Prioritizing genes for pathway impact using network analysis

DNA methylation modifies urine biomarker levels in 1,6-hexamethylene diisocyanate exposed workers: a pilot study

DNA methylation may mediate inter-individual responses to chemical exposure and, thus, modify biomarker levels of exposure and effects. We analyzed inter-individual differences in inhalation and skin exposure to 1,6-hexamethylene diisocyanate (HDI) and urine biomarker 1,6-hexamethylene diamine (HDA) levels in 20 automotive spray-painters. Genome-wide 5-methyl cytosine (CpG) DNA methylation was assessed in each individual's peripheral blood mononuclear cells (PBMC) DNA using the Illumina 450K CpG array. Mediation analysis using linear regression models adjusted for age, ethnicity, and smoking was conducted to identify and assess the association between HDI exposure, CpG methylation, and urine HDA biomarker levels. We did not identify any CpGs common to HDI exposure and biomarker level suggesting that CpG methylation is a mediator that only partially explains the phenotype. Functional significance of genic- and intergenic-CpG methylation status was tested using protein-protein or protein-DNA interactions and gene-ontology enrichment to infer networks. Combined, the results suggest that methylation has the potential to affect HDI mass transport, permeation, and HDI metabolism. We demonstrate the potential use of PBMC methylation along with quantitative exposure and biomarker data to guide further investigation into the mediators of occupational exposure and biomarkers and its role in risk assessment.

Single-nucleotide polymorphisms associated with skin naphthyl-keratin adduct levels in workers exposed to naphthalene

BACKGROUND

Individual genetic variation that results in differences in systemic response to xenobiotic exposure is not accounted for as a predictor of outcome in current exposure assessment models.

OBJECTIVE

We developed a strategy to investigate individual differences in single-nucleotide polymorphisms (SNPs) as genetic markers associated with naphthyl-keratin adduct (NKA) levels measured in the skin of workers exposed to naphthalene.

METHODS

The SNP-association analysis was conducted in PLINK using candidate-gene analysis and genome-wide analysis. We identified significant SNP-NKA associations and investigated the potential impact of these SNPs along with personal and workplace factors on NKA levels using a multiple linear regression model and the Pratt index.

RESULTS

In candidate-gene analysis, a SNP (rs4852279) located near the CYP26B1 gene contributed to the 2-naphthyl-keratin adduct (2NKA) level. In the multiple linear regression model, the SNP rs4852279, dermal exposure, exposure time, task replacing foam, age, and ethnicity all were significant predictors of 2NKA level. In genome-wide analysis, no single SNP reached genome-wide significance for NKA levels (all p ≥ 1.05 × 10(-5)). Pathway and network analyses of SNPs associated with NKA levels were predicted to be involved in the regulation of cellular processes and homeostasis.

CONCLUSIONS

These results provide evidence that a quantitative biomarker can be used as an intermediate phenotype when investigating the association between genetic markers and exposure-dose relationship in a small, well-characterized exposed worker population.

Coping with genetic diversity: the contribution of pathogen and human genomics to modern vaccinology

Vaccine development faces major difficulties partly because of genetic variation in both infectious organisms and humans. This causes antigenic variation in infectious agents and a high interindividual variability in the human response to the vaccine. The exponential growth of genome sequence information has induced a shift from conventional culture-based to genome-based vaccinology, and allows the tackling of challenges in vaccine development due to pathogen genetic variability. Additionally, recent advances in immunogenetics and genomics should help in the understanding of the influence of genetic factors on the interindividual and interpopulation variations in immune responses to vaccines, and could be useful for developing new vaccine strategies. Accumulating results provide evidence for the existence of a number of genes involved in protective immune responses that are induced either by natural infections or vaccines. Variation in immune responses could be viewed as the result of a perturbation of gene networks; this should help in understanding how a particular polymorphism or a combination thereof could affect protective immune responses. Here we will present: i) the first genome-based vaccines that served as proof of concept, and that provided new critical insights into vaccine development strategies; ii) an overview of genetic predisposition in infectious diseases and genetic control in responses to vaccines; iii) population genetic differences that are a rationale behind group-targeted vaccines; iv) an outlook for genetic control in infectious diseases, with special emphasis on the concept of molecular networks that will provide a structure to the huge amount of genomic data.

Construction and analysis of protein-protein interaction networks

Protein–protein interactions form the basis for a vast majority of cellular events, including signal transduction and transcriptional regulation. It is now understood that the study of interactions between cellular macromolecules is fundamental to the understanding of biological systems. Interactions between proteins have been studied through a number of high-throughput experiments and have also been predicted through an array of computational methods that leverage the vast amount of sequence data generated in the last decade. In this review, I discuss some of the important computational methods for the prediction of functional linkages between proteins. I then give a brief overview of some of the databases and tools that are useful for a study of protein–protein interactions. I also present an introduction to network theory, followed by a discussion of the parameters commonly used in analysing networks, important network topologies, as well as methods to identify important network components, based on perturbations.