Kristian Vlahoviček

Histone modification levels are predictive for gene expression

Histones are frequently decorated with covalent modifications. These histone modifications are thought to be involved in various chromatin-dependent processes including transcription. To elucidate the relationship between histone modifications and transcription, we derived quantitative models to predict the expression level of genes from histone modification levels. We found that histone modification levels and gene expression are very well correlated. Moreover, we show that only a small number of histone modifications are necessary to accurately predict gene expression. We show that different sets of histone modifications are necessary to predict gene expression driven by high CpG content promoters (HCPs) or low CpG content promoters (LCPs). Quantitative models involving H3K4me3 and H3K79me1 are the most predictive of the expression levels in LCPs, whereas HCPs require H3K27ac and H4K20me1. Finally, we show that the connections between histone modifications and gene expression seem to be general, as we were able to predict gene expression levels of one cell type using a model trained on another one.

Cell-of-origin chromatin organization shapes the mutational landscape of cancer

Cancer is a disease potentiated by mutations in somatic cells. Cancer mutations are not distributed uniformly along the human genome. Instead, different human genomic regions vary by up to fivefold in the local density of cancer somatic mutations, posing a fundamental problem for statistical methods used in cancer genomics. Epigenomic organization has been proposed as a major determinant of the cancer mutational landscape. However, both somatic mutagenesis and epigenomic features are highly cell-type-specific. We investigated the distribution of mutations in multiple independent samples of diverse cancer types and compared them to cell-type-specific epigenomic features. Here we show that chromatin accessibility and modification, together with replication timing, explain up to 86% of the variance in mutation rates along cancer genomes. The best predictors of local somatic mutation density are epigenomic features derived from the most likely cell type of origin of the corresponding malignancy. Moreover, we find that cell-of-origin chromatin features are much stronger determinants of cancer mutation profiles than chromatin features of matched cancer cell lines. Furthermore, we show that the cell type of origin of a cancer can be accurately determined based on the distribution of mutations along its genome. Thus, the DNA sequence of a cancer genome encompasses a wealth of information about the identity and epigenomic features of its cell of origin.

Prediction of Protein–Protein Interaction Sites in Sequences and 3D Structures by Random Forests

Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.

A Retrotransposon-Driven Dicer Isoform Directs Endogenous Small Interfering RNA Production in Mouse Oocytes

In mammals, a single Dicer participates in biogenesis of small RNAs in microRNA (miRNA) and RNAi pathways. In mice, endogenous RNAi is highly active in oocytes, but not in somatic cells, which we ascribe here to an oocyte-specific Dicer isoform (Dicer(O)). Dicer(O) lacks the N-terminal DExD helicase domain and has higher cleavage activity than the full-length Dicer in somatic cells (Dicer(S)). Unlike Dicer(S), Dicer(O) efficiently produces small RNAs from long double-stranded (dsRNA) substrates. Expression of the Dicer(O) isoform is driven by an intronic MT-C retrotransposon promoter, deletion of which causes loss of Dicer(O) and female sterility. Oocytes from females lacking the MT-C element show meiotic spindle defects and increased levels of endogenous small interfering RNA (endo-siRNA) targets, phenocopying the maternal Dicer null phenotype. The alternative Dicer isoform, whose phylogenetic origin demonstrates evolutionary plasticity of RNA-silencing pathways, is the main determinant of endogenous RNAi activity in the mouse female germline.

PSAIA – Protein Structure and Interaction Analyzer

BackgroundPSAIA (Protein Structure and Interaction Analyzer) was developed to compute geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction sites.ResultsIn addition to most relevant established algorithms, PSAIA offers a new method PIADA (Protein Interaction Atom Distance Algorithm) for the determination of residue interaction pairs. We found that PIADA produced more satisfactory results than comparable algorithms implemented in PSAIA.Particular advantages of PSAIA include its capacity to combine different methods to detect the locations and types of interactions between residues and its ability, without any further automation steps, to handle large numbers of protein structures and complexes. Generally, the integration of a variety of methods enables PSAIA to offer easier automation of analysis and greater reliability of results.PSAIA can be used either via a graphical user interface or from the command-line. Results are generated in either tabular or XML format.ConclusionIn a straightforward fashion and for large sets of protein structures, PSAIA enables the calculation of protein geometric parameters and the determination of location and type for protein-protein interaction sites. XML formatted output enables easy conversion of results to various formats suitable for statistic analysis.Results from smaller data sets demonstrated the influence of geometry on protein interaction sites. Comprehensive analysis of properties of large data sets lead to new information useful in the prediction of protein-protein interaction sites.

Translational Selection Is Ubiquitous in Prokaryotes

Codon usage bias in prokaryotic genomes is largely a consequence of background substitution patterns in DNA, but highly expressed genes may show a preference towards codons that enable more efficient and/or accurate translation. We introduce a novel approach based on supervised machine learning that detects effects of translational selection on genes, while controlling for local variation in nucleotide substitution patterns represented as sequence composition of intergenic DNA. A cornerstone of our method is a Random Forest classifier that outperformed previous distance measure-based approaches, such as the codon adaptation index, in the task of discerning the (highly expressed) ribosomal protein genes by their codon frequencies. Unlike previous reports, we show evidence that translational selection in prokaryotes is practically universal: in 460 of 461 examined microbial genomes, we find that a subset of genes shows a higher codon usage similarity to the ribosomal proteins than would be expected from the local sequence composition. These genes constitute a substantial part of the genome—between 5% and 33%, depending on genome size—while also exhibiting higher experimentally measured mRNA abundances and tending toward codons that match tRNA anticodons by canonical base pairing. Certain gene functional categories are generally enriched with, or depleted of codon-optimized genes, the trends of enrichment/depletion being conserved between Archaea and Bacteria. Prominent exceptions from these trends might indicate genes with alternative physiological roles; we speculate on specific examples related to detoxication of oxygen radicals and ammonia and to possible misannotations of asparaginyl–tRNA synthetases. Since the presence of codon optimizations on genes is a valid proxy for expression levels in fully sequenced genomes, we provide an example of an “adaptome” by highlighting gene functions with expression levels elevated specifically in thermophilic Bacteria and Archaea.

