Different viruses transmit among hosts with different degrees of efficiency. A basic reproductive number (R0) indicates an average number of cases getting infected from a single infected case. R0 can vary widely from a little over 1 to more than 10. Low R0 is usually found among rapidly evolving viruses that are often under a strong positive selection pressure, while high R0 is often found among viruses that are highly stable. The reason for the difference between antigenically diverse viruses with low R0, such as influenza A virus, and antigenically stable viruses with high R0, such as measles virus, is not clear and has been a subject of great interest. Optimization of transmissibility fitness considering intra-host dynamics and inter-host transmissibility was shown to result in strategies for tradeoff between transmissibility and diversity. The nature of transmission, targeting either a naïve children population or an adult population with partial immunity, has been proposed as a contributing factor for the difference in the strategies used by the two groups of viruses. The R0 determines the levels of threshold heard immunity. Lower R0 requires lower herd immunity to terminate an outbreak. Therefore, it can be assumed that the outbreak saturation can be reached more readily when the R0 is low. In addition, one may assume that when the outbreak saturation is reached, herd immunity may provide a strong positive selection pressure that could possibly result in an occurrence of escape mutants. Studies of these hypotheses will give us an important insight into viral evolution. This review discusses the above hypotheses as well as some possible mechanistic explanation for the difference in transmission efficiency of viruses.
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http://dx.doi.org/10.5501/wjv.v1.i5.131 | DOI Listing |
Nature
January 2025
Department of Mathematics & Computer Science, Freie Universität Berlin, Berlin, Germany.
Since the onset of the pandemic, many SARS-CoV-2 variants have emerged, exhibiting substantial evolution in the virus' spike protein, the main target of neutralizing antibodies. A plausible hypothesis proposes that the virus evolves to evade antibody-mediated neutralization (vaccine- or infection-induced) to maximize its ability to infect an immunologically experienced population. Because viral infection induces neutralizing antibodies, viral evolution may thus navigate on a dynamic immune landscape that is shaped by local infection history.
View Article and Find Full Text PDFZoonoses are infectious diseases transmitted from animals to humans. Bats have been suggested to harbour more zoonotic viruses than any other mammalian order. Infections in bats are largely asymptomatic, indicating limited tissue-damaging inflammation and immunopathology.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Sciences and Technology (FAST), Laboratory of Biology and Molecular Typing in Microbiology (LBTMM), University of Abomey-Calavi, Atlantic, Benin.
Background: Antiretroviral treatment increases the risk of accumulation of resistance mutations that negatively impact the possibilities of future treatment. This study aimed to present the frequency of HIV-1 antiretroviral resistance mutations and the genetic diversity among children with virological failure in five pediatric care facilities in Benin.
Methods: A cross-sectional study was carried out from November 20, 2020, to November 30, 2022, in children under 15 years of age who failed ongoing antiretroviral treatment at five facilities care in Benin (VL > 3log10 on two consecutive realizations three months apart).
J Virol
January 2025
MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, United Kingdom.
The unprecedented sequencing efforts during the COVID-19 pandemic paved the way for genomic surveillance to become a powerful tool for monitoring the evolution of circulating viruses. Herein, we discuss how a state-of-the-art artificial intelligence approach called protein language models (pLMs) can be used for effectively analyzing pathogen genomic data. We highlight examples of pLMs applied to predicting viral properties and evolution and lay out a framework for integrating pLMs into genomic surveillance pipelines.
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