Perry Evans

Exome sequencing identifies recurrent mutations in NF1 and RASopathy genes in sun-exposed melanomas

We report on whole-exome sequencing (WES) of 213 melanomas. Our analysis established NF1, encoding a negative regulator of RAS, as the third most frequently mutated gene in melanoma, after BRAF and NRAS. Inactivating NF1 mutations were present in 46% of melanomas expressing wild-type BRAF and RAS, occurred in older patients and showed a distinct pattern of co-mutation with other RASopathy genes, particularly RASA2. Functional studies showed that NF1 suppression led to increased RAS activation in most, but not all, melanoma cases. In addition, loss of NF1 did not predict sensitivity to MEK or ERK inhibitors. The rebound pathway, as seen by the induction of phosphorylated MEK, occurred in cells both sensitive and resistant to the studied drugs. We conclude that NF1 is a key tumor suppressor lost in melanomas, and that concurrent RASopathy gene mutations may enhance its role in melanomagenesis.

Prediction of HIV-1 virus-host protein interactions using virus and host sequence motifs

BackgroundHost protein-protein interaction networks are altered by invading virus proteins, which create new interactions, and modify or destroy others. The resulting network topology favors excessive amounts of virus production in a stressed host cell network. Short linear peptide motifs common to both virus and host provide the basis for host network modification.MethodsWe focused our host-pathogen study on the binding and competing interactions of HIV-1 and human proteins. We showed that peptide motifs conserved across 70% of HIV-1 subtype B and C samples occurred in similar positions on HIV-1 proteins, and we documented protein domains that interact with these conserved motifs. We predicted which human proteins may be targeted by HIV-1 by taking pairs of human proteins that may interact via a motif conserved in HIV-1 and the corresponding interacting protein domain.ResultsOur predictions were enriched with host proteins known to interact with HIV-1 proteins ENV, NEF, and TAT (p-value < 4.26E-21). Cellular pathways statistically enriched for our predictions include the T cell receptor signaling, natural killer cell mediated cytotoxicity, cell cycle, and apoptosis pathways. Gene Ontology molecular function level 5 categories enriched with both predicted and confirmed HIV-1 targeted proteins included categories associated with phosphorylation events and adenyl ribonucleotide binding.ConclusionA list of host proteins highly enriched with those targeted by HIV-1 proteins can be obtained by searching for host protein motifs along virus protein sequences. The resulting set of host proteins predicted to be targeted by virus proteins will become more accurate with better annotations of motifs and domains. Nevertheless, our study validates the role of linear binding motifs shared by virus and host proteins as an important part of the crosstalk between virus and host.

Identification of PLX4032-resistance mechanisms and implications for novel RAF inhibitors

BRAF inhibitors improve melanoma patient survival, but resistance invariably develops. Here we report the discovery of a novel BRAF mutation that confers resistance to PLX4032 employing whole‐exome sequencing of drug‐resistant BRAFV600K melanoma cells. We further describe a new screening approach, a genome‐wide piggyBac mutagenesis screen that revealed clinically relevant aberrations (N‐terminal BRAF truncations and CRAF overexpression). The novel BRAF mutation, a Leu505 to His substitution (BRAFL505H), is the first resistance‐conferring second‐site mutation identified in BRAF mutant cells. The mutation replaces a small nonpolar amino acid at the BRAF‐PLX4032 interface with a larger polar residue. Moreover, we show that BRAFL505H, found in human prostate cancer, is itself a MAPK‐activating, PLX4032‐resistant oncogenic mutation. Lastly, we demonstrate that the PLX4032‐resistant melanoma cells are sensitive to novel, next‐generation BRAF inhibitors, especially the ‘paradox‐blocker’ PLX8394, supporting its use in clinical trials for treatment of melanoma patients with BRAF‐mutations.

Host sequence motifs shared by HIV predict response to antiretroviral therapy

BackgroundThe HIV viral genome mutates at a high rate and poses a significant long term health risk even in the presence of combination antiretroviral therapy. Current methods for predicting a patients response to therapy rely on site-directed mutagenesis experiments and in vitro resistance assays. In this bioinformatics study we treat response to antiretroviral therapy as a two-body problem: response to therapy is considered to be a function of both the host and pathogen proteomes. We set out to identify potential responders based on the presence or absence of host protein and DNA motifs on the HIV proteome.ResultsAn alignment of thousands of HIV-1 sequences attested to extensive variation in nucleotide sequence but also showed conservation of eukaryotic short linear motifs on the protein coding regions. The reduction in viral load of patients in the Stanford HIV Drug Resistance Database exhibited a bimodal distribution after 24 weeks of antiretroviral therapy, with 2,000 copies/ml cutoff. Similarly, patients allocated into responder/non-responder categories based on consistent viral load reduction during a 24 week period showed clear separation. In both cases of phenotype identification, a set of features composed of short linear motifs in the reverse transcriptase region of HIV sequence accurately predicted a patients response to therapy. Motifs that overlap resistance sites were highly predictive of responder identification in single drug regimens but these features lost importance in defining responders in multi-drug therapies.ConclusionHIV sequence mutates in a way that preferentially preserves peptide sequence motifs that are also found in the human proteome. The presence and absence of such motifs at specific regions of the HIV sequence is highly predictive of response to therapy. Some of these predictive motifs overlap with known HIV-1 resistance sites. These motifs are well established in bioinformatics databases and hence do not require identification via in vitro mutation experiments.

