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Marjan Trutschl

Publications and source records attributed to Marjan Trutschl.

2 recordsLinked to original sources

Translation initiation factor eIF4G-1 binds to eIF3 through the eIF3e subunit.

eIF3 in mammals is the largest translation initiation factor ( approximately 800 kDa) and is composed of 13 nonidentical subunits designated eIF3a-m. The role of mammalian eIF3 in assembly of the 48 S complex occurs through high affinity binding to eIF4G. Interactions of eIF4G with eIF4E, eIF4A, eIF3, poly(A)-binding protein, and Mnk1/2 have been mapped to discrete domains on eIF4G, and conversely, the eIF4G-binding sites on all but one of these ligands have been determined. The only eIF4G ligand for which this has not been determined is eIF3. In this study, we have sought to identify the mammalian eIF3 subunit(s) that directly interact(s) with eIF4G. Established procedures for detecting protein-protein interactions gave ambiguous results. However, binding of partially proteolyzed HeLa eIF3 to the eIF3-binding domain of human eIF4G-1, followed by high throughput analysis of mass spectrometric data with a novel peptide matching algorithm, identified a single subunit, eIF3e (p48/Int-6). In addition, recombinant FLAG-eIF3e specifically competed with HeLa eIF3 for binding to eIF4G in vitro. Adding FLAG-eIF3e to a cell-free translation system (i) inhibited protein synthesis, (ii) caused a shift of mRNA from heavy to light polysomes, (iii) inhibited cap-dependent translation more severely than translation dependent on the HCV or CSFV internal ribosome entry sites, which do not require eIF4G, and (iv) caused a dramatic loss of eIF4G and eIF2alpha from complexes sedimenting at approximately 40 S. These data suggest a specific, direct, and functional interaction of eIF3e with eIF4G during the process of cap-dependent translation initiation, although they do not rule out participation of other eIF3 subunits.

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Application of machine learning and visualization of heterogeneous datasets to uncover relationships between translation and developmental stage expression of C. elegans mRNAs.

The relationships between genes in neighboring clusters in a self-organizing map (SOM) and properties attributed to them are sometimes difficult to discern, especially when heterogeneous datasets are used. We report a novel approach to identify correlations between heterogeneous datasets. One dataset, derived from microarray analysis of polysomal distribution, contained changes in the translational efficiency of Caenorhabditis elegans mRNAs resulting from loss of specific eIF4E isoform. The other dataset contained expression patterns of mRNAs across all developmental stages. Two algorithms were applied to these datasets: a classical scatter plot and an SOM. The outputs were linked using a two-dimensional color scale. This revealed that an mRNA's eIF4E-dependent translational efficiency is strongly dependent on its expression during development. This correlation was not detectable with a traditional one-dimensional color scale.

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