Process evaluation design in a cluster randomised controlled childhood obesity prevention trial: The WAVES study

Background: The implementation of a complex intervention is heavily influenced by individual context. Variation in implementation and tailoring of the intervention to the particular context will occur, even in a trial setting. It is recognised that in trials, evaluating the process of implementation of a complex intervention is important, yet process evaluation methods are rarely reported. The WAVES study is a cluster randomised controlled trial to evaluate the effectiveness of an obesity prevention intervention programme targeting children aged 6-7 years, delivered by teachers in primary schools across the West Midlands, UK. The intervention promoted activities encouraging physical activity and healthy eating. This paper presents the methods used to assess implementation of the intervention. Methods: Previous literature was used to identify the dimensions of intervention process and implementation to be assessed, including adherence, exposure, quality of delivery, participant responsiveness, context, and programme differentiation. Results: Multiple methods and tools were developed to capture information on all these dimensions. These included observations, logbooks, qualitative evaluation, questionnaires and research team reflection. Discussion: Data collection posed several challenges, predominantly when relying on teachers to complete paperwork, which they saw as burdensome on top of their teaching responsibilities. However, the use of multiple methods helped to ensure data on each dimension, where possible, was collected using more than one method. This also allowed for triangulation of the findings when several data sources on any one dimension were available. Conclusions: We have reported a comprehensive approach to the assessment of the implementation and processes of a complex childhood obesity prevention intervention within a cluster randomised controlled trial. These approaches can be transferred and adapted for use in other complex intervention trials. Trial registration number: ISRCTN9700058

Elucidating the Stoichiometries of Host–Pathogen Protein Interactions with Mass Photometry

Mass photometry (MP) is a single molecule technique that enables the characterization of individual proteins. Here we show a detailed workflow using the Refeyn OneMP to investigate molecular complexes, using the M53 protein, a plasminogen-binding group A streptococcal M-like protein (PAM), and human plasminogen as exemplar proteins. The methodology described herein confirmed a 1:1 binding stoichiometry for the M53–plasminogen complex. Additionally, MP was used to identify the oligomerization state, homogeneity, purity, and approximate molecular weights of each protein

Current understanding of Group A Streptococcal biofilms

Background: It has been proposed that GAS may form biofilms. Biofilms are microbial communities that aggregate on a surface, and exist within a self-produced matrix of extracellular polymeric substances. Biofilms offer bacteria an increased survival advantage, in which bacteria persist, and resist host immunity and antimicrobial treatment. The biofilm phenotype has long been recognized as a virulence mechanism for many Gram-positive and Gram-negative bacteria, however very little is known about the role of biofilms in GAS pathogenesis. Objective: This review provides an overview of the current knowledge of biofilms in GAS pathogenesis. This review assesses the evidence of GAS biofilm formation, the role of GAS virulence factors in GAS biofilm formation, modelling GAS biofilms, and discusses the polymicrobial nature of biofilms in the oropharynx in relation to GAS. Conclusion: Further study is needed to improve the current understanding of GAS as both a monospecies biofilm, and as a member of a polymicrobial biofilm. Improved modelling of GAS biofilm formation in settings closely mimicking in vivo conditions will ensure that biofilms generated in the lab closely reflect those occurring during clinical infection

Neuroserpin and transthyretin are extracellular chaperones that preferentially inhibit amyloid formation.

Neuroserpin is a secreted protease inhibitor known to inhibit amyloid formation by the Alzheimer’s beta peptide (Aβ). To test whether this effect was constrained to Aβ, we used a range of in vitro assays to demonstrate that neuroserpin inhibits amyloid formation by several different proteins and protects against the associated cytotoxicity but, unlike other known chaperones, has a poor ability to inhibit amorphous protein aggregation. Collectively, these results suggest that neuroserpin has an unusual chaperone selectivity for intermediates on the amyloid-forming pathway. Bioinformatics analyses identified a highly conserved 14-residue region containing an α helix shared between neuroserpin and the thyroxine-transport protein transthyretin, and we subsequently demonstrated that transthyretin also preferentially inhibits amyloid formation. Last, we used rationally designed neuroserpin mutants to demonstrate a direct involvement of the conserved 14-mer region in its chaperone activity. Identification of this conserved region may prove useful in the future design of anti-amyloid reagents

Molecular characterization of the interaction between human IgG and the M-related proteins from Streptococcus pyogenes.

Group A Streptococcal M-related proteins (Mrps) are dimeric α-helical-coiled-coil cell membrane-bound surface proteins. During infection, Mrp recruit the fragment crystallizable region of human immunoglobulin G via their A-repeat regions to the bacterial surface, conferring upon the bacteria enhanced phagocytosis resistance and augmented growth in human blood. However, Mrps show a high degree of sequence diversity, and it is currently not known whether this diversity affects the Mrp-IgG interaction. Herein, we report that diverse Mrps all bind human IgG subclasses with nanomolar affinity, with differences in affinity which ranged from 3.7 to 11.1 nM for mixed IgG. Using surface plasmon resonance, we confirmed Mrps display preferential IgG-subclass binding. All Mrps were found to have a significantly weaker affinity for IgG3 (p < 0.05) compared to all other IgG subclasses. Furthermore, plasma pulldown assays analyzed via Western blotting revealed that all Mrp were able to bind IgG in the presence of other serum proteins at both 25 °C and 37 °C. Finally, we report that dimeric Mrps bind to IgG with a 1:1 stoichiometry, enhancing our understanding of this important host-pathogen interaction

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers