblast effects on cancer cell proliferation across more than four hundred heterotypic cell line pairings. Systems-level modeling of this complex dataset using singular value decomposition revealed that normal tissue fibroblasts variably express at least two functionally distinct activities, one which reflects transcriptional programs associated with activated mesenchymal cells, that act either coordinately or at cross-purposes to modulate cancer cell proliferation. These findings suggest that quantitative approaches may prove useful for identifying organizational principles that govern complex heterotypic cell-cell interactions in cancer and other contexts

Bounding Mean First Passage Times in Population Continuous-Time Markov Chains

We consider the problem of bounding mean first passage times and reachability probabilities for the class of population continuous-time Markov chains, which capture stochastic interactions between groups of identical agents. The quantitative analysis of such models is notoriously difficult since typically neither state-based numerical approaches nor methods based on stochastic sampling give efficient and accurate results. Here, we propose a novel approach that leverages techniques from martingale theory and stochastic processes to generate constraints on the statistical moments of first passage time distributions. These constraints induce a semi-definite program that can be used to compute exact bounds on reachability probabilities and mean first passage times without numerically solving the transient probability distribution of the process or sampling from it. We showcase the method on some test examples and tailor it to models exhibiting multimodality, a class of particularly challenging scenarios from biology

MediPlEx - a tool to combine in silico & experimental gene expression profiles of the model legume Medicago truncatula

Henckel K, Küster H, Stutz L, Goesmann A. MediPlEx - a tool to combine in silico and experimental gene expression profiles of the model legume Medicago truncatula. BMC Research Notes. 2010;3(1): 262.BACKGROUND:Expressed Sequence Tags (ESTs) are in general used to gain a first insight into gene activities from a species of interest. Subsequently, and typically based on a combination of EST and genome sequences, microarray-based expression analyses are performed for a variety of conditions. In some cases, a multitude of EST and microarray experiments are conducted for one species, covering different tissues, cell states, and cell types. Under these circumstances, the challenge arises to combine results derived from the different expression profiling strategies, with the goal to uncover novel information on the basis of the integrated datasets.FINDINGS:Using our new application, MediPlEx (MEDIcago truncatula multiPLe EXpression analysis), expression data from EST experiments, oligonucleotide microarrays and Affymetrix GeneChips can be combined and analyzed, leading to a novel approach to integrated transcriptome analysis. We have validated our tool via the identification of a set of well-characterized AM-specific and AM-induced marker genes, identified by MediPlEx on the basis of in silico and experimental gene expression profiles from roots colonized with AM fungi.CONCLUSIONS:MediPlEx offers an integrated analysis pipeline for different sets of expression data generated for the model legume Medicago truncatula. As expected, in silico and experimental gene expression data that cover the same biological condition correlate well. The collection of differentially expressed genes identified via MediPlEx provides a starting point for functional studies in plant mutants. MediPlEx can freely be used at http://www.cebitec.uni-bielefeld.de/mediplex

DirtyGenes: testing for significant changes in gene or bacterial population compositions from a small number of samples

High throughput genomics technologies are applied widely to microbiomes in humans, animals, soil and water, to detect changes in bacterial communities or the genes they carry, between different environments or treatments. We describe a method to test the statistical significance of differences in bacterial population or gene composition, applicable to metagenomic or quantitative polymerase chain reaction data. Our method goes beyond previous published work in being universally most powerful, thus better able to detect statistically significant differences, and through being more reliable for smaller sample sizes. It can also be used for experimental design, to estimate how many samples to use in future experiments, again with the advantage of being universally most powerful. We present three example analyses in the area of antimicrobial resistance. The first is to published data on bacterial communities and antimicrobial resistance genes (ARGs) in the environment; we show that there are significant changes in both ARG and community composition. The second is to new data on seasonality in bacterial communities and ARGs in hooves from four sheep. While the observed differences are not significant, we show that a minimum group size of eight sheep would provide sufficient power to observe significance of similar changes in further experiments. The third is to published data on bacterial communities surrounding rice crops. This is a much larger data set and is used to verify the new method. Our method has broad uses for statistical testing and experimental design in research on changing microbiomes, including studies on antimicrobial resistance

Characterization of transcriptome dynamics during watermelon fruit development: sequencing, assembly, annotation and gene expression profiles

