Examining the acceptability and feasibility of the Compassionate Mindful Resilience (CMR) programme in adults living with chronic kidney disease: the COSMIC study findings.

BACKGROUND: Individuals with chronic kidney disease experience difficult physical and psychological symptoms, that impact quality of life, and are at increased risk of anxiety and depression. Access to specialist psychological support is limited. This study aimed to support a new service development project, in collaboration with Kidney Care UK, to implement the Compassionate Mindful Resilience (CMR) programme, developed by MindfulnessUK, which provides accessible mindfulness techniques and practices to enhance compassion and resilience, and explore its feasibility for people living with stage 4 or 5 kidney disease and transplant. METHODS: A multi-method feasibility design was utilised. Participants over 18 years, from the UK, with stage 4 or 5 kidney disease or post-transplant, and who were not currently undergoing psychotherapy, were recruited to the four-week CMR programme. Data was collected at baseline, post-intervention and three-months post to measure anxiety, depression, self-compassion, mental wellbeing, resilience, and mindfulness. The acceptability of the intervention for a kidney disease population was explored through qualitative interviews with participants, and the Mindfulness Teacher. RESULTS: In total, 75 participants were recruited to the study, with 65 completing the CMR programme. The majority were female (66.2%) and post-transplant (63.1%). Analysis of completed outcome measures at baseline and post-intervention timepoints (n = 61), and three-months post intervention (n = 45) revealed significant improvements in participant's levels of anxiety (p < .001) and depression (p < .001), self-compassion (p = .005), mental wellbeing (p < .001), resilience (p.001), and mindfulness (p < .001). Thematic analysis of interviews with participants (n = 19) and Mindfulness Teacher (n = 1) generated three themes (and nine-subthemes); experiences of the CMR programme that facilitated subjective benefit, participants lived and shared experiences, and practicalities of programme participation. All participants interviewed reported that they found programme participation to be beneficial. CONCLUSION: The findings suggest that the CMR programme has the potential to improve psychological outcomes among people with chronic kidney disease. Future randomized controlled trials are required to further test its effectiveness.

Synonymous codon substitutions perturb cotranslational protein folding in vivo and impair cell fitness.

In the cell, proteins are synthesized from N to C terminus and begin to fold during translation. Cotranslational folding mechanisms are therefore linked to elongation rate, which varies as a function of synonymous codon usage. However, synonymous codon substitutions can affect many distinct cellular processes, which has complicated attempts to deconvolve the extent to which synonymous codon usage can promote or frustrate proper protein folding in vivo. Although previous studies have shown that some synonymous changes can lead to different final structures, other substitutions will likely be more subtle, perturbing predominantly the protein folding pathway without radically altering the final structure. Here we show that synonymous codon substitutions encoding a single essential enzyme lead to dramatically slower cell growth. These mutations do not prevent active enzyme formation; instead, they predominantly alter the protein folding mechanism, leading to enhanced degradation in vivo. These results support a model in which synonymous codon substitutions can impair cell fitness by significantly perturbing cotranslational protein folding mechanisms, despite the chaperoning provided by the cellular protein homeostasis network.

A Robust and Versatile Automated Glycoanalytical Technology for Serum Antibodies and Acute Phase Proteins: Ovarian Cancer Case Study.

The direct association of the genome, transcriptome, metabolome, lipidome and proteome with the serum glycome has revealed systems of interconnected cellular pathways. The exact roles of individual glycoproteomes in the context of disease have yet to be elucidated. In a move toward personalized medicine, it is now becoming critical to understand disease pathogenesis, and the traits, stages, phenotypes and molecular features that accompany it, as the disruption of a whole system. To this end, we have developed an innovative technology on an automated platform, "GlycoSeqCap," which combines N-glycosylation data from six glycoproteins using a single source of human serum. Specifically, we multiplexed and optimized a successive serial capture and glycoanalysis of six purified glycoproteins, immunoglobulin G (IgG), immunoglobulin M (IgM), immunoglobulin A (IgA), transferrin (Trf), haptoglobin (Hpt) and alpha-1-antitrypsin (A1AT), from 50 µl of human serum. We provide the most comprehensive and in-depth glycan analysis of individual glycoproteins in a single source of human serum to date. To demonstrate the technological application in the context of a disease model, we performed a pilot study in an ovarian cancer cohort (n = 34) using discrimination and classification analyses to identify aberrant glycosylation. In our sample cohort, we exhibit improved selectivity and specificity over the currently used biomarker for ovarian cancer, CA125, for early stage ovarian cancer. This technology will establish a new state-of-the-art strategy for the characterization of individual serum glycoproteomes as a diagnostic and monitoring tool which represents a major step toward understanding the changes that take place during disease.

Experiences of renal healthcare practitioners during the COVID-19 pandemic: a multi-methods approach.

BACKGROUND: Globally, renal healthcare practitioners provide intensive and protracted support to a highly complex multi-morbid patient population however knowledge about the impact of COVID-19 on these practitioners is extremely limited. OBJECTIVE: This study aimed to explore the experiences of COVID-19 with renal healthcare practitioners during the first global lockdown between June 2020 and September 2020. METHODS: A multi-methods approach was carried out including a quantitative survey and qualitative interviews. This was a multinational study of renal healthcare practitioners from 29 countries. Quantitative: A self-designed survey on COVID-19 experiences and standardised questionnaires (General Health Questionnaire-12; Maslach Burnout Inventory). Descriptive statistics were generated for numerical data. Qualitative: Online semi-structured interviews were conducted. Data was subjected to thematic analysis. Renal healthcare practitioners (n = 251) completed an online survey. Thirteen renal healthcare practitioners took part in semi-structured interviews (12 nurses and 1 dietician). RESULTS: The majority of participants surveyed were female (86.9 %; n = 218), nurses (86.9 %; n = 218) with an average 21.5 (SD = 11.1) years' experience since professional qualification, and 16.3 years (SD = 9.3) working in renal healthcare. Survey responses indicated a level of preparedness, training and satisfactory personal protective equipment during the pandemic however approximately 40.3 % experienced fear about attending work, and 49.8 % experienced mental health distress. The highest prevalence of burnout was emotional exhaustion (35.9 %). Three themes emerged from the qualitative analysis highlighting the holistic complexities in managing renal healthcare, a neglected specialist workforce, and the need for appropriate support at work during a pandemic. CONCLUSIONS: Results have highlighted the psychological impact, in terms of emotional exhaustion and mental health distress in our sample of renal healthcare practitioners. As the pandemic has continued, it is important to consider the long-term impact on an already stretched workforce including the risk of developing mental health disorders. Future research and interventions are required to understand and improve the provision of psychological support for specialist medical and nursing personnel.

Mindfulness as a well-being initiative for future nurses: a survey with undergraduate nursing students.

BACKGROUND: Mindfulness can potentially positively impact well-being and resilience in undergraduate nursing students. The psychological well-being of such students undertaking clinical training is paramount to ensure optimal learning, and to equip them with skills to manage their wellbeing in future clinical practice. The aim of our study was to explore the views of undergraduate nursing students in relation to understanding and engaging with mindfulness, and how mindfulness could best be delivered within their university programme. METHODS: An online survey was administered via a cloud-based student response system to a convenience sample of first year undergraduate nursing students completing a Bachelor of Science (BSc) Honours (Hons) degree in nursing at a University in the United Kingdom. Data were analysed using descriptive statistics and thematic analysis. RESULTS: The survey achieved a response rate of 78% (n = 208). Seventy-nine percent of participants had heard of mindfulness and were interested in taking part in a mindfulness programme. Respondents reported that the ideal delivery of the programme would consist of weekly 45-min, in person group sessions, over a 6-week period. Respondents also indicated that a mobile application could potentially facilitate participation in the programme. Thematic analysis of open-ended comments, and free text, within the survey indicated 4 overarching themes: 1) Perceptions of what mindfulness is; 2) Previous mindfulness practice experiences; 3) Impact of mindfulness in nursing; 4) The need for a future well-being initiative for undergraduate nursing students. CONCLUSIONS: Undergraduate nursing students perceived that a mindfulness programme has the potential to enhance well-being and future clinical practice. This student cohort are familiar with mindfulness and want more integrated within their undergraduate curriculum. Further research is required to examine the effectiveness of a tailored mindfulness intervention for this population that incorporates the use of both face-to-face and mobile delivery.

