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

Identification and Exploration of Immunity-Related Genes and Natural Products for Alzheimer's Disease Based on Bioinformatics, Molecular Docking, and Molecular Dynamics

BACKGROUND

Recent research highlights the immune system's role in AD pathogenesis and promising prospects of natural compounds in treatment. This study explores immunity-related biomarkers and potential natural products using bioinformatics, machine learning, molecular docking, and kinetic simulation.

METHODS

Differentially expressed genes (DEGs) in AD were analyzed using GSE5281 and GSE132903 datasets. Important AD module genes were identified using a weighted co-expression algorithm (WGCNA), and immune-related genes (IRGs) were obtained from the ImmPortPortal database. Intersecting these genes yielded important IRGs. Then, the least absolute shrinkage and selection operator (LASSO) and other methods screened common immune-related AD markers. Biological pathways were explored through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). The accuracy of these markers was assessed by subject operator signature (ROC) curves and validated in the GSE122063 dataset. The datasets was then subjected to immunoinfiltration analysis. Multiple compound databases were used to analyze core Chinese medicines and components. Molecular docking and kinetic simulation verification were used for further verification.

RESULTS

A total of 1360 differential genes and 5 biomarkers (PGF, GFAP, GPI, SST, NFKBIA) were identified, showing excellent diagnostic efficiency. GSEA revealed markers associated with Oxidative phosphorylation, Nicotine addiction, and Hippo signaling pathway. Immune infiltration analysis showed dysregulation in multiple immune cell types in AD brains, with significant interactions between markers and 5 immune cell types. A total of 27 possible herbs and 7 core compounds were eventually identified. The binding environment of GPI-luteolin and GPI-stigasterol was relatively stable and showed good affinity.

CONCLUSIONS

PGF, GFAP, SST, GPI, and NFKBIA were identified for early AD diagnosis, associated with immune cells and pathways in AD brains. 7 promising natural compounds, including luteolin and stigmasterol, were screened for targeting these biomarkers.

From genes to drugs: targeting Alzheimer's with circadian insights

Background

Alzheimer's disease (AD) is a typical neurodegenerative disease that presents challenges due to the lack of biomarkers to identify AD. A growing body of evidence highlights the critical role of circadian rhythms in AD.

Methods

The differentially expressed clock genes (DECGs) were identified between AD and ND groups (non-demented controls). Functional enrichment analysis was executed on the DECGs. Candidate diagnostic biomarkers for AD were screened by machine learning. ROC and nomograms were constructed to evaluate candidate biomarkers. In addition, therapeutics targeting predictive biomarkers were screened through the DGIdb website. Finally, the mRNA-miRNA network was constructed.

Results

Nine genes were identified through the DECG analysis between the AD and ND groups. Enrichment analysis of nine genes indicated that the pathways were enriched in long-term potentiation and circadian entrainment. Four clock genes (GSTM3, ERC2, PRKCG, and HLA-DMA) of AD were screened using Lasso regression, random forest, SVM, and GMM. The diagnostic performance of four genes was evaluated by the ROC curve. Furthermore, the nomogram indicated that ERC2, PRKCG, and HLA-DMA are good biomarkers in diagnosing AD. Single-gene GSEA indicated that the main enrichment pathways were oxidative phosphorylation, pathways of neurodegeneration-multiple diseases, etc. The results of immune cell infiltration analysis indicated that there were significant differences in 15 immune cell subsets between AD and ND groups. Moreover, 23 drugs targeting HLA-DMA and 8 drugs targeting PRKCG were identified through the DGIdb website.

Conclusion

We identified three predictive biomarkers for AD associated with clock genes, thus providing promising therapeutic targets for AD.

Mechanism of action and experimental validation of key genes common to diabetic retinopathy and coronary heart disease based on multiple bioinformatics investigations

Introduction

The relationship between diabetic retinopathy (DR) and coronary artery disease (CHD) has been established as a reliable predictor. However, the underlying mechanisms linking these two conditions remain poorly understood. Identifying common key genes could provide new therapeutic targets for both diseases.

