International Journal of Molecular Sciences

Editorial

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5 pages, 179 KiB

Open AccessEditorial

Bioinformatics Methods in Medical Genetics and Genomics

by Yuriy L. Orlov, Ancha V. Baranova and Tatiana V. Tatarinova

Int. J. Mol. Sci. 2020, 21(17), 6224; https://doi.org/10.3390/ijms21176224 - 28 Aug 2020

Cited by 11 | Viewed by 4467

Abstract

Medical genomics relies on next-gen sequencing methods to decipher underlying molecular mechanisms of gene expression. This special issue collects materials originally presented at the “Centenary of Human Population Genetics” Conference-2019, in Moscow. Here we present some recent developments in computational methods tested on [...] Read more.

Medical genomics relies on next-gen sequencing methods to decipher underlying molecular mechanisms of gene expression. This special issue collects materials originally presented at the “Centenary of Human Population Genetics” Conference-2019, in Moscow. Here we present some recent developments in computational methods tested on actual medical genetics problems dissected through genomics, transcriptomics and proteomics data analysis, gene networks, protein–protein interactions and biomedical literature mining. We have selected materials based on systems biology approaches, database mining. These methods and algorithms were discussed at the Digital Medical Forum-2019, organized by I.M. Sechenov First Moscow State Medical University presenting bioinformatics approaches for the drug targets discovery in cancer, its computational support, and digitalization of medical research, as well as at “Systems Biology and Bioinformatics”-2019 (SBB-2019) Young Scientists School in Novosibirsk, Russia. Selected recent advancements discussed at these events in the medical genomics and genetics areas are based on novel bioinformatics tools. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

Research

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18 pages, 6817 KiB

Open AccessArticle

Disparity between Inter-Patient Molecular Heterogeneity and Repertoires of Target Drugs Used for Different Types of Cancer in Clinical Oncology

by Marianna A. Zolotovskaia, Maxim I. Sorokin, Ivan V. Petrov, Elena V. Poddubskaya, Alexey A. Moiseev, Marina I. Sekacheva, Nicolas M. Borisov, Victor S. Tkachev, Andrew V. Garazha, Andrey D. Kaprin, Peter V. Shegay, Alf Giese, Ella Kim, Sergey A. Roumiantsev and Anton A. Buzdin

Int. J. Mol. Sci. 2020, 21(5), 1580; https://doi.org/10.3390/ijms21051580 - 26 Feb 2020

Cited by 17 | Viewed by 3845

Abstract

Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated [...] Read more.

Inter-patient molecular heterogeneity is the major declared driver of an expanding variety of anticancer drugs and personalizing their prescriptions. Here, we compared interpatient molecular heterogeneities of tumors and repertoires of drugs or their molecular targets currently in use in clinical oncology. We estimated molecular heterogeneity using genomic (whole exome sequencing) and transcriptomic (RNA sequencing) data for 4890 tumors taken from The Cancer Genome Atlas database. For thirteen major cancer types, we compared heterogeneities at the levels of mutations and gene expression with the repertoires of targeted therapeutics and their molecular targets accepted by the current guidelines in oncology. Totally, 85 drugs were investigated, collectively covering 82 individual molecular targets. For the first time, we showed that the repertoires of molecular targets of accepted drugs did not correlate with molecular heterogeneities of different cancer types. On the other hand, we found that the clinical recommendations for the available cancer drugs were strongly congruent with the gene expression but not gene mutation patterns. We detected the best match among the drugs usage recommendations and molecular patterns for the kidney, stomach, bladder, ovarian and endometrial cancers. In contrast, brain tumors, prostate and colorectal cancers showed the lowest match. These findings provide a theoretical basis for reconsidering usage of targeted therapeutics and intensifying drug repurposing efforts. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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21 pages, 1127 KiB

Open AccessArticle

The Impact of MNRI Therapy on the Levels of Neurotransmitters Associated with Inflammatory Processes

by Tatiana V. Tatarinova, Trina Deiss, Lorri Franckle, Susan Beaven and Jeffrey Davis

