Unexpected differential metabolic responses of Campylobacter jejuni to the abundant presence of glutamate and fucose.

INTRODUCTION: Campylobacter jejuni is the leading cause of foodborne bacterial enteritis in humans, and yet little is known in regard to how genetic diversity and metabolic capabilities among isolates affect their metabolic phenotype and pathogenicity. OBJECTIVES: For instance, the C. jejuni 11168 strain can utilize both L-fucose and L-glutamate as a carbon source, which provides the strain with a competitive advantage in some environments and in this study we set out to assess the metabolic response of C. jejuni 11168 to the presence of L-fucose and L-glutamate in the growth medium. METHODS: To achieve this, untargeted hydrophilic liquid chromatography coupled to mass spectrometry was used to obtain metabolite profiles of supernatant extracts obtained at three different time points up to 24 h. RESULTS: This study identified both the depletion and the production and subsequent release of a multitude of expected and unexpected metabolites during the growth of C. jejuni 11168 under three different conditions. A large set of standards allowed identification of a number of metabolites. Further mass spectrometry fragmentation analysis allowed the additional annotation of substrate-specific metabolites. The results show that C. jejuni 11168 upon L-fucose addition indeed produces degradation products of the fucose pathway. Furthermore, methionine was faster depleted from the medium, consistent with previously-observed methionine auxotrophy. CONCLUSIONS: Moreover, a multitude of not previously annotated metabolites in C. jejuni were found to be increased specifically upon L-fucose addition. These metabolites may well play a role in the pathogenicity of this C. jejuni strain.

Time-varying risks of infection in patients as they proceed through the phases of 'pre-RA': results from the Scottish Early RA inception cohort.

ObjectiveRheumatoid arthritis (RA) develops after progressing through sequential 'pre-RA' phases. The mechanisms driving progression from one phase to the next remain poorly understood. This study examined the longitudinal rates of community and hospital infections in patients during sequential stages of pre-RA and early arthritis. METHODS: The Scottish Early RA inception cohort recruited patients with newly diagnosed RA. Incidences of infection were determined from community antibiotic prescriptions and serious infections were determined by hospital discharge coding. Dates of diagnosis and symptom onset allowed identification of asymptomatic/symptomatic pre-RA and early arthritis eras to analyse infection rates over time compared with age- and sex-matched controls. RESULTS: The incidence rate ratio (IRR) seen in the period 0-6 months prior to symptom onset was 1.28 (95% CI 1.15 to 1.42). In 'symptomatic pre-RA', the IRR was 1.33 (95% CI 1.18 to 1.49) which persisted into 'early arthritis'. The rate of hospital admissions was numerically greater in 'pre-RA' and significantly greater in 'early arthritis' (IRR 1.82, 95% CI 1.32 to 2.46). CONCLUSION: Antibiotic risk is increased in patients with 'pre-RA' at least 6 months before symptoms develop, and this persists throughout the symptomatic pre-RA phase. Infections may be important in the mechanisms that drive progression to RA or be a manifestation of immune dysfunction (or both). These observations could inform safety and efficacy considerations for interventions in pre-RA to prevent progression. Patients with 'pre-RA' with recurrent antibiotic use may also be an identifiable 'high risk' group that could enrich the study population for intervention studies in pre-RA.

Classification of long-term condition patterns in rheumatoid arthritis and associations with adverse health events: a UK Biobank cohort study.

Purpose: We aimed to classify individuals with RA and ≥2 additional long-term conditions (LTCs) and describe the association between different LTC classes, number of LTCs and adverse health outcomes. Methods: We used UK Biobank participants who reported RA (n=5,625) and employed latent class analysis (LCA) to create classes of LTC combinations for those with ≥2 additional LTCs. Cox-proportional hazard and negative binomial regression were used to compare the risk of all-cause mortality, major adverse cardiac events (MACE), and number of emergency hospitalisations over an 11-year follow-up across the different LTC classes and in those with RA plus one additional LTC. Persons with RA without LTCs were the reference group. Analyses were adjusted for demographic characteristics, smoking, BMI, alcohol consumption and physical activity. Results: A total of 2,566 (46%) participants reported ≥2 LTCs in addition to RA. This involved 1,138 distinct LTC combinations of which 86% were reported by ≤2 individuals. LCA identified 5 morbidity-classes. The distinctive condition in the class with the highest mortality was cancer (class 5; HR 2.66 95%CI (1.91-3.70)). The highest MACE (HR 2.95 95%CI (2.11-4.14)) and emergency hospitalisations (rate ratio 3.01 (2.56-3.54)) were observed in class 3 which comprised asthma, COPD & CHD. There was an increase in mortality, MACE and emergency hospital admissions within each class as the number of LTCs increased. Conclusions: The risk of adverse health outcomes in RA varied with different patterns of multimorbidity. The pattern of multimorbidity should be considered in risk assessment and formulating management plans in patients with RA.

