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Proteomic landscape of Alzheimer's Disease: novel insights into pathogenesis and biomarker discovery.

ABSTRACT: Mass spectrometry-based proteomics empowers deep profiling of proteome and protein posttranslational modifications (PTMs) in Alzheimer's disease (AD). Here we review the advances and limitations in historic and recent AD proteomic research. Complementary to genetic mapping, proteomic studies not only validate canonical amyloid and tau pathways, but also uncover novel components in broad protein networks, such as RNA splicing, development, immunity, membrane transport, lipid metabolism, synaptic function, and mitochondrial activity. Meta-analysis of seven deep datasets reveals 2,698 differentially expressed (DE) proteins in the landscape of AD brain proteome (n = 12,017 proteins/genes), covering 35 reported AD genes and risk loci. The DE proteins contain cellular markers enriched in neurons, microglia, astrocytes, oligodendrocytes, and epithelial cells, supporting the involvement of diverse cell types in AD pathology. We discuss the hypothesized protective or detrimental roles of selected DE proteins, emphasizing top proteins in "amyloidome" (all biomolecules in amyloid plaques) and disease progression. Comprehensive PTM analysis represents another layer of molecular events in AD. In particular, tau PTMs are correlated with disease stages and indicate the heterogeneity of individual AD patients. Moreover, the unprecedented proteomic coverage of biofluids, such as cerebrospinal fluid and serum, procures novel putative AD biomarkers through meta-analysis. Thus, proteomics-driven systems biology presents a new frontier to link genotype, proteotype, and phenotype, accelerating the development of improved AD models and treatment strategies.

SUBMITTER: Bai B

PROVIDER: S-EPMC8359598 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Proteomic landscape of Alzheimer's Disease: novel insights into pathogenesis and biomarker discovery.

Bai Bing B Vanderwall David D Li Yuxin Y Wang Xusheng X Poudel Suresh S Wang Hong H Dey Kaushik Kumar KK Chen Ping-Chung PC Yang Ka K Peng Junmin J

Molecular neurodegeneration 20210812 1

Mass spectrometry-based proteomics empowers deep profiling of proteome and protein posttranslational modifications (PTMs) in Alzheimer's disease (AD). Here we review the advances and limitations in historic and recent AD proteomic research. Complementary to genetic mapping, proteomic studies not only validate canonical amyloid and tau pathways, but also uncover novel components in broad protein networks, such as RNA splicing, development, immunity, membrane transport, lipid metabolism, synaptic ...[more]

PMID: 34384464

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Project description:BackgroundThe increasing incidence of childhood obesity annually has led to a surge in physical and mental health risks, making it a significant global public health concern. This study aimed to discover novel biomarkers of childhood simple obesity through integrative multi-omics analysis, uncovering their potential connections and providing fresh research directions for the complex pathogenesis and treatment strategies.MethodsTranscriptome, untargeted metabolome, and 16 S rDNA sequencing were conducted on subjects to examine transcripts, metabolites in blood, and gut microflora in stool.ResultsTranscriptomic analysis identified 599 differentially expressed genes (DEGs), of which 25 were immune-related genes, and participated in immune pathways such as antimicrobial peptides, neutrophil degranulation, and interferons. The optimal random forest model based on these genes exhibited an AUC of 0.844. The metabolomic analysis examined 71 differentially expressed metabolites (DEMs), including 12 immune-related metabolites. Notably, lauric acid showed an extremely strong positive correlation with BMI and showed a good discriminative power for obesity (AUC = 0.82). DEMs were found to be significantly enriched in four metabolic pathways, namely "Aminoacyl-tRNA biosynthesis", "Valine leucine and isoleucine biosynthesis, and Glycine", "Serine and threonine metabolism", and "Biosynthesis of unsaturated fatty acids". Microbiome analysis revealed 12 differential gut microbiotas (DGMs) at the phylum and genus levels, with p_Firmicutes dominating in the obese group and g_Escherichia-Shigella in the normal group. Subsequently, a Random Forest model was developed based on the DEMs, immune-related DEGs, and metabolites with an AUC value of 0.912. The 14 indicators identified by this model could potentially serve as a set of biomarkers for obesity. The analysis of the inter-omics correlation network found 233 pairs of significant correlations. DEGs BPIFA1, BPI, and SAA1, DEMs Dimethy(tetradecyl)amine, Deoxycholic acid, Pathalic anhydride, and DL-Alanine, and DGMs g_Intestinimonas and g_Turicibacter showed strong connectivity within the network, constituting a large proportion of interactions.ConclusionThis study presents the first comprehensive description of the multi-omics characteristics of childhood simple obesity, recognizing promising biomarkers. Immune-related markers offer a new perspective for researching the immunological mechanisms underlying obesity and its associated complications. The revealed interactions among these biomarkers contribute to a deeper understanding the intricate biological regulatory networks associated with obesity.

Dataset Information

Proteomic landscape of Alzheimer's Disease: novel insights into pathogenesis and biomarker discovery.

Publications

Proteomic landscape of Alzheimer's Disease: novel insights into pathogenesis and biomarker discovery.

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