The RIDDLE syndrome protein mediates a ubiquitin-dependent signaling cascade at sites of DNA damage.

The biological response to DNA double-strand breaks acts to preserve genome integrity. Individuals bearing inactivating mutations in components of this response exhibit clinical symptoms that include cellular radiosensitivity, immunodeficiency, and cancer predisposition. The archetype for such disorders is Ataxia-Telangiectasia caused by biallelic mutation in ATM, a central component of the DNA damage response. Here, we report that the ubiquitin ligase RNF168 is mutated in the RIDDLE syndrome, a recently discovered immunodeficiency and radiosensitivity disorder. We show that RNF168 is recruited to sites of DNA damage by binding to ubiquitylated histone H2A. RNF168 acts with UBC13 to amplify the RNF8-dependent histone ubiquitylation by targeting H2A-type histones and by promoting the formation of lysine 63-linked ubiquitin conjugates. These RNF168-dependent chromatin modifications orchestrate the accumulation of 53BP1 and BRCA1 to DNA lesions, and their loss is the likely cause of the cellular and developmental phenotypes associated with RIDDLE syndrome.

Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique.

The visual detection, classification, and differentiation of cancers within tissues of clinical patients is an extremely difficult and time-consuming process with severe diagnosis implications. To this end, many computational approaches have been developed to analyse tissue samples to supplement histological cancer diagnoses. One approach is the interrogation of the chemical composition of the actual tissue samples through the utilisation of vibrational spectroscopy, specifically Infrared (IR) spectroscopy. Cancerous tissue can be detected by analysing the molecular vibration patterns of tissues undergoing IR irradiation, and even graded, with multivariate and Machine Learning (ML) techniques. This publication serves to review and highlight the potential for the application of infrared microscopy techniques such as Fourier Transform Infrared Spectroscopy (FTIR) and Quantum Cascade Laser Infrared Spectroscopy (QCL), as a means to improve diagnostic accuracy and allow earlier detection of human neoplastic disease. This review provides an overview of the detection and classification of different cancerous tissues using FTIR spectroscopy paired with multivariate and ML techniques, using the F1-Score as a quantitative metric for direct comparison of model performances. Comparisons also extend to data handling techniques, with a provision of a suggested pre-processing protocol for future studies alongside suggestions as to reporting standards for future publication.

Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples.

Identifying nuclei is a standard first step when analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not use fluorescence can also carry useful information. Here, we used brightfield and fluorescence images of fixed cells with fluorescently labelled DNA, and confirmed that three convolutional neural network architectures can be adapted to segment nuclei from the brightfield channel, relying on fluorescence signal to extract the ground truth for training. We found that U-Net achieved the best overall performance, Mask R-CNN provided an additional benefit of instance segmentation, and that DeepCell proved too slow for practical application. We trained the U-Net architecture on over 200 dataset variations, established that accurate segmentation is possible using as few as 16 training images, and that models trained on images from similar cell lines can extrapolate well. Acquiring data from multiple focal planes further helps distinguish nuclei in the samples. Overall, our work helps to liberate a fluorescence channel reserved for nuclear staining, thus providing more information from the specimen, and reducing reagents and time required for preparing imaging experiments.

onlineFDR: an R package to control the false discovery rate for growing data repositories.

SUMMARY: In many areas of biological research, hypotheses are tested in a sequential manner, without having access to future P-values or even the number of hypotheses to be tested. A key setting where this online hypothesis testing occurs is in the context of publicly available data repositories, where the family of hypotheses to be tested is continually growing as new data is accumulated over time. Recently, Javanmard and Montanari proposed the first procedures that control the FDR for online hypothesis testing. We present an R package, onlineFDR, which implements these procedures and provides wrapper functions to apply them to a historic dataset or a growing data repository. AVAILABILITY AND IMPLEMENTATION: The R package is freely available through Bioconductor (http://www.bioconductor.org/packages/onlineFDR). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The MMS22L-TONSL complex mediates recovery from replication stress and homologous recombination.