Association between risk, duration and cause of hospitalisations in people with rheumatoid arthritis and multimorbidity in the UK Biobank and Scottish Early Rheumatoid Arthritis (SERA) cohorts: Longitudinal observational study.

OBJECTIVES: To investigate association between presence of multimorbidity in people with established and early rheumatoid arthritis (RA) and risk, duration and cause of hospitalisations. DESIGN: Longitudinal observational study. SETTING: UK Biobank, population-based cohort recruited between 2006 and 2010, and the Scottish Early Rheumatoid Arthritis (SERA), inception cohort recruited between 2011 and 2015. Both linked to mortality and hospitalisation data. PARTICIPANTS: 4757 UK Biobank participants self-reporting established RA; 825 SERA participants with early RA meeting the 2010 ACR/EULAR classification criteria. Participants stratified by number of long-term conditions (LTCs) in addition to RA (RA only, RA + 1 LTC and RA + ≥ 2 LTCs) and matched to five non-RA controls. MAIN OUTCOME MEASURES: Number and duration of hospitalisations and their causes. Incidence rate ratios (IRR) and 95% confidence intervals (CI) calculated using negative binomial regression models. RESULTS: Participants with RA + ≥ 2 LTCs experienced higher hospitalisation rates compared to those with RA alone (UK Biobank: IRR 2.10, 95% CI 1.91 to 2.30; SERA: IRR 1.74, 95% CI 1.23 to 2.48). Total duration of hospitalisation in RA + ≥ 2 LTCs was also higher (UK Biobank: IRR 2.48, 95% CI 2.17 to 2.84; SERA: IRR 1.90, 95% CI 1.07 to 3.38) than with RA alone. Rate and total duration of hospitalisations was higher in UK Biobank RA participants than non-RA controls with equivalent number of LTCs. Hospitalisations for respiratory infection were higher in early RA than established RA and were the commonest cause of hospital admission in early RA. CONCLUSIONS: Participants with established or early RA with multimorbidity experienced a higher rate and duration of hospitalisations than those with RA alone and with non-RA matched controls.

The impact of autoantibodies against citrullinated, carbamylated, and acetylated peptides on radiographic progression in patients with new-onset rheumatoid arthritis: an observational cohort study.

BACKGROUND: A range of anti-modified protein antibodies (AMPAs) are associated with rheumatoid arthritis. We aimed to assess the relationship between AMPA profiles and radiographic progression in patients with new-onset rheumatoid arthritis. METHODS: In this cohort study, we obtained samples and data from the Scottish Early Rheumatoid Arthritis (SERA) inception cohort and biobank, which recruited patients with new-onset rheumatoid arthritis or undifferentiated arthritis who had at least one swollen joint from 20 hospitals across Scotland. AMPAs in plasma samples were measured by ELISAs at baseline. Paired radiographs of the hands and feet were taken at baseline and at 1 year and were scored with the Sharp-van der Heijde (SvH) method. We calculated differences in radiographic progression using estimated marginal mean changes between baseline and 1 year, with the baseline values of radiographic variables, rheumatoid factor, sex, age at recruitment, symptom duration, and Disease Activity Score 28 with C-reactive protein included as covariates. FINDINGS: Between March 1, 2011, and April, 30, 2015, 1073 patients were recruited to the SERA study. 362 patients with rheumatoid arthritis were included in our study and had their AMPA profiles determined. Patients were grouped into four main autoantibody profiles by reactivities to post-translational modifications: single positivity for anti-citrullinated peptide antibodies (ACPAs; 73 [20%]); double positivity for ACPAs and anti-acetylated peptide antibodies (AAPAs; 45 [12%]); triple positivity for ACPAs, AAPAs, and anti-carbamylated peptide antibodies (151 [42%]); and AMPA negativity (74 [20%]). 19 (5%) patients were in one of the minor autoantibody groups. Of the 233 patients with both antibody data and radiographs of sufficient quality, triple-positive patients had more radiographic progression between baseline and 12 months (estimated mean change in total SvH score 1·8, 95% CI 0·9-2·6, SE 0·4) than did single-positive patients (0·5, 0·1-1·0, 0·2; estimated mean difference in the total change in SvH score 1·2, 95% CI 0·1-2·4, SE 0·5). There was no difference in radiographic progression between single positive patients and AMPA negative patients (estimated mean change in total SvH score 0·7, 95% CI 0·1-1·4, SE 0·3; estimated mean difference in the total change in SvH score -0·2, 95% CI -1·1 to 0·7, SE 0·4). INTERPRETATION: This study suggests that the optimal prediction of future rates of radiographic progression in patients with rheumatoid arthritis will require an assessment of autoantibodies against multiple post-translationally modified proteins or peptides. FUNDING: The EU FP7 HEALTH programme, the Scottish Translational Medicine Research Collaboration, and the Chief Scientist Office Scotland.

