Accounting for human polymorphisms predicted to affect protein function

Improved Litter Size in Thin-Tailed Indonesian Sheep Through Analysis of TGIF1 Gene Polymorphisms

Reproductive traits, particularly the litter size, are crucial for sheep husbandry. Molecular genetic selection methods, including single-nucleotide polymorphism (SNP) analyses, offer potential avenues for enhancing these traits. This study investigated the association between TGIF1 SNPs and litter size in thin-tailed Indonesian sheep. A total of 47 sheep were sampled, and their genomic DNA was analyzed. Bioinformatics, sequencing, and statistical analyses were conducted to identify SNPs, assess genetic parameters, and examine their association with litter size. Nine SNPs, including nonsynonymous variants, were successfully identified through targeted sequencing and Sanger sequencing within exon 3 of TGIF1. Noteworthy polymorphisms at g. 42725867 G>A, g. 42725886 G>A, g. 42725932 A>C, g. 42725950 A>G, g. 42726009 G>A, g.42726036 C>T, g.42726042 A>C, g. 42726051 A>G, and g. 42726059 G>A were revealed. Genetic parameter assessments indicated moderate diversity although no significant association was observed between the TGIF1 SNPs and litter size. This lack of association highlights the potential influence of environmental factors, polygenic effects, or the need for larger sample sizes in future studies. In addition, linkage disequilibrium analysis highlighted strong interconnectivity among six of the nine TGIF1 SNPs, designating them as potential Tag SNPs. Data analysis further demonstrated that the haplotype combination of H3 and Hap 6 within the identified blocks exhibited the highest litter size. This study unveils novel TGIF1 SNPs in thin-tailed Indonesian sheep, prompting the need for additional research to unravel their functional implications and potential impacts on reproductive traits. While no significant associations were found, these findings contribute to the growing body of knowledge on genetic factors influencing litter size and underscore the need for broader investigations, including whole-genome sequencing and validation in larger populations. This investigation provides valuable insights into the genetic factors that influence litter size in this breed and lays the foundation for future genetic improvement strategies.

Using The Cancer Genome Atlas from cBioPortal to Develop Genomic Datasets for Machine Learning Assisted Cancer Treatment

Predicting the impact of genetic mutations is crucial for understanding diseases like cancer. Polymorphism Phenotyping (PolyPhen) and Sorting Intolerant From Tolerant (SIFT) are key tools for assessing how amino acid substitutions affect protein function and mutation pathogenicity. To our knowledge, no ready-to-use genomic dataset exists for prediction models to identify potentially harmful mutations, which could support research and clinical decisions. This study develops genomic and non-genomic datasets using The Cancer Genome Atlas (TCGA) from cBioPortal and applies machine learning models to predict PolyPhen and SIFT scores. We explore three classification models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and an ensemble RF-XGBoost model. Experimental results show that genomic data yields more accurate predictions than non-genomic data. The ensemble RF-XGBoost model performs best on genomic data, achieving average accuracies of 88.43% for PolyPhen and 95.13% for SIFT, highlighting the potential of artificial intelligence in genetic mutation analysis for disease treatment.

Advances in predicting breast cancer driver mutations: Tools for precision oncology (Review)

In the modern era of medicine, prognosis and treatment, options for a number of cancer types including breast cancer have been improved by the identification of cancer‑specific biomarkers. The availability of high‑throughput sequencing and analysis platforms, the growth of publicly available cancer databases and molecular and histological profiling facilitate the development of new drugs through a precision medicine approach. However, only a fraction of patients with breast cancer with few actionable mutations typically benefit from the precision medicine approach. In the present review, the current development in breast cancer driver gene identification, actionable breast cancer mutations, as well as the available therapeutic options, challenges and applications of breast precision oncology are systematically described. Breast cancer driver mutation‑based precision oncology helps to screen key drivers involved in disease development and progression, drug sensitivity and the genes responsible for drug resistance. Advances in precision oncology will provide more targeted therapeutic options for patients with breast cancer, improving disease‑free survival and potentially leading to significant successes in breast cancer treatment in the near future. Identification of driver mutations has allowed new targeted therapeutic approaches in combination with standard chemo‑ and immunotherapies in breast cancer. Developing new driver mutation identification strategies will help to define new therapeutic targets and improve the overall and disease‑free survival of patients with breast cancer through efficient medicine.

Massively parallel experimental interrogation of natural variants in ancient signaling pathways reveals both purifying selection and local adaptation

The nature of standing genetic variation remains a central debate in population genetics, with differing perspectives on whether common variants are mostly neutral or have functional effects. We address this question by directly mapping the fitness effects of over 9,000 natural variants in the Ras/PKA and TOR/Sch9 pathways-key regulators of cell proliferation in eukaryotes-across four conditions in Saccharomyces cerevisiae. While many variants are neutral in our assay, on the order of 3,500 exhibited significant fitness effects. These non-neutral variants tend to be missense and affect conserved, more densely packed, and less solvent-exposed protein regions. They are also typically younger, occur at lower frequencies, and more often found in heterozygous states, suggesting they are subject to purifying selection. A substantial fraction of non-neutral variants showing strong fitness effects in our experiments, however, is present at high frequencies in the population. These variants show signs of local adaptation as they tend to be found specifically in domesticated strains adapted to human-made environments. Our findings support the view that while common variants are often neutral, a significant proportion have adaptive functional consequences and are driven into the population by local positive selection. This study highlights the potential to explore the functional effects of natural genetic variation on a genome scale with quantitative fitness measurements in the laboratory, bridging the gap between population genetics and functional genomics to understand evolutionary dynamics in the wild.

Germline mutational variants of Turkish ovarian cancer patients suspected of Hereditary Breast and Ovarian Cancer (HBOC) by next-generation sequencing

Hereditary Breast and Ovarian Cancer (HBOC) syndrome is characterized by an increased risk of developing breast cancer (BC) and ovarian cancer (OC) due to inherited genetic mutations. Understanding the genetic variants associated with HBOC is crucial for identifying individuals at high risk and implementing appropriate preventive measures. The study included 630 Turkish OC patients with confirmed diagnostic criteria of The National Comprehensive Cancer Network (NCCN) concerning HBOC. Genomic DNA was extracted from peripheral blood samples, and targeted Next-generation sequencing (NGS) was performed. Bioinformatics analysis and variant interpretation were conducted to identify pathogenic variants (PVs). Our analysis revealed a spectrum of germline pathogenic variants associated with HBOC in Turkish OC patients. Notably, several pathogenic variants in BRCA1, BRCA2, and other DNA repair genes were identified. Specifically, we observed germline PVs in 130 individuals, accounting for 20.63% of the total cohort. 76 distinct PVs in genes, BRCA1 (40 PVs), BRCA2 (29 PVs), ATM (1 PV), CHEK2 (2 PVs), ERCC2 (1 PV), MUTYH (1 PV), RAD51C (1 PV), and TP53 (1PV) and also, two different PVs (i.e., c.135-2 A>G p.? in BRCA1 and c.6466_6469delTCTC in BRCA2) were detected in a 34-year-old OC patient. In conclusion, our study contributes to a better understanding of the genetic variants underlying HBOC in Turkish OC patients. These findings provide valuable insights into the genetic architecture of HBOC in the Turkish population and shed light on the potential contribution of specific germline PVs to the increased risk of OC.