Genome integrity is jeopardized each time DNA replication forks stall or collapse. Here we report the identification of a complex composed of MMS22L (C6ORF167) and TONSL (NFKBIL2) that participates in the recovery from replication stress. MMS22L and TONSL are homologous to yeast Mms22 and plant Tonsoku/Brushy1, respectively. MMS22L-TONSL accumulates at regions of ssDNA associated with distressed replication forks or at processed DNA breaks, and its depletion results in high levels of endogenous DNA double-strand breaks caused by an inability to complete DNA synthesis after replication fork collapse. Moreover, cells depleted of MMS22L are highly sensitive to camptothecin, a topoisomerase I poison that impairs DNA replication progression. Finally, MMS22L and TONSL are necessary for the efficient formation of RAD51 foci after DNA damage, and their depletion impairs homologous recombination. These results indicate that MMS22L and TONSL are genome caretakers that stimulate the recombination-dependent repair of stalled or collapsed replication forks.

MolClass: a web portal to interrogate diverse small molecule screen datasets with different computational models.

UNLABELLED: The MolClass toolkit and data portal generate computational models from user-defined small molecule datasets based on structural features identified in hit and non-hit molecules in different screens. Each new model is applied to all datasets in the database to classify compound specificity. MolClass thus defines a likelihood value for each compound entry and creates an activity fingerprint across diverse sets of screens. MolClass uses a variety of machine-learning methods to find molecular patterns and can therefore also assign a priori predictions of bioactivities for previously untested molecules. The power of the MolClass resource will grow as a function of the number of screens deposited in the database. AVAILABILITY AND IMPLEMENTATION: The MolClass webportal, software package and source code are freely available for non-commercial use at http://tyerslab.bio.ed.ac.uk/molclass. A MolClass tutorial and a guide on how to build models from datasets can also be found on the web site. MolClass uses the chemistry development kit (CDK), WEKA and MySQL for its core functionality. A REST service is available at http://tyerslab.bio.ed.ac.uk/molclass/api based on the OpenTox API 1.2.

Combinations of antibiotics and nonantibiotic drugs enhance antimicrobial efficacy.

Combinations of antibiotics are commonly used in medicine to broaden antimicrobial spectrum and generate synergistic effects. Alternatively, combination of nonantibiotic drugs with antibiotics offers an opportunity to sample a previously untapped expanse of bioactive chemical space. We screened a collection of drugs to identify compounds that augment the activity of the antibiotic minocycline. Unexpected synergistic drug combinations exhibited in vitro and in vivo activity against bacterial pathogens, including multidrug-resistant isolates.

Reduction in Nuclear Size by DHRS7 in Prostate Cancer Cells and by Estradiol Propionate in DHRS7-Depleted Cells.

Increased nuclear size correlates with lower survival rates and higher grades for prostate cancer. The short-chain dehydrogenase/reductase (SDR) family member DHRS7 was suggested as a biomarker for use in prostate cancer grading because it is largely lost in higher-grade tumors. Here, we found that reduction in DHRS7 from the LNCaP prostate cancer cell line with normally high levels of DHRS7 increases nuclear size, potentially explaining the nuclear size increase observed in higher-grade prostate tumors where it is lost. An exogenous expression of DHRS7 in the PC3 prostate cancer cell line with normally low DHRS7 levels correspondingly decreases nuclear size. We separately tested 80 compounds from the Microsource Spectrum library for their ability to restore normal smaller nuclear size to PC3 cells, finding that estradiol propionate had the same effect as the re-expression of DHRS7 in PC3 cells. However, the drug had no effect on LNCaP cells or PC3 cells re-expressing DHRS7. We speculate that separately reported beneficial effects of estrogens in androgen-independent prostate cancer may only occur with the loss of DHRS7/ increased nuclear size, and thus propose DHRS7 levels and nuclear size as potential biomarkers for the likely effectiveness of estrogen-based treatments.

Nanoscaled Discovery of a Shunt Rifamycin from Salinispora arenicola Using a Three-Color GFP-Tagged Staphylococcus aureus Macrophage Infection Assay.

