Somatic mosaicism in the diseased brain

Abstract

It is hard to believe that all the cells of a human brain share identical genomes. Indeed, single cell genetic studies have demonstrated intercellular genomic variability in the normal and diseased brain. Moreover, there is a growing amount of evidence on the contribution of somatic mosaicism (the presence of genetically different cell populations in the same individual/tissue) to the etiology of brain diseases. However, brain-specific genomic variations are generally overlooked during the research of genetic defects associated with a brain disease. Accordingly, a review of brain-specific somatic mosaicism in disease context seems to be required. Here, we overview gene mutations, copy number variations and chromosome abnormalities (aneuploidy, deletions, duplications and supernumerary rearranged chromosomes) detected in the neural/neuronal cells of the diseased brain. Additionally, chromosome instability in non-cancerous brain diseases is addressed. Finally, theoretical analysis of possible mechanisms for neurodevelopmental and neurodegenerative disorders indicates that a genetic background for formation of somatic (chromosomal) mosaicism in the brain is likely to exist. In total, somatic mosaicism affecting the central nervous system seems to be a mechanism of brain diseases.

Cytogenomic epileptology

“…Chromosomal abnormalities and CNVs represent an important, albeit poorly explored, genetic causes of epilepsy [ 13 , 14 , 21 ]. The problem of lacking cytogenetic and cytogenomic studies of epilepsy is likely to arise from general decrease in cytogenetic competence [ 144 ].…”

“…For instance, a specific type of chromosomal inability (chromohelkosis or chromosome ulceration/wound) is relatively common in neurodevelopmental cohorts, which include individuals with epilepsy (for more details, see [ 80 ]). In addition, it is pertinent to mention that brain-specific chromosome and genome instability is a key element of the pathogenetic cascades for several brain diseases [ 21 , 24 ]. Consequently, it appears important to test postoperative and postmortem samples from individuals with epileptic disorders in the chromosome instability context.…”

olecular cytogenetic and cytopostgenomic analysis of the human genome

“…Molecular cytogenetic and cytogenomic analyses have become an important part of diagnostic and basic research focused on uncovering genetic mechanisms of a disease [1][2][3][4]. Brilliant discoveries of novel genetic mechanisms for brain disorders [5,6,7] obliged to reevaluate diagnostic workflows and approaches to identification of genomic variations associated with a disease. Nonetheless, there are still numerous problems associated with cytogenomic diagnosis roughly referred to genome mapping, difficulties of interpretation of genome variations (i.e.…”

“…However, the alternative remains questionable, since these approaches do not provide intrinsic data on diseases mechanisms/pathways, inasmuch as genomic mechanisms are much more sophisticated than previously envisaged [20, 28,50]. Moreover, these mechanisms become even more complicated when somatic genomic mosaicism and chromosome/genome instability are taken into account [3,7,15,25,51,52]. Considering the data that applications of the simplest digital tools appreciably increase the yield of molecular cytogenetic genome analysis [1,17,[53][54][55], sophisticated bioinformatic techniques are likely to provide the highest efficiency in diagnostic/research cytogenomic analyses [7,16,28,44,45].…”

“…Moreover, these mechanisms become even more complicated when somatic genomic mosaicism and chromosome/genome instability are taken into account [3,7,15,25,51,52]. Considering the data that applications of the simplest digital tools appreciably increase the yield of molecular cytogenetic genome analysis [1,17,[53][54][55], sophisticated bioinformatic techniques are likely to provide the highest efficiency in diagnostic/research cytogenomic analyses [7,16,28,44,45]. Here, we have added to the algorithm all of our original developments in genomic data processing, which include OMICs data input/processing (variome analysis, data collection, CNV prioritization) [3,10,12,16,28,44], CNV laundering [45], pathway-based analyses of variome for candidate process or CNV prioritization [16,20,28,36, 44] (Fig.…”