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Review

Glucose Metabolic Dysfunction in Neurodegenerative Diseases—New Mechanistic Insights and the Potential of Hypoxia as a Prospective Therapy Targeting Metabolic Reprogramming

1
Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Interdisciplinary Innovation Institute of Medicine and Engineering, Beihang University, Beijing 100191, China
2
School of Engineering Medicine, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2021, 22(11), 5887; https://doi.org/10.3390/ijms22115887
Submission received: 24 April 2021 / Revised: 17 May 2021 / Accepted: 24 May 2021 / Published: 31 May 2021
(This article belongs to the Special Issue Axonopathy in Neurodegenerative Diseases)

Abstract

:
Glucose is the main circulating energy substrate for the adult brain. Owing to the high energy demand of nerve cells, glucose is actively oxidized to produce ATP and has a synergistic effect with mitochondria in metabolic pathways. The dysfunction of glucose metabolism inevitably disturbs the normal functioning of neurons, which is widely observed in neurodegenerative disease. Understanding the mechanisms of metabolic adaptation during disease progression has become a major focus of research, and interventions in these processes may relieve the neurons from degenerative stress. In this review, we highlight evidence of mitochondrial dysfunction, decreased glucose uptake, and diminished glucose metabolism in different neurodegeneration models such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD). We also discuss how hypoxia, a metabolic reprogramming strategy linked to glucose metabolism in tumor cells and normal brain cells, and summarize the evidence for hypoxia as a putative therapy for general neurodegenerative disease.

1. Introduction

Although it only accounts for 2% of the body’s total weight, the brain has a high demand for energy compared to other tissues. Glucose is an essential energy substrate for the adult brain, and at least 25% of glucose is used to maintain basal brain functions [1]. Approximately 70% of the calculated energy is used for neuronal signaling (resting potentials, action potentials, postsynaptic receptors, the glutamate cycle, and postsynaptic Ca2+), and approximately 30% is used for non-signaling conduction activities (proteins, phospholipids, etc.). Glucose metabolism promotes the physiological functions of the brain through glycolysis and mitochondrial oxidative phosphorylation, and its product, ATP, is the electrochemical basis for the maintenance of neurons and non-neuronal cells [2]. Therefore, glucose metabolism and mitochondrial functions are essential for maintaining normal neuronal function.
Increasing studies have found that an abnormal glucose metabolism, including mitochondrial dysfunction, is related to the development of neurodegenerative diseases, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington’s disease (HD), which makes it promising to find a solution to these changes, which could improve the quality of life and prolong the survival of patients with neurodegenerative diseases. As a physical stimulus, hypoxia has a potential role in metabolic reprogramming and even has a protective effect on diseases of mitochondrial dysfunction [3]. In this review, we analyzed the energy metabolism in neurodegenerative diseases and summarized the evidence for hypoxia as a potential treatment for metabolic reprogramming.

2. Energy Metabolism in the Brain

2.1. Glucose Metabolism in the Brain

In the brain, glucose is transported from the arterial blood across endothelial membranes into brain cells via specific glucose transporters, and this process is reversible. The glucose transporters are most commonly the GLUT transporter families, which have different kinetic properties, with GLUT1 located in endothelial cells and astrocytes and GLUT3 and GLUT4 located in neurons [4,5]. When glucose enters the cell, it is irreversibly phosphorylated by hexokinase (HK) to produce glucose 6-phosphate (G6P). Then G6P can be processed by different metabolic pathways: (1) glycolysis (leading to lactic acid production or the tricarboxylic acid (TCA) cycle), (2) the pentose phosphate pathway (PPP), and (3) glycogenesis (Figure 1).
Glycolysis is a non-oxygen-dependent metabolic pathway in which glucose is converted to two pyruvate molecules, resulting in the net production of two ATP, two H2O molecules, and two reduced nicotinamide dinucleotide (NADH) molecules [6]. The two pyruvate molecules resulting from glycolysis are then transported into the mitochondrial matrix and converted to acetyl-CoA via oxidative decarboxylation; thus, acetyl-CoA is complexed with citrate, which undergoes a series of regenerative enzymatic reactions (the TCA cycle). This transformation results in a total of two NADH molecules produced—one NADH per pyruvate molecule. Two TCA cycles produce a total of six NADH, two GTPs (similar to the ATP equivalent), two reduced flavin adenine dinucleotides (FADH2), and four CO2. The NADH produced during glycolysis and NADH and FADH2 produced during the TCA cycle is subsequently re-oxidized in the electron transport chain (ETC), which uses the energy generated by electron transfer through four major protein–metal complexes to transport protons across the inner mitochondrial membrane to the intermembrane space. The flux of protons back to the mitochondrial matrix is mediated by ATP synthase, which uses energy to produce ATP from ADP [7]. The coupling of the electron flow from NADH and FADH2 to molecular oxygen to the production of ATP represents a process known as oxidative phosphorylation. Oxidative phosphorylation produces ~32 ATP per glucose molecule oxidized. The complete oxidation of glucose produces larger amounts of energy in the form of ATP in the mitochondria compared to glycolysis. The metabolic fate of glucose in the brain depends on the cell type and the selective expression of metabolic enzymes. Neurons are predominantly oxidative, while astrocytes are predominantly glycolytic [8].
The PPP metabolizes G6P and generates NADPH, which is used for defense against oxidative stress and biosynthetic reactions. It also produces ribulose-5-phosphate that can convert to ribose-5-phosphate and is used in the biosynthesis of purine ribonucleotides or intermediates for other pathways. These intermediates rearrange to form fructose-6-phosphate plus G3P. In astrocytes, G6P can be produced from glucose and/or glycogen, while in neurons, it is derived only from glucose. The biosynthetic contribution of PPP is the greatest in the developing brain [9,10], whereas, in adults, NADPH production plays an important role in the glutathione (GSH) pathway. Glutathione hydrogen peroxide (together with catalase) is used to detoxify the superoxide produced by the respiratory chain and the biogenic amine neurotransmitters (serotonin, dopamine, and norepinephrine (NE)) metabolized by the monoamine oxidase and hydrogen peroxide that is produced [11]. In addition to inactivating other active compounds, such as methylglyoxal and formaldehyde [12], NADPH is also used in the biosynthesis of fatty acids and cholesterol and for nitric oxide synthase, cytochrome P450 reductase, aldehyde reductase, and aldose reductase, which can produce sorbitol from glucose under hyperglycemic conditions. The pentose phosphate shunt pathway has been historically considered to be a minor flux with a high capacity for upregulation across time [10].
Glycogen, a branched alpha-D glucose polymer, is the major fuel storage in the brain and is present at much lower concentrations than in other organs. In the presence of normal supplies of glucose, there are higher levels of glycogen in the brains of unstressed animals [13]. In the embryonic stages, glycogen appears in the glia and neuronal cells; however, in adults, glycogen is mainly found in astrocytes and is located throughout the cell: in the cytosol, the end-feet surrounding the vasculature, in perisynaptic processes, and is rarely detected in neurons [14]. More recently, small amounts of glycogen, glycogen synthase, and glycogen phosphorylase were detected in neurons using very sensitive assays [15]. Glycogenolysis is more efficient than glycolysis because the activation of glycogen phosphorylase is very fast, and glycogenolysis has almost a 50% higher ATP yield compared with the glycolytic metabolism of glucose. Three ATP are generated from glycogen-derived G6P versus two ATP from glucose-derived G6P because G6P is produced from glycogen without consuming ATP (the cost is “prepaid” during glycogen synthesis), except in the branched points where glucose is released by the debranched enzyme, and one ATP is consumed to produce G6P. Glycogen turnover is highly regulated to maintain normal functions [16], and its dysregulation with an abnormal accumulation of glycogen structures in the brain causes severe seizures and death in patients with Lafora disease [17].

2.2. Other Energetic Sources in the Brain

When glucose supply is restricted or insufficient, e.g., during prolonged fasting, the brain will utilize other substrates such as free fatty acids and amino acids for energy production in the form of ketone bodies. During fasting, free fatty acids are mobilized from adipocytes and transported to the liver, leading to a surplus production of acetyl-CoA, which does not enter the TCA cycle for ATP production but leads to the formation of ketone bodies [18]. Additionally, amino acids (particularly leucine) may contribute 4% of the total ketone body production in the post-absorptive state [19]. The major ketone bodies include acetoacetate (AcAc), acetone, and beta-hydroxybutyrate (BHB). The utilization of ketone bodies seems to depend mainly on the glucose concentration in the blood [20]. When ketone bodies in the plasma are raised either by fasting, diet, or infusion, they are transported to the brain by monocarboxylate transporters (MCTs), which are the only known transporters for ketone bodies and are distributed throughout the brain [21]. Neurons and astrocytes maintain the ability to absorb ketone bodies. Once BHB and acetoacetate are transported to the brain, they are converted back into acetyl-CoA, which enters the TCA cycle to produce ATP. Unlike glucose, the conversion of BHB and acetoacetate into an oxidizable form does not consume ATP [22]. In astrocytes, degradation of fatty acids results in ketone body release to neighboring neurons. Therefore, both exogenous and endogenous ketone bodies can provide fuel for the brain [23]. However, plasma ketone body levels are usually low after an overnight fast (<0.5 mM) and contribute to less than 5% of the brain’s metabolism. Notably, under normal physiological conditions, the brain primarily uses glucose to produce ATP [20].
Approximately 90% of glucose is oxidized to CO2 to produce ATP through oxidative phosphorylation, which takes place in the mitochondria; therefore, mitochondria appear to be involved in the central stage of cellular energy supply, which is particularly important for the brain [24]. The metabolic intermediates of glucose are also used for the synthesis of amino acids for protein synthesis and neurotransmitter production, fatty acids and other lipids required for membrane and myelin synthesis, and five-carbon sugars required for nucleotide synthesis [8]. Thus, any disturbance in the neuronal glucose metabolism, primarily supported by the mitochondria, would affect neuronal function and ultimately affect movement, cognition, learning, and memory. Changes in the mitochondrial energy-transducing capacity, including a decreased neuronal glucose uptake, decreased ETC activity, and glucose metabolic disorders, can be detected in neurodegenerative diseases, including AD, PD, ALS, and HD (Figure 2).

3. Glucose Metabolic Disorders in Neurodegenerative Diseases

Neurodegenerative diseases are marked by the progressive loss of neuronal function, and thus far, there are very limited options for their treatment, which leaves these diseases associated with high morbidity and mortality. These different neurodegenerative diseases share two important characteristics: first, systemic loss of the neurons in the motor, sensory, and cognitive systems leading to cognitive disabilities, such as dementia and motor decline [25]; and second, a correlation between energy metabolic changes and neurodegeneration [26]. Given the limited regenerative ability of neuronal tissue, it is important to restrict neuronal impairment and death. The processes in neurodegenerative disease are quite complex. As deficits in glucose availability and mitochondrial function are well-known hallmarks of many neurodegenerative diseases, it appears reasonable to hypothesize that the high energy demand of the brain renders it sensitive to changes in the energy fuel supply and mitochondrial function. Therefore, it is of primary significance to interpret the neuronal damage in light of the metabolic changes and to look for therapies that can remedy the energy supply of the mitochondria.

3.1. Alzheimer’s Disease (AD)

AD was first described as “presenile dementia” by Alois Alzheimer in 1906 [27]. Alzheimer’s first AD patient was a 51-year-old female with a progressive loss of cognitive functions (comprehension and memory, unpredictable behavior, etc.). The histological analysis of her brain postmortem found senile plaques (SPs) and neurofibrillary tangles (NFTs), which are recognized as two main pathological findings in the brain of AD [28]. SPs are extracellular aggregates composed of amyloid β (Aβ) peptides, while NFTs are intracellular aggregates composed of hyperphosphorylated Tau protein [29,30]. AD begins with the memory loss of recent events (short-term memory impairment) and ultimately deprives the patients of self-awareness, and it is the most common form of dementia involving 50% to 70% of dementia cases. Nearly half of people over the age of 85 suffer from AD.
Some of the first clues hinting at energetic metabolism disorders in AD evolved from observations of the altered brain glucose metabolism in AD patients, which is detectable in living subjects using fluorodeoxyglucose positron emission tomography (FDG-PET) [31,32]. The hypometabolism of glucose and deficits in ATP production is observed in AD [33,34]. Hypometabolism is a key feature of AD that is manifested by mitochondrial dysfunction, decreased neuronal glucose uptake, and diminished glucose metabolism (before Aβ and tau tangles have begun forming) [24].
Extensive studies have indicated that mitochondrial structural dynamics are altered in both AD patients and animal models [35], including a disruption of the balance between mitochondrial fusion and fission, the decreased axonal transport of mitochondria, a lower percentage of mitochondria within a cell, and a change in size, with the mitochondria in AD maintaining much shorter, wider shapes [36]. Quantitative ultrastructural morphometric analysis showed that AD brains contained a significantly lower percentage of normal mitochondria compared with the age-matched control group brains [37] and a significantly higher percentage of mitochondria with broken cristae [38]. In fibroblasts from sporadic AD patients, the mitochondria were longer, with two or more mitochondria often joined together, while those of age-matched normal human fibroblasts were much shorter and appeared as sausage-shaped or rounded [39]. Similar morphological changes were also found in neurons overexpressing wild-type APP. APP-overexpressing cells actually showed mitochondria with heterogeneous morphologies; approximately 50% of cells contained fragmented, punctiform mitochondria, and the mitochondria in some cells demonstrated elongated, net-like structures [40].
Decreased expression of the glucose transporter has also been observed in AD. In a mouse model experiment, there was a decline in the neuronal GLUT3 both in male and female 3×TG-AD mice, which coincides with the brain glucose uptake. However, GLUT4 decreased in male 3×TG-AD mice but not in females [41]. Glucose uptake was also associated with a rise in inactive (phosphorylated) pyruvate dehydrogenase and in ketone body metabolism [42]. In an APP/PS1 model, compared to the wildtype, there was a decreased level of GLUT1 expression in the hippocampus of 18-month old mice, whereas no differences were observed at 8 months old [43]. Additionally, the GLUT1 and GLUT3 expression levels were reduced in six brain regions of AD patients, and the decreased GLUT3 levels in certain neurons compromised the glucose availability and may be responsible for the deficits in glucose metabolism [44]. Postmortem brains studies from AD patients revealed that the expressions of GLUT1 and GLUT3 were significantly decreased, which correlated with abnormal tau hyperphosphorylation and the downregulation of hypoxia-inducible factor 1α (HIF1α, which leads to the transcriptional activation of GLUT); interestingly, the GLUT2 expression was increased, likely due to astrocyte activation [45]. A large reduction in GLUT3 immunoreactivity was found in the dentate gyrus, a region where cells are selectively destroyed in AD [46]. Reduced GLUT expression in AD has also been found in the blood-brain barrier and cerebral cortex [47].
The activity of metabolic enzyme complexes in the TCA cycle decreased in AD, particularly for the pyruvate dehydrogenase complex (PDHC), the α-ketoglutarate dehydrogenase complex (KDHC), and enzymes in the ETC, such as succinate dehydrogenase (in complex II) and cytochrome c oxidase (COX, in complex IV) [48,49]. In 1990, deficient COX activity was found in the platelets of AD patients. A similar finding was made in AD brains in 1992 [50,51]. Subsequently, the finding of reduced COX activity in AD patients has been replicated in platelets [52,53,54], fibroblasts [55], focal brain regions [53], and large parts of the brain [56,57]. COX reduction has also been reported at all stages of the disease, including mild cognitive impairment [49,58]. In a 3xTG-AD model, there was a decreased expression and activity of complex IV in female mice coupled with compromised oxidative phosphorylation [59]. The dysfunction of mitochondrial complexes was also observed in clinical studies [60,61]. A proteomic study in AD patients found that the complex III core protein-1 in the temporal cortex and the complex V β-chain in the frontal cortex were significantly reduced [62], and, in another study, mitochondria from AD patients exhibited suppressed activity in all ETC complexes, with a dramatic decline in complex IV activity [51,63]. Additionally, research demonstrated that these deficits are not specific to AD but are also common across other neurodegenerative disorders [64].
Alterations of the enzymes involved in glucose metabolism were also observed in AD. The downregulation of the canonical WNT/β-catenin pathway inhibits β-catenin target genes (PDK1, MCT-1, c-Myc, cyclin D1, and LDHA) that participate in the regulation of glucose metabolism [65,66,67]. The downregulation of β-catenin also reduces the expression of the PI3K/Akt pathway [68,69]. HIF-1α, a downstream target of the PI3K/Akt pathway, is responsible for the expression of enzymatic enzymes, such as LDHA, PDK1, and PKM2 [70,71]. The levels of HK were found to be decreased in amyloidogenic AD transgenic mouse models as well in post-mortem brain tissues from AD patients [72]. Aβ-toxicity-resistant nerve cells undergo metabolic reprogramming and shifts toward aerobic glycolysis through the activation of HIF-1α, PDK1, and LDHA [73,74]. PDK1 and LDHA activation promotes resistance to Aβ toxicity and leads to the diminution of oxidative phosphorylation [75]. The acute treatment of Aβ induced microglia activation is accompanied by metabolic reprogramming from oxidative phosphorylation (OXPHOS) to glycolysis [76].

