Combination of mangiferin and T0901317 targeting autophagy promotes cholesterol efflux from macrophage foam cell in atherosclerosis.

BACKGROUND: The synthetic liver X receptor ligand (LXR) T0901317 (T0) has been reported to attenuate atherosclerosis (AS) without hyperglyceridemia due to innovative drug combination or nano-sized drug delivery. Given the key roles of mangiferin (MGF) in lipid metabolism and atherogenesis, it is critical to investigate progression of atherosclerotic lesion after combined treatment of MGF and T0. METHODS: Atherosclerotic plaque formation and hepatic lipid accumulation were compared in Apoe-/- mice among T0 and/or MGF treatment. The in vitro functions of MGF and T0 were analyzed by Oil-red O staining, cholesterol efflux assay, transmission electron microscopy and western blot analyses with or without acetylated low density lipoprotein. RESULTS: The combination therapy are effective regulators for atherosclerotic plaque formation in Apoe-/- mice, due to upregulation of ABCA1 and ABCG1 induced by LXR activation. Subsequently, we identified autophagy promoted by MGF and T0 treatment establishes a positive feedback loop that increases cholesterol efflux, resulted from LXRα activation. Under atherogenic conditions, the autophagy inhibitor CQ abolished the enhancement effect on cholesterol outflow of MGF and T0. Mechanically, MGF and T0 promotes LXRα and mTOR/AMPK signaling cascade in macrophage, and promotes AMPK signaling cascade in hepatocyte, leading to lipid metabolic homeostasis. CONCLUSIONS: Altogether, our findings reveal that MGF and T0 engages in AS therapy without side effects by activating AMPK-dependent autophagy to promote macrophage cholesterol efflux, and MGF might serve as a natural compound to assist T0 in AS via targeting autophagy.

The Roles of Long Noncoding RNA in Depression.

Depression is a common psychiatric disorder that brings great pain and burden to patients and their families. However, the pathogenesis underlying the development of depression remains unclear, limiting the development of diagnostic and therapeutic approaches for the disease. Recently, an increasing number of studies have shown that long noncoding RNAs (lncRNAs) play modulatory roles in depression. Here, we summarize the general mechanism of action and their roles in depression. LncRNAs are suggested to exert regulatory functions in depression in various ways, including competing endogenous RNA (ceRNA) networks, interacting with epigenetic modifications, interacting with single-nucleotide polymorphisms (SNPs), acting in cis or trans on target genes and regulating the immune system. A total of 13 lncRNAs (involving 16 ceRNA regulatory axes) have been revealed to have regulatory mechanisms. The potential relationship between methylation modification and lncRNA was also analyzed through lncRNA expression profile data. Functional annotation analysis showed that methylation-related lncRNAs were mainly enriched in postsynaptic specialization, neuron-to-neuron synapses, asymmetric synapses, and postsynaptic density. This indicates that methylation-related lncRNAs may have an impact on the synaptic microenvironment and may thus contribute to the development of depression. Moreover, we predicted potential interactions between SNP sites and lncRNAs in depression by querying the database. Through this review, we hope to deepen the understanding of the regulatory landscape of lncRNAs in depression and propose that future efforts should focus on establishing comprehensive and robust diagnostic models and further revealing the exact mechanism of lncRNA action in depression by experimental evidence.

Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network.

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key posttranslational modification involved in physiology and disease. The ability to robustly and rapidly predict protease-substrate specificity would also enable targeted proteolytic cleavage by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pretrained PGCN model to guide the design of protease libraries for cleaving two noncanonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

Chemical features and machine learning assisted predictions of protein-ligand short hydrogen bonds.

