The Saliva Exposome for Monitoring of Individuals’ Health Trajectories

Bessonneau, V., Pawliszyn, J., & Rappaport, S. M. (2017). The Saliva Exposome for Monitoring of Individuals’ Health Trajectories. Environmental Health Perspectives, 125(7). https://doi.org/10.1289/EHP1011 Reproduced with permission from Environmental Health PerspectivesBackground: There is increasing evidence that environmental, rather than genetic, factors are the major causes of most chronic diseases. By measuring entire classes of chemicals in archived biospecimens, exposome-wide association studies (EWAS) are being conducted to investigate associations between a myriad of exposures received during life and chronic diseases. Objectives: Because the intraindividual variability in biomarker levels, arising from changes in environmental exposures from conception onwards, leads to attenuation of exposure–disease associations, we posit that saliva can be collected repeatedly in longitudinal studies to reduce exposure–measurement errors in EWAS. Methods: From the literature and an open-source saliva–metabolome database, we obtained concentrations of 1,233 chemicals that had been detected in saliva. We connected salivary metabolites with human metabolic pathways and PubMed Medical Subject Heading (MeSH) terms, and performed pathway enrichment and pathway topology analyses. Results: One hundred ninety-six salivary metabolites were mapped into 49 metabolic pathways and connected with human metabolic diseases, central nervous system diseases, and neoplasms. We found that the saliva exposome represents at least 14 metabolic pathways, including amino acid metabolism, TCA cycle, gluconeogenesis, glutathione metabolism, pantothenate and CoA biosynthesis, and butanoate metabolism. Conclusions: Saliva contains molecular information worthy of interrogation via EWAS. The simplicity of specimen collection suggests that saliva offers a practical alternative to blood for measurements that can be used to characterize individual exposomesNatural Sciences and Engineering Research Council of Canada Industrial Research program Canada Research Chairs program U.S. National Institutes of Health || grants P42ES04705 and R33CA19115

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

<p>Abstract</p> <p>Background</p> <p>Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community.</p> <p>Results</p> <p>This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment.</p> <p>Conclusions</p> <p>AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.</p

Physical comorbidities in men with mood and anxiety disorders: a population-based study

Background : The mind-body nexus has been a topic of growing interest. Further data are however required to understand the specific relationship between mood and anxiety disorders and individual physical health conditions, and to verify whether these psychiatric disorders are linked to overall medical burden. Methods : This study examined data collected from 942 men, 20 to 97 years old, participating in the Geelong Osteoporosis Study. A lifetime history of mood and anxiety disorders was identified using the Structured Clinical Interview for DSM-IV-TR Research Version, Non-patient edition (SCID-I/NP). The presence of medical conditions (lifetime) was self-reported and confirmed by medical records, medication use or clinical data. Anthropometric measurements and socioeconomic status (SES) were determined and information on medication use and lifestyle was obtained via questionnaire. Logistic regression models were used to test the associations. Results : After adjustment for age, socioeconomic status, and health risk factors (body mass index, physical activity and smoking), mood disorders were associated with gastro oesophageal reflux disease (GORD), recurrent headaches, blackouts and/or epilepsy, liver disorders and pulmonary disease in older people, whilst anxiety disorders were significantly associated with thyroid, GORD and other gastrointestinal disorders, and psoriasis. Increased odds of high medical burden were associated with both mood and anxiety disorders. Conclusions : Our study provides further population-based evidence supporting the link between mental and physical illness in men. Understanding these associations is not only necessary for individual management, but also to inform the delivery of health promotion messages and health care

Blood lead, cadmium and mercury among children from urban, industrial and rural areas of Fez Boulemane Region (Morocco): Relevant factors and early renal effects

Objectives: To describe blood lead (Pb-B), cadmium (Cd-B) and mercury (Hg-B) levels in children living in urban, industrial and rural areas in Fez city (north of Morocco) and to identify the determinants and some renal effects of exposure. Material and Methods: The study was conducted from June 2007 to January 2008 in 209 school children (113 girls, 96 boys), aged 6-12 years, from urban, industrial and rural areas in Fez city. Interview and questionnaires data were obtained. Blood and urinary samples were analyzed. Results: The mean of blood lead levels (Pb-B) in our population was 55.53 μg/l (range: 7.5-231.1 μg/l). Children from the urban area had higher blood lead levels (BLLs) mean (82.36 μg/l) than children from industrial and rural areas (48.23 and 35.99 μg/l, respectively); with no significant difference between boys and girls. BLLs were associated with traffic intensity, passive smoking and infancy in the urban area. The mean of blood cadmium levels (BCLs) was 0.22 μg/l (range: 0.06-0.68 μg/l), with no difference between various areas. Rural boys had higher BCLs mean than rural girls, but no gender influence was noticed in the other areas. BCLs were associated with the number of cigarettes smoked at children's homes. The blood mercury levels (BMLs) mean was 0.49 μg/l (range: 0.01-5.31 μg/l). The BMLs mean was higher in urban and industrial areas than in the rural area with no gender-related difference. BMLs were associated with amalgam fillings and infancy in the urban area. About 8% of the children had BLLs ≥ 100 μg/l particularly in the urban area, microalbuminuria and a decrease in height were noticed in girls from the inner city of Fez and that can be related to high BLLs (89.45 μg/l). Conclusions: There is a need to control and regulate potential sources of contamination by these trace elements in children; particularly for lead

Light regulation of metabolic pathways in fungi

Light represents a major carrier of information in nature. The molecular machineries translating its electromagnetic energy (photons) into the chemical language of cells transmit vital signals for adjustment of virtually every living organism to its habitat. Fungi react to illumination in various ways, and we found that they initiate considerable adaptations in their metabolic pathways upon growth in light or after perception of a light pulse. Alterations in response to light have predominantly been observed in carotenoid metabolism, polysaccharide and carbohydrate metabolism, fatty acid metabolism, nucleotide and nucleoside metabolism, and in regulation of production of secondary metabolites. Transcription of genes is initiated within minutes, abundance and activity of metabolic enzymes are adjusted, and subsequently, levels of metabolites are altered to cope with the harmful effects of light or to prepare for reproduction, which is dependent on light in many cases. This review aims to give an overview on metabolic pathways impacted by light and to illustrate the physiological significance of light for fungi. We provide a basis for assessment whether a given metabolic pathway might be subject to regulation by light and how these properties can be exploited for improvement of biotechnological processes