Int. J. Mol. Sci. 2020, 21(4), 1358; https://doi.org/10.3390/ijms21041358 - 18 Feb 2020

Cited by 3 | Viewed by 6227

Abstract

The neurotransmitter levels of representatives from five different diagnosis groups were tested before and after participation in the MNRI^®—Masgutova Neurosensorimotor Reflex Intervention. The purpose of this study was to ascertain neurological impact on (1) Developmental disorders, (2) Anxiety disorders/OCD (Obsessive Compulsive [...] Read more.

The neurotransmitter levels of representatives from five different diagnosis groups were tested before and after participation in the MNRI^®—Masgutova Neurosensorimotor Reflex Intervention. The purpose of this study was to ascertain neurological impact on (1) Developmental disorders, (2) Anxiety disorders/OCD (Obsessive Compulsive Disorder), PTSD (Post-Traumatic Stress disorder), (3) Palsy/Seizure disorders, (4) ADD/ADHD (Attention Deficit Disorder/Attention Deficit Disorder Hyperactive Disorder), and (5) ASD (Autism Spectrum Disorder) disorders. Each participant had a form of neurological dysregulation and typical symptoms respective to their diagnosis. These diagnoses have a severe negative impact on the quality of life, immunity, stress coping, cognitive skills, and social assimilation. This study showed a trend towards optimization and normalization of neurological and immunological functioning, thus supporting the claim that the MNRI method is an effective non-pharmacological neuromodulation treatment of neurological disorders. The effects of MNRI on inflammation have not yet been assessed. The resulting post-MNRI changes in participants’ neurotransmitters show significant adjustments in the regulation of the neurotransmitter resulting in being calmer, a decrease of hypervigilance, an increase in stress resilience, behavioral and emotional regulation improvements, a more positive emotional state, and greater control of cognitive processes. In this paper, we demonstrate that the MNRI approach is an intervention that reduces inflammation. It is also likely to reduce oxidative stress and encourage homeostasis of excitatory neurotransmitters. MNRI may facilitate neurodevelopment, build stress resiliency, neuroplasticity, and optimal learning opportunity. There have been no reported side effects of MNRI treatments. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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9 pages, 1791 KiB

Open AccessArticle

A Post-Processing Algorithm for miRNA Microarray Data

by Stepan Nersisyan, Maxim Shkurnikov, Andrey Poloznikov, Andrey Turchinovich, Barbara Burwinkel, Nikita Anisimov and Alexander Tonevitsky

Int. J. Mol. Sci. 2020, 21(4), 1228; https://doi.org/10.3390/ijms21041228 - 12 Feb 2020

Cited by 18 | Viewed by 3970

Abstract

One of the main disadvantages of using DNA microarrays for miRNA expression profiling is the inability of adequate comparison of expression values across different miRNAs. This leads to a large amount of miRNAs with high scores which are actually not expressed in examined [...] Read more.

One of the main disadvantages of using DNA microarrays for miRNA expression profiling is the inability of adequate comparison of expression values across different miRNAs. This leads to a large amount of miRNAs with high scores which are actually not expressed in examined samples, i.e., false positives. We propose a post-processing algorithm which performs scoring of miRNAs in the results of microarray analysis based on expression values, time of discovery of miRNA, and correlation level between the expressions of miRNA and corresponding pre-miRNA in considered samples. The algorithm was successfully validated by the comparison of the results of its application to miRNA microarray breast tumor samples with publicly available miRNA-seq breast tumor data. Additionally, we obtained possible reasons why miRNA can appear as a false positive in microarray study using paired miRNA sequencing and array data. The use of DNA microarrays for estimating miRNA expression profile is limited by several factors. One of them consists of problems with comparing expression values of different miRNAs. In this work, we show that situation can be significantly improved if some additional information is taken into consideration in a comparison. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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41 pages, 5541 KiB

