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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

A loss of function analysis of host factors influencing Vaccinia virus replication by RNA interference.

Vaccinia virus (VACV) is a large, cytoplasmic, double-stranded DNA virus that requires complex interactions with host proteins in order to replicate. To explore these interactions a functional high throughput small interfering RNA (siRNA) screen targeting 6719 druggable cellular genes was undertaken to identify host factors (HF) influencing the replication and spread of an eGFP-tagged VACV. The experimental design incorporated a low multiplicity of infection, thereby enhancing detection of cellular proteins involved in cell-to-cell spread of VACV. The screen revealed 153 pro- and 149 anti-viral HFs that strongly influenced VACV replication. These HFs were investigated further by comparisons with transcriptional profiling data sets and HFs identified in RNAi screens of other viruses. In addition, functional and pathway analysis of the entire screen was carried out to highlight cellular mechanisms involved in VACV replication. This revealed, as anticipated, that many pro-viral HFs are involved in translation of mRNA and, unexpectedly, suggested that a range of proteins involved in cellular transcriptional processes and several DNA repair pathways possess anti-viral activity. Multiple components of the AMPK complex were found to act as pro-viral HFs, while several septins, a group of highly conserved GTP binding proteins with a role in sequestering intracellular bacteria, were identified as strong anti-viral VACV HFs. This screen has identified novel and previously unexplored roles for cellular factors in poxvirus replication. This advancement in our understanding of the VACV life cycle provides a reliable knowledge base for the improvement of poxvirus-based vaccine vectors and development of anti-viral theraputics.

A role for intracellular calcium downstream of G-protein signaling in undifferentiated human embryonic stem cell culture.

Multiple signalling pathways maintain human embryonic stem cells (hESC) in an undifferentiated state. Here we sought to define the significance of G protein signal transduction in the preservation of this state distinct from other cellular processes. Continuous treatment with drugs targeting G(αs)-, G(α-i/o)- and G(α-q/11)-subunit signalling mediators were assessed in independent hESC lines after 7days to discern effects on normalised alkaline phosphatase positive colony frequency vs total cell content. This identified PLCß, intracellular free calcium and CAMKII kinase activity downstream of G(α-q/11) as of particular importance to the former. To confirm the significance of this finding we generated an agonist-responsive hESC line transgenic for a G(α-q/11) subunit-coupled receptor and demonstrated that an undifferentiated state could be promoted in the presence of an agonist without exogenously supplied bFGF and that this correlated with elevated intracellular calcium. Similarly, treatment of unmodified hESCs with a range of intracellular free calcium-modulating drugs in biologically defined mTESR culture system lacking exogenous bFGF promoted an hESC phenotype after 1week of continuous culture as defined by co-expression of OCT4 and NANOG. At least one of these drugs, lysophosphatidic acid significantly elevates phosphorylation of calmodulin and STAT3 in this culture system (p<0.05). These findings substantiate a role for G-protein and calcium signalling in undifferentiated hESC culture.

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.

Characterizing the roles of Met31 and Met32 in coordinating Met4-activated transcription in the absence of Met30.

Yeast sulfur metabolism is transcriptionally regulated by the activator Met4. Met4 lacks DNA-binding ability and relies on interactions with Met31 and Met32, paralogous proteins that bind the same cis-regulatory element, to activate its targets. Although Met31 and Met32 are redundant for growth in the absence of methionine, studies indicate that Met32 has a prominent role over Met31 when Met30, a negative regulator of Met4 and Met32, is inactive. To characterize different roles of Met31 and Met32 in coordinating Met4-activated transcription, we examined transcription in strains lacking either Met31 or Met32 upon Met4 induction in the absence of Met30. Microarray analysis revealed that transcripts involved in sulfate assimilation and sulfonate metabolism were dramatically decreased in met32Δ cells compared to its wild-type and met31Δ counterparts. Despite this difference, both met31Δ and met32Δ cells used inorganic sulfur compounds and sulfonates as sole sulfur sources in minimal media when Met30 was present. This discrepancy may be explained by differential binding of Met31 to Cbf1-dependent promoters between these two conditions. In the absence of Met30, genome-wide chromatin immunoprecipitation analyses found that Met32 bound all Met4-bound targets, supporting Met32 as the main platform for Met4 recruitment. Finally, Met31 and Met32 levels were differentially regulated, with Met32 levels mimicking the profile for active Met4. These different properties of Met32 likely contribute to its prominent role in Met4-activated transcription when Met30 is absent.

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.

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.

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).

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.