Acute stress reduces population-level metabolic and proteomic variation.

BACKGROUND: Variation in omics data due to intrinsic biological stochasticity is often viewed as a challenging and undesirable feature of complex systems analyses. In fact, numerous statistical methods are utilized to minimize the variation among biological replicates. RESULTS: We demonstrate that the common statistics relative standard deviation (RSD) and coefficient of variation (CV), which are often used for quality control or part of a larger pipeline in omics analyses, can also be used as a metric of a physiological stress response. Using an approach we term Replicate Variation Analysis (RVA), we demonstrate that acute physiological stress leads to feature-wide canalization of CV profiles of metabolomes and proteomes across biological replicates. Canalization is the repression of variation between replicates, which increases phenotypic similarity. Multiple in-house mass spectrometry omics datasets in addition to publicly available data were analyzed to assess changes in CV profiles in plants, animals, and microorganisms. In addition, proteomics data sets were evaluated utilizing RVA to identify functionality of reduced CV proteins. CONCLUSIONS: RVA provides a foundation for understanding omics level shifts that occur in response to cellular stress. This approach to data analysis helps characterize stress response and recovery, and could be deployed to detect populations under stress, monitor health status, and conduct environmental monitoring.

Proteomic Analysis of Methanococcus voltae Grown in the Presence of Mineral and Nonmineral Sources of Iron and Sulfur.

Iron sulfur (Fe-S) proteins are essential and ubiquitous across all domains of life, yet the mechanisms underpinning assimilation of iron (Fe) and sulfur (S) and biogenesis of Fe-S clusters are poorly understood. This is particularly true for anaerobic methanogenic archaea, which are known to employ more Fe-S proteins than other prokaryotes. Here, we utilized a deep proteomics analysis of Methanococcus voltae A3 cultured in the presence of either synthetic pyrite (FeS2) or aqueous forms of ferrous iron and sulfide to elucidate physiological responses to growth on mineral or nonmineral sources of Fe and S. The liquid chromatography-mass spectrometry (LCMS) shotgun proteomics analysis included 77% of the predicted proteome. Through a comparative analysis of intra- and extracellular proteomes, candidate proteins associated with FeS2 reductive dissolution, Fe and S acquisition, and the subsequent transport, trafficking, and storage of Fe and S were identified. The proteomic response shows a large and balanced change, suggesting that M. voltae makes physiological adjustments involving a range of biochemical processes based on the available nutrient source. Among the proteins differentially regulated were members of core methanogenesis, oxidoreductases, membrane proteins putatively involved in transport, Fe-S binding ferredoxin and radical S-adenosylmethionine proteins, ribosomal proteins, and intracellular proteins involved in Fe-S cluster assembly and storage. This work improves our understanding of ancient biogeochemical processes and can support efforts in biomining of minerals. IMPORTANCE Clusters of iron and sulfur are key components of the active sites of enzymes that facilitate microbial conversion of light or electrical energy into chemical bonds. The proteins responsible for transporting iron and sulfur into cells and assembling these elements into metal clusters are not well understood. Using a microorganism that has an unusually high demand for iron and sulfur, we conducted a global investigation of cellular proteins and how they change based on the mineral forms of iron and sulfur. Understanding this process will answer questions about life on early earth and has application in biomining and sustainable sources of energy.

Electronic Cigarette Refill Liquids: Nicotine Content, Presence of Child-Resistant Packaging, and in-Shop Compounding.

PURPOSE: To expand on our 2015 study of the nicotine content accuracy of e-liquids, including salts, and the presence of child-resistant packaging. We also describe compounding in shop (CIS). DESIGN AND METHODS: We analyzed samples from 35 shops. CIS processing was observed. Descriptive statistics summarized the data, and inference was performed. RESULTS: Actual nicotine content was significantly less than the identified content, on average, with a mean percent deviation 34.0% below the identified content. Only 3.8% of the samples' actual nicotine content was within 10% of the identified content; the maximum deviation was 213.2%. Of eight uniquely packaged samples, including designs resembling pop cans, ice cream cones, etc., the mean percent deviation was -39.6%; none were within 10% of the identified content. Eight shops compounded samples. After removing outlier values, significant differences were found in the percent deviations between the CIS and non-CIS free-base samples. A significantly higher percentage of CIS samples had nicotine content > 10% above the identified content, and none were within 10%. One shop visually estimated the nicotine quantities to add, e-liquids were not always relabeled to reflect new nicotine levels, and protective materials were not always worn during compounding. Child-resistant packaging was not present for one third of the samples. CONCLUSIONS: Labeling of nicotine content in e-liquids remains inaccurate, child-resistant packaging is inconsistent, and CIS is problematic. Effective e-liquid regulation is needed to protect public health. PRACTICE IMPLICATIONS: Nurses should educate families about the serious health risks of e-liquids and advocate for increased e-liquid regulations.

Metabolic Implications of Using BioOrthogonal Non-Canonical Amino Acid Tagging (BONCAT) for Tracking Protein Synthesis.

BioOrthogonal Non-Canonical Amino acid Tagging (BONCAT) is a powerful tool for tracking protein synthesis on the level of single cells within communities and whole organisms. A basic premise of BONCAT is that the non-canonical amino acids (NCAA) used to track translational activity do not significantly alter cellular physiology. If the NCAA would induce changes in the metabolic state of cells, interpretation of BONCAT studies could be challenging. To address this knowledge-gap, we have used a global metabolomics analyses to assess the intracellular effects of NCAA incorporation. Two NCAA were tested: L-azidohomoalanine (AHA) and L-homopropargylglycine (HPG); L-methionine (MET) was used as a minimal stress baseline control. Liquid Chromatography Mass Spectrometry (LC-MS) and Nuclear Magnetic Resonance (NMR) were used to characterize intracellular metabolite profiles of Escherichia coli cultures, with multivariate statistical analysis using XCMS and MetaboAnalyst. Results show that doping with NCAA induces metabolic changes, however, the metabolic impact was not dramatic. A second set of experiments in which cultures were placed under mild stress to simulate real-world environmental conditions showed a more consistent and more robust perturbation. Pathways that changed include amino acid and protein synthesis, choline and betaine, and the TCA cycle. Globally, these changes were statistically minor, indicating that NCAA are unlikely to exert a significant impact on cells during incorporation. Our results are consistent with previous reports of NCAA doping under replete conditions and extend these results to bacterial growth under environmentally relevant conditions. Our work highlights the power of metabolomics studies in detecting cellular response to growth conditions and the complementarity of NMR and LCMS as omics tools.