Open AccessArticle

Candidate SNP Markers of Atherogenesis Significantly Shifting the Affinity of TATA-Binding Protein for Human Gene Promoters Show Stabilizing Natural Selection as a Sum of Neutral Drift Accelerating Atherogenesis and Directional Natural Selection Slowing It

by Mikhail Ponomarenko, Dmitry Rasskazov, Irina Chadaeva, Ekaterina Sharypova, Irina Drachkova, Dmitry Oshchepkov, Petr Ponomarenko, Ludmila Savinkova, Evgeniya Oshchepkova, Maria Nazarenko and Nikolay Kolchanov

Int. J. Mol. Sci. 2020, 21(3), 1045; https://doi.org/10.3390/ijms21031045 - 5 Feb 2020

Cited by 7 | Viewed by 3591

Abstract

(1) Background: The World Health Organization (WHO) regards atherosclerosis-related myocardial infarction and stroke as the main causes of death in humans. Susceptibility to atherogenesis-associated diseases is caused by single-nucleotide polymorphisms (SNPs). (2) Methods: Using our previously developed public web-service SNP_TATA_Comparator, we estimated statistical [...] Read more.

(1) Background: The World Health Organization (WHO) regards atherosclerosis-related myocardial infarction and stroke as the main causes of death in humans. Susceptibility to atherogenesis-associated diseases is caused by single-nucleotide polymorphisms (SNPs). (2) Methods: Using our previously developed public web-service SNP_TATA_Comparator, we estimated statistical significance of the SNP-caused alterations in TATA-binding protein (TBP) binding affinity for 70 bp proximal promoter regions of the human genes clinically associated with diseases syntonic or dystonic with atherogenesis. Additionally, we did the same for several genes related to the maintenance of mitochondrial genome integrity, according to present-day active research aimed at retarding atherogenesis. (3) Results: In dbSNP, we found 1186 SNPs altering such affinity to the same extent as clinical SNP markers do (as estimated). Particularly, clinical SNP marker rs2276109 can prevent autoimmune diseases via reduced TBP affinity for the human MMP12 gene promoter and therefore macrophage elastase deficiency, which is a well-known physiological marker of accelerated atherogenesis that could be retarded nutritionally using dairy fermented by lactobacilli. (4) Conclusions: Our results uncovered SNPs near clinical SNP markers as the basis of neutral drift accelerating atherogenesis and SNPs of genes encoding proteins related to mitochondrial genome integrity and microRNA genes associated with instability of the atherosclerotic plaque as a basis of directional natural selection slowing atherogenesis. Their sum may be stabilizing the natural selection that sets the normal level of atherogenesis. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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18 pages, 3927 KiB

Open AccessArticle

Unique k-mers as Strain-Specific Barcodes for Phylogenetic Analysis and Natural Microbiome Profiling

by Valery V. Panyukov, Sergey S. Kiselev and Olga N. Ozoline

Int. J. Mol. Sci. 2020, 21(3), 944; https://doi.org/10.3390/ijms21030944 - 31 Jan 2020

Cited by 8 | Viewed by 3932

Abstract

The need for a comparative analysis of natural metagenomes stimulated the development of new methods for their taxonomic profiling. Alignment-free approaches based on the search for marker k-mers turned out to be capable of identifying not only species, but also strains of [...] Read more.

The need for a comparative analysis of natural metagenomes stimulated the development of new methods for their taxonomic profiling. Alignment-free approaches based on the search for marker k-mers turned out to be capable of identifying not only species, but also strains of microorganisms with known genomes. Here, we evaluated the ability of genus-specific k-mers to distinguish eight phylogroups of Escherichia coli (A, B1, C, E, D, F, G, B2) and assessed the presence of their unique 22-mers in clinical samples from microbiomes of four healthy people and four patients with Crohn’s disease. We found that a phylogenetic tree inferred from the pairwise distance matrix for unique 18-mers and 22-mers of 124 genomes was fully consistent with the topology of the tree, obtained with concatenated aligned sequences of orthologous genes. Therefore, we propose strain-specific “barcodes” for rapid phylotyping. Using unique 22-mers for taxonomic analysis, we detected microbes of all groups in human microbiomes; however, their presence in the five samples was significantly different. Pointing to the intraspecies heterogeneity of E. coli in the natural microflora, this also indicates the feasibility of further studies of the role of this heterogeneity in maintaining population homeostasis. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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21 pages, 5659 KiB

