The number of paralogs of proteins involved in translation initiation is larger in trypanosomes than in yeasts or many metazoan and includes two poly(A) binding proteins, PABP1 and PABP2, and four eIF4E variants. In many cases, the paralogs are individually essential and are thus unlikely to have redundant functions although, as yet, distinct functions of different isoforms have not been determined. Here, trypanosome PABP1 and PABP2 have been further characterised. PABP1 and PABP2 diverged subsequent to the differentiation of the Kinetoplastae lineage, supporting the existence of specific aspects of translation initiation regulation. PABP1 and PABP2 exhibit major differences in intracellular localization and distribution on polysome fractionation under various conditions that interfere with mRNA metabolism. Most striking are differences in localization to the four known types of inducible RNP granules. Moreover, only PABP2 but not PABP1 can accumulate in the nucleus. Taken together, these observations indicate that PABP1 and PABP2 likely associate with distinct populations of mRNAs. The differences in localization to inducible RNP granules also apply to paralogs of components of the eIF4F complex: eIF4E1 showed similar localization pattern to PABP2, whereas the localisation of eIF4E4 and eIF4G3 resembled that of PABP1. The grouping of translation initiation as either colocalizing with PABP1 or with PABP2 can be used to complement interaction studies to further define the translation initiation complexes in kinetoplastids.

The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellular metabolome. Metabolic network databases are of increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments. Here we present a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis. In addition to enabling navigation through the BioCyc-based TrypanoCyc interface, we have also implemented a network-based representation of the information through MetExplore, yielding a novel environment in which to visualise the metabolism of this important parasite.