Identification of mitochondrial related signature associated with immune microenvironment in Alzheimer's disease

Bioinformatics analysis characterizes immune infiltration landscape and identifies potential blood biomarkers for heart transplantation

BACKGROUND

Cardiac allograft rejection (AR) remains a significant complication following heart transplantation. The primary objective of our study is to gain a comprehensive understanding of the fundamental mechanisms involved in AR and identify possible therapeutic targets.

METHODS

We acquired the GSE87301 dataset from the Gene Expression Omnibus database. In GSE87301, a comparison was conducted on blood samples from patients with and without cardiac allograft rejection (AR and NAR) to detect differentially expressed genes (DEGs). Enrichment analysis was conducted to identify the pathways that show significant enrichment during AR. Machine learning techniques, including the least absolute shrinkage and selection operator regression (LASSO) and random forest (RF) algorithms, were employed to identify potential genes for the diagnosis of AR. The diagnostic value was evaluated using a nomogram and receiver operating characteristic (ROC) curve. Additionally, immune cell infiltration was analyzed to explore any dysregulation of immune cells in AR.

RESULTS

A total of 114 DEGs were identified from the GSE87301 dataset. These DEGs were mainly found to be enriched in pathways related to the immune system. To identify the signature genes, the LASSO and RF algorithms were used, and four genes, namely ALAS2, HBD, EPB42, and FECH, were identified. The performance of these signature genes was evaluated using the receiver operating characteristic curve (ROC) analysis, which showed that the area under the curve (AUC) values for ALAS2, HBD, EPB42, and FECH were 0.906, 0.881, 0.900, and 0.856, respectively. These findings were further confirmed in the independent datasets and clinical samples. The selection of these specific genes was made to construct a nomogram, which demonstrated excellent diagnostic ability. Additionally, the results of the single-sample gene set enrichment analysis (ssGSEA) revealed that these genes may be involved in immune cell infiltration.

CONCLUSION

We identified four signature genes (ALAS2, HBD, EPB42, and FECH) as potential peripheral blood diagnostic candidates for AR diagnosis. Additionally, a nomogram was constructed to aid in the diagnosis of heart transplantation. This study offers valuable insights into the identification of candidate genes for heart transplantation using peripheral blood samples.

Screening mitochondria-related biomarkers in skin and plasma of atopic dermatitis patients by bioinformatics analysis and machine learning

Background

There is a significant imbalance of mitochondrial activity and oxidative stress (OS) status in patients with atopic dermatitis (AD). This study aims to screen skin and peripheral mitochondria-related biomarkers, providing insights into the underlying mechanisms of mitochondrial dysfunction in AD.

Methods

Public data were obtained from MitoCarta 3.0 and GEO database. We screened mitochondria-related differentially expressed genes (MitoDEGs) using R language and then performed GO and KEGG pathway analysis on MitoDEGs. PPI and machine learning algorithms were also used to select hub MitoDEGs. Meanwhile, the expression of hub MitoDEGs in clinical samples were verified. Using ROC curve analysis, the diagnostic performance of risk model constructed from these hub MitoDEGs was evaluated in the training and validation sets. Further computer-aided algorithm analyses included gene set enrichment analysis (GSEA), immune infiltration and mitochondrial metabolism, centered on these hub MitoDEGs. We also used real-time PCR and Spearman method to evaluate the relationship between plasma circulating cell-free mitochondrial DNA (ccf-mtDNA) levels and disease severity in AD patients.

Results

MitoDEGs in AD were significantly enriched in pathways involved in mitochondrial respiration, mitochondrial metabolism, and mitochondrial membrane transport. Four hub genes (BAX, IDH3A, MRPS6, and GPT2) were selected to take part in the creation of a novel mitochondrial-based risk model for AD prediction. The risk score demonstrated excellent diagnostic performance in both the training cohort (AUC = 1.000) and the validation cohort (AUC = 0.810). Four hub MitoDEGs were also clearly associated with the innate immune cells' infiltration and the molecular modifications of mitochondrial hypermetabolism in AD. We further discovered that AD patients had considerably greater plasma ccf-mtDNA levels than controls (U = 92.0, p< 0.001). Besides, there was a significant relationship between the up-regulation of plasma mtDNA and the severity of AD symptoms.

Conclusions

The study highlights BAX, IDH3A, MRPS6 and GPT2 as crucial MitoDEGs and demonstrates their efficiency in identifying AD. Moderate to severe AD is associated with increased markers of mitochondrial damage and cellular stress (ccf=mtDNA). Our study provides data support for the variation in mitochondria-related functional characteristics of AD patients.

The Influence of Myeloid-Derived Suppressor Cell Expansion in Neuroinflammation and Neurodegenerative Diseases

The neuro-immune axis has a crucial function both during physiological and pathological conditions. Among the immune cells, myeloid-derived suppressor cells (MDSCs) exert a pivotal role in regulating the immune response in many pathological conditions, influencing neuroinflammation and neurodegenerative disease progression. In chronic neuroinflammation, MDSCs could lead to exacerbation of the inflammatory state and eventually participate in the impairment of cognitive functions. To have a complete overview of the role of MDSCs in neurodegenerative diseases, research on PubMed for articles using a combination of terms made with Boolean operators was performed. According to the search strategy, 80 papers were retrieved. Among these, 44 papers met the eligibility criteria. The two subtypes of MDSCs, monocytic and polymorphonuclear MDSCs, behave differently in these diseases. The initial MDSC proliferation is fundamental for attenuating inflammation in Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS), but not in amyotrophic lateral sclerosis (ALS), where MDSC expansion leads to exacerbation of the disease. Moreover, the accumulation of MDSC subtypes in distinct organs changes during the disease. The proliferation of MDSC subtypes occurs at different disease stages and can influence the progression of each neurodegenerative disorder differently.