Open AccessArticle

Elucidating Binding Sites and Affinities of ERα Agonists and Antagonists to Human Alpha-Fetoprotein by In Silico Modeling and Point Mutagenesis

by Nurbubu T. Moldogazieva, Daria S. Ostroverkhova, Nikolai N. Kuzmich, Vladimir V. Kadochnikov, Alexander A. Terentiev and Yuri B. Porozov

Int. J. Mol. Sci. 2020, 21(3), 893; https://doi.org/10.3390/ijms21030893 - 30 Jan 2020

Cited by 14 | Viewed by 3986

Abstract

Alpha-fetoprotein (AFP) is a major embryo- and tumor-associated protein capable of binding and transporting a variety of hydrophobic ligands, including estrogens. AFP has been shown to inhibit estrogen receptor (ER)-positive tumor growth, which can be attributed to its estrogen-binding ability. Despite AFP having [...] Read more.

Alpha-fetoprotein (AFP) is a major embryo- and tumor-associated protein capable of binding and transporting a variety of hydrophobic ligands, including estrogens. AFP has been shown to inhibit estrogen receptor (ER)-positive tumor growth, which can be attributed to its estrogen-binding ability. Despite AFP having long been investigated, its three-dimensional (3D) structure has not been experimentally resolved and molecular mechanisms underlying AFP–ligand interaction remains obscure. In our study, we constructed a homology-based 3D model of human AFP (HAFP) with the purpose of molecular docking of ERα ligands, three agonists (17β-estradiol, estrone and diethylstilbestrol), and three antagonists (tamoxifen, afimoxifene and endoxifen) into the obtained structure. Based on the ligand-docked scoring functions, we identified three putative estrogen- and antiestrogen-binding sites with different ligand binding affinities. Two high-affinity binding sites were located (i) in a tunnel formed within HAFP subdomains IB and IIA and (ii) on the opposite side of the molecule in a groove originating from a cavity formed between domains I and III, while (iii) the third low-affinity binding site was found at the bottom of the cavity. Here, 100 ns molecular dynamics (MD) simulation allowed us to study their geometries and showed that HAFP–estrogen interactions were caused by van der Waals forces, while both hydrophobic and electrostatic interactions were almost equally involved in HAFP–antiestrogen binding. Molecular mechanics/Generalized Born surface area (MM/GBSA) rescoring method exploited for estimation of binding free energies (ΔG_bind) showed that antiestrogens have higher affinities to HAFP as compared to estrogens. We performed in silico point substitutions of amino acid residues to confirm their roles in HAFP–ligand interactions and showed that Thr132, Leu138, His170, Phe172, Ser217, Gln221, His266, His316, Lys453, and Asp478 residues, along with two disulfide bonds (Cys224–Cys270 and Cys269–Cys277), have key roles in both HAFP–estrogen and HAFP–antiestrogen binding. Data obtained in our study contribute to understanding mechanisms underlying protein–ligand interactions and anticancer therapy strategies based on ERα-binding ligands. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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12 pages, 3073 KiB

Open AccessArticle

Practical Guidance in Genome-Wide RNA:DNA Triple Helix Prediction

by Elena Matveishina, Ivan Antonov and Yulia A. Medvedeva

Int. J. Mol. Sci. 2020, 21(3), 830; https://doi.org/10.3390/ijms21030830 - 28 Jan 2020

Cited by 14 | Viewed by 3777

Abstract

Long noncoding RNAs (lncRNAs) play a key role in many cellular processes including chromatin regulation. To modify chromatin, lncRNAs often interact with DNA in a sequence-specific manner forming RNA:DNA triple helices. Computational tools for triple helix search do not always provide genome-wide predictions [...] Read more.

