In silico approaches to mechanistic and predictive toxicology: an introduction to bioinformatics for toxicologists

Crystal structure of Kunitz-type trypsin inhibitor: Entomotoxic effect of native and encapsulated protein targeting gut trypsin of Tribolium castaneum Herbst

Trypsin inhibitors are known to act against insect pests by inhibiting proteases of the digestive tract. In this study, we report structural and functional characterization of ∼ 19 kDa Albizia procera Kunitz-type trypsin inhibitor (ApKTI) protein with potential bio-insecticidal applications. Crystal structure of ApKTI protein has been refined to 1.42 Å and molecular structure (8HNR) showed highly beta sheeted conformation including 12 beta sheets, 15 loops and two small alpha helices. Docking between predicted model of Tribolium castaneum trypsin (TcPT) and 8HNR produced a stable complex (-11.3 kcal/mol) which reflects the inhibitory potential of ApKTI against insect gut trypsin. Significant mortality was observed in all life stages of T. castaneum including egg, larvae, pupae and adults with a 3.0 mg native ApKTI treatment in comparison to negative control. Although standard trypsin inhibitor (Glycine max trypsin inhibitors; GmKTI; 3.0 mg) produced maximum reduction against all above life stages; however, a non-significant mortality difference was observed in comparison to 3.0 mg native ApKTI. The study further explores the synthesis and characterization of Graphene (GNPs) and Zinc oxide (ZnONPs) nanoparticles, followed by the optimization of ApKTI and GmKTI loading on both nanoparticles to evaluate their enhanced insecticidal effectiveness. Encapsulated proteins showed significant mortality against T. castaneum across all concentrations, with GNPs proving more effective than ZnONPs. Additionally, encapsulated GmKTI produced significant mortality of eggs compared to loaded ApKTI treatments while other life stages were non-significantly affected by two proteins. This research highlights the importance of encapsulated ApKTI protein for eco-friendly pest management strategies.

Toxicity testing is evolving!

The efficient management of the continuously increasing number of chemical substances used in today's society is assuming greater importance than ever before. Toxicity testing plays a key role in the regulatory decisions of agencies and governments that aim to protect the public and the environment from the potentially harmful or adverse effects of these multitudinous chemicals. Therefore, there is a critical need for reliable toxicity-testing methods to identify, assess and interpret the hazardous properties of any substance. Traditionally, toxicity-testing approaches have been based on studies in experimental animals. However, in the last 20 years, there has been increasing concern regarding the sustainability of these methodologies. This has created a real need for the development of new approach methodologies (NAMs) that satisfy the regulatory requirements and are acceptable and affordable to society. Numerous initiatives have been launched worldwide in attempts to address this critical need. However, although the science to support this is now available, the legislation and the pace of NAMs acceptance is lagging behind. This review will consider some of the various initiatives in Europe to identify NAMs to replace or refine the current toxicity-testing methods for pharmaceuticals. This paper also presents a novel systematic approach to support the desired toxicity-testing methodologies that the 21st century deserves.

Evolution of Gene Expression Balance Among Homeologs of Natural Polyploids

Polyploidy is a major evolutionary process in eukaryotes, yet the expression balance of homeologs in natural polyploids is largely unknown. To study this expression balance, the expression patterns of 2180 structurally well-characterized genes of wheat were studied, of which 813 had the expected three copies and 375 had less than three. Copy numbers of the remaining 992 ranged from 4 to 14, including homeologs, orthologs, and paralogs. Of the genes with three structural copies corresponding to homeologs, 55% expressed from all three, 38% from two, and the remaining 7% expressed from only one of the three copies. Homeologs of 76-87% of the genes showed differential expression patterns in different tissues, thus have evolved different gene expression controls, possibly resulting in novel functions. Homeologs of 55% of the genes showed tissue-specific expression, with the largest percentage (14%) in the anthers and the smallest (7%) in the pistils. The highest number (1.72/3) of homeologs/gene expression was in the roots and the lowest (1.03/3) in the anthers. As the expression of homeologs changed with changes in structural copy number, about 30% of the genes showed dosage dependence. Chromosomal location also impacted expression pattern as a significantly higher proportion of genes in the proximal regions showed expression from all three copies compared to that present in the distal regions.

