Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy

Background: Urine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with diabetes mellitus type 2 and duration >= 5 years, cases of DN were defined as albuminuria >300 mg/d and diabetic retinopathy (n = 66). Controls were matched for gender and diabetes duration (n = 82). Methodology/Principal Findings: Proteome analysis was performed blinded using high-resolution capillary electrophoresis coupled with mass spectrometry (CE-MS). Data were evaluated employing the previously developed model for DN. Upon unblinding, the model for DN showed 93.8% sensitivity and 91.4% specificity, with an AUC of 0.948 (95% CI 0.898-0.978). Of 65 previously identified peptides, 60 were significantly different between cases and controls of this study. In <10% of cases and controls classification by proteome analysis not entirely resulted in the expected clinical outcome. Analysis of patient's subsequent clinical course revealed later progression to DN in some of the false positive classified DN control patients. Conclusions: These data provide the first independent confirmation that profiling of the urinary proteome by CE-MS can adequately identify subjects with DN, supporting the generalizability of this approach. The data further establish urinary collagen fragments as biomarkers for diabetes-induced renal damage that may serve as earlier and more specific biomarkers than the currently used urinary albumin

Seminal plasma as a source of prostate cancer peptide biomarker candidates for detection of indolent and advanced disease

Background:Extensive prostate specific antigen screening for prostate cancer generates a high number of unnecessary biopsies and over-treatment due to insufficient differentiation between indolent and aggressive tumours. We hypothesized that seminal plasma is a robust source of novel prostate cancer (PCa) biomarkers with the potential to improve primary diagnosis of and to distinguish advanced from indolent disease. <br>Methodology/Principal Findings: In an open-label case/control study 125 patients (70 PCa, 21 benign prostate hyperplasia, 25 chronic prostatitis, 9 healthy controls) were enrolled in 3 centres. Biomarker panels a) for PCa diagnosis (comparison of PCa patients versus benign controls) and b) for advanced disease (comparison of patients with post surgery Gleason score <7 versus Gleason score >>7) were sought. Independent cohorts were used for proteomic biomarker discovery and testing the performance of the identified biomarker profiles. Seminal plasma was profiled using capillary electrophoresis mass spectrometry. Pre-analytical stability and analytical precision of the proteome analysis were determined. Support vector machine learning was used for classification. Stepwise application of two biomarker signatures with 21 and 5 biomarkers provided 83% sensitivity and 67% specificity for PCa detection in a test set of samples. A panel of 11 biomarkers for advanced disease discriminated between patients with Gleason score 7 and organ-confined (<pT3a) or advanced (≥pT3a) disease with 80% sensitivity and 82% specificity in a preliminary validation setting. Seminal profiles showed excellent pre-analytical stability. Eight biomarkers were identified as fragments of N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase​,prostatic acid phosphatase, stabilin-2, GTPase IMAP family member 6, semenogelin-1 and -2. Restricted sample size was the major limitation of the study.</br> <br>Conclusions/Significance: Seminal plasma represents a robust source of potential peptide makers for primary PCa diagnosis. Our findings warrant further prospective validation to confirm the diagnostic potential of identified seminal biomarker candidates.</br&gt

Proteomics as a quality control tool of pharmaceutical probiotic bacterial lysate products

Probiotic bacteria have a wide range of applications in veterinary and human therapeutics. Inactivated probiotics are complex samples and quality control (QC) should measure as many molecular features as possible. Capillary electrophoresis coupled to mass spectrometry (CE/MS) has been used as a multidimensional and high throughput method for the identification and validation of biomarkers of disease in complex biological samples such as biofluids. In this study we evaluate the suitability of CE/MS to measure the consistency of different lots of the probiotic formulation Pro-Symbioflor which is a bacterial lysate of heat-inactivated Escherichia coli and Enterococcus faecalis. Over 5000 peptides were detected by CE/MS in 5 different lots of the bacterial lysate and in a sample of culture medium. 71 to 75% of the total peptide content was identical in all lots. This percentage increased to 87–89% when allowing the absence of a peptide in one of the 5 samples. These results, based on over 2000 peptides, suggest high similarity of the 5 different lots. Sequence analysis identified peptides of both E. coli and E. faecalis and peptides originating from the culture medium, thus confirming the presence of the strains in the formulation. Ontology analysis suggested that the majority of the peptides identified for E. coli originated from the cell membrane or the fimbrium, while peptides identified for E. faecalis were enriched for peptides originating from the cytoplasm. The bacterial lysate peptides as a whole are recognised as highly conserved molecular patterns by the innate immune system as microbe associated molecular pattern (MAMP). Sequence analysis also identified the presence of soybean, yeast and casein protein fragments that are part of the formulation of the culture medium. In conclusion CE/MS seems an appropriate QC tool to analyze complex biological products such as inactivated probiotic formulations and allows determining the similarity between lots

