Search

Background: In the pain field, it is essential to quantify nociceptive responses. The response to the application of von Frey filaments to the skin measures tactile sensitivity and is a surrogate marker of allodynia in states of peripheral and/or central sensitization. The method is widely used across species within the pain field. However, uncertainties appear to exist regarding the appropriate method for analysing obtained data. Therefore, there is a need for refinement of the calculations for transformation of raw data to quantifiable data. Methods: Here, we briefly review the fundamentals behind von Frey testing using the standard up-down method and the associated statistics and show how different parameters of the statistical equation influence the calculated 50% threshold results. We discuss how to obtain the most accurate estimations in a given experimental setting. Results: To enhance accuracy and reproducibility across laboratories, we present an easy to use algorithm that calculates 50% thresholds based on the exact filaments and their interval using math beyond the traditional methods. This tool is available to the everyday user of von Frey filaments and allows the insertion of all imaginable ranges of filaments and is thus applicable to data derived in any species. Conclusion: We advocate for the use of this algorithm to minimize inaccuracies and to improve internal and external reproducibility. Significance: The von Frey testing procedure is standard for assessing peripheral and central sensitization but is associated with inaccuracies and lack of transparency in the associated math. Here, we describe these problems and present a novel statistical algorithm that calculates the exact thresholds using math beyond the traditional methods. The online platform is transparent, free of charge and easy to use also for the everyday user of von Frey filaments. Application of this resource will ultimately reduce errors due to methodological misinterpretations and increase reproducibility across laboratories.

BACKGROUND: Individuals respond differently to dietary intake leading to different associations between diet and traits. Most studies have investigated large cohorts without subgrouping them.OBJECTIVE: The purpose was to identify non-uniform associations between diets and anthropometric traits that appeared to be in conflict with one another across subgroups.DESIGN: We used a cohort comprising 43,790 women and men, the Danish Diet, Cancer and Health study, which includes a baseline examination at age 50-64 years and a follow-up about 5 years later. The baseline examination involved anthropometrics, body fat percentage, a food frequency questionnaire and information on lifestyle. From the questionnaire data we computed association rules between the intake of food groups and changes in waist circumference and body weight. Using association rule mining on subgroups and gender-specific cohorts, we identified non-uniform associations. The two gender-specific cohorts were stratified into subgroups using a non-linear, self-organizing map based method.RESULTS: We found 22 and 7 cases of conflicting rules in 8 participant subgroups for different anthropometric traits in women and men, respectively. For example, in a subgroup of women moderate waist loss was associated with a dietary pattern characterized by low intake in both cabbages and wine, in conflict with the association trends of both dietary factors in the female cohort. The finding of more conflicting rules in women suggests that inter-individual differences in response to dietary intake are stronger in women than in men.CONCLUSIONS: This combined stratification and association discovery approach revealed epidemiological relationships between dietary factors and changes in anthropometric traits in subgroups that take food group interactions into account. Conflicting rules adds an additional layer of complexity that should be integrated into the study of these relationships, for example in relation to genotypes.

Subsets of mononuclear phagocytes, including macrophages and classical dendritic cells (cDC), are highly heterogeneous in peripheral tissues such as the intestine, with each subset playing distinct roles in immune responses. Understanding this complexity at the cellular level has proven difficult due to the expression of overlapping phenotypic markers and the inability to isolate leukocytes of the mucosal lamina propria (LP) effector site, without contamination by the isolated lymphoid follicles (ILFs), which are embedded in the mucosa and which are responsible for the induction of immunity. Here we exploit our novel method for separating lamina propria from isolated lymphoid follicles to carry out single-cell RNA-seq, CITE-seq and flow cytometry analysis of MNPs in the human small intestinal and colonic LP, without contamination by lymphoid follicles. As well as classical monocytes, non-classical monocytes, mature macrophages, cDC1 and CD103+cDC2, we find that a CD1c+CD103-cDC subset, which shares features of both cDC2 and monocytes, is similar to the cDC3 that have recently been described in human peripheral blood. As well as differing between the steady-state small intestine and colon, the proportions of the different MNP subsets change during different stages of inflammatory bowel disease (IBD) inflammation. Putative cDC precursors (pre-cDC) were also present in the intestine, and trajectory analysis revealed clear developmental relationships between these and subsets of mature cDC, as well as between tissue monocytes and macrophages. By providing novel insights into the heterogeneity and development of intestinal MNP, our findings should help develop targeted approaches for modulating intestinal immune responses.

