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Transcriptomic profile of genes encoding proteins responsible for regulation of cells differentiation and neurogenesis in vivo and in vitro – an oocyte model approach

Lisa Moncrieff

Lisa Moncrieff

  • School of Medicine, Medical Sciences and Nutrition, University of AberdeenAberdeen, United Kingdom
  • Department of Histology and Embryology, Poznań University of Medical SciencesPoznań, Poland
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Moncrieff, Lisa
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Ievgeniia Kocherova

Ievgeniia Kocherova

Department of Anatomy, Poznań University of Medical SciencesPoznań, Poland
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Kocherova, Ievgeniia
, 
Artur Bryja

Artur Bryja

Department of Anatomy, Poznań University of Medical SciencesPoznań, Poland
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Bryja, Artur
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Wiesława Kranc

Wiesława Kranc

Department of Anatomy, Poznań University of Medical SciencesPoznań, Poland
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Kranc, Wiesława
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Joanna Perek

Joanna Perek

Department of Anatomy, Poznań University of Medical SciencesPoznań, Poland
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Perek, Joanna
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Magdalena Kulus

Magdalena Kulus

Department of Veterinary Surgery, Institute of Veterinary Medicine, Nicolaus Copernicus University in ToruńToruń, Poland
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Kulus, Magdalena
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Michal Jeseta

Michal Jeseta

Department of Obstetrics and Gynecology, University Hospital and Masaryk UniversityBrno, Czech Republic
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Jeseta, Michal
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Claudia Dompe

Claudia Dompe

  • School of Medicine, Medical Sciences and Nutrition, University of AberdeenAberdeen, United Kingdom
  • Department of Histology and Embryology, Poznań University of Medical SciencesPoznań, Poland
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Dompe, Claudia
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Greg Hutchings

Greg Hutchings

  • School of Medicine, Medical Sciences and Nutrition, University of AberdeenAberdeen, United Kingdom
  • Department of Anatomy, Poznań University of Medical SciencesPoznań, Poland
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Hutchings, Greg
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Krzysztof Janowicz

Krzysztof Janowicz

  • School of Medicine, Medical Sciences and Nutrition, University of AberdeenAberdeen, United Kingdom
  • Department of Anatomy, Poznań University of Medical SciencesPoznań, Poland
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Janowicz, Krzysztof
, 
Piotr Celichowski

Piotr Celichowski

Department of Histology and Embryology, Poznań University of Medical SciencesPoznań, Poland
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Celichowski, Piotr
, 
Małgorzata Bruska

Małgorzata Bruska

Department of Anatomy, Poznań University of Medical SciencesPoznań, Poland
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Bruska, Małgorzata
, 
Maciej Zabel

Maciej Zabel

  • Division of Histology and Embryology, Department of Human Morphology and Embryology, Wrocław Medical UniversityWrocław, Poland
  • Division of Anatomy and Histology, University of Zielona GoraZielona Góra, Poland
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Zabel, Maciej
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Bartosz Kempisty

Bartosz Kempisty

  • Department of Histology and Embryology, Poznań University of Medical SciencesPoznań, Poland
  • Department of Anatomy, Poznań University of Medical SciencesPoznań, Poland
  • Department of Veterinary Surgery, Institute of Veterinary Medicine, Nicolaus Copernicus University in ToruńToruń, Poland
  • Department of Obstetrics and Gynecology, University Hospital and Masaryk UniversityBrno, Czech Republic
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Kempisty, Bartosz
 y 
Paul Mozdziak

Paul Mozdziak

Graduate Physiology Program, North Carolina State UniversityRaleigh, USA
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Mozdziak, Paul
  
29 abr 2020
Transcriptomic profile of genes encoding proteins responsible for regulation of cells differentiation and neurogenesis in vivo and in vitro – an oocyte model approach's Cover Image
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Publicado en línea: 29 abr 2020
Páginas: 1 - 11
Recibido: 19 dic 2019
Aceptado: 05 feb 2020
DOI: https://doi.org/10.2478/acb-2020-0001
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© 2020 Lisa Moncrieff, Ievgeniia Kocherova, Artur Bryja, Wiesława Kranc, Joanna Perek, Magdalena Kulus, Michal Jeseta, Claudia Dompe, Greg Hutchings, Krzysztof Janowicz, Piotr Celichowski, Małgorzata Bruska, Maciej Zabel, Bartosz Kempisty, Paul Mozdziak, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Figure 1

