Association of PICALM with Cognitive Impairment in Parkinson's Disease

Mitochondrial dysfunction, cause or consequence in neurodegenerative diseases?

Neurodegenerative diseases encompass a spectrum of conditions characterized by the gradual deterioration of neurons in the central and peripheral nervous system. While their origins are multifaceted, emerging data underscore the pivotal role of impaired mitochondrial functions and endolysosomal homeostasis to the onset and progression of pathology. This article explores whether mitochondrial dysfunctions act as causal factors or are intricately linked to the decline in endolysosomal function. As research delves deeper into the genetics of neurodegenerative diseases, an increasing number of risk loci and genes associated with the regulation of endolysosomal and autophagy functions are being identified, arguing for a downstream impact on mitochondrial health. Our hypothesis centers on the notion that disturbances in endolysosomal processes may propagate to other organelles, including mitochondria, through disrupted inter-organellar communication. We discuss these views in the context of major neurodegenerative diseases including Alzheimer's and Parkinson's diseases, and their relevance to potential therapeutic avenues.

Cortical hypometabolism in Parkinson's disease is linked to cholinergic basal forebrain atrophy

Cortical hypometabolism on FDG-PET is a well-established neuroimaging biomarker of cognitive impairment in Parkinson's disease (PD), but its pathophysiologic origins are incompletely understood. Cholinergic basal forebrain (cBF) degeneration is a prominent pathological feature of PD-related cognitive impairment and may contribute to cortical hypometabolism through cholinergic denervation of cortical projection areas. Here, we investigated in-vivo associations between subregional cBF volumes on 3T-MRI, cortical hypometabolism on [18F]FDG-PET, and cognitive deficits in a cohort of 95 PD participants with varying degrees of cognitive impairment. We further assessed the spatial correspondence of the cortical pattern of cBF-associated hypometabolism with the pattern of cholinergic denervation in PD as assessed by [18F]FEOBV-PET imaging of presynaptic cholinergic terminal density in a second cohort. Lower volume of the cortically-projecting posterior cBF, but not of the anterior cBF, was significantly associated with extensive neocortical hypometabolism [p(FDR) < 0.05], which mediated the association between cBF atrophy and cognitive impairment (mediated proportion: 43%, p < 0.001). In combined models, posterior cBF atrophy explained more variance in cortical hypometabolism (R2 = 0.26, p < 0.001) than local atrophy in the cortical areas themselves (R2 = 0.16, p = 0.01). Topographic correspondence analysis with the [18F]FEOBV-PET pattern revealed that cortical areas showing most pronounced cBF-associated hypometabolism correspond to those showing most severe cholinergic denervation in PD (Spearman's ρ = 0.57, p < 0.001). In conclusion, posterior cBF atrophy in PD is selectively associated with hypometabolism in denervated cortical target areas, which mediates the effect of cBF atrophy on cognitive impairment. These data provide first-time in-vivo evidence that cholinergic degeneration represents a principle pathological correlate of cortical hypometabolism underlying cognitive impairment in PD.

PICALM as a Novel Prognostic Biomarker and Its Correlation with Immune Infiltration in Breast Cancer

PICALM (phosphatidylinositol-binding clathrin assembly protein) mutations have been linked to a number of human disorders, including leukemia, Alzheimer's disease, and Parkinson's disease. Nevertheless, the effect of PICALM on cancer, particularly on prognosis and immune infiltration in individuals with BRCA, is unknown. We obtained the data of breast cancer patients from The Cancer Genome Atlas (TCGA) database, and analyzed the expression of PICALM in breast cancer, its impact on survival' and its role in tumor immune invasion. Finally, in vitro cellular experiments were performed to validate the results. Research has found that PICALM expression was shown to be downregulated in BRCA and to be substantially linked with clinical stage, histological type, PAM50, and age. PICALM downregulation was linked to a lower overall survival (OS) and disease-specific survival (DSS) in BRCA patients. A multivariate Cox analysis revealed that PICALM is an independent predictor of OS. The enriched pathways revealed by functional enrichment analysis included oxidative phosphorylation, angiogenesis, the TGF signaling pathway, and the IL-6/JAK/STAT3 signaling system. Furthermore, the amount of immune cell infiltration by B cells, eosinophils, mast cells, neutrophils, and T cells was positively linked with PICALM expression. Finally, we experimentally verified that low expression of PICALM can reduce proliferation, migration, and invasion in tumor cells. This evidence shows that PICALM expression impacts prognosis, immune infiltration, and pathway expression in breast cancer patients, and it might be a potential predictive biomarker for the disease.

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features

BACKGROUND

Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.

METHODS

A total of 184 probable AD patients and 3773 healthy individuals aged 65 and over were enrolled. AD-related genes (51 SNPs) and 8 environmental factors were selected as features for multilayer ANN modeling. Random Forest (RF) and Support Vector Machine with RBF kernel (SVM) were also employed for comparison. Model results were verified using traditional statistics.

RESULTS

The ANN achieved high accuracy (0.98), sensitivity (0.95), and specificity (0.96) in the intrinsic test for AD classification. Excluding age and genetic data still yielded favorable results (accuracy: 0.97, sensitivity: 0.94, specificity: 0.96). The assigned weights to ANN features highlighted the importance of mental evaluation, years of education, and specific genetic variations (CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650) for AD classification. Receiver operating characteristic analysis revealed AUC values of 0.99 (intrinsic test), 0.60 (TWB-GWA), and 0.72 (CG-WGS), with slightly lower AUC values (0.96, 0.80, 0.52) when excluding age in ANN. The performance of the ANN model in AD classification was comparable to RF, SVM (linear kernel), and SVM (RBF kernel).

