Deuteros 2.0: Peptide-level significance testing of data from hydrogen deuterium exchange mass spectrometry

Summary: Hydrogen deuterium exchange mass spectrometry (HDX-MS) is becoming increasing routine for monitoring changes in the structural dynamics of proteins. Differential HDX-MS allows comparison of individual protein states, such as in the absence or presence of a ligand. This can be used to attribute changes in conformation to binding events, allowing the mapping of entire con-formational networks. As such, the number of necessary cross-state comparisons quickly increas-es as additional states are introduced to the system of study. There are currently very few software packages available that offer quick and informative comparison of HDX-MS datasets and even few-er which offer statistical analysis and advanced visualization. Following the feedback from our origi-nal software Deuteros, we present Deuteros 2.0 which has been redesigned from the ground up to fulfil a greater role in the HDX-MS analysis pipeline. Deuteros 2.0 features a repertoire of facilities for back exchange correction, data summarization, peptide-level statistical analysis and advanced data plotting features. Availability: Deuteros 2.0 can be downloaded from https://github.com/andymlau/Deuteros_2.0 under the Apache 2.0 license. Installation of Deuteros 2.0 requires the MATLAB Runtime Library available free of charge from MathWorks (https://www.mathworks.com/products/compiler/matlab-runtime.html) and is available for both Windows and Mac operating systems.Comment: Application note with 3 pages, 1 figur

Comprehensive web-based broker for bio-technology design and manufacturing

Synthetic biology, particularly in relation to characterisation experiments relating to the description of bio-parts frequently involves the use of a wide range of equipment, including, for example, plate reader's, flow cytometers, and mass spectrometers. This equipment is often from multiple manufacturers. The study describes broker technology that has been developed which has the ability to connect multiple types of equipment into a common information environment; the connectivity from the databases and equipment is achieved using Visbion's ‘cube’ technology that involves military specification encryption for data security. The broker technology uses a new, developing standard, Digital Imaging and Communication in Medicine (DICOM)-SB, that is based on the highly successful international standard for biomedicine, DICOM. The broker uses a version of the DICOM data model that has been specifically designed for synthetic biology and, in particular, characterisation data

Assessing fitness to drive:A validation study on patients with mild cognitive impairment

Objectives: There is no consensus yet on how to determine which patients with cognitive impairment are able to drive a car safely and which are not. Recently, a strategy was composed for the assessment of fitness to drive, consisting of clinical interviews, a neuropsychological assessment, and driving simulator rides, which was compared with the outcome of an expert evaluation of an on-road driving assessment. A selection of tests and parameters of the new approach revealed a predictive accuracy of 97.4% for the prediction of practical fitness to drive on an initial sample of patients with Alzheimer's dementia. The aim of the present study was to explore whether the selected variables would be equally predictive (i.e., valid) for a closely related group of patients; that is, patients with mild cognitive impairment (MCI).Methods: Eighteen patients with mild cognitive impairment completed the proposed approach to the measurement of fitness to drive, including clinical interviews, a neuropsychological assessment, and driving simulator rides. The criterion fitness to drive was again assessed by means of an on-road driving evaluation. The predictive validity of the fitness to drive assessment strategy was evaluated by receiver operating characteristic (ROC) analyses.Results: Twelve patients with MCI (66.7%) passed and 6 patients (33.3%) failed the on-road driving assessment. The previously proposed approach to the measurement of fitness to drive achieved an overall predictive accuracy of 94.4% in these patients. The application of an optimal cutoff resulted in a diagnostic accuracy of 100% sensitivity toward unfit to drive and 83.3% specificity toward fit to drive. Further analyses revealed that the neuropsychological assessment and the driving simulator rides produced rather stable prediction rates, whereas clinical interviews were not significantly predictive for practical fitness to drive in the MCI patient sample.Conclusions: The selected measures of the previously proposed approach revealed adequate accuracy in identifying fitness to drive in patients with MCI. Furthermore, a combination of neuropsychological test performance and simulated driving behavior proved to be the most valid predictor of practical fitness to drive.</p

Designing and implementing a research integrity promotion plan: recommendations for research funders

Various stakeholders in science have put research integrity high on their agenda. Among them, research funders are prominently placed to foster research integrity by requiring that the organizations and individual researchers they support make an explicit commitment to research integrity. Moreover, funders need to adopt appropriate research integrity practices themselves. To facilitate this, we recommend that funders develop and implement a Research Integrity Promotion Plan (RIPP). This Consensus View offers a range of examples of how funders are already promoting research integrity, distills 6 core topics that funders should cover in a RIPP, and provides guidelines on how to develop and implement a RIPP. We believe that the 6 core topics we put forward will guide funders towards strengthening research integrity policy in their organization and guide the researchers and research organizations they fund

