Translocon Declogger Ste24 Protects against IAPP Oligomer-Induced Proteotoxicity.

Aggregates of human islet amyloid polypeptide (IAPP) in the pancreas of patients with type 2 diabetes (T2D) are thought to contribute to ß cell dysfunction and death. To understand how IAPP harms cells and how this might be overcome, we created a yeast model of IAPP toxicity. Ste24, an evolutionarily conserved protease that was recently reported to degrade peptides stuck within the translocon between the cytoplasm and the endoplasmic reticulum, was the strongest suppressor of IAPP toxicity. By testing variants of the human homolog, ZMPSTE24, with varying activity levels, the rescue of IAPP toxicity proved to be directly proportional to the declogging efficiency. Clinically relevant ZMPSTE24 variants identified in the largest database of exomes sequences derived from T2D patients were characterized using the yeast model, revealing 14 partial loss-of-function variants, which were enriched among diabetes patients over 2-fold. Thus, clogging of the translocon by IAPP oligomers may contribute to ß cell failure.

Unraveling the biology of a fungal meningitis pathogen using chemical genetics.

The fungal meningitis pathogen Cryptococcus neoformans is a central driver of mortality in HIV/AIDS. We report a genome-scale chemical genetic data map for this pathogen that quantifies the impact of 439 small-molecule challenges on 1,448 gene knockouts. We identified chemical phenotypes for 83% of mutants screened and at least one genetic response for each compound. C. neoformans chemical-genetic responses are largely distinct from orthologous published profiles of Saccharomyces cerevisiae, demonstrating the importance of pathogen-centered studies. We used the chemical-genetic matrix to predict novel pathogenicity genes, infer compound mode of action, and to develop an algorithm, O2M, that predicts antifungal synergies. These predictions were experimentally validated, thereby identifying virulence genes, a molecule that triggers G2/M arrest and inhibits the Cdc25 phosphatase, and many compounds that synergize with the antifungal drug fluconazole. Our work establishes a chemical-genetic foundation for approaching an infection responsible for greater than one-third of AIDS-related deaths.

Evaluation of a decision support system using Bayesian network modeling in an applied Multi-INT surveillance environment.

Sensemaking and decision-making are fundamental components of applied Intelligence, Surveillance, and Reconnaissance (ISR). Analysts acquire information from multiple sources over a period of hours, days, or even over the scale of months or years, that must be interpreted and integrated to predict future adversarial events. Sensemaking is essential for developing an appropriate mental model that will lead to accurate predictions sooner. Decision Support Systems (DSS) are one proposed solution to improve analyst decision-making outcomes by leveraging computers to conduct calculations that may be difficult for human operators and provide recommendations. In this study, we tested two simulated DSS that were informed by a Bayesian Network Model as a potential prediction-assistive tool. Participants completed a simulated multi-day, multi-source intelligence task and were asked to make predictions regarding five potential outcomes on each day. Participants in both DSS conditions were able to converge on the correct solution significantly faster than the control group, and between 36-44% more of the sample was able to reach the correct conclusion. Furthermore, we found that a DSS representing projected outcome probabilities as numerical, rather than using verbal ordinal labels, were better able to differentiate which outcomes were extremely unlikely than the control group or verbal-probability DSS.

Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions.

A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that structural similarity is predictive of similar biological activity. However, efforts to systematically benchmark the diverse array of available molecular fingerprints and similarity coefficients have been limited by a lack of large-scale datasets that reflect biological similarities of compounds. To elucidate the relative performance of these alternatives, we systematically benchmarked 11 different molecular fingerprint encodings, each combined with 13 different similarity coefficients, using a large set of chemical-genetic interaction data from the yeast Saccharomyces cerevisiae as a systematic proxy for biological activity. We found that the performance of different molecular fingerprints and similarity coefficients varied substantially and that the all-shortest path fingerprints paired with the Braun-Blanquet similarity coefficient provided superior performance that was robust across several compound collections. We further proposed a machine learning pipeline based on support vector machines that offered a fivefold improvement relative to the best unsupervised approach. Our results generally suggest that using high-dimensional chemical-genetic data as a basis for refining molecular fingerprints can be a powerful approach for improving prediction of biological functions from chemical structures.

