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The ever-increasing number of chemicals and complex mixtures demands a time-saving and cost-effective platform for environmental risk assessment. However, there is limit promising tool for evaluating the contribution of each component to the total toxicity effects of the mixture. Here, four widely distributed environmental pollutants with different mode-of-actions, i.e., cadmium chloride (Cd), nitrofurazone (NFZ), triclosan (TCS), and tris(2-chloroethyl) phosphate (TCEP), were selected as components of artificial mixture. Integration of leave-one-out method and high-dimensional live cell array system was used to explore relative contribution of each component from the mixture. A quaternary mixture (All_4_chems) and four ternary mixtures (Leave_Cd, Leave_NFZ, Leave_TCS and Leave_TCEP) were investigated by Escherichia coli (E. coli) live cell array system with 90 environmental stress genes modified by green fluorescent protein (GFP) expressing reporter vectors. E. coli cytotoxicity tests demonstrated that TCS has antagonism effect with other three chemicals (Cd, NFZ and TCEP), while it was additive effect in other three binary combinations. A total of 26, 23, 13, 31 and 23 genes were significantly altered with fold-change greater than 2 over the 4 h exposure by All_4_chems, Leave_Cd, Leave_NFZ, Leave_TCS and Leave_TCEP, respectively. Clustering analysis based on time-series gene expression patterns and transcriptional effect level index (TELI) showed that Leave_TCEP has similar profiles with All_4_chems, demonstrating TCEP has the least contribution among four components to the quaternary mixture. Leave_NFZ has the least number of significantly altered genes, implying NFZ has the largest toxicity effect contribution to the quaternary mixture. The relative contribution in different pathways indicated that Cd has the most contribution to the mixture in redox stress, while TCS has the least contribution in DNA stress pathway. Collectively, our results demonstrated the utility of high-dimensional toxicogenomics data and leave-one-out method in prioritizing the relative contribution of each component in mixture.

Purpose. Gram-negative Escherichia coli O157:H7 were chosen as model bacteria to evaluate the antimicrobial mechanism of e-polylysine (e-PL). Methodology. The antibacterial activity of e-PL was detected by measuring the minimum inhibitory concentration values as well as the time–kill curve. The membrane integrity was determined by ultraviolet (UV) absorption, membrane potential (MP) assay and flow cytometry (FCM) experiments. The permeability of the inner membrane was detected by β-galactosidase activity assay. Furthermore, electron microscopy [scanning electron microscopy (SEM) and transmission electron microscopy (TEM)] was utilized to observe bacterial morphology. Key findings. These results demonstrated that e-PL showed its antibacterial activity by changing the integrity and permeability of cell membranes, leading to rapid cell death. The electron microscopy analysis (SEM and TEM) results indicated that the bacterial cell morphology, membrane integrity and permeability were spoiled when the E. coli O157:H7 cells were exposed to minimum inhibitory concentrations of e-PL (16 µg ml−1). In addition, the bacterial membrane was damaged more severely when the concentration of e-PL was increased. Conclusion. The present study investigated the antimicrobial mechanism of e-PL by measuring the content of cytoplasmic β-galactosidase, proteins and DNA. In addition, SEM and TEM were carried out to assess the mechanism. These results show that e-PL has the ability to decrease the content of large molecules, cellular soluble proteins and nucleic acids associated with increasing the content of cytoplasmic β-galactosidase in supernatant by causing damage to the cell membranes. Consequently, the use of e-PL as a natural antimicrobial agent should eventually become an appealing method in the field of food preservation.

Functional gene fingerprinting of chemicals could be used to understand the direct gene-chemical interaction in the process of toxification from a genome-wide scale. 2, 2-bis (bromomethyl)-1, 3-propanediol (BMP) is a brominated flame retardant with widespread production but with very limited toxicological data. Here the cytotoxicity of BMP was assessed by Escherichia coli ( E. coli ) functional genome-wide knockout mutants screening and the underlying molecular mechanism was investigated. The median inhibition concentration (IC50) of BMP was 1.608 ± 0.078 mg/ml after 24 h exposure. 119 initial, including 66 sensitive and 53 resistant single gene mutants, were identified by a full library screening of BMP at the concentration of IC50. The resistant genes were significantly enriched in nucleobase-containing compound biosynthetic process (GO: 0034654) by gene ontology (GO) biological process analyses, which suggested that the pathway of DNA repair is a critical cellular process in the survival of cells exposed to BMP. Meanwhile, function annotation of all BMP responsive genes suggested the mechanism of BMP was associated with DNA damage, oxidative stress and cellular transmembrane transport process. Many genes were exclusively responsive to BMP comparing with other chemicals that has been assessed by E. coli mutant screening approach, which indicated that BMP has a distinct mode of toxic action. Overall, the functional genomic screening approach presented here provides a great tool to assess the cellular toxicological mechanism of environmental chemicals.