Utility of Ion-Mobility Spectrometry for Deducing Branching of Multiply Charged Glycans and Glycopeptides in a High-Throughput Positive ion LC-FLR-IMS-MS Workflow.

High-throughput glycan analysis has become an important part of biopharmaceutical production and quality control. However, it is still a significant challenge in the field of glycomics to easily deduce isomeric glycan structures, especially in a high-throughput manner. Ion mobility spectrometry (IMS) is an excellent tool for differentiating isomeric glycan structures. However, demonstrations of the utility of IMS in high-throughput workflows such as liquid chromatography-fluorescence-mass spectrometry (LC-FLR-MS) workflows have been limited with only a small amount of collision cross section (CCS) data available. In particular, IMS data of glycan fragments obtained in positive ion mode are limited in comparison to those obtained in negative ion mode despite positive ion mode being widely used for glycomics. Here, we describe IMS TWCCSN2 data obtained from a high-throughput LC-FLR-IMS-MS workflow in positive ion mode. We obtained IMS data from a selection of RapiFluor-MS (RFMS) labeled N-glycans and also glycopeptides. We describe how IMS is able to distinguish isomeric N-glycans and glycopeptides using both intact IMS and fragment-based IMS glycan sequencing experiments in positive ion mode, without significantly altering the high-throughput nature of the analysis. For the first time, we were able to successfully use IMS in positive ion mode to determine the branching of isomeric glycopeptides and RFMS labeled glycans. Further, we highlight that IMS glycan sequencing of fragments obtained from RFMS labeled glycans was similar to that of glycopeptides. Finally, we show that the IMS glycan sequencing approach can highlight shared structural features of nonisomeric glycans in a high-throughput LC-FLR-IMS-MS workflow.

GlycanAnalyzer: software for automated interpretation of N-glycan profiles after exoglycosidase digestions.

SUMMARY: Many eukaryotic proteins are modified by N-glycans. Liquid chromatography (ultra-performance -UPLC and high-performance-HPLC) coupled with mass spectrometry (MS) is conventionally used to characterize N-glycan structures. Software can automatically assign glycan structures by matching their observed retention times and masses with standardized values in reference databases. However, more precise confirmation of N-glycan structures can be derived using exoglycosidases, enzymes that remove specific monosaccharides from glycans. Exoglycosidase removal of monosaccharides results in signature peak shifts, in both UPLC and MS1, yielding an effective way to verify N-glycan structure with high detail (down to the position and isomeric linkage of each monosaccharide). Because manual interpretation of exoglycosidase data is complex and time consuming, we developed GlycanAnalyzer, a web application that pattern matches N-glycan peak shifts following exoglycosidase digestion and automates structure assignments. GlycanAnalyzer significantly improves assignment accuracy over other auto-assignment methods on tests with a monoclonal antibody and four glycan standards (100% versus 82% for the next best software). By automating data interpretation, GlycanAnalyzer enables the easier use of exoglycosidases to precisely define N-glycan structure. AVAILABILITY AND IMPLEMENTATION: http://glycananalyzer.neb.com. Datasets available online. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A mixed-methods feasibility study of an arts-based intervention for patients receiving maintenance haemodialysis.

BACKGROUND: Haemodialysis can negatively impact quality of life and mental health. Arts-based interventions used successfully in other settings to improve health and well-being, could help address the impact of haemodialysis. This study aimed to evaluate the feasibility and acceptability of conducting a randomised controlled trial (RCT) of an arts-based intervention for patients receiving haemodialysis. METHODS: A parallel convergent mixed-methods design was used, including a pilot cluster RCT and qualitative process evaluation. Phase 1 evaluated recruitment and retention rates through a pilot cluster RCT at a single haemodialysis unit in Northern Ireland. Participants included patients who received haemodialysis for ESKD, were over the age of 18 and had the capacity to consent. These participants were randomised to the intervention or control group according to their haemodialysis shift. The intervention involved six one-hour, one-to-one facilitated arts sessions during haemodialysis. Phase 2 explored intervention and trial acceptability through a qualitative process evaluation using semi-structured interviews based on the RE-AIM framework. Participants included 13 patients who participated in phase 1 of the study, including 9 participants from the experimental group and four participants from the control group, and nine healthcare professionals who were present on the unit during implementation. RESULTS: Out of 122 outpatient haemodialysis patients, 94 were assessed as eligible for participation. Twenty-four participants were randomised, meaning 80% of the target sample size was recruited and the attrition rate at 3 months was 12.5% (n = 3). Participants viewed the arts as more accessible and enjoyable than anticipated following implementation. All participants who started the intervention (n = 11) completed the full six sessions. Qualitative benefits of the intervention suggest improvements in mental well-being. Patient choice and facilitation were important factors for successful implementation. CONCLUSION: An arts-based intervention for patients receiving haemodialysis is acceptable for both patients and healthcare professionals, and a definitive trial is feasible. The intervention may help improve mental-wellbeing in patients receiving haemodialysis, but this requires further investigation in a definitive trial. TRIAL REGISTRATION: The trial was prospectively registered on clinicaltrials.gov on 14/8/2018, registration number NCT03629496 .

