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In silico differential gene expression analysis in tissue databases from patients with Alzheimer’s disease, to identify potential new biomarkers
Author(s) -
del Carmen SilvaLucero Maria,
RiveraOsorio Jared,
Yogesh Muley Vijaykumar,
LopezToledo Gustavo,
Sanchez Cintia P.,
del Carmen CardenasAguayo Maria
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.054368
Subject(s) - fold change , in silico , biology , gene , gene expression , neurodegeneration , microarray analysis techniques , dementia , computational biology , dna microarray , significance analysis of microarrays , microrna , disease , gene expression profiling , microarray , bioinformatics , database , medicine , genetics , pathology , computer science
Background Alzheimer's disease (AD) is a neurodegeneration that accounts for 60‐70% of dementia cases. Symptoms begin with mild memory difficulties and evolve towards cognitive impairment. The underlying risk factors remain largely unclear for this heterogeneous disorder. Discovery of AD biomarkers will allow early detection and the discovery of new treatments. Bioinformatics has become a relevant research tool which have led to the identification of several pathways related to AD. Method Open access databases of RNA microarrays of tissue from the medial temporal lobe (MTL) and peripheral blood of AD patient, were analyzed after background correction and data normalization, Limma package was used for DEA throw statistical R programming language. Data were corrected with the Benjamini and Hochberg approach and the genes with p values equal to or less than 0.05 were considered significant. The direction of the change in gene expression was determined by its variation in the "log2‐fold change" between controls and patients. We performed a functional enrichment analysis of GO using goana and topGO‐Limma (Smyth et al., 2016). Result With MTL brain tissue database (EMEXP 2280) we obtained 359 down regulated genes (DR) and 324 up regulated genes (UR), the UR pathways were: transcription, mitochondrial function and hydrolase activity, while DR were: gene expression and regulation of cell proliferation, nuclear alterations, transmembrane receptor signaling. Analysis with KEGGp showed that metabolic pathways were UR and cytokine pathways and their receptors were DR. The blood samples analysis (PBMC, E‐GEOD‐18309) showed 383 genes DR and 257 genes UR. Finally, the intersection of the differentially expressed genes in the two databases showed 34 genes shared between the brain and blood arrangements and TRIM58 matched in both databases. Conclusion Our in‐silico analysis of AD brain tissue identifies several UR genes related to mitochondria, function, transcription, and hydrolase activity, while gene expression, regulation of proliferation and nuclear alterations were DR. On the other hand, blood databases showed DEG such as TRIM58 (E3 ubiquitin ligase), that is involve in ubiquitination. Protein identified here could be valuable as possible new AD biomarkers.