Garden varieties: how attractive are recommended garden plants to butterflies?

One way the public can engage in insect conservation is through wildlife gardening, including the growing of insect-friendly flowers as sources of nectar. However, plant varieties differ in the types of insects they attract. To determine which garden plants attracted which butterflies, we counted butterflies nectaring on 11 varieties of summer-flowering garden plants in a rural garden in East Sussex, UK. These plants were all from a list of 100 varieties considered attractive to British butterflies, and included the five varieties specifically listed by the UK charity Butterfly Conservation as best for summer nectar. A total of 2659 flower visits from 14 butterfly and one moth species were observed. We performed a principal components analysis which showed contrasting patterns between the species attracted to Origanum vulgare and Buddleia davidii. The “butterfly bush” Buddleia attracted many nymphalines, such as the peacock, Inachis io, but very few satyrines such as the gatekeeper, Pyronia tithonus, which mostly visited Origanum. Eupatorium cannibinum had the highest Simpson’s Diversity score of 0.75, while Buddleia and Origanum were lower, scoring 0.66 and 0.50 respectively. No one plant was good at attracting all observed butterfly species, as each attracted only a subset of the butterfly community. We conclude that to create a butterfly-friendly garden, a variety of plant species are required as nectar sources for butterflies. Furthermore, garden plant recommendations can probably benefit from being more precise as to the species of butterfly they attract

Species-selective killing of bacteria by antimicrobial peptide-PNAs

This is an open-access article distributed under the terms of the Creative Commons Attribution License, CC BY 4.0 which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Broad-spectrum antimicrobials kill indiscriminately, a property that can lead to negative clinical consequences and an increase in the incidence of resistance. Species-specific antimicrobials that could selectively kill pathogenic bacteria without targeting other species in the microbiome could limit these problems. The pathogen genome presents an excellent target for the development of such antimicrobials. In this study we report the design and evaluation of species-selective peptide nucleic acid (PNA) antibacterials. Selective growth inhibition of B. subtilis, E. coli, K. pnuemoniae and S. enterica serovar Typhimurium in axenic or mixed culture could be achieved with PNAs that exploit species differences in the translation initiation region of essential genes. An S. Typhimurium-specific PNA targeting ftsZ resulted in elongated cells that were not observed in E. coli, providing phenotypic evidence of the selectivity of PNA-based antimicrobials. Analysis of the genomes of E. coli and S. Typhimurium gave a conservative estimate of >150 PNA targets that could potentially discriminate between these two closely related species. This work provides a basis for the development of a new class of antimicrobial with a tuneable spectrum of activity.Peer reviewedFinal Published versio

Comment on “The extent of forest in dryland biomes”

This is the author accepted manuscript. The final version is available from American Association for the Advancement of Science via the DOI in this record.Bastin et al. (Reports, 12 May 2017, p. 635) infer forest as more globally extensive than previously estimated using tree cover data. However, their forest definition does not reflect ecosystem function or biotic composition. These structural and climatic definitions inflate forest estimates across the tropics and undermine conservation goals, leading to inappropriate management policies and practices in tropical grassy ecosystems