There are continuous efforts to elucidate the structure and biological functions of short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms reside more than 0.3 Å closer than the sum of their van der Waals radii. In this work, we evaluate 1070 atomic-resolution protein structures and characterize the common chemical features of SHBs formed between the side chains of amino acids and small molecule ligands. We then develop a machine learning assisted prediction of protein-ligand SHBs (MAPSHB-Ligand) model and reveal that the types of amino acids and ligand functional groups as well as the sequence of neighboring residues are essential factors that determine the class of protein-ligand hydrogen bonds. The MAPSHB-Ligand model and its implementation on our web server enable the effective identification of protein-ligand SHBs in proteins, which will facilitate the design of biomolecules and ligands that exploit these close contacts for enhanced functions.

Histologic inflammation and collagen content are not positively correlated in human BPH.

INTRODUCTION: Recent clinical studies have implicated prostate inflammation and fibrosis in the development of bladder outlet obstruction and lower urinary tract symptoms (LUTS). Studies utilizing rodent models, including work in our laboratory, have shown prostate fibrosis to occur as a consequence of inflammation. However, the relationship between collagen content and inflammation in human tissue samples obtained from surgical treatment of benign prostatic hypererplasia (BPH)/LUTS has not to our knowledge been previously examined. METHODS: Prostate tissue specimens from 53 patients (ages 47-88, mean 65.1) treated by open simple prostatectomy or transurethral resection of the prostate for BPH/LUTS were stained to quantitatively assess prostate inflammation and collagen content. Patients with prostate cancer present in greater than 5% of the surgical specimen were excluded. Prostate volume was determined from pelvic CT scan obtained within 2 years of surgery. RESULTS: Analysis of the data showed that inflammation was inversely correlated with collagen content (r = -0.28, p = 0.04). In men with prostates less than 75 cm3 inflammation increases and collagen content decreases with prostate volume (p = 0.002 and p = 0.03, respectively) while in men with prostate volume over 75 cm3 inflammation decreases and collagen content increases with prostate volume (p = 0.30 and p = 0.005, respectively). CONCLUSIONS: Our data do not support the assumed positive association of prostate inflammation with collagen content. Coordinated analysis of scatter plots of inflammation and collagen content with prostate volume revealed a subset of prostates with volumes >50 cm3 prostate characterized by intense inflammation and low collagen content and it is this subgroup that appears most responsible for the inverse correlation of inflammation and collagen.

Critical roles of PU.1/cathepsin S activation in regulating inflammatory responses of macrophages in periodontitis.

OBJECTIVE: To determine the critical roles of PU.1/cathepsin S activation in regulating inflammatory responses of macrophages during periodontitis. BACKGROUND: Cathepsin S (CatS) is a cysteine protease and exerts important roles in the immune response. Elevated CatS has been found in the gingival tissues of periodontitis patients and is involved in alveolar bone destruction. However, the underlying mechanism of CatS-driven IL-6 production in periodontitis remains unclear. METHODS: Western blot was applied to measure mature cathepsin S(mCatS) and IL-6 expression in gingival tissues from periodontitis patients and RAW264.7 cells exposed to lipopolysaccharide from Porphyromonas gingivalis (P.g. LPS). Immunofluorescence was applied to confirm the localization of PU.1, and CatS in the gingival tissues of periodontitis patients. ELISA was performed to determine IL-6 production by the P.g. LPS-exposed RAW264.7 cells. Knockdown by shRNA was used to determine the effects of PU.1 on p38/ nuclear factor (NF)-κB activation, mCatS expression and IL-6 production in RAW264.7 cells. RESULTS: The expressions mCatS and IL-6 were significantly upregulated in gingival macrophages. In cultured RAW264.7 cells, increased mCatS and IL-6 protein paralleled the activation of p38 and NF-κB after exposure to P.g. LPS. CatS knockdown by shRNA significantly decreased P.g. LPS-induced IL-6 expression and p38/NF-κB activation. PU.1 was significantly increased in P.g. LPS-exposed RAW264.7 cells, and PU.1 knockdown dramatically abolished the P.g. LPS-induced upregulation of mCatS and IL-6 and the activation of p38 and NF-κB. Furthermore, PU.1 and CatS colocalized in macrophages within the gingival tissues of periodontitis patients. CONCLUSION: PU.1-dependent CatS drives IL-6 production in macrophages by activating p38 and NF-κB in periodontitis.

