Non-neutral nonsynonymous single nucleotide polymorphisms in human ABC transporters: the first comparison of six prediction methods

Impact of Drug Metabolism/Pharmacokinetics and their Relevance Upon Traditional Medicine-based anti-COVID-19 Drug Research

BACKGROUND

The representative anti-COVID-19 herbs, i.e. Poria cocos, Pogostemon, Prunus, and Glycyrrhiza plants, are commonly used in the prevention and treatment of COVID-19, a pandemic caused by SARS-CoV-2. Diverse medicinal compounds with favorable anti-COVID-19 activities are abundant in these plants, and their unique pharmacological/pharmacokinetic properties are being revealed. However, the current trends of drug metabolism/pharmacokinetic (DMPK) investigations of anti-COVID-19 herbs have not been systematically summarized.

METHODS

Here, the latest awareness, as well as the perception gaps of DMPK attributes, in the anti-COVID-19 drug development and clinical usage was elaborated and critically commented.

RESULTS

The extracts and compounds of P. cocos, Pogostemon, Prunus, and Glycyrrhiza plants show distinct and diverse absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties. The complicated herb-herb interactions (HHIs) and herb-drug interactions (HDIs) of anti-COVID-19 Traditional Chinese Medicine (TCM) herb pair/formula dramatically influence the PK/pharmacodynamic (PD) performance of compounds thereof, which may inspire researchers to design innovative herbal/compound formulas for optimizing the therapeutic outcome of COVID-19 and related epidemic diseases. The ADME/T of some abundant compounds in anti-COVID-19 plants have been elucidated, but DMPK studies should be extended to more compounds of different medicinal parts, species and formulations, and would be facilitated by various omics platforms and computational analyses.

CONCLUSION

In the framework of systems pharmacology and pharmacophylogeny, the DMPK knowledge base would promote the translation of bench findings into the clinical practice of anti-COVID-19, and speed up the anti-COVID-19 drug discovery and development.

Impact of Drug Metabolism/Pharmacokinetics and their Relevance Upon Traditional Medicine-based Cardiovascular Drug Research

BACKGROUND

The representative cardiovascular herbs, i.e. Panax, Ligusticum, Carthamus, and Pueraria plants, are traditionally and globally used in the prevention and treatment of various cardiovascular diseases. Modern phytochemical studies have found many medicinal compounds from these plants, and their unique pharmacological activities are being revealed. However, there are few reviews that systematically summarize the current trends of Drug Metabolism/Pharmacokinetic (DMPK) investigations of cardiovascular herbs.

METHODS

Here, the latest understanding, as well as the knowledge gaps of the DMPK issues in drug development and clinical usage of cardiovascular herbal compounds, was highlighted.

RESULTS

The complicated herb-herb interactions of cardiovascular Traditional Chinese Medicine (TCM) herb pair/formula significantly impact the PK/pharmacodynamic performance of compounds thereof, which may inspire researchers to develop a novel herbal formula for the optimized outcome of different cardiovascular diseases. While the Absorption, Distribution, Metabolism, Excretion and Toxicity (ADME/T) of some compounds has been deciphered, DMPK studies should be extended to more cardiovascular compounds of different medicinal parts, species (including animals), and formulations, and could be streamlined by versatile omics platforms and computational analyses.

CONCLUSION

In the context of systems pharmacology, the DMPK knowledge base is expected to translate bench findings to clinical applications, as well as foster cardiovascular drug discovery and development.

