A scheme to evaluate structural alerts to predict toxicity - Assessing confidence by characterising uncertainties

Structure-activity relationships (SARs) in toxicology have enabled the formation of structural rules which, when coded as structural alerts, are essential tools in in silico toxicology. Whilst other in silico methods have approaches for their evaluation, there is no formal process to assess the confidence that may be associated with a structural alert. This investigation proposes twelve criteria to assess the uncertainty associated with structural alerts, allowing for an assessment of confidence. The criteria are based around the stated purpose, description of the chemistry, toxicology and mechanism, performance and coverage, as well as corroborating and supporting evidence of the alert. Alerts can be given a confidence assessment and score, enabling the identification of areas where more information may be beneficial. The scheme to evaluate structural alerts was placed in the context of various use cases for industrial and regulatory applications. The analysis of alerts, and consideration of the evaluation scheme, identifies the different characteristics an alert may have, such as being highly specific or generic. These characteristics may determine when an alert can be used for specific uses such as identification of analogues for read-across or hazard identification.

•

Structural alerts are useful tools for predictive toxicology.

•

12 criteria to evaluate structural alerts have been identified.

•

A strategy to determine confidence of structural alerts is presented.

•

Different use cases require different characteristics of structural alerts.

•

A Scheme to Evaluate Structural Alerts to Predict Toxicity – Assessing Confidence By Characterising Uncertainties.

Use cases for genetic epidemiology in malaria elimination

Background

While traditional epidemiological approaches have supported significant reductions in malaria incidence across many countries, higher resolution information about local and regional malaria epidemiology will be needed to efficiently target interventions for elimination. The application of genetic epidemiological methods for the analysis of parasite genetics has, thus far, primarily been confined to research settings. To illustrate how these technical methods can be used to advance programmatic and operational needs of National Malaria Control Programmes (NMCPs), and accelerate global progress to eradication, this manuscript presents seven use cases for which genetic epidemiology approaches to parasite genetic data are informative to the decision-making of NMCPs.

Methods

The use cases were developed through a highly iterative process that included an extensive review of the literature and global guidance documents, including the 2017 World Health Organization’s Framework for Malaria Elimination, and collection of stakeholder input. Semi-structured interviews were conducted with programmatic and technical experts about the needs and opportunities for genetic epidemiology methods in malaria elimination.

Results

Seven use cases were developed: Detect resistance, Assess drug resistance gene flow, Assess transmission intensity, Identify foci, Determine connectivity of parasite populations, Identify imported cases, and Characterize local transmission chains. The method currently used to provide the information sought, population unit for implementation, the pre-conditions for using these approaches, and post-conditions intended as a product of the use case were identified for each use case.

Discussion

This framework of use cases will prioritize research and development of genetic epidemiology methods that best achieve the goals of NMCPs, and ultimately, inform the establishment of normative policy guidance for their uses. With significant engagement of stakeholders from malaria endemic countries and collaboration with local programme experts to ensure strategic implementation, genetic epidemiological approaches have tremendous potential to accelerate global malaria elimination efforts.

Electronic supplementary material

The online version of this article (10.1186/s12936-019-2784-0) contains supplementary material, which is available to authorized users.

Systems Genetics Analysis of Iron and Its Regulation in Brain and Periphery

In this contribution, we demonstrate the utility of the systems genetics-systems biology approach to the study of iron regulation while employing a comprehensive database. We describe our work in iron regulation in the brain and periphery under normal iron and iron-restricted dietary conditions in the BXD family of recombinant inbred mouse strains. Using multiple measures, we showed wide variation among the strains in the effect of being fed an iron-restricted diet for 100 days in every measure from brain and from the periphery. All data were entered into GeneNetwork ( www.genenetwork.org ), a database that contains genotypic, phenotypic, and gene expression data (Rosen et al., Methods Mol Biol 401:287-303, 2007). Using this resource, we were able to ask the following four questions concerning possible candidate genes underlying our measures: (1) what is the range of response for each of the measures? (2) Does the pattern of variability show continuous (additive genetic) or discrete (Mendelian) distribution across strains? (3) Are there genetic markers that are associated with the variability in the measures? (4) Are there genes in near the markers that contain associated allelic differences, and whose expression is related to the variability in the measures? Other questions that we could address include: (5) what is the association among the measures between the sexes? (6) What is the association among the measures, e.g., is liver iron status under the diets related to brain iron? (7) What is the relationship between our measures and other phenotypic parameters-i.e., is there an association between our brain iron measures and neurochemical phenotypes extant in the database? And finally, (8) are there gene networks that underlie single or combined measures?