Antimicrobial resistance has emerged as a global public health threat, and development of novel therapeutics for treating infections caused by multi-drug resistant bacteria is urgent. Staphylococcus aureus is a major human and animal pathogen, responsible for high levels of morbidity and mortality worldwide. The intracellular survival of S. aureus in macrophages contributes to immune evasion, dissemination, and resilience to antibiotic treatment. Here, we present a confocal fluorescence imaging assay for monitoring macrophage infection by green fluorescent protein (GFP)-tagged S. aureus as a front-line tool to identify antibiotic leads. The assay was employed in combination with nanoscaled chemical analyses to facilitate the discovery of a new, active rifamycin analogue. Our findings indicate a promising new approach for the identification of antimicrobial compounds with macrophage intracellular activity. The antibiotic identified here may represent a useful addition to our armory in tackling the silent pandemic of antimicrobial resistance.

BioGRID REST Service, BiogridPlugin2 and BioGRID WebGraph: new tools for access to interaction data at BioGRID.

UNLABELLED: The Biological General Repository for Interaction Datasets (BioGRID) representational state transfer (REST) service allows full URL-based access to curated protein and genetic interaction data at the BioGRID database. Appending URL parameters allows filtering of data by various attributes including gene names and identifiers, PubMed ID and evidence type. We also describe two visualization tools that interface with the REST service, the BiogridPlugin2 for Cytoscape and the BioGRID WebGraph. AVAILABILITY AND IMPLEMENTATION: BioGRID data and applications are completely free for commercial and non-commercial use. http://webservice.thebiogrid.org/resources/interactions (REST Service), http://wiki.thebiogrid.org/doku.php/biogridrest(REST Service parameter list and help), http://webservice.thebiogrid.org/resources/application.wadl(REST Service WADL), http://thebiogrid.org/download.php (BiogridPlugin2, v2.1 download), http://wiki.thebiogrid.org/doku.php/biogridplugin2 (BiogridPlugin2 help) and http://tyerslab.bio.ed.ac.uk/tools/BioGRID_webgraph.php(BioGRID WebGraph).

Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole.

Resistance to widely used fungistatic drugs, particularly to the ergosterol biosynthesis inhibitor fluconazole, threatens millions of immunocompromised patients susceptible to invasive fungal infections. The dense network structure of synthetic lethal genetic interactions in yeast suggests that combinatorial network inhibition may afford increased drug efficacy and specificity. We carried out systematic screens with a bioactive library enriched for off-patent drugs to identify compounds that potentiate fluconazole action in pathogenic Candida and Cryptococcus strains and the model yeast Saccharomyces. Many compounds exhibited species- or genus-specific synergism, and often improved fluconazole from fungistatic to fungicidal activity. Mode of action studies revealed two classes of synergistic compound, which either perturbed membrane permeability or inhibited sphingolipid biosynthesis. Synergistic drug interactions were rationalized by global genetic interaction networks and, notably, higher order drug combinations further potentiated the activity of fluconazole. Synergistic combinations were active against fluconazole-resistant clinical isolates and an in vivo model of Cryptococcus infection. The systematic repurposing of approved drugs against a spectrum of pathogens thus identifies network vulnerabilities that may be exploited to increase the activity and repertoire of antifungal agents.

A predictive model for drug bioaccumulation and bioactivity in Caenorhabditis elegans.

The resistance of Caenorhabditis elegans to pharmacological perturbation limits its use as a screening tool for novel small bioactive molecules. One strategy to improve the hit rate of small-molecule screens is to preselect molecules that have an increased likelihood of reaching their target in the worm. To learn which structures evade the worm's defenses, we performed the first survey of the accumulation and metabolism of over 1,000 commercially available drug-like small molecules in the worm. We discovered that fewer than 10% of these molecules accumulate to concentrations greater than 50% of that present in the worm's environment. Using our dataset, we developed a structure-based accumulation model that identifies compounds with an increased likelihood of bioavailability and bioactivity, and we describe structural features that facilitate small-molecule accumulation in the worm. Preselecting molecules that are more likely to reach a target by first applying our model to the tens of millions of commercially available compounds will undoubtedly increase the success of future small-molecule screens with C. elegans.

