Computational analysis of high-risk SNPs in human CHK2 gene responsible for hereditary breast cancer: A functional and structural impact

Computational Insight in the Identification of Non-Synonymous Single-Nucleotide Polymorphism Affecting the Structure and Function of Interleukin-4

BACKGROUND

IL4 is a versatile cytokine essentially known for differentiation, proliferation and cell death in cells. Its dysregulation has been found to be associated with the development of inflammatory disorders.

OBJECTIVE

The goal of the current investigation is to identify and select non-synonymous single-nucleotide polymorphisms (nsSNPs) in the IL-4 gene by employing computational methods which may have a potential functional impact on the occurrence of disease.

METHOD AND RESULT

Six different nsSNPs were predicted to be deleterious based on the consensus of different algorithms: SIFT, Polyphen2 (Humdiv and HumVar), PredictSNP and SNP&GO. I-mutant and MuPro assessment revealed a decrease in the stability of these mutants except K150M. Modelling was then carried out to build the wild type along with its mutants, followed by superimposition of the wild type with mutants to evaluate the RMSD value, which lies between 0.26 and 0.34. Simulation results of mutant models, along with wild type, showed that four of the mutants (N113Y, A118G, R109W and K150M) deviated most and were unstable. A118G showed a significant deviation from the wild type, while V53A and C123R were stable.

CONCLUSION

The finding establishes the evidence that the identified six nsSNPs of IL-4 can be the new entrant presenting their candidature for genetic testing.

Pathogenic single nucleotide polymorphisms in RhoA gene: Insights into structural and functional impacts on RhoA-PLD1 interaction through molecular dynamics simulation

Molecular switches serve as key regulators of biological systems by acting as one of the crucial driving forces in the initiation of signal transduction pathway cascades. The Ras homolog gene family member A (RhoA) is one of the molecular switches that binds with GTP in order to cycle between an active GTP-bound state and an inactive GDP-bound state. Any aberrance in control over this circuit, particularly due to any perturbation in switching, leads to the development of different pathogenicity. Consequently, the single nucleotide polymorphisms (SNPs) within the RhoA gene, especially deleterious genetic variations, are crucial to study to forecast structural alteration and their functional impacts in light of disease onset. In this comprehensive study, we employed a range of computational tools to screen the deleterious SNPs of RhoA from 207 nonsynonymous SNPs (nsSNPs). By utilizing 7 distinct tools for further analysis, 8 common deleterious SNPs were sorted, among them 5 nsSNPs (V9G, G17E, E40K, A61T, F171L) were found to be in the highly conserved regions, with E40K and A61T at G2 and G3 motif of the GTP-binding domain respectively, indicating potential perturbation in GTP/GDP binding ability of the protein. RhoA-GDP complex interacts with the enzyme phospholipase, specifically PLD1, to regulate different cellular activities. PLD1 is also a crucial regulator of thrombosis and cancer. In that line of focus, our initial structural analysis of Y66H, A61T, G17E, I86N, and I151T mutations of RhoA revealed remarkable decreased hydrophobicity from which we further filtered out G17E and I86N which may have potential impact on the RhoA-GDP-PLD1 complex. Intriguingly, the comparative 250 ns (ns) molecular dynamics (MD) simulation of these two mutated complexes revealed overall structural instability and altered interaction patterns. Therefore, further investigation into these deleterious mutations with in vitro and in vivo studies could lead to the identification of potential biomarkers in terms of different pathogenesis and could also be utilized in personalized therapeutic targets in the long run.

Understanding genetic variations associated with familial breast cancer

BACKGROUND

Breast cancer is the most frequent cancer among women. Genetics are the main risk factor for breast cancer. Statistics show that 15-25% of breast cancers are inherited among those with cancer-prone relatives. BRCA1, BRCA2, TP53, CDH1, PTEN, and STK11 are the most frequent genes for familial breast cancer, which occurs 80% of the time. In rare situations, moderate-penetrance gene mutations such CHEK2, BRIP1, ATM, and PALB2 contribute 2-3%.

METHODS

A search of the PubMed database was carried out spanning from 2005 to July 2024, yielding a total of 768 articles that delve into the realm of familial breast cancer, concerning genes and genetic syndromes. After exclusion 150 articles were included in the final review.

RESULTS

We report on a set of 20 familial breast cancer -associated genes into high, moderate, and low penetrance levels. Additionally, 10 genetic disorders were found to be linked with familial breast cancer.

