Biology subjects

Seidel, A.

Publications and source records attributed to Seidel, A..

5 recordsLinked to original sources

bioRxiv2023

Determinants of species-specific utilization of ACE2 by human and animal coronaviruses

Utilization of human ACE2 allowed several bat coronaviruses (CoVs), including the causative agent of COVID-19, to infect humans either directly or via intermediate hosts. Here, we analyzed the ability of Spike proteins from 24 human or animal CoVs to use ACE2 receptors across nine reservoir, potential intermediate and human hosts. We show that overall SARS-CoV-2 Omicron variants evolved more efficient ACE2 usage but mutation of R493Q in BA.5 Spike disrupts utilization of ACE2 from Greater horseshoe bats. Spikes from most CoVs showed species-specific differences in ACE2 usage, partly due to variations in ACE2 residues 31, 41 or 354. Mutation of T403R allowed the RaTG13 bat CoV Spike to use all ACE2 orthologs analysed for viral entry. Sera from COVID-19 vaccinated individuals neutralized the Spike proteins of a range of bat Sarbecoviruses. Our results define determinants of ACE2 receptor usage of diverse CoVs and suggest that COVID-19 vaccination may protect against future zoonoses of SARS-CoV-related bat viruses. HighlightsO_LIMutation of R493Q in BA.5 Spike disrupts utilization of ACE2 from Greater horseshoe bats C_LIO_LIVariations in ACE2 residues 31, 41 or 354 affect utilization by coronavirus Spike proteins C_LIO_LIResidue R403 in the Spike protein of bat coronavirus allow broad and effective ACE2 usage C_LIO_LISera from COVID-19 vaccinated individuals neutralize Spike proteins of bat Sarbecoviruses C_LI

molecular biology↗

bioRxiv2021

SARS-CoV-2 variants B.1.351 and B.1.1.248: Escape from therapeutic antibodies and antibodies induced by infection and vaccination

The global spread of SARS-CoV-2/COVID-19 is devastating health systems and economies worldwide. Recombinant or vaccine-induced neutralizing antibodies are used to combat the COVID-19 pandemic. However, recently emerged SARS-CoV-2 variants B.1.1.7 (UK), B.1.351 (South Africa) and B.1.1.248 (Brazil) harbor mutations in the viral spike (S) protein that may alter virus-host cell interactions and confer resistance to inhibitors and antibodies. Here, using pseudoparticles, we show that entry of UK, South Africa and Brazil variant into human cells is susceptible to blockade by entry inhibitors. In contrast, entry of the South Africa and Brazil variant was partially (Casirivimab) or fully (Bamlanivimab) resistant to antibodies used for COVID-19 treatment and was less efficiently inhibited by serum/plasma from convalescent or BNT162b2 vaccinated individuals. These results suggest that SARS-CoV-2 may escape antibody responses, which has important implications for efforts to contain the pandemic.

molecular biology↗

bioRxiv2020

Alpha-1 antitrypsin inhibits SARS-CoV-2 infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). To identify factors of the respiratory tract that suppress SARS-CoV-2, we screened a peptide/protein library derived from bronchoalveolar lavage, and identified 1-antitrypsin (1-AT) as specific inhibitor of SARS-CoV-2. 1-AT targets the viral spike protein and blocks SARS-CoV-2 infection of human airway epithelium at physiological concentrations. Our findings show that endogenous 1-AT restricts SARS-CoV-2 and repurposes 1-AT-based drugs for COVID-19 therapy.

microbiology↗

bioRxiv2020

Interpretable detection of novel human viruses from genome sequencing data

Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics.

bioinformatics↗

bioRxiv2019

DeePaC: Predicting pathogenic potential of novel DNA with a universal framework for reverse-complement neural networks

MotivationWe expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencing and metagenomics. What is more, recent developments in synthetic biology raise concerns that some strains of bacteria could be modified for malicious purposes. Traditional approaches to open-view pathogen detection depend on databases of known organisms, limiting their performance on unknown, unrecognized, and unmapped sequences. In contrast, machine learning methods can infer pathogenic phenotypes from single NGS reads even though the biological context is unavailable. However, modern neural architectures treat DNA as a simple character string and may predict conflicting labels for a given sequence and its reverse-complement. This undesirable property may impact model performance. ResultsWe present DeePaC, a Deep Learning Approach to Pathogenicity Classification. It includes a universal, extensible framework for neural architectures ensuring identical predictions for any given DNA sequence and its reverse-complement. We implement reverse-complement convolutional neural networks and LSTMs, which outperform the state-of-the-art methods based on both sequence homology and machine learning. Combining a reverse-complement architecture with integrating the predictions for both mates in a read pair results in cutting the error rate almost in half in comparison to the previous state-of-the-art. AvailabilityThe code and the models are available at: https://gitlab.com/rki_bioinformatics/DeePaC

bioinformatics↗