Sequence Alignment Reveals Possible MAPK Docking Motifs on HIV Proteins

Over the course of HIV infection, virus replication is facilitated by the phosphorylation of HIV proteins by human ERK1 and ERK2 mitogen-activated protein kinases (MAPKs). MAPKs are known to phosphorylate their substrates by first binding with them at a docking site. Docking site interactions could be viable drug targets because the sequences guiding them are more specific than phosphorylation consensus sites. In this study we use multiple bioinformatics tools to discover candidate MAPK docking site motifs on HIV proteins known to be phosphorylated by MAPKs, and we discuss the possibility of targeting docking sites with drugs. Using sequence alignments of HIV proteins of different subtypes, we show that MAPK docking patterns previously described for human proteins appear on the HIV matrix, Tat, and Vif proteins in a strain dependent manner, but are absent from HIV Rev and appear on all HIV Nef strains. We revise the regular expressions of previously annotated MAPK docking patterns in order to provide a subtype independent motif that annotates all HIV proteins. One revision is based on a documented human variant of one of the substrate docking motifs, and the other reduces the number of required basic amino acids in the standard docking motifs from two to one. The proposed patterns are shown to be consistent with in silico docking between ERK1 and the HIV matrix protein. The motif usage on HIV proteins is sufficiently different from human proteins in amino acid sequence similarity to allow for HIV specific targeting using small-molecule drugs.

Adjusting for Background Mutation Frequency Biases Improves the Identification of Cancer Driver Genes

A common goal of tumor sequencing projects is finding genes whose mutations are selected for during tumor development. This is accomplished by choosing genes that have more non-synonymous mutations than expected from an estimated background mutation frequency. While this background frequency is unknown, it can be estimated using both the observed synonymous mutation frequency and the non-synonymous to synonymous mutation ratio. The synonymous mutation frequency can be determined across all genes or in a gene-specific manner. This choice introduces an interesting trade-off. A gene-specific frequency adjusts for an underlying mutation bias, but is difficult to estimate given missing synonymous mutation counts. Using a genome-wide synonymous frequency is more robust, but is less suited for adjusting biases. Studying four evaluation criteria for identifying genes with high non-synonymous mutation burden (reflecting preferential selection of expressed genes, genes with mutations in conserved bases, genes with many protein interactions, and genes that show loss of heterozygosity), we find that the gene-specific synonymous frequency is superior in the gene expression and protein interaction tests. In conclusion, the use of the gene-specific synonymous mutation frequency is well suited for assessing a genes non-synonymous mutation burden.

Protein-Protein Interaction Network Alignment by Quantitative Simulation

We adapt a network simulation algorithm called quantitative simulation (QSim) for use in the alignment of biological networks. Unlike most network alignment methods, QSim finds local matches for one network in another, making it asymmetric, and takes full advantage of different edge types. We use QSim to simulate a protein-protein interaction (PPI) network from D. melanogaster using a PPI network from S. cerevisiae, and compare QSims alignment to those from other methods using Gene Ontology (GO) biological process annotations as proxies for correct alignment matches. The best cross-species protein matches obtained from QSim have a higher agreement in GO biological process annotations than those from either BLAST or an alternative network alignment algorithm.

Correlated evolution of positions within mammalian cis elements.

Transcriptional regulation critically depends on proper interactions between transcription factors (TF) and their cognate DNA binding sites. The widely used model of TF-DNA binding – the Positional Weight Matrix (PWM) – presumes independence between positions within the binding site. However, there is evidence to show that the independence assumption may not always hold, and the extent of interposition dependence is not completely known. We hypothesize that the interposition dependence should partly be manifested as correlated evolution at the positions. We report a Maximum-Likelihood (ML) approach to infer correlated evolution at any two positions within a PWM, based on a multiple alignment of 5 mammalian genomes. Application to a genome-wide set of putative cis elements in human promoters reveals a prevalence of correlated evolution within cis elements. We found that the interdependence between two positions decreases with increasing distance between the positions. The interdependent positions tend to be evolutionarily more constrained and moreover, the dependence patterns are relatively similar across structurally related transcription factors. Although some of the detected mutational dependencies may be due to context-dependent genomic hyper-mutation, notably CG to TG, the majority is likely due to context-dependent preferences for specific nucleotide combinations within the cis elements. Patterns of evolution at individual nucleotide positions within mammalian TF binding sites are often significantly correlated, suggesting interposition dependence. The proposed methodology is also applicable to other classes of non-coding functional elements. A detailed investigation of mutational dependencies within specific motifs could reveal preferred nucleotide combinations that may help refine the DNA binding models.

Conservation patterns in cis -elements reveal compensatory mutations

Transcriptional regulation critically depends on proper interactions between transcription factors (TF) and their cognate DNA binding sites or cis elements. A better understanding and modelling of the TF-DNA interaction is an important area of research. The Positional Weight Matrix (PWM) is the most common model of TF-DNA binding and it presumes that the nucleotide preferences at individual positions within the binding site are independent. However, studies have shown that this independence assumption does not always hold. If the nucleotide preference at one position depends on the nucleotide at another position, a chance mutation at one position should exert selection pressures at the other position. By comparing the patterns of evolutionary conservation at individual positions within cis elements, here we show that positional dependence within binding sites is highly prevalent. We also show that dependent positions are more likely to be functional, as evidenced by a higher information content and higher conservation. We discuss two examples—Elk-1 and SAP-1 where the inferred compensatory mutation is consistent with known TF-DNA crystal structure.

Computational analysis in cancer exome sequencing.

Exome sequencing in cancer is a powerful tool for identifying mutational events across the coding region of human genes. Here, we describe computational methods that use exome sequencing reads from cancer samples to identify somatic single nucleotide variants (SNVs), copy number alterations, and short insertions and deletions (InDels). We further describe analytical methods to generate lists of driver genes with more mutational events than expected by chance.