Methods

Public databases were used to compile training and validation datasets for DR and CHD. Machine learning algorithms and expression validation were employed to identify these key genes. To investigate immune cell differences, single-sample gene set enrichment analysis (ssGSEA) and the Wilcoxon test were applied. Spearman correlation analysis further explored the relationship between key genes and immune cell variations. Additionally, potential therapeutic drugs targeting these key genes were identified and a key gene-drug network was constructed. The role of the key genes in the pathogenesis of DR and CHD was further examined through reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results

Consistent expression trends observed across datasets (GSE221521, GSE113079, GSE189005, GSE42148) led to the identification of HIRIP3 and ZNF416 as key genes. In GSE221521, HIRIP3 was positively correlated with CD56 bright natural killer cells (cor = 0.329, P < 0.001) and type 1T helper cells (cor = 0.327, P < 0.001), while ZNF416 showed significant correlations with CD4 T cell activation (cor = 0.340, P < 0.001) and type 1T helper cells (cor = 0.273, P < 0.05). Moreover, 82 transcription factors (TFs) were predicted, including SP3. Binding free energy calculations for key genes and potential drugs suggested stable binding conformations. RT-qPCR results revealed elevated expression of both HIRIP3 and ZNF416 in the control group compared to the DR with CHD (DRwCHD) group, with only ZNF416 showing significant differences between the groups (p < 0.05).

Discussion

These findings highlight HIRIP3 and ZNF416 as crucial genes in DR and CHD detection, providing a foundation for identifying novel therapeutic targets for both diseases.

Fidgetin-like 2 knockdown increases acute neuroinflammation and improves recovery in a rat model of spinal cord injury

Spinal cord injury (SCI) can cause permanent dysfunction proceeding from multifaceted neuroinflammatory processes that contribute to damage and repair. Fidgetin-like 2 (FL2), a microtubule-severing enzyme that negatively regulates axon growth, microglial functions, and wound healing, has emerged as a potential therapeutic target for central nervous system injuries and neuroinflammation. To test the hypothesis that FL2 knockdown increases acute neuroinflammation and improves recovery after SCI, we examined the effects of nanoparticle-encapsulated FL2 siRNA treatment after a moderate contusion SCI in rats. SCI significantly increased FL2 expression in the lesion site and rostral to the lesion 1 day post-injury (dpi). A single treatment of FL2 siRNA after injury led to modestly improved locomotor recovery consistent with the preservation of corticospinal tract function, accompanied by reduced inflammation and increased presence of oligodendrocytes. In determining the acute effects of treatment, RNA sequencing and gene set enrichment analyses revealed that FL2 siRNA modulates early cellular responses, including chemokine signaling, both pro- and anti-inflammatory immune reactions, and neurotransmitter signaling pathways at 1, 4, and 7 dpi. Follow-up analyses at 4 dpi using dual in situ hybridization and immunohistochemistry demonstrated that SCI increased FL2 mRNA and that FL2 was colocalized with microglia/macrophages. FL2 downregulation resulted in a marked accumulation of microglia at the lesion site, accompanied by increased inflammatory markers (IL-1β, TGF-β1, and CD68). The results suggest SCI induces an increase in FL2 expression that undermines acute inflammatory responses as well as spinal cord integrity and growth. Overall, our study suggests that targeting FL2 holds promise as a therapeutic strategy for treating SCI.

Integrated multi-omics analysis revealed the molecular networks and potential targets of cellular senescence in Alzheimer's disease

Cellular senescence (CS) is a hallmark of Alzheimer's disease (AD). However, the mechanisms through which CS contributes to AD pathogenesis remain poorly understood. We found that CS level in AD was higher compared with the healthy control group. Transcriptome-based differential expression analysis identified 113 CS-related genes in blood and 410 in brain tissue as potential candidate genes involved in AD. To further explore the causal role of these genes, an integrative mendelian randomization analysis was conducted, combining AD genome-wide association study summary statistics with expression quantitative trait loci (eQTL) and DNA methylation quantitative trait loci (mQTL) data from blood samples, which identified five putative AD-causal genes (CENPW, EXOSC9, HSPB11, SLC44A2, and SLFN12) and 18 corresponding DNA methylation probes. Additionally, integrative analysis between eQTLs and mQTLs from blood uncovered two genes and 12 corresponding regulatory elements involved in AD. Furthermore, two genes (CDKN2B and ITGAV) were prioritized as putative causal genes in brain tissue and were validated through in vitro experiments. The multi-omics integration study revealed the potential role and underlying biological mechanisms of CS driven by genetic predisposition in AD. This study contributed to fundamental understanding of CS in AD pathogenesis and facilitated the identification of potential therapeutic targets for AD prevention and treatment.