The first murine zygotic transcription is promiscuous and uncoupled from splicing and 3′ processing

Initiation of zygotic transcription in mammals is poorly understood. In mice, zygotic transcription is first detected shortly after pronucleus formation in 1‐cell embryos, but the identity of the transcribed loci and mechanisms regulating their expression are not known. Using total RNA‐Seq, we have found that transcription in 1‐cell embryos is highly promiscuous, such that intergenic regions are extensively expressed and thousands of genes are transcribed at comparably low levels. Striking is that transcription can occur in the absence of defined core‐promoter elements. Furthermore, accumulation of translatable zygotic mRNAs is minimal in 1‐cell embryos because of inefficient splicing and 3′ processing of nascent transcripts. These findings provide novel insights into regulation of gene expression in 1‐cell mouse embryos that may confer a protective mechanism against precocious gene expression that is the product of a relaxed chromatin structure present in 1‐cell embryos. The results also suggest that the first zygotic transcription itself is an active component of chromatin remodeling in 1‐cell embryos.

Demosponge EST sequencing reveals a complex genetic toolkit of the simplest metazoans

Sponges (Porifera) are among the simplest living and the earliest branching metazoans. They hold a pivotal role for studying genome evolution of the entire metazoan branch, both as an outgroup to Eumetazoa and as the closest branching phylum to the common ancestor of all multicellular animals (Urmetazoa). In order to assess the transcription inventory of sponges, we sequenced expressed sequence tag libraries of two demosponge species, Suberites domuncula and Lubomirskia baicalensis, and systematically analyzed the assembled sponge transcripts against their homologs from complete proteomes of six well-characterized metazoans—Nematostella vectensis, Caenorhabditis elegans, Drosophila melanogaster, Strongylocentrotus purpuratus, Ciona intestinalis, and Homo sapiens. We show that even the earliest metazoan species already have strikingly complex genomes in terms of gene content and functional repertoire and that the rich gene repertoire existed even before the emergence of true tissues, therefore further emphasizing the importance of gene loss and spatio-temporal changes in regulation of gene expression in shaping the metazoan genomes. Our findings further indicate that sponge and human genes generally show similarity levels higher than expected from their respective positions in metazoan phylogeny, providing direct evidence for slow rate of evolution in both “basal” and “apical” metazoan genome lineages. We propose that the ancestor of all metazoans had already had an unusually complex genome, thereby shifting the origins of genome complexity from Urbilateria to Urmetazoa.

Highly reactive cysteine residues are part of the substrate binding site of mammalian dipeptidyl peptidases III.

Dipeptidyl peptidase III (DPP III) is a cytosolic zinc-exopeptidase involved in the intracellular protein catabolism of eukaryotes. Although inhibition by thiol reagents is a general feature of DPP III originating from various species, the function of activity important sulfhydryl groups is still inadequately understood. The present study of the reactivity of these groups was undertaken in order to clarify their biological significance. The inactivation kinetics of human and rat DPP III by sulfhydryl reagent p-hydroxy-mercuribenzoate (pHMB) was monitored by determination of the enzymes residual activity with fluorimetric detection. Inactivation of this human enzyme exhibited pseudo-first-order kinetics, suggesting that all reactive SH-groups have equivalent reactivity, and the second-order rate constant was calculated to be 3523+/-567M(-1)min(-1). Rat DPP III was hyperreactive to pHMB and showed biphasic kinetics indicating two classes of reactive SH-groups. The second-order rate constants of 3540M(-1)s(-1) for slower reacting sulfhydryl, and 21,855M(-1)s(-1) for faster reacting sulfhydryl were obtained from slopes of linear plots of pseudo-first-order constants versus reagent concentration. Peptide substrates protected both mammalian DPPs III from inactivation by pHMB. Physiological concentrations of biological thiols and H(2)O(2) inactivated the rat DPP III. Human enzyme was resistant to H(2)O(2) attack and less affected by reduced glutathione (GSH) than the rat homologue. A significantly lower DPP III level, determined by activity measurement and Western blotting, was found in the cytosols of highly oxygenated rat tissues. These results provide kinetic evidence that cysteine residues are involved in substrate binding of mammalian DPPs III.

The SBASE domain sequence resource, release 12: prediction of protein domain-architecture using support vector machines

SBASE (http://www.icgeb.trieste.it/sbase) is an online resource designed to facilitate the detection of domain homologies based on sequence database search. The present release of the SBASE A library of protein domain sequences contains 972 397 protein sequence segments annotated by structure, function, ligand-binding or cellular topology, clustered into 8547 domain groups. SBASE B contains 169 916 domain sequences clustered into 2526 less well-characterized groups. Domain prediction is based on an evaluation of database search results in comparison with a ‘similarity network’ of inter-sequence similarity scores, using support vector machines trained on similarity search results of known domains.