New Insights into Chloramphenicol Biosynthesis in Streptomyces venezuelae ATCC 10712

Comparative genome analysis revealed seven uncharacterized genes, sven0909 to sven0915, adjacent to the previously identified chloramphenicol biosynthetic gene cluster (sven0916–sven0928) of Streptomyces venezuelae strain ATCC 10712 that was absent in a closely related Streptomyces strain that does not produce chloramphenicol. Transcriptional analysis suggested that three of these genes might be involved in chloramphenicol production, a prediction confirmed by the construction of deletion mutants. These three genes encode a cluster-associated transcriptional activator (Sven0913), a phosphopantetheinyl transferase (Sven0914), and a Na(+)/H(+) antiporter (Sven0915). Bioinformatic analysis also revealed the presence of a previously undetected gene, sven0925, embedded within the chloramphenicol biosynthetic gene cluster that appears to encode an acyl carrier protein, bringing the number of new genes likely to be involved in chloramphenicol production to four. Microarray experiments and synteny comparisons also suggest that sven0929 is part of the biosynthetic gene cluster. This has allowed us to propose an updated and revised version of the chloramphenicol biosynthetic pathway

Visualization of proteomics data using R and bioconductor.

Data visualization plays a key role in high-throughput biology. It is an essential tool for data exploration allowing to shed light on data structure and patterns of interest. Visualization is also of paramount importance as a form of communicating data to a broad audience. Here, we provided a short overview of the application of the R software to the visualization of proteomics data. We present a summary of R's plotting systems and how they are used to visualize and understand raw and processed MS-based proteomics data.LG was supported by the European Union 7th Framework Program (PRIME-XS project, grant agreement number 262067) and a BBSRC Strategic Longer and Larger grant (Award BB/L002817/1). LMB was supported by a BBSRC Tools and Resources Development Fund (Award BB/K00137X/1). TN was supported by a ERASMUS Placement scholarship.This is the final published version of the article. It was originally published in Proteomics (PROTEOMICS Special Issue: Proteomics Data Visualisation Volume 15, Issue 8, pages 1375–1389, April 2015. DOI: 10.1002/pmic.201400392). The final version is available at http://onlinelibrary.wiley.com/doi/10.1002/pmic.201400392/abstract

The MMSE should not be the sole indicator of fitness to drive in mild Alzheimer's dementia

Since Alzheimer’s disease may affect driving performance, patients with Alzheimer’s disease are assessed on fitness to drive. On-road driving assessments are widely used, and attempts have also been made to develop strategies to assess fitness to drive in a clinical setting. Preferably, a first indication of fitness to drive is obtained quickly after diagnosis using a single test such as the Mini-Mental State Examination (MMSE). The aim of this study is to investigate whether the MMSE can be used to predict whether patients with Alzheimer’s disease will pass or fail an on-road driving assessment. Patients with Alzheimer’s disease (n = 81) participated in a comprehensive fitness-to-drive assessment which included the MMSE as well as an on-road driving assessment [PLoS One 11(2):e0149566, 2016]. MMSE cutoffs were applied as suggested by Versijpt and colleagues [Acta Neurol Belg 117(4):811–819, 2017]. All patients with Alzheimer’s disease who scored below the lower cutoff (MMSE ≤ 19) failed the on-road driving assessment. However, a third of the patients with Alzheimer’s disease who scored above the upper cutoff (MMSE ≥ 25) failed the on-road driving assessment as well. We conclude that the MMSE alone has insufficient predictive value to correctly identify fitness to drive in patients with very mild-to-mild Alzheimer’s disease implicating the need for comprehensive assessments to determine fitness to drive in a clinical setting

Synthetic biology to access and expand nature's chemical diversity

Bacterial genomes encode the biosynthetic potential to produce hundreds of thousands of complex molecules with diverse applications, from medicine to agriculture and materials. Accessing these natural products promises to reinvigorate drug discovery pipelines and provide novel routes to synthesize complex chemicals. The pathways leading to the production of these molecules often comprise dozens of genes spanning large areas of the genome and are controlled by complex regulatory networks with some of the most interesting molecules being produced by non-model organisms. In this Review, we discuss how advances in synthetic biology — including novel DNA construction technologies, the use of genetic parts for the precise control of expression and for synthetic regulatory circuits — and multiplexed genome engineering can be used to optimize the design and synthesis of pathways that produce natural products