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An insight to segment based genetic exchange in Influenza A virus: an in silico study
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1  Centre for Interdisciplinary Research and Education, 404B Jodhpur Park, Kolkata 700068, India


Influenza virus is well recognized for high level of mutations that lead to development of new strains and subtypes, primarily through genetic shifts and drifts. Another mechanism of genetic change is through recombination, often observed in mammalian genes but controversial in viral genomes, which involves exchanges of short nucleotide sequences between two strains that coinfect the same cell. While evidence of such recombinations is rare to disputed in influenza genomes, we have observed that well-defined segments of influenza genes such as the hemagglutinin and neuraminidase have shown identical sequences between various strains that is best explained by segment exchange. Thus we had in our earlier study of the spread and proliferation of H5N1 bird flu observed that the neuraminidase with three segments – the transmembrane, stalk and body – shows evidence of exchange of one or other segments between different strains. Extending our work to the hemagglutinin of various subtypes, we noticed the same phenomena: Hemagglutinin has two segments, HA1 and HA2, where we found several instances where the segments seem to have been exchanged. Our analyses was based on RNA descriptors calculated in a 2D graphical representation scheme which have been proved to easily identify identical sequences. In this paper we discuss some of the details of this phenomenon in influenza genes which could be important in monitoring development of new highly pathogenic strains

Keywords: Influenza, Hemagglutinin, Recombination, Graphical representation
Comments on this paper
Humbert G. Díaz
Can 2D descriptors be used as input to seek predictive models in this problem?
Dear authors

In previous works different groups have used different numerical descriptors of DNA/RNA or protein sequence representations as input of Machine Learning algorithms. The idea is to seek predictive models able to discriminate sequences with different properties without relying upon sequence alignment. Have you considered the possibility of using a similar approach in this or related problems using the μ descriptors as input?

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Ashesh Nandy
2D graphical representation and numerical characterization of biomolecular sequences can provide a visual impact of the characteristics of the distribution of bases in a sequence and a numerical measure to compare between representations. Where the base distribution follows some pattern the numerical measures can help in prediction of subsequent modifications that may arise in the distribution.
Such a scenario has been envisaged in another paper published in this conference, viz., (b006) Interdependence of Influenza HA and NA and Possibilities of New Reassortments by Ashesh Nandy and Subhash C Basak. That paper is based on the observations that there is interdependence between the hemagglutinin and neuraminidase of an influenza strain such that (a) mutations in one seem to influence co-operative mutations in the other, and (b) replacement of one of these proteins by another impacts the efficiency of viral infection. This had led the authors to devise a coupling between the hemagglutinin and the neuraminidase genes which, when computed through the -descriptors gave numerical quantities characteristic of each influenza subtype. This characteristic implies that one could predict the most likely infective viral subtypes that may arise through reassortments and that has been done for a current North American epidemic strain.

It is worth mentioning here that the -descriptors have also been used elsewhere for predictive purposes, e.g., in predicting the more stable direction of mutational changes in genes [A. Nandy, Empirical Relationship between Intra-Purine and Intra-Pyrimidine Differences in Conserved Gene Sequences, PLoS ONE 2009, 4(8): e6829.doi:10.1371/journal.pone.0006829]. As other consequences of analysis through 2D graphical representations and 20D protein sequence representations, our group has predicted possible gene sequences in human chromosome 3 contig 7 [Current Science 84 (12), 1534 – 1543, 2003] and rational design of peptide vaccines for viruses [BMC Structural Biology 2010, 10:6 doi:10.1186/1472-6807-10-6; PLoS ONE 7(7): e40749. doi:10.1371/journal.pone.0040749); Comput. Biol. Chem. 59 (2015) 8–15].

Thus, this kind of analysis, used imaginatively, can yield great predictive powers.