An extensive computational approach to analyze and characterize the functional mutations in the galactose-1-phosphate uridyl transferase (GALT) protein responsible for classical galactosemia

Type I galactosemia is a very rare autosomal recessive genetic metabolic disorder that occurs because of the mutations present in the galactose-1-phosphate uridyl transferase (GALT) gene, resulting in a deficiency of the GALT enzyme. The action of the GALT enzyme is to convert galactose-1-phosphate and uridine diphosphate glucose into glucose-1-phosphate (G1P) and uridine diphosphate-galactose, a crucial second step of the Leloir pathway. A missense mutation in the GALT enzyme leads to variable galactosemia's clinical presentations, ranging from mild to severe. Our study aimed to employ a comprehensive computational pipeline to analyze the most prevalent missense mutations (p.S135L, p.K285 N, p.Q188R, and p.N314D) responsible for galactosemia; these genes could serve as potential targets for chaperone therapy. We analyzed the four mutations through different computational analyses, including amino acid conservation, in silico pathogenicity and stability predictions, and macromolecular simulations (MMS) at 50 ns The stability and pathogenicity predictors showed that the p.Q188R and p.S135L mutants are the most pathogenic and destabilizing. In agreement with these results, MMS analysis demonstrated that the p.Q188R and p.S135L mutants possess higher deviation patterns, reduced compactness, and intramolecular H-bonds of the protein. This could be due to the physicochemical modifications that occurred in the mutants p.S135L and p.Q188R compared to the native. Evolutionary conservation analysis revealed that the most prevalent mutations positions were conserved among different species except N314. The proposed research study is intended to provide a basis for the therapeutic development of drugs and future treatment of classical galactosemia and possibly other genetic diseases using chaperone therapy.

The first Taxus rhizosphere microbiome revealed by shotgun metagenomic sequencing

In the present study, the shotgun high throughput metagenomic sequencing was implemented to globally capture the features of Taxus rhizosphere microbiome. Total reads could be assigned to 6925 species belonging to 113 bacteria phyla and 301 species of nine fungi phyla. For archaea and virus, 263 and 134 species were for the first time identified, respectively. More than 720,000 Unigenes were identified by clean reads assembly. The top five assigned phyla were Actinobacteria (363,941 Unigenes), Proteobacteria (182,053), Acidobacteria (44,527), Ascomycota (fungi; 18,267), and Chloroflexi (15,539). KEGG analysis predicted numerous functional genes; 7101 Unigenes belong to "Xenobiotics biodegradation and metabolism." A total of 12,040 Unigenes involved in defense mechanisms (e.g., xenobiotic metabolism) were annotated by eggNOG. Talaromyces addition could influence not only the diversity and structure of microbial communities of Taxus rhizosphere, but also the relative abundance of functional genes, including metabolic genes, antibiotic resistant genes, and genes involved in pathogen-host interaction, bacterial virulence, and bacterial secretion system. The structure and function of rhizosphere microbiome could be sensitive to non-native microbe addition, which could impact on the pollutant degradation. This study, complementary to the amplicon sequencing, more objectively reflects the native microbiome of Taxus rhizosphere and its response to environmental pressure, and lays a foundation for potential combination of phytoremediation and bioaugmentation.

High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE

24,189 are all the possible non-synonymous amino acid changes potentially affecting the human mitochondrial DNA. Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. Since these tools proved to be rather incongruent, we have designed and implemented APOGEE, a machine-learning algorithm that outperforms all existing prediction methods in estimating the harmfulness of mitochondrial non-synonymous genome variations. We provide a detailed description of the underlying algorithm, of the selected and manually curated training and test sets of variants, as well as of its classification ability.

TK, C. GPD, R. S, Zayed H. Determining the role of missense mutations in the POU domain of HNF1A that reduce the DNA-binding affinity: A computational approach

