Novel plasma biomarker of atenolol-induced hyperglycemia identified through a metabolomics-genomics integrative approach

Introduction

While atenolol is an effective antihypertensive agent, its use is also associated with adverse events including hyperglycemia and incident diabetes that may offset the benefits of blood pressure lowering. By combining metabolomic and genomic data acquired from hypertensive individuals treated with atenolol, it may be possible to better understand the pathways that most impact the development of an adverse glycemic state.

Objective

To identify biomarkers that can help predict susceptibility to blood glucose excursions during exposure to atenolol.

Methods

Plasma samples acquired from 234 Caucasian participants treated with atenolol in the Pharmacogenomic Evaluation of Antihypertensive Responses trial were analyzed by gas chromatography Time-Of-Flight Mass Spectroscopy. Metabolomics and genomics data were integrated by first correlating participant’s metabolomic profiles to change in glucose after treatment with atenolol, and then incorporating genotype information from genes involved in metabolite pathways associated with glucose response.

Results

Our findings indicate that the baseline level of β-alanine was associated with glucose change after treatment with atenolol (Q = 0.007, β = 2.97 mg/dL). Analysis of genomic data revealed that carriers of the G allele for SNP rs2669429 in gene DPYS, which codes for dihydropyrimidinase, an enzyme involved in β-alanine formation, had significantly higher glucose levels after treatment with atenolol when compared with non-carriers (Q = 0.05, β = 2.76 mg/dL). This finding was replicated in participants who received atenolol as an add-on therapy (P = 0.04, β = 1.86 mg/dL).

Conclusion

These results suggest that β-alanine and rs2669429 may be predictors of atenolol-induced hyperglycemia in Caucasian individuals and further investigation is warranted.

     

Metabolomic profiling in follicular fluid of patients with infertility-related deep endometriosis

Introduction

Endometriosis is an estrogen-dependent gynecological disease that causes infertility, and potential metabolomic biomarkers related to ovarian endometriosis and poor outcomes after assisted reproductive treatments are still lacking.

Objectives

The present study analyzed the metabolomic profiling of follicular fluid samples from 40 patients undergoing in vitro fertilization.

Methods

The follicular fluid samples were classified as controls (n = 22) and endometriosis patients (n = 18). The samples were submitted to Bligh and Dyer protocol followed by metabolomics analysis by ultra-performance liquid chromatography mass spectrometry. Clinical data was assessed by Students’ T-test and metabolomics data was analyzed by multivariate statistics by MetaboAnalyst 3.0 to obtain intrinsic characteristics that allowed for groups discrimination. The Receiver Operating Characteristic curve was carried out for the proposed biomarkers, aiming to determine their specificity and sensitivity, as a set and individually.

Results

From the metabolomic analysis, 20 ion masses were selected as potential biomarkers from principal component analysis, which showed that all biomarkers were more abundant in the endometriosis group when compared to controls. Tentative attribution was performed by lipid maps database, demonstrating that these potential biomarkers correspond to fatty acids, carnitines, monoacylglycerols, lysophosphatidic acids, lysophosphatidylglycerols, diacylglycerols, lysophosphatidylcholines, phosphatidylserine, lysophosphatidylinositols and Phosphatidic Acid.

Conclusion

The use of mass spectrometry-based metabolomics allowed for the identification of effective biomarkers for ovarian endometriosis, which may contribute for a better comprehension of the disease and how it affects the ovary, as well as assisting in the development of accessory tools for endometriosis diagnosis and infertility management.

     

Characterisation of the main drivers of intra- and inter- breed variability in the plasma metabolome of dogs

Introduction

Dog breeds are a consequence of artificial selection for specific attributes. These closed genetic populations have metabolic and physiological characteristics that may be revealed by metabolomic analysis.

Objectives

To identify and characterise the drivers of metabolic differences in the fasted plasma metabolome and then determine metabolites differentiating breeds.

