Decipher reliable biomarkers of brain aging by integrating literature-based evidence with interactome data

Abstract

Aging is an inevitable progressive decline in every physiological function and serves as a primary risk factor for cognitive decline and Alzheimer’s disease. Thus, age-dependent impairments in cognitive function must be understood in association with general aging processes with an integrative approach in a systemic manner. An integrative aging gene network was constructed based on mutual molecular interactions using literature-curated interactome data and separated into functionally distinct modules. To investigate key surrogate biomarkers of the aging brain in the context of the general aging process, co-expression networks were built on post-mortem and Alzheimer’s brain transcriptome data. In both the normal aging brain and the brain affected by Alzheimer’s disease, the immune-related co-expression module was positively correlated with advancing age, whereas the synaptic transmission-related co-expression module was decreased with age. Importantly, the network topology-based analysis indicated that complement system genes were prioritized as a surrogate biomarker in evaluating the process of brain aging. Our public data-centered analysis coupled with experimental validation revealed that the complement system is likely to be a master regulator in initiating and regulating the immune system in the aging brain and could serve as reliable and surrogate biomarkers for the diagnosis of cognitive dysfunction.

Advances in transcriptome analysis of human brain aging

“…A network analysis of published transcriptomic data obtained from adult hippocampus samples has indicated that a module of genes that display age-dependent upregulation is enriched in myelination 39 . This study also confirmed an immune process-enriched module that correlated positively with age and a synaptic transmission-enriched module that correlated negatively with age 39 .

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Prediction of hub genes of Alzheimer’s disease using a protein interaction network and functional enrichment analysis

“…According to the CFG ranking, the top four genes (with 4 points each) were NPY, CXCL12, CCR5, and CNR2, and the genes in second place (with 2 points each) were PTGER3, MTNR1A, and GRM2 (Table 1). Several approaches have been used in previous research to identify potential target genes, such as ZFHX3, ERBB2, ERBB4, OCT3, MIF, CDK13, and GPI [27][28][29][30][31][32][33]. The most recent gene expression analysis conducted by Yan et al [34] identified the following hub genes: CDC42, VEGFA, BDNF, PDYN, CALB, TH, CACNA1A, OXT, CD44, and TAC1.…”

Blood Plasma Metabolic Profile of Newborns with Hypoxic-Ischaemic Encephalopathy by GC-MS

“…As a lipid metabolism-related molecule, the elevated myo-inositol in PB plasma may indicate the impairment in lipid metabolism in HIE, which was also present at urinary metabolomics [ 31 ]. Besides, metabolite set enrichment analysis also revealed several disturbed pathways closely related to brain dysfunction [ 40 , 41 ], including GABA synthesis, release, reuptake and degradation, fatty acid biosynthesis, and regulation of development of the central nervous system [ 42 ]. This further suggested that each metabolic pathway does not function independently but interacts with each other to perform its biological function, so the regulatory mechanism of this complex metabolic network needs to be further investigated.…”