Rohlin Distance and the Evolution of Influenza A virus: Weak Attractors and Precursors

TL;DR

This study applies an informational metric to analyze Influenza A virus evolution, revealing weak attractors in sequence space that align with epidemiological history and can inform vaccine strategies.

Contribution

It introduces a novel metric-based method to detect structural domains and attractors in viral sequence evolution without prior sequence knowledge.

Findings

Identification of weak attractors in influenza sequence evolution

Robustness of the sequence space structure across parameters

Potential for improved vaccine prediction strategies

Abstract

The evolution of the hemagglutinin amino acids sequences of Influenza A virus is studied by a method based on an informational metrics, originally introduced by Rohlin for partitions in abstract probability spaces. This metrics does not require any previous functional or syntactic knowledge about the sequences and it is sensitive to the correlated variations in the characters disposition. Its efficiency is improved by algorithmic tools, designed to enhance the detection of the novelty and to reduce the noise of useless mutations. We focus on the USA data from 1993/94 to 2010/2011 for A/H3N2 and on USA data from 2006/07 to 2010/2011 for A/H1N1 . We show that the clusterization of the distance matrix gives strong evidence to a structure of domains in the sequence space, acting as weak attractors for the evolution, in very good agreement with the epidemiological history of the virus. The structure proves very robust with respect to the variations of the clusterization parameters, and extremely coherent when restricting the observation window. The results suggest an efficient strategy in the vaccine forecast, based on the presence of "precursors" (or "buds") populating the most recent attractor.