Extracellular GAPDH Promotes Alzheimer Disease Progression by Enhancing Amyloid-β Aggregation and Cytotoxicity

Stratifying risk of Alzheimer's disease in healthy middle-aged individuals with machine learning

Alzheimer's disease has a prolonged asymptomatic phase during which pathological changes accumulate before clinical symptoms emerge. This study aimed to stratify the risk of clinical disease to inform future disease-modifying treatments. Cerebrospinal fluid analysis from participants in the Emory Healthy Brain Study was used to classify individuals based on amyloid beta 42 (Aβ42), total tau (tTau) and phosphorylated tau (pTau) levels. Cognitively normal (CN), biomarker-positive (CN)/BM+individuals were identified using a tTau: Aβ42 ratio > 0.24, determined by Gaussian mixture models. CN/BM+ individuals (n = 134) were classified as having asymptomatic Alzheimer's disease (AsymAD), while CN, biomarker-negative (CN/BM-) individuals served as controls (n = 134). Cognitively symptomatic, biomarker-positive individuals with an Alzheimer's disease diagnosis confirmed by the Emory Cognitive Neurology Clinic were labelled as Alzheimer's disease (n = 134). Study groups were matched for age, sex, race and education. Cerebrospinal fluid samples from these matched Emory Healthy Brain Study groups were analysed using targeted proteomics via selected reaction monitoring mass spectrometry. The targeted cerebrospinal fluid panel included 75 peptides from 58 unique proteins. Machine learning approaches identified a subset of eight peptides (ADQDTIR, AQALEQAK, ELQAAQAR, EPVAGDAVPGPK, IASNTQSR, LGADMEDVCGR, VVSSIEQK, YDNSLK) that distinguished between CN/BM- and symptomatic Alzheimer's disease samples with a binary classifier area under the curve performance of 0.98. Using these eight peptides, Emory Healthy Brain Study AsymAD cases were further stratified into 'Control-like' and 'Alzheimer's disease-like' subgroups, representing varying levels of risk for developing clinical disease. The eight peptides were evaluated in an independent dataset from the Alzheimer's Disease Neuroimaging Initiative, effectively distinguishing CN/BM- from symptomatic Alzheimer's disease cases (area under the curve = 0.89) and stratifying AsymAD individuals into control-like and Alzheimer's disease-like subgroups (area under the curve = 0.89). In the absence of matched longitudinal data, an established cross-sectional event-based disease progression model was employed to assess the generalizability of these peptides for risk stratification. In summary, results from two independent modelling methods and datasets demonstrate that the identified eight peptides effectively stratify the risk of progression from asymptomatic to symptomatic Alzheimer's disease.

Biomarker Identification for Alzheimer's Disease Using a Multi-Filter Gene Selection Approach

There is still a lack of effective therapies for Alzheimer's disease (AD), the leading cause of dementia and cognitive decline. Identifying reliable biomarkers and therapeutic targets is crucial for advancing AD research. In this study, we developed an aggregative multi-filter gene selection approach to identify AD biomarkers. This method integrates hub gene ranking techniques, such as degree and bottleneck, with feature selection algorithms, including Random Forest and Double Input Symmetrical Relevance, and applies ranking aggregation to improve accuracy and robustness. Five publicly available AD-related microarray datasets (GSE48350, GSE36980, GSE132903, GSE118553, and GSE5281), covering diverse brain regions like the hippocampus and frontal cortex, were analyzed, yielding 803 overlapping differentially expressed genes from 464 AD and 492 normal cases. An independent dataset (GSE109887) was used for external validation. The approach identified 50 prioritized genes, achieving an AUC of 86.8 in logistic regression on the validation dataset, highlighting their predictive value. Pathway analysis revealed involvement in critical biological processes such as synaptic vesicle cycles, neurodegeneration, and cognitive function. These findings provide insights into potential therapeutic targets for AD.

Exploring glycolytic enzymes in disease: potential biomarkers and therapeutic targets in neurodegeneration, cancer and parasitic infections

Glycolysis, present in most organisms, is evolutionarily one of the oldest metabolic pathways. It has great relevance at a physiological level because it is responsible for generating ATP in the cell through the conversion of glucose into pyruvate and reducing nicotinamide adenine dinucleotide (NADH) (that may be fed into the electron chain in the mitochondria to produce additional ATP by oxidative phosphorylation), as well as for producing intermediates that can serve as substrates for other metabolic processes. Glycolysis takes place through 10 consecutive chemical reactions, each of which is catalysed by a specific enzyme. Although energy transduction by glucose metabolism is the main function of this pathway, involvement in virulence, growth, pathogen-host interactions, immunomodulation and adaptation to environmental conditions are other functions attributed to this metabolic pathway. In humans, where glycolysis occurs mainly in the cytosol, the mislocalization of some glycolytic enzymes in various other subcellular locations, as well as alterations in their expression and regulation, has been associated with the development and progression of various diseases. In this review, we describe the role of glycolytic enzymes in the pathogenesis of diseases of clinical interest. In addition, the potential role of these enzymes as targets for drug development and their potential for use as diagnostic and prognostic markers of some pathologies are also discussed.

Dehydroamino acids and their crosslinks in Alzheimer's disease aggregates

Alzheimer's disease (AD) is characterized by the accumulation of protein aggregates, which are thought to be influenced by posttranslational modifications (PTMs). Dehydroamino acids (DHAAs) are rarely observed PTMs that contain an electrophilic alkene capable of forming protein-protein crosslinks, which may lead to protein aggregation. We report here the discovery of DHAAs in the protein aggregates from AD, constituting an unknown and previously unsuspected source of extensive proteomic complexity. We used mass spectrometry-based proteomics to discover 404 sites of DHAA formation in 171 proteins from protein aggregate-enriched human brain samples, 6-fold more sites than observed in the soluble protein fractions. The DHAA modifications are observed both directly and in the form of conjugates after reacting with abundant cellular nucleophiles or crosslinking to nucleophilic amino acid residues. We report 11 such crosslinks, including three in the Tau protein, which are 10-fold more abundant in AD samples compared with age-matched controls. Many of the proteins found to contain DHAAs and their conjugates are involved in protein aggregation or pathways dysregulated in AD. DHAAs are prevalent modifications in the AD brain proteome and give rise to protein crosslinks that may contribute to protein aggregation.

GraphkmerDTA: integrating local sequence patterns and topological information for drug-target binding affinity prediction and applications in multi-target anti-Alzheimer's drug discovery

Identifying drug-target binding affinity (DTA) plays a critical role in early-stage drug discovery. Despite the availability of various existing methods, there are still two limitations. Firstly, sequence-based methods often extract features from fixed length protein sequences, requiring truncation or padding, which can result in information loss or the introduction of unwanted noise. Secondly, structure-based methods prioritize extracting topological information but struggle to effectively capture sequence features. To address these challenges, we propose a novel deep learning model named GraphkmerDTA, which integrates Kmer features with structural topology. Specifically, GraphkmerDTA utilizes graph neural networks to extract topological features from both molecules and proteins, while fully connected networks learn local sequence patterns from the Kmer features of proteins. Experimental results indicate that GraphkmerDTA outperforms existing methods on benchmark datasets. Furthermore, a case study on lung cancer demonstrates the effectiveness of GraphkmerDTA, as it successfully identifies seven known EGFR inhibitors from a screening library of over two thousand compounds. To further assess the practical utility of GraphkmerDTA, we integrated it with network pharmacology to investigate the mechanisms underlying the therapeutic effects of Lonicera japonica flower in treating Alzheimer's disease. Through this interdisciplinary approach, three potential compounds were identified and subsequently validated through molecular docking studies. In conclusion, we present not only a novel AI model for the DTA task but also demonstrate its practical application in drug discovery by integrating modern AI approaches with traditional drug discovery methodologies.

Factors Affecting Pathological Amyloid Protein Transformation: From Post-Translational Modifications to Chaperones

The review discusses the influence of various factors (e.g., post-translational modifications and chaperones) on the pathological transformation of amyloidogenic proteins involved in the onset and development of neurodegenerative diseases (Alzheimer's and Parkinson's diseases) and spongiform encephalopathies of various origin with special focus on the role of α-synuclein, prion protein, and, to a lesser extent, beta-amyloid peptide. The factors investigated by the authors of this review are discussed in more detail, including posttranslational modifications (glycation and S-nitrosylation), cinnamic acid derivatives and dendrimers, and chaperonins (eukaryotic, bacterial, and phage). A special section is devoted to the role of the gastrointestinal microbiota in the pathogenesis of amyloid neurodegenerative diseases, in particular, its involvement in the transformation of infectious prions and possibly other proteins capable of prion-like transmission of amyloidogenic diseases.

Unveiling biomarker detection in Alzheimer's disease: a computational approach to microarray analysis

Alzheimer's disease (AD) is a major neurodegenerative condition that affects a significant number of people around the world, making understanding the underlying molecular mechanisms fundamental for identifying predictive biomarkers and therapeutic targets for treating AD. Analysis of the gene expression profile GSE5281, consisting of 161 samples (87 AD and 74 control samples) revealed differentially expressed genes (DEGs) used for KEGG screening to connect dysregulated genes to metabolic pathways or other neurological diseases including Parkinson's, prion, and Huntington's and construction of a protein interaction network. Protein-protein interaction (PPI) network and module analysis uncovered the hub genes ACTB, ACTG1, ATP5A1, CCT2, CDC42, EGFR, FN1, GAPDH, GFAP, GRIA1, HSP90AB1, MAPK1, PSMA3, PSMD14, SNAP25, SNCA, SOD1, SOX2, TPI1, and YWHAZ. The analysis revealed a link between dysregulated genes and processes in AD pathology, including the promotion of osteoporosis, an altered nucleotide metabolism, microtubule stability, and the dysfunctionality of the blood-brain barrier (BBB). These targets might be used as predictive biomarkers or to develop curative and preventive therapeutic approaches for treating AD.

Evaluation of cerebrospinal fluid (CSF) and interstitial fluid (ISF) mouse proteomes for the validation and description of Alzheimer's disease biomarkers

BACKGROUND

Mass spectrometry (MS)-based cerebrospinal fluid (CSF) proteomics is an important method for discovering biomarkers of neurodegenerative diseases. CSF serves as a reservoir for interstitial fluid (ISF), and extensive communication between the two fluid compartments helps to remove waste products from the brain.

NEW METHOD

We performed proteomic analyses of both CSF and ISF fluid compartments using intracerebral microdialysis to validate and detect novel biomarkers of Alzheimer's disease (AD) in APPtg and C57Bl/6J control mice.

RESULTS

We identified up to 625 proteins in ISF and 4483 proteins in CSF samples. By comparing the biofluid profiles of APPtg and C57Bl/6J mice, we detected 37 and 108 significantly up- and downregulated candidates, respectively. In ISF, 7 highly regulated proteins, such as Gfap, Aldh1l1, Gstm1, and Txn, have already been implicated in AD progression, whereas in CSF, 9 out of 14 highly regulated proteins, such as Apba2, Syt12, Pgs1 and Vsnl1, have also been validated to be involved in AD pathogenesis. In addition, we also detected new interesting regulated proteins related to the control of synapses and neurotransmission (Kcna2, Cacng3, and Clcn6) whose roles as AD biomarkers should be further investigated.

COMPARISON WITH EXISTING METHODS

This newly established combined protocol provides better insight into the mutual communication between ISF and CSF as an analysis of tissue or CSF compartments alone.

CONCLUSIONS

The use of multiple fluid compartments, ISF and CSF, for the detection of their biological communication enables better detection of new promising AD biomarkers.

Novel Insights Into DLAT's Role in Alzheimer's Disease-Related Copper Toxicity Through Microglial Exosome Dynamics

BACKGROUND

Alzheimer's disease (AD) is a complex neurodegenerative disorder, with recent research emphasizing the roles of microglia and their secreted extracellular vesicles in AD pathology. However, the involvement of specific molecular pathways contributing to neuronal death in the context of copper toxicity remains largely unexplored.

OBJECTIVE

This study investigates the interaction between pyruvate kinase M2 (PKM2) and dihydrolipoamide S-acetyltransferase (DLAT), particularly focusing on copper-induced neuronal death in Alzheimer's disease.

METHODS

Gene expression datasets were analyzed to identify key factors involved in AD-related copper toxicity. The role of DLAT was validated using 5xFAD transgenic mice, while in vitro experiments were conducted to assess the impact of microglial exosomes on neuronal PKM2 transfer and DLAT expression. The effects of inhibiting the PKM2 transfer via microglial exosomes on DLAT expression and copper-induced neuronal death were also evaluated.

RESULTS

DLAT was identified as a critical factor in the pathology of AD, particularly in copper toxicity. In 5xFAD mice, increased DLAT expression was linked to hippocampal damage and cognitive decline. In vitro, microglial exosomes were shown to facilitate the transfer of PKM2 to neurons, leading to upregulation of DLAT expression and increased copper-induced neuronal death. Inhibition of PKM2 transfer via exosomes resulted in a significant reduction in DLAT expression, mitigating neuronal death and slowing AD progression.

