Finn Drabløs

Human ABH3 structure and key residues for oxidative demethylation to reverse DNA/RNA damage

Methylating agents are ubiquitous in the environment, and central in cancer therapy. The 1‐methyladenine and 3‐methylcytosine lesions in DNA/RNA contribute to the cytotoxicity of such agents. These lesions are directly reversed by ABH3 (hABH3) in humans and AlkB in Escherichia coli. Here, we report the structure of the hABH3 catalytic core in complex with iron and 2‐oxoglutarate (2OG) at 1.5 Å resolution and analyse key site‐directed mutants. The hABH3 structure reveals the β‐strand jelly‐roll fold that coordinates a catalytically active iron centre by a conserved His1‐X‐Asp/Glu‐Xn‐His2 motif. This experimentally establishes hABH3 as a structural member of the Fe(II)/2OG‐dependent dioxygenase superfamily, which couples substrate oxidation to conversion of 2OG into succinate and CO2. A positively charged DNA/RNA binding groove indicates a distinct nucleic acid binding conformation different from that predicted in the AlkB structure with three nucleotides. These results uncover previously unassigned key catalytic residues, identify a flexible hairpin involved in nucleotide flipping and ss/ds‐DNA discrimination, and reveal self‐hydroxylation of an active site leucine that may protect against uncoupled generation of dangerous oxygen radicals.

Identification of a novel, widespread, and functionally important PCNA-binding motif

AlkB PCNA-interacting motif (APIM) is present in >200 proteins and may mediate PCNA binding during genotoxic stress.

Improved benchmarks for computational motif discovery

BackgroundAn important step in annotation of sequenced genomes is the identification of transcription factor binding sites. More than a hundred different computational methods have been proposed, and it is difficult to make an informed choice. Therefore, robust assessment of motif discovery methods becomes important, both for validation of existing tools and for identification of promising directions for future research.ResultsWe use a machine learning perspective to analyze collections of transcription factors with known binding sites. Algorithms are presented for finding position weight matrices (PWMs), IUPAC-type motifs and mismatch motifs with optimal discrimination of binding sites from remaining sequence. We show that for many data sets in a recently proposed benchmark suite for motif discovery, none of the common motif models can accurately discriminate the binding sites from remaining sequence. This may obscure the distinction between the potential performance of the motif discovery tool itself versus the intrinsic complexity of the problem we are trying to solve. Synthetic data sets may avoid this problem, but we show on some previously proposed benchmarks that there may be a strong bias towards a presupposed motif model. We also propose a new approach to benchmark data set construction. This approach is based on collections of binding site fragments that are ranked according to the optimal level of discrimination achieved with our algorithms. This allows us to select subsets with specific properties. We present one benchmark suite with data sets that allow good discrimination between positive and negative instances with the common motif models. These data sets are suitable for evaluating algorithms for motif discovery that rely on these models. We present another benchmark suite where PWM, IUPAC and mismatch motif models are not able to discriminate reliably between positive and negative instances. This suite could be used for evaluating more powerful motif models.ConclusionOur improved benchmark suites have been designed to differentiate between the performance of motif discovery algorithms and the power of motif models. We provide a web server where users can download our benchmark suites, submit predictions and visualize scores on the benchmarks.

Cell cycle regulation of human DNA repair and chromatin remodeling genes.

Maintenance of a genome requires DNA repair integrated with chromatin remodeling. We have analyzed six transcriptome data sets and one data set on translational regulation of known DNA repair and remodeling genes in synchronized human cells. These data are available through our new database: www.dnarepairgenes.com. Genes that have similar transcription profiles in at least two of our data sets generally agree well with known protein profiles. In brief, long patch base excision repair (BER) is enriched for S phase genes, whereas short patch BER uses genes essentially equally expressed in all cell cycle phases. Furthermore, most genes related to DNA mismatch repair, Fanconi anemia and homologous recombination have their highest expression in the S phase. In contrast, genes specific for direct repair, nucleotide excision repair, as well as non-homologous end joining do not show cell cycle-related expression. Cell cycle regulated chromatin remodeling genes were most frequently confined to G1/S and S. These include e.g. genes for chromatin assembly factor 1 (CAF-1) major subunits CHAF1A and CHAF1B; the putative helicases HELLS and ATAD2 that both co-activate E2F transcription factors central in G1/S-transition and recruit DNA repair and chromatin-modifying proteins and DNA double strand break repair proteins; and RAD54L and RAD54B involved in double strand break repair. TOP2A was consistently most highly expressed in G2, but also expressed in late S phase, supporting a role in regulating entry into mitosis. Translational regulation complements transcriptional regulation and appears to be a relatively common cell cycle regulatory mechanism for DNA repair genes. Our results identify cell cycle phases in which different pathways have highest activity, and demonstrate that periodically expressed genes in a pathway are frequently co-expressed. Furthermore, the data suggest that S phase expression and over-expression of some multifunctional chromatin remodeling proteins may set up feedback loops driving cancer cell proliferation.

Detecting periodic patterns in biological sequences.

MOTIVATIONnThe search for repeated patterns in DNA and protein sequences is important in sequence analysis. The rapid increase in available sequences, in particular from large-scale genome sequencing projects, makes it relevant to develop sensitive automatic methods for the identification of repeats.nnnRESULTSnA new method for finding periodic patterns in biological sequences is presented. The method is based on evolutionary distance and phase shifts corresponding to insertions and deletions. A given sequence is aligned to itself in a certain sense, trying to minimize a distance to periodicity. Relationships between different such periodicity measures are discussed. An iterative algorithm is used, and the running time is nearly proportional to the sequence length. The alignment produces a periodic consensus pattern. A phase score is used to indicate a statistical significance of the periodicity. Three examples using both DNA and protein sequences illustrate how the method can be used to find patterns.nnnAVAILABILITYnOn request from the authors.nnnCONTACTnevindc@mat nu.no; finn.drablos@unimed.sintef.no

Gene regulation in the immediate-early response process

Immediate-early genes (IEGs) can be activated and transcribed within minutes after stimulation, without the need for de novo protein synthesis, and they are stimulated in response to both cell-extrinsic and cell-intrinsic signals. Extracellular signals are transduced from the cell surface, through receptors activating a chain of proteins in the cell, in particular extracellular-signal-regulated kinases (ERKs), mitogen-activated protein kinases (MAPKs) and members of the RhoA-actin pathway. These communicate through a signaling cascade by adding phosphate groups to neighboring proteins, and this will eventually activate and translocate TFs to the nucleus and thereby induce gene expression. The gene activation also involves proximal and distal enhancers that interact with promoters to simulate gene expression. The immediate-early genes have essential biological roles, in particular in stress response, like the immune system, and in differentiation. Therefore they also have important roles in various diseases, including cancer development. In this paper we summarize some recent advances on key aspects of the activation and regulation of immediate-early genes.

A manually curated ChIP-seq benchmark demonstrates room for improvement in current peak-finder programs

Chromatin immunoprecipitation (ChIP) followed by high throughput sequencing (ChIP-seq) is rapidly becoming the method of choice for discovering cell-specific transcription factor binding locations genome wide. By aligning sequenced tags to the genome, binding locations appear as peaks in the tag profile. Several programs have been designed to identify such peaks, but program evaluation has been difficult due to the lack of benchmark data sets. We have created benchmark data sets for three transcription factors by manually evaluating a selection of potential binding regions that cover typical variation in peak size and appearance. Performance of five programs on this benchmark showed, first, that external control or background data was essential to limit the number of false positive peaks from the programs. However, >80% of these peaks could be manually filtered out by visual inspection alone, without using additional background data, showing that peak shape information is not fully exploited in the evaluated programs. Second, none of the programs returned peak-regions that corresponded to the actual resolution in ChIP-seq data. Our results showed that ChIP-seq peaks should be narrowed down to 100–400u2009bp, which is sufficient to identify unique peaks and binding sites. Based on these results, we propose a meta-approach that gives improved peak definitions.

