Abstract

   Accumulating evidence links numerous abnormalities in cerebral
   metabolism with the progression of Alzheimer's disease (AD), beginning
   in its early stages. Here, we integrate transcriptomic data from AD
   patients with a genome-scale computational human metabolic model to
   characterize the altered metabolism in AD, and employ state-of-the-art
   metabolic modelling methods to predict metabolic biomarkers and drug
   targets in AD. The metabolic descriptions derived are first tested and
   validated on a large scale versus existing AD proteomics and
   metabolomics data. Our analysis shows a significant decrease in the
   activity of several key metabolic pathways, including the carnitine
   shuttle, folate metabolism and mitochondrial transport. We predict
   several metabolic biomarkers of AD progression in the blood and the
   CSF, including succinate and prostaglandin D2. Vitamin D and steroid
   metabolism pathways are enriched with predicted drug targets that could
   mitigate the metabolic alterations observed. Taken together, this study
   provides the first network wide view of the metabolic alterations
   associated with AD progression. Most importantly, it offers a cohort of
   new metabolic leads for the diagnosis of AD and its treatment.

Introduction

   Alzheimer's disease (AD) is the most common form of dementia. It is
   estimated that AD affects more than 35 million patients worldwide and
   its incidence is expected to increase with the aging of the population.
   Although extensive investigations of AD have taken place over the past
   few decades, its pathogenesis has yet to be elucidated. Currently no
   treatment is available to prevent or halt the progression of AD.
   Moreover, the clinical diagnosis of AD is not possible until a patient
   reaches the dementia phase of the disease [27][1]. A more accurate and
   earlier diagnosis of AD could enable the use of potential
   disease-modifying drugs and thus, there is a need for biological
   markers for the early stages of AD [28][2].

   Metabolic alterations have been proposed to be involved in AD from the
   early stages of the disease [29][3]. Increasing evidence indicates an
   antecedent and potentially causal role of brain hypometabolism in AD
   pathogenesis [30][4]. Perturbations in mitochondrial function have long
   been observed in AD patients, including decreased activity of key
   mitochondrial enzymes [31][4], [32][5]. Consequently, ATP production
   and oxygen consumption become impaired [33][6]. Impaired glucose
   transport has also been reported in AD brains. Moreover, there is a
   link between cholesterol turnover and neurodegenerative diseases and
   hypercholesterolemia has been proposed as a risk factor for AD [34][7].
   However, the relationship between cholesterol levels and the clinical
   manifestation of dementia remains unclear [35][8]. There is also a
   debate regarding the role of certain vitamins such as vitamin D and
   folic acid in the pathogenesis of AD [36][9], [37][10]_ENREF_14.
   Clearly from all of this mounting evidence, multiple metabolic pathways
   may play a key role in AD's progression.

   Recent studies of gene expression from brains of AD patients further
   point to the strong association between metabolic alterations and AD,
   already from the early stages of the disease [38][11], [39][12].
   However, such gene expression analyses have been limited to
   transcriptional alterations and therefore cannot encompass the effects
   of putative post-transcriptional modifications that are known to play
   an important role in metabolism [40][13]. Furthermore, they do not
   allow the identification of biomarkers and drug targets in any direct
   manner. Our aim here is to go beyond these gene expression results and
   to elucidate the metabolic changes in AD by employing the increasingly
   prevalent toolkit of analysis methods provided by the emerging field of
   Genome-Scale Metabolic Modeling (GSMM).

   GSMMs have become trusted tools in the study of metabolic networks
   [41][14], and provide a platform for interpreting omics data in a
   biochemically meaningful manner [42][15]. GSMM analysis mostly relies
   on constraint-based modeling (CBM), in which constraints are
   systematically imposed on the GSMM solution space, and the outcomes of
   the model are limited to physically realizable phenotypes. GSMMs have
   been extensively used for the study of metabolism in microorganisms and
   in humans both in health and disease, enabling the prediction of
   various metabolic phenotypes such as enzyme activities and metabolite
   uptake and secretion fluxes, as well as interpretation of various types
   of high throughput data, often yielding clinically relevant results
   [43][16]–[44][21]. In a recent GSMM paper studying brain metabolism,
   three different neuronal sub-types were reconstructed in a GSMM of
   brain energy metabolism [45][22]. Focused on the core of cerebral
   energy metabolism, this reconstruction has suggested that glutamate
   decarboxylase provides a neuroprotective effect which is correlated
   with the brain regional specificity of AD [46][22].

