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Liver cytochrome P450s (P450s) play critical roles in drug metabolism, toxicology, and metabolic processes. Despite rapid progress in the understanding of these enzymes, a systematic investigation of the full spectrum of functionality of individual P450s, the interrelationship or networks connecting them, and the genetic control of each gene/enzyme is lacking. To this end, we genotyped, expression-profiled, and measured P450 activities of 466 human liver samples and applied a systems biology approach via the integration of genetics, gene expression, and enzyme activity measurements. We found that most P450s were positively correlated among themselves and were highly correlated with known regulators as well as thousands of other genes enriched for pathways relevant to the metabolism of drugs, fatty acids, amino acids, and steroids. Genome-wide association analyses between genetic polymorphisms and P450 expression or enzyme activities revealed sets of SNPs associated with P450 traits, and suggested the existence of both cis-regulation of P450 expression (especially for CYP2D6) and more complex trans-regulation of P450 activity. Several novel SNPs associated with CYP2D6 expression and enzyme activity were validated in an independent human cohort. By constructing a weighted coexpression network and a Bayesian regulatory network, we defined the human liver transcriptional network structure, uncovered subnetworks representative of the P450 regulatory system, and identified novel candidate regulatory genes, namely, EHHADH, SLC10A1, and AKR1D1. The P450 subnetworks were then validated using gene signatures responsive to ligands of known P450 regulators in mouse and rat. This systematic survey provides a comprehensive view of the functionality, genetic control, and interactions of P450s.

Current generation DNA sequencing instruments are moving closer to seamlessly sequencing genomes of entire populations as a routine part of scientific investigation. However, while significant inroads have been made identifying small nucleotide variation and structural variations in DNA that impact phenotypes of interest, progress has not been as dramatic regarding epigenetic changes and base-level damage to DNA, largely due to technological limitations in assaying all known and unknown types of modifications at genome scale. Recently, single-molecule real time (SMRT) sequencing has been reported to identify kinetic variation (KV) events that have been demonstrated to reflect epigenetic changes of every known type, providing a path forward for detecting base modifications as a routine part of sequencing. However, to date no statistical framework has been proposed to enhance the power to detect these events while also controlling for false-positive events. By modeling enzyme kinetics in the neighborhood of an arbitrary location in a genomic region of interest as a conditional random field, we provide a statistical framework for incorporating kinetic information at a test position of interest as well as at neighboring sites that help enhance the power to detect KV events. The performance of this and related models is explored, with the best-performing model applied to plasmid DNA isolated from Escherichia coli and mitochondrial DNA isolated from human brain tissue. We highlight widespread kinetic variation events, some of which strongly associate with known modification events, while others represent putative chemically modified sites of unknown types. [ABSTRACT FROM AUTHOR]