Nrf2 activator tertiary butylhydroquinone enhances neural stem cell differentiation and implantation in Alzheimer's disease by boosting mitochondrial function

AIMS

To investigate the effects of Nrf2 agonist tertiary butylhydroquinone (TBHQ)-stimulated neural stem cells (NSCs) transplantation (NSC(TBHQ)) on neuronal damage and cognitive deficits in an AD model and its underlying principles.

METHODS

BHQ-treated NSCs were examined with or without Aβ1-42 to investigate the effects of TBHQ on the proliferation and differentiation functions. The mitophagy inhibitor Cyclosporine A (CSA) was used to explore the regulation of mitophagy by TBHQ. The no-, ethanol-, and TBHQ-treated NSCs were transplanted into the bilateral hippocampal region of model mice to explore the effects of NSC(TBHQ) on neuronal, cognitive, and mitochondrial functional impairments in mice.

RESULTS

TBHQ reversed the Aβ1-42-caused inhibition on NSC proliferation and differentiation, as well as on levels of mitochondrial membrane potential, adenosine triphosphate (ATP), and mitochondrial fusion-associated proteins. TBHQ alleviated the Aβ1-42-induced increase in apoptosis, mitochondrial damage, mitochondria-derived reactive oxygen species (mtROS), and mitochondrial fission-related proteins. TBHQ activated the Parkin, Beclin, LC3II/I, and COXIV expression, while inhibiting the p62 expression. CSA reversed the effects of TBHQ on NSC proliferation and differentiation. After NSC(TBHQ) transplantation, it not only further extended the dwell time in the target quadrant and shorten the time and distance for finding the hidden platform, but also further decreased the Aβ and p-Tau/Tau levels, while increasing the expression of NeuN. The effects of NSC(TBHQ) transplantation on mitochondrial function were consistent with the in vitro experiments.

CONCLUSIONS

The study shows that NSC(TBHQ) intensifies the beneficial impact of NSCs transplantation on cognitive impairment and neuronal damage in AD models, likely due to TBHQ's role in promoting NSCs growth and differentiation via mitophagy, thus laying a theoretical foundation for improving NSCs transplantation for AD.

Mitochondrial DNA leakage: underlying mechanisms and therapeutic implications in neurological disorders

Mitochondrial dysfunction is a pivotal instigator of neuroinflammation, with mitochondrial DNA (mtDNA) leakage as a critical intermediary. This review delineates the intricate pathways leading to mtDNA release, which include membrane permeabilization, vesicular trafficking, disruption of homeostatic regulation, and abnormalities in mitochondrial dynamics. The escaped mtDNA activates cytosolic DNA sensors, especially cyclic gmp-amp synthase (cGAS) signalling and inflammasome, initiating neuroinflammatory cascades via pathways, exacerbating a spectrum of neurological pathologies. The therapeutic promise of targeting mtDNA leakage is discussed in detail, underscoring the necessity for a multifaceted strategy that encompasses the preservation of mtDNA homeostasis, prevention of membrane leakage, reestablishment of mitochondrial dynamics, and inhibition the activation of cytosolic DNA sensors. Advancing our understanding of the complex interplay between mtDNA leakage and neuroinflammation is imperative for developing precision therapeutic interventions for neurological disorders.

Long Non-Coding RNAs: Crucial Regulators in Alzheimer's Disease Pathogenesis and Prospects for Precision Medicine

Long non-coding RNAs (LncRNAs) have emerged as pivotal regulators in the pathogenesis of Alzheimer's disease (AD), a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. With the capacity to modulate gene expression at various levels, LncRNAs are implicated in multiple pathological mechanisms of AD, including amyloid-beta (Aβ) accumulation, tau protein phosphorylation, neuroinflammation, and neuronal apoptosis. Recent studies have highlighted the potential of LncRNAs as diagnostic biomarkers and therapeutic targets due to their differential expression patterns in AD patients. This review synthesizes current knowledge on the role of LncRNAs in AD, focusing on their involvement in key molecular pathways and their promise as indicators for early diagnosis and prognosis. We discuss the regulatory networks of LncRNAs in the context of AD, their interaction with miRNAs, and the implications for developing novel therapeutic strategies. Despite the complexity and variability in LncRNA function, the prospect of harnessing these molecules for precision medicine in AD is gaining momentum. The translational potential of LncRNA-based interventions offers a new frontier in the quest for effective treatments and a deeper understanding of the molecular underpinnings of AD.

