Molecular interconversion behaviour in comprehensive two-dimensional gas chromatography

Comprehensive two-dimensional gas chromatography (GC x GC) is shown to provide information on dynamic molecular behaviour (interconversion), with the interconversion process occurring on both columns in the coupled-column experiment. The experiment requires suitable adjustment of both experimental conditions and relative dimensions of each of the columns. In this case, a longer column than normally employed in GC x GC allows sufficient retention duration on the second column, which permits the typical plateau-shape recognised for the interconversion process to be observed. The extent of interconversion depends on prevailing temperature, retention time, and the phase type. Polyethylene glycol-based phases were found to result in high interconversion kinetics, although terephthalic acid-terminated polyethylene glycol had a lesser extent of interconversion. Much less interconversion was seen for phenyl-methylpolysiloxane and cyclodextrin phases. This suggests that for the oximes, interconversion largely occurs in the stationary phase. Examples of different extents of interconversion in both dimensions are shown, including peak coalescence on the first column with little interconversion on the second column

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Guidelines for the use of survivorship care plans: a systematic quality appraisal using the AGREE II instrument

Abstract Background Survivorship care plans (SCPs) are written treatment summaries and follow-up care plans that are intended to facilitate communication and coordination of care among survivors, cancer care providers, and primary care providers. A growing number of guidelines for the use of SCPs exist, yet SCP use in the United States remains limited. Limited use of SCPs may be due to poor quality of these guidelines. The purpose of the study was to evaluate the quality of guidelines for SCP use, tools that are intended to promote evidence-based medicine. Methods We conducted a comprehensive search of the literature using MEDLINE/PubMed, EMBASE (Excerpta Medica Database), and CINAHL (Cumulative Index to Nursing and Allied Health Literature) published through April 2014, in addition to grey literature sources and bibliographic and expert reviews. Guideline quality was assessed using the AGREE II instrument (Appraisal of Guidelines for Research and Evaluation, 2nd edition), a tool developed by an international group of scientists to advance the quality of clinical practice guidelines. To promote consistency with extant studies using the AGREE II instrument and to clearly and unambiguously identify potentially useful guidelines for SCP use, we also summarized AGREE II scores by strongly recommending, recommending, or not recommending the guidelines that we evaluated. Results Of 128 documents screened, we included 16 guidelines for evaluation. We did not strongly recommend any of the 16 guidelines that we evaluated; we recommended 5 and we did not recommend 11. Overall, guidelines scored highest on clarity of presentation (i.e., guideline language, structure, and format): Guidelines were generally unambiguous in their recommendations that SCPs should be used. Guidelines scored lowest on applicability (i.e., barriers and facilitators to implementation, implementation strategies, and resource implications of applying the guideline): Few guidelines discussed facilitators and barriers to guideline application; advice and tools for implementing guidelines were vague; and none explicitly discussed resource implications of implementing the guidelines. Conclusions Guidelines often advocated survivorship care plan use without justification or suggestions for implementation. Improved guideline quality may promote survivorship care plan use

Semi-automated non-target processing in GC × GC–MS metabolomics analysis: applicability for biomedical studies

Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC–MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC–MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC–MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC–MS and GC–MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC–MS and GC × GC–MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC–MS processing compared to targeted GC–MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC–MS were somewhat higher than with GC–MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC–MS was demonstrated; many additional candidate biomarkers were found with GC × GC–MS compared to GC–MS

Structure of the cystathionine γ-synthase MetB from Mycobacterium ulcerans

Cystathionine γ-synthase (CGS) is a transferase that catalyzes the reaction between O 4-succinyl-l-homoserine and l-cysteine to produce l-­cystathionine and succinate. The crystal structure of CGS from M. ulcerans is presented covalently linked to the cofactor pyridoxal phosphate (PLP). A second structure contains PLP as well as a highly ordered HEPES molecule in the active site acting as a pseudo-ligand. This is the first structure ever reported from the pathogen M. ulcerans

DECIDE: Delphi Expert Consensus Statement on Inflammatory Bowel Disease Dysplasia Shared Management Decision-Making