Methods

Fasted plasma samples were collected from dogs maintained under two environmental conditions (controlled and client-owned at home). The former (n = 33) consisted of three breeds (Labrador Retriever, Cocker Spaniel and Miniature Schnauzer) fed a single diet batch, the latter (n = 96), client-owned dogs consisted of 9 breeds (Beagle, Chihuahua, Cocker Spaniel, Dachshund, Golden Retriever, Greyhound, German Shepherd, Labrador Retriever and Maltese) consuming various diets under differing feeding regimens. Triplicate samples were taken from Beagle (n = 10) and Labrador Retriever (n = 9) over 3 months. Non-targeted metabolite fingerprinting was performed using flow infusion electrospray-ionization mass spectrometry which was coupled with multivariate data analysis. Metadata factors including age, gender, sexual status, weight, diet and breed were investigated.

Results

Breed differences were identified in the plasma metabolome of dogs housed in a controlled environment. Triplicate samples from two breeds identified intra-individual variability, yet breed separation was still observed. The main drivers of variance in dogs maintained in the home environment were associated with breed and gender. Furthermore, metabolite signals were identified that discriminated between Labrador Retriever and Cocker Spaniels in both environments.

Conclusion

Metabolite fingerprinting of plasma samples can be used to investigate breed differences in client-owned dogs, despite added variance of diet, sexual status and environment.

     

Does centrifugation matter? Centrifugal force and spinning time alter the plasma metabolome

Background

Centrifugation is an indispensable procedure for plasma sample preparation, but applied conditions can vary between labs.

Aim

Determine whether routinely used plasma centrifugation protocols (1500×g 10 min; 3000×g 5 min) influence non-targeted metabolomic analyses.

Methods

Nuclear magnetic resonance spectroscopy (NMR) and High Resolution Mass Spectrometry (HRMS) data were evaluated with sparse partial least squares discriminant analyses and compared with cell count measurements.

Results

Besides significant differences in platelet count, we identified substantial alterations in NMR and HRMS data related to the different centrifugation protocols.

Conclusion

Already minor differences in plasma centrifugation can significantly influence metabolomic patterns and potentially bias metabolomics studies.

     

How resilient is the general population to heatwaves? A knowledge survey from the ENHANCE project in Brussels and Amsterdam

Background

Studies have shown an increase in mortality and morbidity during heatwaves, especially among the elderly. We assessed the knowledge of the general population of Brussels and Amsterdam on groups at risk and protective measures for heat-related health effects.

Results

Six locations with mixed populations were selected in each city. Passer-by’s in both cities were asked to participate in a short survey. Respondents in Brussels (n = 120) had significantly more knowledge on risk groups and protective measures than respondents in Amsterdam (n = 133). In both cities, individuals with higher education had better knowledge on risk groups and protective measures than individuals with lower education.

Conclusions

Efforts at heat-awareness raising must be strengthened, especially in Amsterdam, and public health actions should effectively target vulnerable groups with lower education in both cities.

     

Evaluation of cervical posture improvement of children with cerebral palsy after physical therapy based on head movements and serious games

Background

This paper presents the preliminary results of a novel rehabilitation therapy for cervical and trunk control of children with cerebral palsy (CP) based on serious videogames and physical exercise.

Materials

The therapy is based on the use of the ENLAZA Interface, a head mouse based on inertial technology that will be used to control a set of serious videogames with movements of the head.

Methods

Ten users with CP participated in the study. Whereas the control group (n = 5) followed traditional therapies, the experimental group (n = 5) complemented these therapies with a series of ten sessions of gaming with ENLAZA to exercise cervical flexion–extensions, rotations and inclinations in a controlled, engaging environment.

Results

The ten work sessions yielded improvements in head and trunk control that were higher in the experimental group for Visual Analogue Scale, Goal Attainment Scaling and Trunk Control Measurement Scale (TCMS). Significant differences (27% vs. 2% of percentage improvement) were found between the experimental and control groups for TCMS (p < 0.05). The kinematic assessment shows that there were some improvements in the active and the passive range of motion. However, no significant differences were found pre- and post-intervention.

Conclusions

Physical therapy that combines serious games with traditional rehabilitation could allow children with CP to achieve larger function improvements in the trunk and cervical regions. However, given the limited scope of this trial (n = 10) additional studies are needed to corroborate this hypothesis.