Revealing PPP1R12B and COL1A1 as piRNA pathway genes contributing to abdominal aortic aneurysm through integrated analysis and experimental validation

BACKGROUND

Abdominal aortic aneurysm (AAA) is a permanent dilation of the abdominal aorta, with a high mortality rate when rupturing. Although lots of piRNA pathway genes (piRPGs) have recently been linked to both neoplastic and non-neoplastic illnesses, their role in AAA is still unknown. Utilizing integrative bioinformatics methods, this research discovered piRPGs as biomarkers for AAA and explore possible molecular mechanisms.

METHODS

The datasets were obtained from the Gene Expression Omnibus and piRPGs were identified from the Genecards database. The "limma" and "clusterProfiler" R-packages were used to discover differentially expressed genes and perform enrichment analysis, respectively. Hub piRPGs were further filtered using least absolute shrinkage and selection operator regression, random forests, as well as receiver operating characteristic curve. Additionally, multi-factor logistic regression (MLR), extreme gradient boosting (XGboost), and artificial neural network (ANN) were employed to construct prediction models. The relationship between hub piRPGs and immune infiltrating cells and sgGSEA were further studied. The expression of hub piRPGs was verified by qRT-PCR, immunohistochemistry, and western blotting in AAA and normal vascular tissues and analyzed by scRNA-seq in mouse AAA model. SRAMP and cMAP database were utilized for the prediction of N6-methyladenosine (m6A) targets therapeutic drug.

RESULTS

34 differentially expressed piRPGs were identified in AAA and enriched in pathways of immune regulation and gene silence. Three piRPGs (PPP1R12B, LRP10, and COL1A1) were further screened as diagnostic genes and used to construct prediction model. Compared with MLR and ANN, Xgboost showed better predictive ability, and PPP1R12B might have the ability to distinguish small and large AAA. Furthermore, the expression levels of PPP1R12B and COL1A1 were consistent with the results of bioinformatics analysis, and PPP1R12B showed a downward trend that may be related to m6A.

CONCLUSION

The results suggest that piRPGs might serve a significant role in AAA. PPP1R12B, COL1A1, and LRP10 had potential as diagnostic-specific biomarkers for AAA and performed better in XGboost model. The expression and localization of PPP1R12B and COL1A1 were experimentally verified. Besides, downregulation of PPP1R12B caused by m6A might contribute to the formation of AAA.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

A Continuous Extension of Gene Set Enrichment Analysis Using the Likelihood Ratio Test Statistics Identifies Vascular Endothelial Growth Factor as a Candidate Pathway for Alzheimer's Disease via ITGA5

BACKGROUND

Alzheimer's disease (AD) involves brain neuropathologies such as amyloid plaque and hyperphosphorylated tau tangles and is accompanied by cognitive decline. Identifying the biological mechanisms underlying disease onset and progression based on quantifiable phenotypes will help understand disease etiology and devise therapies.

OBJECTIVE

Our objective was to identify molecular pathways associated with hallmark AD biomarkers and cognitive status, accounting for variables such as age, sex, education, and APOE genotype.

METHODS

We introduce a pathway-based statistical approach, extending the gene set likelihood ratio test to continuous phenotypes. We first analyzed independently each of the three phenotypes (amyloid-β, tau, cognition) using continuous gene set likelihood ratio tests to account for covariates, including age, sex, education, and APOE genotype. The analysis involved 634 subjects with data available for all three phenotypes, allowing for the identification of common pathways.

RESULTS

We identified 14 pathways significantly associated with amyloid-β; 5 associated with tau; and 174 associated with cognition, which showed a larger number of pathways compared to biomarkers. A single pathway, vascular endothelial growth factor receptor binding (VEGF-RB), exhibited associations with all three phenotypes. Mediation analysis showed that among the VEGF-RB family genes, ITGA5 mediates the relationship between cognitive scores and pathological biomarkers.

CONCLUSIONS

We presented a new statistical approach linking continuous phenotypes, gene expression across pathways, and covariates like sex, age, and education. Our results reinforced VEGF RB2's role in AD cognition and demonstrated ITGA5's significant role in mediating the AD pathology-cognition connection.

A Continuous Extension of Gene Set Enrichment Analysis using the Likelihood Ratio Test Statistics Identifies VEGF as a Candidate Pathway for Alzheimer's disease

Background

Alzheimer's disease involves brain pathologies such as amyloid plaque depositions and hyperphosphorylated tau tangles and is accompanied by cognitive decline. Identifying the biological mechanisms underlying disease onset and progression based on quantifiable phenotypes will help understand the disease etiology and devise therapies.

Objective

Our objective was to identify molecular pathways associated with AD biomarkers (Amyloid-β and tau) and cognitive status (MMSE) accounting for variables such as age, sex, education, and APOE genotype.

Methods

We introduce a novel pathway-based statistical approach, extending the gene set likelihood ratio test to continuous phenotypes. We first analyzed independently each of the three phenotypes (Amyloid-β, tau, cognition), using continuous gene set likelihood ratio tests to account for covariates, including age, sex, education, and APOE genotype. The analysis involved a large sample size with data available for all three phenotypes, allowing for the identification of common pathways.

Results

We identified 14 pathways significantly associated with Amyloid-β, 5 associated with tau, and 174 associated with MMSE. Surprisingly, the MMSE outcome showed a larger number of significant pathways compared to biomarkers. A single pathway, vascular endothelial growth factor receptor binding (VEGF-RB), exhibited significant associations with all three phenotypes.

Conclusions

The study's findings highlight the importance of the VEGF signaling pathway in aging in AD. The complex interactions within the VEGF signaling family offer valuable insights for future therapeutic interventions.