Застосування зворотних залежностей у математичних моделях складних об’єктів та систем

Представлено метод побудови апроксимуючих поліноміальних функцій багатьох змінних, який засновано на використанні в поліномах від’ємних степенів та застосуванні до поліномів обмеження на сумарну величину ступеня добутку змінних. Запропоновано використання штрафної функції на кількість членів полінома. Експериментальним шляхом отримано оптимальну величину коефіцієнта запропонованої штрафної функції.Представлен метод построения аппроксимирующих полиномиальных функций многих переменных, основанный на использовании в полиномах отрицательных степеней и применении к полиномам ограничения на суммарную величину степени произведения переменных. Предложено использование штрафной функции на количество членов полинома. Экспериментальным путем получена оптимальная величина коэффициента предложенной штрафной функции.A method of constructing approximating polynomials functions of many variables, based on the use of the negative degrees in polynomials and the application of the limitation on the total value of the product variable to polynoms is presented. The usage of the penalty function for the number of polynomial members is suggested. The optimum value of the proposed penalty functions coefficient is experimentally obtained

Feasibility of predicting allele specific expression from DNA sequencing using machine learning

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo

Dysregulation of miRNA-30e-3p targeting IL-1β in an international cohort of systemic autoinflammatory disease patients

Abstract: Autoinflammation is the standard mechanism seen in systemic autoinflammatory disease (SAID) patients. This study aimed to investigate the effect of a candidate miRNA, miR-30e-3p, which was identified in our previous study, on the autoinflammation phenotype seen in SAID patients and to analyze its expression in a larger group of European SAID patients. We examined the potential anti-inflammatory effect of miR-30e-3p, which we had defined as one of the differentially expressed miRNAs in microarray analysis involved in inflammation-related pathways. This study validated our previous microarray results of miR-30e-3p in a cohort involving European SAID patients. We performed cell culture transfection assays for miR-30e-3p. Then, in transfected cells, we analyzed expression levels of pro-inflammatory genes; IL-1β, TNF-α, TGF-β, and MEFV. We also performed functional experiments, caspase-1 activation by fluorometric assay kit, apoptosis assay by flow cytometry, and cell migration assays by wound healing and filter system to understand the possible effect of miR-30e-3p on inflammation. Following these functional assays, 3'UTR luciferase activity assay and western blotting were carried out to identify the target gene of the aforementioned miRNA. MiR-30e-3p was decreased in severe European SAID patients like the Turkish patients. The functional assays associated with inflammation suggested that miR-30e-3p has an anti-inflammatory effect. 3'UTR luciferase activity assay demonstrated that miR-30e-3p directly binds to interleukin-1-beta (IL-1β), one of the critical molecules of inflammatory pathways, and reduces both RNA and protein levels of IL-1β. miR-30e-3p, which has been associated with IL-1β, a principal component of inflammation, might be of potential diagnostic and therapeutic value for SAIDs. Key Messages: miR-30e-3p, which targets IL-1β, could have a role in the pathogenesis of SAID patients.miR-30e-3p has a role in regulating inflammatory pathways like migration, caspase-1 activation.miR-30e-3p has the potential to be used for future diagnostic and therapeutic approaches.</p

National external quality assessment for next-generation sequencing-based diagnostics of primary immunodeficiencies

Dutch genome diagnostic centers (GDC) use next-generation sequencing (NGS)-based diagnostic applications for the diagnosis of primary immunodeficiencies (PIDs). The interpretation of genetic variants in many PIDs is complicated because of the phenotypic and genetic heterogeneity. To analyze uniformity of variant filtering, interpretation, and reporting in NGS-based diagnostics for PID, an external quality assessment was performed. Four main Dutch GDCs participated in the quality assessment. Unannotated variant call format (VCF) files of two PID patient analyses per laboratory were distributed among the four GDCs, analyzed, and interpreted (eight analyses in total). Variants that would be reported to the clinician and/or advised for further investigation were compared between the centers. A survey measuring the experiences of clinical laboratory geneticists was part of the study. Analysis of samples with confirmed diagnoses showed that all centers reported at least the variants classified as likely pathogenic (LP) or pathogenic (P) variants in all samples, except for variants in two genes (PSTPIP1 and BTK). The absence of clinical information complicated correct classification of variants. In this external quality assessment, the final interpretation and conclusions of the genetic analyses were uniform among the four participating genetic centers. Clinical and immunological data provided by a medical specialist are required to be able to draw proper conclusions from genetic data

CAPICE:a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice.

Curation and expansion of the Human Phenotype Ontology for systemic autoinflammatory diseases improves phenotype-driven disease-matching