Chemical Features and Machine Learning Assisted Predictions of Protein-Ligand Short Hydrogen Bonds.

There are continuous efforts to elucidate the structure and biological functions of short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms reside more than 0.3 A closer than the sum of their van der Waals radii. In this work, we evaluate 1070 atomic-resolution protein structures and characterize the common chemical features of SHBs formed between the side chains of amino acids and small molecule ligands. We then develop a machine learning assisted prediction of protein-ligand SHBs (MAPSHB-Ligand) model and reveal that the types of amino acids and ligand functional groups as well as the sequence of neighboring residues are essential factors that determine the class of protein-ligand hydrogen bonds. The MAPSHB-Ligand model and its implementation on our web server enable the effective identification of protein-ligand SHBs in proteins, which will facilitate the design of biomolecules and ligands that exploit these close contacts for enhanced functions.

Genome-wide analysis of R2R3-MYB genes in cultivated peanut (Arachis hypogaea L.): Gene duplications, functional conservation, and diversification.

The cultivated Peanut (Arachis hypogaea L.), an important oilseed and edible legume, are widely grown worldwide. The R2R3-MYB transcription factor, one of the largest gene families in plants, is involved in various plant developmental processes and responds to multiple stresses. In this study we identified 196 typical R2R3-MYB genes in the genome of cultivated peanut. Comparative phylogenetic analysis with Arabidopsis divided them into 48 subgroups. The motif composition and gene structure independently supported the subgroup delineation. Collinearity analysis indicated polyploidization, tandem, and segmental duplication were the main driver of the R2R3-MYB gene amplification in peanut. Homologous gene pairs between the two subgroups showed tissue specific biased expression. In addition, a total of 90 R2R3-MYB genes showed significant differential expression levels in response to waterlogging stress. Furthermore, we identified an SNP located in the third exon region of AdMYB03-18 (AhMYB033) by association analysis, and the three haplotypes of the SNP were significantly correlated with total branch number (TBN), pod length (PL) and root-shoot ratio (RS ratio), respectively, revealing the potential function of AdMYB03-18 (AhMYB033) in improving peanut yield. Together, these studies provide evidence for functional diversity in the R2R3-MYB genes and will contribute to understanding the function of R2R3-MYB genes in peanut.

Prediction and Design of Protease Enzyme Specificity Using a Structure-Aware Graph Convolutional Network.

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key post-translational modification involved in physiology and disease. The ability to robustly and rapidly predict protease substrate specificity would also enable targeted proteolytic cleavage - editing - of a target protein by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally-derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the three-dimensional structure and energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically-grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases: the NS3/4 protease from the Hepatitis C virus (HCV) and the Tobacco Etch Virus (TEV) proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pre-trained PGCN model to guide the design of TEV protease libraries for cleaving two non-canonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

Assessing PDB macromolecular crystal structure confidence at the individual amino acid residue level.

Approximately 87% of the more than 190,000 atomic-level three-dimensional (3D) biostructures in the PDB were determined using macromolecular crystallography (MX). Agreement between 3D atomic coordinates and experimental data for >100 million individual amino acid residues occurring within ∼150,000 PDB MX structures was analyzed in detail. The real-space correlation coefficient (RSCC) calculated using the 3D atomic coordinates for each residue and experimental-data-derived electron density enables outlier detection of unreliable atomic coordinates (particularly important for poorly resolved side-chain atoms) and ready evaluation of local structure quality by PDB users. For human protein MX structures in PDB, comparisons of the per-residue RSCC metric with AlphaFold2-computed structure model confidence (pLDDT-predicted local distance difference test) document (1) that RSCC values and pLDDT scores are correlated (median correlation coefficient ∼0.41), and (2) that experimentally determined MX structures (3.5 Å resolution or better) are more reliable than AlphaFold2-computed structure models and should be used preferentially whenever possible.