MEROPS: the peptidase database.

Peptidases (proteolytic enzymes or proteases), their substrates and inhibitors are of great relevance to biology, medicine and biotechnology. The MEROPS database (http://merops.sanger.ac.uk) aims to fulfil the need for an integrated source of information about these. The organizational principle of the database is a hierarchical classification in which homologous sets of peptidases and protein inhibitors are grouped into protein species, which are grouped into families and in turn grouped into clans. Important additions to the database include newly written, concise text annotations for peptidase clans and the small molecule inhibitors that are outside the scope of the standard classification; displays to show peptidase specificity compiled from our collection of known substrate cleavages; tables of peptidase-inhibitor interactions; and dynamically generated alignments of representatives of each protein species at the family level. New ways to compare peptidase and inhibitor complements between any two organisms whose genomes have been completely sequenced, or between different strains or subspecies of the same organism, have been devised.

The MEROPS batch BLAST: a tool to detect peptidases and their non-peptidase homologues in a genome.

Many of the 181 families of peptidases contain homologues that are known to have functions other than peptide bond hydrolysis. Distinguishing an active peptidase from a homologue that is not a peptidase requires specialist knowledge of the important active site residues, because replacement or lack of one of these catalytic residues is an important clue that the homologue in question is unlikely to hydrolyse peptide bonds. Now that the rate at which proteins are characterized is outstripped by the rate that genome sequences are determined, many genes are being incorrectly annotated because only sequence similarity is taken into consideration. We present a tool called the MEROPS batch BLAST which not only performs a comparison against the MEROPS sequence collection, but also does a pair-wise alignment with the closest homologue detected and calculates the position of the active site residues. A non-peptidase homologue can be distinguished by the absence or unacceptable replacement of any of these residues. An analysis of peptidase homologues in the genome of the bacterium Erythrobacter litoralis is presented as an example.

MEROPS: the peptidase database.

Peptidases (proteolytic enzymes) and their natural, protein inhibitors are of great relevance to biology, medicine and biotechnology. The MEROPS database (http://merops.sanger.ac.uk) aims to fulfil the need for an integrated source of information about these proteins. The organizational principle of the database is a hierarchical classification in which homologous sets of proteins of interest are grouped into families and the homologous families are grouped in clans. The most important addition to the database has been newly written, concise text annotations for each peptidase family. Other forms of information recently added include highlighting of active site residues (or the replacements that render some homologues inactive) in the sequence displays and BlastP search results, dynamically generated alignments and trees at the peptidase or inhibitor level, and a curated list of human and mouse homologues that have been experimentally characterized as active. A new way to display information at taxonomic levels higher than species has been devised. In the Literature pages, references have been flagged to draw attention to particularly 'hot' topics.

A novel metabolomic approach used for the comparison of Staphylococcus aureus planktonic cells and biofilm samples.

INTRODUCTION: Bacterial cell characteristics change significantly during differentiation between planktonic and biofilm states. While established methods exist to detect and identify transcriptional and proteomic changes, metabolic fluctuations that distinguish these developmental stages have been less amenable to investigation. OBJECTIVES: The objectives of the study were to develop a robust reproducible sample preparation methodology for high throughput biofilm analysis and to determine differences between Staphylococcus aureus in planktonic and biofilm states. METHODS: The method uses bead beating in a chloroform/methanol/water extraction solvent to both disrupt cells and quench metabolism. Verification of the method was performed using liquid-chromatography-mass spectrometry. Raw mass-spectrometry data was analysed using an in-house bioinformatics pipe-line incorporating XCMS, MzMatch and in-house R-scripts, with identifications matched to internal standards and metabolite data-base entries. RESULTS: We have demonstrated a novel mechanical bead beating method that has been optimised for the extraction of the metabolome from cells of a clinical Staphylococcus aureus strain existing in a planktonic or biofilm state. This high-throughput method is fast and reproducible, allowing for direct comparison between different bacterial growth states. Significant changes in arginine biosynthesis were identified between the two cell populations. CONCLUSIONS: The method described herein represents a valuable tool in studying microbial biochemistry at a molecular level. While the methodology is generally applicable to the lysis and extraction of metabolites from Gram positive bacteria, it is particularly applicable to biofilms. Bacteria that exist as a biofilm are shown to be highly distinct metabolically from their 'free living' counterparts, thus highlighting the need to study microbes in different growth states. Metabolomics can successfully distinguish between a planktonic and biofilm growth state. Importantly, this study design, incorporating metabolomics, could be optimised for studying the effects of antimicrobials and drug modes of action, potentially providing explanations and mechanisms of antibiotic resistance and to help devise new antimicrobials.