Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor

BACKGROUND: Transient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement. RESULTS: The presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions. CONCLUSION: Current methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general

Transcriptome Analysis of the Effects of Gomisin A on the Recovery of Carbon Tetrachloride-Induced Damage in Rat Liver

Gomisin A possesses a hepatic function-facilitating property in liver-injured rats. Its preventive action on carbon tetrachloride-induced cholestasis is due to maintenance of the function of the bile acids-independent fraction. To investigate alterations in gene expression after gomisin A treatment on injured rat liver, DNA microarray analyses were performed on a Rat 44K 4-Plex Gene Expression platform with duplicated reactions after gomisin A treatment. We identified 255 up-regulated and 230 down-regulated genes due to the effects of gomisin A on recovery of carbon tetrachloride-induced rat liver damage. For functional characterization of these genes, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes biochemical pathways analyses were performed. Many up-regulated or down-regulated genes were related to cell cycle or focal adhesion and cell death genes, respectively. Our microarray experiment indicated that the liver repair mechanism induced by gomisin A was strongly associated with increased gene expressions related to cell cycle and suppression of the gene expression related in cell death

Considerations for creating and annotating the budding yeast Genome Map at SGD: a progress report

The Saccharomyces Genome Database (SGD) is compiling and annotating a comprehensive catalogue of functional sequence elements identified in the budding yeast genome. Recent advances in deep sequencing technologies have enabled for example, global analyses of transcription profiling and assembly of maps of transcription factor occupancy and higher order chromatin organization, at nucleotide level resolution. With this growing influx of published genome-scale data, come new challenges for their storage, display, analysis and integration. Here, we describe SGD's progress in the creation of a consolidated resource for genome sequence elements in the budding yeast, the considerations taken in its design and the lessons learned thus far. The data within this collection can be accessed at http://browse.yeastgenome.org and downloaded from http://downloads.yeastgenome.org

Highly Tunable Aptasensing Microarrays with Graphene Oxide Multilayers

A highly tunable layer-by-layer (LbL)-assembled graphene oxide (GO) array has been devised for high-throughput multiplex protein sensing. In this array, the fluorescence of different target-bound aptamers labeled with dye is efficiently quenched by GO through fluorescence resonance energy transfer (FRET), and simultaneous multiplex target detection is performed by recovering the quenched fluorescence caused by specific binding between an aptamer and a protein. Thin GO films consisting of 10 bilayers displayed a high quenching ability, yielding over 85% fluorescence quenching with the addition of a 2 mu M dye-labeled aptamer. The limit for human thrombin detection in the 6- and 10-bilayered GO array is estimated to be 0.1 and 0.001 nM, respectively, indicating highly tunable nature of LbL assembled GO multilayers in controlling the sensitivity of graphene-based FRET aptasensor. Furthermore, the GO chip could be reused up to four times simply by cleaning it with distilled water.open4

Gene Expression Profiling of Breast Cancers with Emphasis of β-Catenin Regulation

To gain molecular understanding of carcinogenesis of breast cancer, gene expression profiles were analyzed using cDNA microarray representing 4,600 cDNAs in 10 breast cancer samples and the adjacent noncancerous breast tissues from the same patients. The alterations in gene expression levels were confirmed by reverse-transcription PCR in four randomly selected genes. Genes that were differently expressed in cancer and noncancerous tissues were identified. 106 (of which 55 were known) and 49 (of which 28 were known) genes were up- or down-regulated, respectively, in greater than 60% of the breast cancer samples. In cancer tissues, genes related to cell cycle, transcription, metabolism, cell structure/motility and signal transduction were mostly up-regulated. Furthermore, three cancer tissues showing immunohistochemically aberrant accumulation of β-catenin in the nucleus and/or cytoplasm revealed down-regulation of Siah and Axin genes and up-regulation of Wnt and c-myc genes. These findings were highly consistent with Wnt signaling pathway associated with β-catenin regulation previously suggested by others. Our studies, therefore, provide not only a molecular basis to understand biological processes of breast cancer but also useful resources to define the mechanism of β-catenin expression in tumorigenesis of breast cancer

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication