Topic :
Sequence alignment
Research question and objectives:
Is one vaccine enough for all races of the world?
Use sequence alignment to analyze 2 sets of H3N2 HA sequences of 2 different races/ continents and see if there is any difference.
Method:
With higher percentage of similarity between sequences, their functions or viral feature will be more similar and hence they have more chance to be killed by the same vaccine. In order to find out if there is any difference in H3N2, type A virus of different human race, two groups of sequences from different regions with populations of different races are used. The hemagglutinin (HA) is one of the relevant antigens in Influenza. It is responsible for entry of the virus into human body.
Four, H3N2 type A, HA protein sequences from China and Australia are chosen and downloaded from NCBI influenza virus resource database. 2 from Australia, ADF 83752 (AUS1), ADF83741 (AUS2); and 2 from China, ADK 87306 (CN1), ADK87308 (CN2) .These sequences are full length sequences, Human as the host. In order to maintain uniformity of the chosen sequences for comparison, these sequences are from the same year. These sequences than are compared with sequence of the same geographic location and with different geographic locations using BLAST, from NCBI. Total of six comparisons were made. Algorithm and Scoring Parameters of Blast are set as follow,
Gap cost: Existence 12, extension 1
Scoring Matrix: BLOSUM 62
Compositional adjustment: Conditional compositional score matrix adjustment
Word size:3
Max target sequences: 100
Expect threshold: 10
Max matches in a query range: 0
A multiple sequence alignment was also done for the four selected sequences, using the software provided on NCBI influenza virus resource database, to demonstrate the mismatches between sequences and their locations.
Result:
BLAST scores and percentage of similarity between sequences
Score/ percentage of similarity | ||||
Protein sequence | AUS1 | AUS2 | CN1 | CN2 |
AUS1 | ||||
AUS2 | 1242/99% | |||
CN1 | 1230/99% | 1235/99% | ||
CN2 | 1239/99% | 1244/99% | 1232/99% | |
The table above demonstrates the percentage of identity and similarity between sequences. The scores are relatively close, the higest and the lowest score are 1244 and 1230 respectively. All pairs of sequences have a percentage of similarity of 99%. The sequences pair with the highest score are AUS2 and CN2. The lowest score are obtained from the AUS1 and CN1 pair, CN1 and CN2 pair has a score of 1232, only two marks higher than the lowest score.
Multiple sequences alignment of the four selected sequences
From the multiple sequence alignment(MSA)(please see diagram above), the locations with mismatches between sequences are highlighted in white, and stated the mismatch on the particular sequences. The number of mismatches out of 566 for AUS1 is 4, AUS2 is 2, CN1 is 7 and CN2 is 2. The mismatches are all at different locations along the sequence. For sequence with higher number of mismatches, the alignment score with other sequences is lower. The MSA result correlates well with the BLAST result.
Discussion:
The results suggests the sequences are very similar, the mismatches between sequences are not related to their geographic locations and hence the human races. These mismatches between sequences may due other factors, for example vaccine pressure, weather, life style and so forth. A study done by Rambaut, A in year 2008 suggested that seasonal difference has actually effect on Influenza A antigen drift. Futher, these viruses are under rapid mutations. These factors made the mismatches between sequences due to different races less prominent.
With this result, I would make a conclusion that one vaccine can prevent H3N2 type A virus for all human races of the world. Nevertheless, this have to be confirmed by a larger group of samples and require incoporation of sequences of more different geographical regions. In addition, neuraminidase, the protein responsible to budding of new virion out of host cells, needs to take into account.
Reference:
1. Rambaut, A et al. The genomic and epidemiological dynamics of Human Influenza A virus. Nature 453,615-619 (2008)
2. Zhen X. at al. Using a mutual information-based site transition network to map the genetic evolution of influenza A/H3N2 virus. Bioinformatics 25, 2309-2317(2009)
3. Alexander, P. et al An RNA conformational shift in recent H5N1 influenza A viruses. Bioinformatics 23, 272-276
4. ANSWER.COM http://www.answers.com/topic/antigenic-drift