The Collaborative Cross Resource for Systems Genetics Research of Infectious Diseases

An increasing body of evidence highlights the role of host genetic variation in driving susceptibility to severe disease following pathogen infection. In order to fully appreciate the importance of host genetics on infection susceptibility and resulting disease, genetically variable experimental model systems should be employed. These systems allow for the identification, characterization, and mechanistic dissection of genetic variants that cause differential disease responses. Herein we discuss application of the Collaborative Cross (CC) panel of recombinant inbred strains to study viral pathogenesis, focusing on practical considerations for experimental design, assessment and analysis of disease responses within the CC, as well as some of the resources developed for the CC. Although the focus of this chapter is on viral pathogenesis, many of the methods presented within are applicable to studies of other pathogens, as well as to case-control designs in genetically diverse populations.

Integrative Analysis of Genetic, Genomic, and Phenotypic Data for Ethanol Behaviors: A Network-Based Pipeline for Identifying Mechanisms and Potential Drug Targets

Complex behavioral traits, such as alcohol abuse, are caused by an interplay of genetic and environmental factors, producing deleterious functional adaptations in the central nervous system. The long-term behavioral consequences of such changes are of substantial cost to both the individual and society. Substantial progress has been made in the last two decades in understanding elements of brain mechanisms underlying responses to ethanol in animal models and risk factors for alcohol use disorder (AUD) in humans. However, treatments for AUD remain largely ineffective and few medications for this disease state have been licensed. Genome-wide genetic polymorphism analysis (GWAS) in humans, behavioral genetic studies in animal models and brain gene expression studies produced by microarrays or RNA-seq have the potential to produce nonbiased and novel insight into the underlying neurobiology of AUD. However, the complexity of such information, both statistical and informational, has slowed progress toward identifying new targets for intervention in AUD. This chapter describes one approach for integrating behavioral, genetic, and genomic information across animal model and human studies. The goal of this approach is to identify networks of genes functioning in the brain that are most relevant to the underlying mechanisms of a complex disease such as AUD. We illustrate an example of how genomic studies in animal models can be used to produce robust gene networks that have functional implications, and to integrate such animal model genomic data with human genetic studies such as GWAS for AUD. We describe several useful analysis tools for such studies: ComBAT, WGCNA, and EW_dmGWAS. The end result of this analysis is a ranking of gene networks and identification of their cognate hub genes, which might provide eventual targets for future therapeutic development. Furthermore, this combined approach may also improve our understanding of basic mechanisms underlying gene x environmental interactions affecting brain functioning in health and disease.

Systems Genetics of Obesity

Obesity is a complex trait, determined by many genes and influenced by environmental factors. Mapping genomic loci contributing to obesity helps to identify gene variants responsible for differences in the phenotype. However, measuring fat content alone is often not sufficient to identify the underlying gene or genes. Besides in-depth phenotyping, well-designed genetic populations and the combined analysis of data of different origins are necessary to detect one of several genetic determinants. Structured mouse populations and linking information from different experiments help to simplify the complexity in the search for direct genetic effects or factors that are hidden in the genome. In this chapter we present an example of how the physicochemical characterization of adipose tissue in BXD recombinant inbred lines contributes to enlighten the obese phenotype of mice. We describe the search for gene(s) contributing to collagen content in adipose tissue of BXD strains using the GeneNetwork platform.