Molecular complexity of diffuse large B-cell lymphoma: a molecular perspective and therapeutic implications

Diffuse large B-cell lymphoma (DLBCL) stands as a formidable challenge in the landscape of non-Hodgkin's lymphomas. This review illuminates the remarkable strides made in comprehending DLBCL's molecular intricacies and devising targeted treatments. DLBCL, the most prevalent non-Hodgkin's lymphoma, has seen transformative progress in its characterization. Genetic investigations, led by high-throughput sequencing, have unveiled recurrent mutations in genes such as MYC, BCL2, and BCL6, casting light on the underlying genetic chaos propelling DLBCL's aggressiveness. A pivotal facet of this understanding centers on cell signaling pathways. Dysregulation of B-cell receptor (BCR) signaling, NF-κB, PI3K/Akt/mTOR, JAK/STAT, Wnt/β-Catenin, and Toll-like receptor pathways plays a critical role in DLBCL pathogenesis, offering potential therapeutic targets. DLBCL's complex tumor microenvironment (TME) cannot be overlooked. The dynamic interplay among tumor cells, immune cells, stromal components, and the extracellular matrix profoundly influences DLBCL's course and response to therapies. Epigenetic modifications, including DNA methylation and histone changes, add another layer of intricacy. Aberrant epigenetic regulation plays a significant role in lymphomagenesis, offering prospects for epigenetic-based therapies. Promisingly, these molecular insights have spurred the development of personalized treatments. Targeted therapies and immunotherapies, guided by genomic profiling and molecular classification, are emerging as game-changers in DLBCL management. In conclusion, this review underscores the remarkable strides in understanding DLBCL's molecular underpinnings, spanning genetics, cell signaling, the tumor microenvironment, and epigenetics. These advances pave the way for more effective, personalized treatments, renewing hope for DLBCL patients.

Exploring DNA Damage and Repair Mechanisms: A Review with Computational Insights

DNA damage is a critical factor contributing to genetic alterations, directly affecting human health, including developing diseases such as cancer and age-related disorders. DNA repair mechanisms play a pivotal role in safeguarding genetic integrity and preventing the onset of these ailments. Over the past decade, substantial progress and pivotal discoveries have been achieved in DNA damage and repair. This comprehensive review paper consolidates research efforts, focusing on DNA repair mechanisms, computational research methods, and associated databases. Our work is a valuable resource for scientists and researchers engaged in computational DNA research, offering the latest insights into DNA-related proteins, diseases, and cutting-edge methodologies. The review addresses key questions, including the major types of DNA damage, common DNA repair mechanisms, the availability of reliable databases for DNA damage and associated diseases, and the predominant computational research methods for enzymes involved in DNA damage and repair.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

Biochemical Activity of 17 Cancer-Associated Variants of DNA Polymerase Kappa Predicted by Electrostatic Properties

DNA damage and repair have been widely studied in relation to cancer and therapeutics. Y-family DNA polymerases can bypass DNA lesions, which may result from external or internal DNA damaging agents, including some chemotherapy agents. Overexpression of the Y-family polymerase human pol kappa can result in tumorigenesis and drug resistance in cancer. This report describes the use of computational tools to predict the effects of single nucleotide polymorphism variants on pol kappa activity. Partial Order Optimum Likelihood (POOL), a machine learning method that uses input features from Theoretical Microscopic Titration Curve Shapes (THEMATICS), was used to identify amino acid residues most likely involved in catalytic activity. The μ4 value, a metric obtained from POOL and THEMATICS that serves as a measure of the degree of coupling between one ionizable amino acid and its neighbors, was then used to identify which protein mutations are likely to impact the biochemical activity. Bioinformatic tools SIFT, PolyPhen-2, and FATHMM predicted most of these variants to be deleterious to function. Along with computational and bioinformatic predictions, we characterized the catalytic activity and stability of 17 cancer-associated DNA pol kappa variants. We identified pol kappa variants R48I, H105Y, G147D, G154E, V177L, R298C, E362V, and R470C as having lower activity relative to wild-type pol kappa; the pol kappa variants T102A, H142Y, R175Q, E210K, Y221C, N330D, N338S, K353T, and L383F were identified as being similar in catalytic efficiency to WT pol kappa. We observed that POOL predictions can be used to predict which variants have decreased activity. Predictions from bioinformatic tools like SIFT, PolyPhen-2, and FATHMM are based on sequence comparisons and therefore are complementary to POOL but are less capable of predicting biochemical activity. These bioinformatic and computational tools can be used to identify SNP variants with deleterious effects and altered biochemical activity from a large data set.

A Novel EYA1 Mutation Causing Alternative RNA Splicing in a Chinese Family With Branchio-Oto Syndrome: Implications for Molecular Diagnosis and Clinical Application

OBJECTIVES

Branchio-oto syndrome (BOS) primarily manifests as hearing loss, preauricular pits, and branchial defects. EYA1 is the most common pathogenic gene, and splicing mutations account for a substantial proportion of cases. However, few studies have addressed the structural changes in the protein caused by splicing mutations and potential pathogenic factors, and several studies have shown that middle-ear surgery has limited effectiveness in improving hearing in these patients. BOS has also been relatively infrequently reported in the Chinese population. This study explored the genetic etiology in the family of a proband with BOS and provided clinical treatment to improve the patient's hearing.

METHODS

We collected detailed clinical features and peripheral blood samples from the patients and unaffected individuals within the family. Pathogenic mutations were identified by whole-exome sequencing and cosegregation analysis and classified according to the American College of Medical Genetics and Genomics guidelines. Alternative splicing was verified through a minigene assay. The predicted three-dimensional protein structure and biochemical experiments were used to investigate the pathogenicity of the mutation. The proband underwent middle-ear surgery and was followed up at 1 month and 6 months postoperatively to monitor auditory improvement.

RESULTS

A novel heterozygous EYA1 splicing variant (c.1050+4 A>C) was identified and classified as pathogenic (PVS1(RNA), PM2, PP1). Skipping of exon 11 of the EYA1 pre-mRNA was confirmed using a minigene assay. This mutation may impair EYA1-SIX1 interactions, as shown by an immunoprecipitation assay. The EYA1-Mut protein exhibited cellular mislocalization and decreased protein expression in cytological experiments. Middle-ear surgery significantly improved hearing loss caused by bone-conduction abnormalities in the proband.

CONCLUSION

We reported a novel splicing variant of EYA1 in a Chinese family with BOS and revealed the potential molecular pathogenic mechanism. The significant hearing improvement observed in the proband after middle-ear surgery provides a reference for auditory rehabilitation in similar patients.

Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data

Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and relevance relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data is informative about how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models.

Pan-cancer proteogenomics connects oncogenic drivers to functional states

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying their significant cis-effects and distal trans-effects quantified at the RNA, protein, and phosphoprotein levels. Salient observations include the association of point mutations and copy-number alterations with the rewiring of protein interaction networks, and notably, most cancer genes converge toward similar molecular states denoted by sequence-based kinase activity profiles. A correlation between predicted neoantigen burden and measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns of cancer hallmarks vary by polygenic protein abundance ranging from uniform to heterogeneous. Overall, our work demonstrates the value of comprehensive proteogenomics in understanding the functional states of oncogenic drivers and their links to cancer development, surpassing the limitations of studying individual cancer types.

Evolution: Untangling the woolly mammoth

Twenty-two woolly mammoth genomes have been compared to those of living elephants, identifying genes under strong evolutionary pressure in mammoths, including genes associated with curly, wiry, thick, bushy, coarse, uncombable and (of course) woolly hair.

SUFU promotes GLI activity in a Hedgehog-independent manner in pancreatic cancer

Aberrant activation of the Hedgehog (Hh) signaling pathway, through which the GLI family of transcription factors (TF) is stimulated, is commonly observed in cancer cells. One well-established mechanism of this increased activity is through the inactivation of Suppressor of Fused (SUFU), a negative regulator of the Hh pathway. Relief from negative regulation by SUFU facilitates GLI activity and induction of target gene expression. Here, we demonstrate a novel role for SUFU as a promoter of GLI activity in pancreatic ductal adenocarcinoma (PDAC). In non-ciliated PDAC cells unresponsive to Smoothened agonism, SUFU overexpression increases GLI transcriptional activity. Conversely, knockdown (KD) of SUFU reduces the activity of GLI in PDAC cells. Through array PCR analysis of GLI target genes, we identified B-cell lymphoma 2 (BCL2) among the top candidates down-regulated by SUFU KD. We demonstrate that SUFU KD results in reduced PDAC cell viability, and overexpression of BCL2 partially rescues the effect of reduced cell viability by SUFU KD. Further analysis using as a model GLI1, a major TF activator of the GLI family in PDAC cells, shows the interaction of SUFU and GLI1 in the nucleus through previously characterized domains. Chromatin immunoprecipitation (ChIP) assay shows the binding of both SUFU and GLI1 at the promoter of BCL2 in PDAC cells. Finally, we demonstrate that SUFU promotes GLI1 activity without affecting its protein stability. Through our findings, we propose a novel role of SUFU as a positive regulator of GLI1 in PDAC, adding a new mechanism of Hh/GLI signaling pathway regulation in cancer cells.

