In silico investigation of the single nucleotide polymorphisms (SNPs) in DNMT3A gene as a driver gene in patients with Acute Myeloid Leukemia
In silico investigation of the single nucleotide polymorphisms (SNPs) in DNMT3A gene as a driver gene in patients with Acute Myeloid Leukemia
Helena Choobineh,1,*Mohammad Mahdi Darijani,2
1. Department of Biology, Faculty of Science, Yazd University, Yazd, Iran 2. Department of Biology, Faculty of Science, Yazd University, Yazd, Iran
Introduction: Acute myeloid leukemia (AML) is a complex hematologic malignancy characterized by the uncontrolled proliferation of myeloid progenitor cells, leading to disrupted hematopoiesis and severe clinical outcomes. One of the critical genetic alterations in AML is found in the DNA methyltransferase 3A (DNMT3A) gene, which is supposed to be one of the critical driver genes in AML and plays a vital role in de novo DNA methylation, crucial for regulating gene expression and maintaining genomic integrity.
Disruptions in DNMT3A, especially the R882H hotspot mutation, are prevalent in many AML patients and are associated with poor prognoses and the establishment of a pre-leukemic state in hematopoietic stem cells. Recent advancements in high-throughput sequencing have revealed the frequency of DNMT3A mutations and their interactions with other genetic modifications, highlighting the need for deeper understanding of their functional consequences in AML.
Methods: We identified eighteen non-synonymous single nucleotide polymorphisms (nsSNPs) in the DNMT3A gene: ((rs139293773) (Arg736Leu/Pro/His), (rs147001633) (Arg882 Leu/Pro/His), (rs369713081) (Arg688His), (rs377577594) (Arg882Cys/Gly/Ser), (rs568207978) (Arg598Terminal point/Gly), (rs587777507) (Leu648Pro), (rs587777508) (Ile310Thr/Arg), (rs587777509) (Met548Thr/Lys), (rs587777510)(Phe902Ser), (rs751562376) (Arg635Leu/Pro/Gln), (rs754506713) (Cys586Phe/Tyr), (rs757823678) (Arg771Leu/Pro/Gln), (rs758845779) (Ser770Leu/Trp/Terminal point), (rs797044904) (Gly298Trp/Arg), (rs1418039680) (Val716Phe/Leu/Ile), (rs1573340335) (Asp333Asn), (rs1573340475) (Trp330Arg), (rs1673065619) (Phe909Cys/Ser)) using dbSNP, we analyzed these nsSNPs to determine their effect on the structure of the DNA methyltransferase 3 alpha enzyme using Expasy and PyMOL, also stability by I-Mutant, pathogenicity and protein function through SIFT, PolyPhen-2, and GVGD analysis.
Results: Our study examined the impact of non-synonymous single nucleotide polymorphisms (nsSNPs) on the interaction patterns and properties of the DNA methyltransferase 3 alpha enzyme, including polar groups, hydrogen bond lengths, and hydrophobicity. The results obtained from a few prediction tools revealed significant implications of these nsSNPs on the structural and functional characteristics of the enzyme. Specifically, PolyPhen-2 predicted a score of 1, indicating a high likelihood of functional impact. SIFT scores were below 0.05, suggesting deleterious effects. Furthermore, GVGD classification placed the nsSNPs in class C65, highlighting their potential pathogenicity. I-Mutant analysis indicated that all eighteen identified nsSNPs decrease the stability of the enzyme, leading to significant disruptions in its original function. These findings underscore the critical role of nsSNPs in modulating the behavior and functionality of the DNA methyltransferase 3 alpha enzyme.
Conclusion: According to in silico assays, all of the eighteen mentioned single nucleotide polymorphisms (SNPs) exhibit potential harmful effects. However, it is essential to acknowledge that in silico methods possess both advantages and limitations in predicting the impact of genetic variations. In silico analyses rely on computational algorithms to simulate biological processes, allowing for the rapid assessment of a large number of SNPs. These methods are cost-effective, time-efficient, and can provide valuable insights into the potential functional consequences of genetic variations. Despite their utility, in silico predictions are not worthy without experimental results. One of the primary limitations is the reliance on computational models, which may oversimplify the complexity of biological systems. Hence, it is important to emphasize that the predictive nature of in silico methods necessitates further confirmation of their results. Experimental validation through functional assays, such as in vitro and in vivo studies, is essential to verify the actual impact of the identified SNPs on biological processes. These experimental validations can provide comprehensive insights into the functional consequences of genetic variations and enhance our understanding of their implications for human health.