     

Response of Degarelix treatment in human prostate cancer monitored by HR-MAS <Superscript>1</Superscript>H NMR spectroscopy

Introduction

The androgen receptor (AR) is the master regulator of prostate cancer cell metabolism. Degarelix is a novel gonadotrophin-releasing hormone blocker, used to decrease serum androgen levels in order to treat advanced human prostate cancer. Little is known of the rapid metabolic response of the human prostate cancer tissue samples to the decreased androgen levels.

Objectives

To investigate the metabolic responses in benign and cancerous tissue samples from patients after treatment with Degarelix by using HRMAS ¹H NMR spectroscopy.

Methods

Using non-destructive HR-MAS ¹H NMR spectroscopy we analysed the metabolic changes induced by decreased AR signalling in human prostate cancer tissue samples. Absolute concentrations of the metabolites alanine, lactate, glutamine, glutamate, citrate, choline compounds [t-choline = choline + phosphocholine (PC) + glycerophosphocholine (GPC)], creatine compounds [t-creatine = creatine (Cr) + phosphocreatine (PCr)], taurine, myo-inositol and polyamines were measured in benign prostate tissue samples (n = 10), in prostate cancer specimens from untreated patients (n = 7) and prostate cancer specimens from patients treated with Degarelix (n = 6).

Results

Lactate, alanine and t-choline concentrations were significantly elevated in high-grade prostate cancer samples when compared to benign samples in untreated patients. Decreased androgen levels resulted in significant decreases of lactate and t-choline concentrations in human prostate cancer biopsies.

Conclusions

The reduced concentrations of lactate and t-choline metabolites due to Degarelix could in principle be monitored by in vivo ¹H MRS, which suggests that it would be possible to monitor the effects of physical or chemical castration in patients by that non-invasive method.

     

Identification of serum metabolic markers for diagnosis of women with dormant genital tuberculosis

Introduction

Genital tuberculosis (GTB) in women is one of the common causes of infertility in emerging countries. As an intracellular pathogen, Mycobacterium tuberculosis in the endometrium significantly alters the host metabolism in dormant GTB cases. Nuclear magnetic resonance (NMR) based metabolic profiling has emerged as a useful tool for identification of biomarkers in biological fluids.

Objective

To investigate NMR based serum metabolic profile of dormant GTB women as compared to controls.

Methods

Dormant GTB women (n = 26) and unexplained infertile women (controls; n = 26), healthy proven fertile women undergoing voluntary sterilization (n = 25) and women undergoing recurrent spontaneous miscarriage (RSM) (n = 27) were included in the study. 700 MHz proton NMR spectra of serum collected from these patients were recorded. Multivariate analysis including principal component analysis, partial least squares discriminant analysis and orthogonal projection to latent structure-discriminant analysis was applied to all the spectra. Association of dysregulated serum metabolites with our earlier findings related to altered endometrial tissue metabolites in dormant GTB women was studied using multiple correlation analysis.

Results

This study indicates a clear metabolic differentiation between women with dormant GTB and controls. Metabolites including 3-hydroxybutyrate, succinate, citrate, acetate, l-glutamine, l-lysine, glutamate, l-threonine and 1-methyl histidine were found to be significantly upregulated in serum of women with dormant GTB compared with controls. Pearson’s correlation analysis showed a significant correlation between the expression of endometrial tissue and serum metabolites.

Conclusions

The set of identified metabolites may be considered as candidate markers for the diagnosis of dormant GTB and help clinicians in early therapeutic management.

     

High resolution metabolomic analysis of ASD human brain uncovers novel biomarkers of disease

Introduction

Autism spectrum disorders (ASD) is a group of neurodevelopmental disorders believed to have a multifactorial basis. Presently, diagnosis is based on behavioral and developmental signs in children before the age of 3 and no reliable clinical biomarkers are available for early detection.

Objectives

This study aimed to biochemically profile the cerebellum from post-mortem human brain from ASD sufferers (n = 11) and compare their profiles to that of age-matched controls (n = 11) with no known brain disorder.

Methods

Using liquid chromatography combined with LTQ-Orbitrap mass spectrometry we detected 14,328 features in ESI+ mode in polar extracts of post-mortem brain.

Results

Of these only 37 were found to be statistically significantly different between ASD and controls (p < 0.05; fdr < 0.05). A panel of four features had a predictive power of 96.64 %, following statistical cross validation, for ASD detection. This model produced an AUC = 0.874 (CI 0.768–0.944) and a Fisher’s exact score of p = 4.50E?29.

