An important but understudied class of human exposures is comprised of reactive electrophiles that cannot be measured in vivo because they are short lived. An avenue for assessing these meaningful exposures focuses on adducts from reactions with nucleophilic loci of blood proteins, particularly Cys34 of human serum albumin, which is the dominant scavenger of reactive electrophiles in serum. We developed an untargeted analytical scheme and bioinformatics pipeline for detecting, quantitating and annotating Cys34 adducts in tryptic digests of human serum/plasma. The pipeline interrogates tandem mass spectra to find signatures of the Cys34-containing peptide, obtains accurate masses of putative adducts, quantitates adduct levels relative to a ‘housekeeping peptide’, and annotates modifications based on a combination of retention time, accurate mass, elemental composition and database searches. We used the adductomics pipeline to characterize 43 adduct features in archived plasma from healthy human subjects and found several that were highly associated with smoking status, race and other covariates. Since smoking is a strong risk factor for cancer and cardiovascular disease, our ability to discover adducts that distinguish smokers from nonsmokers with untargeted adductomics indicates that the pipeline is suitable for use in epidemiologic studies. In fact, adduct features were both positively and negatively associated with smoking, indicating that some adducts arise from reactions between Cys34 and constituents of cigarette smoke (e.g. ethylene oxide and acrylonitrile) while others (Cys34 oxidation products and disulfides) appear to reflect alterations in the serum redox state that resulted in reduced adduct levels in smokers.

Age and primary tumor size were significant risk factors for postoperative distant metastases. Based on the findings in this study, we conclude that conservative management is sufficient for younger patients with minimally invasive FTC whose primary tumor is small.

Background Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. Results Using metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering . Conclusions Our proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens. Electronic supplementary material The online version of this article (10.1186/s12859-019-2871-9) contains supplementary material, which is available to authorized users.

Objective: The objective was to evaluate the clinical behavior and