CONCLUSION

Familial breast cancer has been linked to several genetic diseases and mutations, according to studies. Screening for genetic disorders is recommended by National Comprehensive Cancer Network recommendations. Evaluation of breast cancer candidate variations and risk loci may improve individual risk assessment. Only high- and moderate-risk gene variations have clinical guidelines, whereas low-risk gene variants require additional investigation. With increasing use of NGS technology, more linkage with rare genes is being discovered.

In silico analysis of missense SNPs in TNFR1a and their possible therapeutic or pathogenic role in immune diseases

Tumor necrosis factor alpha (TNFa) is an inflammatory cytokine that is involved in the pathogenesis of various inflammatory disorders including rheumatoid arthritis. TNF-alpha receptor I (TNFR1a) is one of the receptors TNFa binds with for its activation. Any variation in this receptor might affect the role of TNFa in successive events. Amino acid residue substitutions might happen in TNFR1a through non-synonymous single nucleotide polymorphisms (nsSNPs) which may alter the functioning of TNFa, hence, identifying any such substitutions is of paramount significance. In this study, six nsSNPs at five different evolutionary conserved regions are predicted to be detrimental to the structure and/or function of TNFR1a by using numerous computational tools. Their 3D models are also proposed in this study. Besides, they were found to reduce the stability and affect the molecular mechanisms of this protein. Two contrasting possibilities might happen because of these substitutions. One, they might reduce the production of TNFa which is overexpressed in inflammatory diseases, hence can play therapeutic role in such diseases. Second, they might possibly hinder the apoptosis to occur which can effectuate the uncontrolled division of cells, hence can be pathogenic in diseases like cancer. Further investigations on these nsSNPs using animal models and at cellular level will open doors to understand the underlying mechanisms behind various diseases.

Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review

The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.

Structural analysis and conformational dynamics of SOCS1 gene mutations involved in diffuse large B-cell lymphoma

OBJECTIVES

The SOCS1 gene is frequently mutated in primary Diffuse Large B-Cell Lymphoma (DLBCL) patients and is associated with a reduced survival rate. Using various computational techniques, the current study aims to identify Single Nucleotide Polymorphisms (SNPs) in the SOCS1 gene that are associated with the mortality rate of DLBCL patients. This study also evaluates the effects of SNPs on the structural instability of the SOCS1 protein in DLBCL patient.

METHODS

The cBioPortal webserver was used for mutations and determining how the SNP mutations affect the SOCS1 protein with various algorithms (PolyPhen-2.0, Provean, PhD-SNPg, SNPs&GO, SIFT, FATHMM, Predict SNP and SNAP). Five webservers (I-Mutant 2.0, MUpro, mCSM, DUET and SDM) were used for protein instability and the conserved status and were also predicted through different tools (ConSurf, Expasy, SOMPA). Lastly, MD simulations were run on the two chosen mutations (S116N and V128G) using GROMACS 5.0.1 to study how the mutations change the structure of SOCS1.

RESULTS

Among the 93 SOCS1 mutations detected in DLBCL patients, nine mutations were found to have a detrimental effect (damaging/deleterious/pathogenic/altered) on the SOCS1 protein. All the nine selected mutations are in the conserved region and four are on the extended strand site, four on the random coil site and one on the alpha helix position of the secondary protein structure. After anticipating the structural effects of these nine mutations, two were chosen (S116N and V128G) based on mutational frequency, location within the protein, structural effect (primary, secondary and tertiary) on stability and conservation status within the SOCS1 protein. The simulation of a 50 ns time interval revealed that the Rg value of S116N (2.17 nm) is higher than that of WT (1.98 nm), indicating a loss of structural compactness. In the case of the RMSD value, this mutated type (V128G) shows more deviation (1.54 nm) in comparison to the wild-type (2.14 nm) and another mutant type (S116N) (2.12 nm). The average RMSF values of wild-type and mutant types (V128G and S116N) were 0.88 nm, 0.49 nm, and 0.93 nm, respectively. The RMSF result shows that the mutant V128G structure is more stable than the wild-type and mutant S116N structures.

CONCLUSION

Based on all these computational predictions, this study finds that certain mutations, particularly S116N, have a destabilising and robust effect on the SOCS1 protein. These results can be used to learn more about the importance of SOCS1 mutations in DLBCL patients and to develop new ways to treat DLBCL.