Irreducible uncertainty in near-term climate projections

Model simulations of the next few decades are widely used in assessments of climate change impacts and as guidance for adaptation. Their non-linear nature reveals a level of irreducible uncertainty which it is important to understand and quantify, especially for projections of near-term regional climate. Here we use large idealised initial condition ensembles of the FAMOUS global climate model with a 1 %/year compound increase in CO2 levels to quantify the range of future temperatures in model-based projections. These simulations explore the role of both atmospheric and oceanic initial conditions and are the largest such ensembles to date. Short-term simulated trends in global temperature are diverse, and cooling periods are more likely to be followed by larger warming rates. The spatial pattern of near-term temperature change varies considerably, but the proportion of the surface showing a warming is more consistent. In addition, ensemble spread in inter-annual temperature declines as the climate warms, especially in the North Atlantic. Over Europe, atmospheric initial condition uncertainty can, for certain ocean initial conditions, lead to 20 year trends in winter and summer in which every location can exhibit either strong cooling or rapid warming. However, the details of the distribution are highly sensitive to the ocean initial condition chosen and particularly the state of the Atlantic meridional overturning circulation. On longer timescales, the warming signal becomes more clear and consistent amongst different initial condition ensembles. An ensemble using a range of different oceanic initial conditions produces a larger spread in temperature trends than ensembles using a single ocean initial condition for all lead times. This highlights the potential benefits from initialising climate predictions from ocean states informed by observations. These results suggest that climate projections need to be performed with many more ensemble members than at present, using a range of ocean initial conditions, if the uncertainty in near-term regional climate is to be adequately quantified

Urinary Proteomics to Support Diagnosis of Stroke

Accurate diagnosis in suspected ischaemic stroke can be difficult. We explored the urinary proteome in patients with stroke (n = 69), compared to controls (n = 33), and developed a biomarker model for the diagnosis of stroke. We performed capillary electrophoresis online coupled to micro-time-of-flight mass spectrometry. Potentially disease-specific peptides were identified and a classifier based on these was generated using support vector machine-based software. Candidate biomarkers were sequenced by liquid chromatography-tandem mass spectrometry. We developed two biomarker-based classifiers, employing 14 biomarkers (nominal p-value <0.004) or 35 biomarkers (nominal p-value <0.01). When tested on a blinded test set of 47 independent samples, the classification factor was significantly different between groups; for the 35 biomarker model, median value of the classifier was 0.49 (−0.30 to 1.25) in cases compared to −1.04 (IQR −1.86 to −0.09) in controls, p<0.001. The 35 biomarker classifier gave sensitivity of 56%, specificity was 93% and the AUC on ROC analysis was 0.86. This study supports the potential for urinary proteomic biomarker models to assist with the diagnosis of acute stroke in those with mild symptoms. We now plan to refine further and explore the clinical utility of such a test in large prospective clinical trials

Identification of a General O-linked Protein Glycosylation System in Acinetobacter baumannii and Its Role in Virulence and Biofilm Formation

Acinetobacter baumannii is an emerging cause of nosocomial infections. The isolation of strains resistant to multiple antibiotics is increasing at alarming rates. Although A. baumannii is considered as one of the more threatening “superbugs” for our healthcare system, little is known about the factors contributing to its pathogenesis. In this work we show that A. baumannii ATCC 17978 possesses an O-glycosylation system responsible for the glycosylation of multiple proteins. 2D-DIGE and mass spectrometry methods identified seven A. baumannii glycoproteins, of yet unknown function. The glycan structure was determined using a combination of MS and NMR techniques and consists of a branched pentasaccharide containing N-acetylgalactosamine, glucose, galactose, N-acetylglucosamine, and a derivative of glucuronic acid. A glycosylation deficient strain was generated by homologous recombination. This strain did not show any growth defects, but exhibited a severely diminished capacity to generate biofilms. Disruption of the glycosylation machinery also resulted in reduced virulence in two infection models, the amoebae Dictyostelium discoideum and the larvae of the insect Galleria mellonella, and reduced in vivo fitness in a mouse model of peritoneal sepsis. Despite A. baumannii genome plasticity, the O-glycosylation machinery appears to be present in all clinical isolates tested as well as in all of the genomes sequenced. This suggests the existence of a strong evolutionary pressure to retain this system. These results together indicate that O-glycosylation in A. baumannii is required for full virulence and therefore represents a novel target for the development of new antibiotics