Using genetic findings in autism for the development of new pharmaceutical compounds

RATIONALE

The main reason for the current lack of effective treatments for the core symptoms of autism is our limited understanding of the biological mechanisms underlying this heterogeneous group of disorders. A primary value of genetic research is enhancing our insight into the biology of autism through the study of identified autism risk genes.

OBJECTIVES

In the current review we discuss (1) the genes and loci that are associated with autism, (2) how these provide us with essential cues as to what neurobiological mechanisms may be involved, and (3) how these mechanisms may be used as targets for novel treatments. Next, we provide an overview of currently ongoing clinical trials registered at clinicaltrials.gov with a variety of compounds. Finally, we review current approaches used to translate knowledge derived from gene discovery into novel pharmaceutical compounds and discuss their pitfalls and problems.

CONCLUSIONS

An increasing number of genetic variants associated with autism have been identified. This will generate new ideas about the biological mechanisms involved in autism, which in turn may provide new leads for the development of novel pharmaceutical compounds. To optimize this pipeline of drug discovery, large-scale international collaborations are needed for gene discovery, functional validation of risk genes, and improvement of clinical outcome measures and clinical trial methodology in autism.

Computational toxicology: application in environmental chemicals

This chapter provides an overview of computational models that describe various aspects of the source-to-health effect continuum. Fate and transport models describe the release, transportation, and transformation of chemicals from sources of emission throughout the general environment. Exposure models integrate the microenvironmental concentrations with the amount of time an individual spends in these microenvironments to estimate the intensity, frequency, and duration of contact with environmental chemicals. Physiologically based pharmacokinetic (PBPK) models incorporate mechanistic biological information to predict chemical-specific absorption, distribution, metabolism, and excretion. Values of parameters in PBPK models can be measured in vitro, in vivo, or estimated using computational molecular modeling. Computational modeling is also used to predict the respiratory tract dosimetry of inhaled gases and particulates [computational fluid dynamics (CFD) models], to describe the normal and xenobiotic-perturbed behaviors of signaling pathways, and to analyze the growth kinetics of preneoplastic lesions and predict tumor incidence (clonal growth models).

Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data

Background

Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.

Results

In this paper, we propose a novel method for finding differentially expressed genes in time-course data and across biological conditions (say C₁ and C₂). We model the expression at C₁ using Principal Component Analysis and represent the expression profile of each gene as a linear combination of the dominant Principal Components (PCs). Then the expression data from C₂ is projected on the developed PCA model and scores are extracted. The difference between the scores is evaluated using a hypothesis test to quantify the significance of differential expression. We evaluate the proposed method to understand differences in two case studies (1) the heat shock response of wild-type and HSF1 knockout mice, and (2) cell-cycle between wild-type and Fkh1/Fkh2 knockout Yeast strains.

Conclusion

In both cases, the proposed method identified biologically significant genes.

Systems toxicology: applications of toxicogenomics, transcriptomics, proteomics and metabolomics in toxicology

Toxicogenomics can facilitate the identification and characterization of toxicity, as illustrated in this review. Toxicogenomics, the application of the functional genomics technologies (transcriptomics, proteomics and metabolomics) in toxicology enables the study of adverse effects of xenobiotic substances in relation to structure and activity of the genome. The advantages and limitations of the different technologies are evaluated, and the prospects for integration of the technologies into a systems biology or systems toxicology approach are discussed. Applications of toxicogenomics in various laboratories around the world show that the crucial steps and sequence of events at the molecular level can be studied to provide detailed insights into mechanisms of toxic action. Toxicogenomics allowed for more sensitive and earlier detection of adverse effects in (animal) toxicity studies. Furthermore, the effects of exposure to mixtures could be studied in more detail. This review argues that in the (near) future, human health risk assessment will truly benefit from toxicogenomics (systems toxicology).