CONCLUSION

The ANN model demonstrated good sensitivity, specificity, and accuracy in AD classification. The top-weighted SNPs for AD prediction were CASS4 rs7274581, PICALM rs3851179, and TOMM40 rs2075650. The ANN model performed similarly to RF and SVM, indicating its capability to handle the complexity of AD as a disease entity.

PICALM exerts a role in promoting CRC progression through ERK/MAPK signaling pathway

Background

Colorectal cancer (CRC) is a common malignant tumor in gastrointestinal tract with high incidence and mortality. In this study, the functions and potential mechanism of phosphatidylinositol-binding clathrin assembly protein (PICALM) in CRC were preliminarily explored.

Methods

Based on the Cancer Genome Atlas database and immunohistochemistry staining, revealing that the expression level of PICALM in CRC tissues was higher than that in adjacent normal tissues.

Results

Moreover, loss-of-function and gain-of-function assays in HCT 116 and RKO cells found that PICALM promotes proliferation and migration of CRC cells and inhibits apoptosis. Consistently, knockdown of PICALM inhibited tumorigenicity of CRC cells in vivo. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that knockdown of PICALM resulted in the enrichment of MAPK signaling pathway. Treatment of CRC cells with MAPK inhibitor reversed the effects of PICALM overexpression on proliferation and apoptosis. In addition, overexpression of PICALM upregulated the protein levels of ERK1/2 (p-ERK1/2), MEK1/2 (p-MEK1/2), p38 (p-p38) and JNK (p-JNK), and these effects were partially alleviated by the treatment of MAPK inhibitor.

Conclusions

In summary, the study presented the new discovery that PICALM promoted CRC progression through ERK/MAPK signaling pathway, which drew further interest regarding its clinical application as a promising therapeutic target.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02577-z.

The correlation of blood pressure variability and cognitive function in hypertension patients: A meta-analysis

BACKGROUND

Cognitive impairment (CI) is very common in patients with hypertension. It is necessary to conduct a meta-analysis to evaluate the association between cognitive function and blood pressure variability in patients with hypertension, to provide insights into the clinical management of hypertension and cognitive impairment.

METHODS

We searched PubMed and other databases for case-control studies on the association between blood pressure variability and cognitive function up to 15 July 2021. Two researchers independently screened the literature and retrieved the data. RevMan 5.3 was used for meta-analysis.

RESULTS

A total of 13 studies involving 2754 patients were included. Meta-analysis indicated that 24-hours systolic (MD = 3.54, 95% CI [2.48, 4.60]) and diastolic (MD = 2.43, 95% CI [1.55, 3.31]) blood pressure variation coefficients in the CI group were significantly higher than that of non-CI group (all P < .05). Standard deviations of systolic (MD = 2.20, 95% CI [0.27, 4.13]) and diastolic (MD = 1.79, 95% CI [0.80, 2.79]) blood pressure variation in the CI group were significantly higher than that of the non-CI group (all P < .05). Mean systolic (MD = 3.73, 95% CI [0.92, 6.53]) and diastolic (MD = 5.41, 95% CI [0.42, 10.40]) blood pressure variation in the CI group were significantly higher than that of non-CI group (all P < .05). There were no statistically significant differences in the morning peak systolic (MD = 7.85, 95% CI [-1.30, 17.01]) and diastolic (MD = 4.44, 95% CI [-6.00, 14.89]) blood pressure drop between the CI group and non-CI group (all P > .05).

CONCLUSION

CI in hypertensive patients is closely associated with increased blood pressure variability. Clinical healthcare providers should pay attention to the management of blood pressure variability in hypertensive patients to reduce the occurrence of CI.

Variable Effects of PD-Risk Associated SNPs and Variants in Parkinsonism-Associated Genes on Disease Phenotype in a Community-Based Cohort

Genetic risk factors for Parkinson's disease (PD) risk and progression have been identified from genome-wide association studies (GWAS), as well as studies of familial forms of PD, implicating common variants at more than 90 loci and pathogenic or likely pathogenic variants at 16 loci. With the goal of understanding whether genetic variants at these PD-risk loci/genes differentially contribute to individual clinical phenotypic characteristics of PD, we used structured clinical documentation tools within the electronic medical record in an effort to provide a standardized and detailed clinical phenotypic characterization at the point of care in a cohort of 856 PD patients. We analyzed common SNPs identified in previous GWAS studies, as well as low-frequency and rare variants at parkinsonism-associated genes in the MDSgene database for their association with individual clinical characteristics and test scores at baseline assessment in our community-based PD patient cohort: age at onset, disease duration, Unified Parkinson's Disease Rating Scale I-VI, cognitive status, initial and baseline motor and non-motor symptoms, complications of levodopa therapy, comorbidities and family history of neurological disease with one or more than one affected family members. We find that in most cases an individual common PD-risk SNP identified in GWAS is associated with only a single clinical feature or test score, while gene-level tests assessing low-frequency and rare variants reveal genes associated in either a unique or partially overlapping manner with the different clinical features and test scores. Protein-protein interaction network analysis of the identified genes reveals that while some of these genes are members of already identified protein networks others are not. These findings indicate that genetic risk factors for PD differentially affect the phenotypic presentation and that genes associated with PD risk are also differentially associated with individual disease phenotypic characteristics at baseline. These findings raise the intriguing possibility that different SNPs/gene effects impact discrete phenotypic characteristics. Furthermore, they support the hypothesis that different gene and protein-protein interaction networks that underlie PD risk, the PD phenotype, and the neurodegenerative process leading to the disease phenotype, and point to the significance of the genetic background on disease phenotype.