Genetic activity profiles and pattern recognition in test battery selection

Computer-generated genetic activity profiles and pairwise matching procedures may aid in the selection of the most appropriate short-term bioassays to be used in test batteries for the evaluation of the genotoxicity of a given chemical or group of chemicals. Selection of test batteries would be based on a quantitative comparative assessment of the past performance of similar tests applied to other chemicals of the same structural group. The information potentially available for test-battery selection through the use of this pattern-recognition technique is considerably greater than the qualitative results obtained from individual short-term tests. Application of the method should further our understanding of the relationships between chemical properties and genotoxic responses obtained in short-term bioassays and also may contribute to our knowledge of the mechanisms of complex processes such as carcinogenesis. This approach to battery selection should be augmented by careful consideration of established principles of genetic toxicity testing; that is, a chemical should be evaluated in a battery of tests representing the full range of relevant genetic endpoints.     

Types and amounts of carcinogens as potential human cancer hazards

Current knowledge on the mechanisms of chemical carcinogenesis forms the basis for application of select short-term in vitro and in vivo tests to detect potential human carcinogens, for ultimate application to hazard assessment. Chemical carcinogenesis involves a series of distinct steps, proceeding from the initiation of a neoplastic cell, through its promotion, development, and progression to cancer. Some chemicals act in each of these stages as initiators, cocarcinogens, promoters, or inhibitors of carcinogenesis. Chemicals can be classified as operating by genotoxic or epigenetic mechanisms, and appropriate tests can be used to detect such properties. These abbreviated tests provide enhanced qualitative decision-making potential since they are based on mechanisms of action. Advances in molecular biology may provide additional tests to detect cancer risk. The quantitative data available from in vitro dose-response studies indicate that carcinogenic effects are dose dependent and, therefore, a threshold or no-effect level probably exists, which is low for potent carcinogens (especially genotoxins) and high for weaker ones (particularly epigenetic agents).Presented at a Symposium: Quantitative Assessment of Cancer Risk-Integration of Biological Events, organized by the Carcinogenesis Specialty Section, Society of Toxicology, (chairmen N.P. Page and D.V. Singh), Washington, D.C., February 23–27,1987.     

Risk Assessment: Replacing Default Options with Specific Science

The history of approaches to evaluating the hazards and risks of chemicals is briefly reviewed. The role of default options (generic approaches based on general knowledge in the absence of specific knowledge to the contrary) is discussed as a part of the risk assessment paradigm advanced by the National Academy of Science/National Research Council in 1983 and 1994. Examples are given of the impact of acquiring specific science to replace default options. An argument is made for developing specific science that would reduce uncertainty in risk assessments. Research on specific science would be guided by identified sources of uncertainty in the risk assessment process. The importance of using a research strategy that builds on human data is emphasized for validating new molecular and cellular biological assessment methods. The paper closes with a discussion of the tension between a hazard-based approach versus quantitative risk assessment in guiding risk management decisions. The former requires limited data, is qualitative, and easy to communicate, while the latter requires substantial data and is difficult to communicate. However, quantitative risk assessment provides a more rational basis for decisions on the allocation of both public and private resources for actions that will effectively minimize overall health risks to the public.     

Structure-activity relationships and prediction of the phototoxicity and phototoxic potential of new drugs

Relationships between the structure and properties of chemicals can be programmed into knowledge-based systems such as DEREK for Windows (DEREK is an acronym for "Deductive Estimation of Risk from Existing Knowledge"). The DEREK for Windows computer system contains a subset of over 60 rules describing chemical substructures (toxophores) responsible for skin sensitisation. As part of the European Phototox Project, the rule base was supplemented by a number of rules for the prospective identification of photoallergens, either by extension of the scope of existing rules or by the generation of new rules where a sound mechanistic rationale for the biological activity could be established. The scope of the rules for photoallergenicity was then further refined by assessment against a list of chemicals identified as photosensitisers by the Centro de Farmacovigilancia de la Comunidad Valenciana, Valencia, Spain. This paper contains an analysis of the mechanistic bases of activity for eight important groups of photoallergens and phototoxins, together with rules for the prospective identification of the photobiological activity of new or untested chemicals belonging to those classes. The mechanism of action of one additional chemical, nitrofurantoin, is well established; however, it was deemed inappropriate to write a rule on the basis of a single chemical structure.     

