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1.
Hippocampus ; 27(5): 495-506, 2017 05.
Article in English | MEDLINE | ID: mdl-28100031

ABSTRACT

The hippocampus is subjected to diurnal/circadian rhythms on both the morphological and molecular levels. Certain aspects of cell proliferation in the adult hippocampus are regulated by melatonin and accompanied by apoptosis to ensure proper tissue maintenance and function. The present study investigated Zeitgeber time (ZT)-dependent changes in cell proliferation and apoptosis in the adult murine hippocampus and their regulation by melatonin receptor type1 and type2 (MT1/2)-mediated signaling. Adult melatonin-proficient C3H/HeN mice and melatonin-proficient (C3H/HeN) mice with targeted deletion of MT1/2 were adapted to a 12-h light, 12-h dark photoperiod and were sacrificed at ZT00, ZT06, ZT12, and ZT18. Immunohistochemistry for Ki67 and activated caspase-3 in combination with different markers for the diverse cell types residing in the hippocampus served to identify and quantify proliferating and apoptotic cells in the hippocampal subregions. ZT-dependent changes in cell proliferation and apoptosis were found exclusively in the subgranular zone (SGZ) and granule cell layer (GCL) of melatonin-proficient mice with functional MT1/2. Cell proliferation in the SGZ showed ZT-dependent changes indicated by an increase of proliferating immature neurons during the dark phase of the 24-h light-dark cycle. Apoptosis showed ZT-dependent changes in the SGZ and GCL indicated by an increase of apoptotic immature neurons at ZT06 (SGZ) and a decrease of immature and mature neurons at ZT18 (GCL). Our results indicate that ZT-dependent changes in proliferation of immature neurons in the SGZ are counterbalanced by ZT-dependent changes in apoptosis of immature and mature neurons in the SGZ and GCL exclusively in mice with functional MT1/2. Therefore, MT1/2-mediated signaling appears to be crucial for generation and timing of ZT-dependent changes in cell proliferation and apoptosis and for differentiation of proliferating cells into neurons in the SGZ. © 2017 Wiley Periodicals, Inc.


Subject(s)
Apoptosis/physiology , Cell Proliferation/physiology , Hippocampus/metabolism , Neurons/metabolism , Receptor, Melatonin, MT1/metabolism , Receptor, Melatonin, MT2/metabolism , Animals , Cell Count , Hippocampus/cytology , Immunohistochemistry , Male , Melatonin/metabolism , Mice, Inbred C3H , Mice, Knockout , Neurogenesis/physiology , Neurons/cytology , Receptor, Melatonin, MT1/genetics , Receptor, Melatonin, MT2/genetics , Stem Cell Niche/physiology
2.
Mol Cell Biol ; 36(4): 545-58, 2016 02 15.
Article in English | MEDLINE | ID: mdl-26644408

ABSTRACT

Organisms adapt their physiology and behavior to the 24-h day-night cycle to which they are exposed. On a cellular level, this is regulated by intrinsic transcriptional-translational feedback loops that are important for maintaining the circadian rhythm. These loops are organized by members of the core clock network, which further regulate transcription of downstream genes, resulting in their circadian expression. Despite progress in understanding circadian gene expression, only a few players involved in circadian transcriptional regulation, including transcription factors, epigenetic regulators, and long noncoding RNAs, are known. Aiming to discover such genes, we performed a high-coverage transcriptome analysis of a circadian time course in murine fibroblast cells. In combination with a newly developed algorithm, we identified many transcription factors, epigenetic regulators, and long intergenic noncoding RNAs that are cyclically expressed. In addition, a number of these genes also showed circadian expression in mouse tissues. Furthermore, the knockdown of one such factor, Zfp28, influenced the core clock network. Mathematical modeling was able to predict putative regulator-effector interactions between the identified circadian genes and may help for investigations into the gene regulatory networks underlying circadian rhythms.


Subject(s)
Circadian Rhythm , Gene Expression Profiling , Gene Regulatory Networks , RNA, Long Noncoding/genetics , Transcription Factors/genetics , Transcriptional Activation , Animals , Computer Simulation , Epigenesis, Genetic , Fibroblasts/metabolism , Mice , Models, Genetic , NIH 3T3 Cells
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