Deletion polymorphism of glutathione S-transferases genes (GSTT1, GSTM1) in patients with breast cancer in Primorye region

Background. Breast cancer (BC) refers to multifactorial polygenic diseases that occur as a result of the combined interaction of genetic and environmental factors. Glutathione-mediated detoxification is of key importance in ensuring the resistance of body cells to the damaging effect of xenobiotics.Objective: to study the prevalence of deletion polymorphisms of the GSTM1 and GSTT1 genes and to establish their influence on the formation of cancer risk in patients with BC in the Primorye region (Russia).Materials and methods. The study involved 176 women with BC, aged 23 to 79 years (mean age 48 ± 13 years) and 66 conditionally healthy individuals without cancer. The detection of deletion (null) genotypes of the GSTM1 and GSTT1 was carried out using multiplex PCR followed by analysis of the melting curves of the reaction products.Results. The frequency of GSTT1-0 genotype among BC patients was higher than in the control group (14.77 % versus 6.06 %), significantly exceeding the indicators in the control group by more than 2.5 times (p <0.1), indicating an association between the carriage of the GSTT1-0 genotype and the risk of developing BC. At the same time, the frequencies of the GSTM1-0 genotype in the study groups were comparable; no statistically significant association with the risk of developing BC was found.Conclusions. Homozygous deletion of GSTT1 (GSTT1-0) can potentially be considered as a low-penetrant risk factor for developing BC in the population of Primorye region.

Hidden Transcriptome Signature in Autism Spectrum Disorder (ASD) is Attributed to the Interpatient Heterogeneity

Recent studies of ASD have mostly supported the existence of heterogeneity and genomic variation in ASD which have hindered and restrained development of any effective and targetable treatment for a long time. As numerous studies have shown, both genetic and phenotypic heterogeneity is presented in ASD, however, heterogeneity in genetic level is not fully understood which is the key challenges for the further research. Even dozens of ASD susceptibility genes have been discovered which is commonly accounting for 10 to 20 percent of ASD cases, the internal complex combination of mutated genes that determine the epigenetic factors of ASD is still not comprehensively recognized by the recent studies. First by discouraging the traditional method that have been applied in most of the current research of diseases, this research will then focus on dissecting the heterogeneity of polygenic diseases and analyzing with an unconventional approach for acquiring Differently Expressed Genes (DEGs) in Gupta's Dataset that provided transcriptome of frontal cortex of ASD patients. Divide categories by using unsupervised learning strategy, the results yielded by analyzing within clusters of ASD have supported the feasibility of the attempts to use heterogeneity to reveal its underlying mechanism. This study puts forward the inference that the heterogeneity of polygenic diseases will obscure the molecular signals related to the disease, and at the same time attempts to use heterogeneity to reveal the underlying mechanism.

Development of data infrastructure and in silico prediction method to promote genomic medicine