High throughput nano-liter RT-qPCR to classify soil contamination using a soil arthropod

Background  

To incorporate genomics data into environmental assessments a mechanistic perspective of interactions between chemicals and induced biological processes needs to be developed. Since chemical compounds with structural similarity often induce comparable biological responses in exposed animals, gene expression signatures can serve as a starting point for the assessment of chemicals and their toxicity, but only when relevant and stable gene panels are available. To design such a panel, we isolated differentially expressed gene fragments from the soil arthropod Folsomia candida, a species often used for ecotoxicological testing. Animals were exposed to two chemically distinct compounds, being a metal (cadmium) and a polycyclic aromatic hydrocarbon (phenanthrene). We investigated the affected molecular responses resulting from either treatment and developed and validated 44 qPCR assays for their responses using a high throughput nano-liter RT-qPCR platform for the analysis of the samples.     

The structural basis of the mutagenicity of chemicals in Salmonella typhimurium: the Gene-Tox data base

The CASE structure-activity methodology has been applied to a Gene-Tox derived Salmonella mutagenicity data base consisting of 808 chemicals. Based upon qualitative structural features, CASE identified 29 activating and 3 inactivating structural determinants which correctly predicted the probability of carcinogenicity of 93.7% of the known mutagens and non-mutagens in the data base (sensitivity = 0.998, and specificity = 0.704). Additionally, based upon a qualitative structure-activity analysis, CASE's performance was even better, leading to a sensitivity of 0.981 and a specificity of 1.000. Using the structural determinants identified in this data base, CASE gave excellent predictions of the mutagenicity of chemicals not included in the data base. The identified biophores and biophobes can also be used to investigate the structural basis of the mutagenicity of various chemical classes.     

A probabilistic methodology for integrating knowledge and experiments on biological networks.

Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed experiments. Modern experimental techniques provide massive data on the global behavior of biological systems, and systematically using these large datasets for refining existing knowledge is a major challenge. Here we introduce an extended computational framework that combines formalization of existing qualitative models, probabilistic modeling, and integration of high-throughput experimental data. Using our methods, it is possible to interpret genomewide measurements in the context of prior knowledge on the system, to assign statistical meaning to the accuracy of such knowledge, and to learn refined models with improved fit to the experiments. Our model is represented as a probabilistic factor graph, and the framework accommodates partial measurements of diverse biological elements. We study the performance of several probabilistic inference algorithms and show that hidden model variables can be reliably inferred even in the presence of feedback loops and complex logic. We show how to refine prior knowledge on combinatorial regulatory relations using hypothesis testing and derive p-values for learned model features. We test our methodology and algorithms on a simulated model and on two real yeast models. In particular, we use our method to explore uncharacterized relations among regulators in the yeast response to hyper-osmotic shock and in the yeast lysine biosynthesis system. Our integrative approach to the analysis of biological regulation is demonstrated to synergistically combine qualitative and quantitative evidence into concrete biological predictions.     

Review of Information Resources to Support Human Exposure Assessment Models

Efforts to model human exposures to chemicals are growing more sophisticated and encompass increasingly complex exposure scenarios. The scope of such analyses has increased, growing from assessments of single exposure pathways to complex evaluations of aggregate or cumulative chemical exposures occurring within a variety of settings and scenarios. In addition, quantitative modeling techniques have evolved from simple deterministic analyses using single point estimates for each necessary input parameter to more detailed probabilistic analyses that can accommodate distributions of input parameters and assessment results. As part of an overall effort to guide development of a comprehensive framework for modeling human exposures to chemicals, available information resources needed to derive input parameters for human exposure assessment models were compiled and critically reviewed. Ongoing research in the area of exposure assessment parameters was also identified. The results of these efforts are summarized and other relevant information that will be needed to apply the available data in a comprehensive exposure model is discussed. Critical data gaps in the available information are also identified. Exposure assessment modeling and associated research would benefit from the collection of additional data as well as by enhancing the accessibility of existing and evolving information resources.     

