Mutual information networks reveal evolutionary relationships within the influenza A virus polymerase

Arcos, Sarah; Han, Alvin X.; Velthuis, Aartjan J.W. te; Russell, Colin A.; Lauring, Adam S.

doi:10.1093/ve/vead037

“…S4B ). Using the subdomains defined in ( 46 ), we grouped site entropy by subdomain. Residues in the fingertips subdomain exhibited lower entropy ( P < 0.005 compared with β-hairpin, C-terminal, fingers, prime loop, and ribbon subdomains; P < 0.05 compared with palm and thumb subdomains, and P > 0.05 compared with N-terminal subdomain), residues in the prime loop and ribbon subdomains exhibited higher entropy (for prime loop subdomain: P < 0.005 compared with fingertips, N-terminal, palm, and thumb subdomains; and P < 0.005 compared with fingertips and thumb subdomains), and the distribution of entropy values across other subdomains were largely similar ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Third, we focused on the mutational effects of single amino acid substitutions and did not account for epistatic interactions within PB1 and between PB1 and other viral proteins. In natural evolution, interacting sites often co-evolve ( 46 , 68 ), and an adaptive mutation towards a stimulus is commonly accompanied by compensatory mutations that maintain effective replication ( 5 ). Finally, we only examined fitness in terms of replication, but various treatments can be applied to the variant virus library and future research can examine mutational fitness under specific conditions such as with drug selection or altered baseline mutational rates.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein

Li,

Arcos,

Sabsay

et al. 2023

J Virol

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The influenza virus polymerase is central to influenza virus evolution. Adaptive mutations within the polymerase are often a prerequisite for efficient spread of novel animal-derived viruses in human populations. The polymerase also determines fidelity and, therefore, the rate at which the virus will acquire mutations that lead to host range expansion, drug resistance, or antigenic drift. Despite its importance to viral replication and evolution, our understanding of the mutational effects and associated constraints on the influenza RNA-dependent RNA polymerase (RdRp) is relatively limited. We performed deep mutational scanning of the A/WSN/1933(H1N1) polymerase basic 1 (PB1), generating a library of 95.4% of amino acid substitutions at 757 sites. After accuracy filters, we were able to measure replicative fitness for 13,354 (84%) of all possible amino acid substitutions, and 13 were validated by results from pairwise competition assays. Functional and structural constraints were better revealed by individual sites involved in RNA or protein interactions than by major subdomains defined by sequence conservation. Mutational tolerance, as defined by site entropy, was correlated with evolutionary potential, as captured by diversity in the available H1N1 sequences. Of the 29 beneficial sites, many have either been identified in the natural evolution of PB1 or shown experimentally to have important impacts on replication and adaptation. Accessibility of amino acid substitutions by single nucleotide mutation was a key factor in determining whether mutations appeared in natural PB1 evolution. Our work provides a comprehensive map of mutational effects on a viral RdRp and a valuable resource for subsequent studies of influenza replication and evolution. IMPORTANCE The influenza virus polymerase is important for adaptation to new hosts and, as a determinant of mutation rate, for the process of adaptation itself. We performed a deep mutational scan of the polymerase basic 1 (PB1) protein to gain insights into the structural and functional constraints on the influenza RNA-dependent RNA polymerase. We find that PB1 is highly constrained at specific sites that are only moderately predicted by the global structure or larger domain. We identified a number of beneficial mutations, many of which have been shown to be functionally important or observed in influenza virus’ natural evolution. Overall, our atlas of PB1 mutations and their fitness impacts serves as an important resource for future studies of influenza replication and evolution.

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MicroNet-MIMRF: a microbial network inference approach based on mutual information and Markov random fields

Feng,

Jia,

Wang

et al. 2024

Bioinformatics Advances

0

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Motivation The human microbiome, comprises complex associations and communication networks among microbial communities, which are crucial for maintaining health. The construction of microbial networks is vital for elucidating these associations and co-occurrence patterns. However, existing microbial networks inference methods cannot solve the issues of zero-inflation and non-linear associations. Therefore, necessitating novel methods to improve the accuracy of microbial networks inference. Results In this study, we introduce the Microbial Network based on Mutual Information and Markov Random Fields (MicroNet-MIMRF) as a novel approach for inferring microbial networks. Abundance data of microbes are modeled through the zero-inflated Poisson distribution, and the discrete matrix is estimated for further calculation. Markov random fields based on mutual information are used to construct accurate microbial networks. MicroNet-MIMRF excels at estimating pairwise associations between microbes, effectively addressing zero-inflation and non-linear associations in microbial abundance data. It outperforms commonly used techniques in simulation experiments, achieving area under the curve values exceeding 0.75 for all parameters. A case study on inflammatory bowel disease data further demonstrates the method’s ability to identify insightful associations. Conclusively, MicroNet-MIMRF is a powerful tool for microbial network inference that handles the biases caused by zero-inflation and overestimation of associations. This novel tool has abundant applications in health research. Availability and implementation The MicroNet-MIMRF is provided at https://github.com/Fionabiostats/MicroNet-MIMRF.

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Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein

Li,

Arcos,

Sabsay

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The influenza virus polymerase is central to influenza virus evolution. Adaptive mutations within the polymerase are often a prerequisite for efficient spread of novel animal-derived viruses in human populations. The polymerase also determines fidelity, and therefore the rate at which the virus will acquire mutations that lead to host range expansion, drug resistance, or antigenic drift. Despite its importance to viral replication and evolution, our understanding of the mutational effects and associated constraints on the influenza RNA-dependent RNA polymerase (RdRp) is relatively limited. We performed deep mutational scanning of the A/WSN/1933(H1N1) PB1, generating a library of 95.4% of amino acid substitutions at 757 sites. After accuracy filters, we were able to measure replicative fitness for 13,354 (84%) of all possible amino acid substitutions, and 16 were validated by results from pairwise competition assays. Functional and structural constraints were better revealed by individual sites involved in RNA or protein interactions than by major subdomains defined by sequence conservation. Mutational tolerance, as defined by site entropy, was correlated with evolutionary potential, as captured by diversity in available H1N1 sequences. Of 29 beneficial sites, many have either been identified in the natural evolution of PB1 or shown experimentally to have important impacts on replication and adaptation. Accessibility of amino acid substitutions by single nucleotide mutation was a key factor in determining whether mutations appeared in natural PB1 evolution. Our work provides a comprehensive map of mutational effects on a viral RdRp and a valuable resource for subsequent studies of influenza replication and evolution.

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Mutual information networks reveal evolutionary relationships within the influenza A virus polymerase

Cited by 4 publications

References 47 publications

Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein

Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein

MicroNet-MIMRF: a microbial network inference approach based on mutual information and Markov random fields

Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein

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