Maturity-onset diabetes of the young type 3 (MODY3) is a non-ketotic form of diabetes associated with poor insulin secretion. Over the past years, several studies have reported the association of missense mutations in the Hepatocyte Nuclear Factor 1 Alpha (HNF1A) with MODY3. Missense mutations in the POU homeodomain (POUH) of HNF1A hinder binding to the DNA, thereby leading to a dysfunctional protein. Missense mutations of the HNF1A were retrieved from public databases and subjected to a three-step computational mutational analysis to identify the underlying mechanism. First, the pathogenicity and stability of the mutations were analyzed to determine whether they alter protein structure and function. Second, the sequence conservation and DNA-binding sites of the mutant positions were assessed; as HNF1A protein is a transcription factor. Finally, the biochemical properties of the biological system were validated using molecular dynamic simulations in Gromacs 4.6.3 package. Two arginine residues (131 and 203) in the HNF1A protein are highly conserved residues and contribute to the function of the protein. Furthermore, the R131W, R131Q, and R203C mutations were predicted to be highly deleterious by in silico tools and showed lower binding affinity with DNA when compared to the native protein using the molecular docking analysis. Triplicate runs of molecular dynamic (MD) simulations (50ns) revealed smaller changes in patterns of deviation, fluctuation, and compactness, in complexes containing the R131Q and R131W mutations, compared to complexes containing the R203C mutant complex. We observed reduction in the number of intermolecular hydrogen bonds, compactness, and electrostatic potential, as well as the loss of salt bridges, in the R203C mutant complex. Substitution of arginine with cysteine at position 203 decreases the affinity of the protein for DNA, thereby destabilizing the protein. Based on our current findings, the MD approach is an important tool for elucidating the impact and affinity of mutations in DNA-protein interactions and understanding their function.

A novel MTTT mutation m.15933G > A revealed in analysis of mitochondrial DNA in patients with suspected mitochondrial disease

Background

Mitochondrial diseases present with variable multi-organ symptoms. Common disease-causing mutations in mitochondrial DNA (mtDNA) are regularly screened in diagnostic work-up, but novel mutations may remain unnoticed.

Methods

Patients (N = 66) with a clinical suspicion of mitochondrial disease were screened for their mtDNA coding region using conformation sensitive gel electrophoresis and sequencing. Long-PCR was used to detect deletions followed by POLG1 sequencing in patients with multiple deletions.

Results

We discovered three novel mtDNA variants that included m.8743G > C, m.11322A > G and m.15933G > A. The novel MTTT variant m.15933G > A is suggested to be pathogenic. Analysis revealed also multiple mtDNA deletions in two patients and five nonsynonymous variants that were putatively pathogenic according to in-silico prediction algorithms. In addition, a rare haplogroup H associated m.7585_7586insT variant was discovered.

Conclusions

Among patients with a suspected mitochondrial disease, a novel MTTT variant m.15933G > A was discovered and is suggested to be pathogenic. In addition, several putatively pathogenic nonsynonymous variants and rare variants were found. These findings highlight the importance of coding region mtDNA screening among patients with clinical features suggesting a mitochondrial disease, but who lack the common mitochondrial disease mutations.

Genetic Epidemiology of Glucose-6-Phosphate Dehydrogenase Deficiency in the Arab World

A systematic search was implemented using four literature databases (PubMed, Embase, Science Direct and Web of Science) to capture all the causative mutations of Glucose-6-phosphate dehydrogenase (G6PD) deficiency (G6PDD) in the 22 Arab countries. Our search yielded 43 studies that captured 33 mutations (23 missense, one silent, two deletions, and seven intronic mutations), in 3,430 Arab patients with G6PDD. The 23 missense mutations were then subjected to phenotypic classification using in silico prediction tools, which were compared to the WHO pathogenicity scale as a reference. These in silico tools were tested for their predicting efficiency using rigorous statistical analyses. Of the 23 missense mutations, p.S188F, p.I48T, p.N126D, and p.V68M, were identified as the most common mutations among Arab populations, but were not unique to the Arab world, interestingly, our search strategy found four other mutations (p.N135T, p.S179N, p.R246L, and p.Q307P) that are unique to Arabs. These mutations were exposed to structural analysis and molecular dynamics simulation analysis (MDSA), which predicting these mutant forms as potentially affect the enzyme function. The combination of the MDSA, structural analysis, and in silico predictions and statistical tools we used will provide a platform for future prediction accuracy for the pathogenicity of genetic mutations.

