The INDIA Mutations and B.1.617 Variant : Is there a global "strategy" for mutations and evolution of variants of the SARS-CoV2 genome?

. In this paper, we run for all INDIA mutations and variants a biomathematical numerical method for analysing mRNA nucleotides sequences based on UA/CG Fibonacci numbers proportions (Perez, 2021). In this study, we limit ourselves to the analysis of whole genomes, all coming from the mutations and variants of SARS-CoV2 sequenced in India in 2020 and 2021. We then demonstrate - both on actual genomes of patients and on variants combining the most frequent mutations to the SARS-CoV2 Wuhan genomes and then to the B.1.617 variant - that the numerical Fibonacci AU / CG metastructures increase considerably in all cases analyzed in ratios of up to 8 times. We can affirm that this property contributes to a greater stability and lifespan of messenger RNAs, therefore, possibly also to a greater INFECTUOSITY of these variant genomes.


I -INTRODUCTION.
After various papers related SARS-CoV2 origins and evolution (Perez, 2020) and , in (Perez, 2021), we presented a biomathematical method based on mRNA genomes and spikes UA/CG Fibonacci nucleotides proportions. Particularly we demonstrated a real corelation between variants evolution (UK, South Africa, California, Brazil) and the amount of long range Fibonacci metastructures. In order to test this hypothesis, we are interested in the 2 countries in which the effect of variants seems uncontrollable: Brazil and India. We chose India because the sequencing of genomes is more systematic and reliable there than in Brazil. For this we proceed in 2 steps: -Analyzing the first variants of 2020. For this we rely on this publication: (Muttineri et al, 2021), https://www.google.com/url?sa=t&source=web&rct=j&url=https://journals.plos.org/plospathogens/article/file %3Fid%3D10.1371/journal.pone.0246173%26type %3Dprintable&ved=2ahUKEwj3zdnZnorwAhUQKBoKHUxnD_EQFjABegQICBAC&usg=AOvVaw1A79ux6Ub etoPoRx_jT-M k 2/ Then we study the most recent changes of 2021. For that we rely on this sydtematic approach: (Srivastava Surabhi et al, 2021), https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895735/ the longest Fibonacci structures would therefore measure 2584 bases. When looking for such structures, the first one found is in 1200 location: therefore, the bases located between 1201 and 3784 (1200 + 2584): These 2584 bases are broken down respectively into: 1597 bases UA et 987 bases CG Here are the first 20 basics that the reader can easily check: SPIKREF[1200+¼20] G U A A UU A G A G G U G A U G A A G U 0 1 1 1 1 1 1 0 1 0 0 1 0 1 1 0 1 1 0 1.../... The SPIKE analyzes of this Wuhan-Hu-1 reference genome reports 63 metastructures of this type if we close the sequence on itself (as in mtDNA or bacteria) and 7 metastructures and if we consider the mRNA sequence in its linear form, as will be the case throughout this study.

-Analyzes of reference variants :
2.21/ Analyzing the first variants of 2020 : -Analyzing the first variants of 2020. For this we rely on this publication:

III -RESULTS and DISCUSSION.
3.1 -Analyzing the first variants of 2020 :

-INDIAN VARIANTS SIMULATIONS with mutations on SARS-CoV2 Wuhan :
We work now from these published data : We test 2 possible variant scenarios: ( APL Language session mutations...  8  INDIAC1  85  8  INDIAC2  84  8  INDIAC3  84  8  INDIAC4  84  8  INDIAC5  42  31  INDIAC6  41  31  INDIAC7  40  46  INDIAC8  25  35  INDIAC9  25  25  INDIAC10  25  10  INDIAC11  23  10  INDIAC12  22  8  INDIAC13  21  8  INDIAC14  20  8  INDIAC15  14  8  INDIAC16  11  29  INDIAC17  10  29  INDIAC18  10  41  INDIAC19  10  41  INDIAC20  10  41  INDIAC21  7  41  INDIAC22  6  41  INDIAC23  6  41  INDIAC24  6  41  INDIAC25  6  48  INDIAC26  6  36  INDIAC27  6  36  INDIAC28 6 36 From this analysis, we can draw 3 conclusions: a / this is a simulation of genomes made from SARS-CoV2 and the most frequently encountered mutations in India. So, if it is certain that the first genomes exist in some patients, some others, towards the end of the list of 28 genomes, may not exist but could potentially emerge. b / it is noted that none of the 28 cases found UA / CG metastructures of 177122 bases in quantity LESS than 8, a value which characterizes SARS-CoV2 Wuhan. So, if there was no correlation between these Fibonacci metastructures and the evolution of variants, we should find cases less than 8. c / out of the 28 cases of genomes studied, 20 of them saw an increase in the number of metastructures of 17,712 bases, or more than 2/3 of the genomes studied. The average of the 28 cases is 26.89, ie 3.36 times more than the SARS-CoV2 Wuhan and D614G reference genomes.