Heat map representations of differentially expressed genes belonging to the “neuron differentiation” and “negative regulation of cell differentiation” GO BP terms. Arbitrary signal intensity acquired from microarray analysis is represented by colours (green, higher; red, lower expression). Log2 signal intensity values for any single gene were resized to Row Z-Score scale (from -2, the lowest expression to +2, the highest expression for single gene)
Heat map representations of differentially expressed genes belonging to the “neuron differentiation” and “negative regulation of cell differentiation” GO BP terms. Arbitrary signal intensity acquired from microarray analysis is represented by colours (green, higher; red, lower expression). Log2 signal intensity values for any single gene were resized to Row Z-Score scale (from -2, the lowest expression to +2, the highest expression for single gene)

Figure 2

The circle plot showing the differently expressed genes and z-score “neuron differentiation” and “negative regulation of cell differentiation” GO BP Terms. The outer circle shows a scatter plot for each term of the fold change of the assigned genes. Red circles display downregulation. The inner circle shows the z-score of each GO BP term. The width of the each bar corresponds to the number of genes within GO BP term and the color corresponds to the z-score
The circle plot showing the differently expressed genes and z-score “neuron differentiation” and “negative regulation of cell differentiation” GO BP Terms. The outer circle shows a scatter plot for each term of the fold change of the assigned genes. Red circles display downregulation. The inner circle shows the z-score of each GO BP term. The width of the each bar corresponds to the number of genes within GO BP term and the color corresponds to the z-score

Figure 3

The representation of the mutual relationship between differently expressed genes that belong to the “neuron differentiation” and “negative regulation of cell differentiation” GO BP Terms. The ribbons indicate which gene belongs to which categories. The middle circle represents logarithm from fold change (LogFC) between before IVM and after IVM respectively. The genes were sorted by logFC from most to least changed gene
The representation of the mutual relationship between differently expressed genes that belong to the “neuron differentiation” and “negative regulation of cell differentiation” GO BP Terms. The ribbons indicate which gene belongs to which categories. The middle circle represents logarithm from fold change (LogFC) between before IVM and after IVM respectively. The genes were sorted by logFC from most to least changed gene

Figure 4

Heatmap showing the gene occurrence between differently expressed genes that belongs to the “neuron differentiation” and “negative regulation of cell differentiation” GO BP Terms. The intensity of the color is corresponding to amount of GO BP terms that each gene belongs to
Heatmap showing the gene occurrence between differently expressed genes that belongs to the “neuron differentiation” and “negative regulation of cell differentiation” GO BP Terms. The intensity of the color is corresponding to amount of GO BP terms that each gene belongs to

Figure 5

STRING-generated interaction network between genes that belongs to the “neuron differentiation” and “negative regulation of cell differentiation” GO BP Terms. The intensity of the edges reflects the strength of interaction score
STRING-generated interaction network between genes that belongs to the “neuron differentiation” and “negative regulation of cell differentiation” GO BP Terms. The intensity of the edges reflects the strength of interaction score

Gene symbols, Entrez gene IDs, ratio and corrected P values of studied genes

GENE SYMBOLENTREZ GENE IDRATIOP VALUE
VEGFA397157-14.349386890.001912689
BTG2100048932-13.443318930.0000955
MCOLN3100625693-7.2037544510.000850265
EGR2100038004-6.0421561750.007949861
TGFBR3397512-5.0884824930.000405979
GJA1100518636-4.8330811470.000107676
FST445002-4.4504464490.000364693
CTNNA2100525337-4.3492396650.000512181
RTN4100170118-4.3219371180.027495815
MDGA1397529-4.2931840010.003493167
SLITRK3106504067-4.1816175060.004260951
INHBA397093-4.1449098490.000148036
CDK6100518921-4.0322350860.006042481
LAMB2101101688-3.5860552370.000187911
ROBO2100517681-3.5242431850.001183495
CUX1100521258-3.3420529210.000372663
IHH397174-3.2787332070.000551261
EMX2100152562-3.123877060.001929388
ZCCHC11100516979-3.1092371390.019809962
APP397663-3.085099970.005602323
WWTR1100522573-3.0562151750.000254025
SMARCA1100188905-3.0382116130.014758847
SEMA5A100737194-2.8297211190.001092396
ITGB1397019-2.730502040.003705215
SMAD4397142-2.718852680.001238681
RORA100156637-2.604679250.021553766
NOTCH2100153369-2.5985756680.002523723
RYK100523513-2.3702130230.00439989
KIT396810-2.3231814140.00255635
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Ciencias de la vida, Biología molecular, Bioquímica
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