SHANK3 genetic polymorphism and susceptibility to ASD: evidence from molecular, in silico, and meta-analysis approaches

BACKGROUND

The SHANK3 gene encodes a master synaptic scaffolding protein at the excitatory synapse's postsynaptic density, which is predominantly responsible for constructing a synapse, maintaining synaptic structure, and functions. Recently, evidence from rare mutations and copy number variation provided an important clue about SHANK3 which acts as a strong candidate gene in the pathogenesis of Autism Spectrum Disorder (ASD).

MATERIALS AND METHODS

To investigate potential allelic variants for the SHANK3 (rs9616915) gene as a genetic risk factor, we performed PCR-RFLP analysis and Sanger sequencing for 90 ASD and 90 healthy subjects. Moreover, to understand the functional and structural impacts of our selected non-synonymous SHANK3 SNP rs9616915, we have performed an in silico analysis. Subsequently, a meta-analysis of rs9616915 with a total of 6 eligible studies (including the present study) containing a total of 795 cases and 12,947 controls was obtained from a comprehensive online database search to evaluate the overall association with ASD.

RESULTS

Our retrieved data, such as Pearson's chi-square test (p = 0.081) as well as logistic regression analysis of co-dominant (p = 0.1131), dominant (p = 0.3656) and recessive models (p = 0.0569) speculated no significant association between rs9616915 and our studied sample. Interestingly, by in silico analysis, we have observed two hydrogen bonds between amino acids instead of one hydrogen bond in the protein structure of rs9616915, which indicates this mutant structure could affect the proteins' stability. The findings of the meta-analysis revealed that four genetic association models were associated with ASD susceptibility.

CONCLUSIONS

Our study suggested that targeted SHANK3 SNP of interest rs9616915 might not be associated with ASD in the southern part of the Bangladeshi population.

Exploring the Structural and Functional Effects of Nonsynonymous SNPs in the Human Serotonin Transporter Gene Through In Silico Approaches

The sodium-dependent serotonin transporter SLC6A4 (solute carrier family 6 member 4) gene encodes an intrinsic membrane protein that transmits the serotonin neurotransmitter from synaptic clefts into presynaptic neurons. The product of the SLC6A4 gene is related to the regulation of mood and social behavior, sleep, appetite, memory, digestion, and sexual desire. This protein is a target for antidepressant and psychostimulant drugs, thus prolonged neurotransmitter signaling remains blocked. In this study, the functional consequences of nsSNPs in the human SLC6A4 gene were explored through computational tools: PhD-SNP, SIFT, Align GVGD, PROVEAN, PMut, nsSNP Analyzer, SNPs&GO, SNAP2, PolyPhen2, and PANTHER to identify the most deleterious and damaging nsSNPs. Then the mutant protein stabilities were assessed using I-Mutant, MUpro, and MutPred2; amino acid conservation using ConSurf, and posttranslational modification analysis using MusiteDEEP and PROSPER. Furthermore, the 3-dimensional (3D) model of the mutated proteins was predicted and validated using SPARKS-X, Verify3D, and PROCHECK. The protein–ligand binding sites were analyzed using the COACH meta-server. Results from this study predicted that T192M, G342E, R607C, W282S, R104C, P131L, P156L, and N351S were the most structurally and functionally significant nsSNPs in the human SLC6A4 gene. Arg607 and Pro156 were the predicted sites for posttranslational modifications, and Thr192 and Try282 were the ligand-binding sites in the human SLC6A4 gene. The analyzed data also suggested that R104C, P131L, P156L, T192M, G342E, and W282S mutants might affect the binding of sodium ions with this protein. Taken together, this study provided important information on structurally and functionally important nsSNPs of the human SLC6A4 gene for further experimental validation. In the future, these damaging nsSNPs of the SLC6A4 gene have the potential to be evaluated as prognostic biomarkers for SLC6A4-related disorder diagnosis and research.

An in-silico analysis to identify structural, functional and regulatory role of SNPs in hMRE11

Meiotic recombination 11 (MRE11) is a component of the tri-molecular MRE11-RAD50-NBS1 (MRN) complex, which functions as an exonuclease and endonuclease which is involved in identifying, signalling, protecting and repairing double-strand breaks in DNA (DSBs). Ataxia-telangiectasia-like disorder (ATLD) 1 and Nijmegen breakage syndrome (NBS)-like disorder are MRE11 associated diseases. In the present study, we used an integrated computational approach to identify the most deleterious SNPs and their structural and functional impact on human MRE11. Five of the 68 observed non-synonymous SNP (nsSNPs; I162T, S273C, W210C, D311Y and R364L) should be worked on due to their strong possible pathogenicity and the risk of changing protein properties. All the nsSNPs were highly conserved and decrease the protein stability located in the MRE11 nuclease and MRE11 DNA binding presumed domain. R364L and I162T were predicted to be involved in post-translational modification (PTM) sites. Furthermore, we also analysed the regulatory effect of noncoding SNPs on MRE11 gene regulation in which 6 SNPs were found to affect gene regulation. All six noncoding SNPs predicted chromatin interactive site whereas only one SNP was noted its association with miRNA binding site which disrupts 5 miRNA conserved site. These findings help future studies to get more insights into the role of these variants in the alteration of the MRE11 function. Communicated by Ramaswamy H. Sarma.