   Our investigation begins with an effort to harness GSMM to
   systematically describe the metabolic state in AD on a global, network
   level. We do this by employing a method termed integrative Metabolic
   Analysis Tool (iMAT), which incorporates gene expression into a GSMM to
   predict metabolic flux activity [47][18]. This method has already been
   shown to successfully predict tissue specific metabolic activity in
   several healthy human tissues, including the brain [48][18]. iMAT
   incorporates gene expression to predict global metabolic flux activity
   that is the most consistent with known constraints across the entire
   metabolic network, and reflects post transcriptional modifications that
   are not evident in the raw expression data ([49]Figure 1). We utilized
   a relatively large dataset of gene expression microarrays from the
   cortex of AD patients and elderly controls [50][23] (including 363
   samples), which we integrated with the human metabolic model to study
   the metabolic changes in AD. This model-based genome-scale view of AD
   metabolism leads to the identification of various pathways whose
   activities are altered significantly in AD, and importantly, are not
   revealed by standard pathway enrichment analysis of the raw gene
   expression solely, in a model-free manner. We next predict novel
   biomarkers for AD by comparing predicted uptake and secretion fluxes of
   various metabolites as the disease progresses. Finally, we predict
   perturbations in the metabolic network that can transform the metabolic
   state of AD back closer to a healthy state, highlighting new potential
   metabolic drug targets for AD that may work on a global, network level.

Figure 1. The workflow of iMAT analysis.

   [51]Figure 1
   [52]Open in a new tab

   A. First, we discretize the expression of each metabolic gene measured
   into 3 levels: high, moderate and low. Next, iMAT integrates these
   expression levels into the human metabolic model by maximizing the
   number of enzymes whose predicted flux activity is consistent with
   their expression level, yielding a prediction of the overall network
   flux distribution that is most consistent with the model's constraints
   under steady state. This analysis is done separately for the control
   and the AD states. B. A Toy example of the integration of the metabolic
   network and gene-expression by iMAT and the prediction of enzyme
   flux-activities (taken and modified from [53][18]). Circular nodes
   represent metabolites, solid edges represent reactions, and diamond
   nodes represent enzymes associated by arrows to the reactions they
   catalyze. Grey, red and green represent moderate, significantly low and
   significantly high expression of the enzyme-encoding genes,
   respectively. The predicted flux involving the activation of reactions
   is shown as green edges. Enzymes E4 and E7 are predicted to be
   post-transcriptionally up-regulated and down-regulated respectively.

Methods

Datasets

   The microarrays data used in this study were obtained from the Gene
   Expression Omnibus (GEO) site ([54]www.ncbi.nlm.nih.gov). The first
   dataset contains expression data from 363 cortical samples of controls
   and AD patients' post-mortem brains ([55]GSE15222) [56][23]. We
   additionally analyzed blood leukocytes gene expression that includes 3
   controls, 3 AD and 3 MCI samples ([57]GSE18309) [58][24]. All datasets
   were filtered for metabolic genes included in the human metabolic model
   [59][16].

iMAT analysis

   We first employed a discrete representation of significantly high or
   low enzyme-expression levels across tissues. Gene expression levels
   from the microarray analysis were discretized to highly (1), lowly
   (−1), or moderately (0) expressed, for each sample. This discretization
   was based on a threshold of the mean expression +0.3 SD for highly
   expressed genes, the mean −0.3 SD for lowly expressed genes. Genes
   between these thresholds were defined as 0, and the entire process is
   applied for each sample separately. As iMAT requires only a single such
   discrete representation, the final input includes only those reactions
   that were classified as highly/lowly expressed in at least 2/3 of the
   samples. The list of genes that were defined as highly and lowly
   expressed as input for iMAT is detailed in [60]Table S1. In the iMAT
   [61][18] analysis, the discretized gene expression levels were
   incorporated into the metabolic model to predict a set of high and low
   activity reactions. Network integration is done by mapping the genes to
   the reactions according to the metabolic model, and by solving a
   constraint-based modeling optimization problem to find a steady-state
   metabolic flux distribution, following [62][18]. By using this CBM
   approach we assign permissible flux ranges to all the reactions in the
   network, in a way that satisfies the stoichiometric and thermodynamic
   constraints embedded in the model and maximizes the number of reactions
   whose activity is consistent with their expression state. The
   simulation conditions that were used were the default ones, i.e. the
   boundaries of the model reaction fluxes are between −1000 to 1000.