Long noncoding RNAs (lncRNAs) play a key role in many cellular processes including chromatin regulation. To modify chromatin, lncRNAs often interact with DNA in a sequence-specific manner forming RNA:DNA triple helices. Computational tools for triple helix search do not always provide genome-wide predictions of sufficient quality. Here, we used four human lncRNAs (MEG3, DACOR1, TERC and HOTAIR) and their experimentally determined binding regions for evaluating triplex parameters that provide the highest prediction accuracy. Additionally, we combined triplex prediction with the lncRNA secondary structure and demonstrated that considering only single-stranded fragments of lncRNA can further improve DNA-RNA triplexes prediction. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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13 pages, 1877 KiB

Open AccessArticle

A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy

by Olga Tarasova, Nadezhda Biziukova, Dmitry Kireev, Alexey Lagunin, Sergey Ivanov, Dmitry Filimonov and Vladimir Poroikov

Int. J. Mol. Sci. 2020, 21(3), 748; https://doi.org/10.3390/ijms21030748 - 23 Jan 2020

Cited by 13 | Viewed by 3171

Abstract

Human Immunodeficiency Virus Type 1 (HIV-1) infection is associated with high mortality if no therapy is provided. Currently, the treatment of an HIV-1 positive patient requires that several drugs should be taken simultaneously. The resistance of the virus to an antiretroviral drug may [...] Read more.

Human Immunodeficiency Virus Type 1 (HIV-1) infection is associated with high mortality if no therapy is provided. Currently, the treatment of an HIV-1 positive patient requires that several drugs should be taken simultaneously. The resistance of the virus to an antiretroviral drug may lead to treatment failure. Our approach focuses on predicting the exposure of a particular viral variant to an antiretroviral drug or drug combination. It also aims at the prediction of drug treatment success or failure. We utilized nucleotide sequences of HIV-1 encoding protease and reverse transcriptase to perform such types of prediction. The PASS (Prediction of Activity Spectra for Substances) algorithm based on the naive Bayesian classifier was used to make a prediction. We calculated the probability of whether a sequence belonged (P₁) or did not belong (P₀) to the class associated with exposure of the viral sequence to the set of drugs that can be associated with resistance to the set of drugs. The accuracy calculated as the average Area Under the ROC (Receiver Operating Characteristic) Curve (AUC/ROC) for classifying exposure of the sequence to the HIV-1 protease inhibitors was 0.81 (±0.07), and for HIV-1 reverse transcriptase, it was 0.83 (±0.07). To predict cases of treatment effectiveness or failure, we used P₁ and P₀ values, obtained in PASS, along with the binary vector constructed based on short nucleotide descriptors and the applied random forest classifier. Average AUC/ROC prediction accuracy for the prediction of treatment effectiveness or failure for the combinations of HIV-1 protease inhibitors was 0.82 (±0.06) and of HIV-1 reverse transcriptase was 0.76 (±0.09). Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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20 pages, 2124 KiB

Open AccessArticle

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology

by Victor Tkachev, Maxim Sorokin, Constantin Borisov, Andrew Garazha, Anton Buzdin and Nicolas Borisov

Int. J. Mol. Sci. 2020, 21(3), 713; https://doi.org/10.3390/ijms21030713 - 22 Jan 2020

Cited by 19 | Viewed by 3726

Abstract

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed [...] Read more.

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41–235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61–0.88 range to 0.70–0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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16 pages, 613 KiB

Open AccessArticle

Epidemiology of Hereditary Diseases in the Karachay-Cherkess Republic

by Rena A. Zinchenko, Amin Kh. Makaov, Andrey V. Marakhonov, Varvara A. Galkina, Vitaly V. Kadyshev, Galina I. El’chinova, Elena L. Dadali, Lyudmila K. Mikhailova, Nika V. Petrova, Nina E. Petrina, Tatyana A. Vasilyeva, Polina Gundorova, Alexander V. Polyakov, Oksana Y. Alexandrova, Sergey I. Kutsev and Eugeny K. Ginter

Int. J. Mol. Sci. 2020, 21(1), 325; https://doi.org/10.3390/ijms21010325 - 3 Jan 2020

Cited by 16 | Viewed by 3477

Abstract

Prevalence and allelic heterogeneity of hereditary diseases (HDs) could vary significantly in different human populations. Current knowledge of HDs distribution in populations is generally limited to either European data or analyses of isolated populations which were performed several decades ago. Thus, an acknowledgement [...] Read more.

Prevalence and allelic heterogeneity of hereditary diseases (HDs) could vary significantly in different human populations. Current knowledge of HDs distribution in populations is generally limited to either European data or analyses of isolated populations which were performed several decades ago. Thus, an acknowledgement of the HDs prevalence in different modern open populations is important. The study presents the results of a genetic epidemiological study of hereditary diseases (HDs) in the population of the Karachay-Cherkess Republic (KChR). Clinical screening of a population of 410,367 people for the identification of HDs was conducted. The population surveyed is represented by five major ethnic groups—Karachays, Russians, Circassians, Abazins, Nogais. The study of the populations was carried out in accordance with the proprietary protocol of genetic epidemiological examination designed to identify >3500 HDs easily diagnosed during clinical examination by qualified specialists specializing in the HDs. The protocol consists of the population genetic and medical genetic sections and is intended for comprehensive population analysis based on the data on different genetic systems, including the genes of HDs, DNA polymorphisms, demographic data collected during hospital-based survey. 8950 families (with 10,125 patients) with presumably the HDs were initially identified as a result of the survey and data collection through various sources of registration (from 1156 medical workers from 163 medical institutions). A diagnosis of hereditary pathology was established in 1849 patients (from 1295 families). Two hundred and thirty nosological forms were revealed (in 1857 patients from 1295 families). The total prevalence of HDs was 1:221. Differences between populations and ethnic groups were identified: 1:350 in Russians, 1:195 in Karachays, 1:199 in Circassians, 1:218 in Abazins, 1:135 in Nogais. Frequent diseases were determined, the presence of marked genetic heterogeneity was identified during the confirmatory DNA diagnosis. To explain the reasons for the differentiation of populations by load of HD, a correlation analysis was carried out between the F_ST (random inbreeding) in populations and HDs load values. This analysis showed genetic drift is probably one of the leading factors determining the differentiation of KChR populations by HDs load. For the first time, the size of the load and spectrum of HDs in the populations of the KChR are determined. We have demonstrated genetic drift to be one of the main factors of the population dynamics in studied population. A significant genetic heterogeneity of HDs, both allelic and locus, was revealed in KChR. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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12 pages, 1828 KiB

Open AccessArticle

The Human Isoform of RNA Polymerase II Subunit hRPB11bα Specifically Interacts with Transcription Factor ATF4

by Sergey A. Proshkin, Elena K. Shematorova and George V. Shpakovski

Int. J. Mol. Sci. 2020, 21(1), 135; https://doi.org/10.3390/ijms21010135 - 24 Dec 2019

Cited by 7 | Viewed by 3610

Abstract

Rpb11 subunit of RNA polymerase II of Eukaryotes is related to N-terminal domain of eubacterial α subunit and forms a complex with Rpb3 subunit analogous to prokaryotic α₂ homodimer, which is involved in RNA polymerase assembly and promoter recognition. In humans, a [...] Read more.