3.2. Parkinson’s Disease (PD)

PD is cited to be the second most common age-related neuronal degenerative disorder after AD and affects 0.6% of the population who are 65–69 years old and 2.6% of the population between 85–89 years of age [77]. The cardinal clinical features of PD are rest tremors, slowness of movement, rigidity, and postural instability [78]. In addition to motor symptoms, PD patients also show some non-motor symptoms, such as hyposmia/anosmia, sleep abnormalities, gastrointestinal motility disturbances, anxiety, depression, dementia, and impaired cognition [79]. The pathological hallmarks of PD are the progressive loss of dopaminergic neurons within the substantia nigra pars compacta (SNpc). Motor symptoms of PD do not become clinically apparent until 60–80% loss of dopaminergic neurons from the striatum, but many nonmotor symptoms may precede the onset of motor symptoms by years or decades [79]. The formation of Lewy bodies (LBs), which are composed primarily of post-translationally modified (ubiquitinated, phosphorylated, and/or S-nitrosylated) α-synuclein [80,81], and gliosis have been identified. In addition to the involvement of the nigrostriatal pathway, neurodegeneration and LBs are also found in the locus ceruleus, nucleus basalis, hypothalamus, cerebral cortex, cranial nerve motor nuclei, and central and peripheral components of the autonomic nervous system [79,82]. Large amounts of studies have demonstrated the mechanism and a variety of hypotheses have been proposed; however, the detailed mechanisms of PD remain unclear.
The idea that mitochondrial dysfunction could be implicated in the pathogenesis of PD was originally reported in the early 1980s by Dr. William Langston and colleagues. They found that a group of young drug abusers exposed to 1-methyl-4-phenyl-1,2,3,4-tetrahydropyridine (MPTP) displayed motoric features that bore an uncanny resemblance to those exhibited by sporadic PD patients [83]. It turned out that the active metabolite of MPTP results in marked dopaminergic neurotoxicity selectively for dopamine transporter and significant inhibition of mitochondrial complex I once it enters the neurons, which highlighted, for the first time, that mitochondrial dysfunction may be responsible for the neurodegeneration in PD. Among a number of proposed mechanisms involved in PD pathogenesis, mitochondrial dysfunction has been repeatedly implicated as the cause of the death of dopaminergic neurons in PD [84,85,86,87]. A number of familial forms of PD are associated with mutations in genes encoding both mitochondrially targeted proteins and proteins involved in mitochondrial function and/or oxidative stress responses. Examples of such genes are α-synuclein, Parkin, phosphate, and tensin homolog-induced kinase 1 (PINK1), DJ1, leucine-rich repeat kinase 2 (LRRK2), and Htr A serine peptidase 2 (HTRA2) [88]. Genetically modified organisms based on the knockout, overexpression or mutant versions of these genes have since been generated for the purposes of PD animal modeling. Many of these nuclear genes also implicate a role for the mitochondria in PD pathogenesis. α–synuclein can directly affect the mitochondria in mice that overexpress mutant α-synuclein [89]. In transgenic mice overexpressing α-synuclein, mitochondrial function is impaired, and oxidative stress increases [90]. Parkin, a ubiquitin ligase, is believed to protect neuron mitochondria, and researchers reported, in drosophila and mouse models, that parkin deficiency or mutations led to increased oxidative stress and mitochondrial impairment [91,92]. In contrast, mitochondrial dysfunction and oxidative stress also affect parkin function and exacerbate the consequences of parkin mutations [93]. PINK1, a mitochondrion-localized kinase, appears to protect against cell death [94]. This protective effect is abrogated by PD-related mutations that disable its kinase function [95]. PINK deficiency increases the sensitivity of mitochondria to rotenone and induces the degeneration of dopaminergic neurons in drosophila [96]. Consistent with the evidence from basic science, clinical studies also showed that mitochondrial damage plays a predominant role in the development of PD in patients.
Mitochondrial dysfunction could lead to metabolic disorders. Glucose hypometabolism in PD brains has also been documented using magnetic resonance imaging (MRI) and PET methods [97]. Studies demonstrated that the plasma levels of a-synuclein regulated glucose uptake in adipocytes [98]. The glycolytic enzyme glucose-6-phosphate isomerase, which catalyzes the conversion of G6P to F6P, was recently identified as a conserved modifier of dopamine metabolism, protein aggregation, and neurodegeneration in Caenorhabditis elegans, Drosophila melanogaster, and murine neurons [99]. Decreased levels of the PPP enzymes G6P dehydrogenase and 6-phosphogluconate dehydrogenase were detected at early stages in the putamen and cerebellum of PD brains [100]. A mild deficiency in mitochondrial respiratory electron transport chain NADH dehydrogenase (Complex I) activity has been reported in the substantia nigra [101], as well as in platelets [102,103] and lymphocytes [104,105] in PD patients, which suggests a systemic inhibition of complex I activity in PD patients. Interventions directed to improve mitochondrial bioenergetics have been shown to ameliorate neuropathology and motor deficits in animal models of PD [106].

3.3. Amyotrophic Lateral Sclerosis (ALS)

ALS is a progressive paralytic disease that was first clinicopathologically described by Jean-Martin Charcot in Paris in the mid-nineteenth century [107] and is characterized by the selective degeneration and death of motor neurons. ALS is the third most frequent neurodegenerative disease after AD and PD, of which the annual population incidence is 2/100,000 per year. ALS is categorized in two forms. The most common form is sporadic (90–95%), which has no obvious genetically inherited component. The remaining 5–10% of the cases are familial-type ALS due to their associated genetic dominant inheritance factor [108,109,110,111,112,113]. The first onset of symptoms is typically between the ages of 50 and 65 [114,115,116,117]. The most common symptoms that appear in both types of ALS are muscle weakness, twitching, and cramping, which eventually can lead to the impairment of muscles [118,119]. In the most advanced stages, ALS patients will develop symptoms of dyspnea and dysphagia [120,121]. The malignant nature of the disease progression is indicated by the fact that the median survival is approximately 2 years from diagnosis and 3 years from symptom onset [122]. However, there is considerable clinical variation, with a long ‘tail’ in the survival curve; approximately 10% of patients are still alive 10 years after onset. A better understanding of the biological underpinning of this variation in outcomes could shed considerable light on the nature of ALS.
Abnormal structures, numbers, and localizations of mitochondria have been reported in the motor neurons, muscles, and intra-muscular nerves of ALS patients [123,124,125]. Altered mitochondrial morphology in motor neurons is also one of the earliest pathological signs in mutant SOD1 mouse models of ALS [126,127,128]. Mutant SOD1 mice show vacuolated mitochondria in motor neurons that are lacking any apoptotic features [128,129]. In addition, mitochondrial morphology is also abnormal in TDP-43 models [130,131].
Mitochondrial abnormalities may lead to defects in glucose utilization. Studies have shown that the glucose uptake and levels within CNS tissues were severely diminished prior to pathological signs in SOD1G93A mice [132,133], and abnormalities in glucose utilization in the cerebral motor cortex were accompanied by marked reductions in ATP generation; however, glucose utilization in the spinal cord was unaltered at this stage but became impaired with disease progression [132]. Decreased glucose uptake was also seen in the motor-sensory cortex of ALS patients [134,135]. One FDG-PET study consisting of 81 patients with a suspected diagnosis of ALS was able to correctly classify 95% of ALS cases [136]. Another FDG-PET study showed that a reduction in glucose uptake and phosphorylation was associated with the severity of the disease [137]. Glucose uptake in the spinal cord increased pre-symptomatically in mutant SOD1G93A mice but declined progressively during disease progression [133]. These studies indicate that reduced glucose uptake may be an early diagnostic event in ALS.
Diminished glucose metabolism has been repeatedly reported in numerous studies. The expression of phosphoglucomutase-2-like 1 (PGM2L1) and phosphoglycerate kinase (PGK), two key enzymes in glycolysis, is downregulated in fibroblasts from sporadic ALS patients, which may lead to the reduction in the ability to catabolize carbohydrates in human ALS patients [138]. In agreement, a marked reduction in the components of glycolysis was observed in a recent proteomic study in sporadic ALS skin fibroblasts [139]. Whole-genome expression profiling in the motor cortex of sporadic ALS patients also showed significant downregulation of glycolytic genes [140]. Another study in the post-mortem cortex of ALS patients identified an over two-fold reduction in the PFKFB3 mRNA content [141]. In contrast, there are also studies showing that ALS patients displayed increased systemic energy expenditure at rest and were hypercatabolic [142]. Introducing mutant SOD1 in human fibroblasts or NSC34 motor neuron-like cells was found to increase glycolysis and reduce mitochondrial ATP generation [143,144]. In line with this, a recent metabolomics analysis identified increased glycolysis and deficits in the amino acid metabolism in a cellular model of ALS. Researchers have hypothesized that the overall increase in energy demands may reflect major cellular activity to stimulate CNS and muscle repair in an attempt to control the ALS neurodegenerative process [145]. Given the limited capacity of neurons to upregulate glycolysis [146,147], the physiological relevance to ALS regarding the upregulation of glycolysis in these cells remains to be established. Nevertheless, neurons were found to upregulate glycolysis [148], and oxidative stress was evident in post-mortem samples of ALS patients [149]. The inconsistency in the studies above may reflect different metabolic changes in different disease stages. Additionally, many studies have reported alterations in the mitochondrial electron transport chain, such as reduced complex I activity and cytochrome c oxidase activity in the skeletal muscle, spinal cords, and motor cortex of ALS patients [150,151,152,153]. The maximal oxidative phosphorylation capacity of skeletal muscle mitochondria was significantly increased in early-stage ALS patients, and the muscular mitochondrial respiratory complex IV activity was significantly decreased as the disease advanced [154]. Impaired activities of the complexes I + III, II + III, and IV were also observed in mutant SOD1G93A mice [155]. In SOD1G93A mice, decreased activity of mitochondrial complex I was detected as early as 2 months [155,156]. Together, these findings indicate that altered metabolic homeostasis is associated with ALS pathology and varies according to the disease progression.

3.4. Huntington’s Disease (HD)

HD was first described by George Huntington in 1872. This is an autosomal-dominant inherited disease that affects approximately 5–10 individuals out of 100,000 worldwide [157]. The clinical syndromes are characterized by abnormal choreic involuntary movements, psychiatric, psychological, and intellectual disabilities, and radiologically characterized by varying degrees of striatal atrophy [158]. HD is caused by an expansion of the CAG tract within exon 1 of the huntingtin gene (htt), which encodes a polyglutamine stretch in the HTT protein. In affected individuals, the number of CAG repeats extends from the normal population range (an average of 16 to 20 repeats) to >35 replicates [159], with a maximum expansion of 121 trinucleotides observed [160]. Glutamine itself is not toxic as it is present in all humans. However, the expansion of the polyglutamine tract is prone to misfolding and the formation of toxic aggregates, which may be one of the factors leading to HD [161,162]. This aggregation is responsible for secondary complications, such as apoptosis, excitotoxicity, mitochondrial dysfunction, and transcriptional disorders, which lead to the progressive loss of neurons in the brain, specifically in the striatum and cortex, and ultimately lead to disturbed neuropathological features.
Mitochondrial morphology has been widely observed to be impaired in HD. htt controls both the anterograde (from the cell body to axon terminal) and retrograde (from the axon ends toward the cell body) movements of the mitochondria [163]. Normal htt interacts with htt-interacting proteins 1 and 14 (Hip1 and Hip14), endophilin3, clathrin, and dynamin to control the fission and fusion processes of mitochondria [164]. Normal huntingtin localizes in the mitochondrial outer membrane, which makes the mitochondria vulnerable to any mutations of htt [165]. htt also has a role in the regulation of the mitochondrial membrane potential [166]. Thus, the functions of the mitochondria could certainly be affected in HD.
Energy metabolic deficits were also reported in numerous studies. Studies of the cerebral glucose metabolism using F-18 fluorodeoxyglucose PET provide strong evidence for an impairment of the energy metabolism in the caudate putamen and cortex of presymptomatic HD patients [167]. The striatal metabolism has been shown to be decreased prior to atrophy, and the disease progression is strongly correlated with glucose hypometabolism [168]. In line with this, research has observed that during the early stages of striatum degeneration, HD patients displayed decreased brain glucose uptake [169]. The expression of GLUT3 was shown to be diminished in the striata and cortices of HD mice compared to wild-type mice [170]. Interestingly, increasing the copy numbers of the gene encoding GLUT3 correlated with delayed disease onset in HD patients, and the overexpression of GLUT3, phosphofructokinase, and G6P-dehydrogenase protected against the development of HD phenotypes in animal models [171].
The study of the mitochondrial oxidative metabolism in the striatum of presymptomatic HD patients with PET through directly measuring the molar ratio of the cerebral oxygen metabolism to the cerebral glucose metabolism demonstrated a selective defect of glycolysis in early HD striatum [172]; these data suggest that a metabolic deficit is an early event in HD, and metabolic impairment precedes neuropathology and clinical symptoms in HD patients. Another study showed that an impaired basal ganglia metabolism was highly correlated with the functional capacity of HD patients and the degree of their motor dysfunction [173]. A system-wide analysis of the spatial proteome combined with mass spectrometric analysis recently identified alterations in key proteins related to the brain energy metabolism, and particularly the glia metabolism in a mouse model of HD [174]. In HD patients, increased lactate levels were observed in the striatum and occipital cortex using magnetic resonance spectroscopy imaging [175], which may reflect inefficient oxidative phosphorylation, which leads to the accumulation of lactate from pyruvate via lactate dehydrogenase. However, another magnetic resonance spectroscopy study showed reduced levels of both lactate and citrate in the cerebrospinal fluid from HD patients, which may indicate impairment of both glycolysis and the tricarboxylic acid cycle function in HD patients [176]. Enzyme complex abnormalities were also seen in HD. Postmortem studies showed a marked deficiency of mitochondrial complex II in the striatum of HD patients [177,178]. Decreased complex II enzymatic activity associated with the selective depletion of succinate dehydrogenase (SDH) was observed in cultured striatal neurons transfected with N-terminus mutant htt, and the overexpression of complex II/SDH subunits had a protective effect in this model [179]. The expression of full-length mutant htt in immortalized striatal progenitor cells (derived from the HdhQ111 knock-in mouse model) decreased complex II activity and increased the sensitivity of cells to a Ca2+-induced decrease in oxygen consumption and the mitochondrial membrane potential, whereas the overexpression of complex II prevented mitochondrial dysfunction and cell death [180]. These studies indicate that interventions that improve neuronal energy metabolism may ameliorate HD pathogenesis.
In summary, energy metabolic disorders caused by mitochondrial dysfunction play an important role in the development of neurodegenerative diseases. As hypometabolism is a hallmark of neurodegenerative diseases and the mitochondria are the main supplier of cellular energy, mitochondrial dysfunction is bound to influence the energy metabolism in a cell. Therefore, we speculate that strategies that can alter the glucose metabolism pathway to compensate for the metabolic disorder caused by mitochondrial dysfunction may have a therapeutic effect on neurodegenerative diseases. Here, hypoxia may be a potential strategy.

