Nuclear Magnetic Resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma

RCC usually develops and progresses asymptomatically and, when detected, it is frequently at advanced stages and metastatic, entailing a dismal prognosis. Therefore, there is an obvious demand for new strategies enabling an earlier diagnosis. The importance of metabolic rearrangements for carcinogenesis unlocked a new approach for cancer research, catalyzing the increased use of metabolomics. The present study aimed the NMR metabolic profiling of RCC in urine samples from a cohort of RCC patients (n = 42) and controls (n = 49). The methodology entailed variable selection of the spectra in tandem with multivariate analysis and validation procedures. The retrieval of a disease signature was preceded by a systematic evaluation of the impacts of subject age, gender, BMI, and smoking habits. The impact of confounders on the urine metabolomics profile of this population is residual compared to that of RCC. A 32-metabolite/resonance signature descriptive of RCC was unveiled, successfully distinguishing RCC patients from controls in principal component analysis. This work demonstrates the value of a systematic metabolomics workflow for the identification of robust urinary metabolic biomarkers of RCC. Future studies should entail the validation of the 32-metabolite/resonance signature found for RCC in independent cohorts, as well as biological validation of the putative hypotheses advanced

Construction of a Heterologous Expression Vector for Plantaricin F, One of the Peptides Constituting the Two-Peptide Bacteriocin Plantaricin EF

Certain species of lactic acid bacteria produce and secrete bacteriocins, which are ribosomally synthesized antimicrobial peptides. These peptides recognize and kill target cells by rendering their membrane permeable for various small molecules. There has been an increased interest in lactic acid bacteria bacteriocins because of their potential use as food additives and pharmaceuticals. Plantaricin EF is a two-peptide bacteriocin produced by the lactic acid bacteria Lactobacillus plantarum C11. The two peptides constituting this bacteriocin are called Plantaricin E (PlnE) and Plantaricin F (PlnF). For optimal antimicrobial effect, the two peptides have to be present in equal molar amounts. Circular dichroism studies suggest that the peptides interact physically with each other upon contact with target membranes. The inter-peptide interactions between PlnE and PlnF are thought to be mediated by GxxxG motifs, which are located in their amphiphilic α-helical region. GxxxG motifs are known to confer helix-helix interactions between membrane-inserted polypeptides. In addition, tyrosine and tryptophan residues tend to be prominent in trans-membrane proteins, especially in the parts of proteins exposed to the interface region of the membrane. It is believed that these aromatic residues enhance stability because of interactions with membrane-lipids in the interface region. To study the importance of GxxxG motifs in helix-helix interactions between PlnE and PlnF, the glycine-residues in PlnF have been altered by in vitro site-directed mutagenesis. The tyrosine and tryptophan residues in PlnF were also altered by in vitro site-directed mutagenesis, in order to investigate how PlnF will orient itself in target cell membranes. In order to do this, the gene encoding PlnF, plnF, and the gene encoding its cognate immunity protein, plnI, have been connected to the sakacin P leader-sequence, and cloned into the shuttle-vector pLPV111. The vector was transformed into the lactic acid bacteria Lactobacillus sake Lb790 containing the plasmid pSAK20. pLPV111 and pSAK20 are part of a heterologous expression system designed for expression of the many different bacteriocin. By using this expression system, PlnF is expressed separate from PlnE. This makes it easier to isolate and purify PlnF, as well as constructing and purifying the mutant version of PlnF, for subsequent structure and function analysis

Association between speeding and use of alcohol and medicinal and illegal drugs and involvement in road traffic crashes among motor vehicle drivers

Objective: The objective of this study was to study the association between self-reported road traffic crashes (RTCs) and recent use of alcohol and medicinal and illicit drug use and self-reported speeding in the previous 2 years. Methods: During the period from April 2016 to April 2017, drivers of cars, vans, motorcycles, and mopeds were stopped in a Norwegian roadside survey performed in collaboration with the police. Participation was voluntary and anonymous. The drivers were asked to deliver an oral fluid sample (mixed saliva), which was analyzed for alcohol and 39 illicit and medicinal drugs and metabolites. In addition, data on age, sex, and self-reported speeding tickets and RTCs during the previous 2 years were collected. Results: A total of 5,031 participants were included in the study, and 4.9% tested positive for the use of one or more illicit or medicinal drugs or alcohol. We found a significant, positive association between the use of cannabis and RTC involvement (odds ratio [OR] = 1.93; 95% confidence interval [CI], 1.05–3.57; P = 0.035) and also between previous speeding tickets and RTC involvement (OR = 1.39; 95% CI, 1.08–1.80; P = 0.012). In addition, older age groups were found to have a significant, negative association with RTC involvement, with ORs equal to or less than 0.49, when using the age group 16–24 as reference. Conclusion: Speeding, as an indicator of risk behavior, and the use of cannabis were associated with previous RTC involvement, whereas increasing age was significantly associated with lower risk. This is consistent with previous studies on RTCs

The Association between the Alcohol Biomarker Phosphatidylethanol (PEth) and Self-Reported Alcohol Consumption among Russian and Norwegian Medical Patients

Abstract Aims Valid measures to identify harmful alcohol use are important. Alcohol Use Disorders Identification Test (AUDIT) is a validated questionnaire used to self-report harmful drinking in several cultures and settings. Phosphatidylethanol 16:0/18:1 (PEth) is a direct alcohol biomarker measuring alcohol consumption levels. The aim of this study was to investigate how PEth levels correlate with AUDIT-QF and weekly grams of alcohol consumed among patients in two urban hospitals. In addition, we wanted to investigate the predictive value of PEth in identifying harmful alcohol use as defined by AUDIT-QF and weekly grams of alcohol cutoffs. Methods A cross-sectional study comprising acute medically ill patients with measurable PEth levels (≥0.030 μM) admitted to two urban hospitals in Oslo, Norway (N = 931) and Moscow, Russia (N = 953) was conducted using PEth concentrations in whole blood, sociodemographic data and AUDIT-QF questionnaires. Results PEth levels from patients with measurable PEth were found to be positively correlated with AUDIT-QF scores, with PEth cutpoints of 0.128 μM (Oslo) and 0.270 μM (Moscow) providing optimal discrimination for harmful alcohol use defined by AUDIT-QF (the difference between cities probably reflecting different national drinking patterns in QF). When converting AUDIT-QF into weekly grams of alcohol consumed, the predictive value of PEth improved, with optimal PEth cutpoints of 0.327 (Oslo) and 0.396 (Moscow) μM discriminating between harmful and non-harmful alcohol use as defined in grams (≥350 grams/week). Conclusions By using PEth levels and converting AUDIT-QF into weekly grams of alcohol it was possible to get an improved rapid and sensitive determination of harmful alcohol use among hospitalized patients

Metabolomic profiles across the three study groups.

<p>(a) Mean metabolite levels within each cluster for the three groups. The error bars show standard error of the mean (SEM), and cluster LC3 is the only significant cluster. Nominal p-values are shown (one-way ANOVA). (b) Profiles of selected representative metabolites from different clusters in all three groups. The metabolite levels are shown as beanplots [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134184#pone.0134184.ref027" target="_blank">27</a>], which provide information on the mean level (solid line), individual data point (short black lines), and the density of the distribution.</p

Mean metabolite-levels for significant molecular lipids and polar metabolites.

<p>The metabolite-levels are referred to as mean±SD. Abbreviations: PE phosphatidylethanolamine, PC phosphatidylcholine, TG triglyceride. P-values are given as one-way ANOVA. Pairwise comparisons with Tukey’s range test corrected p-values are labeled:</p><p>^aP<0.05 between G+/LVH+ and control group, given as Tukey’s range test.</p><p>^bP<0.05 between G+/LVH+ and G+/LVH-, given as Tukey’s range test.</p><p>*Not significant</p><p>Mean metabolite-levels for significant molecular lipids and polar metabolites.</p

Echocardiographic parameters for the study subjects.

<p>The parameters are referred to as mean ± SD. Abbreviations: LVEDD left ventricular (LV) internal diameter in diastole, LVESD LV internal diameter in systole, LVEF left ventricular ejection fraction, MWT maximum wall thickness, LV Mass left ventricular mass, LAD left atrium diameter, LVOT gradient left ventricular outflow tract gradient, MV E velocity mitral valve E-wave peak velocity, TDI Lateral Sm TDI peak systolic velocity at lateral mitral annulus, TDI Septal Sm TDI peak systolic velocity at septal mitral annulus, TDI Septal Em TDI peak early diastolic velocity at septal mitral annulus, TDI Lateral Em peak early diastolic velocity at lateral mitral annulus. P-values are given as one-way ANOVA. Pairwise comparisons with Bonferroni corrected p-values are labeled:</p><p>^aP<0.05 between G+/LVH+ and control group,</p><p>^bP<0.05 between G+/LVH+ and G+/LVH-,</p><p>^cP<0.05 between G+/LVH- and control group.</p><p>Echocardiographic parameters for the study subjects.</p

Description of metabolite clusters from the lipidomics (LC) and metabolomics (MC) platforms.

<p>Abbreviations: PC phosphatidylcholine, PE phosphatidylethanolamine, TG triglyceride, ChoE cholesteryl ester, SM sphingomyelin, PUFA polyunsaturated fatty acid, lysoPC lysophosphatidylcholine.</p><p>Description of metabolite clusters from the lipidomics (LC) and metabolomics (MC) platforms.</p