Targeting autophagy in atherosclerosis: Advances and therapeutic potential of natural bioactive compounds from herbal medicines and natural products.

Atherosclerosis (AS) is the most common causes of cardiovascular disease characterized by the formation of atherosclerotic plaques in the arterial wall, and it has become a dominant public health problem that seriously threaten people worldwide. Autophagy is a cellular self-catabolism process, which is critical to protect cellular homeostasis against harmful conditions. Emerging evidence suggest that dysregulated autophagy is involved in the development of AS. Therefore, pharmacological interventions have been developed to inhibit the AS via autophagy induction. Among various AS treating methods, herbal medicines and natural products have been applied as effective complementary and alternative medicines to ameliorate AS and its associated cardiovascular disease. Recently, mounting evidence revealed that natural bioactive compounds from herbs and natural products could induce autophagy to suppress the occurrence and development of AS, by promoting cholesterol efflux, reducing plaque inflammation, and inhibiting apoptosis or senescence. In the present review, we highlight recent findings regarding possible effects and molecular mechanism of natural compounds in autophagy-targeted mitigation of atherosclerosis, aiming to provide new potential therapeutic strategies for the atherosclerosis treatment preclinically and clinically.

Global Survey, Expressions and Association Analysis of CBLL Genes in Peanut.

Cystathionine γ-synthase (CGS), methionine γ-lyase (MGL), cystathionine ß-lyase (CBL) and cystathionine γ-lyase (CGL) share the Cys_Met_Meta_PP domain and play important roles in plant stress response and development. In this study, we defined the genes containing the Cys_Met_Meta_PP domain (PF01053.20) as CBL-like genes (CBLL). Twenty-nine CBLL genes were identified in the peanut genome, including 12 from cultivated peanut and 17 from wild species. These genes were distributed unevenly at the ends of different chromosomes. Evolution, gene structure, and motif analysis revealed that CBLL proteins were composed of five different evolutionary branches. Chromosome distribution pattern and synteny analysis strongly indicated that whole-genome duplication (allopolyploidization) contributed to the expansion of CBLL genes. Comparative genomics analysis showed that there were three common collinear CBLL gene pairs among peanut, Arabidopsis, grape, and soybean, but no collinear CBLL gene pairs between peanut and rice. The prediction results of cis-acting elements showed that AhCBLLs, AdCBLLs, and AiCBLLs contained different proportions of plant growth, abiotic stress, plant hormones, and light response elements. Spatial expression profiles revealed that almost all AhCBLLs had significantly higher expression in pods and seeds. All AhCBLLs could respond to heat stress, and some of them could be rapidly induced by cold, salt, submergence, heat and drought stress. Furthermore, one polymorphic site in AiCBLL7 was identified by association analysis which was closely associated with pod length (PL), pod width (PW), hundred pod weight (HPW) and hundred seed weight (HSW). The results of this study provide a foundation for further research on the function of the CBLL gene family in peanut.

Ion Channel Dysfunction in Astrocytes in Neurodegenerative Diseases.

Astrocytes play an important role in the central nervous system (CNS). Ion channels in these cells not only function in ion transport, and maintain water/ion metabolism homeostasis, but also participate in physiological processes of neurons and glial cells by regulating signaling pathways. Increasing evidence indicates the ion channel proteins of astrocytes, such as aquaporins (AQPs), transient receptor potential (TRP) channels, adenosine triphosphate (ATP)-sensitive potassium (K-ATP) channels, and P2X7 receptors (P2X7R), are strongly associated with oxidative stress, neuroinflammation and characteristic proteins in neurodegenerative disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD) and amyotrophic lateral sclerosis (ALS). Since ion channel protein dysfunction is a significant pathological feature of astrocytes in neurodegenerative diseases, we discuss these critical proteins and their signaling pathways in order to understand the underlying molecular mechanisms, which may yield new therapeutic targets for neurodegenerative disorders.