4. Hypoxia and Glucose Metabolic Reprogramming

4.1. Hypoxia Response In Vivo

Hypoxia (a low oxygen availability) in vivo can be caused by not only an insufficient oxygen supply from the local circulatory system (e.g., in cancer, ischemia in the heart or brain, and in embryos), but also increased oxygen consumption by cells engaged in certain functions (e.g., inflammation, proliferation, and hormone secretion). Hypoxia-inducible factor (HIF) is an important transcription factor that regulates oxygen consumption and morphologically changes in response to hypoxic stress in normal and pathological conditions by activating the transcription of numerous genes responsible for oxygen delivery, angiogenesis, cell proliferation, cell differentiation, and metabolism [181,182]. In normoxic conditions, HIF is constitutively synthesized and hydroxylated by the prolyl-hydroxylase (PHD) enzymes [183,184,185]. The hydroxylated form is recognized by the ubiquitin ligase Von Hippel-Lindau (VHL) and targeted for proteasomal degradation. In hypoxic conditions, the PHD reaction does not take place, allowing HIF stabilization and activation of the hypoxia transcriptional program (Figure 3).
In vivo, cells have developed two methods of dramatic alterations in the energy metabolism to adapt to oxygen deprivation: one is oxygen-independent ATP production, and the other is a reduction in the mitochondrial oxygen consumption. The former is the enhancement of glycolysis. The expression of glucose transporters (GLUT1 and GLUT3) [186,187] and glycolytic enzymes, including hexokinase (HK1 and HK2) [188] and phosphoglycerate kinase 1 (PGK1), are activated by HIF, which is stable in hypoxia conditions [189]. In addition, HIF also upregulates lactate dehydrogenase A (LDHA), which regenerates NAD+ for a continuous supply in glycolysis [190]. Although glycolysis produces less ATP per glucose molecule than oxidative phosphorylation, the cooperative induction of glucose uptake and glycolysis can result in rapid energy production that compensates for the low efficiency. The second is metabolic suppression in mitochondria to decrease oxygen consumption. The activity of ETC complexes is also suppressed. The hypoxic alterations in the energy metabolism are essential for the functions of different cells; for example, hematopoietic stem cells can maintain their stemness in the bone marrow niche, where the oxygen concentrations are low enough to activate HIF constitutively under physiological conditions [191,192].

4.2. Hypoxia and Tumor Metabolic Reprogramming

Normal tissue uses glycolysis to generate approximately 10% of the cell’s ATP, with mitochondria accounting for 90%. In contrast to normal cells, cancerous cells have long been known to show high glycolytic rates even under normoxic conditions (Warburg effect) [193], and over 50% of the cellular energy is produced by glycolysis, with the remainder being generated in the mitochondria. This shift is termed aerobic glycolysis, as it occurs even when there is enough O2 present to support mitochondrial functions. Tumor cells consume more glucose during glycolysis for energy production because of the low efficiency of glycolysis in generating ATP [194].
One of the most recognized reasons for the altered tumor metabolism is the unique physiological stresses that exist within the tumor. The tumor microenvironment suffers from hypoxia, acidosis, and increased interstitial fluid pressure [195]. These microenvironmental stresses are largely the result of poorly formed tumor vasculature [196]. Hypoxia is perhaps the most pervasive of these stresses and variably exists when O2 delivery does not meet the demands within the tumor tissue. Tumor cells respond to hypoxia conditions and adapt their metabolism to adjust the O2 demands to meet the limited supply [197,198]. Perhaps the most important aspect of how cells respond to this unique microenvironment is the activation of HIF-1 [199]. The net result of hypoxic HIF-1 activation is to shift the energy production by increasing glycolysis and suppressing the mitochondrial function. HIF-1 promotes glycolytic energy production by activating the transcription of genes involved in extracellular glucose import (such as GLUT1) and the enzymes responsible for the glycolytic breakdown of intracellular glucose (such as phosphofructokinase 1 (PFK1) and aldolase). Additionally, HIF-1 also downregulates oxidative phosphorylation within the mitochondria by transactivating genes, such as pyruvate dehydrogenase kinase 1 (PDK1) [200,201] and MAX interactor 1 (MXI1) [202]. It has been shown that oncogenes, such as c-Myc and v-Src promote metabolic reprogramming, in part, by cooperation with and activation of HIF-1, respectively [203,204]. In addition, the loss of tumor suppressor genes, including PTEN [205] and VHL [206], is involved in the development of Warburg effects via HIF-1 activation, independent of oxygen concentration.
Not limited to glucose, tumor cells also develop additional nutrition sources to bypass oxygen limitations. Studies reported that tumor cells consume acetate and ketone bodies to fuel tumor cell growth and metastases [207,208,209]. Hypoxia-produced lactate also serves as a carbon source for tumor cells. In human non-small-cell lung cancers, lactate was absorbed avidly by MCT1 and subsequently transformed into pyruvate to fuel the TCA cycle and facilitate cancer progression [210,211]. A recent study performed genome-wide CRISPR growth screens at 21%, 5%, and 1% oxygen to systematically identify gene knockouts with relative fitness defects in high oxygen or low oxygen. The knockouts of many mitochondrial pathways thought to be essential, including complex I and enzymes in Fe-S biosynthesis, grew relatively well with low oxygen, indicating that hypoxia could buffer mitochondrial defects. By contrast, in certain cell types, the knockout of lipid biosynthetic and peroxisomal genes caused fitness defects only with low oxygen [212]. These data suggest that hypoxia may promote metabolic reprogramming by modulating mitochondrial functions.

4.3. Hypoxia and Brain Metabolic Reprogramming

Though the severe hypoxic condition caused by ischemic stroke triggers neuronal cell death, mild oxygen deprivation or low oxygen could promote cortical progenitor cell expansion [213] and neurogenesis [214], suggesting a regulatory mechanism of oxygen tension in the brain. Consistently, in response to hypoxia, the brain also experiences a series of gene expressions such as HIF and its targeting genes and glucose metabolic changes. As in other normal cells discussed above, brain cells also temper their reliance on oxidative phosphorylation and concomitantly upregulate glycolytic enzymes and glucose transporters to promote glycolysis, which makes glycolysis a more prominent role in cellular ATP production to meet cellular energy requirements [215]. Notably, both ATP demand and supply downregulation are essential mechanisms of hypoxia tolerance [216]. The resulting attenuated electron flux through the mitochondrial respiratory chain has been demonstrated to be at least partially due to inhibition of complex I activity [217]. Compare with astrocytes that are highly glycolytic, neurons rely on oxidative metabolism to meet their high energy needs and are more sensitive to hypoxia. As the energy metabolic complement at the cell level, the astrocyte-neuron lactate shuttle is known as the way for astrocytes to support the energy expenditure of neurons [8]. Controversially, though, the neuron could consume glucose directly in response to the energy expenditure challenges [148], which mimics energy metabolism regulation and reprogramming during hypoxia.

4.4. Hypoxia and Neurodegenerative Disease

Metabolic reprogramming with hypoxia is critical for aerobic cells and organisms to survive as the oxygen concentrations surrounding them vary drastically in disease conditions and even under physiological ones. Jain et al. published in 2016 that hypoxia had protective effects on mitochondrial disease. They found that the hypoxia response elicited by genetic or small molecule activation had a protective effect on mitochondrial toxicity in cultured cells and zebrafish models. Specifically, activation of HIF through VHL knockout or FG-4592 treatment alleviated zebrafish embryo death caused by mitochondria respiratory chain inhibition. Furthermore, chronic hypoxia treatment benefited the genetic mouse model of Leigh syndrome, the most common pediatric manifestation of mitochondrial disease, and the survival rates, behavior, neuropathology, body weight, and disease biomarkers were significantly improved [3]. In 2017, one study also reported that breathing normobaric 11% O2 prevents neurodegeneration in Leigh syndrome, and hypoxia can prevent and even reverse the brain lesions in mice with advanced neuropathology [218]. Notably, when grown in 1% ambient O2, FXN-null yeast, human cells, and nematodes were fully viable. In human cells, hypoxia restored the steady-state levels of Fe-S clusters and normalized the ATF4, NRF2, and IRP2 signaling events associated with Friedreich’s ataxia (FRDA). In the mouse model of FRDA, breathing 11% O2 can significantly reduce the progression of ataxia [219]. In addition, intermittent hypoxic conditioning (IHC) prevented anxiety-like behavior and memory and learning deficits and significantly reduced cortical Aβ levels in 3×Tg-AD mice. In affecting the brain energy metabolism, IHC caused a significant increase in brain cortical glucose levels and robustly improved mitochondrial bioenergetic profile in 3×Tg-AD mice [220]. Another pilot clinical study examined the effect of intermittent hypoxia-hyperoxia training (IHHT) in elderly patients with mild cognitive impairment (MCI), the precursor to AD. IHHT significantly promoted cognitive function and functional exercise capacity in these elderly patients who received a multimodal training intervention [221]. Consistent with this, moderate IHC adaptation can improve cerebral oxygenation and hypoxia-induced cerebral vasodilation in elderly patients with MCI while improving short-term memory and attention [222]. IHHT also improved cognitive test scores and decreased the Aβ expression and NET formation significantly one month after the three-week IHHT intervention [223], suggesting a long-term neuroprotective mechanism for neurodegeneration disease of hypoxia.
In addition, high-altitude hypoxia may also have neuroprotective effects. Richardson et al. reported that there were no adverse effects of chronic hypoxia in adolescents who resided from birth at 3700 m in Bolivia, and instead, evidence of successful neurophysiological adaptations was found [224]. Another study showed that in California, where the average altitude is as high as 1800 m, Alzheimer’s disease mortality was negatively correlated with residential altitude [225]. Populations in certain high-altitude may also have special protection against dementia, such as a tribe in the Himalayas of North Indian [226]. Recently, it has been shown that IHC (simulation at 5000 m height, and 4 h per day for 15 consecutive days) can reduce cognitive deficits and anxiety in 9 month-old APP/PS1 mice while reducing amyloid β and pro-apoptotic protein contents in the cerebral cortex and hippocampus and augmenting hippocampal neurogenesis and BDNF content [227]. Together, these studies suggested that hypoxia has potential use as a new nonpharmacological therapy to improve the cognitive function of patients with neurodegenerative diseases and slow the development of neurodegenerative diseases.

5. Conclusions and Future Perspectives

Abnormal energy metabolism and mitochondrial dysfunction appear to have a role in neurodegenerative disease. Mitochondrial dysfunction dramatically decreases the energy supply to the neurons, which may aggravate the progress of neurodegeneration. As a consequence, targeting defects in the energy metabolism in neurodegeneration represents a rational therapeutic strategy. A strategy that manipulates the energy metabolism would be a particularly potent therapy to treat neurodegenerative disease. Hypoxia, as a treatment strategy, activates an evolutionarily conserved adaptive program that allows mammals to cope with limiting oxygen levels, and this program decreases an organism’s reliance on mitochondrial oxidative metabolism. This metabolic alteration may be helpful to improve neurodegenerative disease. Further preclinical studies are required to assess whether hypoxic exposure can be developed into a safe and effective treatment for human diseases associated with mitochondrial dysfunction.