Conclusion

Whilst at this time we were unable to chemically identify the four features of interest we believe that this study underscores the potential value of high resolution metabolomics for the study of ASD. Further characterization of the polar metabolome of post mortem ASD brains could lead to the identification of potential biomarkers and novel therapeutics for the disease. The development of accurate biomarkers could assist in the early detection of ASD and promote early intervention strategies to improve outcome.

     

Quantitative metabolome profiling reveals the involvement of the kynurenine pathway in influenza-associated encephalopathy

Introduction

Influenza-associated encephalopathy is a serious complication of influenza and is the most common form of acute encephalitis/encephalopathy in Japan. The number of reports from other countries is increasing, reflecting international recognition and concern.

Objectives

Identification of a specific biomarker could provide important clues about the pathophysiology of influenza-associated encephalopathy.

Methods

During the 2009–2011 flu seasons, 34 pediatric patients hospitalized with influenza complications, including influenza-associated encephalopathy, were enrolled in the study. Serum samples were collected during the acute and convalescent phases of disease. Patients were classified into encephalopathy (n = 12) and non-encephalopathy (n = 22) groups. Serum metabolites were identified and quantified by capillary electrophoresis coupled with time-of-flight mass spectrometry. Quantified data were evaluated for comparative analysis. Subsequently, a total of 55 patients with or without encephalopathy were enrolled for absolute quantification of serum kynurenine and quinolinic acid.

Results

Based on m/z values and migration times, 136 metabolites were identified in serum samples. During the acute phase of disease, three metabolites (succinic acid, undecanoic acid, and kynurenine) were significantly higher, and two other metabolites (decanoic acid and cystine) were significantly lower, in the encephalopathy group compared to the non-encephalopathy group (p = 0.012, 0.022, 0.044, 0.038, 0.046, respectively). In a larger patient group, serum kynurenine and its downstream product in tryptophan metabolism, quinolinic acid, a known neurotoxin, were significantly higher in the encephalopathy than the non-encephalopathy without febrile seizure group.

Conclusion

Comprehensive metabolite profiles revealed five metabolites as potential biomarkers for influenza-associated encephalopathy; the tryptophan–kynurenine metabolic process could be associated with its pathophysiology.

     

Metabolomics profiling of concussion in adolescent male hockey players: a novel diagnostic method

Introduction

Concussions are a major health concern as they cause significant acute symptoms and in some athletes, long-term neurologic dysfunction. Diagnosis of concussion can be difficult, as are the decisions to stop play.

Objective

To determine if concussions in adolescent male hockey players could be diagnosed using plasma metabolomics profiling.

Methods

Plasma was obtained from 12 concussed and 17 non-concussed athletes, and assayed for 174 metabolites with proton nuclear magnetic resonance and direct injection liquid chromatography tandem mass spectrometry. Data were analysed with multivariate statistical analysis and machine learning.

Results

The estimated time from concussion occurrence to blood draw at the first clinic visit was 2.3 ± 0.7 days. Using principal component analysis, the leading 10 components, each containing 9 metabolites, were shown to account for 82 % of the variance between cohorts, and relied heavily on changes in glycerophospholipids. Cross-validation of the classifier using a leave-one out approach demonstrated a 92 % accuracy rate in diagnosing a concussion (P < 0.0001). The number of metabolites required to achieve the 92 % diagnostic accuracy was minimized from 174 to as few as 17 metabolites. Receiver operating characteristic analyses generated an area under the curve of 0.91, indicating excellent concussion diagnostic potential.

Conclusion

Metabolomics profiling, together with multivariate statistical analysis and machine learning, identified concussed athletes with >90 % certainty. Metabolomics profiling represents a novel diagnostic method for concussion, and may be amenable to point-of-care testing.

     

A putative urinary biosignature for diagnosis and follow-up of tuberculous meningitis in children: outcome of a metabolomics study disclosing host–pathogen responses

Introduction

Tuberculous meningitis (TBM) is a severe manifestation of tuberculosis, presenting with high morbidity and mortality in children. Existing diagnostic methods for TBM are invasive and time-consuming and the need for highly sensitive and selective diagnosis remains high on the TBM agenda.