Enrichment of metabolic pathways (gene expression and iMAT)

   Based on iMAT results, which predict the activity of the reactions in
   the metabolic model, a hypergeometric p-value was computed for each
   pathway in the model for being enriched with active or inactive
   reactions in AD. Subsequently, for comparison of the iMAT results to
   the gene expression, gene expression measurements were forst translated
   to the reaction level using the model's gene-protein-reactions mapping,
   and subsequently the list of altered reactions was again analyzed for
   pathway enrichment in a standard manner as above. In both analyses, a
   correction for multiple hypotheses was done using false discovery rate
   (FDR) method of 0.05.

Flux Variability Analysis (FVA)_ENREF_26 [63][25]

   Metabolic biomarkers are predicted based on a comparison of exchange
   reaction intervals between the healthy case and each of the disease
   states. For exchange intervals A = [minA, maxA] and B = [minB, maxB]
   (where A and B represent the flux intervals in the control and AD
   stages), we define: A<B if ((minA < minB) & (maxA ≤ maxB)) | ((minA ≤
   minB) & (maxA < maxB)). To consider only significant changes between
   exchange intervals, a difference in flux, denoted A<B, is considered
   only when A is at least 90% lower than B.

Metabolic Transformation Algorithm (MTA)

   The MTA algorithm gets as input gene expression levels of two metabolic
   states, termed source and targets states. Next, the MTA approach works
   to: (1) infer the most likely distribution of fluxes in the source
   state using iMAT; (2) identify the set of genes that their expression
   have significantly changed between the source and targets states, and
   the set of genes that their expression remain constant. Following, the
   algorithm searches for perturbations that can globally shift all the
   fluxes of the changed reactions in the right direction, while keeping
   the fluxes of the unchanged reaction as close as possible to their
   predicted source state. Finally, MTA outputs a ranked list of candidate
   perturbations according to their ability to result with a successful
   transformation, from the source to the target metabolic state^44. The
   top 10% of the highest scoring reactions were used for calculation of
   the pathways that are enriched with predicted drug targets, as in
   [64][26].

Results

Network-based description of metabolic alterations in AD and their
large-scale validation

   Our first goal in this study was to uncover the major metabolic
   alterations that differentiate AD-afflicted brains from healthy ones.
   As transcriptional regulation plays a major role in controlling
   metabolic functions [65][13], and there is a large body of
   transcriptome data available for study, we approached this problem
   using iMAT, a computational method to systematically predict metabolic
   behavior by incorporating gene expression data into a GSMM [66][18]. We
   started by integrating an expression dataset of metabolic genes from
   the cortex of both healthy and AD elderly subjects [67][23] into the
   human metabolic model (see [68]Methods, [69]Figure 1). To account for
   metabolic flux activity that is not reflected in the mRNA expression
   data, iMAT considers the mRNA levels as cues for the likelihood that
   the enzyme in question carries a metabolic flux in its associated
   reaction(s), and then leverages the GSMM to accumulate these cues into
   a global flux behavior that is stochiometrically consistent and
   maintains mass balance across the entire network [70][27]. Hence, iMAT
   predicts a feasible flux distribution that best agrees with the gene
   expression data.

