Phenotypic cooperation of a KCNQ2 exon 7 partial duplication and compound copy number variations in genes associated to a severe epileptic and neurodevelopmental delay
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE122584 | GEO | 2019/05/22
REPOSITORIES: GEO
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Project description:Background: Age-related cognitive deficits negatively affect quality of life and can presage serious neurodegenerative disorders. Despite sleep disruption’s well-recognized negative influence on cognition, and its prevalence with age, surprisingly few studies have tested sleep’s relationship to cognitive aging. Methodology: We measured sleep stages in young adult and aged F344 rats during inactive (enhanced sleep) and active (enhanced wake) periods. Animals were behaviorally characterized on the Morris water maze and gene expression profiles of their parietal cortices were taken. Principal Findings: Water maze performance was impaired, and inactive period deep sleep was decreased with age. However, increased deep sleep during the active period was most strongly correlated to maze performance. Transcriptional profiles were strongly associated with behavior and age, and were validated against prior studies. Bioinformatic analysis revealed increased translation and decreased myelin/ neuronal pathways. Conclusions: The F344 rat appears to serve as a reasonable model for some common sleep architecture and cognitive changes seen with age in humans, including the cognitively disrupting influence of active period deep sleep. Microarray analysis suggests that the processes engaged by this sleep are consistent with its function. Thus, active period deep sleep appears temporally misaligned but mechanistically intact, leading to the following: first, aged brain tissue appears capable of generating the slow waves necessary for deep sleep, albeit at a weaker intensity than in young. Second, this activity, presented during the active period, seems disruptive rather than beneficial to cognition. Third, this active period deep sleep may be a cognitively pathologic attempt to recover age-related loss of inactive period deep sleep. Finally, therapeutic strategies aimed at reducing active period deep sleep (e.g., by promoting active period wakefulness and/or inactive period deep sleep) may be highly relevant to cognitive function in the aging community. KEYWORDS: frontal cortex, rat, young or aged We implanted young and aged Fischer 344 rats (n = 6/ group) with wireless EEG, EMG and movement monitoring devices to measure sleep architecture. Animals were trained in the Morris water maze to assess cognitive function, and frontal cortices were removed for microarray analysis.sleep disruption and cognitive decline.
2011-04-21 | E-GEOD-24515 | biostudies-arrayexpress
Project description:In humans, a primate-specific variable-number tandem-repeat (VNTR) polymorphism (4 or 5 repeats 54 nt in length) in the circadian gene PER3 is associated with differences in sleep timing and homeostatic responses to sleep loss. We investigated the effects of this polymorphism on circadian rhythmicity and sleep homeostasis by introducing the polymorphism into mice and assessing circadian and sleep parameters at baseline and during and after 12 h of sleep deprivation (SD). Microarray analysis was used to measure hypothalamic and cortical gene expression. Circadian behavior and sleep were normal at baseline. The response to SD of 2 electrophysiological markers of sleep homeostasis, electroencephalography (EEG) M-NM-8 power during wakefulness and M-NM-4 power during sleep, were greater in the Per35/5 mice. During recovery, the Per35/5 mice fully compensated for the SD-induced deficit in M-NM-4 power, but the Per34/4 and wild-type mice did not. Sleep homeostasis-related transcripts (e.g., Homer1, Ptgs2, and Kcna2) were differentially expressed between the humanized mice, but circadian clock genes were not. These data are in accordance with the hypothesis derived from human data that the PER3 VNTR polymorphism modifies the sleep homeostatic response without significantly influencing circadian parameters.-Hasan, S., van der Veen, D. R., Winsky-Sommerer, R., Hogben, A., Laing, E. E., Koentgen, F., Dijk, D.-J., Archer, S. N. A human sleep homeostasis phenotype in mice expressing a primate-specific PER3 variable-number tandem-repeat coding-region polymorphism. Mice recievied 12 hours of sleep restriction during the 12 hours of light in the light-dark cycle Boxhill represents Per35/5 mice and Coach represents Per34/4 mice. A total of 48 samples comprising 24 mice