Extensive rewiring and complex evolutionary dynamics in a C. elegans multiparameter transcription factor network.

Gene duplication results in two identical paralogs that diverge through mutation, leading to loss or gain of interactions with other biomolecules. Here, we comprehensively characterize such network rewiring for C. elegans transcription factors (TFs) within and across four newly delineated molecular networks. Remarkably, we find that even highly similar TFs often have different interaction degrees and partners. In addition, we find that most TF families have a member that is highly connected in multiple networks. Further, different TF families have opposing correlations between network connectivity and phylogenetic age, suggesting that they are subject to different evolutionary pressures. Finally, TFs that have similar partners in one network generally do not in another, indicating a lack of pressure to retain cross-network similarity. Our multiparameter analyses provide unique insights into the evolutionary dynamics that shaped TF networks.

Publisher Correction: Integrating yeast chemical genomics and mammalian cell pathway analysis.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action.

Summary: Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks. Availability and implementation: MOSAIC is available at http://mosaic.cs.umn.edu. Contact: hisyo@riken.jp, yoshidam@riken.jp, charlie.boone@utoronto.ca or chadm@umn.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Functional annotation of chemical libraries across diverse biological processes.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Errata: Functional annotation of chemical libraries across diverse biological processes.

This corrects the article DOI: 10.1038/nchembio.2436.

Errata: Functional annotation of chemical libraries across diverse biological processes.

This corrects the article DOI: 10.1038/nchembio.2436.

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions.

Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Integrating yeast chemical genomics and mammalian cell pathway analysis.

Chemical genomics has been applied extensively to evaluate small molecules that modulate biological processes in Saccharomyces cerevisiae. Here, we use yeast as a surrogate system for studying compounds that are active against metazoan targets. Large-scale chemical-genetic profiling of thousands of synthetic and natural compounds from the Chinese National Compound Library identified those with high-confidence bioprocess target predictions. To discover compounds that have the potential to function like therapeutic agents with known targets, we also analyzed a reference library of approved drugs. Previously uncharacterized compounds with chemical-genetic profiles resembling existing drugs that modulate autophagy and Wnt/ß-catenin signal transduction were further examined in mammalian cells, and new modulators with specific modes of action were validated. This analysis exploits yeast as a general platform for predicting compound bioactivity in mammalian cells.

A New Natural Product Analog of Blasticidin S Reveals Cellular Uptake Facilitated by the NorA Multidrug Transporter.

The permeation of antibiotics through bacterial membranes to their target site is a crucial determinant of drug activity but in many cases remains poorly understood. During screening efforts to discover new broad-spectrum antibiotic compounds from marine sponge samples, we identified a new analog of the peptidyl nucleoside antibiotic blasticidin S that exhibited up to 16-fold-improved potency against a range of laboratory and clinical bacterial strains which we named P10. Whole-genome sequencing of laboratory-evolved strains of Staphylococcus aureus resistant to blasticidin S and P10, combined with genome-wide assessment of the fitness of barcoded Escherichia coli knockout strains in the presence of the antibiotics, revealed that restriction of cellular access was a key feature in the development of resistance to this class of drug. In particular, the gene encoding the well-characterized multidrug efflux pump NorA was found to be mutated in 69% of all S. aureus isolates resistant to blasticidin S or P10. Unexpectedly, resistance was associated with inactivation of norA, suggesting that the NorA transporter facilitates cellular entry of peptidyl nucleosides in addition to its known role in the efflux of diverse compounds, including fluoroquinolone antibiotics.

Using networks to measure similarity between genes: association index selection.

Biological networks can be used to functionally annotate genes on the basis of interaction-profile similarities. Metrics known as association indices can be used to quantify interaction-profile similarity. We provide an overview of commonly used association indices, including the Jaccard index and the Pearson correlation coefficient, and compare their performance in different types of analyses of biological networks. We introduce the Guide for Association Index for Networks (GAIN), a web tool for calculating and comparing interaction-profile similarities and defining modules of genes with similar profiles.