Background/Purpose Carcinoembryonic antigen (CEA) is an attractive target for immunotherapy because it is expressed minimally in normal tissue, but is overexpressed in a wide variety of malignant epithelial tissues. Lactic acid bacteria (LABs), widely used in food processes, are attractive candidates for oral vaccination. Thus, we examined whether LABs could be used as a live vaccine vector to deliver CEA antigen. Methods CEA was cloned into an Escherichia coli / Lactococcus lactis shuttle vector pSEC:LEISS under the control of a nisin promoter. For displaying the CEA on the cell surface of the L. lactis strain, the anchor motif LcsB from the S-layer protein of Lactobacillus crispatus was fused with CEA. Intracellular and cell surface expression of the CEA–LcsB fusion was confirmed by western blot analysis. Results Significantly higher levels of CEA-specific secretory immunoglobulin A in the sera of mice were observed upon oral administration of strain cultures containing the CEA–LcsB fused protein. In addition, the CEA–LcsB antigen group showed a higher spleen index compared to the CEA antigen alone or negative control, demonstrating that surface-displayed CEA antigen could induce a higher immune response. Conclusion These results provided the first evidence for displaying CEA antigen on the cell surfaces of LABs as oral vaccines against cancer or infectious diseases.

The knowledge of gene-chemical interaction can be used to derive toxicological mechanism of chemical pollutants, therefore, it might be useful to discriminate chemicals with different mechanisms. In this study, three narcotic chemicals (4-chlorophenol (4-CP), 3, 4-dichloroaniline (DCA) and 2, 2, 2-trichloroethanol (TCE)) and three specific acting chemicals (triclosan (TCS), clarithromycin (CLARY), sulfamethoxazole (SMX)) were assessed by Escherichia coli ( E. coli ) genome-wide knockout screening. 66, 97, 88, 144, 198 and 180 initial robust hits were identified by exposure to 4-CP, DCA, TCE, TCS, CLARY and SMX with two replicates at the concentration of IC50, respectively. The average fold change values of responsive mutants to the three narcotic chemicals were smaller than the three specific acting chemicals. The common gene ontology (GO) term of biological process enriched by the three narcotic chemicals was “response to external stimulus” (GO: 0009605). Other GO terms like “lipopolysaccharide biosynthetic process” (induced by 4-CP) and “purine nucleotide biosynthetic process” (induced by DCA) were also influenced by the narcotic chemicals. The toxic target of three known specific acting chemicals could be validated by GSEA of responsive genes. Four genes ( flhC, fliN, fliH and flhD ) might serve as potential biomarkers to distinguish narcotic chemicals and specific acting chemicals. The E. coli functional genomic approach presented here has shown great potential not only for the molecular mechanistic screening of chemicals, rather it can discriminate chemicals based on their mode-of-action.

Recently some novel alternative flame retardants (NFRs), which have been widely applied to meet demands for mandated flame retardation of products, have been detected in various matrices of the environment. However, knowledge on toxic effects and associated molecular mechanisms of these chemicals was limited. Here, toxic mechanisms of action of six NFRs, bis (2-ethylhexyl) phosphate (BEHP), chlorendic acid (Het acid), 2,2-bis (bromomethyl)-1,3-propanediol (BMP), tris (2-butoxyethyl) phosphate (TBEP), triethyl phosphate (TEP), tributyl phosphate (TBP) were investigated by use of a library containing ∼1820 modified green fluorescent protein (GFP) expressing promoter reporter vectors constructed from Escherichia coli K12(E.coli). BEHP, Het acid, BMP, TBEP, TEP, TBP inhibited growth of E. coli with 4 h 10%-inhibition concentrations of 53.0-3102.3 μM. A total of 119, 44, 26, 131, 62, 103 genes out of 336 genes selected during preliminary screening were significantly altered with fold-changes greater than 1.5 by BEHP, Het acid, BMP, TBEP, TEP and TBP, respectively. GO analyses of responsive genes suggested that RNA and primary metabolism process were involved in molecular mechanisms of toxicity. Chemical clustering based on expression of 62 multi-responsive genes showed that BEHP, TBP and TBEP were grouped together, which is consistent with similarity of their chemical structures, especially for BEHP and TBP. Clustering by molecular descriptors and molecular activity by use of the multivariate classification system ToxCast was consistent with that by profiles of multi-responsive genes. The results of this study demonstrated the utility of the E. coli, whole-cell assay for determining mechanisms of toxic action of chemicals.

Background Pathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting. Methods Our DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways. Results Using the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach. Conclusions Findings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.