CONCLUSION

This study uncovers a novel pathway involving microglial exosomes and the PKM2-DLAT interaction in copper-induced neuronal death, providing potential therapeutic targets for Alzheimer's disease. Blocking PKM2 transfer could offer new strategies for reducing neuronal damage and slowing disease progression in AD.

Glyceraldehyde-3-phosphate dehydrogenase is involved in the pathogenesis of Alzheimer's disease

One of important characteristics of Alzheimer's disease is a persistent oxidative/nitrosative stress caused by pro-oxidant properties of amyloid-beta peptide (Aβ) and chronic inflammation in the brain. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is easily oxidized under oxidative stress. Numerous data indicate that oxidative modifications of GAPDH in vitro and in cell cultures stimulate GAPDH denaturation and aggregation, and the catalytic cysteine residue Cys152 is important for these processes. Both intracellular and extracellular GAPDH aggregates are toxic for the cells. Interaction of denatured GAPDH with soluble Aβ results in mixed insoluble aggregates with increased toxicity. The above-described properties of GAPDH (sensitivity to oxidation and propensity to form aggregates, including mixed aggregates with Aβ) determine its role in the pathogenesis of Alzheimer's disease.

Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease

Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, suggesting that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, suggesting that DEGs exert more impact on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we observe an overall downregulation of astrocyte and microglia modules across all brain regions in AD, indicating a prevailing trend of functional repression in glial cells across these regions. Notable genes from the CALM and HSP90 families emerged as hub genes across neuronal modules in all brain regions, suggesting conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis to comprehensively understand the cell-type-specific roles of genes in AD-related biological processes.

Systematic Analysis of Biological Processes Reveals Gene Co-expression Modules Driving Pathway Dysregulation in Alzheimer's Disease

Alzheimer's disease (AD) manifests as a complex systems pathology with intricate interplay among various genes and biological processes. Traditional differential gene expression (DEG) analysis, while commonly employed to characterize AD-driven perturbations, does not sufficiently capture the full spectrum of underlying biological processes. Utilizing single-nucleus RNA-sequencing data from postmortem brain samples across key regions-middle temporal gyrus, superior frontal gyrus, and entorhinal cortex-we provide a comprehensive systematic analysis of disrupted processes in AD. We go beyond the DEG-centric analysis by integrating pathway activity analysis with weighted gene co-expression patterns to comprehensively map gene interconnectivity, identifying region- and cell-type-specific drivers of biological processes associated with AD. Our analysis reveals profound modular heterogeneity in neurons and glia as well as extensive AD-related functional disruptions. Co-expression networks highlighted the extended involvement of astrocytes and microglia in biological processes beyond neuroinflammation, such as calcium homeostasis, glutamate regulation, lipid metabolism, vesicle-mediated transport, and TOR signaling. We find limited representation of DEGs within dysregulated pathways across neurons and glial cells, indicating that differential gene expression alone may not adequately represent the disease complexity. Further dissection of inferred gene modules revealed distinct dynamics of hub DEGs in neurons versus glia, highlighting the differential impact of DEGs on neurons compared to glial cells in driving modular dysregulations underlying perturbed biological processes. Interestingly, we note an overall downregulation of both astrocyte and microglia modules in AD across all brain regions, suggesting a prevailing trend of functional repression in glial cells across these regions. Notable genes, including those of the CALM and HSP90 family genes emerged as hub genes across neuronal modules in all brain regions, indicating conserved roles as drivers of synaptic dysfunction in AD. Our findings demonstrate the importance of an integrated, systems-oriented approach combining pathway and network analysis for a comprehensive understanding of the cell-type-specific roles of genes in AD-related biological processes.