2014-03-11 | E-GEOD-55494 | biostudies-arrayexpress
Project description:In humans, a primate-specific variable-number tandem-repeat (VNTR) polymorphism (4 or 5 repeats 54 nt in length) in the circadian gene PER3 is associated with differences in sleep timing and homeostatic responses to sleep loss. We investigated the effects of this polymorphism on circadian rhythmicity and sleep homeostasis by introducing the polymorphism into mice and assessing circadian and sleep parameters at baseline and during and after 12 h of sleep deprivation (SD). Microarray analysis was used to measure hypothalamic and cortical gene expression. Circadian behavior and sleep were normal at baseline. The response to SD of 2 electrophysiological markers of sleep homeostasis, electroencephalography (EEG) M-NM-8 power during wakefulness and M-NM-4 power during sleep, were greater in the Per35/5 mice. During recovery, the Per35/5 mice fully compensated for the SD-induced deficit in M-NM-4 power, but the Per34/4 and wild-type mice did not. Sleep homeostasis-related transcripts (e.g., Homer1, Ptgs2, and Kcna2) were differentially expressed between the humanized mice, but circadian clock genes were not. These data are in accordance with the hypothesis derived from human data that the PER3 VNTR polymorphism modifies the sleep homeostatic response without significantly influencing circadian parameters.-Hasan, S., van der Veen, D. R., Winsky-Sommerer, R., Hogben, A., Laing, E. E., Koentgen, F., Dijk, D.-J., Archer, S. N. A human sleep homeostasis phenotype in mice expressing a primate-specific PER3 variable-number tandem-repeat coding-region polymorphism. Mice were kept under 3.5h light- 3.5 hours dark cycles and samples were collected in the 17th light cycle Boxhill represents Per35/5 mice and Coach represents Per34/4 mice. A total of 30 samples comprizing 30 mice
2014-03-11 | E-GEOD-55495 | biostudies-arrayexpress
Project description:In humans, a primate-specific variable-number tandem-repeat (VNTR) polymorphism (4 or 5 repeats 54 nt in length) in the circadian gene PER3 is associated with differences in sleep timing and homeostatic responses to sleep loss. We investigated the effects of this polymorphism on circadian rhythmicity and sleep homeostasis by introducing the polymorphism into mice and assessing circadian and sleep parameters at baseline and during and after 12 h of sleep deprivation (SD). Microarray analysis was used to measure hypothalamic and cortical gene expression. Circadian behavior and sleep were normal at baseline. The response to SD of 2 electrophysiological markers of sleep homeostasis, electroencephalography (EEG) θ power during wakefulness and δ power during sleep, were greater in the Per35/5 mice. During recovery, the Per35/5 mice fully compensated for the SD-induced deficit in δ power, but the Per34/4 and wild-type mice did not. Sleep homeostasis-related transcripts (e.g., Homer1, Ptgs2, and Kcna2) were differentially expressed between the humanized mice, but circadian clock genes were not. These data are in accordance with the hypothesis derived from human data that the PER3 VNTR polymorphism modifies the sleep homeostatic response without significantly influencing circadian parameters.-Hasan, S., van der Veen, D. R., Winsky-Sommerer, R., Hogben, A., Laing, E. E., Koentgen, F., Dijk, D.-J., Archer, S. N. A human sleep homeostasis phenotype in mice expressing a primate-specific PER3 variable-number tandem-repeat coding-region polymorphism.
2014-03-11 | GSE55495 | GEO
Project description:In humans, a primate-specific variable-number tandem-repeat (VNTR) polymorphism (4 or 5 repeats 54 nt in length) in the circadian gene PER3 is associated with differences in sleep timing and homeostatic responses to sleep loss. We investigated the effects of this polymorphism on circadian rhythmicity and sleep homeostasis by introducing the polymorphism into mice and assessing circadian and sleep parameters at baseline and during and after 12 h of sleep deprivation (SD). Microarray analysis was used to measure hypothalamic and cortical gene expression. Circadian behavior and sleep were normal at baseline. The response to SD of 2 electrophysiological markers of sleep homeostasis, electroencephalography (EEG) θ power during wakefulness and δ power during sleep, were greater in the Per35/5 mice. During recovery, the Per35/5 mice fully compensated for the SD-induced deficit in δ power, but the Per34/4 and wild-type mice did not. Sleep homeostasis-related transcripts (e.g., Homer1, Ptgs2, and Kcna2) were differentially expressed between the humanized mice, but circadian clock genes were not. These data are in accordance with the hypothesis derived from human data that the PER3 VNTR polymorphism modifies the sleep homeostatic response without significantly influencing circadian parameters.-Hasan, S., van der Veen, D. R., Winsky-Sommerer, R., Hogben, A., Laing, E. E., Koentgen, F., Dijk, D.-J., Archer, S. N. A human sleep homeostasis phenotype in mice expressing a primate-specific PER3 variable-number tandem-repeat coding-region polymorphism.
2014-03-11 | GSE55494 | GEO
Project description:Sleep is a fundamental physiological function and is essential for all animals, including humans. Sleep has been reported to be affected by diet compositions including protein (P) and carbohydrates (C), but there has not been a systematic investigation of the effect of dietary macronutrient balance on sleep. Here, we used the nutritional geometry framework (NGF) to explore the interactive effects on sleep of protein (P) and carbohydrate (C) in the model organism Drosophila. Our results showed that the combination of low protein and moderate carbohydrate prolonged sleep time and sleep quality, with fewer sleep episodes and longer sleep duration. We further found that the effects of macronutrients on sleep mirrored levels of haemolymph glucose and whole-body glycogen. Moreover, transcriptome analyses revealed that high protein, low carbohydrate diet elevated the gene expression of amino acid metabolic pathways when compared to a diet lacking protein, with the glycine, serine, threonine metabolism pathway being most strongly regulated. However, sleep was not decreased in flies fed on a protein-deficient diet with added glycine, serine and threonine. In summary, our results demonstrate that sleep is affected by the balance of protein and carbohydrates in the diet and that reduced protein coupled with moderate carbohydrate promote sleep duration and quality.
2023-12-31 | GSE188430 | GEO
Project description:In order to investigate the functional relevance of SUR2 (the protein encoded by ABCC9 in humans) for sleep duration, we knocked down the expression of its Drosophila homologue (dSur) in the flies’ nervous system (both central and peripheral). Unlike humans, flies are active predominantly around dawn and dusk and show two large sleep episodes during the day and during the night. Knockdown of dSur dramatically reduced night-sleep, particularly during the first half of the dark period, but had little effect on the flies’ day-sleep. This was true for both elavgal4 UAS-Sur RNAi genotypes compared with their parental control strains. These differences in sleep duration were not due to changes in the circadian clock, as the period of the free-running locomotor activity rhythm in constant darkness was indistinguishable between knockdown flies and controls. Furthermore, no systematic differences in activity levels were found between experimental genotypes and control. Thus, the major effect of knocking down the ABCC9 homologue in flies is shortening nighttime sleep due to a delay of its onset by 3 h.