Author Contributions

Writing—original draft preparation, R.H.; review, J.L.; review—editing and supervision, B.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 81971198; Beijing Municipal Natural Science Foundation, grant number 7192103; Chinese Ministry of Science and Technology, grant number 2019YFA0508603; and China Postdoctoral Science Foundation, grant number 2020M670103.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Rossi, S.; Zanier, E.R.; Mauri, I.; Columbo, A.; Stocchetti, N. Brain temperature, body core temperature, and intracranial pressure in acute cerebral damage. J. Neurol. Neurosurg. Psychiatry 2001, 71, 448–454. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Mergenthaler, P.; Lindauer, U.; Dienel, G.A.; Meisel, A. Sugar for the brain: The role of glucose in physiological and pathological brain function. Trends Neurosci. 2013, 36, 587–597. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Jain, I.H.; Zazzeron, L.; Goli, R.; Alexa, K.; Schatzman-Bone, S.; Dhillon, H.; Goldberger, O.; Peng, J.; Shalem, O.; Sanjana, N.E.; et al. Hypoxia as a therapy for mitochondrial disease. Science 2016, 352, 54–61. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Ashrafi, G.; Wu, Z.; Farrell, R.J.; Ryan, T.A. GLUT4 Mobilization Supports Energetic Demands of Active Synapses. Neuron 2017, 93, 606–615.e3. [Google Scholar] [CrossRef] [PubMed]
  5. Simpson, I.A.; Carruthers, A.; Vannucci, S.J. Supply and demand in cerebral energy metabolism: The role of nutrient transporters. J. Cereb. Blood Flow Metab. 2007, 27, 1766–1791. [Google Scholar] [CrossRef]
  6. Romano, A.H.; Conway, T. Evolution of carbohydrate metabolic pathways. Res. Microbiol. 1996, 147, 448–455. [Google Scholar] [CrossRef]
  7. Scheffler, I.E. Mitochondria, 2nd ed.; Wiley-Liss: Hoboken, NJ, USA, 2008; p. xviii, 462, 412 of plates. [Google Scholar]
  8. Belanger, M.; Allaman, I.; Magistretti, P.J. Brain energy metabolism: Focus on astrocyte-neuron metabolic cooperation. Cell Metab. 2011, 14, 724–738. [Google Scholar] [CrossRef] [Green Version]
  9. Baquer, N.Z.; Hothersall, J.S.; McLean, P. Function and regulation of the pentose phosphate pathway in brain. Curr. Top. Cell Regul. 1988, 29, 265–289. [Google Scholar]
  10. Baquer, N.Z.; Hothersall, J.S.; McLean, P.; Greenbaum, A.L. Aspects of carbohydrate metabolism in developing brain. Dev. Med. Child Neurol. 1977, 19, 81–104. [Google Scholar] [CrossRef]
  11. Chen, L.; Zhang, Z.; Hoshino, A.; Zheng, H.D.; Morley, M.; Arany, Z.; Rabinowitz, J.D. NADPH production by the oxidative pentose-phosphate pathway supports folate metabolism. Nat. Metab. 2019, 1, 404–415. [Google Scholar] [CrossRef]
  12. Rae, C.D.; Williams, S.R. Glutathione in the human brain: Review of its roles and measurement by magnetic resonance spectroscopy. Anal. Biochem. 2017, 529, 127–143. [Google Scholar] [CrossRef]
  13. Swanson, R.A.; Morton, M.M.; Sagar, S.M.; Sharp, F.R. Sensory stimulation induces local cerebral glycogenolysis: Demonstration by autoradiography. Neuroscience 1992, 51, 451–461. [Google Scholar] [CrossRef]
  14. Sotelo, C.; Palay, S.L. The fine structure of the lateral vestibular nucleus in the rat: I. Neurons and Neuroglial Cells. J. Cell Biol. 1968, 36, 151–179. [Google Scholar] [CrossRef]
  15. Saez, I.; Duran, J.; Sinadinos, C.; Beltran, A.; Yanes, O.; Tevy, M.F.; Martinez-Pons, C.; Milan, M.; Guinovart, J.J. Neurons have an active glycogen metabolism that contributes to tolerance to hypoxia. J. Cereb. Blood Flow Metab. 2014, 34, 945–955. [Google Scholar] [CrossRef]
  16. Obel, L.F.; Muller, M.S.; Walls, A.B.; Sickmann, H.M.; Bak, L.K.; Waagepetersen, H.S.; Schousboe, A. Brain glycogen-new perspectives on its metabolic function and regulation at the subcellular level. Front. Neuroenergetics 2012, 4, 3. [Google Scholar] [CrossRef] [Green Version]
  17. Roach, P.J. Glycogen phosphorylation and Lafora disease. Mol. Asp. Med. 2015, 46, 78–84. [Google Scholar] [CrossRef] [Green Version]
  18. Fukao, T.; Lopaschuk, G.D.; Mitchell, G.A. Pathways and control of ketone body metabolism: On the fringe of lipid biochemistry. Prostaglandins Leukot. Essent. Fat. Acids 2004, 70, 243–251. [Google Scholar] [CrossRef]
  19. Puchalska, P.; Crawford, P.A. Multi-dimensional Roles of Ketone Bodies in Fuel Metabolism, Signaling, and Therapeutics. Cell Metab. 2017, 25, 262–284. [Google Scholar] [CrossRef] [Green Version]
  20. Cunnane, S.C.; Courchesne-Loyer, A.; Vandenberghe, C.; St-Pierre, V.; Fortier, M.; Hennebelle, M.; Croteau, E.; Bocti, C.; Fulop, T.; Castellano, C.A. Can Ketones Help Rescue Brain Fuel Supply in Later Life? Implications for Cognitive Health during Aging and the Treatment of Alzheimer’s Disease. Front. Mol. Neurosci. 2016, 9, 53. [Google Scholar] [CrossRef]
  21. Pierre, K.; Pellerin, L. Monocarboxylate transporters in the central nervous system: Distribution, regulation and function. J. Neurochem. 2005, 94, 1–14. [Google Scholar] [CrossRef]
  22. Koppel, S.J.; Swerdlow, R.H. Neuroketotherapeutics: A modern review of a century-old therapy. Neurochem. Int. 2018, 117, 114–125. [Google Scholar] [CrossRef] [PubMed]
  23. Takahashi, S. Metabolic compartmentalization between astroglia and neurons in physiological and pathophysiological conditions of the neurovascular unit. Neuropathology 2020, 40, 121–137. [Google Scholar] [CrossRef] [Green Version]
  24. Esteves, A.; Arduíno, D.; Silva, D.F.F.; Martins Branco, D.; Oliveira, C.R.; Cardoso, S. Mitochondrial Metabolism in Age-Related Neurodegenerative Disorders: Alzheimer’s and Parkinson’s Revisited; Nova Science Publishers, Inc.: New York, NY, USA, 2011; pp. 187–244. [Google Scholar]
  25. Martin, J.B. Molecular basis of the neurodegenerative disorders. N. Engl. J. Med. 1999, 340, 1970–1980. [Google Scholar] [CrossRef] [PubMed]
  26. Cai, H.; Cong, W.N.; Ji, S.; Rothman, S.; Maudsley, S.; Martin, B. Metabolic dysfunction in Alzheimer’s disease and related neurodegenerative disorders. Curr. Alzheimer Res. 2012, 9, 5–17. [Google Scholar] [CrossRef] [PubMed]
  27. O’Brien, C. Auguste D and Alzheimer’s disease. Science 1996, 273, 28. [Google Scholar] [CrossRef] [PubMed]
  28. Maurer, K.; Volk, S.; Gerbaldo, H. Auguste D and Alzheimer’s disease. Lancet 1997, 349, 1546–1549. [Google Scholar] [CrossRef]
  29. Anand, R.; Gill, K.D.; Mahdi, A.A. Therapeutics of Alzheimer’s disease: Past, present and future. Neuropharmacology 2014, 76, 27–50. [Google Scholar] [CrossRef] [PubMed]
  30. Imtiaz, B.; Tolppanen, A.M.; Kivipelto, M.; Soininen, H. Future directions in Alzheimer’s disease from risk factors to prevention. Biochem. Pharmacol. 2014, 88, 661–670. [Google Scholar] [CrossRef]
  31. Foster, N.L.; Chase, T.N.; Fedio, P.; Patronas, N.J.; Brooks, R.A.; Di Chiro, G. Alzheimer’s disease: Focal cortical changes shown by positron emission tomography. Neurology 1983, 33, 961–965. [Google Scholar] [CrossRef]
  32. Friedland, R.P.; Budinger, T.F.; Ganz, E.; Yano, Y.; Mathis, C.A.; Koss, B.; Ober, B.A.; Huesman, R.H.; Derenzo, S.E. Regional cerebral metabolic alterations in dementia of the Alzheimer type: Positron emission tomography with [18F]fluorodeoxyglucose. J. Comput. Assist. Tomogr. 1983, 7, 590–598. [Google Scholar] [CrossRef]
  33. Demetrius, L.A.; Driver, J. Alzheimer’s as a metabolic disease. Biogerontology 2013, 14, 641–649. [Google Scholar] [CrossRef]
  34. Szablewski, L. Glucose Transporters in Brain: In Health and in Alzheimer’s Disease. J. Alzheimers Dis. 2017, 55, 1307–1320. [Google Scholar] [CrossRef]
  35. Lin, M.T.; Beal, M.F. Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases. Nature 2006, 443, 787–795. [Google Scholar] [CrossRef]
  36. Zhu, X.; Perry, G.; Smith, M.A.; Wang, X. Abnormal mitochondrial dynamics in the pathogenesis of Alzheimer’s disease. J. Alzheimers Dis. 2013, 33 (Suppl. 1), S253–S262. [Google Scholar] [CrossRef] [Green Version]
  37. De la Monte, S.M.; Luong, T.; Neely, T.R.; Robinson, D.; Wands, J.R. Mitochondrial DNA damage as a mechanism of cell loss in Alzheimer’s disease. Lab. Invest. 2000, 80, 1323–1335. [Google Scholar] [CrossRef] [Green Version]
  38. Silva, D.F.; Selfridge, J.E.; Lu, J.; E, L.; Cardoso, S.M.; Swerdlow, R.H. Mitochondrial abnormalities in Alzheimer’s disease: Possible targets for therapeutic intervention. Adv. Pharmacol. 2012, 64, 83–126. [Google Scholar]
  39. Wang, X.; Su, B.; Fujioka, H.; Zhu, X. Dynamin-like protein 1 reduction underlies mitochondrial morphology and distribution abnormalities in fibroblasts from sporadic Alzheimer’s disease patients. Am. J. Pathol. 2008, 173, 470–482. [Google Scholar] [CrossRef] [Green Version]
  40. Wang, X.; Su, B.; Siedlak, S.L.; Moreira, P.I.; Fujioka, H.; Wang, Y.; Casadesus, G.; Zhu, X. Amyloid-beta overproduction causes abnormal mitochondrial dynamics via differential modulation of mitochondrial fission/fusion proteins. Proc. Natl. Acad. Sci. USA 2008, 105, 19318–19323. [Google Scholar] [CrossRef] [Green Version]
  41. Sancheti, H.; Akopian, G.; Yin, F.; Brinton, R.D.; Walsh, J.P.; Cadenas, E. Age-dependent modulation of synaptic plasticity and insulin mimetic effect of lipoic acid on a mouse model of Alzheimer’s disease. PLoS ONE 2013, 8, e69830. [Google Scholar] [CrossRef]
  42. Ding, F.; Yao, J.; Rettberg, J.R.; Chen, S.; Brinton, R.D. Early decline in glucose transport and metabolism precedes shift to ketogenic system in female aging and Alzheimer’s mouse brain: Implication for bioenergetic intervention. PLoS ONE 2013, 8, e79977. [Google Scholar] [CrossRef] [Green Version]
  43. Hooijmans, C.R.; Graven, C.; Dederen, P.J.; Tanila, H.; van Groen, T.; Kiliaan, A.J. Amyloid beta deposition is related to decreased glucose transporter-1 levels and hippocampal atrophy in brains of aged APP/PS1 mice. Brain Res. 2007, 1181, 93–103. [Google Scholar] [CrossRef]
  44. Simpson, I.A.; Chundu, K.R.; Davies-Hill, T.; Honer, W.G.; Davies, P. Decreased concentrations of GLUT1 and GLUT3 glucose transporters in the brains of patients with Alzheimer’s disease. Ann. Neurol. 1994, 35, 546–551. [Google Scholar] [CrossRef]
  45. Liu, Y.; Liu, F.; Iqbal, K.; Grundke-Iqbal, I.; Gong, C.X. Decreased glucose transporters correlate to abnormal hyperphosphorylation of tau in Alzheimer disease. FEBS Lett. 2008, 582, 359–364. [Google Scholar] [CrossRef] [Green Version]
  46. Harr, S.D.; Simonian, N.A.; Hyman, B.T. Functional alterations in Alzheimer’s disease: Decreased glucose transporter 3 immunoreactivity in the perforant pathway terminal zone. J. Neuropathol. Exp. Neurol. 1995, 54, 38–41. [Google Scholar] [CrossRef]
  47. Kalaria, R.N.; Harik, S.I. Reduced glucose transporter at the blood-brain barrier and in cerebral cortex in Alzheimer disease. J. Neurochem. 1989, 53, 1083–1088. [Google Scholar] [CrossRef]
  48. Shoffner, J.M. Oxidative phosphorylation defects and Alzheimer’s disease. Neurogenetics 1997, 1, 13–19. [Google Scholar] [CrossRef]
  49. Swerdlow, R.H.; Kish, S.J. Mitochondria in Alzheimer’s disease. Int. Rev. Neurobiol. 2002, 53, 341–385. [Google Scholar]
  50. Parker, W.D., Jr.; Filley, C.M.; Parks, J.K. Cytochrome oxidase deficiency in Alzheimer’s disease. Neurology 1990, 40, 1302–1303. [Google Scholar] [CrossRef]
  51. Kish, S.J.; Bergeron, C.; Rajput, A.; Dozic, S.; Mastrogiacomo, F.; Chang, L.J.; Wilson, J.M.; DiStefano, L.M.; Nobrega, J.N. Brain cytochrome oxidase in Alzheimer’s disease. J. Neurochem. 1992, 59, 776–779. [Google Scholar] [CrossRef]
  52. Parker, W.D., Jr.; Mahr, N.J.; Filley, C.M.; Parks, J.K.; Hughes, D.; Young, D.A.; Cullum, C.M. Reduced platelet cytochrome c oxidase activity in Alzheimer’s disease. Neurology 1994, 44, 1086–1090. [Google Scholar] [CrossRef]
  53. Bosetti, F.; Brizzi, F.; Barogi, S.; Mancuso, M.; Siciliano, G.; Tendi, E.A.; Murri, L.; Rapoport, S.I.; Solaini, G. Cytochrome c oxidase and mitochondrial F1F0-ATPase (ATP synthase) activities in platelets and brain from patients with Alzheimer’s disease. Neurobiol. Aging 2002, 23, 371–376. [Google Scholar] [CrossRef]
  54. Cardoso, S.M.; Proenca, M.T.; Santos, S.; Santana, I.; Oliveira, C.R. Cytochrome c oxidase is decreased in Alzheimer’s disease platelets. Neurobiol. Aging 2004, 25, 105–110. [Google Scholar] [CrossRef]
  55. Curti, D.; Rognoni, F.; Gasparini, L.; Cattaneo, A.; Paolillo, M.; Racchi, M.; Zani, L.; Bianchetti, A.; Trabucchi, M.; Bergamaschi, S.; et al. Oxidative metabolism in cultured fibroblasts derived from sporadic Alzheimer’s disease (AD) patients. Neurosci. Lett. 1997, 236, 13–16. [Google Scholar] [CrossRef]
  56. Mutisya, E.M.; Bowling, A.C.; Beal, M.F. Cortical cytochrome oxidase activity is reduced in Alzheimer’s disease. J. Neurochem. 1994, 63, 2179–2184. [Google Scholar] [CrossRef]