Chemical Interrogation of Nuclear Size Identifies Compounds with Cancer Cell Line-Specific Effects on Migration and Invasion.

Background: Lower survival rates for many cancer types correlate with changes in nuclear size/scaling in a tumor-type/tissue-specific manner. Hypothesizing that such changes might confer an advantage to tumor cells, we aimed at the identification of commercially available compounds to guide further mechanistic studies. We therefore screened for Food and Drug Administration (FDA)/European Medicines Agency (EMA)-approved compounds that reverse the direction of characteristic tumor nuclear size changes in PC3, HCT116, and H1299 cell lines reflecting, respectively, prostate adenocarcinoma, colonic adenocarcinoma, and small-cell squamous lung cancer. Results: We found distinct, largely nonoverlapping sets of compounds that rectify nuclear size changes for each tumor cell line. Several classes of compounds including, e.g., serotonin uptake inhibitors, cyclo-oxygenase inhibitors, ß-adrenergic receptor agonists, and Na+/K+ ATPase inhibitors, displayed coherent nuclear size phenotypes focused on a particular cell line or across cell lines and treatment conditions. Several compounds from classes far afield from current chemotherapy regimens were also identified. Seven nuclear size-rectifying compounds selected for further investigation all inhibited cell migration and/or invasion. Conclusions: Our study provides (a) proof of concept that nuclear size might be a valuable target to reduce cell migration/invasion in cancer treatment and (b) the most thorough collection of tool compounds to date reversing nuclear size changes specific to individual cancer-type cell lines. Although these compounds still need to be tested in primary cancer cells, the cell line-specific nuclear size and migration/invasion responses to particular drug classes suggest that cancer type-specific nuclear size rectifiers may help reduce metastatic spread.

Off-target effects of psychoactive drugs revealed by genome-wide assays in yeast.

To better understand off-target effects of widely prescribed psychoactive drugs, we performed a comprehensive series of chemogenomic screens using the budding yeast Saccharomyces cerevisiae as a model system. Because the known human targets of these drugs do not exist in yeast, we could employ the yeast gene deletion collections and parallel fitness profiling to explore potential off-target effects in a genome-wide manner. Among 214 tested, documented psychoactive drugs, we identified 81 compounds that inhibited wild-type yeast growth and were thus selected for genome-wide fitness profiling. Many of these drugs had a propensity to affect multiple cellular functions. The sensitivity profiles of half of the analyzed drugs were enriched for core cellular processes such as secretion, protein folding, RNA processing, and chromatin structure. Interestingly, fluoxetine (Prozac) interfered with establishment of cell polarity, cyproheptadine (Periactin) targeted essential genes with chromatin-remodeling roles, while paroxetine (Paxil) interfered with essential RNA metabolism genes, suggesting potential secondary drug targets. We also found that the more recently developed atypical antipsychotic clozapine (Clozaril) had no fewer off-target effects in yeast than the typical antipsychotics haloperidol (Haldol) and pimozide (Orap). Our results suggest that model organism pharmacogenetic studies provide a rational foundation for understanding the off-target effects of clinically important psychoactive agents and suggest a rational means both for devising compound derivatives with fewer side effects and for tailoring drug treatment to individual patient genotypes.

A Novel Screening Approach for the Dissection of Cellular Regulatory Networks of NF-κB Using Arrayed CRISPR gRNA Libraries.

CRISPR/Cas9 is increasingly being used as a tool to prosecute functional genomic screens. However, it is not yet possible to apply the approach at scale across a full breadth of cell types and endpoints. In order to address this, we developed a novel and robust workflow for array-based lentiviral CRISPR/Cas9 screening. We utilized a ß-lactamase reporter gene assay to investigate mediators of TNF-α-mediated NF-κB signaling. The system was adapted for CRISPR/Cas9 through the development of a cell line stably expressing Cas9 and application of a lentiviral gRNA library comprising mixtures of four gRNAs per gene. We screened a 743-gene kinome library whereupon hits were independently ranked by percent inhibition, Z' score, strictly standardized mean difference, and T statistic. A consolidated and optimized ranking was generated using Borda-based methods. Screening data quality was above acceptable limits (Z' ≥ 0.5). In order to determine the contribution of individual gRNAs and to better understand false positives and negatives, a subset of gRNAs, against 152 genes, were profiled in singlicate format. We highlight the use of known reference genes and high-throughput, next-generation amplicon and RNA sequencing to assess screen data quality. Screening with singlicate gRNAs was more successful than screening with mixtures at identifying genes with known regulatory roles in TNF-α-mediated NF-κB signaling and was found to be superior to previous RNAi-based methods. These results add to the available data on TNF-α-mediated NF-κB signaling and establish a high-throughput functional genomic screening approach, utilizing a vector-based arrayed gRNA library, applicable across a wide variety of endpoints and cell types at a genome-wide scale.