Experimentally Determined Long Intrinsically Disordered Protein Regions Are Now Abundant in the Protein Data Bank.

Intrinsically disordered protein regions are commonly defined from missing electron density in X-ray structures. Experimental evidence for long disorder regions (LDRs) of at least 30 residues was so far limited to manually curated proteins. Here, we describe a comprehensive and large-scale analysis of experimental LDRs for 3133 unique proteins, demonstrating an increasing coverage of intrinsic disorder in the Protein Data Bank (PDB) in the last decade. The results suggest that long missing residue regions are a good quality source to annotate intrinsically disordered regions and perform functional analysis in large data sets. The consensus approach used to define LDRs allows to evaluate context dependent disorder and provide a common definition at the protein level.

Clustering and curation of electropherograms: an efficient method for analyzing large cohorts of capillary electrophoresis glycomic profiles for bioprocessing operations.

The accurate assessment of antibody glycosylation during bioprocessing requires the high-throughput generation of large amounts of glycomics data. This allows bioprocess engineers to identify critical process parameters that control the glycosylation critical quality attributes. The advances made in protocols for capillary electrophoresis-laser-induced fluorescence (CE-LIF) measurements of antibody N-glycans have increased the potential for generating large datasets of N-glycosylation values for assessment. With large cohorts of CE-LIF data, peak picking and peak area calculations still remain a problem for fast and accurate quantitation, despite the presence of internal and external standards to reduce misalignment for the qualitative analysis. The peak picking and area calculation problems are often due to fluctuations introduced by varying process conditions resulting in heterogeneous peak shapes. Additionally, peaks with co-eluting glycans can produce peaks of a non-Gaussian nature in some process conditions and not in others. Here, we describe an approach to quantitatively and qualitatively curate large cohort CE-LIF glycomics data. For glycan identification, a previously reported method based on internal triple standards is used. For determining the glycan relative quantities our method uses a clustering algorithm to 'divide and conquer' highly heterogeneous electropherograms into similar groups, making it easier to define peaks manually. Open-source software is then used to determine peak areas of the manually defined peaks. We successfully applied this semi-automated method to a dataset (containing 391 glycoprofiles) of monoclonal antibody biosimilars from a bioreactor optimization study. The key advantage of this computational approach is that all runs can be analyzed simultaneously with high accuracy in glycan identification and quantitation and there is no theoretical limit to the scale of this method.

Combining Glucose Units, m/z, and Collision Cross Section Values: Multiattribute Data for Increased Accuracy in Automated Glycosphingolipid Glycan Identifications and Its Application in Triple Negative Breast Cancer.

Glycan head-groups attached to glycosphingolipids (GSLs) found in the cell membrane bilayer can alter in response to external stimuli and disease, making them potential markers and/or targets for cellular disease states. To identify such markers, comprehensive analyses of glycan structures must be undertaken. Conventional analyses of fluorescently labeled glycans using hydrophilic interaction high-performance liquid chromatography (HILIC) coupled with mass spectrometry (MS) provides relative quantitation and has the ability to perform automated glycan assignments using glucose unit (GU) and mass matching. The use of ion mobility (IM) as an additional level of separation can aid the characterization of closely related or isomeric structures through the generation of glycan collision cross section (CCS) identifiers. Here, we present a workflow for the analysis of procainamide-labeled GSL glycans using HILIC-IM-MS and a new, automated glycan identification strategy whereby multiple glycan attributes are combined to increase accuracy in automated structural assignments. For glycan matching and identification, an experimental reference database of GSL glycans containing GU, mass, and CCS values for each glycan was created. To assess the accuracy of glycan assignments, a distance-based confidence metric was used. The assignment accuracy was significantly better compared to conventional HILIC-MS approaches (using mass and GU only). This workflow was applied to the study of two Triple Negative Breast Cancer (TNBC) cell lines and revealed potential GSL glycosylation signatures characteristic of different TNBC subtypes.

