Heat Shock Proteins, a Key Modulator of Neuroinflammation in Alzheimer’s Disease

Panchal, Komal; Bhatt, Vidhi; Raval, Mahima; Tiwari, Anjana

doi:10.1007/7515_2020_12

Cited by 1 publication

(1 citation statement)

References 461 publications

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“…The upregulation of the heat-shock proteins HSP70 and HSP27 has also been detected within DLB [97,98]. Heat-shock proteins may be involved in the removal of α-synuclein aggregates, and their upregulation may be a response to DLB pathology [99], or they may modulate immune response and be implicated in neuroinflammation [100]. Additional transcriptomic alternations have been discovered within solute carriers involved in synaptic neurotransmitter clearance, glutamate transport, and cell surface interactions [96,101].…”

Section: Other Transcriptomic Signaturesmentioning

confidence: 99%

Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science

Goddard,

Brookes,

Sharma

et al. 2024

Cells

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Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.

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