Objective

Our aim was to exploit metabolomics as an approach to identify metabolites as potential diagnostic predictors for children with TBM through a non-invasive means.

Methods

Urine samples selected for this study were from three paediatric groups: patients with confirmed TBM (n = 12), patients clinically suspected with TBM but later confirmed to be negative (n = 19) and age-matched controls (n = 29). Metabolomics data were generated through gas chromatography–mass spectrometry analysis and important metabolites were identified according to standard statistical procedures used for metabolomics data.

Results

A global metabolite profile that characterized TBM was developed from the data, reflecting the host and microbial responses. Nine different logistic regression models were fitted to selected metabolites for the best combination as predictors for TBM. Four metabolites—methylcitric, 2-ketoglutaric, quinolinic and 4-hydroxyhippuric acids—showed excellent diagnostic ability and provided prognostic insight into our TBM patients.

Conclusions

This study is the first to illustrate holistically the metabolic complexity of TBM and provided proof-of-concept that a biosignature of urinary metabolites can be defined for non-invasive diagnosis and prognosis of paediatric TBM patients. The biosignature should be developed and validated through future prospective studies to generate a medical algorithm for diagnosis in the initial stages of the disease and for monitoring of treatment strategies.

     

Identifying metabolite markers for preterm birth in cervicovaginal fluid by magnetic resonance spectroscopy

Introduction

Preterm birth (PTB) may be preceded by changes in the vaginal microflora and metabolite profiles.

Objectives

We sought to characterise the metabolite profile of cervicovaginal fluid (CVF) of pregnant women by 1H NMR spectroscopy, and assess their predictive value for PTB.

Methods

A pair of high-vaginal swabs was obtained from pregnant women with no evidence of clinical infection and grouped as follows: asymptomatic low risk (ALR) women with no previous history of PTB, assessed at 20–22 gestational weeks, g.w., n = 83; asymptomatic high risk (AHR) women with a previous history of PTB, assessed at both 20–22 g.w., n = 71, and 26–28 g.w., n = 58; and women presenting with symptoms of preterm labor (PTL) (SYM), assessed at 24–36 g.w., n = 65. Vaginal secretions were dissolved in phosphate buffered saline and scanned with a 9.4 T NMR spectrometer.

Results

Six metabolites (lactate, alanine, acetate, glutamine/glutamate, succinate and glucose) were analysed. In all study cohorts vaginal pH correlated with lactate integral (r = ?0.62, p < 0.0001). Lactate integrals were higher in the term ALR compared to the AHR (20–22 g.w.) women (p = 0.003). Acetate integrals were higher in the preterm versus term women for the AHR (20–22 g.w.) (p = 0.048) and SYM (p = 0.003) groups; and was predictive of PTB < 37 g.w. (AUC 0.78; 95 % CI 0.61–0.95), and delivery within 2 weeks of the index assessment (AUC 0.84; 95 % CI 0.64–1) in the SYM women, whilst other metabolites were not.

Conclusion

High CVF acetate integral of women with symptoms of PTL appears predictive of preterm delivery, as well as delivery within 2 weeks of presentation.

     

From correlation to causation: analysis of metabolomics data using systems biology approaches

Introduction

Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

Objectives

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

Methods

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

Results

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

Conclusions

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

     

A study of long-term complications associated with enteral ostomy and their contributory factors

Background

Complications of ostomy significantly affect the quality of life of ostomates. There is little evidence on the rate of long-term complications in ostomates, especially from the developing countries which include Sri Lanka. This study was aimed to describe the long-term complications of enteral ostomies and their contributory factors.

Methods

A retrospective analysis was carried out on 192 patients who underwent ostomy creation over a period of 5 years. Data on type of complications, age, sex, type of ostomy, type of surgery and perioperative care by enteric stoma therapist were gathered. Associations were established using Chi square test and multiple logistic regression.