Simplified quality assessment for small-molecule ligands in the Protein Data Bank.

More than 70% of the experimentally determined macromolecular structures in the Protein Data Bank (PDB) contain small-molecule ligands. Quality indicators of ∼643,000 ligands present in ∼106,000 PDB X-ray crystal structures have been analyzed. Ligand quality varies greatly with regard to goodness of fit between ligand structure and experimental data, deviations in bond lengths and angles from known chemical structures, and inappropriate interatomic clashes between the ligand and its surroundings. Based on principal component analysis, correlated quality indicators of ligand structure have been aggregated into two largely orthogonal composite indicators measuring goodness of fit to experimental data and deviation from ideal chemical structure. Ranking of the composite quality indicators across the PDB archive enabled construction of uniformly distributed composite ranking score. This score is implemented at RCSB.org to compare chemically identical ligands in distinct PDB structures with easy-to-interpret two-dimensional ligand quality plots, allowing PDB users to quickly assess ligand structure quality and select the best exemplars.

Effective prediction of short hydrogen bonds in proteins via machine learning method.

Short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms lie within 2.7 Å, exhibit prominent quantum mechanical characters and are connected to a wide range of essential biomolecular processes. However, exact determination of the geometry and functional roles of SHBs requires a protein to be at atomic resolution. In this work, we analyze 1260 high-resolution peptide and protein structures from the Protein Data Bank and develop a boosting based machine learning model to predict the formation of SHBs between amino acids. This model, which we name as machine learning assisted prediction of short hydrogen bonds (MAPSHB), takes into account 21 structural, chemical and sequence features and their interaction effects and effectively categorizes each hydrogen bond in a protein to a short or normal hydrogen bond. The MAPSHB model reveals that the type of the donor amino acid plays a major role in determining the class of a hydrogen bond and that the side chain Tyr-Asp pair demonstrates a significant probability of forming a SHB. Combining electronic structure calculations and energy decomposition analysis, we elucidate how the interplay of competing intermolecular interactions stabilizes the Tyr-Asp SHBs more than other commonly observed combinations of amino acid side chains. The MAPSHB model, which is freely available on our web server, allows one to accurately and efficiently predict the presence of SHBs given a protein structure with moderate or low resolution and will facilitate the experimental and computational refinement of protein structures.

Identification of key factors shaping integrated levels of ACE2 and TMPRSS2 expression in head and neck squamous cell carcinoma.

Objectives: To quantify the integrated levels of ACE2 and TMPRSS2, the two well-recognized severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) entry-related genes, and to further identify key factors contributing to SARS-CoV-2 susceptibility in head and neck squamous cell carcinoma (HNSC). Methods: We developed a metric of the potential for tissue infected with SARS-CoV-2 ("TPSI") based on ACE2 and TMPRSS2 transcript levels and compared TPSI levels between tumor and matched normal tissues across 11 tumor types. For further analysis of HNSC, weighted gene co-expression network analysis (WGCNA), functional analysis, and single sample gene set enrichment analysis (ssGSEA) were conducted to investigate TPSI-relevant biological processes and their relationship with the immune landscape. TPSI-related factors were identified from clinical and mutational domains, followed by lasso regression to determine their relative effects on TPSI levels. Results: TPSI levels in tumors were generally lower than in the normal tissues. In HNSC, the genes highly associated with TPSI were enriched in viral entry-related processes, and TPSI levels were positively correlated with both eosinophils and T helper 17 (Th17) cell infiltration. Furthermore, the site of onset, human papillomaviruses (HPV) status, and nuclear receptor binding SET domain protein 1 (NSD1) mutations were identified as the most important factors shaping TPSI levels. Conclusions: This study identified the infection risk of SARS-CoV-2 between tumor and normal tissues, and provided evidence for the risk stratification of HNSC.

Robust estimation and variable selection for the accelerated failure time model.

