Integrative genomic analysis of PPP3R1 in Alzheimer's disease: a potential biomarker for predictive, preventive, and personalized medical approach

Multi-cohort cerebrospinal fluid proteomics identifies robust molecular signatures for asymptomatic and symptomatic Alzheimer's disease

Changes in Amyloid-β (A), hyperphosphorylated Tau (T) in brain and cerebrospinal fluid (CSF) precedes AD symptoms, making CSF proteome a potential avenue to understand the pathophysiology and facilitate reliable diagnostics and therapies. Using the AT framework and a three-stage study design (discovery, replication, and meta-analysis), we identified 2,173 proteins dysregulated in AD, that were further validated in a third totally independent cohort. Machine learning was implemented to create and validate highly accurate and replicable (AUC>0.90) models that predict AD biomarker positivity and clinical status. These models can also identify people that will convert to AD and those AD cases with faster progression. The associated proteins cluster in four different protein pseudo-trajectories groups spanning the AD continuum and were enrichment in specific pathways including neuronal death, apoptosis and tau phosphorylation (early stages), microglia dysregulation and endolysosomal dysfuncton(mid-stages), brain plasticity and longevity (mid-stages) and late microglia-neuron crosstalk (late stages).

Identification of immune microenvironment subtypes and signature genes for Alzheimer's disease diagnosis and risk prediction based on explainable machine learning

Background

Using interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.

Methods

ssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.

Results

It was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.

Conclusion

The hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.

Downregulation of PIK3CB Involved in Alzheimer's Disease via Apoptosis, Axon Guidance, and FoxO Signaling Pathway

OBJECTIVE

To investigate the molecular function of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta (PIK3CB) underlying Alzheimer's disease (AD).

METHODS

RNA sequencing data were used to filtrate differentially expressed genes (DEGs) in AD/nondementia control and PIK3CB-low/high groups. An unbiased coexpression network was established to evaluate module-trait relationships by using weight gene correlation network analysis (WGCNA). Global regulatory network was constructed to predict the protein-protein interaction. Further cross-talking pathways of PIK3CB were identified by functional enrichment analysis.

RESULTS

The mean expression of PIK3CB in AD patients was significantly lower than those in nondementia controls. We identified 2,385 DEGs from 16,790 background genes in AD/control and PIK3CB-low/high groups. Five coexpression modules were established using WGCNA, which participated in apoptosis, axon guidance, long-term potentiation (LTP), regulation of actin cytoskeleton, synaptic vesicle cycle, FoxO, mitogen-activated protein kinase (MAPK), and vascular endothelial growth factor (VEGF) signaling pathways. DEGs with strong relation to AD and low PIK3CB expression were extracted to construct a global regulatory network, in which cross-talking pathways of PIK3CB were identified, such as apoptosis, axon guidance, and FoxO signaling pathway. The occurrence of AD could be accurately predicted by low PIK3CB based on the area under the curve of 71.7%.

CONCLUSIONS

These findings highlight downregulated PIK3CB as a potential causative factor of AD, possibly mediated via apoptosis, axon guidance, and FoxO signaling pathway.