Flu Mutation Explorer: an Interactive Platform for Mapping Host Adaptation Mutations in Influenza A Viruses

Technical Summary: Flu Mutation Explorer

Problem Statement
The rapid expansion of Influenza A Virus (IAV) genome sequencing has transformed global surveillance but created significant challenges in interpreting the biological significance of viral mutations, particularly amino acid replacements linked to host adaptation. While existing resources support mutation annotation and phylogenetic analysis, there is a critical gap in tools that integrate experimentally derived phenotypic evidence with evolutionary context in a framework accessible to users without bioinformatics training. Current tools often fail to effectively combine the functional effects of mutations with their phylogenetic placement, limiting the ability to detect clade-specific adaptation patterns or the emergence of mutations within specific lineages.

Methodology
The authors developed the Flu Mutation Explorer, an interactive web application built on the R Shiny framework. The system relies on a multi-step pipeline for data assembly, curation, and visualization:

1. Database Assembly:

   • Data Retrieval: Over 1.5 million IAV nucleotide sequences and metadata were retrieved from NCBI Entrez (as of June 2026).
   • Curation & Alignment: Sequences were filtered to exclude non-IAV entries and those failing length thresholds. A two-step alignment strategy was employed: sequences were first aligned against their closest reference using Nextalign, then merged using a curated master reference alignment to ensure positional consistency. Nucleotide alignments were trimmed to coding sequences (CDS) and translated to amino acid alignments.
   • Clustering: To mitigate bias from over-sampled seasonal strains and improve visualization performance, sequences were clustered at the nucleotide level using MMseqs2 (95% identity threshold). Representative sequences were extracted for phylogenetic tree construction using IQ-TREE (maximum-likelihood, midpoint-rooted).

2. Adaptation Mutation Curation:

   • A manual curation process compiled a database of over 1,000 mutations associated with mammalian adaptation.
   • Sources included existing mutation resources, structured literature searches (Google Scholar, Litsense), and deep mutational scanning (DMS) studies.
   • Specific effect size thresholds were applied for DMS data (e.g., effect size >1 for HA binding stability; >1 for PB2 in human cells).
   • Mutations were retained only if there was no evidence of reduced fitness in humans, and all entries were linked to primary literature.

3. Interactive Interface:

   • Tree Module: Renders interactive phylogenies for all eight IAV segments using Taxonium. Users can query via amino acid sequences (triggering BLASTP for segment detection) or specific positions. The tool handles residue numbering conversions (e.g., between Group 1 and Group 2 HA subtypes).
   • Adaptation Mutations Module: Displays a curated catalogue of mammalian adaptation mutations. Users can align query sequences to reference sequences to identify mismatches, which are cross-referenced against the catalogue. Results include frequency distributions of amino acids across host groups (birds, mammals, humans).
   • Visualization: Standardized amino acid coloring based on chemical properties and the ability to export publication-ready vector (SVG) or bitmap (PNG) figures.

Key Results

• Database Scale: The final curated database contains 1,179,380 sequences after curation and clustering. The largest proportion corresponds to the HA segment.
• Mutation Catalogue: The authors compiled 1,179 unique mammalian adaptation mutations across all eight segments, derived from 234 publications.
  • The majority of mutations are found in the HA and PB2 segments (619 of the 1,179 were identified from DMS studies).
  • Mutations map to 666 unique sites, representing 2–12% of possible sites per segment.
  • NA and PB1 showed fewer reported unique sites, with NA having a large region (sites 120–275) with no reported mammalian adaptations.
• Case Study 1 (Site Conservation): The tool was used to assess phosphorylation sites in the H1N1 strain A/WSN/33. It successfully demonstrated the ability to identify homologous sites across divergent lineages (Group 1 vs. Group 2 HA) and visualize conservation patterns specific to hosts or subtypes, allowing for rapid hypothesis generation regarding functional importance.
• Case Study 2 (H5N1 Panzootic): During the 2024 H5N1 outbreak in US dairy cattle, the tool was used to screen viral sequences. It identified:
  • PB2 M631L: Present in all sequenced bovine isolates.
  • PA K497R: Observed in most sequenced strains.
  • The tool facilitated the tracking of emerging polymerase mutations (e.g., PB2 E627K, D701N) in cattle while confirming the absence of human-adaptive mutations in HA, thereby aiding risk assessment.

Significance and Claims
The paper positions the Flu Mutation Explorer as a bridge between surveillance pipelines and exploratory research tools. Its primary significance lies in:

• Accessibility: Lowering barriers for non-specialists to interpret influenza sequences by integrating genomic, phylogenetic, and functional information into an intuitive interface.
• Contextualization: Enabling users to evaluate mutations not in isolation, but within their phylogenetic and host context (e.g., determining if a mutation arises independently in multiple lineages or is restricted to specific clades).
• Traceability: Prioritizing manual curation to ensure every mutation is explicitly linked to primary literature, allowing users to assess the strength and context of the evidence.
• Risk Assessment: Providing a mechanism for rapid screening of emerging strains (such as the H5N1 cattle outbreak) for signatures of mammalian adaptation, supporting pre-pandemic risk assessments and public health decision-making.

The authors acknowledge limitations, noting that clustering may obscure mutations unique to single sequences, and that the dataset reflects biases in published literature (heavily skewed toward PB2 and HA). They emphasize that while the tool identifies candidate mutations, biological context and epistatic interactions must still be considered for definitive phenotypic interpretation.