ORIENTAL JOURNAL OF CHEMISTRY

Recurrent unprovoked seizures are a hallmark of epilepsy, a chronic neurological condition caused by momentary changes in the way cortical neurons conduct electricity. Epilepsy patients have been treated with more than 20 anti-epileptic medicines (AEDs) to control their seizures, but one-third of them are resistant to the drugs. The pharmacological treatment of epilepsy frequently involves therapeutic drug monitoring (TDM), although the plasma levels of various AEDs do not always correspond well with the doses and/or the therapeutic or harmful effects of the medications. The genetic polymorphisms of numerous enzymes involved in the metabolism of these medications may be to blame for this. The study's goal was to examine UGT1A6 and UGT2B7 gene polymorphisms using bioinformatics methods.


INTRODUCTION Single Nucleotide Polymorphisms
SNPs, also known as single nucleotide polymorphisms, are variations in the DNA sequence brought on by changes to one single nucleotide (A, T, C or G).Around 90% of the genetic diversity in humans is made up of SNPs.SNPs are distributed differently throughout areas of the 3-billion-base human genome, occurring every 100-300 bases 1 .SNPs can exist in both coding and noncoding areas of the genome.SNPs can have a variety of effects on how cells behave, from no effect to a disease state or a changed reaction to a treatment.Since they account for almost half of all known genetic differences, nonsynonymous SNPs (nsSNPs) that cause an amino acid residue substitution in the protein product are of particular interest related to human inherited disease 2 .Coding synonymous SNPs (sSNPs) and SNPs outside of the gene promoter or coding regions may nonetheless have an impact on transcription factor binding, splicing, or gene expression 3,4 .
Identification of the SNPs is crucial since they cause particular symptoms.This is a difficult operation, though, as it necessitates repeatedly evaluating thousands of SNPs in candidate genes 5 .The selection of a collection of SNPs is a challenging undertaking whenever a study is being prepared to determine the role of an SNP in disease.To distinguish between neutral and functional SNPs in such circumstances, bioinformatics prediction techniques may be of tremendous use.They might also show the structural underpinnings of the mutations.
The sole purpose of these bioinformatics tools is to rank SNPs according to their functional importance 6,7 .
By employing bioinformatics methods for in silico gene analysis, it is possible to detect a link between a gene and a disease at a level of statistical significance without screening a sizable number of people.In other words, these techniques aid in the selection of SNPs prior to analysis 5 .
If disease-associated SNPs can be distinguished from neutral SNPs before using wet lab-based techniques, it would be very helpful.When the illness connections could not be verified by further independent investigations, in silico analyses are helpful 6,8 .Consequently, independent confirmation of SNP functioning gained through the use of prediction tools could potentially be used as additional resources to discriminate true associations from false positives.

Epilepsy AND valprote metabolism
Because of its several modes of action and favourable safety profile, sodium valproate (VPA) is frequently used to treat paediatric epilepsy.VPA is metabolised primarily by three pathways: glucuronidation, mitochondrial oxidation, and cytochrome P450 (CYP)-mediated oxidation.The first two are the main pathways, respectively metabolising 50% and 40% of the injected dose, while the third is regarded as a minor route, metabolising 10%.Uridine-5'-diphosphate glucuronosyl transferases (UGTs), a superfamily of enzymes that catalyses the binding of glucuronic acid to a wide range of endobiotics and xenobiotics, catalyse the glucuronidation reaction.VPA metabolism may change as a result of polymorphisms in the genes that code for UGT enzymes.Drug toxicity or inadequate treatment may result from differences in the blood level of VPA due to changes in its rate of glucuronidation.Additionally, a decrease in these enzymes' ability to glucuronidate VPA may result in a switch in metabolic pathways in favour of two additional pathways that have been linked to the hepatotoxic effects of VPA.UGT genetic variants may affect VPA metabolism, which could change the drug's steady-state plasma levels.To maintain the therapeutic levels, this may necessitate giving epileptics a higher or lower dose of VPA.Monitoring VPA concentrations is important for clinic use in order to prevent adverse responses, decrease adverse reactions, and manage seizures.
The high inter-individual variability may be caused by genetic variations in the genes encoding drug-metabolizing enzymes and their functional effects.The idea of "individualised medicine" is developing, and the idea of "one drug fits all" has given way to the idea of "right drug for the right patient at the right dose and time."Therefore, it is crucial to look into any potential involvement that genetic polymorphisms may have in the metabolism of VPA.To the best of our knowledge, there aren't many studies on the impact of UGT1A6 and UGT2B7 gene polymorphism on VPA metabolism, particularly in Indian contexts.Although there are already a number of studies documenting the relationship between SNPs in the UGT gene and various disorders, computational investigation of the functional effects of SNPs in this gene has not yet been done.To detect potentially harmful SNPs, a variety of publically accessible computational techniques, including Sorting Intolerant From Tolerant (SIFT) 9 and Polymorphism Phenotyping (PolyPhen) 10 , were used 11 .
The SIFT method makes predictions about how changing amino acids would impact protein function.Sequence homology between related genes and domains and the physical-chemical characteristics of the amino acid residues are the foundations for its operation [12][13][14] .
PolyPhen also considers the nature of the relevant amino acid residues and the preservation of the sequence, but it also gives importance to the substitution's location within the protein's structural properties that are listed in the SwissProt annotated database 5,15 .It has been estimated that the "false negative" and "false positive" error rates of SIFT are 31% and 20%, and 31% and 9% for PolyPhen, respectively, in benchmarking studies using amino acid substitutions assumed to have a significant negative impact on the residual activity of the variant protein as the test set 10,13,[15][16][17] .
Objective of the study was to evaluate the single nucleotide polymorphisms of UGT 1A6 and UGT2B7 gene using bioinformatics tools.