Background and Aims Inflammatory bowel disease colitis-associated dysplasia is managed with either enhanced surveillance and endoscopic resection or prophylactic surgery. The rate of progression to cancer after a dysplasia diagnosis remains uncertain in many cases and patients have high thresholds for accepting proctocolectomy. Individualised discussion of management options is encouraged to take place between patients and their multidisciplinary teams for best outcomes. We aimed to develop a toolkit to support a structured, multidisciplinary and shared decision-making approach to discussions about dysplasia management options between clinicians and their patients. Methods Evidence from systematic literature reviews, mixed-methods studies conducted with key stakeholders, and decision-making expert recommendations were consolidated to draft consensus statements by the DECIDE steering group. These were then subjected to an international, multidisciplinary modified electronic Delphi process until an a priori threshold of 80% agreement was achieved to establish consensus for each statement. Results In all, 31 members [15 gastroenterologists, 14 colorectal surgeons and two nurse specialists] from nine countries formed the Delphi panel. We present the 18 consensus statements generated after two iterative rounds of anonymous voting. Conclusions By consolidating evidence for best practice using literature review and key stakeholder and decision-making expert consultation, we have developed international consensus recommendations to support health care professionals counselling patients on the management of high cancer risk colitis-associated dysplasia. The final toolkit includes clinician and patient decision aids to facilitate shared decision-making.</p

Unpredictability of metabolism—the key role of metabolomics science in combination with next-generation genome sequencing

Next-generation sequencing provides technologies which sequence whole prokaryotic and eukaryotic genomes in days, perform genome-wide association studies, chromatin immunoprecipitation followed by sequencing and RNA sequencing for transcriptome studies. An exponentially growing volume of sequence data can be anticipated, yet functional interpretation does not keep pace with the amount of data produced. In principle, these data contain all the secrets of living systems, the genotype–phenotype relationship. Firstly, it is possible to derive the structure and connectivity of the metabolic network from the genotype of an organism in the form of the stoichiometric matrix N. This is, however, static information. Strategies for genome-scale measurement, modelling and predicting of dynamic metabolic networks need to be applied. Consequently, metabolomics science—the quantitative measurement of metabolism in conjunction with metabolic modelling—is a key discipline for the functional interpretation of whole genomes and especially for testing the numerical predictions of metabolism based on genome-scale metabolic network models. In this context, a systematic equation is derived based on metabolomics covariance data and the genome-scale stoichiometric matrix which describes the genotype–phenotype relationship

Recent advances of metabolomics in plant biotechnology

Biotechnology, including genetic modification, is a very important approach to regulate the production of particular metabolites in plants to improve their adaptation to environmental stress, to improve food quality, and to increase crop yield. Unfortunately, these approaches do not necessarily lead to the expected results due to the highly complex mechanisms underlying metabolic regulation in plants. In this context, metabolomics plays a key role in plant molecular biotechnology, where plant cells are modified by the expression of engineered genes, because we can obtain information on the metabolic status of cells via a snapshot of their metabolome. Although metabolome analysis could be used to evaluate the effect of foreign genes and understand the metabolic state of cells, there is no single analytical method for metabolomics because of the wide range of chemicals synthesized in plants. Here, we describe the basic analytical advancements in plant metabolomics and bioinformatics and the application of metabolomics to the biological study of plants

Structure of a Burkholderia pseudomallei Trimeric Autotransporter Adhesin Head

Pathogenic bacteria adhere to the host cell surface using a family of outer membrane proteins called Trimeric Autotransporter Adhesins (TAAs). Although TAAs are highly divergent in sequence and domain structure, they are all conceptually comprised of a C-terminal membrane anchoring domain and an N-terminal passenger domain. Passenger domains consist of a secretion sequence, a head region that facilitates binding to the host cell surface, and a stalk region.Pathogenic species of Burkholderia contain an overabundance of TAAs, some of which have been shown to elicit an immune response in the host. To understand the structural basis for host cell adhesion, we solved a 1.35 A resolution crystal structure of a BpaA TAA head domain from Burkholderia pseudomallei, the pathogen that causes melioidosis. The structure reveals a novel fold of an intricately intertwined trimer. The BpaA head is composed of structural elements that have been observed in other TAA head structures as well as several elements of previously unknown structure predicted from low sequence homology between TAAs. These elements are typically up to 40 amino acids long and are not domains, but rather modular structural elements that may be duplicated or omitted through evolution, creating molecular diversity among TAAs.The modular nature of BpaA, as demonstrated by its head domain crystal structure, and of TAAs in general provides insights into evolution of pathogen-host adhesion and may provide an avenue for diagnostics

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided