Decoding the protein-DNA recognition rules

The C2H2 zinc finger (ZF) transcription factors (TF) form the largest family of DNA binding proteins in eukaryotes. TFs are key proteins involved in gene regulation that bind to specific DNA sites. A major obstacle towards understanding the molecular basis of transcriptional regulation is the lack of a recognition code for protein-DNA interactions. We aim to understand molecular mechanisms of DNA recognition and to quantitatively estimate recognition rules for TF-DNA interactions. We identified key residues playing an important role in ZF-DNA interactions and found that they are prealigned to conformations observed in the bound state prior to binding. A binding site for Cl- ions corresponding to the pocket where DNA phosphates are found most buried in the complex of ZFs is identified. Bound ions constrain conformations of important residues consistent with observations of increased binding affinity with increased ionic strength in protein-DNA interactions. These results suggest a general mechanism where ZFs, through their key residues, rapidly form encounter complexes amenable for a fast readout of the DNA. We developed a novel experimentally-based approach using crystal structures and binding data on the C2H2 ZFs and decoded ten fundamental specific interactions for protein-DNA recognition. These are: Five hydrogen bonds, three desolvation penalties, a non-polar energy, and a novel water accessibility factor. The code is applied to three data sets with a total of 89 ZF mutants on three ZFs of EGR. Guided by simulations of individual ZFs, we mapped the interactions into homology models with all feasible intra- and inter- molecular bonds and selected the structure with the lowest free energy for each ZF. The interactions reproduce changes in affinity of 35 mutants of finger I (FI) (R2 = 0.99), 23 mutants of FII (R2 = 0.97) and 31 human ZFs on FIIII (R2 = 0.95). The method predicts bound ZF-DNA complexes for all mutants, decoding molecular basis of ZF-DNA specificity. These findings reveal recognition rules that depend on DNA sequence/structure, molecular water at the interface and induced fit of the C2H2 TFs. In summary, our method provides the first robust framework to decode the molecular basis of TFs binding to DNA

Human Papillomavirus E6 Triggers Upregulation of the Antiviral and Cancer Genomic DNA Deaminase APOBEC3B

ABSTRACT Several recent studies have converged upon the innate immune DNA cytosine deaminase APOBEC3B (A3B) as a significant source of genomic uracil lesions and mutagenesis in multiple human cancers, including those of the breast, head/neck, cervix, bladder, lung, ovary, and other tissues. A3B is upregulated in these tumor types relative to normal tissues, but the mechanism is unclear. Because A3B also has antiviral activity in multiple systems and is a member of the broader innate immune response, we tested the hypothesis that human papillomavirus (HPV) infection causes A3B upregulation. We found that A3B mRNA expression and enzymatic activity were upregulated following transfection of a high-risk HPV genome and that this effect was abrogated by inactivation of E6. Transduction experiments showed that the E6 oncoprotein alone was sufficient to cause A3B upregulation, and a panel of high-risk E6 proteins triggered higher A3B levels than did a panel of low-risk or noncancer E6 proteins. Knockdown experiments in HPV-positive cell lines showed that endogenous E6 is required for A3B upregulation. Analyses of publicly available head/neck cancer data further support this relationship, as A3B levels are higher in HPV-positive cancers than in HPV-negative cancers. Taken together with the established role for high-risk E6 in functional inactivation of TP53 and published positive correlations in breast cancer between A3B upregulation and genetic inactivation of TP53, our studies suggest a model in which high-risk HPV E6, possibly through functional inactivation of TP53, causes derepression of A3B gene transcription. This would lead to a mutator phenotype that explains the observed cytosine mutation biases in HPV-positive head/neck and cervical cancers