In silico prediction of clinical efficacy

Drug development is a high risk and costly process, and the ability to predict clinical efficacy in silico (in a computer) can save the pharmaceutical industry time and resources. Additionally, such an approach will result in more targeted, personalized therapies. To date, a number of in silico strategies have been developed to provide better information about the human response to novel therapies earlier in the drug development process. Some of the most prominent include physiological modeling of disease and disease processes, analytical tools for population pharmacodynamics, tools for the analysis of genomic expression data, Monte Carlo simulation technologies, and predictive biosimulation. These strategies are likely to contribute significantly to reducing the failure rate of drugs entering clinical trials.

Mutagenic probability estimation of chemical compounds by a novel molecular electrophilicity vector and support vector machine

MOTIVATION

Mutagenicity is among the toxicological end points that pose the highest concern. The accelerated pace of drug discovery has heightened the need for efficient prediction methods. Currently, most available tools fall short of the desired degree of accuracy, and can only provide a binary classification. It is of significance to develop a discriminative and informative model for the mutagenicity prediction.

RESULTS

Here we developed a mutagenic probability prediction model addressing the problem, based on datasets covering a large chemical space. A novel molecular electrophilicity vector (MEV) is first devised to represent the structure profile of chemical compounds. An extended support vector machine (SVM) method is then used to derive the posterior probabilistic estimation of mutagenicity from the MEVs of the training set. The results show that our model gives a better performance than TOPKAT (http://www.accelrys.com) and other previously published methods. In addition, a confidence level related to the prediction can be provided, which may help people make more flexible decisions on chemical ordering or synthesis.

AVAILABILITY

The binary program (ZGTOX_1.1) based on our model and samples of input datasets on Windows PC are available at http://dddc.ac.cn/adme upon request from the authors.

In silico assessment of gene function involved in cysteine biosynthesis in Arabidopsis: expression analysis of multiple isoforms of serine acetyltransferase

In plants, the inorganic sulfur is first fixed into cysteine by the cysteine biosynthetic pathway. This biosynthetic pathway of cysteine involves several enzymatic reactions. In Arabidopsis thaliana, multiple isoforms seem to participate in each enzymatic step for cysteine biosynthesis. To obtain more insights on the specific role of each isoform involved in the cysteine biosynthesis, in silico analysis of these isoforms using Arabidopsis expressed sequence tags (EST) database was carried out. This EST database analysis revealed distinct population distribution of ESTs among multiple isoforms, suggesting that each isoform has its particular expression pattern, presumably associated with its specific role in cysteine biosynthesis. As another in silico analysis, co-expression analysis of genes involved in sulfur metabolism in Arabidopsis was performed using a public transcriptome database of DNA microarrays. This co-expression analysis also suggested specific function and co-regulation of some isoform genes for cysteine biosynthesis by consideration on the clustering of co-expressed genes. From the results of sensitivity to feedback regulation, subcellular localization and expression of mRNA analyses, each serine acetyltransferase (SATase) isoform seems to have its specific role for cysteine biosynthesis. Similar expression patterns were observed between the experimental results of expression data for SATase isoforms and the in silico results of "digital northern" analysis using EST database.

A systems biology approach to developmental toxicology

Recent advances in developmental biology have yielded detailed models of gene regulatory networks (GRNs) involved in cell specification and other processes in embryonic differentiation. Such networks form the bedrock on which a systems biology approach to developmental toxicology can be built. In this review, an introduction to GRNs in general is followed by a description of specific networks involved in sea urchin and Drosophila development. A hypothesis is presented regarding the role of GRN analysis in the determination of mechanisms of chemical toxicity during embryonic development. Potential for future directions and research approaches in this area is discussed.