Complex Genetics of Cardiovascular Traits in Mice: F2-Mapping of QTLs and Their Underlying Genes

In this chapter, we will use the example of the identification of Tnni3k as a modulator of cardiac conduction to introduce you to the use of a murine F2-generation intercross as a powerful method for the identification of novel genes relevant for cardiovascular traits. Murine F2-progeny is a genetically diverse panel of mice with differences in phenotype manifestations, e.g. cardiovascular traits such as cardiomyopathy and ECG parameters. This chapter discusses the best strategies for using F2-mice for genetic mapping. Moreover, we provide an example of the feasibility of identification of new genes modulating cardiac function utilizing the technique of mapping quantitative trait loci (QTLs) and a systems genetics integration of available genetic, gene expression, and phenotypic data.

Systems Genetics Analysis to Identify the Genetic Modulation of a Glaucoma-Associated Gene

Loss of retinal ganglion cells (RGCs) is one of the hallmarks of retinal neurodegenerative diseases, glaucoma being one of the most common. Recently, γ-synuclein (SNCG) was shown to be highly expressed in the somas and axons of RGCs. In various mouse models of glaucoma, downregulation of Sncg gene expression correlates with RGC loss. To investigate the regulation of Sncg in RGCs, we used a systems genetics approach to identify a gene that modulates the expression of Sncg, followed by confirmatory studies in both healthy and diseased retinas. We found that chromosome 1 harbors an eQTL that modulates the expression of Sncg in the mouse retina and identified Pfdn2 as the candidate upstream modulator of Sncg expression. Downregulation of Pfdn2 in enriched RGCs causes a concomitant reduction in Sncg. In this chapter, we describe our strategy and methods for identifying and confirming a genetic modulation of a glaucoma-associated gene. A similar method can be applied to other genes expressed in other tissues.

QTL Mapping and Identification of Candidate Genes in DO Mice: A Use Case Model Derived from a Benzene Toxicity Experiment

Diversity Outbred (DO) mice are a multiparental advanced generation intercross population derived from eight inbred strains which are genetically very diverse. They are maintained as an outbred population using a randomized mating design. Thus DO mice represent an ideal population to map phenotypic traits. Here, we provide a case study in which male DO mice were exposed to benzene and phenotyped for the number of micronucleated reticulocytes. We provide step-by-step R scripts for the analysis of phenotypes, genotypes, mapping of resistance gene loci and identification of candidate genes.

Overcoming challenges integrating patient-generated data into the clinical EHR: lessons from the CONtrolling Disease Using Inexpensive IT--Hypertension in Diabetes (CONDUIT-HID) Project

INTRODUCTION

The CONDUIT-HID intervention integrates patients' electronic blood pressure measurements directly into the clinical EHR using Microsoft HealthVault as an intermediary data store. The goal of this paper is to describe generalizable categories of patient and technical challenges encountered in the development and implementation of this inexpensive, commercial off-the-shelf consumer health informatics intervention, examples of challenges within each category, and how the example challenges were resolved prior to conducting an RCT of the intervention.

METHODS

The research team logged all challenges and mediation strategies during the technical development of the intervention, conducted home visits to observe patients using the intervention, and conducted telephone calls with patients to understand challenges they encountered. We then used these data to iteratively refine the intervention.

RESULTS

The research team identified a variety of generalizable categories of challenges associated with patients uploading data from their homes, patients uploading data from clinics because they did not have or were not comfortable using home computers, and patients establishing the connection between HealthVault and the clinical EHR. Specific challenges within these categories arose because: (1) the research team had little control over the device and application design, (2) multiple vendors needed to coordinate their actions and design changes, (3) the intervention use cases were not anticipated by the device and application designers, (4) PHI accessed on clinic computers needed to be kept secure, (5) the research team wanted the data in the clinical EHR to be valid and reliable, (6) patients needed the ability to share only the data they wanted, and (7) the development of some EHR functionalities were new to the organization. While these challenges were varied and complex, the research team was able to successfully resolve each one prior to the start of the RCT.

CONCLUSIONS

By identifying these generalizable categories of challenges, we aim to help others proactively search for and remedy potential challenges associated with their interventions, rather than reactively responding to problems as they arise. We posit that this approach will significantly increase the likelihood that these types of interventions will be successful.