  57. Wong-Riley, M.; Antuono, P.; Ho, K.C.; Egan, R.; Hevner, R.; Liebl, W.; Huang, Z.; Rachel, R.; Jones, J. Cytochrome oxidase in Alzheimer’s disease: Biochemical, histochemical, and immunohistochemical analyses of the visual and other systems. Vision Res. 1997, 37, 3593–3608. [Google Scholar] [CrossRef] [Green Version]
  58. Valla, J.; Schneider, L.; Niedzielko, T.; Coon, K.D.; Caselli, R.; Sabbagh, M.N.; Ahern, G.L.; Baxter, L.; Alexander, G.; Walker, D.G.; et al. Impaired platelet mitochondrial activity in Alzheimer’s disease and mild cognitive impairment. Mitochondrion 2006, 6, 323–330. [Google Scholar] [CrossRef] [Green Version]
  59. Yao, J.; Irwin, R.W.; Zhao, L.; Nilsen, J.; Hamilton, R.T.; Brinton, R.D. Mitochondrial bioenergetic deficit precedes Alzheimer’s pathology in female mouse model of Alzheimer’s disease. Proc. Natl. Acad. Sci. USA 2009, 106, 14670–14675. [Google Scholar] [CrossRef] [Green Version]
  60. Blass, J.P.; Sheu, R.K.; Gibson, G.E. Inherent abnormalities in energy metabolism in Alzheimer disease. Interaction with cerebrovascular compromise. Ann. N. Y. Acad. Sci. 2000, 903, 204–221. [Google Scholar] [CrossRef]
  61. Fukuyama, R.; Hatanpaa, K.; Rapoport, S.I.; Chandrasekaran, K. Gene expression of ND4, a subunit of complex I of oxidative phosphorylation in mitochondria, is decreased in temporal cortex of brains of Alzheimer’s disease patients. Brain Res. 1996, 713, 290–293. [Google Scholar] [CrossRef]
  62. Kim, S.H.; Vlkolinsky, R.; Cairns, N.; Lubec, G. Decreased levels of complex III core protein 1 and complex V beta chain in brains from patients with Alzheimer’s disease and Down syndrome. Cell Mol. Life Sci. 2000, 57, 1810–1816. [Google Scholar] [CrossRef]
  63. Parker, W.D., Jr.; Parks, J.; Filley, C.M.; Kleinschmidt-DeMasters, B.K. Electron transport chain defects in Alzheimer’s disease brain. Neurology 1994, 44, 1090–1096. [Google Scholar] [CrossRef]
  64. Santos, R.X.; Correia, S.C.; Wang, X.; Perry, G.; Smith, M.A.; Moreira, P.I.; Zhu, X. Alzheimer’s disease: Diverse aspects of mitochondrial malfunctioning. Int. J. Clin. Exp. Pathol. 2010, 3, 570–581. [Google Scholar]
  65. He, T.C.; Sparks, A.B.; Rago, C.; Hermeking, H.; Zawel, L.; da Costa, L.T.; Morin, P.J.; Vogelstein, B.; Kinzler, K.W. Identification of c-MYC as a target of the APC pathway. Science 1998, 281, 1509–1512. [Google Scholar] [CrossRef]
  66. Shtutman, M.; Zhurinsky, J.; Simcha, I.; Albanese, C.; D’Amico, M.; Pestell, R.; Ben-Ze’ev, A. The cyclin D1 gene is a target of the beta-catenin/LEF-1 pathway. Proc. Natl. Acad. Sci. USA 1999, 96, 5522–5527. [Google Scholar] [CrossRef] [Green Version]
  67. Angers, S.; Moon, R.T. Proximal events in Wnt signal transduction. Nat. Rev. Mol. Cell Biol. 2009, 10, 468–477. [Google Scholar] [CrossRef]
  68. Park, K.S.; Lee, R.D.; Kang, S.K.; Han, S.Y.; Park, K.L.; Yang, K.H.; Song, Y.S.; Park, H.J.; Lee, Y.M.; Yun, Y.P.; et al. Neuronal differentiation of embryonic midbrain cells by upregulation of peroxisome proliferator-activated receptor-gamma via the JNK-dependent pathway. Exp. Cell Res. 2004, 297, 424–433. [Google Scholar] [CrossRef]
  69. Yue, X.; Lan, F.; Yang, W.; Yang, Y.; Han, L.; Zhang, A.; Liu, J.; Zeng, H.; Jiang, T.; Pu, P.; et al. Interruption of beta-catenin suppresses the EGFR pathway by blocking multiple oncogenic targets in human glioma cells. Brain Res. 2010, 1366, 27–37. [Google Scholar] [CrossRef]
  70. Sun, Q.; Chen, X.; Ma, J.; Peng, H.; Wang, F.; Zha, X.; Wang, Y.; Jing, Y.; Yang, H.; Chen, R.; et al. Mammalian target of rapamycin up-regulation of pyruvate kinase isoenzyme type M2 is critical for aerobic glycolysis and tumor growth. Proc. Natl. Acad. Sci. USA 2011, 108, 4129–4134. [Google Scholar] [CrossRef] [Green Version]
  71. Semenza, G.L. HIF-1: Upstream and downstream of cancer metabolism. Curr. Opin. Genet. Dev. 2010, 20, 51–56. [Google Scholar] [CrossRef] [Green Version]
  72. Cuadrado-Tejedor, M.; Vilarino, M.; Cabodevilla, F.; Del Rio, J.; Frechilla, D.; Perez-Mediavilla, A. Enhanced expression of the voltage-dependent anion channel 1 (VDAC1) in Alzheimer’s disease transgenic mice: An insight into the pathogenic effects of amyloid-beta. J. Alzheimers Dis. 2011, 23, 195–206. [Google Scholar] [CrossRef] [Green Version]
  73. Soucek, T.; Cumming, R.; Dargusch, R.; Maher, P.; Schubert, D. The regulation of glucose metabolism by HIF-1 mediates a neuroprotective response to amyloid beta peptide. Neuron 2003, 39, 43–56. [Google Scholar] [CrossRef] [Green Version]
  74. Newington, J.T.; Pitts, A.; Chien, A.; Arseneault, R.; Schubert, D.; Cumming, R.C. Amyloid beta resistance in nerve cell lines is mediated by the Warburg effect. PLoS ONE 2011, 6, e19191. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Newington, J.T.; Rappon, T.; Albers, S.; Wong, D.Y.; Rylett, R.J.; Cumming, R.C. Overexpression of pyruvate dehydrogenase kinase 1 and lactate dehydrogenase A in nerve cells confers resistance to amyloid beta and other toxins by decreasing mitochondrial respiration and reactive oxygen species production. J. Biol. Chem. 2012, 287, 37245–37258. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Baik, S.H.; Kang, S.; Lee, W.; Choi, H.; Chung, S.; Kim, J.I.; Mook-Jung, I. A Breakdown in Metabolic Reprogramming Causes Microglia Dysfunction in Alzheimer’s Disease. Cell Metab. 2019, 30, 493–507.e496. [Google Scholar] [CrossRef]
  77. De Lau, L.M.; Breteler, M.M. Epidemiology of Parkinson’s disease. Lancet Neurol. 2006, 5, 525–535. [Google Scholar] [CrossRef]
  78. Jankovic, J.; Aguilar, L.G. Current approaches to the treatment of Parkinson’s disease. Neuropsychiatr. Dis. Treat. 2008, 4, 743–757. [Google Scholar] [CrossRef] [Green Version]
  79. Chaudhuri, K.R.; Odin, P.; Antonini, A.; Martinez-Martin, P. Parkinson’s disease: The non-motor issues. Parkinsonism. Relat. Disord. 2011, 17, 717–723. [Google Scholar] [CrossRef]
  80. Spillantini, M.G.; Schmidt, M.L.; Lee, V.M.; Trojanowski, J.Q.; Jakes, R.; Goedert, M. Alpha-synuclein in Lewy bodies. Nature 1997, 388, 839–840. [Google Scholar] [CrossRef]
  81. Lee, V.M.; Trojanowski, J.Q. Mechanisms of Parkinson’s disease linked to pathological alpha-synuclein: New targets for drug discovery. Neuron 2006, 52, 33–38. [Google Scholar] [CrossRef] [Green Version]
  82. Goedert, M.; Spillantini, M.G.; Del Tredici, K.; Braak, H. 100 years of Lewy pathology. Nat. Rev. Neurol. 2013, 9, 13–24. [Google Scholar] [CrossRef]
  83. Langston, J.W.; Ballard, P.; Tetrud, J.W.; Irwin, I. Chronic Parkinsonism in humans due to a product of meperidine-analog synthesis. Science 1983, 219, 979–980. [Google Scholar] [CrossRef] [Green Version]
  84. AIDS and mortality. Rev. Infirm. 1992, 42, 5.
  85. Dawson, T.M.; Dawson, V.L. Molecular pathways of neurodegeneration in Parkinson’s disease. Science 2003, 302, 819–822. [Google Scholar] [CrossRef]
  86. Yao, Z.; Wood, N.W. Cell death pathways in Parkinson’s disease: Role of mitochondria. Antioxid. Redox Signal. 2009, 11, 2135–2149. [Google Scholar] [CrossRef]
  87. Ellis, C.E.; Murphy, E.J.; Mitchell, D.C.; Golovko, M.Y.; Scaglia, F.; Barcelo-Coblijn, G.C.; Nussbaum, R.L. Mitochondrial lipid abnormality and electron transport chain impairment in mice lacking alpha-synuclein. Mol. Cell Biol. 2005, 25, 10190–10201. [Google Scholar] [CrossRef] [Green Version]
  88. Smidt, M.P.; van Schaick, H.S.; Lanctot, C.; Tremblay, J.J.; Cox, J.J.; van der Kleij, A.A.; Wolterink, G.; Drouin, J.; Burbach, J.P. A homeodomain gene Ptx3 has highly restricted brain expression in mesencephalic dopaminergic neurons. Proc. Natl. Acad. Sci. USA 1997, 94, 13305–13310. [Google Scholar] [CrossRef] [Green Version]
  89. Martin, L.J.; Pan, Y.; Price, A.C.; Sterling, W.; Copeland, N.G.; Jenkins, N.A.; Price, D.L.; Lee, M.K. Parkinson’s disease alpha-synuclein transgenic mice develop neuronal mitochondrial degeneration and cell death. J. Neurosci. 2006, 26, 41–50. [Google Scholar] [CrossRef] [Green Version]
  90. Song, D.D.; Shults, C.W.; Sisk, A.; Rockenstein, E.; Masliah, E. Enhanced substantia nigra mitochondrial pathology in human alpha-synuclein transgenic mice after treatment with MPTP. Exp. Neurol. 2004, 186, 158–172. [Google Scholar] [CrossRef]
  91. Palacino, J.J.; Sagi, D.; Goldberg, M.S.; Krauss, S.; Motz, C.; Wacker, M.; Klose, J.; Shen, J. Mitochondrial dysfunction and oxidative damage in parkin-deficient mice. J. Biol. Chem. 2004, 279, 18614–18622. [Google Scholar] [CrossRef] [Green Version]
  92. Pesah, Y.; Pham, T.; Burgess, H.; Middlebrooks, B.; Verstreken, P.; Zhou, Y.; Harding, M.; Bellen, H.; Mardon, G. Drosophila parkin mutants have decreased mass and cell size and increased sensitivity to oxygen radical stress. Development 2004, 131, 2183–2194. [Google Scholar] [CrossRef] [Green Version]
  93. Chung, K.K.; Thomas, B.; Li, X.; Pletnikova, O.; Troncoso, J.C.; Marsh, L.; Dawson, V.L.; Dawson, T.M. S-nitrosylation of parkin regulates ubiquitination and compromises parkin’s protective function. Science 2004, 304, 1328–1331. [Google Scholar] [CrossRef]
  94. Silvestri, L.; Caputo, V.; Bellacchio, E.; Atorino, L.; Dallapiccola, B.; Valente, E.M.; Casari, G. Mitochondrial import and enzymatic activity of PINK1 mutants associated to recessive parkinsonism. Hum. Mol. Genet. 2005, 14, 3477–3492. [Google Scholar] [CrossRef] [Green Version]
  95. Petit, A.; Kawarai, T.; Paitel, E.; Sanjo, N.; Maj, M.; Scheid, M.; Chen, F.; Gu, Y.; Hasegawa, H.; Salehi-Rad, S.; et al. Wild-type PINK1 prevents basal and induced neuronal apoptosis, a protective effect abrogated by Parkinson disease-related mutations. J. Biol. Chem. 2005, 280, 34025–34032. [Google Scholar] [CrossRef] [Green Version]
  96. Yang, Y.; Gehrke, S.; Imai, Y.; Huang, Z.; Ouyang, Y.; Wang, J.W.; Yang, L.; Beal, M.F.; Vogel, H.; Lu, B. Mitochondrial pathology and muscle and dopaminergic neuron degeneration caused by inactivation of Drosophila Pink1 is rescued by Parkin. Proc. Natl. Acad. Sci. USA 2006, 103, 10793–10798. [Google Scholar] [CrossRef] [Green Version]
  97. Borghammer, P.; Chakravarty, M.; Jonsdottir, K.Y.; Sato, N.; Matsuda, H.; Ito, K.; Arahata, Y.; Kato, T.; Gjedde, A. Cortical hypometabolism and hypoperfusion in Parkinson’s disease is extensive: Probably even at early disease stages. Brain Struct. Funct. 2010, 214, 303–317. [Google Scholar] [CrossRef]
  98. Rodriguez-Araujo, G.; Nakagami, H.; Hayashi, H.; Mori, M.; Shiuchi, T.; Minokoshi, Y.; Nakaoka, Y.; Takami, Y.; Komuro, I.; Morishita, R.; et al. Alpha-synuclein elicits glucose uptake and utilization in adipocytes through the Gab1/PI3K/Akt transduction pathway. Cell Mol. Life Sci. 2013, 70, 1123–1133. [Google Scholar] [CrossRef]
  99. Knight, A.L.; Yan, X.; Hamamichi, S.; Ajjuri, R.R.; Mazzulli, J.R.; Zhang, M.W.; Daigle, J.G.; Zhang, S.; Borom, A.R.; Roberts, L.R.; et al. The glycolytic enzyme, GPI, is a functionally conserved modifier of dopaminergic neurodegeneration in Parkinson’s models. Cell Metab. 2014, 20, 145–157. [Google Scholar] [CrossRef] [Green Version]
  100. Dunn, L.; Allen, G.F.; Mamais, A.; Ling, H.; Li, A.; Duberley, K.E.; Hargreaves, I.P.; Pope, S.; Holton, J.L.; Lees, A.; et al. Dysregulation of glucose metabolism is an early event in sporadic Parkinson’s disease. Neurobiol. Aging 2014, 35, 1111–1115. [Google Scholar] [CrossRef] [Green Version]
  101. Mann, V.M.; Cooper, J.M.; Daniel, S.E.; Srai, K.; Jenner, P.; Marsden, C.D.; Schapira, A.H. Complex I, iron, and ferritin in Parkinson’s disease substantia nigra. Ann. Neurol. 1994, 36, 876–881. [Google Scholar] [CrossRef] [PubMed]
  102. Blandini, F.; Nappi, G.; Greenamyre, J.T. Quantitative study of mitochondrial complex I in platelets of parkinsonian patients. Mov. Disord. 1998, 13, 11–15. [Google Scholar] [CrossRef] [PubMed]
  103. Haas, R.H.; Nasirian, F.; Nakano, K.; Ward, D.; Pay, M.; Hill, R.; Shults, C.W. Low platelet mitochondrial complex I and complex II/III activity in early untreated Parkinson’s disease. Ann. Neurol. 1995, 37, 714–722. [Google Scholar] [CrossRef] [PubMed]
  104. Barroso, N.; Campos, Y.; Huertas, R.; Esteban, J.; Molina, J.A.; Alonso, A.; Gutierrez-Rivas, E.; Arenas, J. Respiratory chain enzyme activities in lymphocytes from untreated patients with Parkinson disease. Clin. Chem. 1993, 39, 667–669. [Google Scholar] [CrossRef] [PubMed]
  105. Yoshino, H.; Nakagawa-Hattori, Y.; Kondo, T.; Mizuno, Y. Mitochondrial complex I and II activities of lymphocytes and platelets in Parkinson’s disease. J. Neural. Transm. Park Dis. Dement. Sect. 1992, 4, 27–34. [Google Scholar] [CrossRef] [PubMed]
  106. Tieu, K.; Perier, C.; Caspersen, C.; Teismann, P.; Wu, D.C.; Yan, S.D.; Naini, A.; Vila, M.; Jackson-Lewis, V.; Ramasamy, R.; et al. D-beta-hydroxybutyrate rescues mitochondrial respiration and mitigates features of Parkinson disease. J. Clin. Invest. 2003, 112, 892–901. [Google Scholar] [CrossRef] [Green Version]
  107. Talbot, K. Amyotrophic lateral sclerosis: Cell vulnerability or system vulnerability? J. Anat. 2014, 224, 45–51. [Google Scholar] [CrossRef]
  108. Abhinav, K.; Stanton, B.; Johnston, C.; Hardstaff, J.; Orrell, R.W.; Howard, R.; Clarke, J.; Sakel, M.; Ampong, M.A.; Shaw, C.E.; et al. Amyotrophic lateral sclerosis in South-East England: A population-based study. The South-East England register for amyotrophic lateral sclerosis (SEALS Registry). Neuroepidemiology 2007, 29, 44–48. [Google Scholar] [CrossRef]
  109. Greenway, M.J.; Andersen, P.M.; Russ, C.; Ennis, S.; Cashman, S.; Donaghy, C.; Patterson, V.; Swingler, R.; Kieran, D.; Prehn, J.; et al. ANG mutations segregate with familial and ‘sporadic’ amyotrophic lateral sclerosis. Nat. Genet. 2006, 38, 411–413. [Google Scholar] [CrossRef]
  110. Chio, A.; Logroscino, G.; Traynor, B.J.; Collins, J.; Simeone, J.C.; Goldstein, L.A.; White, L.A. Global epidemiology of amyotrophic lateral sclerosis: A systematic review of the published literature. Neuroepidemiology 2013, 41, 118–130. [Google Scholar] [CrossRef] [Green Version]
  111. Byrne, S.; Walsh, C.; Lynch, C.; Bede, P.; Elamin, M.; Kenna, K.; McLaughlin, R.; Hardiman, O. Rate of familial amyotrophic lateral sclerosis: A systematic review and meta-analysis. J. Neurol. Neurosurg. Psychiatry 2011, 82, 623–627. [Google Scholar] [CrossRef]
  112. Ticozzi, N.; Vance, C.; Leclerc, A.L.; Keagle, P.; Glass, J.D.; McKenna-Yasek, D.; Sapp, P.C.; Silani, V.; Bosco, D.A.; Shaw, C.; et al. Mutational analysis reveals the FUS homolog TAF15 as a candidate gene for familial amyotrophic lateral sclerosis. Am. J. Med. Genet. B Neuropsychiatr. Genet. 2011, 156B, 285–290. [Google Scholar] [CrossRef]
  113. Ticozzi, N.; Tiloca, C.; Morelli, C.; Colombrita, C.; Poletti, B.; Doretti, A.; Maderna, L.; Messina, S.; Ratti, A.; Silani, V. Genetics of familial Amyotrophic lateral sclerosis. Arch. Ital. Biol. 2011, 149, 65–82. [Google Scholar]
  114. Logroscino, G.; Traynor, B.J.; Hardiman, O.; Chio, A.; Couratier, P.; Mitchell, J.D.; Swingler, R.J.; Beghi, E. Eurals, Descriptive epidemiology of amyotrophic lateral sclerosis: New evidence and unsolved issues. J. Neurol. Neurosurg. Psychiatry 2008, 79, 6–11. [Google Scholar] [CrossRef]
  115. Logroscino, G.; Traynor, B.J.; Hardiman, O.; Chio, A.; Mitchell, D.; Swingler, R.J.; Millul, A.; Benn, E.; Beghi, E. Eurals, Incidence of amyotrophic lateral sclerosis in Europe. J. Neurol. Neurosurg. Psychiatry 2010, 81, 385–390. [Google Scholar] [CrossRef]
  116. Mazzini, L.; Balzarini, C.; Colombo, R.; Mora, G.; Pastore, I.; De Ambrogio, R.; Caligari, M. Effects of creatine supplementation on exercise performance and muscular strength in amyotrophic lateral sclerosis: Preliminary results. J. Neurol. Sci. 2001, 191, 139–144. [Google Scholar] [CrossRef]
  117. O’Toole, O.; Traynor, B.J.; Brennan, P.; Sheehan, C.; Frost, E.; Corr, B.; Hardiman, O. Epidemiology and clinical features of amyotrophic lateral sclerosis in Ireland between 1995 and 2004. J. Neurol. Neurosurg. Psychiatry 2008, 79, 30–32. [Google Scholar] [CrossRef]
  118. Goetz, C.G. Amyotrophic lateral sclerosis: Early contributions of Jean-Martin Charcot. Muscle Nerve 2000, 23, 336–343. [Google Scholar] [CrossRef]
  119. Wijesekera, L.C.; Leigh, P.N. Amyotrophic lateral sclerosis. Orphanet J. Rare Dis. 2009, 4, 3. [Google Scholar] [CrossRef] [Green Version]
  120. Leigh, P.N.; Abrahams, S.; Al-Chalabi, A.; Ampong, M.A.; Goldstein, L.H.; Johnson, J.; Lyall, R.; Moxham, J.; Mustfa, N.; Rio, A.; et al. The management of motor neurone disease. J. Neurol. Neurosurg. Psychiatry 2003, 74 (Suppl. 4), iv32–iv47. [Google Scholar] [CrossRef]
  121. Polkey, M.I.; Lyall, R.A.; Moxham, J.; Leigh, P.N. Respiratory aspects of neurological disease. J. Neurol. Neurosurg. Psychiatry 1999, 66, 5–15. [Google Scholar] [CrossRef] [Green Version]
  122. Chio, A.; Calvo, A.; Moglia, C.; Mazzini, L.; Mora, G. Phenotypic heterogeneity of amyotrophic lateral sclerosis: A population based study. J. Neurol. Neurosurg. Psychiatry 2011, 82, 740–746. [Google Scholar] [CrossRef]
  123. Atsumi, T. The ultrastructure of intramuscular nerves in amyotrophic lateral sclerosis. Acta Neuropathol. 1981, 55, 193–198. [Google Scholar] [CrossRef]
  124. Sasaki, S.; Iwata, M. Ultrastructural study of synapses in the anterior horn neurons of patients with amyotrophic lateral sclerosis. Neurosci. Lett. 1996, 204, 53–56. [Google Scholar] [CrossRef]
  125. Siklos, L.; Engelhardt, J.; Harati, Y.; Smith, R.G.; Joo, F.; Appel, S.H. Ultrastructural evidence for altered calcium in motor nerve terminals in amyotropic lateral sclerosis. Ann. Neurol. 1996, 39, 203–216. [Google Scholar] [CrossRef]
  126. Dal Canto, M.C.; Gurney, M.E. Neuropathological changes in two lines of mice carrying a transgene for mutant human Cu, Zn SOD, and in mice overexpressing wild type human SOD: A model of familial amyotrophic lateral sclerosis (FALS). Brain Res. 1995, 676, 25–40. [Google Scholar] [CrossRef]
  127. Kong, J.; Xu, Z. Massive mitochondrial degeneration in motor neurons triggers the onset of amyotrophic lateral sclerosis in mice expressing a mutant SOD1. J. Neurosci. 1998, 18, 3241–3250. [Google Scholar] [CrossRef]
  128. Wong, P.C.; Pardo, C.A.; Borchelt, D.R.; Lee, M.K.; Copeland, N.G.; Jenkins, N.A.; Sisodia, S.S.; Cleveland, D.W.; Price, D.L. An adverse property of a familial ALS-linked SOD1 mutation causes motor neuron disease characterized by vacuolar degeneration of mitochondria. Neuron 1995, 14, 1105–1116. [Google Scholar] [CrossRef] [Green Version]
  129. Bendotti, C.; Calvaresi, N.; Chiveri, L.; Prelle, A.; Moggio, M.; Braga, M.; Silani, V.; De Biasi, S. Early vacuolization and mitochondrial damage in motor neurons of FALS mice are not associated with apoptosis or with changes in cytochrome oxidase histochemical reactivity. J. Neurol. Sci. 2001, 191, 25–33. [Google Scholar] [CrossRef]
  130. Shan, X.; Chiang, P.M.; Price, D.L.; Wong, P.C. Altered distributions of Gemini of coiled bodies and mitochondria in motor neurons of TDP-43 transgenic mice. Proc. Natl. Acad. Sci. USA 2010, 107, 16325–16330. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  131. Xu, Y.F.; Gendron, T.F.; Zhang, Y.J.; Lin, W.L.; D’Alton, S.; Sheng, H.; Casey, M.C.; Tong, J.; Knight, J.; Yu, X.; et al. Wild-type human TDP-43 expression causes TDP-43 phosphorylation, mitochondrial aggregation, motor deficits, and early mortality in transgenic mice. J. Neurosci. 2010, 30, 10851–10859. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  132. Browne, S.E.; Yang, L.; DiMauro, J.P.; Fuller, S.W.; Licata, S.C.; Beal, M.F. Bioenergetic abnormalities in discrete cerebral motor pathways presage spinal cord pathology in the G93A SOD1 mouse model of ALS. Neurobiol. Dis. 2006, 22, 599–610. [Google Scholar] [CrossRef] [PubMed]
  133. Miyazaki, K.; Masamoto, K.; Morimoto, N.; Kurata, T.; Mimoto, T.; Obata, T.; Kanno, I.; Abe, K. Early and progressive impairment of spinal blood flow-glucose metabolism coupling in motor neuron degeneration of ALS model mice. J. Cereb. Blood Flow Metab. 2012, 32, 456–467. [Google Scholar] [CrossRef] [Green Version]
  134. Hatazawa, J.; Brooks, R.A.; Dalakas, M.C.; Mansi, L.; Di Chiro, G. Cortical motor-sensory hypometabolism in amyotrophic lateral sclerosis: A PET study. J. Comput. Assist. Tomogr. 1988, 12, 630–636. [Google Scholar] [CrossRef]
  135. Pagani, M.; Chio, A.; Valentini, M.C.; Oberg, J.; Nobili, F.; Calvo, A.; Moglia, C.; Bertuzzo, D.; Morbelli, S.; De Carli, F.; et al. Functional pattern of brain FDG-PET in amyotrophic lateral sclerosis. Neurology 2014, 83, 1067–1074. [Google Scholar] [CrossRef] [Green Version]
  136. Van Laere, K.; Vanhee, A.; Verschueren, J.; De Coster, L.; Driesen, A.; Dupont, P.; Robberecht, W.; Van Damme, P. Value of 18fluorodeoxyglucose-positron-emission tomography in amyotrophic lateral sclerosis: A prospective study. JAMA Neurol. 2014, 71, 553–561. [Google Scholar] [CrossRef] [Green Version]
  137. Dalakas, M.C.; Hatazawa, J.; Brooks, R.A.; Di Chiro, G. Lowered cerebral glucose utilization in amyotrophic lateral sclerosis. Ann. Neurol. 1987, 22, 580–586. [Google Scholar] [CrossRef]
  138. Raman, R.; Allen, S.P.; Goodall, E.F.; Kramer, S.; Ponger, L.L.; Heath, P.R.; Milo, M.; Hollinger, H.C.; Walsh, T.; Highley, J.R.; et al. Gene expression signatures in motor neurone disease fibroblasts reveal dysregulation of metabolism, hypoxia-response and RNA processing functions. Neuropathol. Appl. Neurobiol. 2015, 41, 201–226. [Google Scholar] [CrossRef]
  139. Szelechowski, M.; Amoedo, N.; Obre, E.; Leger, C.; Allard, L.; Bonneu, M.; Claverol, S.; Lacombe, D.; Oliet, S.; Chevallier, S.; et al. Metabolic Reprogramming in Amyotrophic Lateral Sclerosis. Sci. Rep. 2018, 8, 3953. [Google Scholar] [CrossRef] [Green Version]
  140. Lederer, C.W.; Torrisi, A.; Pantelidou, M.; Santama, N.; Cavallaro, S. Pathways and genes differentially expressed in the motor cortex of patients with sporadic amyotrophic lateral sclerosis. BMC Genom. 2007, 8, 26. [Google Scholar] [CrossRef] [Green Version]
  141. Wang, X.S.; Simmons, Z.; Liu, W.; Boyer, P.J.; Connor, J.R. Differential expression of genes in amyotrophic lateral sclerosis revealed by profiling the post mortem cortex. Amyotroph. Lateral Scler. 2006, 7, 201–210. [Google Scholar] [CrossRef]
  142. Funalot, B.; Desport, J.C.; Sturtz, F.; Camu, W.; Couratier, P. High metabolic level in patients with familial amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. 2009, 10, 113–117. [Google Scholar] [CrossRef]
  143. Allen, S.P.; Rajan, S.; Duffy, L.; Mortiboys, H.; Higginbottom, A.; Grierson, A.J.; Shaw, P.J. Superoxide dismutase 1 mutation in a cellular model of amyotrophic lateral sclerosis shifts energy generation from oxidative phosphorylation to glycolysis. Neurobiol. Aging 2014, 35, 1499–1509. [Google Scholar] [CrossRef]
  144. Valbuena, G.N.; Rizzardini, M.; Cimini, S.; Siskos, A.P.; Bendotti, C.; Cantoni, L.; Keun, H.C. Metabolomic Analysis Reveals Increased Aerobic Glycolysis and Amino Acid Deficit in a Cellular Model of Amyotrophic Lateral Sclerosis. Mol. Neurobiol. 2016, 53, 2222–2240. [Google Scholar] [CrossRef] [Green Version]
  145. Rodriguez, M.J.; Mahy, N. Neuron-Microglia Interactions in Motor Neuron Degeneration. The Inflammatory Hypothesis in Amyotrophic Lateral Sclerosis Revisited. Curr. Med. Chem. 2016, 23, 4753–4772. [Google Scholar] [CrossRef] [Green Version]
  146. Herrero-Mendez, A.; Almeida, A.; Fernandez, E.; Maestre, C.; Moncada, S.; Bolanos, J.P. The bioenergetic and antioxidant status of neurons is controlled by continuous degradation of a key glycolytic enzyme by APC/C-Cdh1. Nat. Cell Biol. 2009, 11, 747–752. [Google Scholar] [CrossRef]
  147. Rodriguez-Rodriguez, P.; Fernandez, E.; Almeida, A.; Bolanos, J.P. Excitotoxic stimulus stabilizes PFKFB3 causing pentose-phosphate pathway to glycolysis switch and neurodegeneration. Cell Death Differ. 2012, 19, 1582–1589. [Google Scholar] [CrossRef] [Green Version]
  148. Diaz-Garcia, C.M.; Mongeon, R.; Lahmann, C.; Koveal, D.; Zucker, H.; Yellen, G. Neuronal Stimulation Triggers Neuronal Glycolysis and Not Lactate Uptake. Cell Metab. 2017, 26, 361–374.e364. [Google Scholar] [CrossRef]
  149. Ferrante, R.J.; Browne, S.E.; Shinobu, L.A.; Bowling, A.C.; Baik, M.J.; MacGarvey, U.; Kowall, N.W.; Brown, R.H., Jr.; Beal, M.F. Evidence of increased oxidative damage in both sporadic and familial amyotrophic lateral sclerosis. J. Neurochem. 1997, 69, 2064–2074. [Google Scholar] [CrossRef]
  150. Borthwick, G.M.; Johnson, M.A.; Ince, P.G.; Shaw, P.J.; Turnbull, D.M. Mitochondrial enzyme activity in amyotrophic lateral sclerosis: Implications for the role of mitochondria in neuronal cell death. Ann. Neurol. 1999, 46, 787–790. [Google Scholar] [CrossRef]
  151. Bowling, A.C.; Schulz, J.B.; Brown, R.H., Jr.; Beal, M.F. Superoxide dismutase activity, oxidative damage, and mitochondrial energy metabolism in familial and sporadic amyotrophic lateral sclerosis. J. Neurochem. 1993, 61, 2322–2325. [Google Scholar] [CrossRef]
  152. Fujita, K.; Yamauchi, M.; Shibayama, K.; Ando, M.; Honda, M.; Nagata, Y. Decreased cytochrome c oxidase activity but unchanged superoxide dismutase and glutathione peroxidase activities in the spinal cords of patients with amyotrophic lateral sclerosis. J. Neurosci. Res. 1996, 45, 276–281. [Google Scholar] [CrossRef]
