Array comparative genomic hybridization (CGH) has been repeatedly shown to be a successful tool for the identification of genomic variations in a clinical population. During the last decade, the implementation of array CGH has resulted in the identification of new causative submicroscopic chromosome imbalances and copy number variations (CNVs) in neuropsychiatric (neurobehavioral) diseases. Currently, array-CGH-based technologies have become an integral part of molecular diagnosis and research in individuals with neuropsychiatric disorders and children with intellectual disability (mental retardation) and congenital anomalies. Here, we introduce the Russian cohort of children with intellectual disability, autism, epilepsy and congenital anomalies analyzed by BAC array CGH and a novel bioinformatic strategy.

Long contiguous stretches of homozygosity (LCSH) (regions/runs of homozygosity) are repeatedly detected by single-nucleotide polymorphism (SNP) chromosomal microarrays. Providing important clues regarding parental relatedness (consanguinity), uniparental disomy, chromosomal recombination or rearrangements, LCSH are rarely considered as a possible epigenetic cause of neurodevelopmental disorders. Additionally, despite being relevant to imprinting, LCSH at imprinted loci have not been truly addressed in terms of pathogenicity. In this study, we examined LCSH in children with unexplained intellectual disability, autism, congenital malformations and/or epilepsy focusing on chromosomal regions which harbor imprinted disease genes. Out of 267 cases, 14 (5.2 %) were found to have LCSH at imprinted loci associated with a clinical outcome. There were 5 cases of LCSH at 15p11.2, 4 cases of LCSH at 7q31.2, 3 cases of LCSH at 11p15.5, and 2 cases of LCSH at 7q21.3. Apart from a case of LCSH at 7q31.33q32.3 (~4 Mb in size), all causative LCSH were 1–1.5 Mb in size. Clinically, these cases were characterized by a weak resemblance to corresponding imprinting diseases (i.e., Silver-Russell, Beckwith-Wiedemann, and Prader-Willi/Angelman syndromes), exhibiting distinctive intellectual disability, autistic behavior, developmental delay, seizures and/or facial dysmorphisms. Parental consanguinity was detected in 8 cases (3 %), and these cases did not exhibit LCSH at imprinted loci. This study demonstrates that shorter LCSH at chromosomes 7q21.3, 7q31.2, 11p15.5, and 15p11.2 occur with a frequency of about 5 % in the children with intellectual disability, autism, congenital malformations and/or epilepsy. Consequently, this type of epigenetic mutations appears to be the most common one among children with neurodevelopmental diseases. Finally, since LCSH less than 2.5–10 Mb in size are generally ignored in diagnostic SNP microarray studies, one can conclude that an important epigenetic cause of intellectual disability, autism or epilepsy is actually overlooked.

Mechanisms for somatic chromosomal mosaicism (SCM) and chromosomal instability (CIN) are not completely understood. During molecular karyotyping and bioinformatic analyses of children with neurodevelopmental disorders and congenital malformations (n = 612), we observed colocalization of regular chromosomal imbalances or copy number variations (CNV) with mosaic ones (n = 47 or 7.7%). Analyzing molecular karyotyping data and pathways affected by CNV burdens, we proposed a mechanism for SCM/CIN, which had been designated as “chromohelkosis” (from the Greek words chromosome ulceration/open wound). Briefly, structural chromosomal imbalances are likely to cause local instability (“wreckage”) at the breakpoints, which results either in partial/whole chromosome loss (e.g., aneuploidy) or elongation of duplicated regions. Accordingly, a function for classical/alpha satellite DNA (protection from the wreckage towards the centromere) has been hypothesized. Since SCM and CIN are ubiquitously involved in development, homeostasis and disease (e.g., prenatal development, cancer, brain diseases, aging), we have metaphorically (ironically) designate the system explaining chromohelkosis contribution to SCM/CIN as the cytogenomic “theory of everything”, similar to the homonymous theory in physics inasmuch as it might explain numerous phenomena in chromosome biology. Recognizing possible empirical and theoretical weaknesses of this “theory”, we nevertheless believe that studies of chromohelkosis-like processes are required to understand structural variability and flexibility of the genome.

Rett syndrome (RTT) is an X-linked neurodevelopmental disease affecting predominantly females caused by MECP2 mutations. Although RTT is classically considered a monogenic disease, a stable proportion of patients, who do not exhibit MECP2 sequence variations, does exist. Here, we have attempted at uncovering genetic causes underlying the disorder in mutation-negative cases by whole genome analysis using array comparative genomic hybridization (CGH) and a bioinformatic approach.

BACKGROUND: The global amount of investment in companies developing artificial intelligence (AI)-based software technologies for medical diagnostics reached $80 million in 2016, rose to $152 million in 2017, and is expected to continue growing. While software manufacturing companies should comply with existing clinical, bioethical, legal, and methodological frameworks and standards, there is a lack of uniform national and international standards and protocols for testing and monitoring AI-based software. AIM: This objective of this study is to develop a universal methodology for testing and monitoring AI-based software for medical diagnostics, with the aim of improving its quality and implementing its integration into practical healthcare. MATERIALS AND METHODS: The research process involved an analytical phase in which a literature review was conducted on the PubMed and eLibrary databases. The practical stage included the approbation of the developed methodology within the framework of an experiment focused on the use of innovative technologies in the field of computer vision to analyze medical images and further application in the health care system of the city of Moscow. RESULTS: A methodology for testing and monitoring AI-based software for medical diagnostics has been developed, aimed at improving its quality and introducing it into practical healthcare. The methodology consists of seven stages: self-testing, functional testing, calibration testing, technological monitoring, clinical monitoring, feedback, and refinement. CONCLUSION: Distinctive features of the methodology include its cyclical stages of monitoring and software development, leading to continuous improvement of its quality, the presence of detailed requirements for the results of the software work, and the participation of doctors in software evaluation. The methodology will allow software developers to achieve significant outcomes and demonstrate achievements across various areas. It also empowers users to make informed and confident choices among software options that have passed an independent and comprehensive quality check.

Somatic genome variations (mosaicism) seem to represent a common mechanism for human intercellular/interindividual diversity in health and disease. However, origins and mechanisms of somatic mosaicism remain a matter of conjecture. Recently, it has been hypothesized that zygotic genomic variation naturally occurring in humans is likely to predispose to nonheritable genetic changes (aneuploidy) acquired during the lifetime through affecting cell cycle regulation, genome stability maintenance, and related pathways. Here, we have evaluated genomic copy number variation (CNV) in genes implicated in the cell cycle pathway (according to Kyoto Encyclopedia of Genes and Genomes/KEGG) within a cohort of patients with intellectual disability, autism, and/or epilepsy, in which the phenotype was not associated with genomic rearrangements altering this pathway. Benign CNVs affecting 20 genes of the cell cycle pathway were detected in 161 out of 255 patients (71.6%). Among them, 62 individuals exhibited >2 CNVs affecting the cell cycle pathway. Taking into account the number of individuals demonstrating CNV of these genes, a support for this hypothesis appears to be presented. Accordingly, we speculate that further studies of CNV burden across the genes implicated in related pathways might clarify whether zygotic genomic variation generates somatic mosaicism in health and disease.