Invited review: Good practices in genome-wide association studies to identify candidate sequence variants in dairy cattle

Genotype data from dairy cattle selection programs have greatly facilitated GWAS to identify variants related to economic traits. Results can enhance the accuracy of genomic prediction, analyze more complex models that go beyond additive effects, elucidate the genetic architecture of a trait, and finally, decipher the underlying biology of traits. The entire process, comprising data generation, quality control, statistical analyses, interpretation of association results, and linking results to biology should be designed and executed to minimize the generation of false-positive and false-negative associations and misleading links to biological processes. This review aims to provide general guidelines for data analysis that address data quality control, association tests, adjustment for population stratification, and significance evaluation to improve the reliability of conclusions. We also provide guidance on post-GWAS strategy and the interpretation of results. These guidelines are tailored to dairy cattle, which are characterized by long-range linkage disequilibrium, large half-sib families, and routinely collected phenotypes, requiring different approaches than those applied in human GWAS. We discuss common limitations and challenges that have been overlooked in the analysis and interpretation of GWAS to identify candidate sequence variants in dairy cattle.

Antioxidant enzyme and DNA base repair genetic risk scores' associations with systemic oxidative stress biomarker in pooled cross-sectional studies

Background: Oxidative stress is hypothesized to contribute to the pathogenesis of several chronic diseases. Numerous dietary and lifestyle factors are associated with oxidative stress; however, little is known about associations of genetic factors, individually or jointly with dietary and lifestyle factors, with oxidative stress in humans. Methods: We genotyped 22 haplotype-tagging single nucleotide polymorphisms (SNPs) in 3 antioxidant enzyme (AE) genes and 79 SNPs in 14 DNA base excision repair (BER) genes to develop oxidative stress-specific AE and BER genetic risk scores (GRS) in two pooled cross-sectional studies (n = 245) of 30-74-year-old, White, cancer- and inflammatory bowel disease-free adults. Of the genotypes, based on their associations with a systemic oxidative stress biomarker, plasma F₂-isoprostanes (FiP) concentrations, we selected 4 GSTP1 SNPs for an AE GRS, and 12 SNPs of 5 genes (XRCC1, TDG, PNKP, MUTYH, and FEN1) for a BER GRS. We also calculated a previously-reported, validated, questionnaire-based, oxidative stress biomarker-weighted oxidative balance score (OBS) comprising 17 anti- and pro-oxidant dietary and lifestyle exposures, with higher scores representing a higher predominance of antioxidant exposures. We used general linear regression to assess adjusted mean FiP concentrations across GRS and OBS tertiles, separately and jointly. Results: The adjusted mean FiP concentrations among those in the highest relative to the lowest oxidative stress-specific AE and BER GRS tertiles were, proportionately, 11.8% (p = 0.12) and 21.2% (p = 0.002) higher, respectively. In the joint AE/BER GRS analysis, the highest estimated mean FiP concentration was among those with jointly high AE/BER GRS. Mean FiP concentrations across OBS tertiles were similar across AE and BER GRS strata. Conclusion: Our pilot study findings suggest that DNA BER, and possibly AE, genotypes collectively may be associated with systemic oxidative stress in humans, and support further research in larger, general populations.

Non-synonymous Single Nucleotide Polymorphisms in Human ACE2 Gene May Affect the Infectivity of SARS-CoV-2 Omicron Subvariants

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the coronavirus disease 2019 (COVID-19), which first appeared in December 2019. Angiotensin I converting enzyme 2 (ACE2) receptor, present on the host cells, interacts with the receptor binding domain (RBD) of spike (S) protein of SARS-CoV-2 and facilitates the viral entry into host cells.

METHODS

Non-synonymous single nucleotide polymorphisms (nsSNPs) in the ACE2 gene may have an impact on the protein's stability and its function. The deleterious or harmful nsSNPs of the ACE2 gene that can change the strength as well as the pattern of interaction with the RBD of S protein were selected for this study.

RESULTS

The ACE2:RBD interactions were analyzed by protein-protein docking study. The missense mutations A242V, R708W, G405E, D292N, Y633C, F308L, and G405E in ACE2 receptor were found to interact with RBD of Omicron subvariants with stronger binding affinity. Among the other selected nsSNPs of human ACE2 (hACE2), R768W, Y654S, F588S, R710C, R710C, A191P, and R710C were found to have lower binding affinity for RBD of Omicron subvariants.

CONCLUSION

The findings of this study suggest that the nsSNPs present in the human ACE2 gene alter the structure and function of the protein and, consequently, the susceptibility to Omicron subvariants.

The Somatic Mutation Landscape of UDP-Glycosyltransferase (UGT) Genes in Human Cancers

The human UDP-glycosyltransferase (UGTs) superfamily has a critical role in the metabolism of anticancer drugs and numerous pro/anti-cancer molecules (e.g., steroids, lipids, fatty acids, bile acids and carcinogens). Recent studies have shown wide and abundant expression of UGT genes in human cancers. However, the extent to which UGT genes acquire somatic mutations within tumors remains to be systematically investigated. In the present study, our comprehensive analysis of the somatic mutation profiles of 10,069 tumors from 33 different TCGA cancer types identified 3427 somatic mutations in UGT genes. Overall, nearly 18% (1802/10,069) of the assessed tumors had mutations in UGT genes with huge variations in mutation frequency across different cancer types, ranging from over 25% in five cancers (COAD, LUAD, LUSC, SKCM and UCSC) to less than 5% in eight cancers (LAML, MESO, PCPG, PAAD, PRAD, TGCT, THYM and UVM). All 22 UGT genes showed somatic mutations in tumors, with UGT2B4, UGT3A1 and UGT3A2 showing the largest number of mutations (289, 307 and 255 mutations, respectively). Nearly 65% (2260/3427) of the mutations were missense, frame-shift and nonsense mutations that have been predicted to code for variant UGT proteins. Furthermore, about 10% (362/3427) of the mutations occurred in non-coding regions (5' UTR, 3' UTR and splice sites) that may be able to alter the efficiency of translation initiation, miRNA regulation or the splicing of UGT transcripts. In conclusion, our data show widespread somatic mutations of UGT genes in human cancers that may affect the capacity of cancer cells to metabolize anticancer drugs and endobiotics that control pro/anti-cancer signaling pathways. This highlights their potential utility as biomarkers for predicting therapeutic efficacy and clinical outcomes.

Comprehensive In Silico Functional Prediction Analysis of CDKL5 by Single Amino Acid Substitution in the Catalytic Domain

Cyclin-dependent kinase-like 5 (CDKL5) is a serine/threonine protein kinase whose pathological mutations cause CDKL5 deficiency disorder. Most missense mutations are concentrated in the catalytic domain. Therefore, anticipating whether mutations in this region affect CDKL5 function is informative for clinical diagnosis. This study comprehensively predicted the pathogenicity of all 5700 missense substitutions in the catalytic domain of CDKL5 using in silico analysis and evaluating their accuracy. Each missense substitution was evaluated as “pathogenic” or “benign”. In silico tools PolyPhen-2 HumDiv mode/HumVar mode, PROVEAN, and SIFT were selected individually or in combination with one another to determine their performance using 36 previously reported mutations as a reference. Substitutions predicted as pathogenic were over 88.0% accurate using each of the three tools. The best performance score (accuracy, 97.2%; sensitivity, 100%; specificity, 66.7%; and Matthew’s correlation coefficient (MCC), 0.804) was achieved by combining PolyPhen-2 HumDiv, PolyPhen-2 HumVar, and PROVEAN. This provided comprehensive information that could accurately predict the pathogenicity of the disease, which might be used as an aid for clinical diagnosis.

Characterization of hotspot exonuclease domain mutations in the DNA polymerase ϵ gene in endometrial cancer

Objective

This study was aimed to profile hotspot exonuclease domain mutations (EDMs) of the DNA polymerase ϵ gene (POLE) in endometrial cancer (EC) and to investigate the effects of EDMs on tumor cell behavior and catalytic activities of Polϵ.