The somatic autosomal mutation matrix in cancer genomes

DNA damage in somatic cells originates from both environmental and endogenous sources, giving rise to mutations through multiple mechanisms. When these mutations affect the function of critical genes, cancer may ensue. Although identifying genomic subsets of mutated genes may inform therapeutic options, a systematic survey of tumor mutational spectra is required to improve our understanding of the underlying mechanisms of mutagenesis involved in cancer etiology. Recent studies have presented genome-wide sets of somatic mutations as a 96-element vector, a procedure that only captures the immediate neighbors of the mutated nucleotide. Herein, we present a 32 × 12 mutation matrix that captures the nucleotide pattern two nucleotides upstream and downstream of the mutation. A somatic autosomal mutation matrix (SAMM) was constructed from tumor-specific mutations derived from each of 909 individual cancer genomes harboring a total of 10,681,843 single-base substitutions. In addition, mechanistic template mutation matrices (MTMMs) representing oxidative DNA damage, ultraviolet-induced DNA damage, 5mCpG deamination, and APOBEC-mediated cytosine mutation, are presented. MTMMs were mapped to the individual tumor SAMMs to determine the maximum contribution of each mutational mechanism to the overall mutation pattern. A Manhattan distance across all SAMM elements between any two tumor genomes was used to determine their relative distance. Employing this metric, 89.5 % of all tumor genomes were found to have a nearest neighbor from the same tissue of origin. When a distance-dependent 6-nearest neighbor classifier was used, 86.9 % of all SAMMs were assigned to the correct tissue of origin. Thus, although tumors from different tissues may have similar mutation patterns, their SAMMs often display signatures that are characteristic of specific tissues

Guanine Holes Are Prominent Targets for Mutation in Cancer and Inherited Disease

Albino Bacolla, Guliang Wang, Aklank Jain, Karen M. Vasquez, Division of Pharmacology and Toxicology, The University of Texas at Austin, Dell Pediatric Research Institute, Austin, Texas, United States of AmericaAlbino Bacolla, Nuri A. Temiz, Ming Yi, Joseph Ivanic, Regina Z. Cer, Duncan E. Donohue, Uma S. Mudunuri, Natalia Volfovsky, Brian T. Luke, Robert M., Stephens, Jack R. Collins, Advanced Biomedical Computing Center, SAIC-Frederick, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of AmericaEdward V. Ball, David N. Cooper, Institute of Medical Genetics, School of Medicine, Cardiff University, Cardiff, United KingdomSingle base substitutions constitute the most frequent type of human gene mutation and are a leading cause of cancer and inherited disease. These alterations occur non-randomly in DNA, being strongly influenced by the local nucleotide sequence context. However, the molecular mechanisms underlying such sequence context-dependent mutagenesis are not fully understood. Using bioinformatics, computational and molecular modeling analyses, we have determined the frequencies of mutation at G•C bp in the context of all 64 5′-NGNN-3′ motifs that contain the mutation at the second position. Twenty-four datasets were employed, comprising >530,000 somatic single base substitutions from 21 cancer genomes, >77,000 germline single-base substitutions causing or associated with human inherited disease and 16.7 million benign germline single-nucleotide variants. In several cancer types, the number of mutated motifs correlated both with the free energies of base stacking and the energies required for abstracting an electron from the target guanines (ionization potentials). Similar correlations were also evident for the pathological missense and nonsense germline mutations, but only when the target guanines were located on the non-transcribed DNA strand. Likewise, pathogenic splicing mutations predominantly affected positions in which a purine was located on the non-transcribed DNA strand. Novel candidate driver mutations and tissue-specific mutational patterns were also identified in the cancer datasets. We conclude that electron transfer reactions within the DNA molecule contribute to sequence context-dependent mutagenesis, involving both somatic driver and passenger mutations in cancer, as well as germline alterations causing or associated with inherited disease.This work was supported by grants from the NIH (CA097175 and CA093729) to KMV, NCI/NIH contract HHSN261200800001E to AB and the Frederick National Laboratory for Cancer Research, and CBIIT/caBIG ISRCE yellow task #09-260 to the Frederick National Laboratory for Cancer Research. DNC and EVB received financial support from BIOBASE GmbH through a license agreement (for HGMD) with Cardiff University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.PharmacyEmail: [email protected]

The Role of Methylation in the Intrinsic Dynamics of B- and Z-DNA

Methylation of cytosine at the 5-carbon position (5mC) is observed in both prokaryotes and eukaryotes. In humans, DNA methylation at CpG sites plays an important role in gene regulation and has been implicated in development, gene silencing, and cancer. In addition, the CpG dinucleotide is a known hot spot for pathologic mutations genome-wide. CpG tracts may adopt left-handed Z-DNA conformations, which have also been implicated in gene regulation and genomic instability. Methylation facilitates this B-Z transition but the underlying mechanism remains unclear. Herein, four structural models of the dinucleotide d(GC)5 repeat sequence in B-, methylated B-, Z-, and methylated Z-DNA forms were constructed and an aggregate 100 nanoseconds of molecular dynamics simulations in explicit solvent under physiological conditions was performed for each model. Both unmethylated and methylated B-DNA were found to be more flexible than Z-DNA. However, methylation significantly destabilized the BII, relative to the BI, state through the Gp5mC steps. In addition, methylation decreased the free energy difference between B- and Z-DNA. Comparisons of α/γ backbone torsional angles showed that torsional states changed marginally upon methylation for B-DNA, and Z-DNA. Methylation-induced conformational changes and lower energy differences may contribute to the transition to Z-DNA by methylated, over unmethylated, B-DNA and may be a contributing factor to biological function

