Metayer, C. | Search bioRxiv

bioRxiv2021

Neutralising antibodies against the SARS-CoV-2 Delta variant induced by Alhydroxyquim-II-adjuvanted trimeric spike antigens

Global control of COVID-19 will require the deployment of vaccines capable of inducing long-term protective immunity against SARS-CoV-2 variants. In this report, we describe an adjuvanted subunit candidate vaccine that affords elevated, sustained and cross-variant SARS-CoV-2 neutralising antibodies (NAbs) in multiple animal models. Alhydroxiquim-II is a TLR7/8 small-molecule agonist chemisorbed on aluminium hydroxide. Vaccination with Alhydroxiquim-II combined with a stabilized, trimeric form of the SARS-CoV-2 spike protein (termed CoVac-II) resulted in high-titre NAbs in mice, with no decay in responses over an 8-month period. NAbs from sera of CoVac-II-immunized mice, horses and rabbits were broadly neutralising against SARS-CoV-2 variants. Boosting long-term CoVac-II-immunized mice with adjuvanted spike protein from the Beta variant markedly increased levels of NAb titres against multiple SARS-CoV-2 variants; notably high titres against the Delta variant were observed. These data strongly support the clinical assessment of Alhydroxiquim-II-adjuvanted spike proteins to protect against SARS-CoV-2 variants of concern.

microbiology↗

bioRxiv2018

Data-adaptive pipeline for filtering and normalizing metabolomics data.

IntroductionUntargeted metabolomics datasets contain large proportions of uninformative features and are affected by a variety of nuisance technical effects that can bias subsequent statistical analyses. Thus, there is a need for versatile and data-adaptive methods for filtering and normalizing data prior to investigating the underlying biological phenomena.\n\nObjectivesHere, we propose and evaluate a data-adaptive pipeline for metabolomics data that are generated by liquid chromatography-mass spectrometry platforms.\n\nMethodsOur data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. It also incorporates a variant of k-nearest-neighbor imputation of missing values. Finally, we adapted an RNA-Seq approach and R package, scone, to select an appropriate normalization scheme for removing unwanted variation from metabolomics datasets.\n\nResultsUsing two metabolomics datasets that were generated in our laboratory from samples of human blood serum and neonatal blood spots, we compared our data-adaptive pipeline with a traditional filtering and normalization scheme. The data-adaptive approach outperformed the traditional pipeline in almost all metrics related to removal of unwanted variation and maintenance of biologically relevant signatures. The R code for running the data-adaptive pipeline is provided with an example dataset at https://github.com/courtneyschiffman/Data-adaptive-metabolomics.\n\nConclusionOur proposed data-adaptive pipeline is intuitive and effectively reduces technical noise from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

bioinformatics↗