   Following the iMAT analysis, we examined which pathways had altered
   activity in AD versus the control ([71]Table 1, [72]Methods). This step
   was performed by employing flux variability analysis (FVA) [73][25] on
   the metabolic states inferred by iMAT for each of the AD vs. healthy
   states examined. The FVA analysis computes permissible flux intervals
   for each reaction, i.e., the minimal and maximal flux for each reaction
   that is yet consistent with the output of iMAT ([74]Table S2). Then, by
   comparing the flux intervals of each reaction in its normal state and
   in AD, one can detect reactions whose activity is likely to be altered,
   and predict altered metabolic pathways. A number of pathways that were
   not manifested in a standard gene set enrichment analysis based on the
   gene expression alone were uncovered by our model-augmented analysis
   ([75]Table 1, [76]Figure 2). To bolster confidence in our results, we
   examined three sets of thresholds for determining when a given reaction
   is altered – that is, marking a ‘difference’ between its control and AD
   flux states. The pathways of carnitine shuttle, folate metabolism, and
   mitochondrial transport emerged robustly as the most over represented
   pathways with reduced flux activity in AD in all three cases (see
   [77]Table S3). As expected, most of the fluxes across the network
   decrease in the disease, in accordance with the accepted notion of
   increased hypometabolism associated with AD.

Table 1. iMAT's predictions of metabolic pathways whose activity is
significantly decreased in AD.

   Pathway                                     p-value
   Carnitine shuttle                           3.53E^-18
   Folate metabolism*                          3.78E^-13
   Transport, Mitochondrial*                   4.77E^-11
   Fatty acid oxidation, peroxisome*           1.16E^-08
   Transport, Lysosomal                        2.31E^-06
   Biotin metabolism                           2.56E^-06
   N-glycan degradation                        7.52E^-06
   IMP biosynthesis                            2.21E^-05
   Valine, Leucine, and Isoleucine metabolism* 6.31E^-05
   Pyrimidine catabolism                       1.13E^-03
   Arginine and Proline metabolism             1.17E^-03
   Phenylalanine metabolism                    1.62E^-03
   Fatty acid metabolism*                      4.76E^-03
   [78]Open in a new tab

   The table lists the pathways that are significantly decreased in AD
   according to iMAT predictions, as compared with the activity of control
   reactions. * Metabolic pathways that were significantly altered both in
   gene expression itself and in the model. All the results presented pass
   FDR of 0.05.

Figure 2. Key flux alterations in central metabolism predicted by iMAT for AD
versus control states.

   [79]Figure 2
   [80]Open in a new tab

   The figure depicts the changes in energy metabolism in both cytosol (c)
   and mitochondrion (m). Several key enzymes whose activity was reported
   to decrease in AD patients are detailed in blue: pyruvate dehydrogenase
   (PDH), α-ketoglutarate dehydrogenase (AKGDH) and carnitine
   acetyltransferase (CAT). * Reactions whose activity changed
   significantly already at the transcription level.

   As mentioned earlier, differences between gene expression levels and
   enzyme flux activities as predicted by iMAT can indicate whether enzyme
   activity is post-transcriptionally increased or decreased compared to
   the original mRNA levels [81][18] ([82]Figure 1). To test the metabolic
   descriptions we have obtained, we compared the predicted alterations in
   enzyme activities to the measured protein levels of these enzymes,
   according to proteomic data from temporal cortex of AD patients
   [83][28]. Reassuringly, we find significant overlap between predicted
   and experimentally determined differences in the levels of these
   proteins (hypergeometric p-value of 0.002). When focusing on reactions
   that are predicted by the AD model to be post-transcriptionally
   regulated, the calculated overlap p-value with the alterations reported
   in the proteomics data is 9.16e^−12. Tryptophan metabolism was enriched
   among these reactions (p-value 2.5e^−4, [84]Table S4).

   As a further testing of the metabolic descriptions obtained with the
   iMAT analysis, we identified the predicted alterations in metabolites
   exchange (secretion and uptake) between the cortex and biofluids in AD
   and normal patients, and compared our findings to experimentally
   determined metabolomic profiles in two patient sets in the CSF and the
   blood ([85]Table S5). Our predicted alterations showed highly
   significant overlap with reported metabolomic alterations in both
   fluids (p-values: 8.4e^−26 and 1.06e^−15 in CSF and blood,
   respectively).

   Finally, several key central metabolism enzymes whose flux has been
   predicted to decrease indeed have been reported to decrease their
   activity in AD [86][29]–[87][31]. These enzymes include PDH, AKGDH and
   cytochrome c oxidase (COX). All enzymes fluxes in this set are
   significantly decreased in the AD vs control predicted flux states,
   with p-values of 5e^−4, 5e^−3 and 2e^−7, respectively.