IA-Lab: A MATLAB framework for efficient microscopy image analysis development, applied to quantifying intracellular transport of internalized peptide-drug conjugate.

This work presents a MATLAB-based software package for high-throughput microscopy image analysis development, making such development more accessible for a large user community. The toolbox provides a GUI and a number of analysis workflows, and can serve as a general framework designed to allow for easy extension. For a new application, only a minor part of the object-oriented code needs to be replaced by new components, making development efficient. This makes it possible to quickly develop solutions for analysis not available in existing tools. We show its use in making a tool for quantifying intracellular transport of internalized peptide-drug conjugates. The code is freely available as open source on GitHub (https://github.com/amcorrigan/ia-lab).

Effects of Air Temperature on Climate-Sensitive Mortality and Morbidity Outcomes in the Elderly; a Systematic Review and Meta-analysis of Epidemiological Evidence.

INTRODUCTION: Climate change and rapid population ageing are significant public health challenges. Understanding which health problems are affected by temperature is important for preventing heat and cold-related deaths and illnesses, particularly in the elderly. Here we present a systematic review and meta-analysis on the effects of ambient hot and cold temperature (excluding heat/cold wave only studies) on elderly (65+ years) mortality and morbidity. METHODS: Time-series or case-crossover studies comprising cause-specific cases of elderly mortality (n=3,933,398) or morbidity (n=12,157,782) were pooled to obtain a percent change (%) in risk for temperature exposure on cause-specific disease outcomes using a random-effects meta-analysis. RESULTS: A 1°C temperature rise increased cardiovascular (3.44%, 95% CI 3.10-3.78), respiratory (3.60%, 3.18-4.02), and cerebrovascular (1.40%, 0.06-2.75) mortality. A 1°C temperature reduction increased respiratory (2.90%, 1.84-3.97) and cardiovascular (1.66%, 1.19-2.14) mortality. The greatest risk was associated with cold-induced pneumonia (6.89%, 20-12.99) and respiratory morbidity (4.93% 1.54-8.44). A 1°C temperature rise increased cardiovascular, respiratory, diabetes mellitus, genitourinary, infectious disease and heat-related morbidity. DISCUSSION: Elevated risks for the elderly were prominent for temperature-induced cerebrovascular, cardiovascular, diabetes, genitourinary, infectious disease, heat-related, and respiratory outcomes. These risks will likely increase with climate change and global ageing.

Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism.

The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery.

Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning.

The structure of genetic interaction networks predicts that, analogous to synthetic lethal interactions between non-essential genes, combinations of compounds with latent activities may exhibit potent synergism. To test this hypothesis, we generated a chemical-genetic matrix of 195 diverse yeast deletion strains treated with 4,915 compounds. This approach uncovered 1,221 genotype-specific inhibitors, which we termed cryptagens. Synergism between 8,128 structurally disparate cryptagen pairs was assessed experimentally and used to benchmark predictive algorithms. A model based on the chemical-genetic matrix and the genetic interaction network failed to accurately predict synergism. However, a combined random forest and Naive Bayesian learner that associated chemical structural features with genotype-specific growth inhibition had strong predictive power. This approach identified previously unknown compound combinations that exhibited species-selective toxicity toward human fungal pathogens. This work demonstrates that machine learning methods trained on unbiased chemical-genetic interaction data may be widely applicable for the discovery of synergistic combinations in different species.