Quantitative profiling brain proteomes revealed mitochondrial dysfunction in Alzheimer's disease

Mitochondria dysfunction in the pathogenesis of Alzheimer's disease: recent advances

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases, characterized by impaired cognitive function due to progressive loss of neurons in the brain. Under the microscope, neuronal accumulation of abnormal tau proteins and amyloid plaques are two pathological hallmarks in affected brain regions. Although the detailed mechanism of the pathogenesis of AD is still elusive, a large body of evidence suggests that damaged mitochondria likely play fundamental roles in the pathogenesis of AD. It is believed that a healthy pool of mitochondria not only supports neuronal activity by providing enough energy supply and other related mitochondrial functions to neurons, but also guards neurons by minimizing mitochondrial related oxidative damage. In this regard, exploration of the multitude of mitochondrial mechanisms altered in the pathogenesis of AD constitutes novel promising therapeutic targets for the disease. In this review, we will summarize recent progress that underscores the essential role of mitochondria dysfunction in the pathogenesis of AD and discuss mechanisms underlying mitochondrial dysfunction with a focus on the loss of mitochondrial structural and functional integrity in AD including mitochondrial biogenesis and dynamics, axonal transport, ER-mitochondria interaction, mitophagy and mitochondrial proteostasis.

Quantitative proteomic characterization of microvesicles/exosomes from the cerebrospinal fluid of patients with acute bilirubin encephalopathy

Severe hyperbilirubinemia causes neurotoxicity and may lead to acute bilirubin encephalopathy (ABE) during the critical period of central nervous system development. The aim of the present study was to identify differentially expressed proteins (DEPs) in microvesicles/exosomes (MV/E) isolated from the cerebrospinal fluid (CSF) of patients with ABE. Co-precipitation was used to isolate the MV/E from the CSF of patients with ABE and age-matched controls. Isobaric tagging for relative and absolute quantification-based proteomic technology combined with liquid chromatography/tandem mass spectrometry was used to identify DEPs in the MV/E. Bioinformatics analysis was subsequently performed to investigate Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes enriched signaling pathways of these DEPs. A total of four proteins were selected for further validation via western blotting. A total of 291 dysregulated proteins were identified by comparing the patients with ABE with the controls. Bioinformatics analysis indicated the involvement of immune-inflammation-associated cellular processes and signaling pathways in the pathophysiology of ABE. In conclusion, the present study identified the proteomic profile of MV/E isolated from the CSF of patients with ABE. These results may provide an improved understanding of the pathogenesis of ABE and may help to identify early diagnostic biomarkers and therapeutic targets.

Hypoxia-Induced Degenerative Protein Modifications Associated with Aging and Age-Associated Disorders

Aging is an inevitable time-dependent decline of various physiological functions that finally leads to death. Progressive protein damage and aggregation have been proposed as the root cause of imbalance in regulatory processes and risk factors for aging and neurodegenerative diseases. Oxygen is a modulator of aging. The oxygen-deprived conditions (hypoxia) leads to oxidative stress, cellular damage and protein modifications. Despite unambiguous evidence of the critical role of spontaneous non-enzymatic Degenerative Protein Modifications (DPMs) such as oxidation, glycation, carbonylation, carbamylation, and deamidation, that impart deleterious structural and functional protein alterations during aging and age-associated disorders, the mechanism that mediates these modifications is poorly understood. This review summarizes up-to-date information and recent developments that correlate DPMs, aging, hypoxia, and age-associated neurodegenerative diseases. Despite numerous advances in the study of the molecular hallmark of aging, hypoxia, and degenerative protein modifications during aging and age-associated pathologies, a major challenge remains there to dissect the relative contribution of different DPMs in aging (either natural or hypoxia-induced) and age-associated neurodegeneration.

A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction

Background: Alzheimer's disease (AD) is the most common type of dementia. Scientists have discovered that the causes of AD may include a combination of genetic, lifestyle, and environmental factors, but the exact cause has not yet been elucidated. Effective strategies to prevent and treat AD therefore remain elusive. The identified genetic causes of AD mainly focus on individual genes, but growing evidence has shown that complex diseases are usually affected by the interaction of genes in a network. Few studies have focused on the interactions and correlations between genes and how they are gradually destroyed or disappear during AD progression. A differential network analysis has been recognized as an essential tool for identifying the underlying pathogenic mechanisms and significant genes for prediction analysis. We therefore aim to conduct a differential network analysis to reveal potential networks involved in the neuropathogenesis of AD and identify genes for AD prediction. Methods: In this paper, we selected 365 samples from the Religious Orders Study and the Rush Memory and Aging Project, including 193 clinically and neuropathologically confirmed AD subjects and 172 no cognitive impairment (NCI) controls. Then, we selected 158 genes belonging to the AD pathway (hsa05010) of the Kyoto Encyclopedia of Genes and Genomes. We employed a machine learning method, namely, joint density-based non-parametric differential interaction network analysis and classification (JDINAC), in the analysis of gene expression data (RNA-seq data). We searched for the differential networks in the RNA-seq data with a pathological diagnosis of AD. Finally, an optimal prediction model was built through cross-validation, which showed good discrimination and calibration for AD prediction. Results: We used JDINAC to derive a gene co-expression network and to explore the relationship between the interaction of gene pairs and AD, and the top 10 differential gene pairs were identified. We then compared the prediction performance between JDINAC and individual genes based on prediction methods. JDINAC provides better accuracy of classification than the latest methods, such as random forest and penalized logistic regression. Conclusions: The interaction between gene pairs is related to AD and can provide more insight than the individual genes in AD prediction.

Meta-Analysis of Gene Expression Changes in the Blood of Patients with Mild Cognitive Impairment and Alzheimer's Disease Dementia

Background: Dementia is a major public health concern affecting approximately 47 million people worldwide. Mild cognitive impairment (MCI) is one form of dementia that affects an individual’s memory with or without affecting their daily life. Alzheimer’s disease dementia (ADD) is a more severe form of dementia that usually affects elderly individuals. It remains unclear whether MCI is a distinct disorder from or an early stage of ADD. Methods: Gene expression data from blood were analyzed to identify potential biomarkers that may be useful for distinguishing between these two forms of dementia. Results: A meta-analysis revealed 91 genes dysregulated in individuals with MCI and 387 genes dysregulated in ADD. Pathway analysis identified seven pathways shared between MCI and ADD and nine ADD-specific pathways. Fifteen transcription factors were associated with MCI and ADD, whereas seven transcription factors were specific for ADD. Mir-335-5p was specific for ADD, suggesting that it may be useful as a biomarker. Diseases that are associated with MCI and ADD included developmental delays, cognition impairment, and movement disorders. Conclusion: These results provide a better molecular understanding of peripheral changes that occur in MCI and ADD patients and may be useful in the identification of diagnostic and prognostic biomarkers.