Tissue-specific differences in the spatial interposition of X-chromosome and 3R chromosome regions in the malaria mosquito Anopheles messeae fall

Spatial organization of a chromosome in a nucleus is very important in biology but many aspects of it are still generally unresolved. We focused on tissue-specific features of chromosome architecture in closely related malaria mosquitoes, which have essential inter-specific differences in polytene chromosome attachments in nurse cells. We showed that the region responsible for X-chromosome attachment interacts with nuclear lamina stronger in nurse cells, then in salivary glands cells in Anopheles messeae Fall. The inter-tissue differences were demonstrated more convincingly in an experiment of two distinct chromosomes interposition in the nucleus space of cells from four tissues. Microdissected DNA-probes from nurse cells X-chromosome (2BC) and 3R chromosomes (32D) attachment regions were hybridized with intact nuclei of nurse cells, salivary gland cells, follicle epithelium cells and imaginal disсs cells in 3D-FISH experiments. We showed that only salivary gland cells and follicle epithelium cells have no statistical differences in the interposition of 2BC and 32D. Generally, the X-chromosome and 3R chromosome are located closer to each other in cells of the somatic system in comparison with nurse cells on average. The imaginal disсs cell nuclei have an intermediate arrangement of chromosome interposition, similar to other somatic cells and nurse cells. In spite of species-specific chromosome attachments there are no differences in interposition of nurse cells chromosomes in An. messeae and An. atroparvus Thiel. Nurse cells have an unusual chromosome arrangement without a chromocenter, which could be due to the special mission of generative system cells in ontogenesis and evolution

Tissue-Specific Differences in the Spatial Interposition of X-Chromosome and 3R Chromosome Regions in the Malaria Mosquito <i>Anopheles messeae</i> Fall.

<div><p>Spatial organization of a chromosome in a nucleus is very important in biology but many aspects of it are still generally unresolved. We focused on tissue-specific features of chromosome architecture in closely related malaria mosquitoes, which have essential inter-specific differences in polytene chromosome attachments in nurse cells. We showed that the region responsible for X-chromosome attachment interacts with nuclear lamina stronger in nurse cells, then in salivary glands cells in <i>Anopheles messeae</i> Fall. The inter-tissue differences were demonstrated more convincingly in an experiment of two distinct chromosomes interposition in the nucleus space of cells from four tissues. Microdissected DNA-probes from nurse cells X-chromosome (2BC) and 3R chromosomes (32D) attachment regions were hybridized with intact nuclei of nurse cells, salivary gland cells, follicle epithelium cells and imaginal disсs cells in 3D-FISH experiments. We showed that only salivary gland cells and follicle epithelium cells have no statistical differences in the interposition of 2BC and 32D. Generally, the X-chromosome and 3R chromosome are located closer to each other in cells of the somatic system in comparison with nurse cells on average. The imaginal disсs cell nuclei have an intermediate arrangement of chromosome interposition, similar to other somatic cells and nurse cells. In spite of species-specific chromosome attachments there are no differences in interposition of nurse cells chromosomes in <i>An. messeae</i> and <i>An. atroparvus</i> Thiel. Nurse cells have an unusual chromosome arrangement without a chromocenter, which could be due to the special mission of generative system cells in ontogenesis and evolution.</p></div

Differences in linear organization of polytene chromosome from nurse cells and salivary glands cells of <i>Anopheles messeae</i>.

<p>Localization of the DNA-probe of the X-chromosome attachment region (2BC) on the X-chromosome from nurse cells (a-c); localization of DNA-probe of the 3R chromosome attachment region (32D) on 3R chromosome (d-f); localization of 2BC and 32D DNA on salivary gland chromosomes (g-j). Ch—chromocenter. Scale bar 20 μm.</p

Differences in lamin localization on X-chromosomes of nurse cells (a-c) and salivary gland cells (d-f).

<p>The arrow shows the 2BC region.</p

Summaries for the X-chromosome and 3R-chromsome tissue-specifics in connection with developmental trees for <i>An</i>. <i>messeae</i> and <i>An</i>. <i>atroparvus</i>.

<p>Summaries for the X-chromosome and 3R-chromsome tissue-specifics in connection with developmental trees for <i>An</i>. <i>messeae</i> and <i>An</i>. <i>atroparvus</i>.</p

Comparison of X-chromosome and 3R chromosome regions interposition in different tissues by <i>t</i>-test (<i>p</i>-values).

<p>NC- nurse cells</p><p>IDC—imaginal discs cells</p><p>SGC—salivary glands cells</p><p>FEC- follicle epithelium cells</p><p><i>p</i>-Values are bold, if differences are significant (<i>p</i><0.05)</p><p>Comparison of X-chromosome and 3R chromosome regions interposition in different tissues by <i>t</i>-test (<i>p</i>-values).</p

Interposition of the X-chromosome region (2BC) and 3R chromosome region (32B) in the nucleus space (A) and the frequencies of nuclei with juxtaposed, average and distant interposition of these regions (B).

<p>Interposition of the X-chromosome region (2BC) and 3R chromosome region (32B) in the nucleus space (A) and the frequencies of nuclei with juxtaposed, average and distant interposition of these regions (B).</p

Arrangement of 32D, 2BC points and a center point in the nucleus required for analysis of distance between X-chromosome and 3R chromosome regions by α angle calculation.

<p>Arrangement of 32D, 2BC points and a center point in the nucleus required for analysis of distance between X-chromosome and 3R chromosome regions by α angle calculation.</p

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented

Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression

This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented