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Appears in Networks 4

In-Edges 9

bp(MESH:Aging) increases p(FPLX:HSPB) View Subject | View Object

Ranked by decreasing median aging correlation, the induction of sHSPs and TPR genes consistently ranked high and the HSP60s, HSP40s, and HSP70s were consistently repressed. PubMed:25437566

bp(MESH:Aging) increases p(FPLX:HSPB) View Subject | View Object

Among the genes that are induced in brain aging and disease are sHSPs and TPR-containing chaperone genes (Figures S3A–S3D). PubMed:25437566

bp(MESH:Aging) increases p(FPLX:HSPB) View Subject | View Object

Additionally, an investigation of chaperone and cochaperone gene expression in young (36±4 years of age) and aged (73 ±4 years of age) human brain tissue revealed that of 332 genes examined, 101 are significantly repressed with age, including HSP70, HSP40, HSP90, and TRiC genes (113). Furthermore, 62 chaperone genes, including several small HSPs, were found to be significantly induced, likely as a result of the cellular response to accumulating protein damage with age (113). PubMed:25784053

Annotations
Cell Ontology (CL)
motor neuron

bp(GO:aging) increases p(FPLX:HSPB) View Subject | View Object

Furthermore, sHSPs were found to be consistently upregulated in the aging human brain and in the context of neurodegenerative diseases (Brehme et al., 2014). PubMed:27491084

bp(HBP:Proteostasis) association p(FPLX:HSPB) View Subject | View Object

Many studies based on model systems support a role for candidates from each of the major chaperome families; HSP100, HSP90, HSP70, HSP60, HSP40, sHSPs, and TPR-domain-containing proteins in proteostasis. PubMed:27491084

bp(HBP:misfolding) association p(FPLX:HSPB) View Subject | View Object

Our summary (Table 1) points towards specific sHSPs that play a prominent role in misfolding diseases, as judged by frequency of observations, including CRYAB, HSPB1, HSPB3 and HSPB8 (each 7×), HSPB6 (6×), and CRYAA (5×) (Fig. 1). PubMed:27491084

bp(MESH:Aging) increases p(FPLX:HSPB) View Subject | View Object

The HSP40, HSP60 and HSP70 families were amongst the most repressed chaperones, with HSP70s being the most repressed group overall. However, in contrast with the broad spectrum of repressed chaperone families, sHSPs and the TPR co-chaperone proteins were the only families that were significantly induced. PubMed:27491084

path(MESH:"Alzheimer Disease") increases p(FPLX:HSPB) View Subject | View Object

Furthermore, sHSPs were found to be consistently upregulated in the aging human brain and in the context of neurodegenerative diseases (Brehme et al., 2014). PubMed:27491084

a(CHEBI:ATP) causesNoChange act(p(FPLX:HSPB)) View Subject | View Object

ATP-independent chaperones, such as the small Hsps, may function as additional holdases that buffer aggregation. PubMed:23746257

Out-Edges 4

p(FPLX:HSPB) association bp(HBP:Proteostasis) View Subject | View Object

Many studies based on model systems support a role for candidates from each of the major chaperome families; HSP100, HSP90, HSP70, HSP60, HSP40, sHSPs, and TPR-domain-containing proteins in proteostasis. PubMed:27491084

p(FPLX:HSPB) association bp(HBP:misfolding) View Subject | View Object

Our summary (Table 1) points towards specific sHSPs that play a prominent role in misfolding diseases, as judged by frequency of observations, including CRYAB, HSPB1, HSPB3 and HSPB8 (each 7×), HSPB6 (6×), and CRYAA (5×) (Fig. 1). PubMed:27491084

act(p(FPLX:HSPB)) decreases a(MESH:"Protein Aggregates") View Subject | View Object

ATP-independent chaperones, such as the small Hsps, may function as additional holdases that buffer aggregation. PubMed:23746257

About

BEL Commons is developed and maintained in an academic capacity by Charles Tapley Hoyt and Daniel Domingo-Fernández at the Fraunhofer SCAI Department of Bioinformatics with support from the IMI project, AETIONOMY. It is built on top of PyBEL, an open source project. Please feel free to contact us here to give us feedback or report any issues. Also, see our Publishing Notes and Data Protection information.

If you find BEL Commons useful in your work, please consider citing: Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018). BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language. Database, 2018(3), 1–11.