Results

Out of 192 patients, only 146 patients presented regularly for follow up. The mean follow up duration was 28 months (range: 3–183). Around 34.2% developed surgical long-term complications related to the ostomy. Common complications were prolapse (n = 24, 16.4%), skin excoriation (n = 22, 15.1%) and parastomal hernia (n = 14, 9.6%). Overall complication rate was significantly less in loop ostomies (p < 0.05) and defunctioning ostomies (p < 0.05). Skin excoriation was significantly high in males (p < 0.05) and in ileostomies (p < 0.001). Parastomal hernia was commoner in end ostomies (p < 0.05). Perioperative care by enteric stoma therapist reduced the overall and specific complications (p < 0.001).

Conclusion

The overall complication rate in our cohort of patients was 34.2%. The perioperative care of a stoma therapist may be very effective in preventing complications particularly in a setting with limited resources.

     

Distinct proteome pathology of circulating microparticles in systemic lupus erythematosus

Background

The pathogenesis of systemic lupus erythematosus (SLE) is poorly understood but has been linked to defective clearance of subcellular particulate material from the circulation. This study investigates the origin, formation, and specificity of circulating microparticles (MPs) in patients with SLE based on comprehensive MP proteome profiling using patients with systemic sclerosis (SSc) and healthy donors (HC) as controls.

Methods

We purified MPs from platelet-poor plasma using differential centrifugation of samples from SLE (n = 45), SSc (n = 38), and two sets of HC (n = 35, n = 25). MP proteins were identified and quantitated after trypsin digestion by liquid chromatography-tandem mass spectrometry. The abundance of specific proteins was compared between the groups using univariate statistics and false discovery rate correction for multiple comparisons. Specific proteins and protein ratios were explored for diagnostic and disease activity information using receiver-operating characteristic curves and by analysis of correlations of protein abundance with disease activity scores.

Results

We identify and quantitate more than 1000 MP proteins and show that a subpopulation of SLE-MPs (which we propose to call luposomes) are highly specific for SLE, i.e. not found in MP preparations from HC or patients with another autoimmune, systemic disease, SSc. In SLE-MPs platelet proteins and mitochondrial proteins are significantly diminished, cytoskeletal proteins deranged, and glycolytic enzymes and apoptotic proteins significantly increased.

Conclusions

Normal MPs are efficiently removed in SLE, but aberrant MPs, derived from non-lymphoid leukocytes, are less efficiently removed and abundantly produced leading to an altered MP proteome in SLE. The data suggest that an abnormal generation of MPs may partake in the pathology of SLE and that new diagnostic, monitoring, and treatment strategies targeting these processes may be advantageous.

     

A metabolomics guided exploration of marine natural product chemical space

Introduction

Natural products from culture collections have enormous impact in advancing discovery programs for metabolites of biotechnological importance. These discovery efforts rely on the metabolomic characterization of strain collections.

Objective

Many emerging approaches compare metabolomic profiles of such collections, but few enable the analysis and prioritization of thousands of samples from diverse organisms while delivering chemistry specific read outs.

Method

In this work we utilize untargeted LC–MS/MS based metabolomics together with molecular networking to inventory the chemistries associated with 1000 marine microorganisms.

Result

This approach annotated 76 molecular families (a spectral match rate of 28 %), including clinically and biotechnologically important molecules such as valinomycin, actinomycin D, and desferrioxamine E. Targeting a molecular family produced primarily by one microorganism led to the isolation and structure elucidation of two new molecules designated maridric acids A and B.

Conclusion

Molecular networking guided exploration of large culture collections allows for rapid dereplication of know molecules and can highlight producers of uniques metabolites. These methods, together with large culture collections and growing databases, allow for data driven strain prioritization with a focus on novel chemistries.

     

Glomerular abundance of complement proteins characterized by proteomic analysis of laser-captured microdissected glomeruli associates with progressive disease in IgA nephropathy

Background

The clinical course of IgA nephropathy (IgAN) is variable and complement activation may predict prognosis. The present study investigated whether glomerular abundance of complement proteins associates with progression to end-stage renal disease (ESRD) in patients for whom prognosis could not be predicted based on clinical variables.

Methods

Based on data from the Norwegian Kidney Biopsy Registry and the Norwegian Renal Registry, three groups were included: IgAN patients with (n = 9) or without (n = 16) progression to ESRD during 10 years, and controls (n = 15) with a normal kidney biopsy. IgAN patients had eGFR > 45 ml/min/1.73 m² and non-nephrotic proteinuria at time of biopsy. Using stored formalin-fixed paraffin embedded kidney biopsy tissue, about 100 glomerular cross sections were microdissected for each patient. Samples were analyzed by liquid chromatography–tandem mass spectrometry and relative abundances of complement proteins were compared between groups.