Methods

POLE sequencing was performed in tumor tissue samples from patients with EC to identify hotspot EDMs. Bioinformatics tools were used to select the potential pathogenic EDMs. The association of EDMs with the clinical outcomes of patients was assessed. EC cells were transfected with wildtype POLE or POLE variants to examine the effects of the EDMs on EC cell behavior, including cell cycle, migration, and invasion. Co-immunoprecipitation was employed to obtain FLAG-tagged wildtype and mutant catalytic subunits of Polϵ, followed by the assessment of polymerase and exonuclease activities.

Results

In addition to previously reported P286R and V411L, R375Q and P452L were identified as novel, and deleterious POLE hotspot EDMs of EC. Patients in EDM group had significantly better clinical outcomes than the rest of the cohort. Compared with wildtype POLE, overexpression of POLE variants promoted cisplatin resistance, G0/G1 cell cycle arrest, and cell migration and invasion in EC cells. Overexpression of POLE variants significantly increased the abundance of 3’-OH and upregulated the expression of DNA mismatch repair genes in HEK293T cells. Compared with wildtype Polϵ, Pol ϵ mutants exhibited undermined polymerase and exonuclease abilities in the presence of mismatched nucleotides in HEK293 cells.

Conclusion

We characterized the of hotspot exonuclease domain mutations in the DNA polymerase ϵ gene and identified P286R, V411L, R375Q, and P452L as pathogenic POLE hotspot EDMs in endometrial cancer. These hotspot EDMs are associated with the malignant behavior of endometrial cancer cells in vitro and favorable prognosis in patients, suggesting that POLE affects a wide range of cellular processes beyond DNA replication and proofreading.

Functional and Structural Impact of Deleterious Missense Single Nucleotide Polymorphisms in the NR3C1, CYP3A5, and TNF-α Genes: An In Silico Analysis

Human diseases are generally influenced by SNPs (single nucleotide polymorphisms). The mutations in amino acid residues generated by deleterious SNPs contribute to the structural and functional diversity of the encoded protein. Tumor necrosis factor-α (TNF-α), Glucocorticoid receptor gene (NR3C1), and Cytochrome P450 3A5 (CYP3A5) play a key role in glucocorticoid resistance susceptibility in humans. Possible causative mutations could be used as therapeutic targets and diagnostic markers for glucocorticoid resistance. This study evaluated the missense SNPs of TNF-α, NR3C1, and CYP3A5 to predict their impact on amino acid changes, protein interaction, and functional stability. The protein sequence of dbSNP was obtained and used online in silico method to screen deleterious mutants for the in silico analysis. In the coding regions of TNF-α, NR3C1, and CYP3A5, 14 deleterious mutations were discovered. The protein functional and stability changes in the amino acid between native and mutant energy were identified by analyzing the changes in the hydrogen bonding of these mutants from native, which were all measured using Swiss PDB and PyMOL. F446S and R439K had the highest root-mean-square deviation (RMSD) values among the 14 deleterious mutants. Additionally, the conserved region of amino acid protein interaction was analyzed. This study could aid in the discovery of new detrimental mutations in TNF-α, NR3C1, and CYP3A5, as well as the development of long-term therapy for corticosteroid resistance in several inflammatory diseases. However, more research into the deleterious mutations of the TNF-α, NR3C1, and CYP3A5 genes is needed to determine their role in corticosteroid resistance.

Locating a novel autosomal recessive genetic variant in the cattle glucokinase gene using only WGS data from three cases and six carriers

New Mendelian genetic conditions, which adversely affect livestock, arise all the time. To manage them effectively, some methods need to be devised that are quick and accurate. Until recently, finding the causal genomic site of a new autosomal recessive genetic disease has required a two-stage approach using single-nucleotide polymorphism (SNP) chip genotyping to locate the region containing the new variant. This region is then explored using fine-mapping methods to locate the actual site of the new variant. This study explores bioinformatic methods that can be used to identify the causative variants of recessive genetic disorders with full penetrance with just nine whole genome-sequenced animals to simplify and expedite the process to a one-step procedure. Using whole genome sequencing of only three cases and six carriers, the site of a novel variant causing perinatal mortality in Irish moiled calves was located. Four methods were used to interrogate the variant call format (VCF) data file of these nine animals, they are genotype criteria (GCR), autozygosity-by-difference (ABD), variant prediction scoring, and registered SNP information. From more than nine million variants in the VCF file, only one site was identified by all four methods (Chr4: g.77173487A>T (ARS-UCD1.2 (GCF_002263795.1)). This site was a splice acceptor variant located in the glucokinase gene (GCK). It was verified on an independent sample of animals from the breed using genotyping by polymerase chain reaction at the candidate site and autozygosity-by-difference using SNP-chips. Both methods confirmed the candidate site. Investigation of the GCR method found that sites meeting the GCR were not evenly spread across the genome but concentrated in regions of long runs of homozygosity. Locating GCR sites was best performed using two carriers to every case, and the carriers should be distantly related to the cases, within the breed concerned. Fewer than 20 animals need to be sequenced when using the GCR and ABD methods together. The genomic site of novel autosomal recessive Mendelian genetic diseases can be located using fewer than 20 animals combined with two bioinformatic methods, autozygosity-by-difference, and genotype criteria. In many instances it may also be confirmed with variant prediction scoring. This should speed-up and simplify the management of new genetic diseases to a single-step process.

Molecular Insights into the Role of Pathogenic nsSNPs in GRIN2B Gene Provoking Neurodevelopmental Disorders

The GluN2B subunit of N-methyl-D-aspartate receptors plays an important role in the physiology of different neurodevelopmental diseases. Genetic variations in the GluN2B coding gene (GRIN2B) have consistently been linked to West syndrome, intellectual impairment with focal epilepsy, developmental delay, macrocephaly, corticogenesis, brain plasticity, as well as infantile spasms and Lennox–Gastaut syndrome. It is unknown, however, how GRIN2B genetic variation impacts protein function. We determined the cumulative pathogenic impact of GRIN2B variations on healthy participants using a computational approach. We looked at all of the known mutations and calculated the impact of single nucleotide polymorphisms on GRIN2B, which encodes the GluN2B protein. The pathogenic effect, functional impact, conservation analysis, post-translation alterations, their driving residues, and dynamic behaviors of deleterious nsSNPs on protein models were then examined. Four polymorphisms were identified as phylogenetically conserved PTM drivers and were related to structural and functional impact: rs869312669 (p.Thr685Pro), rs387906636 (p.Arg682Cys), rs672601377 (p.Asn615Ile), and rs1131691702 (p.Ser526Pro). The combined impact of protein function is accounted for by the calculated stability, compactness, and total globularity score. GluN2B hydrogen occupancy was positively associated with protein stability, and solvent-accessible surface area was positively related to globularity. Furthermore, there was a link between GluN2B protein folding, movement, and function, indicating that both putative high and low local movements were linked to protein function. Multiple GRIN2B genetic variations are linked to gene expression, phylogenetic conservation, PTMs, and protein instability behavior in neurodevelopmental diseases. These findings suggest the relevance of GRIN2B genetic variations in neurodevelopmental problems.

Determination of deleterious single-nucleotide polymorphisms of human LYZ C gene: an in silico study

Background

Single-nucleotide polymorphisms (SNPs) have a crucial function in affecting the susceptibility of individuals to diseases and also determine how an individual responds to different treatment options. The present study aimed to predict and characterize deleterious missense nonsynonymous SNPs (nsSNPs) of lysozyme C (LYZ C) gene using different computational methods. Lyz C is an important antimicrobial peptide capable of damaging the peptidoglycan layer of bacteria leading to osmotic shock and cell death. The nsSNPs were first analyzed by SIFT and PolyPhen v2 tools. The nsSNPs predicted as deleterious were then assessed by other in silico tools — SNAP, PROVEAN, PhD-SNP, and SNPs & GO. These SNPs were further examined by I-Mutant 3.0 and ConSurf. GeneMANIA and STRING tools were used to study the interaction network of the LYZ C gene. NetSurfP 2.0 was used to predict the secondary structure of Lyz C protein. The impact of variations on the structural characteristics of the protein was studied by HOPE analysis. The structures of wild type and variants were predicted by SWISS-MODEL web server, and energy minimization was carried out using XenoPlot software. TM-align tool was used to predict root-mean-square deviation (RMSD) and template modeling (TM) scores.

Results

Eight missense nsSNPs (T88N, I74T, F75I, D67H, W82R, D85H, R80C, and R116S) were found to be potentially deleterious. I-Mutant 3.0 determined that the variants decreased the stability of the protein. ConSurf predicted rs121913547, rs121913549, and rs387906536 nsSNPs to be conserved. Interaction network tools showed that LYZ C protein interacted with lactoferrin (LTF). HOPE tool analyzed differences in physicochemical properties between wild type and variants. TM-align tool predicted the alignment score, and the protein folding was found to be identical. PyMOL was used to visualize the superimposition of variants over wild type.

Conclusion

This study ascertained the deleterious missense nsSNPs of the LYZ C gene and could be used in further experimental analysis. These high-risk nsSNPs could be used as molecular targets for diagnostic and therapeutic interventions.

High-Risk Polymorphisms Associated with the Molecular Function of Human HMGCR Gene Infer the Inhibition of Cholesterol Biosynthesis

HMG-CoA reductase or HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase) is a rate-limiting enzyme involved in cholesterol biosynthesis. HMGCR plays an important role in the possible occurrence of hypercholesterolemia leading to atherosclerosis and coronary heart disease. This enzyme is a major target for cholesterol-lowering drugs such as "statin" which blocks the synthesis of mevalonate, a precursor for cholesterol biosynthesis. This study is aimed at characterizing deleterious mutations and classifying functional single nucleotide polymorphisms (SNPs) of the HMGCR gene through analysis of functional and structural evaluation, domain association, solvent accessibility, and energy minimization studies. The functional and characterization tools such as SIFT, PolyPhen, SNPs and GO, Panther, I-Mutant, and Pfam along with programming were employed to explore all the available SNPs in the HMGCR gene in the database. Among 6815 SNP entries from different databases, approximately 388 SNPs were found to be missense. Analysis showed that seven missense SNPs are more likely to have deleterious effects. A tertiary model of the mutant protein was constructed to determine the functional and structural effects of the HMGCR mutation. In addition, the location of the mutations suggests that they may have deleterious effects because most of the mutations are residing in the functional domain of the protein. The findings from the analysis predicted that rs147043821 and rs193026499 missense SNPs could cause significant structural and functional instability in the mutated proteins of the HMGCR gene. The findings of the current study will likely be useful in future efforts to uncover the mechanism and cause of hypercholesterolemia. In addition, the identified SNPs of HMGCR gene could set up a strong foundation for further therapeutic discovery.

Dynamic insights into the effects of nonsynonymous polymorphisms (nsSNPs) on loss of TREM2 function

Single nucleotide variations in Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) are associated with many neurodegenerative diseases, including Nasu-Hakola disease (NHD), frontotemporal dementia (FTD), and late-onset Alzheimer's disease because they disrupt ligand binding to the extracellular domain of TREM2. However, the effects of nonsynonymous single nucleotide polymorphisms (nsSNPs) in TREM2 on disease progression remain unknown. In this study, we identified several high-risk nsSNPs in the TREM2 gene using various deleterious SNP predicting algorithms and analyzed their destabilizing effects on the ligand recognizing region of the TREM2 immunoglobulin (Ig) domain by molecular dynamics (MD) simulation. Cumulative prediction by all tools employed suggested the three most deleterious nsSNPs involved in loss of TREM2 function are rs549402254 (W50S), rs749358844 (R52C), and rs1409131974 (D104G). MD simulation showed that these three variants cause substantial structural alterations and conformational remodeling of the apical loops of the TREM2 Ig domain, which is responsible for ligand recognition. Detailed analysis revealed that these variants substantially increased distances between apical loops and induced conformation remodeling by changing inter-loop nonbonded contacts. Moreover, all nsSNPs changed the electrostatic potentials near the putative ligand-interacting region (PLIR), which suggested they might reduce specificity or loss of binding affinity for TREM2 ligands. Overall, this study identifies three potential high-risk nsSNPs in the TREM2 gene. We propose further studies on the molecular mechanisms responsible for loss of TREM2 function and the associations between TREM2 nsSNPs and neurodegenerative diseases.

The Role of Natural Polymorphic Variants of DNA Polymerase β in DNA Repair

DNA polymerase β (Polβ) is considered the main repair DNA polymerase involved in the base excision repair (BER) pathway, which plays an important part in the repair of damaged DNA bases usually resulting from alkylation or oxidation. In general, BER involves consecutive actions of DNA glycosylases, AP endonucleases, DNA polymerases, and DNA ligases. It is known that protein-protein interactions of Polβ with enzymes from the BER pathway increase the efficiency of damaged base repair in DNA. However natural single-nucleotide polymorphisms can lead to a substitution of functionally significant amino acid residues and therefore affect the catalytic activity of the enzyme and the accuracy of Polβ action. Up-to-date databases contain information about more than 8000 SNPs in the gene of Polβ. This review summarizes data on the in silico prediction of the effects of Polβ SNPs on DNA repair efficacy; available data on cancers associated with SNPs of Polβ; and experimentally tested variants of Polβ. Analysis of the literature indicates that amino acid substitutions could be important for the maintenance of the native structure of Polβ and contacts with DNA; others affect the catalytic activity of the enzyme or play a part in the precise and correct attachment of the required nucleotide triphosphate. Moreover, the amino acid substitutions in Polβ can disturb interactions with enzymes involved in BER, while the enzymatic activity of the polymorphic variant may not differ significantly from that of the wild-type enzyme. Therefore, investigation regarding the effect of Polβ natural variants occurring in the human population on enzymatic activity and protein-protein interactions is an urgent scientific task.

An overview of human proteins and genes involved in SARS-CoV-2 infection

As of July 2021, the outbreak of coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has led to more than 200 million infections and more than 4.2 million deaths globally. Complications of severe COVID-19 include acute kidney injury, liver dysfunction, cardiomyopathy, and coagulation dysfunction. Thus, there is an urgent need to identify proteins and genetic factors associated with COVID-19 susceptibility and outcome. We comprehensively reviewed recent findings of host-SARS-CoV-2 interactome analyses. To identify genetic variants associated with COVID-19, we focused on the findings from genome and transcriptome wide association studies (GWAS and TWAS) and bioinformatics analysis. We described established human proteins including ACE2, TMPRSS2, 40S ribosomal subunit, ApoA1, TOM70, HLA-A, and PALS1 interacting with SARS-CoV-2 based on cryo-electron microscopy results. Furthermore, we described approximately 1000 human proteins showing evidence of interaction with SARS-CoV-2 and highlighted host cellular processes such as innate immune pathways affected by infection. We summarized the evidence on more than 20 identified candidate genes in COVID-19 severity. Predicted deleterious and disruptive genetic variants with possible effects on COVID-19 infectivity have been also summarized. These findings provide novel insights into SARS-CoV-2 biology and infection as well as potential strategies for development of novel COVID therapeutic targets and drug repurposing.

Higher order genetic interactions switch cancer genes from two-hit to one-hit drivers

The classic two-hit model posits that both alleles of a tumor suppressor gene (TSG) must be inactivated to cause cancer. In contrast, for some oncogenes and haploinsufficient TSGs, a single genetic alteration can suffice to increase tumor fitness. Here, by quantifying the interactions between mutations and copy number alterations (CNAs) across 10,000 tumors, we show that many cancer genes actually switch between acting as one-hit or two-hit drivers. Third order genetic interactions identify the causes of some of these switches in dominance and dosage sensitivity as mutations in other genes in the same biological pathway. The correct genetic model for a gene thus depends on the other mutations in a genome, with a second hit in the same gene or an alteration in a different gene in the same pathway sometimes representing alternative evolutionary paths to cancer.

