Identification of genes involved in ceramide-dependent neuronal apoptosis using cDNA arrays

BACKGROUND: Ceramide is important in many cell responses, such as proliferation, differentiation, growth arrest and apoptosis. Elevated ceramide levels have been shown to induce apoptosis in primary neuronal cultures and neuronally differentiated PC 12 cells. RESULTS: To investigate gene expression during ceramide-dependent apoptosis, we carried out a global study of gene expression in neuronally differentiated PC 12 cells treated with C(2)-ceramide using an array of 9,120 cDNA clones. Although the criteria adopted for differential hybridization were stringent, modulation of expression of 239 genes was identified during the effector phase of C(2)-ceramide-induced cell death. We have made an attempt at classifying these genes on the basis of their putative functions, first with respect to known effects of ceramide or ceramide-mediated transduction systems, and then with respect to regulation of cell growth and apoptosis. CONCLUSIONS: Our cell-culture model has enabled us to establish a profile of gene expression during the effector phase of ceramide-mediated cell death. Of the 239 genes that met the criteria for differential hybridization, 10 correspond to genes previously involved in C(2)-ceramide or TNF-α signaling pathways and 20 in neuronal disorders, oncogenesis or more broadly in the regulation of proliferation. The remaining 209 genes, with or without known functions, constitute a pool of genes potentially implicated in the regulation of neuronal cell death

SAPHIR - a multi-scale, multi-resolution modeling environment targeting blood pressure regulation and fluid homeostasis.

International audienceWe present progress on a comprehensive, modular, interactive modeling environment centered on overall regulation of blood pressure and body fluid homeostasis. We call the project SAPHIR, for "a Systems Approach for PHysiological Integration of Renal, cardiac, and respiratory functions". The project uses state-of-the-art multi-scale simulation methods. The basic core model will give succinct input-output (reduced-dimension) descriptions of all relevant organ systems and regulatory processes, and it will be modular, multi-resolution, and extensible, in the sense that detailed submodules of any process(es) can be "plugged-in" to the basic model in order to explore, eg. system-level implications of local perturbations. The goal is to keep the basic core model compact enough to insure fast execution time (in view of eventual use in the clinic) and yet to allow elaborate detailed modules of target tissues or organs in order to focus on the problem area while maintaining the system-level regulatory compensations

Predicting RNA secondary structure by the comparative approach: how to select the homologous sequences

<p>Abstract</p> <p>Background</p> <p>The secondary structure of an RNA must be known before the relationship between its structure and function can be determined. One way to predict the secondary structure of an RNA is to identify covarying residues that maintain the pairings (Watson-Crick, Wobble and non-canonical pairings). This "comparative approach" consists of identifying mutations from homologous sequence alignments. The sequences must covary enough for compensatory mutations to be revealed, but comparison is difficult if they are too different. Thus the choice of homologous sequences is critical. While many possible combinations of homologous sequences may be used for prediction, only a few will give good structure predictions. This can be due to poor quality alignment in stems or to the variability of certain sequences. This problem of sequence selection is currently unsolved.</p> <p>Results</p> <p>This paper describes an algorithm, <it>SSCA</it>, which measures the suitability of sequences for the comparative approach. It is based on evolutionary models with structure constraints, particularly those on sequence variations and stem alignment. We propose three models, based on different constraints on sequence alignments. We show the results of the <it>SSCA </it>algorithm for predicting the secondary structure of several RNAs. <it>SSCA </it>enabled us to choose sets of homologous sequences that gave better predictions than arbitrarily chosen sets of homologous sequences.</p> <p>Conclusion</p> <p><it>SSCA </it>is an algorithm for selecting combinations of RNA homologous sequences suitable for secondary structure predictions with the comparative approach.</p

SSCA, an algorithm for selecting sequences to use in RNA secondary structure prediction by the comparative approach

National audienceOne way to predict the secondary structure of an RNA is to use the comparative approach, which consists of identifying mutations and identitties from homologous sequence alignments. The sequences must covary enough for compensatory mutations to be revealed, but comparison is difficult if they are too different. Thus the choice of homologous sequences is critical. While many possible combinations of homologous sequences may be used for prediction, only a few will give good structure predictions. We have developed an algorithm, SSCA, which measures the suitability of sequences for the comparative approach. It is based on evolutionary models with structure constraints, particularly those on sequence variations and stem alignment

Geum canadense Jacq.

https://thekeep.eiu.edu/herbarium_specimens_byname/8610/thumbnail.jp

Tfold, a complete and interactive system for predicting non coding RNA secondary structures

Tfold, a complete and interactive system for predicting non coding RNA secondary structuresPredicting RNA secondary structures is a very important task, and is still a challenging problem, even several methods and algorithms are proposed in the literature. We propose in this paper a complete and interactive system called Tfold, for predicting non coding RNA secondary structures. Tfold takes in input a RNA sequence for which the secondary structure is searched, and a set of aligned homologous sequences. It combines conservation, covariation and thermodynamic criteria for searching for stems. It searches for all kinds of pseudo-knots. It uses the principle of "divide and conquer" and searches for stems from the most stable to the less stable ones, by subdividing the sequence. Tfold uses an algorithm, called SSCA for selecting, from a large set of homologous sequences (taken from a database for example), the most appropriate sequences to use for the prediction. Tfold can take into account one or several stems considered by the user as belonging to the secondary structure. Besides, Tfold can return several structures (if requested by the user) in the case where it finds incompatible stems with close scores. Tfold has a complexity of O(n2), with n the sequence length. Tfold allows predicting a secondary structure in very few seconds. The developed tool, which offers several possibilities of using, is available on the web site: http://tfold.ibisc.univ-evry.fr/TFold