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EEF1A: thinking outside the ribosome.

ABSTRACT: Eukaryotic translation elongation factor 1A (eEF1A) is one of the most abundant protein synthesis factors. eEF1A is responsible for the delivery of all aminoacyl-tRNAs to the ribosome, aside from initiator and selenocysteine tRNAs. In addition to its roles in polypeptide chain elongation, unique cellular and viral activities have been attributed to eEF1A in eukaryotes from yeast to plants and mammals. From preliminary, speculative associations to well characterized biochemical and biological interactions, it is clear that eEF1A, of all the translation factors, has been ascribed the most functions outside of protein synthesis. A mechanistic understanding of these non-canonical functions of eEF1A will shed light on many important biological questions, including viral-host interaction, subcellular organization, and the integration of key cellular pathways.

SUBMITTER: Mateyak MK

PROVIDER: S-EPMC2898402 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

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eEF1A: thinking outside the ribosome.

Mateyak Maria K MK Kinzy Terri Goss TG

The Journal of biological chemistry 20100505 28

Eukaryotic translation elongation factor 1A (eEF1A) is one of the most abundant protein synthesis factors. eEF1A is responsible for the delivery of all aminoacyl-tRNAs to the ribosome, aside from initiator and selenocysteine tRNAs. In addition to its roles in polypeptide chain elongation, unique cellular and viral activities have been attributed to eEF1A in eukaryotes from yeast to plants and mammals. From preliminary, speculative associations to well characterized biochemical and biological int ...[more]

PMID: 20444696

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Project description:BackgroundGreater epidemiologic understanding of the relationships among fetal-infant mortality and its prognostic factors, including birthweight, could have vast public health implications. A key step toward that understanding is a realistic and tractable framework for analyzing birthweight distributions and fetal-infant mortality. The present paper is the second of a two-part series that introduces such a framework.MethodsWe propose estimating birthweight-specific mortality within each component of a normal mixture model representing a birthweight distribution, the number of components having been determined from the data rather than fixed a priori.ResultsWe address a number of methodological issues related to our proposal, including the construction of confidence intervals for mortality risk at any given birthweight within a component, for odds ratios comparing mortality within two different components from the same population, and for odds ratios comparing mortality within analogous components from two different populations. As an illustration we find that, for a population of white singleton infants, the odds of mortality at 3000 g are an estimated 4.15 times as large in component 2 of a 4-component normal mixture model as in component 4 (95% confidence interval, 2.04 to 8.43). We also outline an extension of our framework through which covariates could be probabilistically related to mixture components. This extension might allow the assertion of approximate correspondences between mixture components and identifiable subpopulations.ConclusionsThe framework developed in this paper does not require infants from compromised pregnancies to share a common birthweight-specific mortality curve, much less assume the existence of an interval of birthweights over which all infants have the same curve. Hence, the present framework can reveal heterogeneity in mortality that is undetectable via a contaminated normal model or a 2-component normal mixture model.

Dataset Information

EEF1A: thinking outside the ribosome.

Publications

eEF1A: thinking outside the ribosome.

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