A viral perspective on worldwide non-pharmaceutical interventions against COVID-19

Rasigade, Jean‐Philippe; Barray, Anaïs; Shapiro, Julie Teresa; Coquisart, Charlène; Vigouroux, Yoann; Bal, Antonin; Destras, Grégory; Vanhems, Philippe; Lina, Bruno; Josset, Laurence; Wirth, Thierry

doi:10.21203/rs.3.rs-101080/v1

Cited by 7 publications

(6 citation statements)

References 48 publications

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“…If we were able to track the movement of lineages with finer spatial resolution at the scale of individual communities where changes in human behavior appear to be most strongly correlated with reported cases, we could likely quantify how changes in human mobility or other behaviors shape transmission rates from phylogenies. We believe that this is an important direction for future work, as it would provide an independent means of measuring the impact of public health interventions on transmission rates using increasingly abundant pathogen sequence data (Ragonnet-Cronin et al, 2021;Rasigade et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Decomposing the sources of SARS-CoV-2 fitness variation in the United States

2021

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The fitness of a pathogen is a composite phenotype determined by many different factors influencing growth rates both within and between hosts. Determining what factors shape fitness at the host population-level is especially challenging because both intrinsic factors like pathogen genetics and extrinsic factors such as host behavior influence between-host transmission potential. This challenge has been highlighted by controversy surrounding the population-level fitness effects of mutations in the SARS-CoV-2 genome and their relative importance when compared against non-genetic factors shaping transmission dynamics. Building upon phylodynamic birth–death models, we develop a new framework to learn how hundreds of genetic and non-genetic factors have shaped the fitness of SARS-CoV-2. We estimate the fitness effects of all amino acid variants and several structural variants that have circulated in the United States between February 2020 and March 2021 from viral phylogenies. We also estimate how much fitness variation among pathogen lineages is attributable to genetic versus non-genetic factors such as spatial heterogeneity in transmission rates. Before September 2020, most fitness variation between lineages can be explained by background spatial heterogeneity in transmission rates across geographic regions. Starting in late 2020, genetic variation in fitness increased dramatically with the emergence of several new lineages including B.1.1.7, B.1.427, B.1.429 and B.1.526. Our analysis also indicates that genetic variants in less well-explored genomic regions outside of Spike may be contributing significantly to overall fitness variation in the viral population.

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Section: Discussionmentioning

confidence: 99%

Decomposing the sources of SARS-CoV-2 fitness variation in the United States

2021

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“…For each of the previously described models, the point estimate of the pooled coefficients of proteins in a category was obtained as the sum of the coefficients of individual proteins. The variance of the pooled coefficients was obtained as the sum of the covariance matrix of the individual coefficients, as described elsewhere [45].…”

Section: Methodsmentioning

confidence: 99%

Staphylococcus aureus virulence factor expression matters: input from targeted proteomics shows Panton-Valentine leucocidin impact on mortality

Pivard

Bastien

Macavei

et al. 2022

Preprint

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In the case of commensal bacteria such as Staphylococcus aureus, the transition from commensalism to invasion and disease as well as disease severity in the course of an infection remain poorly predictable on the sole basis of virulence gene content. To determine whether variations in the levels of expression of the numerous S. aureus virulence factors could affect disease occurrence and/or severity, we developed a targeted proteomic approach that monitored 149 peptides surrogates targeting 44 proteins. Semi-quantification was achieved by normalization on the signal of ribosomal proteins. We then evaluated this approach on a series of S. aureus strains from 136 patients presenting a severe community-acquired pneumonia, all admitted to an intensive care unit. After adjusting to the Charlson Comorbidity Index score the multivariate analysis of severity parameters found that HlgB, Nuc, and Tsst-1 were positively associated while BlaI and HlgC were negatively associated with leucopenia. BlaZ and HlgB were positively associated with hemoptysis and HlgC was negatively associated with hemoptysis. Regarding mortality, both the multivariate (1.28; 95%CI[1.02;1.60]) and survival (1.15; 95%CI[1.016;1.302]) analyses showed that only PVL was associated with death in a dose-dependent manner. Beyond highlighting the decisive role of PVL in community-acquired pneumonia severity, this study brings the proof of concept that "expression matters" and proposes a method that can be routinely implemented in laboratories, for any Staphylococcal disease, and which could be developed for other commensal bacteria.

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“…Considering the Alpha VoC as an example, it has been possible to study its infectious power in lab experiments, finding a higher rate of transmission by 67–75%, compared to the previous ones 11 . The transmission advantage has been confirmed by epidemiological data in the UK 15 , 16 . Most analyses of the epidemiological data are done applying the time-honoured compartmental models of the SIR type 17 – 19 , appropriately extended by including more compartments 20 .…”

Section: Introductionmentioning

confidence: 65%

Variant-driven early warning via unsupervised machine learning analysis of spike protein mutations for COVID-19

Hoffer

Vatani

Cot

et al. 2022

Sci Rep

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Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its spread within the population, in real time. To this end, we focused on the Spike protein for its central role in mediating viral outbreak and replication in host cells. Employing the Levenshtein distance on the Spike protein sequences, we designed a machine learning algorithm yielding a temporal clustering of the available dataset. From this, we were able to identify and define emerging persistent variants that are in agreement with known evidences. Our novel algorithm allowed us to define persistent variants as chains that remain stable over time and to highlight emerging variants of epidemiological interest as branching events that occur over time. Hence, we determined the relationship and temporal connection between variants of interest and the ensuing passage to dominance of the current variants of concern. Remarkably, the analysis and the relevant tools introduced in our work serve as an early warning for the emergence of new persistent variants once the associated cluster reaches 1% of the time-binned sequence data. We validated our approach and its effectiveness on the onset of the Alpha variant of concern. We further predict that the recently identified lineage AY.4.2 (‘Delta plus’) is causing a new emerging variant. Comparing our findings with the epidemiological data we demonstrated that each new wave is dominated by a new emerging variant, thus confirming the hypothesis of the existence of a strong correlation between the birth of variants and the pandemic multi-wave temporal pattern. The above allows us to introduce the epidemiology of variants that we described via the Mutation epidemiological Renormalisation Group framework.

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A viral perspective on worldwide non-pharmaceutical interventions against COVID-19

Cited by 7 publications

References 48 publications

Decomposing the sources of SARS-CoV-2 fitness variation in the United States

Decomposing the sources of SARS-CoV-2 fitness variation in the United States

Staphylococcus aureus virulence factor expression matters: input from targeted proteomics shows Panton-Valentine leucocidin impact on mortality

Variant-driven early warning via unsupervised machine learning analysis of spike protein mutations for COVID-19

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