| 1. |
- Fontal-Cazalla, F.M., Buffington, M., Nordlander, G., Liljeblad, J., Ros-Farré, P., Nieves-Aldrey, J.L., Pujade-Villar, J. & Ronquist, F.
(författare)
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Phylogeny of the Eucoilinae (Hymenoptera: Cynipoidea: Figitidae).
- 2002
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Ingår i: Cladistics. ; :18, s. 154-199
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Ibarrola, Nieves, et al.
(författare)
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Cloning of a novel signaling molecule, AMSH-2, that potentiates transforming growth factor beta signaling.
- 2004
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Ingår i: BMC Cell Biology. - 1471-2121. ; 5:1, s. 2-
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Transforming growth factor-betas (TGF-betas), bone morphogenetic proteins (BMPs) and activins are important regulators of developmental cell growth and differentiation. Signaling by these factors is mediated chiefly by the Smad family of latent transcription factors. RESULTS: There are a large number of uncharacterized cDNA clones that code for novel proteins with homology to known signaling molecules. We have identified a novel molecule from the HUGE database that is related to a previously known molecule, AMSH (associated molecule with the SH3 domain of STAM), an adapter shown to be involved in BMP signaling. Both of these molecules contain a coiled-coil domain located within the amino-terminus region and a JAB (Domain in Jun kinase activation domain binding protein and proteasomal subunits) domain at the carboxy-terminus. We show that this novel molecule, which we have designated AMSH-2, is widely expressed and its overexpression potentiates activation of TGF-beta-dependent promoters. Coimmunoprecipitation studies indicated that Smad7 and Smad2, but not Smad3 or 4, interact with AMSH-2. We show that overexpression of AMSH-2 decreases the inhibitory effect of Smad7 on TGF-beta signaling. Finally, we demonstrate that knocking down AMSH-2 expression by RNA interference decreases the activation of 3TP-lux reporter in response to TGF-beta. CONCLUSIONS: This report implicates AMSH and AMSH-2 as a novel family of molecules that positively regulate the TGF-beta signaling pathway. Our results suggest that this effect could be partially explained by AMSH-2 mediated decrease of the action of Smad7 on TGF-beta signaling pathway.
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| 3. |
- Nylander, Johan A. A., et al.
(författare)
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Bayesian phylogenetic analysis of combined data
- 2004
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Ingår i: Systematic Biology. - : Oxford University Press (OUP). - 1063-5157 .- 1076-836X. ; 53:1, s. 47-67
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Tidskriftsartikel (populärvet., debatt m.m.)abstract
- The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.
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