Identification of Lipophagy-Related Gene Signature for Diagnosis and Risk Prediction of Alzheimer's Disease

Background: Recent research indicates that lipid metabolism and autophagy play crucial roles in the development of Alzheimer's disease (AD). Investigating the relationship between AD diagnosis and gene expression related to lipid metabolism, autophagy, and lipophagy may improve early diagnosis and the identification of therapeutic targets. Methods: Transcription datasets from AD patients were obtained from the Gene Expression Omnibus (GEO). Genes associated with lipid metabolism, autophagy, and lipophagy were sourced from the Gene Set Enrichment Analysis (GSEA) database and the Human Autophagy Database (HADb). Lipophagy-related hub genes were identified using a combination of Limma analysis, weighted gene co-expression network analysis (WGCNA), and machine learning techniques. Based on these hub genes, we developed an AD risk prediction nomogram and validated its diagnostic accuracy using three external validation datasets. Additionally, the expression levels of the hub genes were assessed through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: Our analysis identified three hub genes-ACBD5, GABARAPL1, and HSPA8-as being associated with AD progression. The nomogram constructed from these hub genes achieved an area under the curve (AUC) value of 0.894 for AD risk prediction, with all validation sets yielding AUC values greater than 0.8, indicating excellent diagnostic efficacy. qRT-PCR results further corroborated the associations between these hub genes and AD development. Conclusions: This study identified and validated three lipophagy-related hub genes and developed a reliable diagnostic model, offering insights into the pathology of AD and facilitating the diagnosis of AD patients.

Pan-cancer association of a mitochondrial function score with genomic alterations and clinical outcome

Mitochondria are pivotal in cellular energy metabolism and have garnered significant attention for their roles in cancer progression and therapy resistance. Despite this, the functional diversity of mitochondria across various cancer types remains inadequately characterized. This study seeks to fill this knowledge gap by introducing and validating MitoScore-a novel metric designed to quantitatively assess mitochondrial function across a wide array of cancers. Our investigation evaluates the capacity of MitoScore not only to distinguish between tumor and adjacent normal tissues but also to serve as a predictive marker for clinical outcomes. We analyzed gene expression data from 24 cancer types and corresponding normal tissues using the TCGA database. MitoScore was calculated by summing the normalized expression levels of six mitochondrial genes known to be consistently altered across multiple cancers. Differential gene expression was assessed using DESeq2, with a focus on identifying significant changes in mitochondrial function. MitoScore's associations with tumor proliferation, hypoxia, aneuploidy, and clinical outcomes were evaluated using Spearman's correlation, linear regression, and Kaplan-Meier survival analyses. MitoScore was significantly higher in tumor tissues compared to normal tissues across most cancer types (p < 0.001). It positively correlated with tumor proliferation rates (r = 0.46), hypoxia scores (r = 0.61), and aneuploidy (r = 0.44), indicating its potential as a marker of aggressive tumor behavior. High MitoScore was also associated with poorer prognosis in several cancer types, suggesting its utility as a predictive biomarker for clinical outcomes. This study introduces MitoScore, a metric for mitochondrial activity often elevated in tumors and linked to poor prognosis. It correlates positively with hypoxia and negatively with stromal and immune infiltration, highlighting mitochondria's role in the tumor microenvironment. MitoScore's association with genomic instability, such as aneuploidy, suggests mitochondrial dysfunction contributes to cancer progression. Despite challenges in mitochondrial-targeted therapies, MitoScore may identify tumors responsive to such treatments, warranting further research for clinical application.

Association between Alzheimer's disease pathologic products and age and a pathologic product-based diagnostic model for Alzheimer's disease

Background

Alzheimer's disease (AD) has a major negative impact on people's quality of life, life, and health. More research is needed to determine the relationship between age and the pathologic products associated with AD. Meanwhile, the construction of an early diagnostic model of AD, which is mainly characterized by pathological products, is very important for the diagnosis and treatment of AD.