Comprehensive Characterization of the Coding and Non-Coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools

Single nucleotide polymorphisms (SNPs) help to understand the phenotypic variations in humans. Genome-wide association studies (GWAS) have identified SNPs located in the tumor protein 63 (TP63) locus to be associated with the genetic susceptibility of cancers. However, there is a lack of in-depth characterization of the structural and functional impacts of the SNPs located at the TP63 gene. The current study was designed for the comprehensive characterization of the coding and non-coding SNPs in the human TP63 gene for their functional and structural significance. The functional and structural effects of the SNPs were investigated using a wide variety of computational tools and approaches, including molecular dynamics (MD) simulation. The deleterious impact of eight nonsynonymous SNPs (nsSNPs) affecting protein stability, structure, and functions was measured by using 13 bioinformatics tools. These eight nsSNPs are in highly conserved positions in protein and were predicted to decrease protein stability and have a deleterious impact on the TP63 protein function. Molecular docking analysis showed five nsSNPs to reduce the binding affinity of TP63 protein to DNA with significant results for three SNPs (R319H, G349E, and C347F). Further, MD simulations revealed the possible disruption of TP63 and DNA binding, hampering the essential protein function. PolymiRTS study found five non-coding SNPs in miRNA binding sites, and the GTEx portal recognized five eQTLs SNPs in single tissue of the lung, heart (LV), and cerebral hemisphere (brain). Characterized nsSNPs and non-coding SNPs will help researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

A computational in silico approach to predict high-risk coding and non-coding SNPs of human PLCG1 gene

PLCG1 gene is responsible for many T-cell lymphoma subtypes, including peripheral T-cell lymphoma (PTCL), angioimmunoblastic T-cell lymphoma (AITL), cutaneous T-cell lymphoma (CTCL), adult T-cell leukemia/lymphoma along with other diseases. Missense mutations of this gene have already been found in patients of CTCL and AITL. The non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein structure as well as its functions. In this study, probable deleterious and disease-related nsSNPs in PLCG1 were identified using SIFT, PROVEAN, PolyPhen-2, PhD-SNP, Pmut, and SNPS&GO tools. Further, their effect on protein stability was checked along with conservation and solvent accessibility analysis by I-mutant 2.0, MUpro, Consurf, and Netsurf 2.0 server. Some SNPs were finalized for structural analysis with PyMol and BIOVIA discovery studio visualizer. Out of the 16 nsSNPs which were found to be deleterious, ten nsSNPs had an effect on protein stability, and six mutations (L411P, R355C, G493D, R1158H, A401V and L455F) were predicted to be highly conserved. Among the six highly conserved mutations, four nsSNPs (R355C, A401V, L411P and L455F) were part of the catalytic domain. L411P, L455F and G493D made significant structural change in the protein structure. Two mutations-Y210C and R1158H had post-translational modification. In the 5' and 3' untranslated region, three SNPs, rs139043247, rs543804707, and rs62621919 showed possible miRNA target sites and DNA binding sites. This in silico analysis has provided a structured dataset of PLCG1 gene for further in vivo researches. With the limitation of computational study, it can still prove to be an asset for the identification and treatment of multiple diseases associated with the target gene.

Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays

Single-nucleotide polymorphisms (SNPs) are the most common genetic variations for various complex human diseases, including cancers. Genome-wide association studies (GWAS) have identified numerous SNPs that increase cancer risks, such as breast cancer, colorectal cancer, and leukemia. These SNPs were cataloged for scientific use. However, GWAS are often conducted on certain populations in which the Orang Asli and Malays were not included. Therefore, we have developed a bioinformatic pipeline to mine the whole-genome sequence databases of the Orang Asli and Malays to determine the presence of pathogenic SNPs that might increase the risks of cancers among them. Five different in silico tools, SIFT, PROVEAN, Poly-Phen-2, Condel, and PANTHER, were used to predict and assess the functional impacts of the SNPs. Out of the 80 cancer-related nsSNPs from the GWAS dataset, 52 nsSNPs were found among the Orang Asli and Malays. They were further analyzed using the bioinformatic pipeline to identify the pathogenic variants. Three nsSNPs; rs1126809 (TYR), rs10936600 (LRRC34), and rs757978 (FARP2), were found as the most damaging cancer pathogenic variants. These mutations alter the protein interface and change the allosteric sites of the respective proteins. As TYR, LRRC34, and FARP2 genes play important roles in numerous cellular processes such as cell proliferation, differentiation, growth, and cell survival; therefore, any impairment on the protein function could be involved in the development of cancer. rs1126809, rs10936600, and rs757978 are the important pathogenic variants that increase the risks of cancers among the Orang Asli and Malays. The roles and impacts of these variants in cancers will require further investigations using in vitro cancer models.

Impact of non-synonymous mutations on the structure and function of telomeric repeat binding factor 1

Telomeric repeat binding factor 1 (TRF1) is one of the major components of the shelterin complex. It directly binds to the telomere and controls its function by regulating the telomerase acting on it. Several variations are reported in the TRF1 gene; some are associated with variety of diseases. Here, we have studied the structural and functional significance of these variations in the TRFH domain of TRF1. We have used cutting-edge computational methods such as SIFT, PolyPhen-2, PROVEAN, Mutation Assessor, mCSM, SDM, STRUM, MAESTRO, and DUET to predict the effects of 124 mutations in the TRFH domain of TRF1. Out of 124 mutations, we have identified 12 deleterious mutations with high confidence based on their prediction. To see the impact of the finally selected mutations on the structure and stability of TRF1, all-atom molecular dynamics (MD) simulations on TRF1-Wild type (WT), L79R and P150R mutants for 200 ns were carried out. A significant conformational change in the structure of the P150R mutant was observed. Our integrated computational study provides a comprehensive understanding of structural changes in TRF1 incurred due to the mutations and subsequent function, leading to the progression of many diseases.Communicated by Ramaswamy H. Sarma.

Experimental Models as Refined Translational Tools for Breast Cancer Research

Breast cancer is one of the most common cancers worldwide, which makes it a very impactful malignancy in the society. Breast cancers can be classified through different systems based on the main tumor features and gene, protein, and cell receptors expression, which will determine the most advisable therapeutic course and expected outcomes. Multiple therapeutic options have already been proposed and implemented for breast cancer treatment. Nonetheless, their use and efficacy still greatly depend on the tumor classification, and treatments are commonly associated with invasiveness, pain, discomfort, severe side effects, and poor specificity. This has demanded an investment in the research of the mechanisms behind the disease progression, evolution, and associated risk factors, and on novel diagnostic and therapeutic techniques. However, advances in the understanding and assessment of breast cancer are dependent on the ability to mimic the properties and microenvironment of tumors in vivo, which can be achieved through experimentation on animal models. This review covers an overview of the main animal models used in breast cancer research, namely in vitro models, in vivo models, in silico models, and other models. For each model, the main characteristics, advantages, and challenges associated to their use are highlighted.

Prediction of potential deleterious nonsynonymous single nucleotide polymorphisms of HIF1A gene: A computational approach

Hypoxia-inducible factor-1α (HIF-1α) is the oxygen sensitive subunit of HIF1 transcription factor. Its variations is associated with several diseases including different type of cancer, cardiovascular diseases, and liver and kidney failure. Despite all the investigations carried out on the single nucleotide polymorphisms (SNPs) of HIF1A gene and diseases, there are many uncharacterized nonsynonymous SNPs of this gene, which might have damaging effect on the protein function. Therefore, it is worthwhile to analyze these potential damaging nsSNPs, using different bioinformatics tools before launching large population studies. The objective of the present study was to predict the possible deleterious nsSNPs of HIF1A gene and their effects on the function and structure of HIF-1alpha protein, using different bioinformatics tools. Various prediction servers were used including SIFT, PROVEAN, PolyPhen-2, PANTHER, phD-SNP, SNP-GO, I-Mutant 2.0, Fathmm, SNPeffect 4.0, Mutation taster, CADD and RAMPAGE in a stepwise approach. After analyzing all 454 missense variants of the HIF1A gene using the abovementioned tools, we reported 11 variants with a significant impact on the function or structure of HIF-1α protein. Furthermore, among these variants only S274 P was predicted as stability enhancing variant with effect on protein function by increasing its stability. Although there are many advantages for computational analysis, the results has to be confirmed by experimental investigations.