Systems pharmacology and multi-scale mechanism of Enicostema axillare bioactives in treating Alzheimer disease

As a progressive neurological disease with increased morbidity and mortality, Alzheimer Disease (AD) is characterized by neuron damage that controls memory and mental functions. Enicostema axillare (EA), an herb with a history of combativeness and effectiveness in treating Rheumatoid Arthritis, Cancer, and Diabetes, is used in Indian folk medicine from a holistic point of view. Though the herb is used for many illnesses, the molecular mechanism of its bioactive on AD has not been deciphered by intricate research. A unique pharmacology approach based on ADME drug screening and targeting, pathway enrichment (GO and KEGG), and network pharmacology, was established to explore the molecular mechanisms of E. axillare (EA) bioactive compounds for the treatment of AD. In brief, we bring to light the three active compounds of EA and seven potential molecular targets of AD, which are mainly implicated in four signaling pathways, i.e., MAPK, Apoptosis, neurodegeneration, and the TNF pathway. Moreover, the network analysis of the active compounds, molecular targets, and their pathways reveals the pharmacological nature of the compounds. Further, molecular docking studies were carried out to explore the interactions between the EA bioactive compounds and the targets and examine the binding affinity. The outcome of the work reflects the potential therapeutic effects of the compounds for treating AD through the modulation of the key proteins, which further corroborates the reliability of our network pharmacology analysis. This study not only helps in understanding the molecular mechanism of the drugs but also helps in finding and sorting new drugs for the treatment of AD, and other complex diseases through modern medicine.

Supplementation of Medium-Chain Triglycerides Combined with Docosahexaenoic Acid Inhibits Amyloid Beta Protein Deposition by Improving Brain Glucose Metabolism in APP/PS1 Mice

The deterioration of brain glucose metabolism predates the clinical onset of Alzheimer's disease (AD). Medium-chain triglycerides (MCTs) and docosahexaenoic acid (DHA) positively improve brain glucose metabolism and decrease the expression of AD-related proteins. However, the effects of the combined intervention are unclear. The present study explored the effects of the supplementation of MCTs combined with DHA in improving brain glucose metabolism and decreasing AD-related protein expression levels in APP/PS1 mice. The mice were assigned into four dietary treatment groups: the control group, MCTs group, DHA group, and MCTs + DHA group. The corresponding diet of the respective groups was fed to mice from the age of 3 to 11 months. The results showed that the supplementation of MCTs combined with DHA could increase serum octanoic acid (C8:0), decanoic acid (C10:0), DHA, and β-hydroxybutyrate (β-HB) levels; improve glucose metabolism; and reduce nerve cell apoptosis in the brain. Moreover, it also aided with decreasing the expression levels of amyloid beta protein (Aβ), amyloid precursor protein (APP), β-site APP cleaving enzyme-1 (BACE1), and presenilin-1 (PS1) in the brain. Furthermore, the supplementation of MCTs + DHA was significantly more beneficial than that of MCTs or DHA alone. In conclusion, the supplementation of MCTs combined with DHA could improve energy metabolism in the brain of APP/PS1 mice, thus decreasing nerve cell apoptosis and inhibiting the expression of Aβ.

The role of sperm-specific glyceraldehyde-3-phosphate dehydrogenase in the development of pathologies-from asthenozoospermia to carcinogenesis

The review considers various aspects of the influence of the glycolytic enzyme, sperm-specific glyceraldehyde-3-phosphate dehydrogenase (GAPDS) on the energy metabolism of spermatozoa and on the occurrence of several pathologies both in spermatozoa and in other cells. GAPDS is a unique enzyme normally found only in mammalian spermatozoa. GAPDS provides movement of the sperm flagellum through the ATP formation in glycolytic reactions. Oxidation of cysteine residues in GAPDS results in inactivation of the enzyme and decreases sperm motility. In particular, reduced sperm motility in diabetes can be associated with GAPDS oxidation by superoxide anion produced during glycation reactions. Mutations in GAPDS gene lead in the loss of motility, and in some cases, disrupts the formation of the structural elements of the sperm flagellum, in which the enzyme incorporates during spermiogenesis. GAPDS activation can be used to increase the spermatozoa fertility, and inhibitors of this enzyme are being tried as contraceptives. A truncated GAPDS lacking the N-terminal fragment of 72 amino acids that attaches the enzyme to the sperm flagellum was found in melanoma cell lines and then in specimens of melanoma and other tumors. Simultaneous production of the somatic form of GAPDH and sperm-specific GAPDS in cancer cells leads to a reorganization of their energy metabolism, which is accompanied by a change in the efficiency of metastasis of certain forms of cancer. Issues related to the use of GAPDS for the diagnosis of cancer, as well as the possibility of regulating the activity of this enzyme to prevent metastasis, are discussed.