2018-03-08 | GSE52764 | GEO
Project description:We used Affymetrix microarray profiling to analyze gene expression patterns in healthy donor liver as well as tumor and paired non-tumor tissue of HCC patients. For gene expression profiling, tumors and paired non-tumor tissues were profiled separately using a single channel array platform. Tumor and paired non-tumor samples of 22 patients of cohort1 and the normal liver pool were carried out on Affymetrix GeneChip HG-U133A 2.0 arrays (Affymetrix, Santa Clara, CA) according to the manufacturer's protocol. The fluorescent intensities were determined with an Affymetrix GeneChip Scanner 3000, controlled by GCOS Affymetrix software. All samples of cohort2 and 42 tumor and paired non-tumor samples of cohort2 were processed on the 96 HT HG-U133A microarray platform. The fluorescent intensities were determined with an Affymetrix GeneChip HT Array Plate Scanner, controlled by GCOS Affymetrix software. Cohort 1 Training Group: LCS-005A, LCS-019A, LCS-029A, LCS-039A, LCS-039B, LCS-254A, LCS-254B, LCS-068A, LCS-068B, LCS-076A, LCS-076B, LCS-004A, LCS-004B, LCS-007A, LCS-007B, LCS-024B, LCS-042B, LCS-062B, LCS-023A, LCS-023B, LCS-067A, LCS-065A, LCS-073A, LCS-067B, LCS-065B, LCS-073B, LCS-024A, LCS-042A, LCS-062A, LCS-015A, LCS-069A, LCS-015B, LCS-069B, LCS-033A, LCS-061A, LCS-020A, LCS-045A, LCS-033B, LCS-020B, LCS-045B, LCS-057A, LCS-009A, LCS-057B, LCS-009B, LCS-049A, LCS-049B, LCS-061B, LCS-047A, LCS-047B, LCS-075A, LCS-075B, LCS-034A, LCS-034B, LCS-041A, LCS-041B, LCS-044A, LCS-044B, LCS-071A, LCS-071B, LCS-036A, LCS-064A, LCS-064B, LCS-038A, LCS-038B, LCS-002A, LCS-028B, LCS-016B, LCS-054A, LCS-063A, LCS-022A, LCS-054B, LCS-063B, LCS-022B, LCS-036B, LCS-025A, LCS-066A, LCS-027A, LCS-048A, LCS-012A, LCS-025B, LCS-066B, LCS-027B, LCS-048B, LCS-012B, LCS-051A, LCS-056A, LCS-051B, LCS-056B, LCS-028A, LCS-016A, LCS-196A, LCS-196B, LCS-072A, LCS-072B, LCS-333A, LCS-333B, LCS-339A, LCS-339B, LCS-341A, LCS-341B, LCS-343A, LCS-343B, LCS-344A, LCS-344B, LCS-346A, LCS-346B, LCS-347A, LCS-347B, LCS-393A, LCS-393B, LCS-400A, LCS-400B, LCS-401A, LCS-401B, LCS-403A, LCS-403B, LCS-406A, LCS-406B, LCS-415A, LCS-415B, LCS-424A, LCS-424B, LCS-426A, LCS-426B Cohort 2 Testing Group: LCS-079A, LCS-092A, LCS-101A, LCS-079B, LCS-092B, LCS-101B, LCS-083A, LCS-095A, LCS-102A, LCS-083B, LCS-095B, LCS-102B, LCS-085A, LCS-096A, LCS-103A, LCS-085B, LCS-096B, LCS-103B, LCS-091A, LCS-099A, LCS-105A, LCS-091B, LCS-099B, LCS-105B, LCS-122A, LCS-130A, LCS-123A, LCS-135A, LCS-123B, LCS-135B, LCS-125A, LCS-127A, LCS-136A, LCS-125B, LCS-127B, LCS-136B, LCS-122B, LCS-130B, LCS-212B, LCS-180B, LCS-183B, LCS-153B, LCS-018B, LCS-188A, LCS-185A, LCS-154A, LCS-120A, LCS-286A, LCS-086A, LCS-177A, LCS-140A, LCS-035A, LCS-188B, LCS-185B, LCS-154B, LCS-120B, LCS-286B, LCS-238B, LCS-086B, LCS-177B, LCS-035B, LCS-275B, LCS-132A, LCS-205A, LCS-274A, LCS-159A, LCS-150A, LCS-014A, LCS-227A, LCS-283A, LCS-162A, LCS-104A, LCS-272A, LCS-132B, LCS-205B, LCS-274B, LCS-159B, LCS-227B, LCS-046B, LCS-283B, LCS-272B, LCS-237A, LCS-152A, LCS-209A, LCS-211A, LCS-230A, LCS-262A, LCS-144A, LCS-158B, LCS-237B, LCS-152B, LCS-209B, LCS-211B, LCS-230B, LCS-262B, LCS-144B, LCS-212A, LCS-180A, LCS-183A, LCS-153A, LCS-018A, LCS-275A, LCS-118A, LCS-234A, LCS-210A, LCS-265A, LCS-149A, LCS-040A, LCS-169A, LCS-256A, LCS-190B, LCS-118B, LCS-234B, LCS-210B, LCS-265B, LCS-149B, LCS-040B, LCS-169B, LCS-213A, LCS-164A, LCS-223A, LCS-245A, LCS-281B, LCS-191A, LCS-171A, LCS-249A, LCS-213B, LCS-164B, LCS-245B, LCS-094B, LCS-094A, LCS-191B, LCS-171B, LCS-249B, LCS-131A, LCS-241A, LCS-074A, LCS-160A, LCS-179A, LCS-134A, LCS-224A, LCS-078A, LCS-131B, LCS-201B, LCS-074B, LCS-160B, LCS-179B, LCS-134B, LCS-147B, LCS-224B, LCS-078B, LCS-093A, LCS-253A, LCS-279A, LCS-008A, LCS-291A, LCS-281A, LCS-156A, LCS-282A, LCS-090A, LCS-151A, LCS-253B, LCS-279B, LCS-008B, LCS-172B, LCS-291B, LCS-156B, LCS-282B, LCS-090B, LCS-151B, LCS-259A, LCS-010A, LCS-264A, LCS-264B, LCS-163A, LCS-163B, LCS-089A, LCS-089B, LCS-143A, LCS-143B, LCS-199A, LCS-199B, LCS-278A, LCS-278B, LCS-119A, LCS-119B, LCS-184A, LCS-184B, LCS-203A, LCS-203B, LCS-178A, LCS-178B, LCS-284A, LCS-284B, LCS-108A, LCS-261B, LCS-043A, LCS-043B, LCS-174A, LCS-174B, LCS-166A, LCS-193A, LCS-193B, LCS-215A, LCS-215B, LCS-021A, LCS-021B, LCS-129A, LCS-129B, LCS-198A, LCS-198B, LCS-084A, LCS-084B, LCS-250A, LCS-011A, LCS-108B, LCS-011B, LCS-219A, LCS-219B, LCS-194A, LCS-194B, LCS-170A, LCS-170B, LCS-204A, LCS-204B, LCS-261A, LCS-195A, LCS-195B, LCS-200B, LCS-206A, LCS-206B, LCS-161B, LCS-145A, LCS-145B, LCS-236A, LCS-236B, LCS-270A, LCS-270B, LCS-182A, LCS-182B, LCS-197B, LCS-173B, LCS-240B, LCS-266B, LCS-167B, LCS-032B, LCS-050B, LCS-285B, LCS-031B, LCS-189A, LCS-277A, LCS-117A, LCS-267A, LCS-100A, LCS-228A, LCS-268A, LCS-222A, LCS-192A, LCS-175A, LCS-277B, LCS-117B, LCS-267B, LCS-100B, LCS-228B, LCS-268B, LCS-222B, LCS-192B, LCS-248A, LCS-121A, LCS-157A, LCS-148A, LCS-126A, LCS-260A, LCS-223B, LCS-121B, LCS-157B, LCS-148B, LCS-260B, LCS-247A, LCS-088A, LCS-243A, LCS-165A, LCS-208A, LCS-110A, LCS-146A, LCS-247B, LCS-243B, LCS-165B, LCS-208B, LCS-110B, LCS-146B, LCS-147A, LCS-173A, LCS-240A, LCS-266A, LCS-167A, LCS-032A, LCS-050A, LCS-285A, LCS-031A, LCS-106A, LCS-106B, LCS-107A, LCS-107B, LCS-109A, LCS-109B, LCS-116A, LCS-137A, LCS-137B, LCS-138A, LCS-138B, LCS-139A, LCS-139B, LCS-142A, LCS-142B, LCS-207A, LCS-231A, LCS-231B, LCS-251A, LCS-263A, LCS-263B, LCS-269A, LCS-269B, LCS-273A, LCS-273B, LCS-289A, LCS-289B, LCS-290A, LCS-290B, LCS-093B, LCS-097A, LCS-097B, LCS-104B, LCS-140B, LCS-150B, LCS-158A, LCS-161A, LCS-162B, LCS-014B, LCS-166B, LCS-172A, LCS-197A, LCS-200A, LCS-201A, LCS-216A, LCS-216B, LCS-238A, LCS-241B, LCS-046A, LCS-248B, LCS-259B, Normal1, Normal2, LCS-003B, LCS-006B, LCS-026B, LCS-128B, LCS-168B, LCS-181B, LCS-190A, X02-342A, X02-342B, X02-362A, X02-362B
2010-12-01 | E-GEOD-14520 | biostudies-arrayexpress
