Contextual factors related to vector-control interventions for malaria: a scoping review and evidence and gap map protocol.

Objective: This scoping review will identify existing literature regarding contextual factors relevant to vector-control interventions to prevent malaria. We will use the findings of the scoping review to produce an interactive evidence and gap map. The map will assist in the priority setting, development, and conduct of targeted systematic reviews. These systematic reviews seek to assist the Vector Control and Insecticide Resistance Unit of the World Health Organization's Global Malaria Programme by informing recommendation development by their Guidelines Development Group. Introduction: Malaria contributes substantially to the global burden of disease, with an estimated 247 million cases and 619,000 deaths in 2021. Vector-control is key in reducing malaria transmission. Vector-control interventions directly target the mosquito, reducing the potential for parasite infections. These interventions commonly include insecticides used in indoor residual spraying or insecticide-treated nets and larval source management. Several new vector-control interventions are under evaluation to complement these. In addition to estimating the effects of interventions on health outcomes, it is critical to understand how populations at risk of malaria consider them in terms of their feasibility, acceptability, and values. Inclusion Criteria: Eligible studies will have assessed the contextual factors of feasibility or acceptability of the interventions of interest, or the valuation of the outcomes of interests. These assessments will be from the perspective of people who receive (residents) or deliver (workers or technicians) the vector-control intervention for the purpose of preventing malaria. Methods: We will conduct this scoping review in accordance with the JBI methodology for scoping reviews and report in line with the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews (PRISMA-ScR). We will construct the evidence and gap map following guidance from the Campbell Collaboration.

Guidelines for releasing a variant effect predictor.

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders.

Identifying causal mutations accelerates genetic disease diagnosis, and therapeutic development. Missense variants present a bottleneck in genetic diagnoses as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are increasingly successful at prediction for variants in known disease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome1-6. To address this, we developed a deep generative model, popEVE, that combines evolutionary information with population sequence data7 and achieves state-of-the-art performance at ranking variants by severity to distinguish patients with severe developmental disorders8 from potentially healthy individuals9. popEVE identifies 442 genes in patients this developmental disorder cohort, including evidence of 123 novel genetic disorders, many without the need for gene-level enrichment and without overestimating the prevalence of pathogenic variants in the population. A majority of these variants are close to interacting partners in 3D complexes. Preliminary analyses on child exomes indicate that popEVE can identify candidate variants without the need for inheritance labels. By placing variants on a unified scale, our model offers a comprehensive perspective on the distribution of fitness effects across the entire proteome and the broader human population. popEVE provides compelling evidence for genetic diagnoses even in exceptionally rare single-patient disorders where conventional techniques relying on repeated observations may not be applicable.

Deep generative modeling of the human proteome reveals over a hundred novel genes involved in rare genetic disorders.

Identifying causal mutations accelerates genetic disease diagnosis, and therapeutic development. Missense variants present a bottleneck in genetic diagnoses as their effects are less straightforward than truncations or nonsense mutations. While computational prediction methods are increasingly successful at prediction for variants in known disease genes, they do not generalize well to other genes as the scores are not calibrated across the proteome. To address this, we developed a deep generative model, popEVE, that combines evolutionary information with population sequence data and achieves state-of-the-art performance at ranking variants by severity to distinguish patients with severe developmental disorders from potentially healthy individuals. popEVE identifies 442 genes in a cohort of developmental disorder cases, including evidence of 119 novel genetic disorders without the need for gene-level enrichment and without overestimating the prevalence of pathogenic variants in the population. By placing variants on a unified scale, our model offers a comprehensive perspective on the distribution of fitness effects across the entire proteome and the broader human population. popEVE provides compelling evidence for genetic diagnoses even in exceptionally rare single-patient disorders where conventional techniques relying on repeated observations may not be applicable. Interactive web viewer and downloads available at pop.evemodel.org.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction.

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

Biolayer Interferometry Analysis for a Higher Throughput Quantification of In-Process Samples of a Rotavirus Vaccine.

