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To test the hypothesis that early-life adversity accelerates the pace of biological aging, we analyzed data from the Dutch Hunger Winter Families Study (DHWFS, N = 951). DHWFS is a natural-experiment birth-cohort study of survivors of in-utero exposure to famine conditions caused by the German occupation of the Western Netherlands in Winter 1944 to 1945, matched controls, and their siblings. We conducted DNA methylation analysis of blood samples collected when the survivors were aged 58 to quantify biological aging using the DunedinPACE, GrimAge, and PhenoAge epigenetic clocks. Famine survivors had faster DunedinPACE, as compared with controls. This effect was strongest among women. Results were similar for GrimAge, although effect-sizes were smaller. We observed no differences in PhenoAge between survivors and controls. Famine effects were not accounted for by blood-cell composition and were similar for individuals exposed early and later in gestation. Findings suggest in-utero undernutrition may accelerate biological aging in later life. [ABSTRACT FROM AUTHOR]

Multiple studies have illustrated that gene expression profiling of primary breast cancers throughout the final stages of tumor development can provide valuable markers for risk prediction of metastasis and disease sub typing. However, the identification of a biologically interpretable and universally shared set of markers proved to be difficult. Here, we propose a method for de novo grouping of genes by dissecting the proteinprotein interaction network into disjoint sub networks using pair wise gene expression correlation measures. We show that the obtained sub networks are functionally coherent and are consistently identified when applied on a compendium composed of six different breast cancer studies. Application of the proposed method using different integration approaches underlines the robustness of the identified sub network related to cell cycle and identifies putative new sub network markers for metastasis related to cell-cell adhesion, the proteasome complex and JUN-FOS signalling. Although gene selection with the proposed method does not directly improve upon previously reported cross study classification performances, it shows great promises for applications in data integration and result interpretation.

BACKGROUND: Lead is an environmental pollutant that causes acute and chronic toxicity. Surveys have related mean blood lead concentrations to exogenous sources, including industrial activity, use of lead-based paints, or traffic density. However, there has been little investigation of individual differences in lead absorption, distribution, or toxicity, or of genetic causes of such variation. OBJECTIVES: We assessed the genetic contribution to variation in blood lead concentration in adults and conducted a preliminary search for genes producing such variation. METHODS: Erythrocyte lead concentration was measured by inductively coupled plasma mass spectrometry in venous blood samples from 2,926 Australian adult male and female twins. Mean lead concentrations were compared by place of residence, social class and education, and by the subjects' age, sex, alcohol intake, smoking habits, iron status, and HFE genotype. RESULTS: After adjustment for these covariates, there was strong evidence of genetic effects but not for shared environmental effects persisting into adult life. Linkage analysis showed suggestive evidence (logarithm of odds = 2.63, genome-wide p = 0.170) for a quantitative trait locus affecting blood lead values on chromosome 3 with the linkage peak close to SLC4A7, a gene whose product affects lead transport. CONCLUSIONS: We conclude that genetic variation plays a significant role in determining lead absorption, lead distribution within the body, or both. KEY WORDS: blood lead, heritability, linkage, toxicogenetics, twin study. Environ Health Perspect 115:1224-1230 (2007). doi:10.1289/ehp.8847 available via http://dx.doi.org/ [Online 14 June 2007] John B. Whitfield, Veronica Dy, Robert McQuilty, Gu Zhu, Grant W. Montgomery, Manuel A.R. Ferreira, David L. Duffy, Michael C. Neale, Bas T. Heijmans, Andrew C. Heath, and Nicholas G. Martin (1)Department of Clinical Biochemistry, Royal Prince Alfred Hospital, Sydney, Australia; (2)Genetic Epidemiology Unit, Queensland Institute of Medical Research, Brisbane, Australia; (3)Department of Psychiatry and Human Genetics, Virginia Commonwealth University, Richmond, Virginia, USA; (4)Section of Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands; (5)Missouri Alcoholism Research Center, Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA, Many elements and compounds present in the environment pose significant health risks to exposed individuals, and contribute to the burden of disease for society. For any exposed person the outcome [...]

The bodily decline that occurs with advancing age strongly impacts on the prospects for future health and life expectancy. Despite the profound role of age in disease etiology, knowledge about the molecular mechanisms driving the process of aging in humans is limited. Here, we used an integrative network-based approach for combining multiple large-scale expression studies in blood (2539 individuals) with protein-protein Interaction ( PPI) data for the detection of consistently coexpressed PPI modules that may reflect key processes that change throughout the course of normative aging. Module detection followed by a meta-analysis on chronological age identified fifteen consistently coexpressed PPI modules associated with chronological age, including a highly significant module ( P = 3.5 × 10−38) enriched for ' T-cell activation' marking age-associated shifts in lymphocyte blood cell counts ( R2 = 0.603; P = 1.9 × 10−10). Adjusting the analysis in the compendium for the ' T-cell activation' module showed five consistently coexpressed PPI modules that robustly associated with chronological age and included modules enriched for ' Translational elongation', ' Cytolysis' and ' DNA metabolic process'. In an independent study of 3535 individuals, four of five modules consistently associated with chronological age, underpinning the robustness of the approach. We found three of five modules to be significantly enriched with aging-related genes, as defined by the GenAge database, and association with prospective survival at high ages for one of the modules including ASF1A. The hereby-detected age-associated and consistently coexpressed PPI modules therefore may provide a molecular basis for future research into mechanisms underlying human aging. [ABSTRACT FROM AUTHOR]

To test the hypothesis that early-life adversity accelerates the pace of biological aging, we analyzed data from the Dutch Hunger Winter Families Study (DHWFS, N=951). DHWFS is a natural-experiment birth-cohort study of survivors of in-utero exposure to famine conditions caused by the German occupation of the Western Netherlands in Winter 1944-5, matched controls, and their siblings. We conducted DNA methylation analysis of blood samples collected when the survivors were aged 58 to quantify biological aging using the DunedinPACE, GrimAge, and PhenoAge epigenetic clocks. Famine survivors had faster DunedinPACE, as compared with controls. This effect was strongest among women. Results were similar for GrimAge, although effect-sizes were smaller. We observed no differences in PhenoAge between survivors and controls. Famine effects were not accounted for by blood-cell composition and were similar for individuals exposed early and later in gestation. Findings suggest in-utero undernutrition may accelerate biological aging in later life., Competing Interests: Conflict of Interest DWB is listed as an inventor on the Duke University and University of Otago Invention DunedinPACE, which is licensed to TruDiagnostic.

Multiple studies have illustrated that gene expression profiling of primary breast cancers throughout the final stages of tumor development can provide valuable markers for risk prediction of metastasis and disease sub typing. However, the identification of a biologically interpretable and universally shared set of markers proved to be difficult. Here, we propose a method for de novo grouping of genes by dissecting the protein-protein interaction network into disjoint sub networks using pair wise gene expression correlation measures. We show that the obtained sub networks are functionally coherent and are consistently identified when applied on a compendium composed of six different breast cancer studies. Application of the proposed method using different integration approaches underlines the robustness of the identified sub network related to cell cycle and identifies putative new sub network markers for metastasis related to cell-cell adhesion, the proteasome complex and JUN-FOS signalling. Although gene selection with the proposed method does not directly improve upon previously reported cross study classification performances, it shows great promises for applications in data integration and result interpretation.