Comparison of pathogenicity prediction tools on missense variants in RYR1 and CACNA1S associated with malignant hyperthermia

BACKGROUND

Malignant hyperthermia (MH) is a pharmacogenetic disorder that has been linked to the skeletal muscle calcium release channel (RYR1) and the α1S subunit of the voltage-dependent L-type calcium channel (CACNA1S). Genomic DNA capture and next generation sequencing are becoming the preferred method to identify mutations in these genes. Bioinformatic pathogenicity prediction of identified variants may help to determine if these variants are in fact disease causing.

METHODS

Eight pathogenicity prediction programmes freely available on the web were used to determine their ability to correctly predict the impact of a missense variant on RyR1 or dihydropyridine receptor (DHPR) protein function. We tested MH-causative variants, variants that had been shown to alter calcium release in cells, and common sequence variants in RYR1 and CACNA1S.

RESULTS

None of the prediction programmes was able to identify all of the variants tested correctly as either 'damaging' (MH-causative variants, variants that had been shown to alter calcium release in cells) or as 'benign' (common sequence variants). The overall sensitivity of predictions ranged from 84% to 100% depending on the programme used, with specificity from 25% to 83%.

CONCLUSIONS

In this study we determined the sensitivity and specificity of bioinformatic pathogenicity prediction tools for RYR1 and CACNA1S. We suggest that the prediction results should be treated with caution, as none of the programmes tested predicted all the variants correctly and should only be used in combination with other available data (functional assays, segregation analysis).

MitImpact: an exhaustive collection of pre-computed pathogenicity predictions of human mitochondrial non-synonymous variants

Mitochondrial DNA carries a tiny, but fundamental portion of the eukaryotic genetic code. As its nuclear counterpart, it is susceptible to point mutations. Their level of pathogenicity has been assessed for the newly discovered mutations only, leaving some degree of uncertainty on the potential impact of the unknown mutations. Here we present Mitochondrial mutation Impact (MitImpact), a queryable lightweight web interface to a reasoned collection of structurally and evolutionary annotated pathogenicity predictions, obtained by assembling pre-computed with on-the-fly-computed sets of pathogenicity estimations, for all the possible mitochondrial missense variants. It presents itself as a resource for fast and reliable evaluation of gene-specific susceptibility of unknown and verified amino acid changes. MitImpact is freely available at http://bioinformatics.css-mendel.it/ (tools section). ©2014 Wiley Periodicals, Inc.

Evolution- and structure-based computational strategy reveals the impact of deleterious missense mutations on MODY 2 (maturity-onset diabetes of the young, type 2)

Heterozygous mutations in the central glycolytic enzyme glucokinase (GCK) can result in an autosomal dominant inherited disease, namely maturity-onset diabetes of the young, type 2 (MODY 2). MODY 2 is characterised by early onset: it usually appears before 25 years of age and presents as a mild form of hyperglycaemia. In recent years, the number of known GCK mutations has markedly increased. As a result, interpreting which mutations cause a disease or confer susceptibility to a disease and characterising these deleterious mutations can be a difficult task in large-scale analyses and may be impossible when using a structural perspective. The laborious and time-consuming nature of the experimental analysis led us to attempt to develop a cost-effective computational pipeline for diabetic research that is based on the fundamentals of protein biophysics and that facilitates our understanding of the relationship between phenotypic effects and evolutionary processes. In this study, we investigate missense mutations in the GCK gene by using a wide array of evolution- and structure-based computational methods, such as SIFT, PolyPhen2, PhD-SNP, SNAP, SNPs&GO, fathmm, and Align GVGD. Based on the computational prediction scores obtained using these methods, three mutations, namely E70K, A188T, and W257R, were identified as highly deleterious on the basis of their effects on protein structure and function. Using the evolutionary conservation predictors Consurf and Scorecons, we further demonstrated that most of the predicted deleterious mutations, including E70K, A188T, and W257R, occur in highly conserved regions of GCK. The effects of the mutations on protein stability were computed using PoPMusic 2.1, I-mutant 3.0, and Dmutant. We also conducted molecular dynamics (MD) simulation analysis through in silico modelling to investigate the conformational differences between the native and the mutant proteins and found that the identified deleterious mutations alter the stability, flexibility, and solvent-accessible surface area of the protein. Furthermore, the functional role of each SNP in GCK was identified and characterised using SNPeffect 4.0, F-SNP, and FASTSNP. We hope that the observed results aid in the identification of disease-associated mutations that affect protein structure and function. Our in silico findings provide a new perspective on the role of GCK mutations in MODY2 from an evolution-based structure-centric point of view. The computational architecture described in this paper can be used to predict the most appropriate disease phenotypes for large-genome sequencing projects and to provide individualised drug therapy for complex diseases such as diabetes.