Rpb11 subunit of RNA polymerase II of Eukaryotes is related to N-terminal domain of eubacterial α subunit and forms a complex with Rpb3 subunit analogous to prokaryotic α₂ homodimer, which is involved in RNA polymerase assembly and promoter recognition. In humans, a POLR2J gene family has been identified that potentially encodes several hRPB11 proteins differing mainly in their short C-terminal regions. The functions of the different human specific isoforms are still mainly unknown. To further characterize the minor human specific isoform of RNA polymerase II subunit hRPB11bα, the only one from hRPB11 (POLR2J) homologues that can replace its yeast counterpart in vivo, we used it as bait in a yeast two-hybrid screening of a human fetal brain cDNA library. By this analysis and subsequent co-purification assay in vitro, we identified transcription factor ATF4 as a prominent partner of the minor RNA polymerase II (RNAP II) subunit hRPB11bα. We demonstrated that the hRPB11bα interacts with leucine b-Zip domain located on the C-terminal part of ATF4. Overexpression of ATF4 activated the reporter more than 10-fold whereas co-transfection of hRPB11bα resulted in a 2.5-fold enhancement of ATF4 activation. Our data indicate that the mode of interaction of human RNAP II main (containing major for of hRPB11 subunit) and minor (containing hRPB11bα isoform of POLR2J subunit) transcription enzymes with ATF4 is certainly different in the two complexes involving hRPB3–ATF4 (not hRPB11a–ATF4) and hRpb11bα–ATF4 platforms in the first and the second case, respectively. The interaction of hRPB11bα and ATF4 appears to be necessary for the activation of RNA polymerase II containing the minor isoform of the hRPB11 subunit (POLR2J) on gene promoters regulated by this transcription factor. ATF4 activates transcription by directly contacting RNA polymerase II in the region of the heterodimer of α-like subunits (Rpb3–Rpb11) without involving a Mediator, which provides fast and highly effective activation of transcription of the desired genes. In RNA polymerase II of Homo sapiens that contains plural isoforms of the subunit hRPB11 (POLR2J), the strength of the hRPB11–ATF4 interaction appeared to be isoform-specific, providing the first functional distinction between the previously discovered human forms of the Rpb11 subunit. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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11 pages, 1146 KiB

Open AccessArticle

Prediction of Protein–Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences

by Dmitry Karasev, Boris Sobolev, Alexey Lagunin, Dmitry Filimonov and Vladimir Poroikov

Int. J. Mol. Sci. 2020, 21(1), 24; https://doi.org/10.3390/ijms21010024 - 18 Dec 2019

Cited by 11 | Viewed by 3383

Abstract

The affinity of different drug-like ligands to multiple protein targets reflects general chemical–biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein–ligand interactions based on pairwise sequence alignment provides reasonable [...] Read more.

The affinity of different drug-like ligands to multiple protein targets reflects general chemical–biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein–ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands’ specificity well coincides with the phylogenic taxonomy of the proteins. Methods using multiple alignment require an accurate match of functionally significant residues. Such conditions may not be met in the case of diverged protein families. To overcome these limitations, we propose an approach based on the analysis of local sequence similarity within the set of analyzed proteins. The positional scores, calculated by sequence fragment comparisons, are used as input data for the Bayesian classifier. Our approach provides a prediction accuracy comparable or exceeding those of other methods. It was demonstrated on the popular Gold Standard test sets, presenting different sequence heterogeneity and varying from the group, including different protein families to the more specific groups. A reasonable prediction accuracy was also found for protein kinases, displaying weak relationships between sequence phylogeny and inhibitor specificity. Thus, our method can be applied to the broad area of protein–ligand interactions. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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14 pages, 3330 KiB

Open AccessArticle

The mTOR Signaling Pathway Activity and Vitamin D Availability Control the Expression of Most Autism Predisposition Genes

by Ekaterina A. Trifonova, Alexandra I. Klimenko, Zakhar S. Mustafin, Sergey A. Lashin and Alex V. Kochetov

Int. J. Mol. Sci. 2019, 20(24), 6332; https://doi.org/10.3390/ijms20246332 - 15 Dec 2019

Cited by 26 | Viewed by 6234

Abstract

Autism spectrum disorder (ASD) has a strong and complex genetic component with an estimate of more than 1000 genes implicated cataloged in SFARI (Simon′s Foundation Autism Research Initiative) gene database. A significant part of both syndromic and idiopathic autism cases can be attributed [...] Read more.