AIM It is debated whether the inhibition of glucagon secretion by glucose results from direct effects of glucose on the α-cell (intrinsic regulation) or by paracrine effects exerted by beta- or delta-cell products. METHODS To study this in a more physiological model than isolated islets, we perfused isolated rat pancreases and measured glucagon, insulin and somatostatin secretion in response to graded increases in perfusate glucose concentration (from 3.5 to 4, 5, 6, 7, 8, 10, 12 mmol/L) as well as glucagon responses to blockage/activation of insulin/GABA/somatostatin signalling with or without addition of glucose. RESULTS Glucagon secretion was reduced by about 50% (compared to baseline secretion at 3.5 mmol/L) within minutes after increasing glucose from 4 to 5 mmol/L (P

Numerous genetic studies have established a role for rare genomic variants in Congenital Heart Disease (CHD) at the copy number variation (CNV) and de novo variant (DNV) level. To identify novel haploinsufficient CHD disease genes, we performed an integrative analysis of CNVs and DNVs identified in probands with CHD including cases with sporadic thoracic aortic aneurysm. We assembled CNV data from 7,958 cases and 14,082 controls and performed a gene-wise analysis of the burden of rare genomic deletions in cases versus controls. In addition, we performed variation rate testing for DNVs identified in 2,489 parent-offspring trios. Our analysis revealed 21 genes which were significantly affected by rare CNVs and/or DNVs in probands. Fourteen of these genes have previously been associated with CHD while the remaining genes (FEZ1, MYO16, ARID1B, NALCN, WAC, KDM5B and WHSC1) have only been associated in small cases series or show new associations with CHD. In addition, a systems level analysis revealed affected protein-protein interaction networks involved in Notch signaling pathway, heart morphogenesis, DNA repair and cilia/centrosome function. Taken together, this approach highlights the importance of re-analyzing existing datasets to strengthen disease association and identify novel disease genes and pathways., Author summary Congenital heart disease (CHD) is the most common congenital anomaly and represents a major global health burden. Multiple studies have identified a key genetic component contributing to the aetiology of CHD. However, despite the advances in the field of CHD within the last three decades, the genetic causes underlying CHD are still not fully understood. Herein we have assembled a large patient CHD cohort and performed a data-driven meta-analysis of genomic variants in CHD. This analysis has allowed us to strengthen the disease association of known CHD genes, as well as identifying novel haploinsufficient CHD candidate genes.

Non-oncogene addiction (NOA) genes are essential for supporting the stress-burdened phenotype of tumours and thus vital for their survival. Although NOA genes are acknowledged to be potential drug targets, there has been no large-scale attempt to identify and characterise them as a group across cancer types. Here we provide the first method for the identification of conditional NOA genes and their rewired neighbours using a systems approach. Using copy number data and expression profiles from The Cancer Genome Atlas (TCGA) we performed comparative analyses between high and low genomic stress tumours for 15 cancer types. We identified 101 condition-specific differential coexpression modules, mapped to a high-confidence human interactome, comprising 133 candidate NOA rewiring hub genes. We observe that most modules lose coexpression in the high-stress state and that activated stress modules and hubs take part in homoeostasis maintenance processes such as chromosome segregation, oxireductase activity, mitotic checkpoint (PLK1 signalling), DNA replication initiation and synaptic signalling. We furthermore show that candidate NOA rewiring hubs are unique for each cancer type, but that their respective rewired neighbour genes largely are shared across cancer types., Using the computer to find the Achilles heel of cancer Tumour cells are under many types of stresses from their environments and they activate certain genes to stay alive. These ‘Achilles heel genes’ could be potential new drug targets for cancer. A team led by Søren Brunak at Copenhagen University in Denmark used a catalogue of cancer data available online to identify these Achilles heel genes using network biology. They found several networks across 15 cancer types that are significantly more active in the stressed state. This study is the first to use systems level approaches to identify stress genes in 15 cancer types. A key result is that the stress-activated genes themselves are not shared across cancer types, but their rewired neighbours are. This underscores the idea of an overall stress-specific mechanism which is not necessarily cancer type-specific. Condition-specific genes will have mounting impact on cancer research and personalised cancer therapeutics.

