Antibody escape and global spread of SARS-CoV-2 lineage A.27

Kaleta, Tamara; Kern, Lisa; Hong, Samuel L.; Hölzer, Martin; Kochs, Georg; Beer, Julius; Schnepf, Daniel; Schwemmle, Martin; Bollen, Nena; Kolb, Philipp; Huber, Magdalena; Ulferts, Svenja; Weigang, Sebastian; Dudas, Gytis; Wittig, Alice; Jaki, Lena; Padane, Abdou; Lagaré, Adamou; Salou, Mounérou; Ozer, Egon A.; Ndodo, Nnaemeka; Odoom, John Kofi; Rutayisire, Robert; Benkahla, Alia; Akoua‐Koffi, Chantal; Ouédraogo, Abdoul–Salam; Simon‐Lorière, Etienne; Enouf, Vincent; Kröger, Stefan; Calvignac‐Spencer, Sébastien; Baele, Guy; Panning, Marcus; Fuchs, Jonas

doi:10.1038/s41467-022-28766-y

Cited by 24 publications

(20 citation statements)

References 80 publications

(128 reference statements)

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“…Second, we also selected L452R and T478K, which are both strongly represented in Delta, but only with low frequencies < 0.1% in Alpha, Beta, Gamma. The L452R change has been shown to increase infectivity in vitro and decrease neutralization by sera from COVID-19 patients and vaccinated individuals [20, 21] and has also been detected in other lineages such as A.27 which rose during the early months in 2021 in Germany [22]. The T478K change is commonly known from Delta and Omicron and has been predicted to affect the spike/ACE2 interaction [23].…”

Section: Resultsmentioning

confidence: 99%

CovRadar: Continuously tracking and filtering SARS-CoV-2 mutations for molecular surveillance

Wittig

Miranda

Tang

et al. 2021

Preprint

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The ongoing pandemic caused by SARS-CoV-2 emphasizes the importance of molecular surveillance to understand the evolution of the virus and to monitor and plan the epidemiological responses. Quick analysis, easy visualization and convenient filtering of the latest viral sequences are essential for this purpose. We present CovRadar, a tool for molecular surveillance of the Corona spike protein. The spike protein contains the receptor binding domain (RBD) that is used as a target for most vaccine candidates. CovRadar consists of a workflow pipeline and a web application that enable the analysis and visualization of over 250,000 sequences. First, CovRadar extracts the regions of interest using local alignment, then builds a multiple sequence alignment, infers variants, consensus sequences and phylogenetic trees and finally presents the results in an interactive PDF-like app, making reporting fast, easy and flexible. CovRadar is freely accessible at https://covradar.net, its open-source code is available at https://gitlab.com/dacs-hpi/covradar.

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Section: Resultsmentioning

confidence: 99%

CovRadar: Continuously tracking and filtering SARS-CoV-2 mutations for molecular surveillance

Wittig

Miranda

Tang

et al. 2021

Preprint

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“…CTMC is a fast algorithm that can handle many sequences while facing little convergence issues, which made it the predominant approach. For example, CTMC and its extensions have been extensively used during the SARS-CoV-2 pandemic (Candido et al 2020; Dellicour, Durkin, et al 2020; Lemey et al 2020; Alteri et al 2021; Butera et al 2021; Dellicour et al 2021; Kaleta et al 2022; Perez et al 2022). In general, researchers analyzed large datasets whose composition was corrected or reflected case counts (Candido et al 2020; Lemey et al 2020) or the number of hospitalizations per geographical location (Dellicour et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Impact and mitigation of sampling bias to determine viral spread: evaluating discrete phylogeography through CTMC modeling and structured coalescent model approximations

Layan

Mueller

Dellicour

et al. 2022

Preprint

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Bayesian phylogeographic inference is a powerful tool in molecular epidemiological studies that enables reconstructing the origin and subsequent geographic spread of pathogens. Such inference is, however, potentially affected by geographic sampling bias. Here, we investigated the impact of sampling bias on the spatiotemporal reconstruction of viral epidemics using Bayesian discrete phylogeographic models and explored different operational strategies to mitigate this impact. We considered the continuous-time Markov chain (CTMC) model and two structured coalescent approximations (BASTA and MASCOT). For each approach, we compared the estimated and simulated spatiotemporal histories in biased and unbiased conditions based on simulated epidemics of rabies virus (RABV) in dogs in Morocco. While the reconstructed spatiotemporal histories were impacted by sampling bias for the three approaches, BASTA and MASCOT reconstructions were also biased when employing unbiased samples. Increasing the number of analyzed genomes led to more robust estimates at low sampling bias for CTMC. Alternative sampling strategies that maximize the spatiotemporal coverage greatly improved the inference at intermediate sampling bias for CTMC, and to a lesser extent, for BASTA and MASCOT. In contrast, allowing for time-varying population sizes in MASCOT resulted in robust inference. We further applied these approaches to two empirical datasets: a RABV dataset from the Philippines and a SARS-CoV-2 dataset describing its early spread across the world. In conclusion, sampling biases are ubiquitous in phylogeographic analyses but may be accommodated by increasing sample size, balancing spatial and temporal composition in the samples, and informing structured coalescent models with reliable case count data.

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“…This lineage was also described in Cote d’Ivoire in January–February 2021 [ 11 ]. In our series, most of the A.27 sequences from Tunisia were detected in February and April 2021, and the lineage was then apparently displaced by the B.1.1.7 UK lineage that was introduced in February 2021 [ 29 ]. Most sequences from Niger were detected earlier, in November–December 2020, with only few sequences in January and February 2021.…”

Section: Discussionmentioning

confidence: 99%

SARS-CoV-2 Lineage A.27: New Data from African Countries and Dynamics in the Context of the COVID-19 Pandemic

Chouikha

Lagaré

Ghedira

et al. 2022

Viruses

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SARS-CoV-2 is constantly evolving with lineages emerging and others eclipsing. Some lineages have an important epidemiological impact and are known as variants of interest (VOIs), variants under monitoring (VUMs) or variants of concern (VOCs). Lineage A.27 was first defined as a VUM since it holds mutations of concern. Here, we report additional lineage A.27 data and sequences from five African countries and describe the molecular characteristics, and the genetic history of this lineage worldwide. Based on the new sequences investigated, the most recent ancestor (tMRCA) of lineage A.27 was estimated to be from April 2020 from Niger. It then spread to Europe and other parts of the world with a peak observed between February and April 2021. The detection rate of A.27 then decreased with only a few cases reported during summer 2021. The phylogenetic analysis revealed many sub-lineages. Among them, one was defined by the substitution Q677H in the spike (S) gene, one was defined by the substitution D358N in the nucleoprotein (N) gene and one was defined by the substitution A2143V in the ORF1b gene. This work highlights the importance of molecular characterization and the timely submission of sequences to correctly describe the circulation of particular strains in order to be proactive in monitoring the pandemic.

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Antibody escape and global spread of SARS-CoV-2 lineage A.27

Cited by 24 publications

References 80 publications

CovRadar: Continuously tracking and filtering SARS-CoV-2 mutations for molecular surveillance

CovRadar: Continuously tracking and filtering SARS-CoV-2 mutations for molecular surveillance

Impact and mitigation of sampling bias to determine viral spread: evaluating discrete phylogeography through CTMC modeling and structured coalescent model approximations

SARS-CoV-2 Lineage A.27: New Data from African Countries and Dynamics in the Context of the COVID-19 Pandemic

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