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Markus Friberg

Publications and source records attributed to Markus Friberg.

3 recordsLinked to original sources

The Iron control element, acting in positive and negative control of iron-regulated Bradyrhizobium japonicum genes, is a target for the Irr protein.

Bradyrhizobium japonicum, the nitrogen-fixing soybean symbiont, possesses a heme uptake system encoded by the gene cluster hmuVUT-hmuR-exbBD-tonB. Transcription of the divergently oriented hmuT and hmuR genes was previously found to be induced by iron limitation and to depend on a 21-bp promoter-upstream iron control element (ICE). Here, we show by deletion analysis that the full-length ICE is needed for this type of positive control. Additional genes associated with ICE-like motifs were identified in the B. japonicum genome, of which bll6680 and blr7895 code for bacterioferritin and rubrerythrin homologs, respectively. Transcription start site mapping revealed that their ICEs directly overlap with either the -10 promoter region or the transcription initiation site, suggesting an involvement of the ICE in negative control of both genes. Consistent with this inference was the observed down-regulation of both genes under iron limitation, which in the case of bll6680 was shown to require an intact ICE motif. Using a yeast one-hybrid system, we demonstrated in vivo interaction of the iron response regulator (Irr) with all three ICEs. Moreover, specific in vitro binding of purified Irr protein to the ICE motifs of bll6680 and blr7895 was shown in electrophoretic mobility shift experiments. A genome-wide survey for iron-regulated genes with a custom-made Affymetrix gene chip revealed 17 genes to be induced and 68 to be repressed under iron-replete conditions. Remarkably, ICE-like motifs are associated with a large subset of those B. japonicum genes. We propose the ICE as an important cis-acting element in B. japonicum which represents the DNA-binding site for the Irr protein and, depending on its location within promoter regions, is involved in positive or negative control of the associated iron-regulated genes.

Bacterial Proteins↗

Scoring functions for transcription factor binding site prediction.

BACKGROUND: Transcription factor binding site (TFBS) prediction is a difficult problem, which requires a good scoring function to discriminate between real binding sites and background noise. Many scoring functions have been proposed in the literature, but it is difficult to assess their relative performance, because they are implemented in different software tools using different search methods and different TFBS representations. RESULTS: Here we compare how several scoring functions perform on both real and semi-simulated data sets in a common test environment. We have also developed two new scoring functions and included them in the comparison. The data sets are from the yeast (S. cerevisiae) genome. Our new scoring function LLBG (least likely under the background model) performs best in this study. It achieves the best average rank for the correct motifs. Scoring functions based on positional bias performed quite poorly in this study. CONCLUSION: LLBG may provide an interesting alternative to current scoring functions for TFBS prediction.

Algorithms↗

Limitations of codon adaptation index and other coding DNA-based features for prediction of protein expression in Saccharomyces cerevisiae.

The relationship between codon usage and protein/mRNA expression in S. cerevisiae has been extensively studied. Recently, protein expression data for the whole yeast genome was published. We investigate which properties of coding DNA sequences can be used to predict expression levels. The new algorithm by Carbone et al. for computing dominating codon bias in a genome is evaluated. It is concluded that it works at least as well as existing methods, and eliminates the need to arbitrarily choose a set of highly expressed genes. Also, the hypothesis that information on codon pair frequencies can be used to predict expression is investigated. Our conclusion is that codon pairs do not contribute more information than do single codon frequencies. Overall correlation between predicted and actual expression data using properties of coding DNA sequences is around 0.65. Hence, while being a useful source of information, the expression levels predicted by these methods should only be used as a rule of thumb.

Algorithms↗