Assessment of composite motif discovery methods

BackgroundComputational discovery of regulatory elements is an important area of bioinformatics research and more than a hundred motif discovery methods have been published. Traditionally, most of these methods have addressed the problem of single motif discovery – discovering binding motifs for individual transcription factors. In higher organisms, however, transcription factors usually act in combination with nearby bound factors to induce specific regulatory behaviours. Hence, recent focus has shifted from single motifs to the discovery of sets of motifs bound by multiple cooperating transcription factors, so called composite motifs or cis-regulatory modules. Given the large number and diversity of methods available, independent assessment of methods becomes important. Although there have been several benchmark studies of single motif discovery, no similar studies have previously been conducted concerning composite motif discovery.ResultsWe have developed a benchmarking framework for composite motif discovery and used it to evaluate the performance of eight published module discovery tools. Benchmark datasets were constructed based on real genomic sequences containing experimentally verified regulatory modules, and the module discovery programs were asked to predict both the locations of these modules and to specify the single motifs involved. To aid the programs in their search, we provided position weight matrices corresponding to the binding motifs of the transcription factors involved. In addition, selections of decoy matrices were mixed with the genuine matrices on one dataset to test the response of programs to varying levels of noise.ConclusionAlthough some of the methods tested tended to score somewhat better than others overall, there were still large variations between individual datasets and no single method performed consistently better than the rest in all situations. The variation in performance on individual datasets also shows that the new benchmark datasets represents a suitable variety of challenges to most methods for module discovery.

Vitamin D receptor ChIP-seq in primary CD4+ cells: relationship to serum 25-hydroxyvitamin D levels and autoimmune disease.

BackgroundVitamin D insufficiency has been implicated in autoimmunity. ChIP-seq experiments using immune cell lines have shown that vitamin D receptor (VDR) binding sites are enriched near regions of the genome associated with autoimmune diseases. We aimed to investigate VDR binding in primary CD4+ cells from healthy volunteers.MethodsWe extracted CD4+ cells from nine healthy volunteers. Each sample underwent VDR ChIP-seq. Our results were analyzed in relation to published ChIP-seq and RNA-seq data in the Genomic HyperBrowser. We used MEMEChIP for de novo motif discovery. 25-Hydroxyvitamin D levels were measured using liquid chromatography–tandem mass spectrometry and samples were divided into vitamin D sufficient (25(OH)D ≥75 nmol/L) and insufficient/deficient (25(OH)D <75 nmol/L) groups.ResultsWe found that the amount of VDR binding is correlated with the serum level of 25-hydroxyvitamin D (r = 0.92, P= 0.0005). In vivo VDR binding sites are enriched for autoimmune disease associated loci, especially when 25-hydroxyvitamin D levels (25(OH)D) were sufficient (25(OH)D ≥75: 3.13-fold, P<0.0001; 25(OH)D <75: 2.76-fold, P<0.0001; 25(OH)D ≥75 enrichment versus 25(OH)D <75 enrichment: P= 0.0002). VDR binding was also enriched near genes associated specifically with T-regulatory and T-helper cells in the 25(OH)D ≥75 group. MEME ChIP did not identify any VDR-like motifs underlying our VDR ChIP-seq peaks.ConclusionOur results show a direct correlation between in vivo 25-hydroxyvitamin D levels and the number of VDR binding sites, although our sample size is relatively small. Our study further implicates VDR binding as important in gene-environment interactions underlying the development of autoimmunity and provides a biological rationale for 25-hydroxyvitamin D sufficiency being based at 75 nmol/L. Our results also suggest that VDR binding in response to physiological levels of vitamin D occurs predominantly in a VDR motif-independent manner.

EpiFactors: a comprehensive database of human epigenetic factors and complexes

Epigenetics refers to stable and long-term alterations of cellular traits that are not caused by changes in the DNA sequence per se. Rather, covalent modifications of DNA and histones affect gene expression and genome stability via proteins that recognize and act upon such modifications. Many enzymes that catalyse epigenetic modifications or are critical for enzymatic complexes have been discovered, and this is encouraging investigators to study the role of these proteins in diverse normal and pathological processes. Rapidly growing knowledge in the area has resulted in the need for a resource that compiles, organizes and presents curated information to the researchers in an easily accessible and user-friendly form. Here we present EpiFactors, a manually curated database providing information about epigenetic regulators, their complexes, targets and products. EpiFactors contains information on 815 proteins, including 95 histones and protamines. For 789 of these genes, we include expressions values across several samples, in particular a collection of 458 human primary cell samples (for approximately 200 cell types, in many cases from three individual donors), covering most mammalian cell steady states, 255 different cancer cell lines (representing approximately 150 cancer subtypes) and 134 human postmortem tissues. Expression values were obtained by the FANTOM5 consortium using Cap Analysis of Gene Expression technique. EpiFactors also contains information on 69 protein complexes that are involved in epigenetic regulation. The resource is practical for a wide range of users, including biologists, pharmacologists and clinicians. Database URL: http://epifactors.autosome.ru