GlycoStore: a database of retention properties for glycan analysis.

Summary: GlycoStore is a curated chromatographic, electrophoretic and mass-spectrometry composition database of N-, O-, glycosphingolipid (GSL) glycans and free oligosaccharides associated with a range of glycoproteins, glycolipids and biotherapeutics. The database is built on publicly available experimental datasets from GlycoBase developed in the Oxford Glycobiology Institute and then the National Institute for Bioprocessing Research and Training (NIBRT). It has now been extended to include recently published and in-house data collections from the Bioprocessing Technology Institute (BTI) A*STAR, Macquarie University and Ludger Ltd. GlycoStore provides access to approximately 850 unique glycan structure entries supported by over 8500 retention positions determined by: (i) hydrophilic interaction chromatography (HILIC) ultra-high performance liquid chromatography (U/HPLC) and reversed phase (RP)-U/HPLC with fluorescent detection; (ii) porous graphitized carbon (PGC) chromatography in combination with ESI-MS/MS detection; and (iii) capillary electrophoresis with laser induced fluorescence detection (CE-LIF). GlycoStore enhances many features previously available in GlycoBase while addressing the limitations of the data collections and model of this popular resource. GlycoStore aims to support detailed glycan analysis by providing a resource that underpins current workflows. It will be regularly updated by expert annotation of published data and data obtained from the project partners. Availability and implementation: http://www.glycostore.org. Supplementary information: Supplementary data are available at Bioinformatics online.

Evaluating mindfulness training for medical and PhD nursing students.

BACKGROUND: it is recognised that mindfulness training can positively impact holistic wellbeing and can improve mood, focus and resilience. Health service students often experience high levels of psychological morbidity. Engaging in mindfulness techniques may positively impact the psychological wellbeing of healthcare students. This is of vital importance to ensure optimum learning for healthcare practice with its associated challenges. METHOD: an evaluation was conducted with medical students (n=4) and PhD nursing students (n=6), who took part in a mindfulness-based workshop, followed by five weekly 30-minute sessions of mindfulness training, including a 15-minute meditation exercise. Data collection was carried out at baseline and post intervention, followed by a focus group discussion to elucidate qualitative experiences. RESULTS: significant findings were identified with respect to cognitive mindfulness scores (P=0.02) and resilience (P=0.04). DISCUSSION: data reflected three themes: the impact of mindfulness and maintaining practice, improvements in wellbeing, and improvements in academic endeavour. CONCLUSIONS: this evaluation found significant improvements in the ability to cope with stress and increased attention and resilience in all students. Although results are not generalisable in this small evaluation, students reported increased concentration levels and improved focus, both of which are likely to impact positively on any psychological symptoms, particularly those related to workload pressures.

Correct machine learning on protein sequences: a peer-reviewing perspective.

Machine learning methods are becoming increasingly popular to predict protein features from sequences. Machine learning in bioinformatics can be powerful but carries also the risk of introducing unexpected biases, which may lead to an overestimation of the performance. This article espouses a set of guidelines to allow both peer reviewers and authors to avoid common machine learning pitfalls. Understanding biology is necessary to produce useful data sets, which have to be large and diverse. Separating the training and test process is imperative to avoid over-selling method performance, which is also dependent on several hidden parameters. A novel predictor has always to be compared with several existing methods, including simple baseline strategies. Using the presented guidelines will help nonspecialists to appreciate the critical issues in machine learning.

FELLS: fast estimator of latent local structure.