Predictors of Cognitive Decline in Healthy Middle-Aged Individuals with Asymptomatic Alzheimer's Disease

Alzheimer's disease (AD) progresses through a lengthy asymptomatic period during which pathological changes accumulate prior to development of clinical symptoms. As disease-modifying treatments are developed, tools to stratify risk of clinical disease will be required to guide their use. In this study, we examine the relationship of AD biomarkers in healthy middle-aged individuals to health history, family history, and neuropsychological measures and identify cerebrospinal fluid (CSF) biomarkers to stratify risk of progression from asymptomatic to symptomatic AD. CSF from cognitively normal (CN) individuals (N=1149) in the Emory Healthy Brain Study were assayed for Aβ42, total Tau (tTau), and phospho181-Tau (pTau), and a subset of 134 cognitively normal, but biomarker-positive, individuals were identified with asymptomatic AD (AsymAD) based on a locally-determined cutoff value for ratio of tTau to Aβ42. These AsymAD cases were matched for demographic features with 134 biomarker-negative controls (CN/BM-) and compared for differences in medical comorbidities and family history. Dyslipidemia emerged as a distinguishing feature between AsymAD and CN/BM-groups with significant association with personal and family history of dyslipidemia. A weaker relationship was seen with diabetes, but there was no association with hypertension. Examination of the full cohort by median regression revealed a significant relationship of CSF Aβ42 (but not tTau or pTau) with dyslipidemia and diabetes. On neuropsychological tests, CSF Aβ42 was not correlated with performance on any measures, but tTau and pTau were strongly correlated with visuospatial perception and visual episodic memory. In addition to traditional CSF AD biomarkers, a panel of AD biomarker peptides derived from integrating brain and CSF proteomes were evaluated using machine learning strategies to identify a set of 8 peptides that accurately classified CN/BM- and symptomatic AD CSF samples with AUC of 0.982. Using these 8 peptides in a low dimensional t-distributed Stochastic Neighbor Embedding analysis and k-Nearest Neighbor (k=5) algorithm, AsymAD cases were stratified into "Control-like" and "AD-like" subgroups based on their proximity to CN/BM- or AD CSF profiles. Independent analysis of these cases using a Joint Mutual Information algorithm selected a set of 5 peptides with 81% accuracy in stratifying cases into AD-like and Control-like subgroups. Performance of both sets of peptides was evaluated and validated in an independent data set from the Alzheimer's Disease Neuroimaging Initiative. Based on our findings, we conclude that there is an important role of lipid metabolism in asymptomatic stages of AD. Visuospatial perception and visual episodic memory may be more sensitive than language-based abilities to earliest stages of cognitive decline in AD. Finally, candidate CSF peptides show promise as next generation biomarkers for predicting progression from asymptomatic to symptomatic stages of AD.

Protein Interactome of Amyloid-β as a Therapeutic Target

The amyloid concept of Alzheimer's disease (AD) assumes the β-amyloid peptide (Aβ) as the main pathogenic factor, which injures neural and other brain cells, causing their malfunction and death. Although Aβ has been documented to exert its cytotoxic effect in a solitary manner, there is much evidence to claim that its toxicity can be modulated by other proteins. The list of such Aβ co-factors or interactors includes tau, APOE, transthyretin, and others. These molecules interact with the peptide and affect the ability of Aβ to form oligomers or aggregates, modulating its toxicity. Thus, the list of potential substances able to reduce the harmful effects of the peptide should include ones that can prevent the pathogenic interactions by specifically binding Aβ and/or its partners. In the present review, we discuss the data on Aβ-based complexes in AD pathogenesis and on the compounds directly targeting Aβ or the destructors of its complexes with other polypeptides.

Exploring the Role of Glycolytic Enzymes PFKFB3 and GAPDH in the Modulation of Aβ and Neurodegeneration and Their Potential of Therapeutic Targets in Alzheimer's Disease

Alzheimer's disease (AD) is presently the 6th major cause of mortality across the globe. However, it is expected to rise rapidly, following cancer and heart disease, as a leading cause of death among the elderly peoples. AD is largely characterized by metabolic changes linked to glucose metabolism and age-induced mitochondrial failure. Recent research suggests that the glycolytic pathway is required for a range of neuronal functions in the brain including synaptic transmission, energy production, and redox balance; however, alteration in glycolytic pathways may play a significant role in the development of AD. Moreover, it is hypothesized that targeting the key enzymes involved in glucose metabolism may help to prevent or reduce the risk of neurodegenerative disorders. One of the major pro-glycolytic enzyme is 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3); it is normally absent in neurons but abundant in astrocytes. Similarly, another key of glycolysis is glyceraldehyde-3-phosphate dehydrogenase (GAPDH) which catalyzes the conversion of aldolase and glyceraldehyde 3 phosphates to 1,3 bisphosphoglycerate. GAPDH has been reported to interact with various neurodegenerative disease-associated proteins, including the amyloid-β protein precursor (AβPP). These findings indicate PFKFB3 and GAPDH as a promising therapeutic target to AD. Current review highlight the contributions of PFKFB3 and GAPDH in the modulation of Aβand AD pathogenesis and further explore the potential of PFKFB3 and GAPDH as therapeutic targets in AD.