Reflections on the implications of thresholds of mutagenic activity for the labelling of chemicals by the European Union

During the course of the safety evaluation and regulatory control of chemicals it is important to distinguish between "potential hazard" and "actual risk" of exposure to toxins. In the case of DNA reactive chemicals, it has been prudent to assume that hazard is expressed as risk at low exposure concentrations. However, analysis of the dose-response relationships of both DNA reactive and non-DNA reactive genotoxins (e.g., aneugens) indicate that there are exposure concentrations below which protective mechanisms such as DNA repair activity and the presence of multiple targets may lead to the prediction of no risk until threshold concentrations are achieved. Current European Union management procedures for mutagenic chemicals are based predominantly upon hazard assessment rather than assessment of actual risk under likely exposure scenarios. As our knowledge of protective mechanisms increases, the time is now appropriate to undertake a re-evaluation of European Union criteria and to base the clarification mutagenic chemical more firmly upon the basis of actual risks to the human population and to the environment.     

Three perspectives on the prediction of chemical effects in ecosystems

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction of chemical effects: the suborganismal, organismal and ecological perspective, which are currently largely separated. Each of the outlined perspectives includes essential and complementary theories and tools for prediction but captures only part of the phenomenon of chemical effects. Links between the perspectives may foster predictive modelling of chemical effects in ecosystems and extrapolation between species. A major challenge for the linkage is the lack of data sets simultaneously covering different levels of biological organisation (here referred to as biological levels) as well as varying temporal and spatial scales. Synthesising the three perspectives, some central aspects and associated types of data seem particularly necessary to improve prediction. First, suborganism- and organism-level responses to chemicals need to be recorded and tested for relationships with chemical groups and organism traits. Second, metrics that are measurable at many biological levels, such as energy, need to be scrutinised for their potential to integrate across levels. Third, experimental data on the simultaneous response over multiple biological levels and spatiotemporal scales are required. These could be collected in nested and interconnected micro- and mesocosm experiments. Lastly, prioritisation of processes involved in the prediction framework needs to find a balance between simplification and capturing the essential complexity of a system. For example, in some cases, eco-evolutionary dynamics and interactions may need stronger consideration. Prediction needs to move from a static to a real-world eco-evolutionary view.     

Endocrine disruptors in marine organisms: approaches and perspectives

Organic pollutants exhibiting endocrine disrupting activity (Endocrine Disruptors--EDs) are prevalent over a wide range in the aquatic ecosystems; most EDs are resistant to environmental degradation and are considered ubiquitous contaminants. The actual potency of EDs is low compared to that of natural hormones, but environmental concentrations may still be sufficiently high to produce detrimental biological effects. Most information on the biological effects and mechanisms of action of EDs has been focused on vertebrates. Here we summarize recent progress in studies on selected aspects of endocrine disruption in marine organisms that are still poorly understood and that certainly deserve further research in the near future. This review, divided in four sections, focuses mainly on invertebrates (effects of EDs and mechanisms of action) and presents data on top predators (large pelagic fish and cetaceans), a group of vertebrates that are particularly at risk due to their position in the food chain. The first section deals with basic pathways of steroid biosynthesis and metabolism as a target for endocrine disruption in invertebrates. In the second section, data on the effects and alternative mechanisms of action of estrogenic compounds in mussel immunocytes are presented, addressing to the importance of investigating full range responses to estrogenic chemicals in ecologically relevant invertebrate species. In the third section we review the potential use of vitellogenin (Vtg)-like proteins as a biomarker of endocrine disruption in marine bivalve molluscs, used worldwide as sentinels in marine biomonitoring programmes. Finally, we summarize the results of a recent survey on ED accumulation and effects on marine fish and mammals, utilizing both classical biomarkers of endocrine disruption in vertebrates and non-lethal techniques, such as non-destructive biomarkers, indicating the toxicological risk for top predator species in the Mediterranean. Overall, the reviewed data underline the potential to identify specific types of responses to specific groups of chemicals such as EDs in order to develop suitable biomarkers that could be useful as diagnostic tools for endocrine disruption in marine invertebrates and vertebrates.     