This article concerns robust modeling of the survival time for cancer patients. Accurate prediction of patient survival time is crucial to the development of effective therapeutic strategies. To this goal, we propose a unified Expectation-Maximization approach combined with the L1 -norm penalty to perform variable selection and parameter estimation simultaneously in the accelerated failure time model with right-censored survival data of moderate sizes. Our approach accommodates general loss functions, and reduces to the well-known Buckley-James method when the squared-error loss is used without regularization. To mitigate the effects of outliers and heavy-tailed noise in real applications, we recommend the use of robust loss functions under the general framework. Furthermore, our approach can be extended to incorporate group structure among covariates. We conduct extensive simulation studies to assess the performance of the proposed methods with different loss functions and apply them to an ovarian carcinoma study as an illustration.

Cluster analysis of men undergoing surgery for BPH/LUTS reveals prominent roles of both bladder outlet obstruction and diminished bladder contractility.

Lower urinary tract symptoms (LUTS) in aging men are commonly attributed to bladder outlet obstruction from benign prostatic hyperplasia (BPH) but BPH/LUTS often reflects a confluence of many factors. We performed a hierarchical cluster analysis using four objective patient characteristics (age, HTN, DM, and BMI), and five pre-operative urodynamic variables (volume at first uninhibited detrusor contraction, number of uninhibited contractions, Bladder Outlet Obstruction Index (BOOI), Bladder Contractility Index (BCI) and Bladder Power at Qmax) to identify meaningful subgroups within a cohort of 94 men undergoing surgery for BPH/LUTS. Two meaningful subgroups (clusters) were identified. Significant differences between the two clusters included Prostate Volume (95 vs 53 cc; p-value = 0.001), BOOI (mean 70 vs 49; p-value = 0.001), BCI (mean 129 vs 83; p-value <0.001), Power (689 vs 236; p-value <0.001), Qmax (8.3 vs 4.9 cc/sec; p-value <0.001) and post-void residual (106 vs 250 cc; p-value = 0.001). One cluster is distinguished by larger prostate volume, greater outlet resistance and better bladder contractility. The other is distinguished by smaller prostate volume, lower outlet resistance and worse bladder contractility. Remarkably, the second cluster exhibited greater impairment of urine flow and bladder emptying. Surgery improved flow and emptying for patients in both clusters. These findings reveal important roles for both outlet obstruction and diminished detrusor function in development of diminished urine flow and impaired bladder emptying in patients with BPH/LUTS.

Mitochondrial Targeting Therapeutics: Promising Role of Natural Products in Non-alcoholic Fatty Liver Disease.

Non-alcoholic fatty liver disease (NAFLD) has become one of the most common chronic liver diseases worldwide, and its prevalence is still growing rapidly. However, the efficient therapies for this liver disease are still limited. Mitochondrial dysfunction has been proven to be closely associated with NAFLD. The mitochondrial injury caused reactive oxygen species (ROS) production, and oxidative stress can aggravate the hepatic lipid accumulation, inflammation, and fibrosis. which contribute to the pathogenesis and progression of NAFLD. Therefore, pharmacological therapies that target mitochondria could be a promising way for the NAFLD intervention. Recently, natural products targeting mitochondria have been extensively studied and have shown promising pharmacological activity. In this review, the recent research progress on therapeutic effects of natural-product-derived compounds that target mitochondria and combat NAFLD was summarized, aiming to provide new potential therapeutic lead compounds and reference for the innovative drug development and clinical treatment of NAFLD.

Metabolic Dysregulation Contributes to the Progression of Alzheimer's Disease.

Alzheimer's disease (AD) is an incurable neurodegenerative disease. Numerous studies have demonstrated a critical role for dysregulated glucose metabolism in its pathogenesis. In this review, we summarize metabolic alterations in aging brain and AD-related metabolic deficits associated with glucose metabolism dysregulation, glycolysis dysfunction, tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS) deficits, and pentose phosphate pathway impairment. Additionally, we discuss recent treatment strategies targeting metabolic defects in AD, including their limitations, in an effort to encourage the development of novel therapeutic strategies.