Custom made python script using network assignment and scoring to estimate the impact of biological processes.
Data files
Nov 19, 2024 version files 1.40 MB
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datafile_hippocampus.xlsx
1.40 MB
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README.md
2.49 KB
Abstract
Blood-borne factors are essential to maintain neuronal synaptic plasticity and cognitive resilience throughout life. One such factor is osteocalcin (OCN), a pro-youthful hormone produced by bones, that influences hippocampal neuronal homeostasis. However, the way how this blood-borne factor communicates with neurons remains unclear. Here, we show the importance of a core primary cilium (PC)-protein/autophagy axis in mediating OCN's effects. We found that OCN’s receptor, GPR158, is present at the PC of hippocampal neurons and mediates the regulation of autophagy machinery by OCN. During aging, autophagy and PC-core proteins are reduced in neurons and restoring their levels is sufficient to improve cognitive impairments in aged mice. Mechanistically, the induction of this axis by OCN is dependent on the PC-dependent cAMP response element-binding protein signaling pathway. Altogether, this study demonstrates that PC/autophagy axis is a gateway to mediate blood-borne factor-neuron communication and advances our understanding of the mechanism involved in age-related cognitive decline.
README: NATAGING-Code
https://doi.org/10.5061/dryad.dfn2z35bh
Description of the data and file structure
Protein Group (Column A): A group of proteins that cannot be unambiguously identified as individual proteins based on the identified peptides. This occurs when multiple proteins share a significant number of peptides, making it difficult to determine which specific protein(s) are present.
Protein IDs (Column B): Unique identifiers assigned to each protein in a database. These IDs are used to reference specific proteins and their associated information.
Protein Names (Column C): The common names or systematic names assigned to proteins. These names are often derived from their function, structure, or the gene that encodes them.
Genes (Column D): The mapped DNA that are known to encode the proteins in column “Protein Names”. A single gene can encode multiple protein isoforms through alternative splicing.
First Protein Description (Column E): A brief description of the first protein in a protein group. This description may provide information about the protein's function, localization, or other relevant characteristics.
Vehicle: Experimental control group
Vehicle 1 (Column F): hippocampus mouse 1 treated with vehicle
Vehicle 2 (Column G): hippocampus mouse 2 treated with vehicle
Vehicle 3 (Column H): hippocampus mouse 3 treated with vehicle
Vehicle 4 (Column I): hippocampus mouse 4 treated with vehicle
Vehicle 5 (Column J): hippocampus mouse 5 treated with vehicle
Vehicle 6 (Column K): hippocampus mouse 6 treated with vehicle
OCN: Experimental osteocalcin group
OCN 1 (Column L): hippocampus mouse 1 treated with OCN
OCN 2 (Column M): hippocampus mouse 2 treated with OCN
OCN 3 (Column N): hippocampus mouse 3 treated with OCN
OCN 4 (Column O): hippocampus mouse 4 treated with OCN
OCN 5 (Column P): hippocampus mouse 5 treated with OCN
OCN 6 (Column Q): hippocampus mouse 6 treated with OCN
In columns F to Q, numbers represent fluorescent intensity values and have no unit
NA cells in the table mean non available fluorescent intensity
Files and variables
Dataset available at PRIDE under the accession number 1-20241015-110055-3467911.
Code/software
home made python code
Access information
Other publicly accessible locations of the data:
- KEGG
Data was derived from the following sources:
- KEGG
- PRIDE under the accession number 1-20241015-110055-3467911
Methods
A custom made python script using network assignment and scoring to estimate the impact of biological processes and pathways in the context of specific functions. The code uses specific biological pathways by enriching them with gene ontologies (KEGG database, Reactome, etc.) and assigning them to nodes and connections, to determine centrality and peripherality of the factors