   The pathway predicted to decrease most significantly in AD is the
   carnitine shuttle, which, quite surprisingly, does not emerge in a
   standard gene expression enrichment test ([88]Table 1). Carnitine
   shuttle is a carnitine dependent transport of fatty acids into the
   mitochondria for the production of energy via β-oxidation. Brain
   acyl-carnitines can function in synthesizing lipids, altering and
   stabilizing membrane composition, improving mitochondrial function,
   increasing antioxidant activity, and enhancing cholinergic
   neurotransmission [89][32]. A decreased activity of CAT has been
   measured in temporal cortex of AD patients [90][33] (and in our
   analysis as well - [91]Figure 2), and it has been demonstrated that
   acetyl-carnitine administration can improve the cognitive performance
   in patients with mild AD [92][34].

   Another pathway whose activity is predicted to decrease in AD is folate
   metabolism and the uptake of folate into the cell is also predicted to
   decrease ([93]Figure 2 and [94]Table S6). Experimental reports indicate
   a decrease of folate in the CSF of patients with AD [95][35]. Beyond
   the folate pathway itself, we find an overall dramatic decrease in the
   predicted activity of all reactions that have substrates of folate,
   dihydrofolate (DHF), or tetrahydrofolate (THF) ([96]Figure S1).

   Remarkably, the activity of reactions participating in metabolism of
   various neurotransmitters also decreased significantly in AD. This
   includes decreased uptake of acetylcholine and decreased activity of
   acetylcholinesterase, in accordance with reported decreases in levels
   and activity (respectively) in AD [97][36]; decreased secretion of
   norepinephrin, consistent with a previous metabolomic study showing its
   significant depletion in AD [98][37]; and decreased transport of
   4-aminobutanoate (GABA) into the mitochondria.

Prediction of metabolic biomarkers of AD

   A major need in AD is the development of better biomarkers which can be
   read from accessible fluids, such as the blood [99][38]. As a first
   step in identifying potential biomarkers, we focus on predicting
   changes in extracellular transport reactions in the model
   ([100]Methods, [101]Table 2). A full list of metabolites with predicted
   secretion or uptake altered in disease is provided in [102]Tables S6
   and [103]S7, respectively. As expected, most of the secretion and
   uptake fluxes of these biomarkers are predicted to decrease in AD.

Table 2. Metabolites whose secretion or uptake is markedly decreased in AD.

   Metabolite                  Decreased secretion/uptake
   Succinate                   secretion
   Prostaglandin D2            secretion
   D-Mannose                   secretion
   Sphingosylphosphorylcholine uptake
   Pentadecanoate              uptake
   Heptadecanoate              uptake
   D-Glucosamine               uptake
   [104]Open in a new tab

   Among the biomarkers predicted here, succinate has been previously
   reported to significantly decrease in the CSF of AD patients [105][39].
   Prostaglandin D2 (PGD2), whose secretion we predict to decrease as well
   ([106]Table 2), is the most abundant prostaglandin in the brain and
   plays a role in regulation of sleep [107][40]. PGD2 mean level was
   found to slightly decrease in the CSF in AD patients; however, this
   change was not significant [108][41].

   To predict plasma biomarkers in AD, we integrated recently reported
   gene expression data from blood leukocytes of AD and Mild Cognitive
   Impairment (MCI) patients [109][24] with the human metabolic model in a
   manner similar to that described previously with the cortical gene
   expression data (i.e. iMAT, see [110]Methods), thus generating a
   metabolic description of these blood cells in AD. Next, we repeated the
   analyses detailed above and identified pathways that are enriched with
   altered reactions in blood leukocytes in AD and MCI ([111]Figure S2).
   As evident, flux alterations in MCI and AD are quite similar. We found
   a significant overlap in metabolites we predicted to change in the
   blood (versus controls) with those reported in literature (P-value
   1.15e^−18, [112][42]–[113][44]). [114]Table S8 lists our highest
   confidence blood biomarkers. Notably, we predict cholesterol to
   increase in the blood of AD patients as its secretion flux is predicted
   to increase. Altered cholesterol metabolism was suggested before in
   plasma of AD patients compared to MCI patients [115][45].