Lifestyle and sociodemographics are likely to influence dietary patterns, and, as a result, human exposure to chemical contaminants in foods and their associated health impact. We aimed to characterize subgroups of the Danish population based on diet and sociodemographic indicators, and identify those bearing a higher disease burden due to exposure to methylmercury (MeHg), cadmium (Cd) and inorganic arsenic (i-As). We collected dietary, lifestyle, and sociodemographic data on the occurrence of chemical contaminants in foods from Danish surveys. We grouped participants according to similarities in diet, lifestyle, and sociodemographics using Self-Organizing Maps (SOM), and estimated disease burden in disability-adjusted life years (DALY). SOM clustering resulted in 12 population groups with distinct characteristics. Exposure to contaminants varied between clusters and was largely driven by intake of fish, seafood and cereal products. Five clusters had an estimated annual burden >20 DALY/100,000. The cluster with the highest burden had a high proportion of women of childbearing age, with most of the burden attributed to MeHg. Individuals belonging to the top three clusters had higher education and physical activity, were mainly non-smokers and lived in urban areas. Our findings may facilitate the development of preventive strategies targeted to the most affected subgroups.

Propionibacterium acnes is the most abundant bacterium on human skin, particularly in sebaceous areas. P. acnes is suggested to be an opportunistic pathogen involved in the development of diverse medical conditions but is also a proven contaminant of human clinical samples and surgical wounds. Its significance as a pathogen is consequently a matter of debate. In the present study, we investigated the presence of P. acnes DNA in 250 next-generation sequencing data sets generated from 180 samples of 20 different sample types, mostly of cancerous origin. The samples were subjected to either microbial enrichment, involving nuclease treatment to reduce the amount of host nucleic acids, or shotgun sequencing. We detected high proportions of P. acnes DNA in enriched samples, particularly skin tissue-derived and other tissue samples, with the levels being higher in enriched samples than in shotgun-sequenced samples. P. acnes reads were detected in most samples analyzed, though the proportions in most shotgun-sequenced samples were low. Our results show that P. acnes can be detected in practically all sample types when molecular methods, such as next-generation sequencing, are employed. The possibility of contamination from the patient or other sources, including laboratory reagents or environment, should therefore always be considered carefully when P. acnes is detected in clinical samples. We advocate that detection of P. acnes always be accompanied by experiments validating the association between this bacterium and any clinical condition.

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notablyTERTpromoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression inTP53, TLE4, andTCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

Motivation Understanding the specificity of protein receptor–ligand interactions is pivotal for our comprehension of biological mechanisms and systems. Receptor protein families often have a certain level of sequence diversity that converges into fewer conserved protein structures, allowing the exertion of well-defined functions. T and B cell receptors of the immune system and protein kinases that control the dynamic behaviour and decision processes in eukaryotic cells by catalysing phosphorylation represent prime examples. Driven by the large sequence diversity, the receptors within such protein families are often found to share specificities although divergent at the sequence level. This observation has led to the notion that prediction models of such systems are most effectively handled in a receptor-specific manner. Results We show that this approach in many cases is suboptimal, and describe an alternative improved framework for generating models with pan-receptor-predictive power for receptor protein families. The framework is based on deep artificial neural networks and integrates information from individual receptors into a single pan-receptor model, leveraging information across multiple receptor-specific datasets allowing predictions of the receptor specificity for all members of a given protein family including those described by limited or no ligand data. The approach was applied to the protein kinase superfamily, leading to the method NetPhosPan. The method was extensively validated and benchmarked against state-of-the-art prediction methods and was found to have unprecedented performance in particularly for kinase domains characterized by limited or no experimental data. Availability and implementation The method is freely available to non-commercial users and can be downloaded at http://www.cbs.dtu.dk/services/NetPhospan-1.0. Supplementary information Supplementary data are available at Bioinformatics online.