Identification and validation of biomarkers based on cellular senescence in mild cognitive impairment

Background

Mild cognitive impairment (MCI), a syndrome defined as decline of cognitive function greater than expected for an individual's age and education level, occurs in up to 22.7% of elderly patients in United States, causing the heavy psychological and economic burdens to families and society. Cellular senescence (CS) is a stress response that accompanies permanent cell-cycle arrest, which has been reported to be a fundamental pathological mechanism of many age-related diseases. This study aims to explore biomarkers and potential therapeutic targets in MCI based on CS.

Methods

The mRNA expression profiles of peripheral blood samples from patients in MCI and non-MCI group were download from gene expression omnibus (GEO) database (GSE63060 for training and GSE18309 for external validation), CS-related genes were obtained from CellAge database. Weighted gene co-expression network analysis (WGCNA) was conducted to discover the key relationships behind the co-expression modules. The differentially expressed CS-related genes would be obtained through overlapping among the above datasets. Then, pathway and GO enrichment analyses were performed to further elucidate the mechanism of MCI. The protein-protein interaction network was used to extract hub genes and the logistic regression was performed to distinguish the MCI patients from controls. The hub gene-drug network, hub gene-miRNA network as well as transcription factor-gene regulatory network were used to analyze potential therapeutic targets for MCI.

Results

Eight CS-related genes were identified as key gene signatures in MCI group, which were mainly enriched in the regulation of response to DNA damage stimulus, Sin3 complex and transcription corepressor activity. The receiver operating characteristic curves of logistic regression diagnostic model were constructed and presented great diagnostic value in both training and validation set.

Conclusion

Eight CS-related hub genes - SMARCA4, GAPDH, SMARCB1, RUNX1, SRC, TRIM28, TXN, and PRPF19 - serve as candidate biomarkers for MCI and display the excellent diagnostic value. Furthermore, we also provide a theoretical basis for targeted therapy against MCI through the above hub genes.

Serial five-membered lactone ring ions in the treatment of Alzheimer's diseases-comprehensive profiling of arctigenin metabolites and network analysis

Arctigenin is a phenylpropanoid dibenzylbutyro lactone lignan compound with multiple biological functions. Previous studies have shown that arctigenin have neuroprotective effects in Alzheimer's disease (AD) models both in vivo and in vitro; however, its metabolism in vivo has not been studied. Most traditional analytical methods only partially characterize drug metabolite prototypes, so there is an urgent need for a research strategy that can fully characterize drug metabolites. In the present study, ions fishing with a serial five-membered lactone ring as a fishhook strategy based on ultrahigh-performance liquid chromatography-Q-Exactive Orbitrap mass spectrometry (UHPLC-Q-Exactive Orbitrap MS) was utilised to characterise the metabolism of arctigenin, and the establishment of this strategy also solved the challenge of creating a comprehensive metabolic profile of neolignan. Based on the proposed strategy, a total of 105 metabolites were detected and characterised, 76 metabolites of which were found in rats and 49 metabolites in liver microsomes. These metabolites were postulated to be produced through oxidation, reduction, hydrolysis, and complex reactions. Subsequently, network pharmacology was utilized to elucidate the mechanism of arctigenin and its main metabolites against Alzheimer's disease, screening 381 potential targets and 20 major signaling pathways. The study on the comprehensive metabolism of arctigenin provides a holistic metabolic profile, which will help to better understand the mechanism of arctigenin in the treatment of Alzheimer's disease (AD) and also provide a basis for the safe administration of arctigenin.

