Comparative analysis of protein interaction networks

Recent advances in proteomics and computational biology have lead to a flood of protein interaction data and resulting interaction networks (e.g. (Gavin et al., 2002)). Here I first analyse the status and quality of parts lists (genes and proteins), then comparatively assess large-scale protein interaction data (von Mering et al., 2002) and finally try to identify biological meaningful units (e.g. pathways, cellular processes) within interaction networks that are derived from the conservation of gene neighborhood (Snel et al., 2002). Possible extensions of gene neighborhood analysis to eukaryotes (von Mering and Bork, 2002) will be discussed.     

Evolutionary relationships among heterokont algae (the autotrophic stramenopiles) based on combined analyses of small and large subunit ribosomal RNA

In order to study the phylogenetic relationships within the stramenopiles, and particularly among the heterokont algae, we have determined complete or nearly complete large-subunit ribosomal RNA sequences for different species of raphidophytes, phaeophytes, xanthophytes, chrysophytes, synurophytes and pinguiophytes. With the small- and large-subunit ribosomal RNA sequences of representatives for nearly all known groups of heterokont algae, phylogenetic trees were constructed from a concatenated alignment of both ribosomal RNAs, including more than 5,000 positions. By using different tree construction methods, inferred phylogenies showed phaeophytes and xanthophytes as sister taxa, as well as the pelagophytes and dictyochophytes, and the chrysophytes/synurophytes and eustigmatophytes. All these relationships are highly supported by bootstrap analysis. However, apart from these sister group relationships, very few other internodes are well resolved and most groups of heterokont algae seem to have diverged within a relatively short time frame.     

A novel antiinflammatory maintains glucocorticoid efficacy with reduced side effects

Glucocorticoids (GCs) are commonly used to treat inflammatory disease; unfortunately, the long-term use of these steroids leads to a large number of debilitating side effects. The antiinflammatory effects of GCs are a result of GC receptor (GR)-mediated inhibition of expression of proinflammatory genes as well as GR-mediated activation of antiinflammatory genes. Similarly, side effects are most likely due to both activated and repressed GR target genes in affected tissues. An as yet unachieved pharmaceutical goal is the development of a compound capable of separating detrimental side effects from antiinflammatory activity. We describe the discovery and characterization of AL-438, a GR ligand that exhibits an altered gene regulation profile, able to repress and activate only a subset of the genes normally regulated by GCs. When tested in vivo, AL-438 retains full antiinflammatory efficacy and potency comparable to steroids but its negative effects on bone metabolism and glucose control are reduced at equivalently antiinflammatory doses. The mechanism underlying this selective in vitro and in vivo activity may be the result of differential cofactor recruitment in response to ligand. AL-438 reduces the interaction between GR and peroxisomal proliferator-activated receptor gamma coactivator-1, a cofactor critical for steroid-mediated glucose up-regulation, while maintaining normal interactions with GR-interacting protein 1. This compound serves as a prototype for a unique, nonsteroidal alternative to conventional GCs in treating inflammatory disease.     

Nonsense-mediated mRNA decay in Drosophila: at the intersection of the yeast and mammalian pathways

The nonsense-mediated mRNA decay (NMD) pathway promotes the rapid degradation of mRNAs containing premature stop codons (PTCs). In Caenorhabditis elegans, seven genes (smg1-7) playing an essential role in NMD have been identified. Only SMG2-4 (known as UPF1-3) have orthologs in Saccharomyces cerevisiae. Here we show that the Drosophila orthologs of UPF1-3, SMG1, SMG5 and SMG6 are required for the degradation of PTC-containing mRNAs, but that there is no SMG7 ortholog in this organism. In contrast, orthologs of SMG5-7 are encoded by the human genome and all three are required for NMD. In human cells, exon boundaries have been shown to play a critical role in defining PTCs. This role is mediated by components of the exon junction complex (EJC). Contrary to expectation, however, we show that the components of the EJC are dispensable for NMD in Drosophila cells. Consistently, PTC definition occurs independently of exon boundaries in DROSOPHILA: Our findings reveal that despite conservation of the NMD machinery, different mechanisms have evolved to discriminate premature from natural stop codons in metazoa.     

Quantitative variability of 342 plasma proteins in a human twin population

The degree and the origins of quantitative variability of most human plasma proteins are largely unknown. Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2–7 years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors. The data indicate that different proteins show vastly different patterns of abundance variability among humans and that genetic control and longitudinal variation affect protein levels and biological processes to different degrees. The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors. Moreover, we identified 13 cis‐SNPs significantly influencing the level of specific plasma proteins. These results therefore have immediate implications for the effective design of blood‐based biomarker studies.