Most genomic alterations are tolerated while only a minor fraction disrupts molecular function sufficiently to drive disease. Protein kinases play a central biological function and the functional consequences of their variants are abundantly characterized. However, this heterogeneous information is often scattered across different sources, which makes the integrative analysis complex and laborious. wKinMut-2 constitutes a solution to facilitate the interpretation of the consequences of human protein kinase variation. Nine methods predict their pathogenicity, including a kinase-specific random forest approach. To understand the biological mechanisms causative of human diseases and cancer, information from pertinent reference knowledge bases and the literature is automatically mined, digested, and homogenized. Variants are visualized in their structural contexts and residues affecting catalytic and drug binding are identified. Known protein-protein interactions are reported. Altogether, this information is intended to assist the generation of new working hypothesis to be corroborated with ulterior experimental work. The wKinMut-2 system, along with a user manual and examples, is freely accessible at http://kinmut2.bioinfo.cnio.es, the code for local installations can be downloaded from https://github.com/Rbbt-Workflows/KinMut2.

Systemic lupus erythematosus (SLE) is caused by a combination of genetic and acquired immunodeficiencies and environmental factors including infections. An association with Epstein-Barr virus (EBV) has been established by numerous studies over the past decades. Here, we review recent experimental studies on EBV, and present our integrated theory of SLE development.SLE patients have dysfunctional control of EBV infection resulting in frequent reactivations and disease progression. These comprise impaired functions of EBV-specific T-cells with an inverse correlation to disease activity and elevated serum levels of antibodies against lytic cycle EBV antigens. The presence of EBV proteins in renal tissue from SLE patients with nephritis suggests direct involvement of EBV in SLE development. As expected for patients with immunodeficiencies, studies reveal that SLE patients show dysfunctional responses to other viruses as well. An association with EBV infection has also been demonstrated for other autoimmune diseases, including Sjögren's syndrome, rheumatoid arthritis, and multiple sclerosis.Collectively, the interplay between an impaired immune system and the cumulative effects of EBV and other viruses results in frequent reactivation of EBV and enhanced cell death, causing development of SLE and concomitant autoreactivities.

Protein kinases are a superfamily involved in many crucial cellular processes, including signal transmission and regulation of cell cycle. As a consequence of this role, kinases have been reported to be associated with many types of cancer and are considered as potential therapeutic targets. We analyzed the distribution of pathogenic somatic point mutations (drivers) in the protein kinase superfamily with respect to their location in the protein, such as in structural, evolutionary, and functionally relevant regions. We find these driver mutations are more clearly associated with key protein features than other somatic mutations (passengers) that have not been directly linked to tumor progression. This observation fits well with the expected implication of the alterations in protein kinase function in cancer pathogenicity. To explain the relevance of the detected association of cancer driver mutations at the molecular level in the human kinome, we compare these with genetically inherited mutations (SNPs). We find that the subset of nonsynonymous SNPs that are associated to disease, but sufficiently mild to the point of being widespread in the population, tend to avoid those key protein regions, where they could be more detrimental for protein function. This tendency contrasts with the one detected for cancer associated-driver-mutations, which seems to be more directly implicated in the alteration of protein function. The detailed analysis of protein kinase groups and a number of relevant examples, confirm the relation between cancer associated-driver-mutations and key regions for protein kinase structure and function. Proteins 2009; 77:892-903. (C) 2009 Wiley-Liss, Inc.

Cancer genome projects are now being expanded in an attempt to provide complete landscapes of the mutations that exist in tumours. Although the importance of cataloguing genome variations is well recognized, there are obvious difficulties in bridging the gaps between high-throughput resequencing information and the molecular mechanisms of cancer evolution. Here, we describe the current status of the high-throughput genomic technologies, and the current limitations of the associated computational analysis and experimental validation of cancer genetic variants. We emphasize how the current cancer-evolution models will be influenced by the high-throughput approaches, in particular through efforts devoted to monitoring tumour progression, and how, in turn, the integration of data and models will be translated into mechanistic knowledge and clinical applications.