Mechanism of immobilized protein A binding to immunoglobulin G on nanosensor array surfaces.

Protein A is often used for the purification and detection of antibodies such as immunoglobulin G (IgG) because of its quadrivalent domains that bind to the Fc region of these macromolecules. However, the kinetics and thermodynamics of the binding to many sensor surfaces have eluded mechanistic description due to complexities associated with multivalent interactions. In this work, we use a near-infrared (nIR) fluorescent single-walled carbon nanotube sensor array to obtain the kinetics of IgG binding to protein A, immobilized using a chelated Cu(2+)/His-tag chemistry to hydrogel dispersed sensors. A bivalent binding mechanism is able to describe the concentration dependence of the effective dissociation constant, KD,eff, which varies from 100 pM to 1 µM for IgG concentrations from 1 ng mL(-1) to 100 µg mL(-1), respectively. The mechanism is shown to describe the unusual concentration-dependent scaling demonstrated by other sensor platforms in the literature as well, and a comparison is made between resulting parameters. For comparison, we contrast IgG binding with that of human growth hormone (hGH) to its receptor (hGH-R) which displays an invariant dissociation constant at KD = 9 µM. These results should aid in the use of protein A and other recognition elements in a variety of sensor types.

Diagnostic accuracy of tests based on radiologic measurements of dogs and cats: a systematic review.

A systematic review of diagnostic tests based on radiologic measurements of structures in dogs and cats was done in order to reach generalizable conclusions about the value of making such measurements. Literature search was done using the ISI Web of Knowledge(SM) for studies in the subject category Veterinary sciences. Studies were eligible for inclusion that employed length, angle, area or volume measurements from radiographic, ultrasonographic, CT or MR images of dogs or cats as a diagnostic test for a naturally occurring condition, compared the results of imaging with a reference standard, included at least 10 subjects, and sufficient data that a 2 × 2 table of results could be constructed. Quality of studies was assessed using the QUADAS-2 tool. Twenty-six studies were found describing 40 tests that satisfied the inclusion criteria. Tests were radiographic in 22 (55%) instances and ultrasonographic in 18 (45%). Quality of studies was generally low, with a risk of bias in patient selection in 92% studies, performance of the index test in 73% studies, and patient flow in 42% studies. Median (range) number of subjects was 64 (20-305), sensitivity was 77% (38-99%), specificity was 82% (50-99%), positive likelihood ratio was 4.1 (1-103), and negative likelihood ratio was 0.29 (0.01-1). Two studies that compared accuracy of radiographic measurements to subjective image interpretation alone found no difference. Evidence is weak that radiologic measurements of structures in dogs and cats are useful for diagnosis, hence measurements should not be emphasized as a basis for diagnosis in either teaching or clinical imaging reports.

Neurotransmitter detection using corona phase molecular recognition on fluorescent single-walled carbon nanotube sensors.

Temporal and spatial changes in neurotransmitter concentrations are central to information processing in neural networks. Therefore, biosensors for neurotransmitters are essential tools for neuroscience. In this work, we applied a new technique, corona phase molecular recognition (CoPhMoRe), to identify adsorbed polymer phases on fluorescent single-walled carbon nanotubes (SWCNTs) that allow for the selective detection of specific neurotransmitters, including dopamine. We functionalized and suspended SWCNTs with a library of different polymers (n = 30) containing phospholipids, nucleic acids, and amphiphilic polymers to study how neurotransmitters modulate the resulting band gap, near-infrared (nIR) fluorescence of the SWCNT. We identified several corona phases that enable the selective detection of neurotransmitters. Catecholamines such as dopamine increased the fluorescence of specific single-stranded DNA- and RNA-wrapped SWCNTs by 58-80% upon addition of 100 µM dopamine depending on the SWCNT chirality (n,m). In solution, the limit of detection was 11 nM [K(d) = 433 nM for (GT)15 DNA-wrapped SWCNTs]. Mechanistic studies revealed that this turn-on response is due to an increase in fluorescence quantum yield and not covalent modification of the SWCNT or scavenging of reactive oxygen species. When immobilized on a surface, the fluorescence intensity of a single DNA- or RNA-wrapped SWCNT is enhanced by a factor of up to 5.39 ± 1.44, whereby fluorescence signals are reversible. Our findings indicate that certain DNA/RNA coronae act as conformational switches on SWCNTs, which reversibly modulate the SWCNT fluorescence. These findings suggest that our polymer-SWCNT constructs can act as fluorescent neurotransmitter sensors in the tissue-compatible nIR optical window, which may find applications in neuroscience.

