Mode of Action Clustering of Chemicals and Environmental Samples on the Bases of Bacterial Stress Gene Inductions

Abstract

Over the years, environment and the human population have seen an increasing exposure to both existing and newly developed chemicals. It is generally accepted that at least some of those are toxic, albeit as pure compound or in combination with others. In response to a growing public awareness and scientific data, the new European chemicals legislation (Registration, Evaluation and Authorization of Chemicals) is under implementation at the moment. As a consequence, during the coming years about 30,000 chemicals have to be assessed on their potential hazard for man and biota. Part of this assessment will be done using existing and new in vitro tests offering insight into the toxicity of chemicals and into their toxicological mode of action. This study presents data on a battery of 14 bacterial reporter gene assay allowing mode of action determination and statistical grouping of chemicals based on their induction profile. Gene induction results are used to group reference chemicals in a statistical cascade employing hierarchical tree and k-means clustering for initial grouping. Both complementary, yet mathematically different, algorithms are consequently confirmed by principal component analysis (PCA). The gene induction profiles of an environmental extract with documented in vivo effects and a chemical with limited toxicological are data available and projected in the PCA vector space. The projection allows correct mode of action grouping and indicates that effect predictions based on the known toxicological effects of the reference compounds can be made.

Autism-Like Behavior and Epigenetic Changes Associated with Autism as Consequences ofIn UteroExposure to Environmental Pollutants in a Mouse Model

“…Industrial chemicals are being produced and released into the environment at a staggering rate. The average consumer is exposed to up to 10,000 different chemicals every day out of a world market that includes over 30,000 industrial chemicals sold at quantities greater than 400 million tons per year [ 25 ]. These chemicals range in toxicity from being benign to being extremely hazardous and the environmental and public health implications of this overwhelming and pervasive exposure have not been well characterized for the vast majority of chemicals.…”

Bacterial gene profiling assay applied as an alternative method for mode of action classification: Pilot study using chlorinated anilines

“…In addition, FI scores (FIS) were calculated for the mean FI of three independent replicates as the ratio of the measured FI to the maximum FI (set to 100%), obtained for every stress gene on approximately 200 compounds (according to Dardenne et al [24]). …”

“…A BGS of 100% indicated a significant induction in all the different test concentrations of every gene in that particular MOA group. To compare the MOAs of the four anilines with other polar and nonpolar narcotic MOAs, the data set was extended with bacterial gene profiling data from an in-house E. coli gene profiling database [24]. Ethanol (EtOH), methanol (MetOH), tetrachloromethane (CCl 4 ), 1,1,1-trichloroethane (C 2 H 3 Cl 3 ), and dichloromethane (CH 2 Cl 2 ) were included as Verhaar class 1 (i.e., narcotic) reference compounds, whereas phenol was added as another standard Verhaar class 2 (i.e., polar narcotic) chemical.…”

“…An exploratory approach that assists in elucidating the degree of similarity between multivariate data sets of different chemical treatments was applied. Hierarchical clustering (HCL) analysis (Euclidean distance, average linkage) with statistical support for the nodes of the trees by means of bootstrapping (n ¼ 1,000) was performed using MultiExperiment viewer (MeV 4.5.1; TIGR) to compare our data with those of Dardenne et al [24]. Hierarchical clusterings were performed based on FIS and BGS to assess the (presumed) similarities in gene induction responses after chemical exposures of compounds belonging to Verhaar class 1 and class 2.…”

Biogeochemical Typing of Paddy Field by a Data-Driven Approach Revealing Sub-Systems within a Complex Environment - A Pipeline to Filtrate, Organize and Frame Massive Dataset from Multi-Omics Analyses

“…The BGC typing pipeline is based on integrative statistical analysis: namely, Spearman correlation [13] , [14] , least-squares structuring [15] and k-means clustering [16] , [17] . The set formed by the groups of organisms and the chemical profiles associated by this pipeline we call the BGC types which are meaningful a priori only in the specific study, nevertheless we expect to find similar BGC types spread on similar environments under similar conditions and analyses.…”