This article details the assessment process and evaluation results for two categories in the 8th Critical Assessment of Protein Structure Prediction experiment (CASP8). The domain prediction category was evaluated with a range of scores including the Normalized Domain Overlap score and a domain boundary distance measure. Residue-residue contact predictions were evaluated with standard CASP measures, prediction accuracy, and Xd. In the domain boundary prediction category, prediction methods still make reliable predictions for targets that have structural templates, but continue to struggle to make good predictions for the few ab initio targets in CASP. There was little indication of improvement in the domain prediction category. The contact prediction category demonstrated that there was renewed interest among predictors and despite the small sample size the results suggested that there had been an increase in prediction accuracy. In contrast to CASP7 contact specialists predicted contacts more accurately than the majority of tertiary structure predictors. Despite this small success, the lack of free modeling targets makes it unlikely that either category will be included in their present form in CASP9.

Predictions of intramolecular residue-residue contacts were assessed as part of the seventh community-wide Critical Assessment of Structure Prediction experiment (CASP7). As in past assessments, we focused on contacts that lie far apart in sequence as these are likely to be more informative in predicting protein structure. One lab did somewhat better than others according to our assessment, and there is some reason to think that this lab's results represent progress over CASP6. In general, contacts inferred from 3D structural predictions are similar in accuracy to those predicted by contact prediction methods. However, contact prediction methods were more accurate for some targets.

Most genomic alterations are tolerated while only a minor fraction disrupts molecular function sufficiently to drive disease. Protein kinases play a central biological function and the functional consequences of their variants are abundantly characterized. However, this heterogeneous information is often scattered across different sources, which makes the integrative analysis complex and laborious. wKinMut-2 constitutes a solution to facilitate the interpretation of the consequences of human protein kinase variation. Nine methods predict their pathogenicity, including a kinase-specific random forest approach. To understand the biological mechanisms causative of human diseases and cancer, information from pertinent reference knowledge bases and the literature is automatically mined, digested, and homogenized. Variants are visualized in their structural contexts and residues affecting catalytic and drug binding are identified. Known protein-protein interactions are reported. Altogether, this information is intended to assist the generation of new working hypothesis to be corroborated with ulterior experimental work. The wKinMut-2 system, along with a user manual and examples, is freely accessible at http://kinmut2.bioinfo.cnio.es, the code for local installations can be downloaded from https://github.com/Rbbt-Workflows/KinMut2.

Yolken et al. (1) claim detection of Acanthocystis turfacea chlorella virus 1 (ATCV-1, gi119953744) in the normal human oropharyngeal viral flora and associate it with altered cognitive function. However, the reported presence of a freshwater algae virus, previously not known to infect other species, was based on a few sequence reads homologous to ATCV-1 identified with BLASTn. These reads span relatively few bases (97–698 bp) per sample, dispersed over a minor fraction (0.03–0.24%) of the 288 kb ATCV-1 genome.

An entire family of methodologies for predicting protein interactions is based on the observed fact that families of interacting proteins tend to have similar phylogenetic trees due to co-evolution. One application of this concept is the prediction of the mapping between the members of two interacting protein families (which protein within one family interacts with which protein within the other). The idea is that the real mapping would be the one maximizing the similarity between the trees. Since the exhaustive exploration of all possible mappings is not feasible for large families, current approaches use heuristic techniques which do not ensure the best solution to be found. This is why it is important to check the results proposed by heuristic techniques and to manually explore other solutions. Here we present TSEMA, the server for efficient mapping assessment. This system calculates an initial mapping between two families of proteins based on a Monte Carlo approach and allows the user to interactively modify it based on performance figures and/or specific biological knowledge. All the explored mappings are graphically shown over a representation of the phylogenetic trees. The system is freely available at http://pdg.cnb.uam.es/TSEMA. Standalone versions of the software behind the interface are available upon request from the authors.