Rotavirus A infection is a global leading cause of severe acute gastroenteritis associated with life-threatening diarrheal episodes in infants and young children. The disease burden is being reduced, namely due to a wider access to rotavirus vaccines. However, there is a demand to expand rotavirus vaccination programs, and to achieve this, it is critical to improve high-throughput in-process product quality control and vaccine manufacturing monitoring. Here, we present the development of an analytical method for the quantification of rotavirus particles contained in a licensed vaccine. The binding of rotavirus proteins to distinct glycoconjugate receptors and monoclonal antibodies was evaluated using biolayer interferometry analysis, applied on an Octet platform. The antibody strategy presented the best results with a linear response range within 2.5 × 107-1.0 × 108 particles·mL-1 and limits of detection and quantification of 2.5 × 106 and 7.5 × 106 particles·mL-1, respectively. Method suitability for the quantification of in-process samples was shown using samples from different manufacturing stages and their titers were comparable with the approved CCID(50) method. This cell-free method enables a fast and high-throughput analysis, compatible with time constraints during bioprocess development and it is suitable to be adapted to other viral particle-based drug products.

Disease variant prediction with deep generative models of evolutionary data.

Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences1-3. In principle, computational methods could support the large-scale interpretation of genetic variants. However, state-of-the-art methods4-10 have relied on training machine learning models on known disease labels. As these labels are sparse, biased and of variable quality, the resulting models have been considered insufficiently reliable11. Here we propose an approach that leverages deep generative models to predict variant pathogenicity without relying on labels. By modelling the distribution of sequence variation across organisms, we implicitly capture constraints on the protein sequences that maintain fitness. Our model EVE (evolutionary model of variant effect) not only outperforms computational approaches that rely on labelled data but also performs on par with, if not better than, predictions from high-throughput experiments, which are increasingly used as evidence for variant classification12-16. We predict the pathogenicity of more than 36 million variants across 3,219 disease genes and provide evidence for the classification of more than 256,000 variants of unknown significance. Our work suggests that models of evolutionary information can provide valuable independent evidence for variant interpretation that will be widely useful in research and clinical settings.

Insect High Five™ cell line development using site-specific flipase recombination technology.

Insect Trichoplusia ni High Five™ (Hi5) cells have been widely explored for production of heterologous proteins, traditionally mostly using the lytic baculovirus expression vector system (BEVS), and more recently using virus-free transient gene expression systems. Stable expression in such host cells would circumvent the drawbacks associated with both systems when it comes to scale-up and implementation of more efficient high-cell density process modes for the manufacturing of biologics. In this study, we combined Flipase (Flp) recombinase-mediated cassette exchange (RMCE) with fluorescence-activated cell sorting (FACS) for generating a stable master clonal Hi5 cell line with the flexibility to express single or multiple proteins of interest from a tagged genomic locus. The 3-step protocol herein implemented consisted of (i) introducing the RMCE docking cassette into the cell genome by random integration followed by selection in Hygromycin B and FACS (Hi5-tagging population), (ii) eliminating cells tagged in loci with low recombination efficiency by transfecting the tagging population with an eGFP-containing target cassette followed by selection in G418 and FACS (Hi5-RMCE population), and (iii) isolation of pure eGFP-expressing cells by FACS and expansion to suspension cultures (Hi5-RMCE master clone). Exchangeability of the locus in the master clone was demonstrated in small-scale suspension cultures by replacing the target cassette by one containing a single protein (i.e., iCherry, as an intracellular protein model) or two proteins (i.e., influenza HA and M1 for virus-like particles production, as an extracellular protein model). Overall, the stable insect Hi5 cell platform herein assembled has the potential to assist and accelerate biologics development.

The "quality" of JBI qualitative research synthesis: a methodological investigation into the adherence of meta-aggregative systematic reviews to reporting standards and methodological guidance.