Results

Proteomic analyses quantified 2018 proteins, of which 28 proteins belong to the complement system. As compared to IgAN patients without progressive disease, glomeruli from patients with progressive IgAN had significantly higher abundance of components of the classical and the terminal complement pathways, and inhibitory factors such as Factor H and factor H related proteins. Abundance of complement proteins classified progressors from non-progressors with an area under ROC curve of 0.91 (p = 0.001). Clinical and morphological data were similar between the two patient groups and could not predict progressive IgAN.

Conclusions

In conclusion, higher glomerular abundance of complement proteins was associated with a progressive clinical course in IgAN and are candidate biomarkers to predict prognosis.

     

Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait

Introduction

Past studies on plant metabolomes have highlighted the influence of growing environments and varietal differences in variation of levels of metabolites yet there remains continued interest in evaluating the effect of genetic modification (GM).

Objectives

Here we test the hypothesis that metabolomics differences in grain from maize hybrids derived from a series of GM (NK603, herbicide tolerance) inbreds and corresponding negative segregants can arise from residual genetic variation associated with backcrossing and that the effect of insertion of the GM trait is negligible.

Methods

Four NK603-positive and negative segregant inbred males were crossed with two different females (testers). The resultant hybrids, as well as conventional comparator hybrids, were then grown at three replicated field sites in Illinois, Minnesota, and Nebraska during the 2013 season. Metabolomics data acquisition using gas chromatography–time of flight-mass spectrometry (GC–TOF-MS) allowed the measurement of 367 unique metabolite features in harvested grain, of which 153 were identified with small molecule standards. Multivariate analyses of these data included multi-block principal component analysis and ANOVA-simultaneous component analysis. Univariate analyses of all 153 identified metabolites was conducted based on significance testing (α = 0.05), effect size evaluation (assessing magnitudes of differences), and variance component analysis.

Results

Results demonstrated that the largest effects on metabolomic variation were associated with different growing locations and the female tester. They further demonstrated that differences observed between GM and non-GM comparators, even in stringent tests utilizing near-isogenic positive and negative segregants, can simply reflect minor genomic differences associated with conventional back-crossing practices.

Conclusion

The effect of GM on metabolomics variation was determined to be negligible and supports that there is no scientific rationale for prioritizing GM as a source of variation.

     

Plasma lipidomic profiling in patients with rheumatoid arthritis

Introduction

Rheumatoid arthritis (RA) is linked to increased cardiovascular morbidity and mortality, not completely explained by traditional risk factors. Importantly, the increased risk occurs despite lower levels of total and low-density lipoprotein cholesterol. Whilst systemic inflammation may be a factor, it is possible that changes in individual lipid species contribute to the increased cardiovascular risk.

Objectives

In the present study, we characterized plasma lipidomic profiles in patients with RA in comparison with healthy controls.

Methods

Patients with RA (n = 32) and age- and gender-matched healthy volunteers (n = 84) were recruited. Fasting plasma lipid profiles were measured using electrospray-ionisation tandem mass spectrometry. 24 lipid classes and subclasses were measured.

Results

Patients with RA had normal total, low-density lipoprotein and high-density lipoprotein cholesterol, but higher triglycerides than controls. Five lipid classes (dihydroceramides, alkylphosphatidylethanolamine, alkenylphosphatidylethanolamine, lysophosphatidylinositol, phosphatidylserine) differed between patients with RA and controls. Then we measured 36 lipid species within these 5 classes and found that 11 lipid species were different between patients with RA and controls. Three lipid classes (dihydroceramides, lysophosphatidylinositol, phosphatidylserine) and 10 lipid species remained significantly associated with RA after adjusting for age, sex, body mass index, current smoking, systolic blood pressure and anti-hypertensive treatment in a binary logistic regression model.

Conclusion

This study has identified lipid alterations in RA. These alterations of lipids warrant further investigation as they may be associated with accelerated atherosclerosis and joint inflammation in patient with RA.