Autism spectrum disorder (ASD) has a strong and complex genetic component with an estimate of more than 1000 genes implicated cataloged in SFARI (Simon′s Foundation Autism Research Initiative) gene database. A significant part of both syndromic and idiopathic autism cases can be attributed to disorders caused by the mechanistic target of rapamycin (mTOR)-dependent translation deregulation. We conducted gene-set analyses and revealed that 606 out of 1053 genes (58%) included in the SFARI Gene database and 179 out of 281 genes (64%) included in the first three categories of the database (“high confidence”, “strong candidate”, and “suggestive evidence”) could be attributed to one of the four groups: 1. FMRP (fragile X mental retardation protein) target genes, 2. mTOR signaling network genes, 3. mTOR-modulated genes, 4. vitamin D3 sensitive genes. The additional gene network analysis revealed 43 new genes and 127 new interactions, so in the whole 222 out of 281 (79%) high scored genes from SFARI Gene database were connected with mTOR signaling activity and/or dependent on vitamin D3 availability directly or indirectly. We hypothesized that genetic and/or environment mTOR hyperactivation, including provocation by vitamin D deficiency, might be a common mechanism controlling the expressivity of most autism predisposition genes and even core symptoms of autism. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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18 pages, 1507 KiB

Open AccessArticle

Y-chromosome and Surname Analyses for Reconstructing Past Population Structures: The Sardinian Population as a Test Case

by Viola Grugni, Alessandro Raveane, Giulia Colombo, Carmen Nici, Francesca Crobu, Linda Ongaro, Vincenza Battaglia, Daria Sanna, Nadia Al-Zahery, Ornella Fiorani, Antonella Lisa, Luca Ferretti, Alessandro Achilli, Anna Olivieri, Paolo Francalacci, Alberto Piazza, Antonio Torroni and Ornella Semino

Int. J. Mol. Sci. 2019, 20(22), 5763; https://doi.org/10.3390/ijms20225763 - 16 Nov 2019

Cited by 4 | Viewed by 14193

Abstract

Many anthropological, linguistic, genetic and genomic analyses have been carried out to evaluate the potential impact that evolutionary forces had in shaping the present-day Sardinian gene pool, the main outlier in the genetic landscape of Europe. However, due to the homogenizing effect of [...] Read more.

Many anthropological, linguistic, genetic and genomic analyses have been carried out to evaluate the potential impact that evolutionary forces had in shaping the present-day Sardinian gene pool, the main outlier in the genetic landscape of Europe. However, due to the homogenizing effect of internal movements, which have intensified over the past fifty years, only partial information has been obtained about the main demographic events. To overcome this limitation, we analyzed the male-specific region of the Y chromosome in three population samples obtained by reallocating a large number of Sardinian subjects to the place of origin of their monophyletic surnames, which are paternally transmitted through generations in most of the populations, much like the Y chromosome. Three Y-chromosome founding lineages, G2-L91, I2-M26 and R1b-V88, were identified as strongly contributing to the definition of the outlying position of Sardinians in the European genetic context and marking a significant differentiation within the island. The present distribution of these lineages does not always mirror that detected in ancient DNAs. Our results show that the analysis of the Y-chromosome gene pool coupled with a sampling method based on the origin of the family name, is an efficient approach to unravelling past heterogeneity, often hidden by recent movements, in the gene pool of modern populations. Furthermore, the reconstruction and comparison of past genetic isolates represent a starting point to better assess the genetic information deriving from the increasing number of available ancient DNA samples. Full article

(This article belongs to the Special Issue Medical Genetics, Genomics and Bioinformatics)

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