<p>Abstract</p> <p>Background</p> <p>Cultivated watermelon [<it>Citrullus lanatus </it>(Thunb.) Matsum. & Nakai var. <it>lanatus</it>] is an important agriculture crop world-wide. The fruit of watermelon undergoes distinct stages of development with dramatic changes in its size, color, sweetness, texture and aroma. In order to better understand the genetic and molecular basis of these changes and significantly expand the watermelon transcript catalog, we have selected four critical stages of watermelon fruit development and used Roche/454 next-generation sequencing technology to generate a large expressed sequence tag (EST) dataset and a comprehensive transcriptome profile for watermelon fruit flesh tissues.</p> <p>Results</p> <p>We performed half Roche/454 GS-FLX run for each of the four watermelon fruit developmental stages (immature white, white-pink flesh, red flesh and over-ripe) and obtained 577,023 high quality ESTs with an average length of 302.8 bp. <it>De novo </it>assembly of these ESTs together with 11,786 watermelon ESTs collected from GenBank produced 75,068 unigenes with a total length of approximately 31.8 Mb. Overall 54.9% of the unigenes showed significant similarities to known sequences in GenBank non-redundant (nr) protein database and around two-thirds of them matched proteins of cucumber, the most closely-related species with a sequenced genome. The unigenes were further assigned with gene ontology (GO) terms and mapped to biochemical pathways. More than 5,000 SSRs were identified from the EST collection. Furthermore we carried out digital gene expression analysis of these ESTs and identified 3,023 genes that were differentially expressed during watermelon fruit development and ripening, which provided novel insights into watermelon fruit biology and a comprehensive resource of candidate genes for future functional analysis. We then generated profiles of several interesting metabolites that are important to fruit quality including pigmentation and sweetness. Integrative analysis of metabolite and digital gene expression profiles helped elucidating molecular mechanisms governing these important quality-related traits during watermelon fruit development.</p> <p>Conclusion</p> <p>We have generated a large collection of watermelon ESTs, which represents a significant expansion of the current transcript catalog of watermelon and a valuable resource for future studies on the genomics of watermelon and other closely-related species. Digital expression analysis of this EST collection allowed us to identify a large set of genes that were differentially expressed during watermelon fruit development and ripening, which provide a rich source of candidates for future functional analysis and represent a valuable increase in our knowledge base of watermelon fruit biology.</p

Analysis of expressed sequence tags generated from full-length enriched cDNA libraries of melon

Abstract Background Melon (Cucumis melo), an economically important vegetable crop, belongs to the Cucurbitaceae family which includes several other important crops such as watermelon, cucumber, and pumpkin. It has served as a model system for sex determination and vascular biology studies. However, genomic resources currently available for melon are limited. Result We constructed eleven full-length enriched and four standard cDNA libraries from fruits, flowers, leaves, roots, cotyledons, and calluses of four different melon genotypes, and generated 71,577 and 22,179 ESTs from full-length enriched and standard cDNA libraries, respectively. These ESTs, together with ~35,000 ESTs available in public domains, were assembled into 24,444 unigenes, which were extensively annotated by comparing their sequences to different protein and functional domain databases, assigning them Gene Ontology (GO) terms, and mapping them onto metabolic pathways. Comparative analysis of melon unigenes and other plant genomes revealed that 75% to 85% of melon unigenes had homologs in other dicot plants, while approximately 70% had homologs in monocot plants. The analysis also identified 6,972 gene families that were conserved across dicot and monocot plants, and 181, 1,192, and 220 gene families specific to fleshy fruit-bearing plants, the Cucurbitaceae family, and melon, respectively. Digital expression analysis identified a total of 175 tissue-specific genes, which provides a valuable gene sequence resource for future genomics and functional studies. Furthermore, we identified 4,068 simple sequence repeats (SSRs) and 3,073 single nucleotide polymorphisms (SNPs) in the melon EST collection. Finally, we obtained a total of 1,382 melon full-length transcripts through the analysis of full-length enriched cDNA clones that were sequenced from both ends. Analysis of these full-length transcripts indicated that sizes of melon 5' and 3' UTRs were similar to those of tomato, but longer than many other dicot plants. Codon usages of melon full-length transcripts were largely similar to those of Arabidopsis coding sequences. Conclusion The collection of melon ESTs generated from full-length enriched and standard cDNA libraries is expected to play significant roles in annotating the melon genome. The ESTs and associated analysis results will be useful resources for gene discovery, functional analysis, marker-assisted breeding of melon and closely related species, comparative genomic studies and for gaining insights into gene expression patterns.This work was supported by Research Grant Award No. IS-4223-09C from BARD, the United States-Israel Binational Agricultural Research and Development Fund, and by SNC Laboratoire ASL, de Ruiter Seeds B.V., Enza Zaden B.V., Gautier Semences S.A., Nunhems B.V., Rijk Zwaan B.V., Sakata Seed Inc, Semillas Fitó S.A., Seminis Vegetable Seeds Inc, Syngenta Seeds B.V., Takii and Company Ltd, Vilmorin and Cie S.A. and Zeraim Gedera Ltd (all of them as part of the support to ICuGI). CC was supported by CNRS ERL 8196.Peer Reviewe

Deep sequencing of the Mexican avocado transcriptome, an ancient angiosperm with a high content of fatty acids

Background: Avocado (Persea americana) is an economically important tropical fruit considered to be a good source of fatty acids. Despite its importance, the molecular and cellular characterization of biochemical and developmental processes in avocado is limited due to the lack of transcriptome and genomic information. Results: The transcriptomes of seeds, roots, stems, leaves, aerial buds and flowers were determined using different sequencing platforms. Additionally, the transcriptomes of three different stages of fruit ripening (pre-climacteric, climacteric and post-climacteric) were also analyzed. The analysis of the RNAseqatlas presented here reveals strong differences in gene expression patterns between different organs, especially between root and flower, but also reveals similarities among the gene expression patterns in other organs, such as stem, leaves and aerial buds (vegetative organs) or seed and fruit (storage organs). Important regulators, functional categories, and differentially expressed genes involved in avocado fruit ripening were identified. Additionally, to demonstrate the utility of the avocado gene expression atlas, we investigated the expression patterns of genes implicated in fatty acid metabolism and fruit ripening. Conclusions: A description of transcriptomic changes occurring during fruit ripening was obtained in Mexican avocado, contributing to a dynamic view of the expression patterns of genes involved in fatty acid biosynthesis and the fruit ripening process