Project description:We profiled the whole transcriptomes of female rat hypothalamic arcuate nuclei to determine the remodeling of the genomic fabrics responsible for the glutamatergic, GABAergic, dopaminergic, cholinergic and serotonergic transmission in epilepsy and recovery following different treatments. The rats were prenatally exposed (G15) to betamethasone (or just saline for control) followed by repeated adiministration of N-Methyl-D-Aspartic acid (NMDA) on postnatal days 12, 13 and 15 which triggered infantile spasms. Pups were treated with either ATCH, PMX53 (a potent C5ar1 antagonist) or saline to act as a control, on days 13, 14 and 15 prior to NMDA administration to determine what effects each treatment had on transcriptome recovery. Our Genomic Fabric Paradigm (GFP) is proposed as a transformative research approach to enhance the understanding of the brain transcriptomic alterations in epileptic rats and recovery following various treatments. The genomic fabric of a particular synapse is the structured transcriptome associated with the most interconnected and stably expressed gene network responsible for that type of neurotransmission. GFP refines the description of functional pathways by selecting the most prominent genes and determining their networking. Moreover, it quantifies the remodeling of functional pathways and their interplay in disease and recovery in response to a treatment. We found that priming with betamethasone had substantial consequences on the topology of the genomic fabrics of all kind of synaptic transmission and that NMDA-induced spasms strongly exacerbated the remodeling of these fabrics. Contrary to our findings in male rats, neither ACTH or PMX53 treatment recovered the normal synaptic transcriptomes. One tissue (hypothalamic arcuate nucleus) x two prenatal exposures (B = betamethasone, S = saline) x two conditions for the betamethasone expossed rats (with (Y) without (N) infantile spasms) x three postnatal treatments (A = ACTH, P = PMX53, S = saline) x one sex (M) . Biological replicates: 4 ASNSM, 4 ABNSM, 4 ABYSM, 4 ABYAM, 4 ABYPM. Please note that a the multiple yellow strategy has been adopted, in which differently labeled biological replicas are cohybridized, similarly labeled samples of distinct conditions are compared and the results of the green and red comparisons are averaged.
2016-07-20 | E-GEOD-84585 | biostudies-arrayexpress
Project description:The sleep-wake cycle is determined by a circadian and a sleep homeostatic process. However, the molecular impact of these two processes and their interaction on different cell populations in the brain remain unknown. To fill this gap, we have profiled the single-cell transcriptome of adult fruit fly brains across the sleep-wake cycle and different circadian times. We show cell type-specific transcriptomic changes between sleep/wakefulness states, different levels of sleep drive, and varying circadian times, with glial cells displaying the largest variations. Furthermore, the cell types whose transcriptomic dynamics correlate with the sleep homeostat or circadian clock are largely non-overlapping, with the exception of glial cells. Diminishing the circadian clock only in glial cells impairs the homeostatic sleep rebound after sleep deprivation. These findings reveal a comprehensive picture of different effects of sleep homeostatic and circadian processes on different cell types and define glial cells as the interaction sites of these two processes to determine sleep-wake dynamics.
2023-11-29 | GSE221239 | GEO