MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing

We have developed a novel machine-learning approach, MutPred Splice, for the identification of coding region substitutions that disrupt pre-mRNA splicing. Applying MutPred Splice to human disease-causing exonic mutations suggests that 16% of mutations causing inherited disease and 10 to 14% of somatic mutations in cancer may disrupt pre-mRNA splicing. For inherited disease, the main mechanism responsible for the splicing defect is splice site loss, whereas for cancer the predominant mechanism of splicing disruption is predicted to be exon skipping via loss of exonic splicing enhancers or gain of exonic splicing silencer elements. MutPred Splice is available at http://mutdb.org/mutpredsplice.     

Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions

Systematic interrogation of mutation or protein modification data is important to identify sites with functional consequences and to deduce global consequences from large data sets. Mechismo (mechismo.russellab.org) enables simultaneous consideration of thousands of 3D structures and biomolecular interactions to predict rapidly mechanistic consequences for mutations and modifications. As useful functional information often only comes from homologous proteins, we benchmarked the accuracy of predictions as a function of protein/structure sequence similarity, which permits the use of relatively weak sequence similarities with an appropriate confidence measure. For protein–protein, protein–nucleic acid and a subset of protein–chemical interactions, we also developed and benchmarked a measure of whether modifications are likely to enhance or diminish the interactions, which can assist the detection of modifications with specific effects. Analysis of high-throughput sequencing data shows that the approach can identify interesting differences between cancers, and application to proteomics data finds potential mechanistic insights for how post-translational modifications can alter biomolecular interactions.     

DNA Replication Origin Function Is Promoted by H3K4 Di-methylation in Saccharomyces cerevisiae

DNA replication is a highly regulated process that is initiated from replication origins, but the elements of chromatin structure that contribute to origin activity have not been fully elucidated. To identify histone post-translational modifications important for DNA replication, we initiated a genetic screen to identify interactions between genes encoding chromatin-modifying enzymes and those encoding proteins required for origin function in the budding yeast Saccharomyces cerevisiae. We found that enzymes required for histone H3K4 methylation, both the histone methyltransferase Set1 and the E3 ubiquitin ligase Bre1, are required for robust growth of several hypomorphic replication mutants, including cdc6-1. Consistent with a role for these enzymes in DNA replication, we found that both Set1 and Bre1 are required for efficient minichromosome maintenance. These phenotypes are recapitulated in yeast strains bearing mutations in the histone substrates (H3K4 and H2BK123). Set1 functions as part of the COMPASS complex to mono-, di-, and tri-methylate H3K4. By analyzing strains lacking specific COMPASS complex members or containing H2B mutations that differentially affect H3K4 methylation states, we determined that these replication defects were due to loss of H3K4 di-methylation. Furthermore, histone H3K4 di-methylation is enriched at chromosomal origins. These data suggest that H3K4 di-methylation is necessary and sufficient for normal origin function. We propose that histone H3K4 di-methylation functions in concert with other histone post-translational modifications to support robust genome duplication.     

Probing the Boundaries of Orthology: The Unanticipated Rapid Evolution of Drosophila centrosomin

The rapid evolution of essential developmental genes and their protein products is both intriguing and problematic. The rapid evolution of gene products with simple protein folds and a lack of well-characterized functional domains typically result in a low discovery rate of orthologous genes. Additionally, in the absence of orthologs it is difficult to study the processes and mechanisms underlying rapid evolution. In this study, we have investigated the rapid evolution of centrosomin (cnn), an essential gene encoding centrosomal protein isoforms required during syncytial development in Drosophila melanogaster. Until recently the rapid divergence of cnn made identification of orthologs difficult and questionable because Cnn violates many of the assumptions underlying models for protein evolution. To overcome these limitations, we have identified a group of insect orthologs and present conserved features likely to be required for the functions attributed to cnn in D. melanogaster. We also show that the rapid divergence of Cnn isoforms is apparently due to frequent coding sequence indels and an accelerated rate of intronic additions and eliminations. These changes appear to be buffered by multi-exon and multi-reading frame maximum potential ORFs, simple protein folds, and the splicing machinery. These buffering features also occur in other genes in Drosophila and may help prevent potentially deleterious mutations due to indels in genes with large coding exons and exon-dense regions separated by small introns. This work promises to be useful for future investigations of cnn and potentially other rapidly evolving genes and proteins.     

Identification of collaborative driver pathways in breast cancer

Background

An important challenge in cancer biology is to computationally screen mutations in cancer cells, separating those that might drive cancer initiation and progression, from the much larger number of bystanders. Since mutations are large in number and diverse in type, the frequency of any particular mutation pattern across a set of samples is low. This makes statistical distinctions and reproducibility across different populations difficult to establish.