中国生物多样性相关传统知识调查与评估指标体系构建

基于调查获得的数据开展生物多样性相关传统知识评估, 明确面临的主要压力和保护空缺, 可为相关管理部门决策提供科学依据。指标是开展评估的主要工具, 但是目前尚未有关于生物多样性相关传统知识评估指标体系的文献报道。我们基于压力-状态-响应(pressure-state-response, PSR)模型, 充分考虑生物多样性相关传统知识的基本特征、主要威胁因子、保护和传承措施, 初步构建了区域和国家尺度的生物多样性相关传统知识评估指标体系。然后通过专家咨询, 确定了30项指标, 其中压力指标7项、状态指标14项、响应指标9项。这些指标不仅可以用于生物多样性相关传统知识的综合评估, 还可以对其基本状况、受威胁状况、保护与传承状况、相关遗传资源进行单独评价。此外, 基于评估参数计算的数据需求, 我们借鉴国内外民族植物学和生物多样性相关传统知识调查的主要研究成果, 建立了“全国生物多样性相关传统知识调查技术方法体系”, 并通过试点调查进行修改完善。 “全国生物多样性相关传统知识调查”以关键人物访谈、问卷调查和参与观察为主, 并辅以生物学和生态学调查; 采用滚雪球抽样法对目标群体进行抽样, 确定访谈对象。     

All-atom simulations to studying metallodrugs/target interactions

Metallodrugs are extensively used to treat and diagnose distinct disease types. The unique physical–chemical properties of metal ions offer tantalizing opportunities to tailor effective scaffolds for selectively targeting specific biomolecules. Modern experimental techniques have collected a large body of structural data concerning the interactions of metallodrugs with their biomolecular targets, although being unable to exhaustively assess the molecular basis of their mechanism of action.In this scenario, the complementary use of accurate computational methods allows uncovering the minutiae of metallodrugs/targets interactions and their underlying mechanism of action at an atomic-level of detail. This knowledge is increasingly perceived as an invaluable requirement to rationally devise novel and selective metallodrugs. Building on literature studies, selected largely from the last 2 years, this compendium encompasses a cross-section of the current role, advances, and challenges met by computer simulations to decipher the mechanistic intricacies of prototypical metallodrugs.     

Chromosome breakage induced by alkyl-alkane-sulfonates under different physical treatment conditions

A detailed study of chromosome breakage induced by three alkane sulfonates, which differ in chemical structure, functionality and reaction mechanism has been made in barley under different treatment conditions of temperature and hydrogen ion concentration. This study has indicated that, (i) the frequency and the types of chromosome breakage, at mitosis and meiosis, indicate certain qualitative and quantitative differences between the biological action of these three chemicals, (ii) the temperature of the treatment solution profoundly influences the frequency of chromosome breakage — a high frequency of breakage is observed at higher temperature, (iii) the effect of pH, though not very significant, is evident from the production of appreciably low frequency of breakage at alkaline pH for a given dose of chemical, (iv) by appropriate manipulation of treatment conditions, increase or decrease in the frequency of chromosome breakage can be accomplished to a considerable extent, (v) the qualitative and quantitative differences observed with respect to chromosoma breakage reflect the differences in the mode of biological action of these agents.     

Chemical carcinogens. A review and analysis of the literature of selected chemicals and the establishment of the Gene-Tox Carcinogen Data Base. A report of the U.S. Environmental Protection Agency Gene-Tox Program