INTRODUCTION: Accurate and standardized phenotypic descriptions are essential in diagnosing rare diseases and discovering new diseases, and the Human Phenotype Ontology (HPO) system was developed to provide a rich collection of hierarchical phenotypic descriptions. However, although the HPO terms for inborn errors of immunity have been improved and curated, it has not been investigated whether this curation improves the diagnosis of systemic autoinflammatory disease (SAID) patients. Here, we aimed to study if improved HPO annotation for SAIDs enhanced SAID identification and to demonstrate the potential of phenotype-driven genome diagnostics using curated HPO terms for SAIDs. METHODS: We collected HPO terms from 98 genetically confirmed SAID patients across eight different European SAID expertise centers and used the LIRICAL (Likelihood Ratio Interpretation of Clinical Abnormalities) computational algorithm to estimate the effect of HPO curation on the prioritization of the correct SAID for each patient. RESULTS: Our results show that the percentage of correct diagnoses increased from 66% to 86% and that the number of diagnoses with the highest ranking increased from 38 to 45. In a further pilot study, curation also improved HPO-based whole-exome sequencing (WES) analysis, diagnosing 10/12 patients before and 12/12 after curation. In addition, the average number of candidate diseases that needed to be interpreted decreased from 35 to 2. DISCUSSION: This study demonstrates that curation of HPO terms can increase identification of the correct diagnosis, emphasizing the high potential of HPO-based genome diagnostics for SAIDs

ISSAID/EMQN Best Practice Guidelines for the Genetic Diagnosis of Monogenic Autoinflammatory Diseases in the Next-Generation Sequencing Era

Abstract Background Monogenic autoinflammatory diseases are caused by pathogenic variants in genes that regulate innate immune responses, and are characterized by sterile systemic inflammatory episodes. Since symptoms can overlap within this rapidly expanding disease category, accurate genetic diagnosis is of the utmost importance to initiate early inflammation-targeted treatment and prevent clinically significant or life-threatening complications. Initial recommendations for the genetic diagnosis of autoinflammatory diseases were limited to a gene-by-gene diagnosis strategy based on the Sanger method, and restricted to the 4 prototypic recurrent fevers (MEFV, MVK, TNFRSF1A, and NLRP3 genes). The development of best practices guidelines integrating critical recent discoveries has become essential. Methods The preparatory steps included 2 online surveys and pathogenicity annotation of newly recommended genes. The current guidelines were drafted by European Molecular Genetics Quality Network members, then discussed by a panel of experts of the International Society for Systemic Autoinflammatory Diseases during a consensus meeting. Results In these guidelines, we combine the diagnostic strength of next-generation sequencing and recommendations to 4 more recently identified genes (ADA2, NOD2, PSTPIP1, and TNFAIP3), nonclassical pathogenic genetic alterations, and atypical phenotypes. We present a referral-based decision tree for test scope and method (Sanger versus next-generation sequencing) and recommend on complementary explorations for mosaicism, copy-number variants, and gene dose. A genotype table based on the 5-category variant pathogenicity classification provides the clinical significance of prototypic genotypes per gene and disease. Conclusions These guidelines will orient and assist geneticists and health practitioners in providing up-to-date and appropriate diagnosis to their patients

Recommendations for whole genome sequencing in diagnostics for rare diseases

In 2016, guidelines for diagnostic Next Generation Sequencing (NGS) have been published by EuroGentest in order to assist laboratories in the implementation and accreditation of NGS in a diagnostic setting. These guidelines mainly focused on Whole Exome Sequencing (WES) and targeted (gene panels) sequencing detecting small germline variants (Single Nucleotide Variants (SNVs) and insertions/deletions (indels)). Since then, Whole Genome Sequencing (WGS) has been increasingly introduced in the diagnosis of rare diseases as WGS allows the simultaneous detection of SNVs, Structural Variants (SVs) and other types of variants such as repeat expansions. The use of WGS in diagnostics warrants the re-evaluation and update of previously published guidelines. This work was jointly initiated by EuroGentest and the Horizon2020 project Solve-RD. Statements from the 2016 guidelines have been reviewed in the context of WGS and updated where necessary. The aim of these recommendations is primarily to list the points to consider for clinical (laboratory) geneticists, bioinformaticians, and (non-)geneticists, to provide technical advice, aid clinical decision-making and the reporting of the results

Curation and expansion of Human Phenotype Ontology for defined groups of inborn errors of immunity.

BACKGROUND: Accurate, detailed, and standardized phenotypic descriptions are essential to support diagnostic interpretation of genetic variants and to discover new diseases. The Human Phenotype Ontology (HPO), extensively used in rare disease research, provides a rich collection of vocabulary with standardized phenotypic descriptions in a hierarchical structure. However, to date, the use of HPO has not yet been widely implemented in the field of inborn errors of immunity (IEIs), mainly due to a lack of comprehensive IEI-related terms. OBJECTIVES: We sought to systematically review available terms in HPO for the depiction of IEIs, to expand HPO, yielding more comprehensive sets of terms, and to reannotate IEIs with HPO terms to provide accurate, standardized phenotypic descriptions. METHODS: We initiated a collaboration involving expert clinicians, geneticists, researchers working on IEIs, and bioinformaticians. Multiple branches of the HPO tree were restructured and extended on the basis of expert review. Our ontology-guided machine learning coupled with a 2-tier expert review was applied to reannotate defined subgroups of IEIs. RESULTS: We revised and expanded 4 main branches of the HPO tree. Here, we reannotated 73 diseases from 4 International Union of Immunological Societies-defined IEI disease subgroups with HPO terms. We achieved a 4.7-fold increase in the number of phenotypic terms per disease. Given the new HPO annotations, we demonstrated improved ability to computationally match selected IEI cases to their known diagnosis, and improved phenotype-driven disease classification. CONCLUSIONS: Our targeted expansion and reannotation presents enhanced precision of disease annotation, will enable superior HPO-based IEI characterization, and hence benefit both IEI diagnostic and research activities