Method

We collected clinical study data from September 2005 to August 2024 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Using correlation analysis method like cor function, we analyzed the pathology products (t-Tau, p-Tau, and Aβ proteins), age, gender, and Minimum Mental State Examination (MMSE) scores in the ADNI data. Next, we investigated the relationship between pathologic products and age in the AD and non-AD groups using linear regression. Ultimately, we used these features to build a diagnostic model for AD.

Results

A total of 1,255 individuals were included in the study (mean [SD] age, 73.27 [7.26] years; 691male [55.1%]; 564 female [44.9%]). The results of the correlation analysis showed that the correlations between pathologic products and age were, in descending order, Tau (Corr=0.75), p-Tau (Corr=0.71), and Aβ (Corr=0.54). In the AD group, t-Tau protein showed a tendency to decrease with age, but it was not statistically significant. p-Tau protein levels similarly decreased with age and its decrease was statistically significant. In contrast to Tau protein, in the AD group, Aβ levels increased progressively with age. In the non-AD group, the trend of pathologic product levels with age was consistently opposite to that of the AD group. We finally screened the optimal AD diagnostic model (AUC=0.959) based on the results of correlation analysis and by using the Xgboost algorithm and SVM algorithm.

Conclusion

In a novel finding, we observed that Tau protein and Aβ had opposite trends with age in both the AD and non-AD groups. The linear regression curves of the AD and non-AD groups had completely opposite trends. Through a machine learning approach, we constructed an AD diagnostic model with excellent performance based on the selected features.

Mitochondrial Alterations in Alzheimer's Disease: Insight from the 5xFAD Mouse Model

Mitochondrial dysfunction is increasingly recognized as a key factor in Alzheimer's disease (AD) pathogenesis, but the precise relationship between mitochondrial dynamics and proteinopathies in AD remains unclear. This study investigates the role of mitochondrial dynamics and function in the hippocampal tissue and peripheral blood mononuclear cells (PBMCs) of 5xFAD transgenic mice, as a model of AD. The levels of mitochondrial fusion proteins OPA1 and MFN2 and fission proteins DRP1 and phospho-DRP1 (S616) at 3, 6, and 9 months of age were assessed. Western blot analysis revealed significantly lower levels of OPA1 and MFN2 in the hippocampus of 6- and 9-month-old transgenic (TG) 5xFAD mice compared to controls (CTR), while DRP1 and pDRP1 levels were increased in 9-month-old TG mice. Additionally, MFN2 were decreased in the PBMCs of 9-month-old TG mice, indicating systemic mitochondrial alterations. Ultrastructural analysis of hippocampal tissues showed substantial alterations in mitochondrial morphology, including abnormalities in size and shape, a preponderance of teardrop-shaped mitochondria, and alterations in the somatic mitochondria-ER complex. Notably, mitochondria-associated ER contact sites were more distant in TG mice, suggesting functional impairments. Flow cytometric measurements demonstrated decreased mitochondrial membrane potential and mass, along with increased superoxide production, in the PBMCs of TG mice, particularly at 9 months, highlighting compromised mitochondrial function. Levels of key mitochondrial proteins including VDAC, TOM2O, and mitophagy-related protein PINK1 levels altered in both central and peripheral tissue of TG mice. These findings suggest that mitochondrial dysfunction and altered dynamics are early events in AD development in 5xFAD mice, manifesting in both central and peripheral tissues, and support the notion that mitochondrial abnormalities are an integral component of AD pathology. These insights might lead to the development of targeted therapies that modulate mitochondrial dynamics and function to mitigate AD progression.

The role of TRAP1 in regulating mitochondrial dynamics during acute hypoxia-induced brain injury

Brain damage caused by acute hypoxia is associated with the physiological activities of mitochondria. Although mitochondria being dynamically regulated, our comprehensive understanding of the response of specific brain cell types to acute hypoxia remains ambiguous. Tumor necrosis factor receptor-associated protein 1 (TRAP1), a mitochondrial-based molecular chaperone, plays a role in controlling mitochondrial movements. Herein, we demonstrated that acute hypoxia significantly alters mitochondria morphology and functionality in both in vivo and in vitro brain injury experiments. Summary-data-based Mendelian Randomization (SMR) analyses revealed possible causative links between mitochondria-related genes and hypoxia injury. Advancing the protein-protein interaction network and molecular docking further elucidated the associations between TRAP1 and mitochondrial dynamics. Furthermore, it was shown that TRAP1 knockdown levels variably affected the expression of key mitochondrial dynamics proteins (DRP1, FIS1, and MFN1/2) in primary hippocampal neurons, astrocytes, and BV-2 cell, leading to changes in mitochondrial structure and function. Understanding the function of TRAP1 in altering mitochondrial physiological activity during hypoxia-induced acute brain injury could help serve as a potential therapeutic target to mitigate neurological damage.