  153. Wiedemann, F.R.; Winkler, K.; Kuznetsov, A.V.; Bartels, C.; Vielhaber, S.; Feistner, H.; Kunz, W.S. Impairment of mitochondrial function in skeletal muscle of patients with amyotrophic lateral sclerosis. J. Neurol. Sci. 1998, 156, 65–72. [Google Scholar] [CrossRef]
  154. Echaniz-Laguna, A.; Zoll, J.; Ponsot, E.; N’Guessan, B.; Tranchant, C.; Loeffler, J.P.; Lampert, E. Muscular mitochondrial function in amyotrophic lateral sclerosis is progressively altered as the disease develops: A temporal study in man. Exp. Neurol. 2006, 198, 25–30. [Google Scholar] [CrossRef] [PubMed]
  155. Mattiazzi, M.; D’Aurelio, M.; Gajewski, C.D.; Martushova, K.; Kiaei, M.; Beal, M.F.; Manfredi, G. Mutated human SOD1 causes dysfunction of oxidative phosphorylation in mitochondria of transgenic mice. J. Biol. Chem. 2002, 277, 29626–29633. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  156. Jung, C.; Higgins, C.M.; Xu, Z. Mitochondrial electron transport chain complex dysfunction in a transgenic mouse model for amyotrophic lateral sclerosis. J. Neurochem. 2002, 83, 535–545. [Google Scholar] [CrossRef]
  157. Munoz-Sanjuan, I.; Bates, G.P. The importance of integrating basic and clinical research toward the development of new therapies for Huntington disease. J. Clin. Invest. 2011, 121, 476–483. [Google Scholar] [CrossRef] [Green Version]
  158. Neylan, T.C. Neurodegenerative disorders: George Huntington’s description of hereditary chorea. J. Neuropsychiatry Clin. Neurosci. 2003, 15, 108. [Google Scholar] [CrossRef]
  159. Warby, S.C.; Visscher, H.; Collins, J.A.; Doty, C.N.; Carter, C.; Butland, S.L.; Hayden, A.R.; Kanazawa, I.; Ross, C.J.; Hayden, M.R. HTT haplotypes contribute to differences in Huntington disease prevalence between Europe and East Asia. Eur. J. Hum. Genet. 2011, 19, 561–566. [Google Scholar] [CrossRef]
  160. Kremer, B.; Goldberg, P.; Andrew, S.E.; Theilmann, J.; Telenius, H.; Zeisler, J.; Squitieri, F.; Lin, B.; Bassett, A.; Almqvist, E.; et al. A worldwide study of the Huntington’s disease mutation. The sensitivity and specificity of measuring CAG repeats. N. Engl. J. Med. 1994, 330, 1401–1406. [Google Scholar] [CrossRef]
  161. DiFiglia, M.; Sapp, E.; Chase, K.O.; Davies, S.W.; Bates, G.P.; Vonsattel, J.P.; Aronin, N. Aggregation of huntingtin in neuronal intranuclear inclusions and dystrophic neurites in brain. Science 1997, 277, 1990–1993. [Google Scholar] [CrossRef]
  162. Becher, M.W.; Kotzuk, J.A.; Sharp, A.H.; Davies, S.W.; Bates, G.P.; Price, D.L.; Ross, C.A. Intranuclear neuronal inclusions in Huntington’s disease and dentatorubral and pallidoluysian atrophy: Correlation between the density of inclusions and IT15 CAG triplet repeat length. Neurobiol. Dis. 1998, 4, 387–397. [Google Scholar] [CrossRef] [Green Version]
  163. Trushina, E.; Dyer, R.B.; Badger, J.D., 2nd; Ure, D.; Eide, L.; Tran, D.D.; Vrieze, B.T.; Legendre-Guillemin, V.; McPherson, P.S.; Mandavilli, B.S.; et al. Mutant huntingtin impairs axonal trafficking in mammalian neurons in vivo and in vitro. Mol. Cell Biol. 2004, 24, 8195–8209. [Google Scholar] [CrossRef] [Green Version]
  164. Bossy-Wetzel, E.; Petrilli, A.; Knott, A.B. Mutant huntingtin and mitochondrial dysfunction. Trends Neurosci. 2008, 31, 609–616. [Google Scholar] [CrossRef] [Green Version]
  165. Choo, Y.S.; Johnson, G.V.; MacDonald, M.; Detloff, P.J.; Lesort, M. Mutant huntingtin directly increases susceptibility of mitochondria to the calcium-induced permeability transition and cytochrome c release. Hum. Mol. Genet. 2004, 13, 1407–1420. [Google Scholar] [CrossRef]
  166. Ismailoglu, I.; Chen, Q.; Popowski, M.; Yang, L.; Gross, S.S.; Brivanlou, A.H. Huntingtin protein is essential for mitochondrial metabolism, bioenergetics and structure in murine embryonic stem cells. Dev. Biol. 2014, 391, 230–240. [Google Scholar] [CrossRef] [Green Version]
  167. Feigin, A.; Leenders, K.L.; Moeller, J.R.; Missimer, J.; Kuenig, G.; Spetsieris, P.; Antonini, A.; Eidelberg, D. Metabolic network abnormalities in early Huntington’s disease: An [(18)F]FDG PET study. J. Nucl. Med. 2001, 42, 1591–1595. [Google Scholar]
  168. Antonini, A.; Leenders, K.L.; Spiegel, R.; Meier, D.; Vontobel, P.; Weigell-Weber, M.; Sanchez-Pernaute, R.; de Yebenez, J.G.; Boesiger, P.; Weindl, A.; et al. Striatal glucose metabolism and dopamine D2 receptor binding in asymptomatic gene carriers and patients with Huntington’s disease. Brain 1996, 119 (Pt. 6), 2085–2095. [Google Scholar] [CrossRef] [Green Version]
  169. Ciarmiello, A.; Cannella, M.; Lastoria, S.; Simonelli, M.; Frati, L.; Rubinsztein, D.C.; Squitieri, F. Brain white-matter volume loss and glucose hypometabolism precede the clinical symptoms of Huntington’s disease. J. Nucl. Med. 2006, 47, 215–222. [Google Scholar]
  170. McClory, H.; Williams, D.; Sapp, E.; Gatune, L.W.; Wang, P.; DiFiglia, M.; Li, X. Glucose transporter 3 is a rab11-dependent trafficking cargo and its transport to the cell surface is reduced in neurons of CAG140 Huntington’s disease mice. Acta Neuropathol. Commun. 2014, 2, 179. [Google Scholar] [CrossRef] [Green Version]
  171. Vittori, A.; Breda, C.; Repici, M.; Orth, M.; Roos, R.A.; Outeiro, T.F.; Giorgini, F.; Hollox, E.J.; Network, R. Copy-number variation of the neuronal glucose transporter gene SLC2A3 and age of onset in Huntington’s disease. Hum. Mol. Genet. 2014, 23, 3129–3137. [Google Scholar] [CrossRef] [Green Version]
  172. Powers, W.J.; Videen, T.O.; Markham, J.; McGee-Minnich, L.; Antenor-Dorsey, J.V.; Hershey, T.; Perlmutter, J.S. Selective defect of in vivo glycolysis in early Huntington’s disease striatum. Proc. Natl. Acad. Sci. USA 2007, 104, 2945–2949. [Google Scholar] [CrossRef] [Green Version]
  173. Young, A.B.; Penney, J.B.; Starosta-Rubinstein, S.; Markel, D.S.; Berent, S.; Giordani, B.; Ehrenkaufer, R.; Jewett, D.; Hichwa, R. PET scan investigations of Huntington’s disease: Cerebral metabolic correlates of neurological features and functional decline. Ann. Neurol. 1986, 20, 296–303. [Google Scholar] [CrossRef] [PubMed]
  174. Skotte, N.H.; Andersen, J.V.; Santos, A.; Aldana, B.I.; Willert, C.W.; Norremolle, A.; Waagepetersen, H.S.; Nielsen, M.L. Integrative Characterization of the R6/2 Mouse Model of Huntington’s Disease Reveals Dysfunctional Astrocyte Metabolism. Cell Rep. 2018, 23, 2211–2224. [Google Scholar] [CrossRef] [PubMed]
  175. Jenkins, B.G.; Koroshetz, W.J.; Beal, M.F.; Rosen, B.R. Evidence for impairment of energy metabolism in vivo in Huntington’s disease using localized 1H NMR spectroscopy. Neurology 1993, 43, 2689–2695. [Google Scholar] [CrossRef]
  176. Garseth, M.; Sonnewald, U.; White, L.R.; Rod, M.; Zwart, J.A.; Nygaard, O.; Aasly, J. Proton magnetic resonance spectroscopy of cerebrospinal fluid in neurodegenerative disease: Indication of glial energy impairment in Huntington chorea, but not Parkinson disease. J. Neurosci. Res. 2000, 60, 779–782. [Google Scholar] [CrossRef]
  177. Gu, M.; Gash, M.T.; Mann, V.M.; Javoy-Agid, F.; Cooper, J.M.; Schapira, A.H. Mitochondrial defect in Huntington’s disease caudate nucleus. Ann. Neurol. 1996, 39, 385–389. [Google Scholar] [CrossRef]
  178. Maksimovic, I.D.; Jovanovic, M.D.; Colic, M.; Mihajlovic, R.; Micic, D.; Selakovic, V.; Ninkovic, M.; Malicevic, Z.; Rusic-Stojiljkovic, M.; Jovicic, A. Oxidative damage and metabolic dysfunction in experimental Huntington’s disease: Selective vulnerability of the striatum and hippocampus. Vojnosanit. Pregl. 2001, 58, 237–242. [Google Scholar]
  179. Benchoua, A.; Trioulier, Y.; Zala, D.; Gaillard, M.C.; Lefort, N.; Dufour, N.; Saudou, F.; Elalouf, J.M.; Hirsch, E.; Hantraye, P.; et al. Involvement of mitochondrial complex II defects in neuronal death produced by N-terminus fragment of mutated huntingtin. Mol. Biol. Cell 2006, 17, 1652–1663. [Google Scholar] [CrossRef] [Green Version]
  180. Milakovic, T.; Quintanilla, R.A.; Johnson, G.V. Mutant huntingtin expression induces mitochondrial calcium handling defects in clonal striatal cells: Functional consequences. J. Biol. Chem. 2006, 281, 34785–34795. [Google Scholar] [CrossRef] [Green Version]
  181. Poellinger, L.; Johnson, R.S. HIF-1 and hypoxic response: The plot thickens. Curr. Opin. Genet. Dev. 2004, 14, 81–85. [Google Scholar] [CrossRef]
  182. Semenza, G.L. Life with oxygen. Science 2007, 318, 62–64. [Google Scholar] [CrossRef]
  183. Wang, G.L.; Jiang, B.H.; Rue, E.A.; Semenza, G.L. Hypoxia-inducible factor 1 is a basic-helix-loop-helix-PAS heterodimer regulated by cellular O2 tension. Proc. Natl. Acad. Sci. USA 1995, 92, 5510–5514. [Google Scholar] [CrossRef] [Green Version]
  184. Majmundar, A.J.; Wong, W.J.; Simon, M.C. Hypoxia-inducible factors and the response to hypoxic stress. Mol. Cell 2010, 40, 294–309. [Google Scholar] [CrossRef] [Green Version]
  185. Ivan, M.; Haberberger, T.; Gervasi, D.C.; Michelson, K.S.; Gunzler, V.; Kondo, K.; Yang, H.; Sorokina, I.; Conaway, R.C.; Conaway, J.W.; et al. Biochemical purification and pharmacological inhibition of a mammalian prolyl hydroxylase acting on hypoxia-inducible factor. Proc. Natl. Acad. Sci. USA 2002, 99, 13459–13464. [Google Scholar] [CrossRef] [Green Version]
  186. Gleadle, J.M.; Ratcliffe, P.J. Induction of hypoxia-inducible factor-1, erythropoietin, vascular endothelial growth factor, and glucose transporter-1 by hypoxia: Evidence against a regulatory role for Src kinase. Blood 1997, 89, 503–509. [Google Scholar] [CrossRef] [Green Version]
  187. Maxwell, P.H.; Dachs, G.U.; Gleadle, J.M.; Nicholls, L.G.; Harris, A.L.; Stratford, I.J.; Hankinson, O.; Pugh, C.W.; Ratcliffe, P.J. Hypoxia-inducible factor-1 modulates gene expression in solid tumors and influences both angiogenesis and tumor growth. Proc. Natl. Acad. Sci. USA 1997, 94, 8104–8109. [Google Scholar] [CrossRef] [Green Version]
  188. Mathupala, S.P.; Rempel, A.; Pedersen, P.L. Glucose catabolism in cancer cells: Identification and characterization of a marked activation response of the type II hexokinase gene to hypoxic conditions. J. Biol. Chem. 2001, 276, 43407–43412. [Google Scholar] [CrossRef] [Green Version]
  189. Semenza, G.L.; Roth, P.H.; Fang, H.M.; Wang, G.L. Transcriptional regulation of genes encoding glycolytic enzymes by hypoxia-inducible factor 1. J. Biol. Chem. 1994, 269, 23757–23763. [Google Scholar] [CrossRef]
  190. Semenza, G.L.; Jiang, B.H.; Leung, S.W.; Passantino, R.; Concordet, J.P.; Maire, P.; Giallongo, A. Hypoxia response elements in the aldolase A, enolase 1, and lactate dehydrogenase A gene promoters contain essential binding sites for hypoxia-inducible factor 1. J. Biol. Chem. 1996, 271, 32529–32537. [Google Scholar] [CrossRef] [Green Version]
  191. Suda, T.; Takubo, K.; Semenza, G.L. Metabolic regulation of hematopoietic stem cells in the hypoxic niche. Cell Stem Cell 2011, 9, 298–310. [Google Scholar] [CrossRef] [Green Version]
  192. Takubo, K.; Goda, N.; Yamada, W.; Iriuchishima, H.; Ikeda, E.; Kubota, Y.; Shima, H.; Johnson, R.S.; Hirao, A.; Suematsu, M.; et al. Regulation of the HIF-1alpha level is essential for hematopoietic stem cells. Cell Stem Cell 2010, 7, 391–402. [Google Scholar] [CrossRef] [Green Version]
  193. Warburg, O.; Wind, F.; Negelein, E. The Metabolism of Tumors in the Body. J. Gen. Physiol. 1927, 8, 519–530. [Google Scholar] [CrossRef] [Green Version]
  194. Warburg, O. On respiratory impairment in cancer cells. Science 1956, 124, 269–270. [Google Scholar] [PubMed]
  195. Milosevic, M.; Fyles, A.; Hedley, D.; Hill, R. The human tumor microenvironment: Invasive (needle) measurement of oxygen and interstitial fluid pressure. Semin. Radiat. Oncol. 2004, 14, 249–258. [Google Scholar] [CrossRef] [PubMed]
  196. Brown, J.M.; Giaccia, A.J. The unique physiology of solid tumors: Opportunities (and problems) for cancer therapy. Cancer Res. 1998, 58, 1408–1416. [Google Scholar] [PubMed]
  197. Denko, N.C.; Fontana, L.A.; Hudson, K.M.; Sutphin, P.D.; Raychaudhuri, S.; Altman, R.; Giaccia, A.J. Investigating hypoxic tumor physiology through gene expression patterns. Oncogene 2003, 22, 5907–5914. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  198. Hockel, M.; Vaupel, P. Tumor hypoxia: Definitions and current clinical, biologic, and molecular aspects. J. Natl. Cancer Inst. 2001, 93, 266–276. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  199. Iyer, N.V.; Kotch, L.E.; Agani, F.; Leung, S.W.; Laughner, E.; Wenger, R.H.; Gassmann, M.; Gearhart, J.D.; Lawler, A.M.; Yu, A.Y.; et al. Cellular and developmental control of O2 homeostasis by hypoxia-inducible factor 1 alpha. Genes Dev. 1998, 12, 149–162. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  200. Kim, J.W.; Tchernyshyov, I.; Semenza, G.L.; Dang, C.V. HIF-1-mediated expression of pyruvate dehydrogenase kinase: A metabolic switch required for cellular adaptation to hypoxia. Cell Metab. 2006, 3, 177–185. [Google Scholar] [CrossRef] [Green Version]
  201. Papandreou, I.; Cairns, R.A.; Fontana, L.; Lim, A.L.; Denko, N.C. HIF-1 mediates adaptation to hypoxia by actively downregulating mitochondrial oxygen consumption. Cell Metab. 2006, 3, 187–197. [Google Scholar] [CrossRef] [Green Version]
  202. Zhang, H.; Gao, P.; Fukuda, R.; Kumar, G.; Krishnamachary, B.; Zeller, K.I.; Dang, C.V.; Semenza, G.L. HIF-1 inhibits mitochondrial biogenesis and cellular respiration in VHL-deficient renal cell carcinoma by repression of C-MYC activity. Cancer Cell 2007, 11, 407–420. [Google Scholar] [CrossRef] [Green Version]
  203. Kim, J.W.; Gao, P.; Liu, Y.C.; Semenza, G.L.; Dang, C.V. Hypoxia-inducible factor 1 and dysregulated c-Myc cooperatively induce vascular endothelial growth factor and metabolic switches hexokinase 2 and pyruvate dehydrogenase kinase 1. Mol. Cell Biol. 2007, 27, 7381–7393. [Google Scholar] [CrossRef] [Green Version]
  204. Jiang, B.H.; Agani, F.; Passaniti, A.; Semenza, G.L. V-SRC induces expression of hypoxia-inducible factor 1 (HIF-1) and transcription of genes encoding vascular endothelial growth factor and enolase 1: Involvement of HIF-1 in tumor progression. Cancer Res. 1997, 57, 5328–5335. [Google Scholar]
  205. Tandon, P.; Gallo, C.A.; Khatri, S.; Barger, J.F.; Yepiskoposyan, H.; Plas, D.R. Requirement for ribosomal protein S6 kinase 1 to mediate glycolysis and apoptosis resistance induced by Pten deficiency. Proc. Natl. Acad. Sci. USA 2011, 108, 2361–2365. [Google Scholar] [CrossRef] [Green Version]
  206. Minchenko, A.; Leshchinsky, I.; Opentanova, I.; Sang, N.; Srinivas, V.; Armstead, V.; Caro, J. Hypoxia-inducible factor-1-mediated expression of the 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) gene. Its possible role in the Warburg effect. J. Biol. Chem. 2002, 277, 6183–6187. [Google Scholar] [CrossRef] [Green Version]
  207. Comerford, S.A.; Huang, Z.; Du, X.; Wang, Y.; Cai, L.; Witkiewicz, A.K.; Walters, H.; Tantawy, M.N.; Fu, A.; Manning, H.C.; et al. Acetate dependence of tumors. Cell 2014, 159, 1591–1602. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  208. Mashimo, T.; Pichumani, K.; Vemireddy, V.; Hatanpaa, K.J.; Singh, D.K.; Sirasanagandla, S.; Nannepaga, S.; Piccirillo, S.G.; Kovacs, Z.; Foong, C.; et al. Acetate is a bioenergetic substrate for human glioblastoma and brain metastases. Cell 2014, 159, 1603–1614. [Google Scholar] [CrossRef] [Green Version]
  209. Huang, D.; Li, T.; Wang, L.; Zhang, L.; Yan, R.; Li, K.; Xing, S.; Wu, G.; Hu, L.; Jia, W.; et al. Hepatocellular carcinoma redirects to ketolysis for progression under nutrition deprivation stress. Cell Res. 2016, 26, 1112–1130. [Google Scholar] [CrossRef] [Green Version]
  210. Hui, S.; Ghergurovich, J.M.; Morscher, R.J.; Jang, C.; Teng, X.; Lu, W.; Esparza, L.A.; Reya, T.; Le, Z.; Yanxiang Guo, J.; et al. Glucose feeds the TCA cycle via circulating lactate. Nature 2017, 551, 115–118. [Google Scholar] [CrossRef] [Green Version]
  211. Faubert, B.; Li, K.Y.; Cai, L.; Hensley, C.T.; Kim, J.; Zacharias, L.G.; Yang, C.; Do, Q.N.; Doucette, S.; Burguete, D.; et al. Lactate Metabolism in Human Lung Tumors. Cell 2017, 171, 358–371.e9. [Google Scholar] [CrossRef] [Green Version]
  212. Jain, I.H.; Calvo, S.E.; Markhard, A.L.; Skinner, O.S.; To, T.L.; Ast, T.; Mootha, V.K. Genetic Screen for Cell Fitness in High or Low Oxygen Highlights Mitochondrial and Lipid Metabolism. Cell 2020, 181, 716–727.e711. [Google Scholar] [CrossRef]
  213. Wagenfuhr, L.; Meyer, A.K.; Marrone, L.; Storch, A. Oxygen Tension Within the Neurogenic Niche Regulates Dopaminergic Neurogenesis in the Developing Midbrain. Stem Cells Dev. 2016, 25, 227–238. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  214. Marti-Fabregas, J.; Romaguera-Ros, M.; Gomez-Pinedo, U.; Martinez-Ramirez, S.; Jimenez-Xarrie, E.; Marin, R.; Marti-Vilalta, J.L.; Garcia-Verdugo, J.M. Proliferation in the human ipsilateral subventricular zone after ischemic stroke. Neurology 2010, 74, 357–365. [Google Scholar] [CrossRef] [PubMed]
  215. Lendahl, U.; Lee, K.L.; Yang, H.; Poellinger, L. Generating specificity and diversity in the transcriptional response to hypoxia. Nat. Rev. Genet. 2009, 10, 821–832. [Google Scholar] [CrossRef] [PubMed]
  216. Hochachka, P.W.; Buck, L.T.; Doll, C.J.; Land, S.C. Unifying theory of hypoxia tolerance: Molecular/metabolic defense and rescue mechanisms for surviving oxygen lack. Proc. Natl. Acad. Sci. USA 1996, 93, 9493–9498. [Google Scholar] [CrossRef] [Green Version]
  217. Tello, D.; Balsa, E.; Acosta-Iborra, B.; Fuertes-Yebra, E.; Elorza, A.; Ordonez, A.; Corral-Escariz, M.; Soro, I.; Lopez-Bernardo, E.; Perales-Clemente, E.; et al. Induction of the mitochondrial NDUFA4L2 protein by HIF-1alpha decreases oxygen consumption by inhibiting Complex I activity. Cell Metab. 2011, 14, 768–779. [Google Scholar] [CrossRef] [Green Version]
  218. Ferrari, M.; Jain, I.H.; Goldberger, O.; Rezoagli, E.; Thoonen, R.; Cheng, K.H.; Sosnovik, D.E.; Scherrer-Crosbie, M.; Mootha, V.K.; Zapol, W.M. Hypoxia treatment reverses neurodegenerative disease in a mouse model of Leigh syndrome. Proc. Natl. Acad. Sci. USA 2017, 114, E4241–E4250. [Google Scholar] [CrossRef] [Green Version]
  219. Ast, T.; Meisel, J.D.; Patra, S.; Wang, H.; Grange, R.M.H.; Kim, S.H.; Calvo, S.E.; Orefice, L.L.; Nagashima, F.; Ichinose, F.; et al. Hypoxia Rescues Frataxin Loss by Restoring Iron Sulfur Cluster Biogenesis. Cell 2019, 177, 1507–1521.e16. [Google Scholar] [CrossRef]
  220. Correia, S.C.; Machado, N.J.; Alves, M.G.; Oliveira, P.F.; Moreira, P.I. Intermittent Hypoxic Conditioning Rescues Cognition and Mitochondrial Bioenergetic Profile in the Triple Transgenic Mouse Model of Alzheimer’s Disease. Int. J. Mol. Sci. 2021, 22, 461. [Google Scholar] [CrossRef]
  221. Bayer, U.; Likar, R.; Pinter, G.; Stettner, H.; Demschar, S.; Trummer, B.; Neuwersch, S.; Glazachev, O.; Burtscher, M. Intermittent hypoxic-hyperoxic training on cognitive performance in geriatric patients. Alzheimers Dement. 2017, 3, 114–122. [Google Scholar] [CrossRef]
  222. Wang, H.; Shi, X.; Schenck, H.; Hall, J.R.; Ross, S.E.; Kline, G.P.; Chen, S.; Mallet, R.T.; Chen, P. Intermittent Hypoxia Training for Treating Mild Cognitive Impairment: A Pilot Study. Am. J. Alzheimers Dis. Other Demen. 2020, 35. [Google Scholar] [CrossRef]
  223. Serebrovska, Z.O.; Serebrovska, T.V.; Kholin, V.A.; Tumanovska, L.V.; Shysh, A.M.; Pashevin, D.A.; Goncharov, S.V.; Stroy, D.; Grib, O.N.; Shatylo, V.B.; et al. Intermittent Hypoxia-Hyperoxia Training Improves Cognitive Function and Decreases Circulating Biomarkers of Alzheimer’s Disease in Patients with Mild Cognitive Impairment: A Pilot Study. Int. J. Mol. Sci. 2019, 20, 5405. [Google Scholar] [CrossRef] [Green Version]
  224. Richardson, C.; Hogan, A.M.; Bucks, R.S.; Baya, A.; Virues-Ortega, J.; Holloway, J.W.; Rose-Zerilli, M.; Palmer, L.J.; Webster, R.J.; Kirkham, F.J.; et al. Neurophysiological evidence for cognitive and brain functional adaptation in adolescents living at high altitude. Clin. Neurophysiol. 2011, 122, 1726–1734. [Google Scholar] [CrossRef]
  225. Wu, K.C.; Wu, P.; Xu, Y.Z.; Li, Y.H.; Shen, Y.L. Formation Mechanism of Aerobic Granular Sludge and Removal Efficiencies in Integrated ABR-CSTR Reactor. Huan Jing Ke Xue 2015, 36, 2947–2953. [Google Scholar]
  226. Raina, S.K.; Chander, V.; Bhardwaj, A. Dementia in a tribal landlocked elderly population at high altitude: What explains the lower prevalence? J. Neurosci. Rural Pract. 2016, 7, 419–422. [Google Scholar] [CrossRef]
  227. Meng, S.X.; Wang, B.; Li, W.T. Intermittent hypoxia improves cognition and reduces anxiety-related behavior in APP/PS1 mice. Brain Behav. 2020, 10, e01513. [Google Scholar] [CrossRef]
Figure 1. Glucose metabolism and energy homeostasis in the brain. Glucose enters the cell through GLUT and is converted to G6P by HK. Then, G6P can be processed by different metabolic pathways: (1) Glycolysis (shown by red arrows), which leads to lactic acid production or the tricarboxylic acid (TCA) cycle. NADH and FADH2 are subsequently re-oxidized in ETC to produce ATP. (2) The pentose phosphate pathway (PPP) (shown by green arrows), which metabolizes G6P and generates NADPH. NADPH is then used for oxidative stress defense and biosynthetic reactions. (3) Glycogenesis (shown by blue arrows). Abbreviations are as follows: GLUT: glucose transporters; HK: Hexokinase; G6P: glucose-6-phosphate; G1P: glucose-1-phosphate; 6-PG: 6-phosphogluconate; G3P: glyceraldehyde-3-phosphate; 1,3-BPG: 1,3-bisphosphoglycerate; Ru-5P: ribulose-5-phosphate; R-5P: ribose-5-phosphate.
Figure 1. Glucose metabolism and energy homeostasis in the brain. Glucose enters the cell through GLUT and is converted to G6P by HK. Then, G6P can be processed by different metabolic pathways: (1) Glycolysis (shown by red arrows), which leads to lactic acid production or the tricarboxylic acid (TCA) cycle. NADH and FADH2 are subsequently re-oxidized in ETC to produce ATP. (2) The pentose phosphate pathway (PPP) (shown by green arrows), which metabolizes G6P and generates NADPH. NADPH is then used for oxidative stress defense and biosynthetic reactions. (3) Glycogenesis (shown by blue arrows). Abbreviations are as follows: GLUT: glucose transporters; HK: Hexokinase; G6P: glucose-6-phosphate; G1P: glucose-1-phosphate; 6-PG: 6-phosphogluconate; G3P: glyceraldehyde-3-phosphate; 1,3-BPG: 1,3-bisphosphoglycerate; Ru-5P: ribulose-5-phosphate; R-5P: ribose-5-phosphate.
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Figure 2. The crosstalk of metabolic dysfunction and neurodegenerative disease. Accumulating evidence has suggested the presence of a strong correlation between metabolic dysregulation and neurodegenerative disorders, such as AD, PD, ALS, and HD.
Figure 2. The crosstalk of metabolic dysfunction and neurodegenerative disease. Accumulating evidence has suggested the presence of a strong correlation between metabolic dysregulation and neurodegenerative disorders, such as AD, PD, ALS, and HD.
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Figure 3. Glucose metabolic reprogramming induced by hypoxia. In normoxia conditions, HIF is constitutively made and hydroxylated by prolyl-hydroxylase (PHD) enzymes; the hydroxylated form is recognized by ubiquitin ligase, Von Hippel-Lindau (VHL), and targeted for proteasomal degradation. In response to the environmental hypoxia, the PHD reaction does not take place, allowing HIF stabilization and translocation to the nucleus. HIF binds with other transcriptional factors to enhance the transcription of genes encoding the enzymes involved in glycolysis.
Figure 3. Glucose metabolic reprogramming induced by hypoxia. In normoxia conditions, HIF is constitutively made and hydroxylated by prolyl-hydroxylase (PHD) enzymes; the hydroxylated form is recognized by ubiquitin ligase, Von Hippel-Lindau (VHL), and targeted for proteasomal degradation. In response to the environmental hypoxia, the PHD reaction does not take place, allowing HIF stabilization and translocation to the nucleus. HIF binds with other transcriptional factors to enhance the transcription of genes encoding the enzymes involved in glycolysis.
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Han, R.; Liang, J.; Zhou, B. Glucose Metabolic Dysfunction in Neurodegenerative Diseases—New Mechanistic Insights and the Potential of Hypoxia as a Prospective Therapy Targeting Metabolic Reprogramming. Int. J. Mol. Sci. 2021, 22, 5887. https://doi.org/10.3390/ijms22115887

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Han R, Liang J, Zhou B. Glucose Metabolic Dysfunction in Neurodegenerative Diseases—New Mechanistic Insights and the Potential of Hypoxia as a Prospective Therapy Targeting Metabolic Reprogramming. International Journal of Molecular Sciences. 2021; 22(11):5887. https://doi.org/10.3390/ijms22115887

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Han, Rongrong, Jing Liang, and Bing Zhou. 2021. "Glucose Metabolic Dysfunction in Neurodegenerative Diseases—New Mechanistic Insights and the Potential of Hypoxia as a Prospective Therapy Targeting Metabolic Reprogramming" International Journal of Molecular Sciences 22, no. 11: 5887. https://doi.org/10.3390/ijms22115887

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