The literature on 506 selected chemicals has been evaluated for evidence that these chemicals induce tumors in experimental animals and this assessment comprises the Gene-Tox Carcinogen Data Base. Three major sources of information were used to create this evaluated data base: all 185 chemicals determined by the International Agency for Research on Cancer to have Sufficient evidence of carcinogenic activity in experimental animals, 28 selected chemicals bioassayed for carcinogenic activity by the National Toxicology Program/National Cancer Institute and found to induce tumors in mice and rats, and 293 selected chemicals which had been evaluated in genetic toxicology and related bioassays as determined from previous Gene-Tox reports. The literature data on the 239 chemicals were analyzed by the Gene-Tox Carcinogenesis Panel in an organized, rational and consistent manner. Criteria were established to assess individual studies employing single chemicals and 4 categories of response were developed: Positive, Negative, Inconclusive (Equivocal) and Inconclusive. After evaluating each of the individual studies on the 293 chemicals, the Panel placed each of the 506 chemicals in an overall classification category based on the strength of the evidence indicating the presence or absence of carcinogenic effects. An 8-category decision scheme was established using a modified version of the International Agency for Research on Cancer approach. This scheme included two categories of Positive (Sufficient and Limited), two categories of Negative (Sufficient and Limited), a category of Equivocal (the evidence of carcinogenicity from well-conducted and well-reported lifetime studies had uncertain significance and was neither clearly positive nor negative), and three categories of Inadequate (the evidence of carcinogenicity was insufficient to make a decision, however, the data suggested a positive or negative indication). Of the 506 chemicals in the Gene-Tox Carcinogen Data Base, 252 were evaluated as Sufficient Positive, 99 as Limited Positive, 40 as Sufficient Negative, 21 as Limited Negative, 1 as Equivocal, 13 as Inadequate with the data suggesting a positive indication, 32 as Inadequate with the data suggesting a negative indication, and 48 Inadequate with the data not suggesting any indication of activity. This data base was analyzed and examined according to chemical class, using a 29 chemical class scheme.(ABSTRACT TRUNCATED AT 400 WORDS)     

Generalized logical model based on network topology to capture the dynamical trends of cellular signaling pathways

Background

Cellular responses to extracellular perturbations require signaling pathways to capture and transmit the signals. However, the underlying molecular mechanisms of signal transduction are not yet fully understood, thus detailed and comprehensive models may not be available for all the signaling pathways. In particular, insufficient knowledge of parameters, which is a long-standing hindrance for quantitative kinetic modeling necessitates the use of parameter-free methods for modeling and simulation to capture dynamic properties of signaling pathways.

Results

We present a computational model that is able to simulate the graded responses to degradations, the sigmoidal biological relationships between signaling molecules and the effects of scheduled perturbations to the cells. The simulation results are validated using experimental data of protein phosphorylation, demonstrating that the proposed model is capable of capturing the main trend of protein activities during the process of signal transduction. Compared with existing simulators, our model has better performance on predicting the state transitions of signaling networks.

Conclusion

The proposed simulation tool provides a valuable resource for modeling cellular signaling pathways using a knowledge-based method.

     

Toxicogenomic approach for assessing toxicant-related disease

The problems of identifying environmental factors involved in the etiology of human disease and performing safety and risk assessments of drugs and chemicals have long been formidable issues. Three principal components for predicting potential human health risks are: (1) the diverse structure and properties of thousands of chemicals and other stressors in the environment; (2) the time and dose parameters that define the relationship between exposure and disease; and (3) the genetic diversity of organisms used as surrogates to determine adverse chemical effects. The global techniques evolving from successful genomics efforts are providing new exciting tools with which to address these intractable problems of environmental health and toxicology. In order to exploit the scientific opportunities, the National Institute of Environmental Health Sciences has created the National Center for Toxicogenomics (NCT). The primary mission of the NCT is to use gene expression technology, proteomics and metabolite profiling to create a reference knowledge base that will allow scientists to understand mechanisms of toxicity and to be able to predict the potential toxicity of new chemical entities and drugs. A principal scientific objective underpinning the use of microarray analysis of chemical exposures is to demonstrate the utility of signature profiling of the action of drugs or chemicals and to utilize microarray methodologies to determine biomarkers of exposure and potential adverse effects. The initial approach of the NCT is to utilize proof-of-principle experiments in an effort to "phenotypically anchor" the altered patterns of gene expression to conventional parameters of toxicity and to define dose and time relationships in which the expression of such signature genes may precede the development of overt toxicity. The microarray approach is used in conjunction with proteomic techniques to identify specific proteins that may serve as signature biomarkers. The longer-range goal of these efforts is to develop a reference relational database of chemical effects in biological systems (CEBS) that can be used to define common mechanisms of toxicity, chemical and drug actions, to define cellular pathways of response, injury and, ultimately, disease. In order to implement this strategy, the NCT has created a consortium of research organizations and private sector companies to actively collaborative in populating the database with high quality primary data. The evolution of discrete databases to a knowledge base of toxicogenomics will be accomplished through establishing relational interfaces with other sources of information on the structure and activity of chemicals such as that of the National Toxicology Program (NTP) and with databases annotating gene identity, sequence, and function.