MOTIVATION: The behavior of a protein is encoded in its sequence, which can be used to predict distinct features such as secondary structure, intrinsic disorder or amphipathicity. Integrating these and other features can help explain the context-dependent behavior of proteins. However, most tools focus on a single aspect, hampering a holistic understanding of protein structure. Here, we present Fast Estimator of Latent Local Structure (FELLS) to visualize structural features from the protein sequence. FELLS provides disorder, aggregation and low complexity predictions as well as estimated local propensities including amphipathicity. A novel fast estimator of secondary structure (FESS) is also trained to provide a fast response. The calculations required for FELLS are extremely fast and suited for large-scale analysis while providing a detailed analysis of difficult cases. AVAILABILITY AND IMPLEMENTATION: The FELLS web server is available from URL: http://protein.bio.unipd.it/fells/ . The server also exposes RESTful functionality allowing programmatic prediction requests. An executable version of FESS for Linux can be downloaded from URL: protein.bio.unipd.it/download/. CONTACT: silvio.tosatto@unipd.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MobiDB 2.0: an improved database of intrinsically disordered and mobile proteins.

MobiDB (http://mobidb.bio.unipd.it/) is a database of intrinsically disordered and mobile proteins. Intrinsically disordered regions are key for the function of numerous proteins. Here we provide a new version of MobiDB, a centralized source aimed at providing the most complete picture on different flavors of disorder in protein structures covering all UniProt sequences (currently over 80 million). The database features three levels of annotation: manually curated, indirect and predicted. Manually curated data is extracted from the DisProt database. Indirect data is inferred from PDB structures that are considered an indication of intrinsic disorder. The 10 predictors currently included (three ESpritz flavors, two IUPred flavors, two DisEMBL flavors, GlobPlot, VSL2b and JRONN) enable MobiDB to provide disorder annotations for every protein in absence of more reliable data. The new version also features a consensus annotation and classification for long disordered regions. In order to complement the disorder annotations, MobiDB features additional annotations from external sources. Annotations from the UniProt database include post-translational modifications and linear motifs. Pfam annotations are displayed in graphical form and are link-enabled, allowing the user to visit the corresponding Pfam page for further information. Experimental protein-protein interactions from STRING are also classified for disorder content.

Comprehensive large-scale assessment of intrinsic protein disorder.

MOTIVATION: Intrinsically disordered regions are key for the function of numerous proteins. Due to the difficulties in experimental disorder characterization, many computational predictors have been developed with various disorder flavors. Their performance is generally measured on small sets mainly from experimentally solved structures, e.g. Protein Data Bank (PDB) chains. MobiDB has only recently started to collect disorder annotations from multiple experimental structures. RESULTS: MobiDB annotates disorder for UniProt sequences, allowing us to conduct the first large-scale assessment of fast disorder predictors on 25 833 different sequences with X-ray crystallographic structures. In addition to a comprehensive ranking of predictors, this analysis produced the following interesting observations. (i) The predictors cluster according to their disorder definition, with a consensus giving more confidence. (ii) Previous assessments appear over-reliant on data annotated at the PDB chain level and performance is lower on entire UniProt sequences. (iii) Long disordered regions are harder to predict. (iv) Depending on the structural and functional types of the proteins, differences in prediction performance of up to 10% are observed. AVAILABILITY: The datasets are available from Web site at URL: http://mobidb.bio.unipd.it/lsd. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PASTA 2.0: an improved server for protein aggregation prediction.

The formation of amyloid aggregates upon protein misfolding is related to several devastating degenerative diseases. The propensities of different protein sequences to aggregate into amyloids, how they are enhanced by pathogenic mutations, the presence of aggregation hot spots stabilizing pathological interactions, the establishing of cross-amyloid interactions between co-aggregating proteins, all rely at the molecular level on the stability of the amyloid cross-beta structure. Our redesigned server, PASTA 2.0, provides a versatile platform where all of these different features can be easily predicted on a genomic scale given input sequences. The server provides other pieces of information, such as intrinsic disorder and secondary structure predictions, that complement the aggregation data. The PASTA 2.0 energy function evaluates the stability of putative cross-beta pairings between different sequence stretches. It was re-derived on a larger dataset of globular protein domains. The resulting algorithm was benchmarked on comprehensive peptide and protein test sets, leading to improved, state-of-the-art results with more amyloid forming regions correctly detected at high specificity. The PASTA 2.0 server can be accessed at http://protein.bio.unipd.it/pasta2/.