miR-21T IAV Segment-Function Atlas

Primary owner: Ryla Cantorgenali
Scientific lead: Ben tenOever
Project family: Programmable influenza pathogenesis
Primary arm: Mechanistic pathogenesis
Secondary arm: Programmable immune instruction
Status: Active
Stage: Manuscript development
Evidence status: Working model
Visibility: Confidential
Last reviewed: 2026-07-12

Project thesis

Systematic miR-21-mediated silencing of individual influenza A virus segments can reveal which viral gene products shape distinct host transcriptional states and can separate canonical immune antagonism from indirect effects caused by altered viral load, replication competence, and RNA polarity.

Biological question

How does selective loss of each IAV segment alter host transcription, viral RNA polarity, and the regulatory programs that define the infected-cell state?

Experimental system

The project compares mock, wild-type PR8, and eight PR8-based segment-targeted viruses: PB2-T, PB1-T, PA-T, HA-T, NP-T, NA-T, M-T, and NS-T. The available program includes two bulk RNA-seq timepoints with biological replicates and a linked viral-polarity analysis comparing mRNA/cRNA-oriented reads with vRNA-oriented reads.

Current interpretation

The dataset should not be interpreted as eight equivalent single-variable perturbations. The targeted conditions fall into at least three analytical classes:

  1. Candidate segment-specific host regulators: NS-T, M-T, NA-T, and potentially HA-T may expose nonredundant host-response programs.
  2. Polymerase-limited conditions: PB2-T, PB1-T, and PA-T share substantial reductions in viral replication and an apparent shift toward higher mRNA/cRNA relative to vRNA.
  3. Extreme replication-collapse control: NP-T contains very few viral reads and produces an extreme host-signaling state that is likely driven in large part by failure of viral replication and antagonism.

The immediate analytical objective is therefore to determine which host signatures remain segment-specific after accounting for viral load, infection efficiency, and viral RNA polarity.

Source manuscript concept

The uploaded planning document proposes a systems-transcriptomics paper built around co-expression networks, transcription-factor and kinase inference, segment-specificity matrices, polarity analysis, and a predictive machine-learning model. This is a useful roadmap, but many numerical values and statistical outcomes in that document are anticipated or illustrative rather than verified. They must not be treated as results until reproduced from the actual datasets. fileciteturn46file0

Evidence map

Established or directly available

Supported interpretations requiring formal analysis

Proposed analyses, not yet established findings

Publication trajectory

Most defensible current paper

A discovery-oriented systems-virology paper showing that systematic segment silencing produces structured host-response states, with explicit correction for viral load and RNA-polarity differences.

Candidate central claim

Systematic segment silencing reveals a structured map of IAV control over host transcription, while viral-load and polarity analysis identifies which apparent immune phenotypes reflect direct segment function versus replication-state collapse.

Alternative manuscript routes

  1. Integrated segment-function paper: Emphasize robust NS, M, NA, and polymerase-associated modules after viral-load adjustment.
  2. Polymerase-focused paper: Elevate the PB2/PB1/PA polarity defect if validated independently and mechanistically.
  3. Methods/platform paper: Emphasize the segment-silencing library and analysis framework if biological claims remain heterogeneous.

The manuscript should not yet commit to all three routes simultaneously.

Proposed figure architecture

Figure 1 — Platform and dataset quality

Figure 2 — Segment-specific host-response landscape

Figure 3 — Viral RNA polarity and polymerase-limited states

Figure 4 — Robust modules and upstream-regulator hypotheses

Figure 5 — Biological validation and synthesis

A machine-learning figure should remain optional unless it contributes biological interpretation beyond PCA and conventional classification.

Immediate priorities

  1. Build a complete sample-level QC panel.
  2. Generate a pre-specified DESeq2 contrast matrix for every condition versus WT at each timepoint.
  3. Quantify viral load and segment composition for every replicate.
  4. Finalize mRNA/cRNA:vRNA ratios and assess alignment robustness.
  5. Re-run pathway and module analyses after defining how low-viral-load samples will be handled.
  6. Select one or two segment-specific signatures for protein-level validation.

Current blockers

Decisions needed