   Intriguingly, several pathways whose activity is predicted to change in
   blood leukocytes of AD patients are also altered in the AD cortex.
   Among them, IMP biosynthesis was the only pathway that did not change
   in MCI blood leukocytes. Notably, the activities of IMP biosynthesis
   and fatty acid oxidation pathways increase in AD blood leukocytes but
   decrease in the cortex. Biomarkers predicted by both the cortical and
   the blood leukocytes analyses in AD are detailed in [116]Table 3.

Table 3. Biomarkers predicted by both analyses of the cortex and the blood
leukocytes in AD.

   Metabolite name          Cortex                    Blood
   diacylglycerol[117]^**   secretion decrease        secretion increase
   triacylglycerol[118]^**  uptake decrease           uptake increase
   hyaluronan[119]^**       uptake decrease           secretion decrease[120]^*
   prostaglandin D2[121]^** secretion decrease[122]^* secretion decrease
   metanephrine             secretion decrease        secretion decrease
   [123]Open in a new tab

   * Highly confident biomarkers (no overlap between flux intervals that
   is predicted for the control and AD).

   ** Biomarkers that are altered only in AD blood leukocytes and not in
   MCI.

Prediction of drug targets by Metabolic Transformation Algorithm

   Metabolic changes occur from the very earliest stages of AD. Although
   it is not known whether metabolism is the primary cause of the disease,
   these changes are extensive and may cause further feedback and
   exacerbation of neuronal death and disease progression [124][4].
   Therefore, a drug that could reverse metabolic damage might have
   important therapeutic benefits. To predict candidate drug targets for
   AD we analyzed here the effects of metabolic gene knockouts using the
   human model. Our analysis is based on an algorithm termed Metabolic
   Transformation Algorithm (MTA) [125][26], which aims to identify gene
   perturbations that can transform metabolism from a given disease state
   back to a healthy one. This approach has already obtained promising
   results by identifying novel lifespan extending genes in yeast, which
   were then experimentally validated [126][26]. Here, we perform a
   systematic knockout of each gene in the human metabolic network (using
   the cortical gene expression data) and predict which knockouts will
   most likely transform the AD metabolic state back closer to the healthy
   one ([127]Figure 3). The pathways enriched with reactions whose
   knockout is predicted by MTA to reverse AD's key metabolic alterations
   back closer to the healthy state are Vitamin D, nucleotides and Steroid
   metabolism (p-values 1.63e^−8, 2.83e^−5, 2.16e^−4, respectively).

Figure 3. MTA workflow.

   [128]Figure 3
   [129]Open in a new tab

   MTA performs knockouts for each of the reactions in the metabolic model
   at the AD state (source), and assigns a score for each gene knockout
   reflecting the predicted extent by which this knockout may transform
   the metabolic state back to the healthy (target) state. Next, we
   aggregate the gene/reaction level predictions to identify the pathways
   whose knockout is predicted to be most successful in transforming the
   metabolic state as close as possible back to the healthy state.

   Vitamin D has been studied in recent years for its relation to
   cognitive performance and AD [130][9], [131][46], but its associations
   remain uncertain. Nevertheless, it has been increasingly recognized to
   play an active role in the nervous system [132][47], and a genome-wide
   association study of late-onset AD found evidence for involvement of
   the vitamin D receptor [133][47]. Steroid metabolism is another pathway
   we found enriched with predicted drug targets for AD. Intriguingly, the
   reaction that received the highest score within this pathway is
   11-beta-hydroxysteroid dehydrogenase type 1 (11β-HSD1), an enzyme that
   catalyzes the intracellular regeneration of active glucocorticoids
   (i.e., cortisol and corticosterone). 11β-HSD1 knock-out mice have shown
   improved cognition, and 11β-HSD1 inhibitors improved memory in elderly
   men [134][48]. In general, steroids offer interesting therapeutic
   opportunities because of their varying roles in the nervous system:
   they regulate neurotransmitter systems, they promote the viability of
   neurons, and they influence cognitive processes [135][49].

   Finally, a recent study by Searcy et al. showed that long-term
   Pioglitazone (PIO) treatment improved learning and decreased Aβ and tau
   deposits in a mouse model of AD [136][50]. Gene expression from the
   brains of these mice before and after the PIO treatment was also
   measured. For validation of the MTA predictions, we examined whether
   our set of top 10% knock-out predictions in humans is enriched with
   mouse orthologous genes whose expression was significantly decreased in
   the PIO treated mice with the improved phenotype. Encouragingly, we
   find such a significant overlap p-value of 0.025.

