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Exploring new antiviral targets for influenza and COVID-19: Mapping promising hot spots in viral RNA polymerases.

ABSTRACT: Influenza and COVID-19 are infectious respiratory diseases that represent a major concern to public health with social and economic impact worldwide, for which the available therapeutic options are not satisfactory. The RdRp has a central role in viral replication and thus represents a major target for the development of antiviral approaches. In this study, we focused on Influenza A virus PB1 polymerase protein and the betacoronaviruses nsp12 polymerase protein, considering their functional and structural similarities. We have performed conservation and druggability analysis to map conserved druggable regions, that may have functional or structural importance in these proteins. We disclosed the most promising and new targeting regions for the discovery of new potential polymerase inhibitors. Conserved druggable regions of putative interaction with favipiravir and molnupiravir were also mapped. We have also compared and integrated the current findings with previous research.

SUBMITTER: Figueiredo-Nunes I

PROVIDER: S-EPMC9674405 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Exploring new antiviral targets for influenza and COVID-19: Mapping promising hot spots in viral RNA polymerases.

Figueiredo-Nunes Inês I Trigueiro-Louro João J Rebelo-de-Andrade Helena H

Virology 20221118

Influenza and COVID-19 are infectious respiratory diseases that represent a major concern to public health with social and economic impact worldwide, for which the available therapeutic options are not satisfactory. The RdRp has a central role in viral replication and thus represents a major target for the development of antiviral approaches. In this study, we focused on Influenza A virus PB1 polymerase protein and the betacoronaviruses nsp12 polymerase protein, considering their functional and ...[more]

PMID: 36463618

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Project description:Modern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome. Vector integration can activate local gene expression, which can cause cancer if the vector inserts near an oncogene. Viral integration hot-spots or 'common insertion sites' (CIS) are scrutinized to evaluate and predict patient safety. CIS are typically defined by a minimum density of insertions (such as 2-4 within a 30-100 kb region), which unfortunately depends on the total number of observed VIS. This is problematic for comparing hot-spot distributions across data sets and patients, where the VIS numbers may vary.We develop two new methods for defining hot-spots that are relatively independent of data set size. Both methods operate on distributions of VIS across consecutive 1 Mb 'bins' of the genome. The first method 'z-threshold' tallies the number of VIS per bin, converts these counts to z-scores, and applies a threshold to define high density bins. The second method 'BCP' applies a Bayesian change-point model to the z-scores to define hot-spots. The novel hot-spot methods are compared with a conventional CIS method using simulated data sets and data sets from five published human studies, including the X-linked ALD (adrenoleukodystrophy), CGD (chronic granulomatous disease) and SCID-X1 (X-linked severe combined immunodeficiency) trials. The BCP analysis of the human X-linked ALD data for two patients separately (774 and 1627 VIS) and combined (2401 VIS) resulted in 5-6 hot-spots covering 0.17-0.251% of the genome and containing 5.56-7.74% of the total VIS. In comparison, the CIS analysis resulted in 12-110 hot-spots covering 0.018-0.246% of the genome and containing 5.81-22.7% of the VIS, corresponding to a greater number of hot-spots as the data set size increased. Our hot-spot methods enable one to evaluate the extent of VIS clustering, and formally compare data sets in terms of hot-spot overlap. Finally, we show that the BCP hot-spots from the repopulating samples coincide with greater gene and CpG island density than the median genome density.The z-threshold and BCP methods are useful for comparing hot-spot patterns across data sets of disparate sizes. The methodology and software provided here should enable one to study hot-spot conservation across a variety of VIS data sets and evaluate vector safety for gene therapy trials.

Dataset Information

Exploring new antiviral targets for influenza and COVID-19: Mapping promising hot spots in viral RNA polymerases.

Publications

Exploring new antiviral targets for influenza and COVID-19: Mapping promising hot spots in viral RNA polymerases.

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