Computational and structural based approach to identify malignant nonsynonymous single nucleotide polymorphisms associated with CDK4 gene

Cycline-dependent kinase 4 (CDK4), an enzyme of the cycline dependent or Ser/Thr protein kinase family, plays a role in cell cycle progression (G1 phase) by phosphorylating a tumor suppressor protein called pRB. Alteration of this enzyme due to missense mutation/ nonsynonymous single nucleotide polymorphisms (nsSNPs) are responsible for various types of cancer progression, e.g. melanoma, lung cancer, and breast cancer. Hence, this study is designed to identify the malignant missense mutation of CDK4 from the single nucleotide polymorphism database (dbSNP) by incorporating computational algorithms. Out of 239 nsSNPs; G15S, D140Y and D140H were predicted to be highly malignant variants which may have a devastating impact on protein structure or function. We also found defective binding motif of these three mutants with the CDK4 inhibitor ribociclib and ATP. However, by incorporating molecular dynamic simulation, our study concludes that the superiority of G15S than the other two mutants (D140Y and D140H) in destabilizing proteins nature.

Computational algorithmic and molecular dynamics study of functional and structural impacts of non-synonymous single nucleotide polymorphisms in human DHFR gene

Human dihydrofolate reductase (DHFR) is a conserved enzyme that is central to folate metabolism and is widely targeted in pathogenic diseases as well as cancers. Although studies have reported the fact that genetic mutations in DHFR leads to a rare autosomal recessive inborn error of folate metabolism and drug resistance, there is a lack of an extensive study on how the deleterious non-synonymous SNPs (nsSNPs) disrupt its phenotypic effects. In this study, we aim at discovering the structural and functional consequences of nsSNPs in DHFR by employing a combined computational approach consisting of ten recently developed in silico tools for identification of damaging nsSNPs and molecular dynamics (MD) simulation for getting deeper insights into the magnitudes of damaging effects. Our study revealed the presence of 12 most deleterious nsSNPs affecting the native phenotypic effects, with three (R71T, G118D, Y122D) identified in the co-factor and ligand binding active sites. MD simulations also suggested that these three SNPs particularly Y122D, alter the overall structural flexibility and dynamics of the native DHFR protein which can provide more understandings into the crucial roles of these mutants in influencing the loss of DHFR function.

TLR8 is highly conserved among the Saudi population and its mutations have no effect on the severity of COVID-19 symptoms

Coronavirus 2019 (COVID-19) is an infection caused by the newly discovered severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The innate system is the first line of defense against pathogens and diverse infectious agents. It has been suggested to play a key role in the development of the cytokine storm and promoting other severe forms of chronic inflammation. Toll-like receptors (TLRs) are crucial for the innate immune response to pathogens. TLR8 is expressed on myeloid cells and phagocytes, where it acts as an endosomal sensor of RNA degradation. The present study aimed to investigate whether the severity of COVID-19 symptoms could be associated with certain genetic variations of TLR8. We collected blood samples from 45 participants who had moderate to severe respiratory symptoms and a positive COVID-19 PCR test result within 3-5 days of sample collection. Genomic DNA was extracted from the blood samples, then exon 2 of the TLR8 gene was amplified with polymerase chain reaction (PCR), and PCR products were utilized for sequencing. DNA sequencing showed an average of 99.63% sequence homology in TLR8 across all samples. Base-pair homology analysis revealed variations in TLR8 at two positions: X:12937804 (rs5744080) and X:12937513 (rs2159377). The results revealed that these two mutations had no detrimental effect on symptoms in the target population. Our results show that specific SNPs did not affect the final receptor function of TLR8. This finding also indicates that the innate immune response, once activated, does not depend on the innate immune receptor's level of affinity for identifying their respective glycoprotein structures on the SARS-CoV-2 virus.

Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability

Genetic polymorphisms in DNA damage repair and tumor suppressor genes have been associated with increasing the risk of several types of cancer. Analyses of putative functional single nucleotide polymorphisms (SNP) in such genes can greatly improve human health by guiding choice of therapeutics. In this study, we selected nine genes responsible for various cancer types for gene enrichment analysis and found that BRCA1, ATM, and TP53 were more enriched in connectivity. Therefore, we used different computational algorithms to classify the nonsynonymous SNPs which are deleterious to the structure and/or function of these three proteins. The present study showed that the major pathogenic variants (V1687G and V1736G of BRCA1, I2865T and V2906A of ATM, V216G and L194H of TP53) might have a greater impact on the destabilization of the proteins. To stabilize the high-risk SNPs, we performed mutation site-specific molecular docking analysis and validated using molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) studies. Additionally, SNPs of untranslated regions of these genes affecting miRNA binding were characterized. Hence, this study will assist in developing precision medicines for cancer types related to these polymorphisms.

A Missense Mutation in the MYBPH Gene Is Associated With Abdominal Fat Traits in Meat-Type Chickens

Chicken is an important source of protein for human nutrition and a model system for growth and developmental biology. Although the genetic architecture of quantitative traits in meat-type chickens has been the subject of ongoing investigation, the identification of mutations associated with carcass traits of economic interest remains challenging. Therefore, our aim was to identify predicted deleterious mutation, which potentially affects protein function, and test if they were associated with carcass traits in chickens. For that, we performed a genome-wide association analysis (GWAS) for breast, thigh and drumstick traits in meat-type chickens and detected 19 unique quantitative trait loci (QTL). We then used: (1) the identified windows; (2) QTL for abdominal fat detected in a previous study with the same population and (3) previously obtained whole genome sequence data, to identify 18 predicted deleterious single nucleotide polymorphisms (SNPs) in those QTL for further association with breast, thigh, drumstick and abdominal fat traits. Using the additive model, a predicted deleterious SNP c.482C > T (SIFT score of 0.4) was associated (p-value < 0.05) with abdominal fat weight and percentage. This SNP is in the second exon of the MYBPH gene, and its allele frequency deviates from Hardy–Weinberg equilibrium. In conclusion, our study provides evidence that the c.482C > T SNP in the MYBPH gene is a putative causal mutation for fat deposition in meat-type chickens.

A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants

BACKGROUND

The field of pharmacogenomics focuses on the way a person's genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient's genome, hence rendering its incorporation into clinical routine practice a realistic possibility.

METHODS

This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would categorize novel variants according to their expected effect on protein functionality. This categorization is based on the available in silico prediction and/or conservation scores, which are selected with the use of recursive feature elimination process. Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation.

RESULTS

All models achieved similar performance toward making informed conclusions, with RF model achieving the highest accuracy (85%, 95% CI: 0.79, 0.90), as well as improved overall performance (precision 85%, sensitivity 84%, specificity 94%) and being used for subsequent analyses. When applied on real world WGS data, the selected RF model identified 2 missense variants, expected to lead to decreased function proteins and 1 to increased. As expected, a greater number of variants were highlighted when the approach was used on NGS data derived from targeted resequencing of coding regions. Specifically, 71 variants (out of 156 with sufficient annotation information) were classified as to "Decreased function," 41 variants as "No" function proteins, and 1 variant in "Increased function."

CONCLUSION

Overall, the proposed RF-based classification model holds promise to lead to an extremely useful variant prioritization and act as a scoring tool with interesting clinical applications in the fields of pharmacogenomics and personalized medicine.

Global spectrum of population-specific common missense variation in cytochrome P450 pharmacogenes

Next-generation sequencing technology has afforded the discovery of many novel variants that are of significance to inheritable pharmacogenomics (PGx) traits but a large proportion of them have unknown consequences. These include missense variants resulting in single amino acid substitutions in cytochrome P450 (CYP) proteins that can impair enzyme function, leading to altered drug efficacy and toxicity. While most unknown variants are rare, an overlooked minority are variants that are collectively rare but enriched in specific populations. Here, we analyzed sequence variation data in 141,456 individuals from across eight study populations in gnomAD for 38 CYP genes to identify such variants in addition to common variants. By further comparison with data from two PGx-specific databases (PharmVar and PharmGKB) and ClinVar, we identified 234 missense variants in 35 CYP genes, of which 107 were unknown to these databases. Most unknown variants (n = 83) were population-specific common variants and several (n = 7) were found in important CYP pharmacogenes (CYP2D6, CYP4F2, and CYP2C19). Overall, 29% (n = 31) of 107 unknown variants were predicted to affect CYP enzyme function although further biochemical characterization is necessary. These variants may elucidate part of the unexplained interpopulation differences observed in drug response.