Discussion

   In the current study, we used genome scale metabolic modeling
   approaches to integrate gene expression measurements in the cortex of
   AD patients to address three key research questions: (1) what are the
   main metabolic alterations occurring in AD? (2) Which metabolites may
   serve as candidates for metabolic biomarkers of AD in the CSF and in
   the blood? And finally, (3) which metabolic genes may be silenced to
   most efficiently reverse the metabolic alterations observed in AD to a
   state of healthy aged matched controls?

   We described the metabolic alterations in AD in both the cortex and
   blood leukocytes. The cortical analysis was based on a very large
   dataset of AD and control patients. However, for the analysis of the
   blood leukocyte we used a small dataset of gene expression that is
   publicly available ([137]Methods) for comparison to the cortical
   predictions and between MCI and AD patients. A further analysis in the
   future utilizing richer gene expression datasets from blood cells of AD
   and MCI patients will aid to support this study's findings. Both
   analyses shared several pathways whose activity significantly increased
   in the blood and decreased in the brain, implying a possible
   compensation mechanism. Moreover, we predict biomarkers that are common
   to both analyses (i.e, cortex and blood), strengthening the potential
   of these metabolites as candidates for early diagnosis of AD. The MTA
   analysis yielded predictions of drug targets that may reverse the
   metabolic state of the disease back to the healthy one. Vitamin D and
   steroid metabolism appear in our analysis to be important in reversing
   the metabolic state in the disease. Furthermore, although it did not
   pass the FDR cutoff, our findings may hint to the importance of
   cholesterol in the pathogenesis of AD (P-value 0.015) and the potential
   value of keeping its levels in check [138][7]. The use of MTA for
   finding potential drug targets holds an advantage for finding drug
   candidates that act globally to reverse the entire metabolic network
   state to the healthy state, and thus may have lesser side effects.

   Our analysis is in line with the common view that metabolism is overall
   decreased in AD. Several transport pathways appear throughout our
   analyses, further emphasizing the importance of metabolite transport in
   the disease. The predicted candidate biomarkers and drug targets that
   were discovered in this analysis may offer new metabolic leads for
   advancing the diagnosis of AD and its treatment. Hopefully, this work
   will motivate and guide future experimental studies geared at studying
   some of these leads.

Supporting Information

   Figure S1

   Maximal fluxes of reactions in which folate, DHF or THF act as
   substrates.

   (DOCX)
   [139]Click here for additional data file.^ (767.3KB, docx)
   Figure S2

   Pathways enriched with reactions that are altered in AD and MCI blood
   leukocytes.

   (DOCX)
   [140]Click here for additional data file.^ (110.5KB, docx)
   Table S1

   The list of genes that were defined as input for iMAT.

   (XLSX)
   [141]Click here for additional data file.^ (70.7KB, xlsx)
   Table S2

   The list of reactions which their fluxes are predicted to alter in the
   disease.

   (XLSX)
   [142]Click here for additional data file.^ (110.4KB, xlsx)
   Table S3

   Over represented pathways with altered reactions for different
   thresholds.

   (DOCX)
   [143]Click here for additional data file.^ (12.3KB, docx)
   Table S4

   Tryptophan metabolism reactions which are PTR.

   (DOCX)
   [144]Click here for additional data file.^ (11.5KB, docx)
   Table S5

   Metabolites level prediction in biofluids and experimental support.

   (DOCX)
   [145]Click here for additional data file.^ (83KB, docx)
   Table S6

   Exchange reactions which their uptake fluxes alter in the cortex in AD.

   (DOCX)
   [146]Click here for additional data file.^ (13.7KB, docx)
   Table S7

   Exchange reactions which their secretion fluxes alter in the cortex in
   AD.

   (DOCX)
   [147]Click here for additional data file.^ (13.7KB, docx)
   Table S8

   Metabolites whose secretion or uptake are altered significantly in
   blood leukocytes in AD.

   (DOCX)
   [148]Click here for additional data file.^ (12.1KB, docx)

Acknowledgments