INTRODUCTION: Approaches to the synthesis of qualitative research have existed for more than 20 years and have evolved significantly during that time. One common approach is meta-aggregation, as advocated by JBI. There is now a considerable number of published reviews that claim to follow the JBI approach to meta-aggregation. This methodological review sought to determine the extent to which a selection of these reviews follow the available guidance, with a view to establishing compliance and identifying potential areas for improvement. METHODS: The JBI Database of Systematic Reviews and Implementation Reports (JBISRIR) was searched from 2015 to 2017 to identify all qualitative systematic reviews following the JBI approach. Citations were screened by two independent reviewers, and data extraction was conducted independently by at least two reviewers. Eligible reviews were then assessed against the JBI methodological guidance and ENTREQ statement to determine compliance. RESULTS: From the search, 33 health care-related reviews that met the inclusion criteria were identified. Several areas were identified where reviewers consistently made errors or did not clearly report their findings, including study screening and selection issues (particularly how this was done and by whom), transparent rationale for study exclusion, who performed data extraction and how, processes for developing synthesized findings, and the development and presentation of recommendations. CONCLUSION: Although qualitative synthesis has come a long way, there are still some areas for improvement in conduct and reporting. This has implications for those who develop guidance and provide education to systematic reviewers.

Guidelines rarely used GRADE and applied methods inconsistently: A methodological study of Australian guidelines.

OBJECTIVES: The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach is accepted methodology to assess the certainty of the evidence included in systematic reviews and clinical practice guidelines. The GRADE approach is endorsed globally, in Australia, the National Health and Medical Research Council advocated for the use of the GRADE approach in 2011. The purpose of this methodological review was to assess how GRADE has been adopted for Australian practice guidelines. STUDY DESIGN AND SETTING: This methodological review searched of the National Health and Medical Research Council Clinical Practice Guidelines Portal from 2011 to 2018, in an effort to retrieve all practice guidelines available via this medium. RESULTS: 240 guidelines were retrieved authored by 51 different organizations. 15 guidelines followed GRADE methodology. Application of GRADE methods varied between guidelines, some misreported and altered aspects of the GRADE process. Guidelines that closely adhered to the guidance from the GRADE Working Group scored higher in domain 3 (rigor of development) of the Appraisal of Guidelines for Research and Evaluation II tool, indicating a positive linear relationship between GRADE adherence and rigor of development scores. CONCLUSION: The results of our project suggest that the use of GRADE in Australian guidelines is increasing, however, strategies to increase uptake and reporting within the guideline community need to be explored.

Adherence of meta-aggregative systematic reviews to reporting standards and methodological guidance: a methodological review protocol.

REVIEW OBJECTIVE: The objective of the review is to examine Joanna Briggs Institute (JBI) qualitative meta-aggregative reviews to determine.

Use of GRADE in Australian clinical practice guidelines: a methodological review protocol.

RESEARCH QUESTION/OBJECTIVE: The purpose of this methodological review is to determine whether and to what extent GRADE (Grading of Recommendations, Assessment, Development and Evaluation) methodology has been and is currently being used in Australian clinical practice guidelines.

RMCE-based insect cell platform to produce membrane proteins captured on HIV-1 Gag virus-like particles.

Conformationally complex membrane proteins (MPs) are therapeutic targets in many diseases, but drug discovery has been slowed down by the lack of efficient production tools. Co-expression of MPs with matrix proteins from enveloped viruses is a promising approach to obtain correctly folded proteins at the surface of virus-like particles (VLPs), preserving their native lipidic environment. Here, we implemented a site-specific recombinase-mediated cassette exchange (RMCE) strategy to establish a reusable HIV-1 Gag-expressing insect cell line for fast production of target MPs on the surface of Gag-VLPs. The Sf9 cell line was initially tagged with a Gag-GFP-expressing cassette incorporating two flipase recognition target sites (FRTs), one within the fusion linker of Gag-GFP. The GFP cassette was afterwards replaced by a Cherry cassette via flipase (Flp) recombination. The fusion of Gag to fluorescent proteins enabled high-throughput screening of cells with higher Gag expression and Flp-mediated cassette exchange ability, while keeping the functionality of the VLP scaffold unaltered. The best cell clone was then Flp-recombinated to produce Gag-VLPs decorated with a human ß2-adrenergic receptor (ß2AR). Release of a fluorescently labeled ß2AR into the culture supernatant was confirmed by immunoblotting, and its co-localization with Gag-VLPs was visualized by confocal microscopy. Furthermore, the differential avidity of ß2AR-dsplaying Gag-VLPs versus "naked" Gag-VLPs to an anti-ß2AR antibody measured by ELISA corroborated the presence of ß2AR at the surface of the Gag-VLPs. In conclusion, this novel insect cell line represents a valuable platform for fast production of MPs in their native conformation, which can accelerate small-molecule and antibody drug discovery programs.