Spatially interacting phosphorylation sites and mutations in cancer

Advances in mass-spectrometry have generated increasingly large-scale proteomics datasets containing tens of thousands of phosphorylation sites (phosphosites) that require prioritization. We develop a bioinformatics tool called HotPho and systematically discover 3D co-clustering of phosphosites and cancer mutations on protein structures. HotPho identifies 474 such hybrid clusters containing 1255 co-clustering phosphosites, including RET p.S904/Y928, the conserved HRAS/KRAS p.Y96, and IDH1 p.Y139/IDH2 p.Y179 that are adjacent to recurrent mutations on protein structures not found by linear proximity approaches. Hybrid clusters, enriched in histone and kinase domains, frequently include expression-associated mutations experimentally shown as activating and conferring genetic dependency. Approximately 300 co-clustering phosphosites are verified in patient samples of 5 cancer types or previously implicated in cancer, including CTNNB1 p.S29/Y30, EGFR p.S720, MAPK1 p.S142, and PTPN12 p.S275. In summary, systematic 3D clustering analysis highlights nearly 3,000 likely functional mutations and over 1000 cancer phosphosites for downstream investigation and evaluation of potential clinical relevance.

Identifying the causes of recurrent pregnancy loss in consanguineous couples using whole exome sequencing on the products of miscarriage with no chromosomal abnormalities

Recurrent miscarriages occur in about 5% of couples trying to conceive. In the past decade, the products of miscarriage have been studied using array comparative genomic hybridization (a-CGH). Within the last decade, an association has been proposed between miscarriages and single or multigenic changes, introducing the possibility of detecting other underlying genetic factors by whole exome sequencing (WES). We performed a-CGH on the products of miscarriage from 1625 Iranian women in consanguineous or non-consanguineous marriages. WES was carried out on DNA extracted from the products of miscarriage from 20 Iranian women in consanguineous marriages and with earlier normal genetic testing. Using a-CGH, a statistically significant difference was detected between the frequency of imbalances in related vs. unrelated couples (P < 0.001). WES positively identified relevant alterations in 11 genes in 65% of cases. In 45% of cases, we were able to classify these variants as pathogenic or likely pathogenic, according to the American College of Medical Genetics and Genomics guidelines, while in the remainder, the variants were classified as of unknown significance. To the best of our knowledge, our study is the first to employ WES on the products of miscarriage in consanguineous families with recurrent miscarriages regardless of the presence of fetal abnormalities. We propose that WES can be helpful in making a diagnosis of lethal disorders in consanguineous couples after prior genetic testing.

Implementation of computational approaches to explore the deleterious effects of non-synonymous SNPs on pRB protein

Retinoblastoma 1 (RB1) is the first discovered tumor suppressor gene and recognized as the simple model system whose encoded defective protein can cause a pediatric cancer retinoblastoma. It functions as a negative regulator of the cell cycle through the interactions with members of the E2F transcription factors family. The protein of the RB1 gene (pRB) is engaged in various cell cycle processes including apoptosis, cell cycle arrest and chromatin remodeling. Recent studies on Retinoblastoma also exhibited multiple sets of point mutation in the associated protein due to its large polymorphic information in the local database. In this study, we identified the list of disease associated non-synonymous single nucleotide polymorphisms (nsSNPs) in RB1 by incorporating different computational algorithms, web servers, modeling of the mutants and finally superimposing it. Out of 826 nsSNPs, W516G and W563G were predicted to be highly deleterious variants in the conserved regions and found to have an impact on protein structure and protein-protein interaction. Moreover, our study concludes the effect of W516G variant was more detrimental in destabilizing protein's nature as compared to W563G variant. We also found defective binding of pRB having W516G mutation with E2F2 protein. Findings of this study will aid in shortening of the expensive experimental cost of identifying disease associated SNPs in retinoblastoma for which specialized personalized treatment or therapy can be formulated.Communicated by Ramaswamy H. Sarma.

Whole exome sequencing reveals pathogenic variants in MYO3A, MYO15A and COL9A3 and differential frequencies in ancestral alleles in hearing impairment genes among individuals from Cameroon

There is scarcity of known gene variants of hearing impairment (HI) in African populations. This knowledge deficit is ultimately affecting the development of genetic diagnoses. We used whole exome sequencing to investigate gene variants, pathways of interactive genes and the fractions of ancestral overderived alleles for 159 HI genes among 18 Cameroonian patients with non-syndromic HI (NSHI) and 129 ethnically matched controls. Pathogenic and likely pathogenic (PLP) variants were found in MYO3A, MYO15A and COL9A3, with a resolution rate of 50% (9/18 patients). The study identified significant genetic differentiation in novel population-specific gene variants at FOXD4L2, DHRS2L6, RPL3L and VTN between HI patients and controls. These gene variants are found in functional/co-expressed interactive networks with other known HI-associated genes and in the same pathways with VTN being a hub protein, that is, focal adhesion pathway and regulation of the actin cytoskeleton (P-values <0.05). The results suggest that these novel population-specific gene variants are possible modifiers of the HI phenotypes. We found a high proportion of ancestral allele versus derived at low HI patients-specific minor allele frequency in the range of 0.0-0.1. The results showed a relatively low pickup rate of PLP variants in known genes in this group of Cameroonian patients with NSHI. In addition, findings may signal an evolutionary enrichment of some variants of HI genes in patients, as the result of polygenic adaptation, and suggest the possibility of multigenic influence on the phenotype of congenital HI, which deserves further investigations.

In silico analysis of non-synonymous single nucleotide polymorphisms of human DEFB1 gene

Background

Single nucleotide polymorphisms (SNPs) play a significant role in differences in individual’s susceptibility to diseases, and it is imperative to differentiate potentially harmful SNPs from neutral ones. Defensins are small cationic antimicrobial peptides that serve as antimicrobial and immunomodulatory molecules, and SNPs in β-defensin 1 (DEFB1 gene) have been associated with several diseases. In this study, we have determined deleterious SNPs of the DEFB1 gene that can affect the susceptibility to diseases by using different computational methods. Non-synonymous SNPs (nsSNPs) of the DEFB1 gene that have the ability to affect protein structure and functions were determined by several in silico tools—SIFT, PolyPhen v2, PROVEAN, SNAP, PhD-SNP, and SNPs&GO. Then, nsSNPs identified to be potentially deleterious were further analyzed by I-Mutant and ConSurf. Post-translational modifications mediated by nsSNPs were predicted by ModPred, and gene-gene interaction was studied by GeneMANIA. Finally, nsSNPs were submitted to Project HOPE analysis.

Results

Ten nsSNPs of the DEFB1 gene were found to be potentially deleterious: rs1800968, rs55874920, rs56270143, rs140503947, rs145468425, rs146603349, rs199581284, rs201260899, rs371897938, rs376876621. I-Mutant server showed that nsSNPs rs140503947 and rs146603349 decreased stability of the protein, and ConSurf analysis revealed that SNPs were located in conserved regions. The physiochemical properties of the polymorphic amino acid residues and their effect on structure were determined by Project HOPE.

Conclusion

This study has determined high-risk deleterious nsSNPs of β-defensin 1 and could increase the knowledge of nsSNPs towards the impact of mutations on structure and functions mediated by β-defensin 1 protein.

Inferring the molecular and phenotypic impact of amino acid variants with MutPred2

Identifying pathogenic variants and underlying functional alterations is challenging. To this end, we introduce MutPred2, a tool that improves the prioritization of pathogenic amino acid substitutions over existing methods, generates molecular mechanisms potentially causative of disease, and returns interpretable pathogenicity score distributions on individual genomes. Whilst its prioritization performance is state-of-the-art, a distinguishing feature of MutPred2 is the probabilistic modeling of variant impact on specific aspects of protein structure and function that can serve to guide experimental studies of phenotype-altering variants. We demonstrate the utility of MutPred2 in the identification of the structural and functional mutational signatures relevant to Mendelian disorders and the prioritization of de novo mutations associated with complex neurodevelopmental disorders. We then experimentally validate the functional impact of several variants identified in patients with such disorders. We argue that mechanism-driven studies of human inherited disease have the potential to significantly accelerate the discovery of clinically actionable variants.

GJB4 and GJC3 variants in non-syndromic hearing impairment in Ghana

IMPACT STATEMENT

Although connexins are known to be the major genetic factors associated with HI, only a few studies have investigated GJB4 and GJC3 variants among hearing-impaired patients. This study is the first to report GJB4 and GJC3 variants from an African HI cohort. We have demonstrated that GJB4 and GJC3 genes may not contribute significantly to HI in Ghana, hence these genes should not be considered for routine clinical screening in Ghana. However, it is important to study a larger population to determine the association of GJB4 and GJC3 variants with HI.

Integrated genomic analysis reveals mutated ELF3 as a potential gallbladder cancer vaccine candidate

Gallbladder cancer (GBC) is an aggressive gastrointestinal malignancy with no approved targeted therapy. Here, we analyze exomes (n = 160), transcriptomes (n = 115), and low pass whole genomes (n = 146) from 167 gallbladder cancers (GBCs) from patients in Korea, India and Chile. In addition, we also sequence samples from 39 GBC high-risk patients and detect evidence of early cancer-related genomic lesions. Among the several significantly mutated genes not previously linked to GBC are ETS domain genes ELF3 and EHF, CTNNB1, APC, NSD1, KAT8, STK11 and NFE2L2. A majority of ELF3 alterations are frame-shift mutations that result in several cancer-specific neoantigens that activate T-cells indicating that they are cancer vaccine candidates. In addition, we identify recurrent alterations in KEAP1/NFE2L2 and WNT pathway in GBC. Taken together, these define multiple targetable therapeutic interventions opportunities for GBC treatment and management.

Gene-wise variant burden and genomic characterization of nearly every gene

Aim: Current gene-level prioritization methods aim to provide information for further prioritization of ‘disease-causing’ mutations. Since, they are inherently biased toward disease genes, methods specific to pharmacogenetic (PGx) genes are required. Methods: We proposed a gene-wise variant burden (GVB) method that integrates in silico deleteriousness scores of the multitude of variants of a given gene at a personal-genome level. Results: GVB in its simplest form outperformed the two state-of-the-art methods with regard to predicting pharmacogenes and complex disease genes but not for rare Mendelian disease genes. GVB* adjusted by paralog counts robustly performed well in most of the pharmacogenetic subcategories. Seven molecular genetic features well characterized the unique genomic properties of PGx, complex, and Mendelian disease genes. Conclusion: Altogether, GVB is an individual-specific genescore, especially advantageous for PGx studies.

Two Novel Rare Strongly Linked Missense SNPs (P27R and A85G) Within the GDF9 Gene Were Significantly Associated With Litter Size in Shaanbei White Cashmere (SBWC) Goats

Growth differentiation factor 9 (GDF9) is a high-fertility candidate gene that plays a crucial role in early folliculogenesis in female mammals. In this study, direct sequencing was used to screen possible SNP loci in the goat GDF9 gene. Three SNP loci, p.proline27alanine (P27R), p.leucine61leucine (L61L), and p.alanine85glycine (A85G), were identified in Shaanbei white cashmere (SBWC) goats. Among the three SNPs, two rare missense SNP loci (P27R and A85G) were discovered to be strongly linked with each other (D′ value = 0.926, r² value = 0.703). Both P27R and A85G loci had two genotypes: wild type and heterozygous type. A85G exerted a significant effect on litter size (P = 0.029) in SBWC goats, and the heterozygous genotype was superior in comparison with the wild type. The heterozygous genotype was also superior in P27R but no significant association was found. However, the combination genotypes of P27R and A85G were identified to have superior effects on litter size (P = 3.8E−15). This information suggested that these two SNPs influenced litter size in goats synergistically. Combining this information with our previous studies, we propose that the GDF9 gene is the principal high-fertility candidate gene and that the A85G locus is a promising SNP that affects litter size in goats. These results may fill a research gap regarding rare mutations as well as provide crucial molecular markers that could be useful in marker-assisted selection (MAS) goat rearing when selecting superior individuals.

Genotypic and phenotypic characteristics of Korean children with childhood-onset Leber's hereditary optic neuropathy

PURPOSE

We sought to identify the phenotypic and genotypic characteristics of Korean children with genetically confirmed Leber's hereditary optic neuropathy (LHON).

METHODS

The medical records of 64 genetically confirmed LHON patients were reviewed. Seventeen patients aged 13 years or younger with optic atrophy with positive mitochondrial DNA (mtDNA) mutations were considered to demonstrate childhood-onset LHON. The non-childhood-onset group included 47 patients with genetically confirmed LHON who experienced disease onset later than 13 years of age. The type of mtDNA mutation, visual acuity (VA), color vision, fundus photography, retinal nerve fiber layer (RNFL) thickness, and visual field were investigated.

RESULTS

Sequence analysis of the mitochondrial genome revealed five different kinds of LHON-associated mtDNA mutations among our childhood-onset patients, including m.11778G>A (58.8%), m.3496G>T (11.8%), m.3497C>T (5.9%), m.11696G>A (5.9%), and m.14502T>C (5.9%). The mean final best-corrected VA in the childhood-onset group was better than that in the non-childhood-onset group with the value of logMAR 0.29 (0.09-0.75) vs. 0.55 (0.27-1.29) (expressed as median (interquartile range); p = 0.05). Spontaneous visual recovery was observed in 35.3% of the childhood-onset group but in only 12.8% of the non-childhood-onset group (p = 0.04). Eight patients (47.1%) showed interocular asymmetry of the disease, with two presenting true unilateral involvement of the optic nerve and the other six patients demonstrating unilateral subclinical manifestations with bilateral optic atrophy.

CONCLUSION

Involvement of secondary mitochondrial mutations was confirmed in patients with childhood-onset LHON. Characteristic clinical features of childhood-onset LHON included a higher proportion of subacute or insidious onset of symptoms, better VA, higher spontaneous recovery, and asymmetrical ocular involvement.

InMeRF: prediction of pathogenicity of missense variants by individual modeling for each amino acid substitution

In predicting the pathogenicity of a nonsynonymous single-nucleotide variant (nsSNV), a radical change in amino acid properties is prone to be classified as being pathogenic. However, not all such nsSNVs are associated with human diseases. We generated random forest (RF) models individually for each amino acid substitution to differentiate pathogenic nsSNVs in the Human Gene Mutation Database and common nsSNVs in dbSNP. We named a set of our models ‘Individual Meta RF’ (InMeRF). Ten-fold cross-validation of InMeRF showed that the areas under the curves (AUCs) of receiver operating characteristic (ROC) and precision–recall curves were on average 0.941 and 0.957, respectively. To compare InMeRF with seven other tools, the eight tools were generated using the same training dataset, and were compared using the same three testing datasets. ROC-AUCs of InMeRF were ranked first in the eight tools. We applied InMeRF to 155 pathogenic and 125 common nsSNVs in seven major genes causing congenital myasthenic syndromes, as well as in VANGL1 causing spina bifida, and found that the sensitivity and specificity of InMeRF were 0.942 and 0.848, respectively. We made the InMeRF web service, and also made genome-wide InMeRF scores available online (https://www.med.nagoya-u.ac.jp/neurogenetics/InMeRF/).

PG-path: Modeling and personalizing pharmacogenomics-based pathways

A pharmacogenomics-based pathway represents a series of reactions that occur between drugs and genes in the human body after drug administration. PG-path is a pharmacogenomics-based pathway that standardizes and visualizes the components (nodes) and actions (edges) involved in pharmacokinetic and pharmacodynamic processes. It provides an intuitive understanding of the drug response in the human body. A pharmacokinetic pathway visualizes the absorption, distribution, metabolism, and excretion (ADME) at the systemic level, and a pharmacodynamic pathway shows the action of the drug in the target cell at the cellular-molecular level. The genes in the pathway are displayed in locations similar to those inside the body. PG-path allows personalized pathways to be created by annotating each gene with the overall impact degree of deleterious variants in the gene. These personalized pathways play a role in assisting tailored individual prescriptions by predicting changes in the drug concentration in the plasma. PG-path also supports counseling for personalized drug therapy by providing visualization and documentation.