In genome medicine, which is now being implemented in medical care, variants detected by genome analysis such as next-generation sequencers are clinically interpreted to determine the diagnosis and treatment plan. The clinical interpretation is performed based on the detailed clinical background and the information from journal papers and public databases, such as frequencies in the population and their relationship to the disease. A large amount of genomic data has been accumulated so far, and many genomic variant databases related to diseases have been developed, including ClinVar. On the other hand, the genes and variants involved in diseases are different between populations with different genetic backgrounds. Furthermore, it has been reported that there is a racial bias in the information shared in current public databases, which affects clinical interpretation. Therefore, increasing the diversity of genomic variant data has become an important issue worldwide. In Japan, the Japan Agency for Medical Research and Development (AMED) launched a project to develop an integrated clinical genome information database in 2016. This project targeted “Cancer,” “Rare/Intractable diseases,” “Infectious diseases,” “Dementia,” and “Hearing loss”, and in collaboration with research institutes that provide genomic medicine in Japan, we developed an integrated database named MGeND (Medical Genomics Japan Database). The MGeND is a freely accessible database, which provides disease-related genomic information detected from the Japanese population. The MGeND widely collects variant data for monogenic diseases represented by rare diseases and polygenic diseases such as dementia and infectious disease. The genome variant data are integrated by genomic position for these diseases and can be searched across diseases. The useful genome analysis methods differ depending on the disease area. Therefore, in addition to “SNV, short indel, SV, and CNV” data handled by ClinVar, MGeND includes GWAS (Genome-Wide Association Study) data, which is widely used in studies of polygenic diseases, and HLA (Human Leukemia Virus) allele frequency data, which is used in immune-related diseases such as infectious diseases. As of September 2021, more than 150,000 variants have been registered in MGeND, and 60,000 unique variants have been made public. Of these variants, about 70% were variants registered only in MGeND and not registered in ClinVar. This fact shows the importance of the efforts to collect genomic information by each ethnic group. On the other hands, many variants have not been annotated with any clinical interpretation because the effects on molecular function and the mechanisms of disease are not clear at this time. These variants of uncertain significance (VUS) are a bottleneck for genomic medicine because they cannot be used for diagnosis or treatment selection. The evaluation of VUS requires detailed experimental validation and a vast amount of knowledge integration, which is costly. In order to understand the molecular function and disease relevance of VUS and to enable optimal drug selection, we have been developing a machine learning-based method for predicting the pathogenicity of variants and a computational platform for estimating the effect of variants on drug sensitivity. Many methods for predicting the pathogenicity of genomic variants using machine learning have been developed. Most of them use the conservation of amino acid or nucleotide sequences among closely related species, physicochemical properties of proteins as features for prediction. There are also many prediction methods based on ensemble learning that aggregate the predicted scores by existing tools. These approaches focus on individual genes and variants and evaluate their effects. However, in many diseases, multiple molecules play a complex role in the pathogenesis of the disease. In other words, to assess the pathological significance of variants more accurately, it is necessary to consider the molecular association. Therefore, we constructed a knowledge graph based on molecular networks, genomic variants, and predicted scores by existing methods and proposed a prediction model using Graph Convolutional Network (GCN). The prediction performance evaluation using a benchmark set showed that the GCN-based method outperformed existing methods. It is known that variants can affect the interaction between a molecule and a drug. For optimal drug selection, it is necessary to clarify the effect of the variant on drug affinity. It is time-consuming and costly to perform experiments on a large number of VUSs. Our previous studies show that molecular dynamics calculation can evaluate the affinity between mutants and drugs energetically and estimate with high accuracy. We are currently working on a project to estimate the effects of a large number of VUSs using the supercomputer Fugaku. To realize calculations for many VUS in this project, we are developing a data platform for seamlessly performing molecular dynamics simulation from genome information. Moreover, we are constructing a database to publish calculation results and their outcomes for contributing a selection of optimal drugs. In the presentation, I will introduce the development of the databases and prediction methods to improve the efficiency of genomic medicine.

Design of efficacious somatic cell genome editing strategies for recessive and polygenic diseases

Compound heterozygous recessive or polygenic diseases could be addressed through gene correction of multiple alleles. However, targeting of multiple alleles using genome editors could lead to mixed genotypes and adverse events that amplify during tissue morphogenesis. Here we demonstrate that Cas9-ribonucleoprotein-based genome editors can correct two distinct mutant alleles within a single human cell precisely. Gene-corrected cells in an induced pluripotent stem cell model of Pompe disease expressed the corrected transcript from both corrected alleles, leading to enzymatic cross-correction of diseased cells. Using a quantitative in silico model for the in vivo delivery of genome editors into the developing human infant liver, we identify progenitor targeting, delivery efficiencies, and suppression of imprecise editing outcomes at the on-target site as key design parameters that control the efficacy of various therapeutic strategies. This work establishes that precise gene editing to correct multiple distinct gene variants could be highly efficacious if designed appropriately.

Design of efficacious somatic cell genome editing strategies for recessive and polygenic diseases

Gene correction of multiple alleles for compound heterozygous recessive or polygenic diseases is a promising therapeutic strategy. However, the targeting of multiple alleles using genome editors in a single cell could lead to mixed genotypes and adverse events that amplify during tissue morphogenesis. Here we demonstrate that SpyCas9-based S1mplex genome editors can be designed and developed to correct two distinct mutant alleles within a single human cell precisely. Gene-corrected cells in a patient-derived, induced pluripotent stem cell (iPSC) model of Pompe disease robustly expressed the corrected transcript from both corrected alleles. The translated protein from the gene-corrected cells was properly processed after translation and was able to enzymatically cross-correct diseased cells at levels equivalent to standard-of-care, enzyme replacement therapy (ERT). Using a novel in silico model for the in vivo delivery of these and many other genome editors into a developing liver of a human infant, we identify progenitor cell targeting, delivery efficiencies, and suppression of imprecise editing outcomes at the on-target site as key design parameters controlling the potency and efficacy of in vivo somatic cell genome editing. Both single and double gene correction are efficacious for in vivo somatic cell editing strategies, while double gene correction is more effective than single-gene correction for autologous cell therapy with ex vivo gene-corrected cells. This work establishes that precise gene correction using genome editors to correct multiple distinct gene variants could be efficacious in the treatment of recessive and polygenic disorders.

Cell type-specific proteogenomic signal diffusion for integrating multi-omics data predicts novel schizophrenia risk genes

Accumulation of diverse types of omics data on schizophrenia (SCZ) requires a systems approach to jointly modeling the interplay between genome, transcriptome and proteome. Proteome dynamics, as the definitive cellular machinery in human body, has been lagging behind the research on genome/transcriptome in the context of SCZ, both at tissue and single-cell resolution. We introduce a Markov Affinity-based Proteogenomic Signal Diffusion (MAPSD) method to model intra-cellular protein trafficking paradigms and tissue-wise single-cell protein abundances. MAPSD integrates multi-omics data to amplify the signals at SCZ risk loci with small effect sizes, and reveal convergent disease-associated gene modules in the brain interactome as well as more than 130 tissue/cell-type combinations. We predicted a set of high-confidence SCZ risk genes, the majority of which are not directly connected to SCZ susceptibility risk genes. We characterized the subcellular localization of proteins encoded by candidate SCZ risk genes in various brain regions, and illustrated that most are enriched in neuronal and Purkinje cells in cerebral cortex. We demonstrated how the identified gene set may be involved in different developmental stages of the brain, how they alter SCZ-related biological pathways, and how they can be effectively leveraged for drug repurposing. MAPSD can be applied to other polygenic diseases, yet our case study on SCZ signifies how tissue-adjusted protein-protein interaction networks can assist in generating deeper insights into the orchestration of polygenic diseases.

Osteocyte Transcriptome Mapping Identifies a Molecular Landscape Controlling Skeletal Homeostasis and Susceptibility to Skeletal Disease

Osteocytes are master regulators of the skeleton. We mapped the transcriptome of osteocytes from different skeletal sites, across age and sexes in mice to reveal genes and molecular programs that control this complex cellular-network. We define an osteocyte transcriptome signature of 1239 genes that distinguishes osteocytes from other cells. 77% have no previously known role in the skeleton and are enriched for genes regulating neuronal network formation, suggesting this program is important in osteocyte communication. We evaluated 19 skeletal parameters in 733 knockout mouse lines and reveal 26 osteocyte transcriptome signature genes that control bone structure and function. We showed osteocyte transcriptome signature genes are enriched for human orthologs that cause monogenic skeletal disorders (P=2.4×10-22) and are associated with the polygenic diseases osteoporosis (P=1.8×10-13) and osteoarthritis (P=1.6×10-7). Thus, we reveal the molecular landscape that regulates osteocyte network formation and function and establish the importance of osteocytes in human skeletal disease.

Development of an Algorithm for Determining of Genetic Risk at the Primary Healthcare Level – A New Tool for Primary Prevention: A Study Protocol

Introduction Family history (FH) is an important part of the patients’ medical history during preventive management at model family medicine practices (MFMP). It currently includes a one (or two) generational inquiry, predominately in terms of cardiovascular diseases, arterial hypertension, and diabetes, but not of other diseases with a probable genetic aetiology. Beside family history, no application-based algorithm is available to determine the risk level for specific chronic diseases in Slovenia. Methods A web application-based algorithm aimed at determining the risk level for selected monogenic and polygenic diseases will be developed. The data will be collected in MFMP; approximately 40 overall with a sample including healthy preventive examination attendees (approximately 1,000). Demographic data, a three-generational FH, a medical history of acquired and congenital risk factors for the selected diseases, and other important clinical factors will be documented. Results The results will be validated by a clinical genetic approach based on family pedigrees and the next-generation genetic sequencing method. After the risk of genetic diseases in the Slovenian population has been determined, clinical pathways for acting according to the assessed risk level will be prepared. Conclusion By means of a public health tool providing an assessment of family predisposition, a contribution to the effective identification of people at increased risk of the selected monogenic and polygenic diseases is expected, lessening a significant public health burden.

The role of genetic research with family design in the study of affective disorders

Affective disorders (recurrent depressive disorder and bipolar affective disorder) are multifactorial and polygenic diseases, which suggests the involvement of multiple neurobiological mechanisms. The phenotype of affective disorders is a heterogeneous group of clinically similar psychopathological symptoms, which also makes it difficult to detect potential biomarkers and new therapeutic targets. To study families at high risk of developing affective disorders using both clinical and molecular genetic approaches can help to study the neurobiological basis of depressive conditions, as well as to identify endophenotypes of affective disorders. The most important criterion for an endophenotype is its heritability, which can be proved only within the framework of the family design of the study. Comprehensive clinical and molecular genetic studies based on family design have the best prospects.