Abstract

Cigarette smoke (CS) is an important source of morbidity and early mortality worldwide. Besides causing various life-threatening diseases, CS is also known to cause hypoxia. Chronic hypoxia would induce early aging and premature death. Continuation of smoking during pregnancy is a known risk for the unborn child. Although carbon monoxide (CO) is considered to be a cause of hypoxia, the effect of other component(s) of CS on hypoxia is not known. Here we show by immunoblots and mass spectra analyses that in smoker’s blood p -benzoquinone (p -BQ) derived from CS forms covalent adducts with cysteine 93 residues in both the β chains of hemoglobin (Hb) producing Hb-p -BQ adducts. UV–vis spectra and CD spectra analyses show that upon complexation with p -BQ the structure of Hb is altered. Compared to nonsmoker’s Hb, the content of α-helix decreased significantly in smoker’s Hb (p = 0.0224). p -BQ also induces aggregation of smoker’s Hb as demonstrated by SDS-PAGE, dynamic light scattering and atomic force microscopy. Alteration of Hb structure in smoker’s blood is accompanied by reduced oxygen binding capacity. Our results provide the first proof that p -BQ is a cause of hypoxia in smokers. We also show that although both p -BQ and CO are responsible for causing hypoxia in smokers, exposure to CO further affects the function over and above that produced by Hb-p -BQ adduct.

Abstract

The exposome is a complement to the genome that includes non-genetic causes of disease. Multiple definitions are available, with salient points being global inclusion of exposures and behaviors, and cumulative integration of associated biologic responses. As such, the concept is both refreshingly simple and dauntingly complex. This article reviews high-resolution metabolomics (HRM) as an affordable approach to routinely analyze samples for a broad spectrum of environmental chemicals and biologic responses. HRM has been successfully used in multiple exposome research paradigms and is suitable to implement in a prototype universal exposure surveillance system. Development of such a structure for systematic monitoring of environmental exposures is an important step toward sequencing the exposome because it builds upon successes of exposure science, naturally connects external exposure to body burden and partitions the exposome into workable components. Practical results would be repositories of quantitative data on chemicals according to geography and biology. This would support new opportunities for environmental health analysis and predictive modeling. Complementary approaches to hasten development of exposome theory and associated biologic response networks could include experimental studies with model systems, analysis of archival samples from longitudinal studies with outcome data and study of relatively short-lived animals, such as household pets (dogs and cats) and non-human primates (common marmoset). International investment and cooperation to sequence the human exposome will advance scientific knowledge and also provide an important foundation to control adverse environmental exposures to sustain healthy living spaces and improve prediction and management of disease.

Abstract

Background The massive characterization of host-associated and environmental microbial communities has represented a real breakthrough in the life sciences in the last years. In this context, metaproteomics specifically enables the transition from assessing the genomic potential to actually measuring the functional expression of a microbiome. However, significant research efforts are still required to develop analysis pipelines optimized for metaproteome characterization. Results This work presents an efficient analytical pipeline for shotgun metaproteomic analysis, combining bead-beating/freeze-thawing for protein extraction, filter-aided sample preparation for cleanup and digestion, and single-run liquid chromatography-tandem mass spectrometry for peptide separation and identification. The overall procedure is more time-effective and less labor-intensive when compared to state-of-the-art metaproteomic techniques. The pipeline was first evaluated using mock microbial mixtures containing different types of bacteria and yeasts, enabling the identification of up to over 15,000 non-redundant peptide sequences per run with a linear dynamic range from 104 to 108 colony-forming units. The pipeline was then applied to the mouse fecal metaproteome, leading to the overall identification of over 13,000 non-redundant microbial peptides with a false discovery rate of "1%, belonging to over 600 different microbial species and 250 functionally relevant protein families. An extensive mapping of the main microbial metabolic pathways actively functioning in the gut microbiome was also achieved. Conclusions The analytical pipeline presented here may be successfully used for the in-depth and time-effective characterization of complex microbial communities, such as the gut microbiome, and represents a useful tool for the microbiome research community. Electronic supplementary material The online version of this article (doi:10.1186/s40168-014-0049-2) contains supplementary material, which is available to authorized users. Document type: Article