Results

In this paper we develop a novel method that promises to partially ameliorate these problems. The basic idea is although mutations are highly heterogeneous and vary from one sample to another, the processes that are disrupted when cells undergo transformation tend to be invariant across a population for a particular cancer or cancer subtype. Specifically, we focus on finding mutated pathway-groups that are invariant across samples of breast cancer subtypes. The identification of informative pathway-groups consists of two steps. The first is identification of pathways significantly enriched in genes containing non-synonymous mutations; the second uses pathways so identified to find groups that are functionally related in the largest number of samples. An application to 4 subtypes of breast cancer identified pathway-groups that can highly explicate a particular subtype and rich in processes associated with transformation.

Conclusions

In contrast to previous methods that identify pathways across a set of samples without any further validation, we show that mutated pathway-groups can be found in each breast cancer subtype and that such groups are invariant across the majority of samples. The algorithm is available at http://www.visantnet.org/misi/MUDPAC.zip.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-605) contains supplementary material, which is available to authorized users.     

Birds do it,bees do it,worms and ciliates do it too: DNA methylation from unexpected corners of the tree of life

Studies describing intricate patterns of DNA methylation in nematode and ciliate are controversial due to the uncertainty of genomic evolutionary conservation of DNA methylation enzymes.See related research articles http://genomebiology.com/2012/13/10/R99 and http://genomebiology.com/2012/13/10/R100     

State Transitions in the TORC1 Signaling Pathway and Information Processing in Saccharomyces cerevisiae

TOR kinase complex I (TORC1) is a key regulator of cell growth and metabolism in all eukaryotes. Previous studies in yeast have shown that three GTPases—Gtr1, Gtr2, and Rho1—bind to TORC1 in nitrogen and amino acid starvation conditions to block phosphorylation of the S6 kinase Sch9 and activate protein phosphatase 2A (PP2A). This leads to downregulation of 450 Sch9-dependent protein and ribosome synthesis genes and upregulation of 100 PP2A-dependent nitrogen assimilation and amino acid synthesis genes. Here, using bandshift assays and microarray measurements, we show that the TORC1 pathway also populates three other stress/starvation states. First, in glucose starvation conditions, the AMP-activated protein kinase (AMPK/Snf1) and at least one other factor push the TORC1 pathway into an off state, in which Sch9-branch signaling and PP2A-branch signaling are both inhibited. Remarkably, the TORC1 pathway remains in the glucose starvation (PP2A inhibited) state even when cells are simultaneously starved for nitrogen and glucose. Second, in osmotic stress, the MAPK Hog1/p38 drives the TORC1 pathway into a different state, in which Sch9 signaling and PP2A-branch signaling are inhibited, but PP2A-branch signaling can still be activated by nitrogen starvation. Third, in oxidative stress and heat stress, TORC1-Sch9 signaling is blocked while weak PP2A-branch signaling occurs. Together, our data show that the TORC1 pathway acts as an information-processing hub, activating different genes in different conditions to ensure that available energy is allocated to drive growth, amino acid synthesis, or a stress response, depending on the needs of the cell.     

RIDDLE: reflective diffusion and local extension reveal functional associations for unannotated gene sets via proximity in a gene network

The growing availability of large-scale functional networks has promoted the development of many successful techniques for predicting functions of genes. Here we extend these network-based principles and techniques to functionally characterize whole sets of genes. We present RIDDLE (Reflective Diffusion and Local Extension), which uses well developed guilt-by-association principles upon a human gene network to identify associations of gene sets. RIDDLE is particularly adept at characterizing sets with no annotations, a major challenge where most traditional set analyses fail. Notably, RIDDLE found microRNA-450a to be strongly implicated in ocular diseases and development. A web application is available at http://www.functionalnet.org/RIDDLE.     

Analysis of Individual Protein Regions Provides Novel Insights on Cancer Pharmacogenomics

The promise of personalized cancer medicine cannot be fulfilled until we gain better understanding of the connections between the genomic makeup of a patient''s tumor and its response to anticancer drugs. Several datasets that include both pharmacologic profiles of cancer cell lines as well as their genomic alterations have been recently developed and extensively analyzed. However, most analyses of these datasets assume that mutations in a gene will have the same consequences regardless of their location. While this assumption might be correct in some cases, such analyses may miss subtler, yet still relevant, effects mediated by mutations in specific protein regions. Here we study such perturbations by separating effects of mutations in different protein functional regions (PFRs), including protein domains and intrinsically disordered regions. Using this approach, we have been able to identify 171 novel associations between mutations in specific PFRs and changes in the activity of 24 drugs that couldn''t be recovered by traditional gene-centric analyses. Our results demonstrate how focusing on individual protein regions can provide novel insights into the mechanisms underlying the drug sensitivity of cancer cell lines. Moreover, while these new correlations are identified using only data from cancer cell lines, we have been able to validate some of our predictions using data from actual cancer patients. Our findings highlight how gene-centric experiments (such as systematic knock-out or silencing of individual genes) are missing relevant effects mediated by perturbations of specific protein regions. All the associations described here are available from http://www.cancer3d.org.     

dSPRINT: predicting DNA,RNA, ion,peptide and small molecule interaction sites within protein domains

Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties of proteins are frequently analyzed based upon whether they contain one of these domains. To date, however, knowledge of whether and how protein domains interact with ligands has been limited to domains that have been observed in co-crystal structures; this leaves approximately two-thirds of human protein domain families uncharacterized with respect to whether and how they bind DNA, RNA, small molecules, ions and peptides. To fill this gap, we introduce dSPRINT, a novel ensemble machine learning method for predicting whether a domain binds DNA, RNA, small molecules, ions or peptides, along with the positions within it that participate in these types of interactions. In stringent cross-validation testing, we demonstrate that dSPRINT has an excellent performance in uncovering ligand-binding positions and domains. We also apply dSPRINT to newly characterize the molecular functions of domains of unknown function. dSPRINT’s predictions can be transferred from domains to sequences, enabling predictions about the ligand-binding properties of 95% of human genes. The dSPRINT framework and its predictions for 6503 human protein domains are freely available at http://protdomain.princeton.edu/dsprint.     

Phyletic Profiling with Cliques of Orthologs Is Enhanced by Signatures of Paralogy Relationships

New microbial genomes are sequenced at a high pace, allowing insight into the genetics of not only cultured microbes, but a wide range of metagenomic collections such as the human microbiome. To understand the deluge of genomic data we face, computational approaches for gene functional annotation are invaluable. We introduce a novel model for computational annotation that refines two established concepts: annotation based on homology and annotation based on phyletic profiling. The phyletic profiling-based model that includes both inferred orthologs and paralogs—homologs separated by a speciation and a duplication event, respectively—provides more annotations at the same average Precision than the model that includes only inferred orthologs. For experimental validation, we selected 38 poorly annotated Escherichia coli genes for which the model assigned one of three GO terms with high confidence: involvement in DNA repair, protein translation, or cell wall synthesis. Results of antibiotic stress survival assays on E. coli knockout mutants showed high agreement with our model''s estimates of accuracy: out of 38 predictions obtained at the reported Precision of 60%, we confirmed 25 predictions, indicating that our confidence estimates can be used to make informed decisions on experimental validation. Our work will contribute to making experimental validation of computational predictions more approachable, both in cost and time. Our predictions for 998 prokaryotic genomes include ∼400000 specific annotations with the estimated Precision of 90%, ∼19000 of which are highly specific—e.g. “penicillin binding,” “tRNA aminoacylation for protein translation,” or “pathogenesis”—and are freely available at http://gorbi.irb.hr/.     

Network-based Survival Analysis Reveals Subnetwork Signatures for Predicting Outcomes of Ovarian Cancer Treatment

Cox regression is commonly used to predict the outcome by the time to an event of interest and in addition, identify relevant features for survival analysis in cancer genomics. Due to the high-dimensionality of high-throughput genomic data, existing Cox models trained on any particular dataset usually generalize poorly to other independent datasets. In this paper, we propose a network-based Cox regression model called Net-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer datasets. Net-Cox integrates gene network information into the Cox''s proportional hazard model to explore the co-expression or functional relation among high-dimensional gene expression features in the gene network. Net-Cox was applied to analyze three independent gene expression datasets including the TCGA ovarian cancer dataset and two other public ovarian cancer datasets. Net-Cox with the network information from gene co-expression or functional relations identified highly consistent signature genes across the three datasets, and because of the better generalization across the datasets, Net-Cox also consistently improved the accuracy of survival prediction over the Cox models regularized by or . This study focused on analyzing the death and recurrence outcomes in the treatment of ovarian carcinoma to identify signature genes that can more reliably predict the events. The signature genes comprise dense protein-protein interaction subnetworks, enriched by extracellular matrix receptors and modulators or by nuclear signaling components downstream of extracellular signal-regulated kinases. In the laboratory validation of the signature genes, a tumor array experiment by protein staining on an independent patient cohort from Mayo Clinic showed that the protein expression of the signature gene FBN1 is a biomarker significantly associated with the early recurrence after 12 months of the treatment in the ovarian cancer patients who are initially sensitive to chemotherapy. Net-Cox toolbox is available at http://compbio.cs.umn.edu/Net-Cox/.     