The role of APOBEC3B in lung tumor evolution and targeted cancer therapy resistance

In this study, the impact of the apolipoprotein B mRNA-editing catalytic subunit-like (APOBEC) enzyme APOBEC3B (A3B) on epidermal growth factor receptor (EGFR)-driven lung cancer was assessed. A3B expression in EGFR mutant (EGFRmut) non-small-cell lung cancer (NSCLC) mouse models constrained tumorigenesis, while A3B expression in tumors treated with EGFR-targeted cancer therapy was associated with treatment resistance. Analyses of human NSCLC models treated with EGFR-targeted therapy showed upregulation of A3B and revealed therapy-induced activation of nuclear factor kappa B (NF-κB) as an inducer of A3B expression. Significantly reduced viability was observed with A3B deficiency, and A3B was required for the enrichment of APOBEC mutation signatures, in targeted therapy-treated human NSCLC preclinical models. Upregulation of A3B was confirmed in patients with NSCLC treated with EGFR-targeted therapy. This study uncovers the multifaceted roles of A3B in NSCLC and identifies A3B as a potential target for more durable responses to targeted cancer therapy.</p

<i>MYC</i> and <i>PVT1</i> synergize to regulate RSPO1 levels in breast cancer

<p>Copy number gain of the 8q24 region including the v-myc avian myelocytomatosis viral oncogene homolog (<i>MYC)</i> oncogene has been observed in many different cancers and is associated with poor outcomes. While the role of <i>MYC</i> in tumor formation has been clearly delineated, we have recently shown that co-operation between adjacent long non-coding RNA plasmacytoma variant transcription 1 (<i>PVT1)</i> and <i>MYC</i> is necessary for tumor promotion. Chromosome engineered mice containing an increased copy of <i>Myc-Pvt1</i> (Gain <i>Myc-Pvt1</i>) accelerates mammary tumors in <i>MMTV-Neu</i> mice, while single copy increase of each is not sufficient. In addition, mammary epithelium from the Gain <i>Myc-Pvt1</i> mouse show precancerous phenotypes, notably increased DNA replication, elevated -<i>H2AX</i> phosphorylation and increased ductal branching. In an attempt to capture the molecular signatures in pre-cancerous cells we utilized RNA sequencing to identify potential targets of supernumerary <i>Myc-Pvt1</i> cooperation in mammary epithelial cells. In this extra view we show that an extra copy of both <i>Myc</i> and <i>Pvt1</i> leads to increased levels of <i>Rspo1</i>, a crucial regulator of canonical β-catenin signaling required for female development. Human breast cancer tumors with high levels of <i>MYC</i> transcript have significantly more <i>PVT1</i> transcript and <i>RSPO1</i> transcript than tumors with low levels of MYC showing that the murine results are relevant to a subset of human tumors. Thus, this work identifies a key mechanism in precancerous and cancerous tissue by which a main player in female differentiation is transcriptionally activated by supernumerary <i>MYC</i> and <i>PVT1</i>, leading to increased premalignant features, and ultimately to tumor formation.</p

Relative free energy profiles across the ε−ζ reaction coordinates in B- and 5mCB-DNA.

<p>The plots show the changes in free energy (y-axis) across the ε−ζ coordinate range (x-axis) that define the BI and BII sub-states. (A) Overall relative free energy profiles for unmethylated B-DNA (<i>black</i>) and methylated 5mCB-DNA (<i>red</i>). (B) Relative free energy profiles for unmethylated and methylated CpG and GpC steps. <i>Black</i>, CpG steps of B-DNA; <i>red</i>, 5mCpG steps of 5mCB-DNA; <i>blue</i>, GpC steps of B-DNA; <i>green</i>, Gp5mC steps of 5mCB-DNA.</p