Mitochondrial dysfunction gene expression, DNA methylation, and inflammatory cytokines interaction activate Alzheimer's disease: a multi-omics Mendelian randomization study

BACKGROUND

Mitochondrial dysfunction (MD) is increasingly recognized as a key pathophysiological contributor in Alzheimer disease (AD). As differential MD genes expression may serve as either a causative factor or a consequence in AD, and expression of these genes could be influenced by epigenetic modifications or interact with inflammatory cytokines, hence, the precise role of MD in AD remains uncertain.

METHODS

Meta-analysis of brain transcriptome datasets was conducted to pinpoint differentially expressed genes (DEGs) associated with MD in AD. We utilized three-step SMR to analyze the AD genome-wide association study summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs from the blood and brain tissues, respectively. Through SMR and colocalization analysis, we further explored the interactions between brain eQTLs and inflammatory cytokines.

RESULTS

Five datasets were meta-analyzed to prioritize 825 DEGs in AD from 1339 MD-related genes. Among these, seven genes from blood samples such as NDUFS8 and SPG7 and thirty-two genes from brain tissue including CLU and MAPT were identified as candidate AD-causal MD genes and regulated by methylation level. Furthermore, we revealed 13 MD gene expression-inflammatory pathway pairs involving LDLR, ACE and PTPMT1 along with interleukin-17C, interleukin-18 and hepatocyte growth factor.

CONCLUSIONS

This study highlighted that the AD-causal MD genes could be regulated by epigenetic changes and interact with inflammatory cytokines, providing evidence for AD prevention and intervention.

Recent advances in biomaterials based near-infrared mild photothermal therapy for biomedical application: A review

Mild photothermal therapy (MPTT) generates heat therapeutic effect at the temperature below 45 °C under near-infrared (NIR) irradiation, which has the advantages of controllable treatment efficacy, lower hyperthermia temperatures, reduced dosage, and minimized damage to surrounding tissues. Despite significant progress has been achieved in MPTT, it remains primarily in the stage of basic and clinical research and has not yet seen widespread clinical adoption. Herein, a comprehensive overview of the recent NIR MPTT development was provided, aiming to emphasize the mechanism and obstacles, summarize the used photothermal agents, and introduce various biomedical applications such as anti-tumor, wound healing, and vascular disease treatment. The challenges of MPTT were proposed with potential solutions, and the future development direction in MPTT was outlooked to enhance the prospects for clinical translation.

Mitochondrial plasticity and synaptic plasticity crosstalk; in health and Alzheimer's disease

Synaptic plasticity is believed to underlie the cellular and molecular basis of memory formation. Mitochondria are one of the main organelles involved in metabolism and energy maintenance as plastic organelles that change morphologically and functionally in response to cellular needs and regulate synaptic function and plasticity through multiple mechanisms, including ATP generation, calcium homeostasis, and biogenesis. An increased neuronal activity enhances synaptic efficiency, during which mitochondria's spatial distribution and morphology change significantly. These organelles build up in the pre-and postsynaptic zones to produce ATP, which is necessary for several synaptic processes like neurotransmitter release and recycling. Mitochondria also regulate calcium homeostasis by buffering intracellular calcium, which ensures proper synaptic activity. Furthermore, mitochondria in the presynaptic terminal have distinct morphological properties compared to dendritic or postsynaptic mitochondria. This specialization enables precise control of synaptic activity and plasticity. Mitochondrial dysfunction has been linked to synaptic failure in many neurodegenerative disorders, like Alzheimer's disease (AD). In AD, malfunctioning mitochondria cause delays in synaptic vesicle release and recycling, ionic gradient imbalances, and mostly synaptic failure. This review emphasizes mitochondrial plasticity's contribution to synaptic function. It also explores the profound effect of mitochondrial malfunction on neurodegenerative disorders, focusing on AD, and provides an overview of how they sustain cellular health under normal conditions and how their malfunction contributes to neurodegenerative diseases, highlighting their potential as a therapeutic target for such conditions.

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.