-INDIAN VARIANTS SIMULATIONS with mutations on B.1.617 variant :
The strain of the variant B.1.617 has grown exponentially in India since the beginning of 2021. We are going to redo the 28 previous analyzes no longer from the SARS-CoV2 Wuhan genome but by inserting the SINDIAFULL spike already analyzed in (Perez, 2021).
This therefore amounts to applying the successive mutations to a type B 1.617 genome, at least at the level of its Spike sequence.

B.1.617 lineage
This strain, also known as the "double mutant virus", has spread rapidly through India.
The strain has been dubbed the "double mutant virus" due to two of the concerning mutations it carries.
These two key mutations are: Further studies on the strain are needed to determine its transmissibility, although it is suspected to do so due to its spike protein mutations which are thought to increase immune evasion and receptor binding. Whether vaccine efficacy is affected also needs further research.
SINDIAFULL is the Spike B.1.617 from (Perez-2021 INDIAC1  85  31  INDIAC2  84  31  INDIAC3  84  31  INDIAC4  84  46  INDIAC5  42  35  INDIAC6  41  35  INDIAC7  40  25  INDIAC8  25  10  INDIAC9  25  10  INDIAC10  25  8  INDIAC11  23  29  INDIAC12  22  29  INDIAC13  21  29  INDIAC14  20  29  INDIAC15  14  29  INDIAC16  11  41  INDIAC17  10  48  INDIAC18  10  36  INDIAC19  10  36  INDIAC20  10  36  INDIAC21  7  36  INDIAC22  6  36  INDIAC23  6  36  INDIAC24  6  36  INDIAC25  6  34  INDIAC26  6  62  INDIAC27  6  62  INDIAC28 6 62 The most remarkable result is the fact that the very simple combination of the 4 most frequent mutations (85% of cases) and the variant B.1.617 is sufficient to multiply by 4 to 6 (31 to 46 against 8 for SARS-CoV2 Wuhan (the number of Fibonacci metastructures of 17,712 AU / CG bases. We also note that out of the 28 genomes studied, only one of them possesses the 8 characteristic metastructures of SARS-COV2 Wuhan. The average of the other 27 is 34.57, ie 4.32 times more and some cases are 8 times more INDIA26-28 : 62). In (Pragya Yadav et al, 2021), the authors provide a list of the 33 main mutations characterizing the genomes of the Indian variant B 1.617. On the other hand, we have just studied the impact of the 28 most frequent mutations in India, those which represent more than 5% of contaminations). It is clear that these 2 sets of mutations partially overlap. However it would be interesting to simulate the effect of some of the 28 mutations when they are absent in B.1.617. Indeed, their high frequency makes it possible to suggest their possible future addition to B.1.617. This is what we will simulate in this last paragraph.   Here, unlike the 2 previous simulations where most of the mutations INCREASED the number of long AU / CG metastructures, here almost all of the mutations DECREASE the number of these long metastructures. It is true that the level of these metastructures of 17711 AU / CG bases is very IMPORTANT in the reference genome B.1.617 Ref.
The level of the B.1.617 consensus reference variant genome is however more than 6.6 times higher than that of the Wuhan SARS-CoV2 reference genome.
The average level of these 22 nested mutations applied to the variant genome consensus reference B.1.617 is however more than 4.7 times higher than that of the reference genome SARS-CoV2 Wuhan.

CONCLUSIONS.
In this study, we limit ourselves to the analysis of whole genomes, all coming from the mutations and variants of SARS-CoV2 sequenced in India in 2020 and 2021. We then demonstrate -both on actual genomes of patients and on variants combining the most frequent mutations to the SARS-CoV2 Wuhan genomes and then to the B.1.617 variantthat the numerical Fibonacci AU / CG metastructures increase considerably in all cases analyzed in ratios of up to 8 times. We can affirm that this property contributes to a greater stability and lifespan of messenger RNAs, therefore, possibly also to a greater INFECTUOSITY of these variant genomes.