Experimental tools to study molecular recognition within the nanoparticle corona.

Advancements in optical nanosensor development have enabled the design of sensors using synthetic molecular recognition elements through a recently developed method called Corona Phase Molecular Recognition (CoPhMoRe). The synthetic sensors resulting from these design principles are highly selective for specific analytes, and demonstrate remarkable stability for use under a variety of conditions. An essential element of nanosensor development hinges on the ability to understand the interface between nanoparticles and the associated corona phase surrounding the nanosensor, an environment outside of the range of traditional characterization tools, such as NMR. This review discusses the need for new strategies and instrumentation to study the nanoparticle corona, operating in both in vitro and in vivo environments. Approaches to instrumentation must have the capacity to concurrently monitor nanosensor operation and the molecular changes in the corona phase. A detailed overview of new tools for the understanding of CoPhMoRe mechanisms is provided for future applications.

Automated algorithm development to assess survival of human neurons using longitudinal single-cell tracking: Application to synucleinopathy.

The development of phenotypic assays with appropriate analyses is an important step in the drug discovery process. Assays using induced pluripotent stem cell (iPSC)-derived human neurons are emerging as powerful tools for drug discovery in neurological disease. We have previously shown that longitudinal single cell tracking enabled the quantification of survival and death of neurons after overexpression of α-synuclein with a familial Parkinson's disease mutation (A53T). The reliance of this method on manual counting, however, rendered the process labor intensive, time consuming and error prone. To overcome these hurdles, we have developed automated detection algorithms for neurons using the BioStation CT live imaging system and CL-Quant software. In the current study, we use these algorithms to successfully measure the risk of neuronal death caused by overexpression of α-synuclein (A53T) with similar accuracy and improved consistency as compared to manual counting. This novel method also provides additional key readouts of neuronal fitness including total neurite length and the number of neurite nodes projecting from the cell body. Finally, the algorithm reveals the neuroprotective effects of brain-derived neurotrophic factor (BDNF) treatment in neurons overexpressing α-synuclein (A53T). These data show that an automated algorithm improves the consistency and considerably shortens the analysis time of assessing neuronal health, making this method advantageous for small molecule screening for inhibitors of synucleinopathy and other neurodegenerative diseases.

Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose.

Rapid detection of volatile organic compounds (VOCs) is growing in importance in many sectors. Noninvasive medical diagnoses may be based upon particular combinations of VOCs in human breath; detecting VOCs emitted from environmental hazards such as fungal growth could prevent illness; and waste could be reduced through monitoring of gases produced during food storage. Electronic noses have been applied to such problems, however, a common limitation is in improving selectivity. Graphene is an adaptable material that can be functionalized with many chemical receptors. Here, we use this versatility to demonstrate selective and rapid detection of multiple VOCs at varying concentrations with graphene-based variable capacitor (varactor) arrays. Each array contains 108 sensors functionalized with 36 chemical receptors for cross-selectivity. Multiplexer data acquisition from 108 sensors is accomplished in tens of seconds. While this rapid measurement reduces the signal magnitude, classification using supervised machine learning (Bootstrap Aggregated Random Forest) shows excellent results of 98% accuracy between 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) at 4 concentrations each. With the addition of 1-octene, an analyte highly similar in structure to octane, an accuracy of 89% is achieved. These results demonstrate the important role of the choice of analysis method, particularly in the presence of noisy data. This is an important step toward fully utilizing graphene-based sensor arrays for rapid gas sensing applications from environmental monitoring to disease detection in human breath.