Empirical bayes gene screening tool for time-course or dose-response microarray data

An efficient method to reduce the dimensionality of microarray gene expression data from thousands or tens of thousands of cDNA clones down to a subset of the most differentially expressed cDNA clones is essential in order to simplify the massive amount of data generated from microarray experiments. An extension to the methods of Efron et al. [Efron, B., Tibshirani, R., Storey, J., Tusher, V. (2001). Empirical Bayes analysis of a microarray experiment. J. Am. Statist. Assoc. 96:1151-1160] is applied to a differential time-course experiment to determine a subset of cDNAs that have the largest probability of being differentially expressed with respect to treatment conditions across a set of unequally spaced time points. The proposed extension, which is advocated to be a screening tool, allows for inference across a continuous variable in addition to incorporating a more complex experimental design and allowing for multiple design replications. With the current data the focus is on a time-course experiment; however, the proposed methods can easily be implemented on a dose-response experiment, or any other microarray experiment that contains a continuous variable of interest. The proposed empirical Bayes gene-screening tool is compared with the Efron et al. (2001) method in addition to an adjusted model-based t-value using a time-course data set where the toxicological effect of a specific mixture of chemicals is being studied.

Interaction networks: coordinating responses to xenobiotic exposure

In the last decade the increased usage of '-omic' technologies, plus the sequencing of over 800 complete genomes has led to a vast increase in the amount of information available to the researcher for examining cellular responses to xenobiotics. Much effort has been put into the identification and analysis of expression profiles associated with pathobiological conditions and/or xenobiotic exposure. These profiles are commonly used in two applications. Firstly, comparative profile experiments are used to classify pathobiological states and for the screening of novel chemical entities to predict their action(s) on the body. Secondly, mechanistic investigations will gain information on the molecular mechanisms underlying toxic responses/pathobiological states. During the course of such analysis it has become increasingly clear that a series of highly refined interaction networks exist within the body, regulating both the sensitivity and selectivity of the body's response to pathobiological states/xenobiotic exposure. These interaction networks exist at several levels: Firstly, within individual cells, the interaction between factors that transmit xenobiotics signals will determine the overall cellular response. Secondly, intraorgan communication occurs between the different cell types/sub-types which makes up an organ, coordinating the overall organ response. Finally, interorgan interactions provide axes of response through the body.

Does pharmacotherapy for preterm labor sensitize the developing brain to environmental neurotoxicants? Cellular and synaptic effects of sequential exposure to terbutaline and chlorpyrifos in neonatal rats

It is increasingly clear that environmental toxicants target specific human subpopulations. In the current study, we examined the effects of prior developmental exposure to a beta(2)-adrenoceptor agonist used to arrest preterm labor, terbutaline, on the subsequent effects of exposure to the organophosphate insecticide, chlorpyrifos (CPF). Neonatal rats were given terbutaline on postnatal day (PN) 2-5, followed by CPF on PN11-14. Although neither treatment affected growth or viability, each elicited alterations in indices of brain cell differentiation and cholinergic innervation in the immediate posttreatment period (PN15), persisting into adulthood (PN60). Biomarkers of brain cell number (DNA concentration and content), cell size (protein/DNA ratio) and neuritic projections (membrane/total protein) were affected by either agent alone, with patterns consistent with neuronal and neuritic damage accompanied by reactive gliosis. The combined exposure augmented these effects by both additive and synergistic mechanisms. Similarly, choline acetyltransferase (ChAT), a constitutive marker for cholinergic nerve terminals, was affected only by combined exposure to both terbutaline and CPF. Indices of cholinergic synaptic activity [hemicholinium-3 and m(2)-muscarinic acetylcholine receptor binding] showed impairment after exposure to either terbutaline or CPF but the effects were more severe when the treatments were combined. These findings suggest that terbutaline, like CPF, is a developmental neurotoxicant, and that its use in the therapy of preterm labor may create a subpopulation that is sensitized to the adverse neural effects of a subsequent exposure to organophosphate insecticides.

Liver gene expression profiles of rats treated with clofibric acid: comparison of whole liver and laser capture microdissected liver

Clofibric acid (CLO) is a peroxisome proliferator (PP) that acts through the peroxisome proliferator activated receptor alpha, leading to hepatocarcinogenesis in rodents. CLO-induced hepatocarcinogenesis is a multi-step process, first transforming normal liver cells into foci. The combination of laser capture microdissection (LCM) and genomics has the potential to provide expression profiles from such small cell clusters, giving an opportunity to understand the process of cancer development in response to PPs. To our knowledge, this is the first evaluation of the impact of the successive steps of LCM procedure on gene expression profiling by comparing profiles from LCM samples to those obtained with non-microdissected liver samples collected after a 1 month CLO treatment in the rat. We showed that hematoxylin and eosin (H&E) staining and laser microdissection itself do not impact on RNA quality. However, the overall process of the LCM procedure affects the RNA quality, resulting in a bias in the gene profiles. Nonetheless, this bias did not prevent accurate determination of a CLO-specific molecular signature. Thus, gene-profiling analysis of microdissected foci, identified by H&E staining may provide insight into the mechanisms underlying non-genotoxic hepatocarcinogenesis in the rat by allowing identification of specific genes that are regulated by CLO in early pre-neoplastic foci.

An in silico assessment of gene function and organization of the phenylpropanoid pathway metabolic networks in Arabidopsis thaliana and limitations thereof

The Arabidopsis genome sequencing in 2000 gave to science the first blueprint of a vascular plant. Its successful completion also prompted the US National Science Foundation to launch the Arabidopsis 2010 initiative, the goal of which is to identify the function of each gene by 2010. In this study, an exhaustive analysis of The Institute for Genomic Research (TIGR) and The Arabidopsis Information Resource (TAIR) databases, together with all currently compiled EST sequence data, was carried out in order to determine to what extent the various metabolic networks from phenylalanine ammonia lyase (PAL) to the monolignols were organized and/or could be predicted. In these databases, there are some 65 genes which have been annotated as encoding putative enzymatic steps in monolignol biosynthesis, although many of them have only very low homology to monolignol pathway genes of known function in other plant systems. Our detailed analysis revealed that presently only 13 genes (two PALs, a cinnamate-4-hydroxylase, a p-coumarate-3-hydroxylase, a ferulate-5-hydroxylase, three 4-coumarate-CoA ligases, a cinnamic acid O-methyl transferase, two cinnamoyl-CoA reductases) and two cinnamyl alcohol dehydrogenases can be classified as having a bona fide (definitive) function; the remaining 52 genes currently have undetermined physiological roles. The EST database entries for this particular set of genes also provided little new insight into how the monolignol pathway was organized in the different tissues and organs, this being perhaps a consequence of both limitations in how tissue samples were collected and in the incomplete nature of the EST collections. This analysis thus underscores the fact that even with genomic sequencing, presumed to provide the entire suite of putative genes in the monolignol-forming pathway, a very large effort needs to be conducted to establish actual catalytic roles (including enzyme versatility), as well as the physiological function(s) for each member of the (multi)gene families present and the metabolic networks that are operative. Additionally, one key to identifying physiological functions for many of these (and other) unknown genes, and their corresponding metabolic networks, awaits the development of technologies to comprehensively study molecular processes at the single cell level in particular tissues and organs, in order to establish the actual metabolic context.

What is the relevance of bioinformatics to pharmacology?

Although bioinformatics achieved prominence because of its central role in genome data storage, management and analysis, its focus has shifted as the life sciences exploit these data. In pharmacology, genomic, transcriptomic and proteomic data are being used in the quest for drugs that fulfill unmet medical needs, are disease modifying or curative and are more effective and safer than current drugs. Bioinformatics is used in drug target identification and validation and in the development of biomarkers and toxicogenomic and pharmacogenomic tools to maximize the therapeutic benefit of drugs. Now that the 'parts list' of cellular signalling pathways is available, integrated computational and experimental programmes are being developed, with the goal of enabling in silico pharmacology by linking the genome, transcriptome and proteome to cellular pathophysiology.