Asymmetric relationships between proteins shape genome evolution

An investigation of metabolic networks in E. coli and S. cerevisiae reveals that asymmetric protein interactions affect gene expression, the relative effect of gene-knockouts and genome evolution

Identification of cyanobacterial non-coding RNAs by comparative genome analysis

BACKGROUND: Whole genome sequencing of marine cyanobacteria has revealed an unprecedented degree of genomic variation and streamlining. With a size of 1.66 megabase-pairs, Prochlorococcus sp. MED4 has the most compact of these genomes and it is enigmatic how the few identified regulatory proteins efficiently sustain the lifestyle of an ecologically successful marine microorganism. Small non-coding RNAs (ncRNAs) control a plethora of processes in eukaryotes as well as in bacteria; however, systematic searches for ncRNAs are still lacking for most eubacterial phyla outside the enterobacteria. RESULTS: Based on a computational prediction we show the presence of several ncRNAs (cyanobacterial functional RNA or Yfr) in several different cyanobacteria of the Prochlorococcus-Synechococcus lineage. Some ncRNA genes are present only in two or three of the four strains investigated, whereas the RNAs Yfr2 through Yfr5 are structurally highly related and are encoded by a rapidly evolving gene family as their genes exist in different copy numbers and at different sites in the four investigated genomes. One ncRNA, Yfr7, is present in at least seven other cyanobacteria. In addition, control elements for several ribosomal operons were predicted as well as riboswitches for thiamine pyrophosphate and cobalamin. CONCLUSION: This is the first genome-wide and systematic screen for ncRNAs in cyanobacteria. Several ncRNAs were both computationally predicted and their presence was biochemically verified. These RNAs may have regulatory functions and each shows a distinct phylogenetic distribution. Our approach can be applied to any group of microorganisms for which more than one total genome sequence is available for comparative analysis

An unexpected twist to the activation of IKKβ:TAK1 primes IKKβ for activation by autophosphorylation

IKKβ {IκB [inhibitor of NF-κB (nuclear factor κB)] kinase β} is required to activate the transcription factor NF-κB, but how IKKβ itself is activated in vivo is still unclear. It was found to require phosphorylation by one or more ‘upstream’ protein kinases in some reports, but by autophosphorylation in others. In the present study, we resolve this contro-versy by demonstrating that the activation of IKKβ induced by IL-1 (interleukin-1) or TNF (tumour necrosis factor) in embryonic fibroblasts, or by ligands that activate Toll-like receptors in macrophages, requires two distinct phosphorylation events: first, the TAK1 [TGFβ (transforming growth factor β)-activated kinase-1]-catalysed phosphorylation of Ser(177) and, secondly, the IKKβ-catalysed autophosphorylation of Ser(181). The phosphorylation of Ser(177) by TAK1 is a priming event required for the subsequent autophosphorylation of Ser(181), which enables IKKβ to phosphorylate exogenous substrates. We also provide genetic evidence which indicates that the IL-1-stimulated, LUBAC (linear ubiquitin chain assembly complex)-catalysed formation of linear ubiquitin chains and their interaction with the NEMO (NF-κB essential modulator) component of the canonical IKK complex permits the TAK1-catalysed priming phosphorylation of IKKβ at Ser(177) and IKKα at Ser(176). These findings may be of general significance for the activation of other protein kinases

ACEseq – allele specific copy number estimation from whole genome sequencing

ACEseq is a computational tool for allele-specific copy number estimation in tumor genomes based on whole genome sequencing. In contrast to other tools it features GC-bias correction, unique replication timing-bias correction and integration of structural variant (SV) breakpoints for improved genome segmentation. ACEseq clearly outperforms widely used state-of-the art methods, provides a fully automated estimation of tumor cell content and ploidy, and additionally computes homologous recombination deficiency scores.</jats:p

The anti-inflammatory drug BAY 11-7082 suppresses the MyD88-dependent signalling network by targeting the ubiquitin system

The compound BAY 11-7082 inhibits IκBα [inhibitor of NF-κB (nuclear factor κB)α] phosphorylation in cells and has been used to implicate the canonical IKKs (IκB kinases) and NF-κB in >350 publications. In the present study we report that BAY 11-7082 does not inhibit the IKKs, but suppresses their activation in LPS (lipopolysaccharide)-stimulated RAW macrophages and IL (interleukin)-1-stimulated IL-1R (IL-1 receptor) HEK (human embryonic kidney)-293 cells. BAY 11-7082 exerts these effects by inactivating the E2-conjugating enzymes Ubc (ubiquitin conjugating) 13 and UbcH7 and the E3 ligase LUBAC (linear ubiquitin assembly complex), thereby preventing the formation of Lys(63)-linked and linear polyubiquitin chains. BAY 11-7082 prevents ubiquitin conjugation to Ubc13 and UbcH7 by forming a covalent adduct with their reactive cysteine residues via Michael addition at the C(3) atom of BAY 11-7082, followed by the release of 4-methylbenzene-sulfinic acid. BAY 11-7082 stimulated Lys(48)-linked polyubiquitin chain formation in cells and protected HIF1α (hypoxia-inducible factor 1α) from proteasomal degradation, suggesting that it inhibits the proteasome. The results of the present study indicate that the anti-inflammatory effects of BAY 11-7082, its ability to induce B-cell lymphoma and leukaemic T-cell death and to prevent the recruitment of proteins to sites of DNA damage are exerted via inhibition of components of the ubiquitin system and not by inhibiting NF-κB

Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution

The gap between the amount of genome information released by genome sequencing projects and our knowledge about the proteins' functions is rapidly increasing. To fill this gap, various ‘genomic-context’ methods have been proposed that exploit sequenced genomes to predict the functions of the encoded proteins. One class of methods, phylogenetic profiling, predicts protein function by correlating the phylogenetic distribution of genes with that of other genes or phenotypic characteristics. The functions of a number of proteins, including ones of medical relevance, have thus been predicted and subsequently confirmed experimentally. Additionally, various approaches to measure the similarity of phylogenetic profiles and to account for the phylogenetic bias in the data have been proposed. We review the successful applications of phylogenetic profiling and analyse the performance of various profile similarity measures with a set of one microsporidial and 25 fungal genomes. In the fungi, phylogenetic profiling yields high-confidence predictions for the highest and only the highest scoring gene pairs illustrating both the power and the limitations of the approach. Both practical examples and theoretical considerations suggest that in order to get a reliable and specific picture of a protein's function, results from phylogenetic profiling have to be combined with other sources of evidence