Mechanism of GAPDH Redox Signaling by H2O2 Activation of a Two-Cysteine Switch

Oxidation of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) by reactive oxygen species such as H2O2 activate pleiotropic signaling pathways is associated with pathophysiological cell fate decisions. Oxidized GAPDH binds chaperone proteins with translocation of the complex to the nucleus and mitochondria initiating autophagy and cellular apoptosis. In this study, we establish the mechanism by which H2O2-oxidized GAPDH subunits undergo a subunit conformational rearrangement. H2O2 oxidizes both the catalytic cysteine and a vicinal cysteine (four residues downstream) to their respective sulfenic acids. A 'two-cysteine switch' is activated, whereby the sulfenic acids irreversibly condense to an intrachain thiosulfinic ester resulting in a major metastable subunit conformational rearrangement. All four subunits of the homotetramer are uniformly and independently oxidized by H2O2, and the oxidized homotetramer is stabilized at low temperatures. Over time, subunits unfold forming disulfide-linked aggregates with the catalytic cysteine oxidized to a sulfinic acid, resulting from thiosulfinic ester hydrolysis via the highly reactive thiosulfonic ester intermediate. Molecular Dynamic Simulations provide additional mechanistic insights linking GAPDH subunit oxidation with generating a putative signaling conformer. The low-temperature stability of the H2O2-oxidized subunit conformer provides an operable framework to study mechanisms associated with gain-of-function activities of oxidized GAPDH to identify novel targets for the treatment of neurodegenerative diseases.

Monitoring GAPDH activity and inhibition with cysteine-reactive chemical probes

Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) is a central enzyme in glycolysis that regulates the Warburg effect in cancer cells. In addition to its role in metabolism, GAPDH is also implicated in diverse cellular processes, including transcription and apoptosis. Dysregulated GAPDH activity is associated with a variety of pathologies, and GAPDH inhibitors have demonstrated therapeutic potential as anticancer and immunomodulatory agents. Given the critical role of GAPDH in pathophysiology, it is important to have access to tools that enable rapid monitoring of GAPDH activity and inhibition within a complex biological system. Here, we report an electrophilic peptide-based probe, SEC1, which covalently modifies the active-site cysteine, C152, of GAPDH to directly report on GAPDH activity within a proteome. We demonstrate the utility of SEC1 to assess changes in GAPDH activity in response to oncogenic transformation, reactive oxygen species (ROS) and small-molecule GAPDH inhibitors, including Koningic acid (KA). We then further evaluated KA, to determine the detailed mechanism of inhibition. Our mechanistic studies confirm that KA is a highly effective irreversible inhibitor of GAPDH, which acts through a NAD⁺-uncompetitive and G3P-competitive mechanism. Proteome-wide evaluation of the cysteine targets of KA demonstrated high selectivity for the active-site cysteine of GAPDH over other reactive cysteines within the proteome. Lastly, the therapeutic potential of KA was investigated in an autoimmune model, where treatment with KA resulted in decreased cytokine production by Th1 effector cells. Together, these studies describe methods to evaluate GAPDH activity and inhibition within a proteome, and report on the high potency and selectivity of KA as an irreversible inhibitor of GAPDH.

Cysteine-reactive chemical probes can covalently modify the active-site cysteine of GAPDH.

Identification of molecular signatures associated with sleep disorder and Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized.

METHODS

Differentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls.

RESULTS

DEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder.

CONCLUSION

AD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.

Modification of Glyceraldehyde-3-Phosphate Dehydrogenase with Nitric Oxide: Role in Signal Transduction and Development of Apoptosis

This review focuses on the consequences of GAPDH S-nitrosylation at the catalytic cysteine residue. The widespread hypothesis according to which S-nitrosylation causes a change in GAPDH structure and its subsequent binding to the Siah1 protein is considered in detail. It is assumed that the GAPDH complex with Siah1 is transported to the nucleus by carrier proteins, interacts with nuclear proteins, and induces apoptosis. However, there are several conflicting and unproven elements in this hypothesis. In particular, there is no direct confirmation of the interaction between the tetrameric GAPDH and Siah1 caused by S-nitrosylation of GAPDH. The question remains as to whether the translocation of GAPDH into the nucleus is caused by S-nitrosylation or by some other modification of the catalytic cysteine residue. The hypothesis of the induction of apoptosis by oxidation of GAPDH is considered. This oxidation leads to a release of the coenzyme NAD+ from the active center of GAPDH, followed by the dissociation of the tetramer into subunits, which move to the nucleus due to passive transport and induce apoptosis. In conclusion, the main tasks are summarized, the solutions to which will make it possible to more definitively establish the role of nitric oxide in the induction of apoptosis.