Correlated Mutations: A Hallmark of Phenotypic Amino Acid Substitutions

Point mutations resulting in the substitution of a single amino acid can cause severe functional consequences, but can also be completely harmless. Understanding what determines the phenotypical impact is important both for planning targeted mutation experiments in the laboratory and for analyzing naturally occurring mutations found in patients. Common wisdom suggests using the extent of evolutionary conservation of a residue or a sequence motif as an indicator of its functional importance and thus vulnerability in case of mutation. In this work, we put forward the hypothesis that in addition to conservation, co-evolution of residues in a protein influences the likelihood of a residue to be functionally important and thus associated with disease. While the basic idea of a relation between co-evolution and functional sites has been explored before, we have conducted the first systematic and comprehensive analysis of point mutations causing disease in humans with respect to correlated mutations. We included 14,211 distinct positions with known disease-causing point mutations in 1,153 human proteins in our analysis. Our data show that (1) correlated positions are significantly more likely to be disease-associated than expected by chance, and that (2) this signal cannot be explained by conservation patterns of individual sequence positions. Although correlated residues have primarily been used to predict contact sites, our data are in agreement with previous observations that (3) many such correlations do not relate to physical contacts between amino acid residues. Access to our analysis results are provided at http://webclu.bio.wzw.tum.de/~pagel/supplements/correlated-positions/.     

Genetic Analysis of Resistance and Sensitivity to 2-Deoxyglucose in Saccharomyces cerevisiae

Aerobic glycolysis is a metabolic pathway utilized by human cancer cells and also by yeast cells when they ferment glucose to ethanol. Both cancer cells and yeast cells are inhibited by the presence of low concentrations of 2-deoxyglucose (2DG). Genetic screens in yeast used resistance to 2-deoxyglucose to identify a small set of genes that function in regulating glucose metabolism. A recent high throughput screen for 2-deoxyglucose resistance identified a much larger set of seemingly unrelated genes. Here, we demonstrate that these newly identified genes do not in fact confer significant resistance to 2-deoxyglucose. Further, we show that the relative toxicity of 2-deoxyglucose is carbon source dependent, as is the resistance conferred by gene deletions. Snf1 kinase, the AMP-activated protein kinase of yeast, is required for 2-deoxyglucose resistance in cells growing on glucose. Mutations in the SNF1 gene that reduce kinase activity render cells hypersensitive to 2-deoxyglucose, while an activating mutation in SNF1 confers 2-deoxyglucose resistance. Snf1 kinase activated by 2-deoxyglucose does not phosphorylate the Mig1 protein, a known Snf1 substrate during glucose limitation. Thus, different stimuli elicit distinct responses from the Snf1 kinase.     

The Mechanism of Nucleotide Excision Repair-Mediated UV-Induced Mutagenesis in Nonproliferating Cells

Following the irradiation of nondividing yeast cells with ultraviolet (UV) light, most induced mutations are inherited by both daughter cells, indicating that complementary changes are introduced into both strands of duplex DNA prior to replication. Early analyses demonstrated that such two-strand mutations depend on functional nucleotide excision repair (NER), but the molecular mechanism of this unique type of mutagenesis has not been further explored. In the experiments reported here, an ade2 adeX colony-color system was used to examine the genetic control of UV-induced mutagenesis in nondividing cultures of Saccharomyces cerevisiae. We confirmed a strong suppression of two-strand mutagenesis in NER-deficient backgrounds and demonstrated that neither mismatch repair nor interstrand crosslink repair affects the production of these mutations. By contrast, proteins involved in the error-prone bypass of DNA damage (Rev3, Rev1, PCNA, Rad18, Pol32, and Rad5) and in the early steps of the DNA-damage checkpoint response (Rad17, Mec3, Ddc1, Mec1, and Rad9) were required for the production of two-strand mutations. There was no involvement, however, for the Pol η translesion synthesis DNA polymerase, the Mms2-Ubc13 postreplication repair complex, downstream DNA-damage checkpoint factors (Rad53, Chk1, and Dun1), or the Exo1 exonuclease. Our data support models in which UV-induced mutagenesis in nondividing cells occurs during the Pol ζ-dependent filling of lesion-containing, NER-generated gaps. The requirement for specific DNA-damage checkpoint proteins suggests roles in recruiting and/or activating factors required to fill such gaps.