Methodology Evaluation of the functional impact of missense SNPs using SIFT
The total number of non-intronic missense mutations in the UGT1A6 FASTA amino acid sequence with protein accession ID NP_001063.2was 231; rs numbers and the locations of the SNPs on the chromosomes were noted in a format suitable for analysis using the online tool Sort the Intolerant from Tolerant (SIFT).Polyphen tool was used to the selected SNPs of UGT gene.

RESULTS AND DISCUSSION
Following the analysis, we found the following things; Coding variants had a 100% success rate, but anticipated ones had a 98% success rate (227 out of 231), were tolerated at 46% (106/227), and were harmful at 54% (121/227).Only 1% (2 of 231) were synonymous, while 91% (229 of 231) were non-synonymous.192 out of 231 of them, or 83 percent, were new.The SIFT scale runs from 0 to 1. SNPs with a SIFT score of less than or equal to 0.05 are deemed harmful, whereas those with a value higher than that are deemed tolerable.The optimal range for the median information is between 2.75 and 3.5.This is used to gauge the diversity of the prediction sequences.An indicator indicating the prediction was based on closely similar sequences is a number larger than 3.25.The number of sequences at a certain place is known as the number of sequences at prediction.SIFT automatically selects sequences, however if the alteration is at the start or end of the protein, there might only be a few sequences represented there, as this column demonstrates.Three SNPs were chosen from 231 missense mutations: rs1105879 (A552C), rs6759892 (T19G), and rs2070959 (A541G).A detrimental variant, rs1105879 (A552C), was identified with a SIFT score of 0.026.The PolyPhen tool also looked at these SNPs.Scores between 0.0-0.15indicate benign mutation, 0.15 and 1.0 may be harmful, and scores between 0.85 and 1.0 are harmful, as firmly expected.To forecast the effects of single point protein mutations of the first three SNPs of UGT1A6, I mutant suite 3.0 was utilised.All three SNPs of UGT1A6 had DDG values for binary classification that were 0, indicating decreased stability.A/G Thr181Ala 0.0 1 0 Tolerated -1.17/decr stability Fo l l ow i n g t h e d i s c ove r y o f S N P s associated with the protein accession ID NP_001065.2 in NCBI, the UGT2B7 gene was also examined using the same methods.A total of 240 SNPs were found after missense mutations were removed.The majority (100%) of the changes were coding variations, of which 91% (220 of 240) were anticipated, 38% (85 of 220) were tolerated, 62% (135 of 220) were damaged, 93% (225 of 240) were non-synonymous, and 7% (15 of 240) were synonymous.206 out of 240 SNPs, or 85%, were new. the pre-selection of SNPs.For the wet lab investigation, three SNPs can be chosen: rs6759892, rs2070959, rs1105879 of UGT1A6 and rs7662029 (UGT2B7).Studies have revealed that even though the chosen variations were deemed to be tolerable, they play a substantial e f fe c t i n c e r t a i n g r o u p s. C o n s e q u e n t l y, bioinfor matics methods may be helpful in determining the negative impact of SNPs.