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.

Examining Pathways of Iron and Sulfur Acquisition, Trafficking, Deployment, and Storage in Mineral-Grown Methanogen Cells.

Methanogens have a high demand for iron (Fe) and sulfur (S); however, little is known of how they acquire, deploy, and store these elements and how this, in turn, affects their physiology. Methanogens were recently shown to reduce pyrite (FeS2), generating aqueous iron sulfide (FeSaq) clusters that are likely assimilated as a source of Fe and S. Here, we compared the phenotypes of Methanococcus voltae grown with FeS2 or ferrous iron [Fe(II)] and sulfide (HS-). FeS2-grown cells are 33% smaller yet have 193% more Fe than Fe(II)/HS--grown cells. Whole-cell electron paramagnetic resonance revealed similar distributions of paramagnetic Fe, although FeS2-grown cells showed a broad spectral feature attributed to intracellular thioferrate-like nanoparticles. Differential proteomic analyses showed similar expression of core methanogenesis enzymes, indicating that Fe and S source does not substantively alter the energy metabolism of cells. However, a homolog of the Fe(II) transporter FeoB and its putative transcriptional regulator DtxR were up-expressed in FeS2-grown cells, suggesting that cells sense Fe(II) limitation. Two homologs of IssA, a protein putatively involved in coordinating thioferrate nanoparticles, were also up-expressed in FeS2-grown cells. We interpret these data to indicate that, in FeS2-grown cells, DtxR cannot sense Fe(II) and therefore cannot downregulate FeoB. We suggest this is due to the transport of Fe(II) complexed with sulfide (FeSaq), leading to excess Fe that is sequestered by IssA as a thioferrate-like species. This model provides a framework for the design of targeted experiments aimed at further characterizing Fe acquisition and homeostasis in M. voltae and other methanogens. IMPORTANCE FeS2 is the most abundant sulfide mineral in the Earth's crust and is common in environments inhabited by methanogenic archaea. FeS2 can be reduced by methanogens, yielding aqueous FeSaq clusters that are thought to be a source of Fe and S. Here, we show that growth of Methanococcus voltae on FeS2 results in smaller cell size and higher Fe content per cell, with Fe likely stored intracellularly as thioferrate-like nanoparticles. Fe(II) transporters and storage proteins were upregulated in FeS2-grown cells. These responses are interpreted to result from cells incorrectly sensing Fe(II) limitation due to assimilation of Fe(II) as FeSaq. These findings have implications for our understanding of how Fe/S availability influences methanogen physiology and the biogeochemical cycling of these elements.

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.

Implementing professional behaviour change in teams under pressure: results from phase one of a prospective process evaluation (the Implementing Nutrition Screening in Community Care for Older People (INSCCOPe) project).

OBJECTIVES: To evaluate the implementation of a new procedure for screening and treatment of malnutrition for older people in community settings and to identify factors promoting or inhibiting its implementation as a routine aspect of care. DESIGN: Prospective process evaluation using mixed methods with pre/post-implementation measures. SETTING AND PARTICIPANTS: Community teams (nursing and allied health professionals) within a UK National Health Service Community Trust. 73 participants were recruited, of which 32 completed both pre-implemetation and post-implementation surveys. MAIN OUTCOME MEASURES: NoMAD survey for pre-post-intervention measures; telephone interviews exploring participant experiences and wider organisational/contextual processes. METHODS: Data prior to implementation of training, baseline (T0-survey and telephone interview) and 2 months following training (T1-follow-up survey). Quantitative data described using frequency tables reporting team type, healthcare provider role group and total study sample; analysis using Wilcoxon rank-sum (subgroup comparison) and Wilcoxon signed-rank (within-group observation point comparison) tests. Qualitative interview data (audio and transcription) analysed through directed content analysis using normalisation process theory. RESULTS: High support for nutrition screening and treatment indicated by participants. Concerns expressed around logistical, organisational and specialist dietetic support. Pre-post-training measures indicated a positive impact of training on knowledge of the new procedure; however, most implementation measures saw no significant changes between time points or between subgroups (training participants vs non-participants). Implementation barriers included the following: high levels of training non-completion; vulnerability to attrition of trained staff; lack of monitoring of post-intervention compliance and lack of access to dietetic support. CONCLUSION: Greater support necessary to support implementation in relation to monitoring of training completion, and organisational support for nutrition screening and treatment activity. Recommended changes to implementation design are as follows: appointment of a key person to support and monitor procedure compliance; adoption of training as an e-learning module within the existing organisational platform to increase participation in changeable working conditions.

What factors promote or inhibit implementation of a new procedure for screening and treatment of malnutrition in community settings? A prospective process evaluation of the Implementing Nutrition Screening in Community Care for Older People (INSCCOPe) project (UK).

INTRODUCTION: Malnutrition remains underdetected, undertreated and often overlooked by those working with older people in primary care in the UK. A new procedure for screening and treatment of malnutrition is currently being implemented by a large National Health Service (NHS) trust in England, incorporating a programme of training for staff working within Integrated Community Teams and Older People's Mental Health teams. Running in parallel, the Implementing Nutrition Screening in Community Care for Older People process evaluation study explores factors that may promote or inhibit its implementation and longer term embedding in routine care, with the aim of optimising sustainability and scalability. METHODS AND ANALYSIS: Implementation will be assessed through observation of staff within a single area of the trust, in addition to the procedure development and delivery group (PDDG). Data collection will occur at three observation points: prior to implementation of training, baseline (T0); 2 months following training (T1); and 8 months following training (T2). Observation points will consist of a survey and follow-up semistructured telephone interview with staff. Investigation of the PDDG will involve: observations of discussions around development of the procedure; semistructured telephone interviews prior to implementation, and at 6 months following implementation. Quantitative data will be described using frequency tables reporting by team type, healthcare provider role group, and total study sample (Wilcoxon rank-sum and Wilcoxon signed-rank tests may also be conducted if appropriate. Audio and transcription data will be analysed using Nomarlization Process Theory as a framework for deductive thematic analysis (using the NVIVO CAQDAS software package). ETHICS AND DISSEMINATION: Ethical approval for the study has been granted through institutional ethical review (Bournemouth University); NHS Research Ethics committee approval was not required. Dissemination will occur through presentations to academic and practitioner audiences and publication results in peer-reviewed academic journals.