Biology subjects

Lutz, B.

Publications and source records attributed to Lutz, B..

3 recordsLinked to original sources

bioRxiv2021

Multivalent designed proteins protect against SARS-CoV-2 variants of concern

Escape variants of SARS-CoV-2 are threatening to prolong the COVID-19 pandemic. To address this challenge, we developed multivalent protein-based minibinders as potential prophylactic and therapeutic agents. Homotrimers of single minibinders and fusions of three distinct minibinders were designed to geometrically match the SARS-CoV-2 spike (S) trimer architecture and were optimized by cell-free expression and found to exhibit virtually no measurable dissociation upon binding. Cryo-electron microscopy (cryoEM) showed that these trivalent minibinders engage all three receptor binding domains on a single S trimer. The top candidates neutralize SARS-CoV-2 variants of concern with IC50 values in the low pM range, resist viral escape, and provide protection in highly vulnerable human ACE2-expressing transgenic mice, both prophylactically and therapeutically. Our integrated workflow promises to accelerate the design of mutationally resilient therapeutics for pandemic preparedness. One-Sentence SummaryWe designed, developed, and characterized potent, trivalent miniprotein binders that provide prophylactic and therapeutic protection against emerging SARS-CoV-2 variants of concern.

synthetic biology↗

bioRxiv2021

Peripheral CB1 receptor blockade acts as a memory enhancer through an adrenergic-dependent mechanism

Peripheral inputs to the brain continuously shape its function and can influence the formation of non-emotional memory, but the underlying mechanisms have not been fully understood. Cannabinoid type-1 receptors (CB1R), widely distributed in the organism, is a well-recognized player in memory performance, and its systemic modulation significantly influences memory function. By assessing non-emotional memory in mice, we have now found a relevant role of peripheral CB1R in the formation of persistent memory. Indeed, peripherally restricted CB1R antagonism by using AM6545 showed a mnemonic effect that was occluded in adrenalectomized mice, after peripheral adrenergic blockade, or when vagus nerve was chemogenetically inhibited. Genetic CB1R deletion in dopamine {beta}-hydroxylase-expressing cells enhanced the formation of persistent memory, supporting a role of peripheral CB1R modulating the adrenergic tone. Notably, brain connectivity was affected by peripheral CB1R inhibition, and locus coeruleus activity and extracellular hippocampal norepinephrine, were increased. In agreement, intra-hippocampal {beta}-adrenergic blockade prevented AM6545 mnemonic effects. Together, we disclose a novel peripheral mechanism relevant for the modulation of the formation of persistent non-emotional memory.

neuroscience↗

bioRxiv2020

Leveraging UMLS-driven NLP to enhance identification of influenza predictors derived from electronic medical record data

ObjectiveMultiple clinical prediction rules have been developed, but lack validation. This study aims to identify a set of prediction algorithms for influenza, based on electronic health record (EHR) structured data and clinical notes derived data using Unified Medical Language System (UMLS) driven natural language processing (NLP). Materials and MethodsData were extracted from an enterprise-wide data warehouse for all patients who tested positive for influenza and were seen in ambulatory care between 2009 and 2019 (N = 7,278). A text processing pipeline was used to analyze chart notes for UMLS terms for symptoms of interest to improve data quality completeness. Three models, which step up complexity of the dataset and predictors, were tested with least absolute shrinkage and selection operator (LASSO)-selected parameters to identify predictors for influenza. Receiver operating characteristic (ROC) curves compared test accuracy across the three models. ResultsThree models identified 7, 8, and 10 predictors, and the most complex model performed best. The addition of the UMLS-driven NLP symptoms data improved data quality (false negatives) and increased the number of significant predictors. NLP also increased the strength of the models, as did the addition of two-way predictor interactions. DiscussionThe EHR is a feasible source for offering rapidly accessible datasets for influenza related prediction research that was used to produce a prediction model for influenza. Combining data collected in routine care with data science methods improved a prediction model for influenza, and in the future, could be used to drive diagnostics at the point of care.

bioinformatics↗