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. 2015 Apr;138:202-16.
doi: 10.1016/j.envres.2014.12.031. Epub 2015 Feb 27.

Transcriptional Profiling and Biological Pathway Analysis of Human Equivalence PCB Exposure in Vitro: Indicator of Disease and Disorder Development in Humans

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Transcriptional Profiling and Biological Pathway Analysis of Human Equivalence PCB Exposure in Vitro: Indicator of Disease and Disorder Development in Humans

Somiranjan Ghosh et al. Environ Res. .
Free PMC article

Abstract

Background and aims: Our earlier gene-expression studies with a Slovak PCBs-exposed population have revealed possible disease and disorder development in accordance with epidemiological studies. The present investigation aimed to develop an in vitro model system that can provide an indication of disrupted biological pathways associated with developing future diseases, well in advance of the clinical manifestations that may take years to appear in the actual human exposure scenario.

Methods: We used human Primary Blood Mononuclear Cells (PBMC) and exposed them to a mixture of human equivalence levels of PCBs (PCB-118, -138, -153, -170, -180) as found in the PCBs-exposed Slovak population. The microarray studies of global gene expression were conducted on the Affymetrix platform using Human Genome U133 Plus 2.0 Array along with Ingenuity Pathway Analysis (IPA) to associate the affected genes with their mechanistic pathways. High-throughput qRT-PCR Taqman Low Density Array (TLDA) was done to further validate the selected 6 differentially expressed genes of our interest, viz., ARNT, CYP2D6, LEPR, LRP12, RRAD, TP53, with a small population validation sample (n=71).

Results: Overall, we revealed a discreet gene expression profile in the experimental model that resembled the diseases and disorders observed in PCBs-exposed population studies. The disease pathways included endocrine system disorders, genetic disorders, metabolic diseases, developmental disorders, and cancers, strongly consistent with the evidence from epidemiological studies.

Interpretation: These gene finger prints could lead to the identification of populations and subgroups at high risk for disease, and can pose as early disease biomarkers well ahead of time, before the actual disease becomes visible.

Keywords: Biomarkers; Disease and disorders; Gene expression; Human PBMC; PCBs; Pathway analysis; Taqman Low-Density Array (TLDA).

Conflict of interest statement

Conflict of Interest

There is no conflict of interest among the authors in the present work.

Figures

Fig. 1
Fig. 1
Hierarchical cluster analysis along with the heat map of the differentially expressed gene set (100) in human PBMCs in vitro study following human equivalence PCBs exposures. Red denotes up-regulation, blue down-regulation, and gray no difference; where brighter color (+/−) denotes the increasing intensities of up/down-regulations induced by PCBs exposures. The Hierarchical clustering (Dendrogram) displayed the results systematically, and showed that control and treated are grouped together and was based on average linkage with Pearson correlation.
Fig. 2
Fig. 2
The key (Top) bio-functions in developing toxicities with the differentially expressed gene set following PCBs mixed-exposures in vitro as obtained through IPA analysis physiological system development and functions (A), disease and disorder development (B), and in molecular and cellular functions (C). The most statistically significant top biofunctions that were identified in the IPA Tox analysis are listed here according to their p value (−Log). The threshold line corresponds to a p value of 0.05.
Fig. 3
Fig. 3
Connectivity of differentially expressed genes in the important signaling pathway following mixed PCBs exposure in human PBMC in vitro depicting the connectivity between genes expressed (with ≥1.5 fold change, t-test, p <0.05). Geometric figures in red denote up-regulated genes and those are green indicate down-regulation. Genes in the top 6 networks (our experimental 100 gene sets) were allowed to grow our pathway with the direct/indirect relationship from the IPA knowledge base with the stringent filter, experimentally observed, those who were only from human study. Solid interconnecting lines show the genes that are directly connected and the dotted lines signify the indirect connection between the genes and cellular functions. Canonical functions (signaling) that are highly represented are shown within the box. Genes in uncolored notes were not identified as differentially expressed in our experiment and were integrated into computational generated networks based on evidence stored in the IPA knowledge base indicating relevance of this network.
Fig. 4
Fig. 4
Top biofunctions in disease and disorder development with the in vitro studies (PCBs mixed) generated through IPA analysis. The gene sets from the study were filtered, uploaded, and run through in the IPA comparative data Analysis module. The important disease and disorders that are represented here were at or above the threshold value (corresponds to a p value of 0.05). Fischer’s exact test was used to calculate a p-value determining the probability that each biological function and/or disease assigned to the dataset.
Fig. 5
Fig. 5
In vitro Quantitative Real-time PCR (qRT-PCR) validation of the selected 14 genes (both experimental and IPA knowledge base) by Taqman Low Density Array (TLDA) in ABI platform (7900HT Fast Real-Time PCR System) after analyzed by SDS RQ Manager Version 1.2.1 (ΔΔCt). Each panel shows the relative quantification of the selected genes up/down-regulation among the experimentally exposed condition (Subjects 1–6). The relative quantification is calculated in contrast to calibrator samples, i.e.; no-exposure in in vitro studies (control).
Fig. 6
Fig. 6
Quantitative Real-time PCR (qRT-PCR) validation of the selected 6 genes of interest by Taqman Low Density Array (TLDA) in ABI platform (7900HT Fast Real-Time PCR System) after analyzed by SDS RQ Manager Version 1.2.1(ΔΔCt). The panels A–F (with the respective genes) represent the relative quantification of the genes upon small population validation (the population with high PCBs in their blood; n=71) with the same gene transcript that has been used in in vitro studies. The relative quantification is calculated in contrast to calibrator samples, i.e.; the subjects with no/background PCBs exposures in the population.

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