Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Abstract

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Keywords: Arsenic; Carcinogenesis; Cellular Transformation; In Vitro Model; Stochasticity; Transcriptomics.

Fig.1.

Chronic iAs exposure suppressed transcripts with stochastic expression at 7, 19 and 28-weeks. For each panel, the black dots above the green line represent the number of transcripts with stochastic expression at that time point under that experimental condition. These numbers are also provided in each panel and in Table S1.

Fig. 2.

Deterministic changes in mRNA expression and pathway dysregulation from chronic iAs exposure at 7, 19 and 28-weeks. A. Venn diagram depicting the distribution of mRNAs that are differentially expressed (p< 0.05) deterministically across all 5 comparisons (all included; H-H Removed; L-L Removed; H-L Removed; L-H Removed) at each time point along with the number of overlaps at different time points. B. Heat Map of predicted activated/inhibited pathways based on mRNAs that are differentially expressed (p< 0.05) deterministically across all 5 comparisons (all included; H-H Removed; L-L Removed; H-L Removed; L-H Removed) at 7, 19 and 28-weeks (presented in the same order as in Table S4). The color code bar on the right refers to the z-score values. Absence of a bar (represented by white) signifies that the pathway was not predicted to be activated or inhibited at that time point. The pathways are presented in the same order as in Table S4.

Fig. 3.

Stochastic changes in mRNA expression and pathway dysregulation from chronic iAs exposure at 7, 19 and 28-weeks. A. Venn diagram depicting the distribution of mRNAs (p< 0.05) that are differentially expressed stochastically in only one out of all 5 comparisons (all included; H-H Removed; L-L Removed; H-L Removed; L-H Removed) at each time point along with the number of overlaps at different time points. B. Heat Map of predicted activated/inhibited pathways based on mRNAs (p< 0.05) that are differentially expressed stochastically in only one out of all 5 comparisons (all included; H-H Removed; L-L Removed; H-L Removed; L-H Removed) at 7, 19 and 28-weeks (presented in the same order as in Table S4). The color code bar on the right refers to the z-score values. Absence of a bar (represented by white) signifies that the pathway was not predicted to be activated or inhibited at that time point. The pathways are presented in the same order as in Table S8.

Fig. 4.

mRNAs that are differentially expressed deterministically and stochastically populate unique dysregulated pathways. A. Heat map of predicted unique activated/inhibited pathways that are populated exclusively by mRNAs that are differentially expressed (p< 0.05) deterministically across all 5 comparisons (all included; H-H Removed; L-L Removed; H-L Removed; L-H Removed) at 7, 19 and 28-weeks. B. Heat map of predicted unique activated/inhibited pathways that are populated exclusively by mRNAs that are differentially expressed (p< 0.05) stochastically in only one out of all 5 comparisons (all included; H-H Removed; L-L Removed; H-L Removed; L-H Removed) at 7, 19 and 28-weeks. For both panels, the color code bar on the right refers to the z-score values, while the absence of a bar (represented by white) signifies that the pathway was not predicted to be activated or inhibited at that time point.

Fig. 5.

A few dysregulated pathways are modulated by mRNAs that are differentially expressed either deterministically or stochastically by chronic iAs exposure. A. Venn diagram depicting the distribution of dysregulated pathways that are populated by mRNAs (p< 0.05) that are differentially expressed either deterministically or stochastically at each time point along with the number of overlaps at different time points. B. Heat Map of predicted activated/inhibited pathways that are populated by mRNAs (p< 0.05) that are differentially expressed either deterministically or stochastically at 7, 19 and 28-weeks. The color code bar on the right refers to the z-score values. Absence of a bar (represented by white) signifies that the pathway was not predicted to be activated or inhibited at that time point. The pathways are presented in the same order as in Table S9.

Fig. 6.

Simplified model depicting how deterministic and stochastic changes both contribute to different stages of carcinogenesis. The cellular characteristics at each time point representing a stage in carcinogenesis are presented in the boxes on the left. On the right, a representative dysregulated pathway associated with these cellular characteristics are presented. Dysregulated pathways exclusively populated by deterministic or stochastic gene expression changes are presented in blue or purple text respectively.