The Routine Clinical use of Pharmacogenetic Tests: What it Will Require?

Pharmacogenetic testing aims to personalize drug therapy with a view to optimising drug efficacy and minimise toxicity. However, despite the potential benefits, pharmacogenetic testing is mostly confined to specialised medical areas, laboratories and centres. Widespread integration into routine clinical practice has been limited by a complex set of issues including regulatory and reimbursement frameworks, evidence of clinical utility and clinician perspectives, practices and education. Here we assess the current barriers to widespread clinical uptake and identify the key issue necessary to address to accelerate routine testing.

Simple Emergent Power Spectra from Complex Inflationary Physics.

We construct ensembles of random scalar potentials for N_{f}-interacting scalar fields using nonequilibrium random matrix theory, and use these to study the generation of observables during small-field inflation. For N_{f}=O(few), these heavily featured scalar potentials give rise to power spectra that are highly nonlinear, at odds with observations. For N_{f}≫1, the superhorizon evolution of the perturbations is generically substantial, yet the power spectra simplify considerably and become more predictive, with most realizations being well approximated by a linear power spectrum. This provides proof of principle that complex inflationary physics can give rise to simple emergent power spectra. We explain how these results can be understood in terms of large N_{f} universality of random matrix theory.

Meta-analysis comparing the efficacy of anti-EGFR monoclonal antibody therapy between KRAS G13D and other KRAS mutant metastatic colorectal cancer tumours.

BACKGROUND: Metastatic colorectal cancer (mCRC) tumours harbouring a RAS mutation are associated with a lack of treatment benefit from anti-EGFR monoclonal antibodies (mAbs). However, observational evidence has led to speculation that mCRC patients with KRAS G13D mutant (MT) tumours may derive a benefit from treatment with anti-EGFR mAbs. METHODS: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to evaluate whether the efficacy of anti-EGFR mAbs for mCRC differs between tumours harbouring a KRAS G13D mutation (KRAS G13D) and KRAS mutations other than G13D (other KRAS MT). RESULTS: Eight RCTs (n = 5967) met the inclusion criteria for assessment of both overall survival (OS) and progression-free survival (PFS). For other KRAS MT the hazard ratio for OS benefit with addition of anti-EGFR mAb therapy was 1.06 (95% confidence interval [CI]; 0.96, 1.17), compared to 1.08 (95% CI; 0.73, 1.60) for KRAS G13D [test for interaction p=0.99]. In contrast, the hazard ratio for KRAS wild-type (WT) tumours was 0.85 (95% CI; 0.76, 0.95). Regarding PFS benefit with anti-EGFR mAbs, the hazard ratio was 1.07 (95% CI; 0.92, 1.26) for other KRAS MT, 0.96 (95% CI; 0.73, 1.27) for KRAS G13D, and 0.68 (95% CI; 0.54, 0.85) for KRAS WT. Again, the test for interaction (p=0.46) demonstrated no significant difference in PFS benefit for anti-EGFR mAb therapy between KRAS G13D and other KRAS MT. CONCLUSION: This meta-analysis demonstrates no significant difference between KRAS G13D and other KRAS MT tumours in terms of treatment benefit from anti-EGFR mAbs for mCRC.