Protein kinases play a crucial role in a plethora of significant physiological functions and a number of mutations in this superfamily have been reported in the literature to disrupt protein structure and/or function. Computational and experimental research aims to discover the mechanistic connection between mutations in protein kinases and disease with the final aim of predicting the consequences of mutations on protein function and the subsequent phenotypic alterations. In this article, we will review the possibilities and limitations of current computational methods for the prediction of the pathogenicity of mutations in the protein kinase superfamily. In particular we will focus on the problem of benchmarking the predictions with independent gold standard datasets. We will propose a pipeline for the curation of mutations automatically extracted from the literature. Since many of these mutations are not included in the databases that are commonly used to train the computational methods to predict the pathogenicity of protein kinase mutations we propose them to build a valuable gold standard dataset in the benchmarking of a number of these predictors. Finally, we will discuss how text mining approaches constitute a powerful tool for the interpretation of the consequences of mutations in the context of disease genome analysis with particular focus on cancer.

Background\ud Protein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites.\ud \ud Results\ud Pathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families.\ud \ud Conclusions\ud Disease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development.

Protein kinases are a superfamily involved in many crucial cellular processes, including signal transmission and regulation of cell cycle. As a consequence of this role, kinases have been reported to be associated with many types of cancer and are considered as potential therapeutic targets. We analyzed the distribution of pathogenic somatic point mutations (drivers) in the protein kinase superfamily with respect to their location in the protein, such as in structural, evolutionary, and functionally relevant regions. We find these driver mutations are more clearly associated with key protein features than other somatic mutations (passengers) that have not been directly linked to tumor progression. This observation fits well with the expected implication of the alterations in protein kinase function in cancer pathogenicity. To explain the relevance of the detected association of cancer driver mutations at the molecular level in the human kinome, we compare these with genetically inherited mutations (SNPs). We find that the subset of nonsynonymous SNPs that are associated to disease, but sufficiently mild to the point of being widespread in the population, tend to avoid those key protein regions, where they could be more detrimental for protein function. This tendency contrasts with the one detected for cancer associated-driver-mutations, which seems to be more directly implicated in the alteration of protein function. The detailed analysis of protein kinase groups and a number of relevant examples, confirm the relation between cancer associated-driver-mutations and key regions for protein kinase structure and function.

Background\ud The phenotypic effects of sequence variations in protein-coding regions come about primarily via their effects on the resulting structures, for example by disrupting active sites or affecting structural stability. In order better to understand the mechanisms behind known mutant phenotypes, and predict the effects of novel variations, biologists need tools to gauge the impacts of DNA mutations in terms of their structural manifestation. Although many mutations occur within domains whose structure has been solved, many more occur within genes whose protein products have not been structurally characterized.\ud \ud Results\ud Here we present 3DSim (3D Structural Implication of Mutations), a database and web application facilitating the localization and visualization of single amino acid polymorphisms (SAAPs) mapped to protein structures even where the structure of the protein of interest is unknown. The server displays information on 6514 point mutations, 4865 of them known to be associated with disease. These polymorphisms are drawn from SAAPdb, which aggregates data from various sources including dbSNP and several pathogenic mutation databases. While the SAAPdb interface displays mutations on known structures, 3DSim projects mutations onto known sequence domains in Gene3D. This resource contains sequences annotated with domains predicted to belong to structural families in the CATH database. Mappings between domain sequences in Gene3D and known structures in CATH are obtained using a MUSCLE alignment. 1210 three-dimensional structures corresponding to CATH structural domains are currently included in 3DSim; these domains are distributed across 396 CATH superfamilies, and provide a comprehensive overview of the distribution of mutations in structural space.\ud \ud Conclusion\ud The server is publicly available at http://3DSim.bioinfo.cnio.es/ webcite. In addition, the database containing the mapping between SAAPdb, Gene3D and CATH is available on request and most of the functionality is available through programmatic web service access.

Computational methods for predicting protein interaction partners are becoming increasingly popular. Many of them are mature enough to be widely used by molecular biologists who can look for proteins related to the protein of interest in order to infer information about its context in the cell. In this chapter we describe the use of the mirrortree set of programs and related software for predicting protein interactions. They are all based on the idea that interacting or functionally related proteins tend to show similar phylogenetic trees due to coevolution. The basic mirrortree program can be used to calculate the similarity between the phylogenetic trees implicit in the multiple sequence alignments of two protein families. The ECID database contains protein interactions and relationships from different computational and experimental sources for the model organism Escherichia coli, including the ones generated with mirrortree. Finally, the TSEMA server uses the concept of tree similarity between interacting families to look for the best mapping between two families of interacting proteins: which member in one family interacts with which member in the other.

Computational methods for predicting protein interaction partners are becoming increasingly popular. Many of them are mature enough to be widely used by molecular biologists who can look for proteins related to the protein of interest in order to infer information about its context in the cell. In this chapter we describe the use of the mirrortree set of programs and related software for predicting protein interactions. They are all based on the idea that interacting or functionally related proteins tend to show similar phylogenetic trees due to coevolution. The basic mirrortree program can be used to calculate the similarity between the phylogenetic trees implicit in the multiple sequence alignments of two protein families. The ECID database contains protein interactions and relationships from different computational and experimental sources for the model organism Escherichia coli, including the ones generated with mirrortree. Finally, the TSEMA server uses the concept of tree similarity between interacting families to look for the best mapping between two families of interacting proteins: which member in one family interacts with which member in the other.

Reliable biomarkers predicting resistance or sensitivity to anti-cancer therapy are critical for oncologists to select proper therapeutic drugs in individual cancer patients. Ovarian and breast cancer patients carrying germline mutations in BRCA1 or BRCA2 genes are often sensitive to DNA damaging drugs and relative to non-mutation carriers present a favorable clinical outcome following therapy. Genome sequencing studies have shown a high number of mutations in the tumor genome in patients carrying BRCA1 or BRCA2 mutations (mBRCA). The present study used exome-sequencing and SNP 6 array data of The Cancer Genome Atlas (TCGA) to correlate the total exome mutation number (Nmut) to progression-free survival (PFS) and overall survival (OS) in the patients (n = 316) with high grade serous ovarian cancer (HGSOC) after debulking surgery and platinum-based chemotherapy. HGSOC in 70 patients of this cohort had either germlines or somatic mutations of BRCA1 or BRCA2 genes. The results revealed that the Nmut was significantly lower in the chemotherapy-resistant mBRCA HGSOC defined by progression within 6 months after completion of first line platinum-based chemotherapy. We found a significant association between low Nmut and shorter PFS and OS in mBRCA HGSOC by Cox regression and Kaplan-Meier analyses. The association was also significant when the analysis was limited to germline BRCA1 or BRCA2 mutated patients with SNP array-determined loss of heterozygosity of the BRCA1 or BRCA2 locus in the tumors. In the mBRCA HGSOC tumors, Nmut was correlated with the genome fraction with loss of heterozygosity and with number of telomeric allelic imbalance, genomic measures evaluating chromosomal instability. However, no significant association between Nmut and PFS or OS was found in HGSOC carrying wild-type BRCA1 and BRCA2 genes. These results suggest that in cancers with DNA repair deficiency caused by functional BRCA loss, higher versus lower Nmut may reflect the status of deficiency or rescue by alternative mechanism(s) for DNA repair, with lower Nmut predicting for resistance to DNA-damaging drugs in mBRCA HGSOC. Our observations are consistent with the new concept that BRCA1/2 critically regulate error-free repair of nucleotide damage to suppress mutation formation, and may imply an activation of alternative repair mechanism(s) capable of bypassing the BRCA defect and restoring error-free DNA repair. Citation Format: Nicolai Juul Birkbak, Bose Kochupurakkal, Jose MG Izarzugaza, Yang Li, Joyce Liu, Zoltan Szallasi, Ursula Matulonis, Andrea L. Richardson, J Dirk Iglehart, Zhigang C. Wang. Exome mutation burden predicts clinical outcome in ovarian cancer carrying mutated BRCA1 and BRCA2 genes. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-255. doi:10.1158/1538-7445.AM2013-LB-255