Lessons from next-generation sequencing analysis in hematological malignancies

Next-generation sequencing has led to a revolution in the study of hematological malignancies with a substantial number of publications and discoveries in the last few years. Significant discoveries associated with disease diagnosis, risk stratification, clonal evolution and therapeutic intervention have been generated by this powerful technology. As part of the post-genomic era, sequencing analysis will likely become part of routine clinical testing and the challenge will ultimately be successfully transitioning from gene discovery to preventive and therapeutic intervention as part of individualized medicine strategies. In this report, we review recent advances in the understanding of hematological malignancies derived through genome-wide sequence analysis.

Systematic investigation of predicted effect of nonsynonymous SNPs in human prion protein gene: a molecular modeling and molecular dynamics study

Nonsynonymous mutations in the human prion protein (HuPrP) gene contribute to the conversion of HuPrP(C) to HuPrP(Sc) and amyloid formation which in turn leads to prion diseases such as familial Creutzfeldt-Jakob disease and Gerstmann-Straussler-Scheinker disease. In order to better understand and predict the role of HuPrP mutations, we developed the following procedure: first, we consulted the Human Genome Variation database and dbSNP databases, and we reviewed literature for the retrieval of aggregation-related nsSNPs of the HuPrP gene. Next, we used three different methods - Polymorphism Phenotyping (PolyPhen), PANTHER, and Auto-Mute - to predict the effect of nsSNPs on the phenotype. We compared the predictions against experimentally reported effects of these nsSNPs to evaluate the accuracy of the three methods: PolyPhen predicted 17 out of 22 nsSNPs as "probably damaging" or "possibly damaging"; PANTHER predicted 8 out of 22 nsSNPs as "Deleterious"; and Auto-Mute predicted 9 out of 20 nsSNPs as "Disease". Finally, structural analyses of the native protein against mutated models were investigated using molecular modeling and molecular dynamics (MD) simulation methods. In addition to comparing predictor methods, our results show the applicability of our procedure for the prediction of damaging nsSNPs. Our study also elucidates the obvious relationship between predicted values of aggregation-related nsSNPs in HuPrP gene and molecular modeling and MD simulations results. In conclusion, this procedure would enable researchers to select outstanding candidates for extensive MD simulations in order to decipher more details of HuPrP aggregation. An animated interactive 3D complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:34.

Congruency in the prediction of pathogenic missense mutations: state-of-the-art web-based tools

A remarkable degree of genetic variation has been found in the protein-encoding regions of DNA through deep sequencing of samples obtained from thousands of subjects from several populations. Approximately half of the 20 000 single nucleotide polymorphisms present, even in normal healthy subjects, are nonsynonymous amino acid substitutions that could potentially affect protein function. The greatest challenges currently facing investigators are data interpretation and the development of strategies to identify the few gene-coding variants that actually cause or confer susceptibility to disease. A confusing array of options is available to address this problem. Unfortunately, the overall accuracy of these tools at ultraconserved positions is low, and predictions generated by current computational tools may mislead researchers involved in downstream experimental and clinical studies. First, we have presented an updated review of these tools and their primary functionalities, focusing on those that are naturally prone to analyze massive variant sets, to infer some interesting similarities among their results. Additionally, we have evaluated the prediction congruency for real whole-exome sequencing data in a proof-of-concept study on some of these web-based tools.