Abstract
Dysregulated central-energy metabolism is a hallmark of brain aging.
Supplying enough energy for neurotransmission relies on the
neuron-astrocyte metabolic network. To identify genes contributing to
age-associated brain functional decline, we formulated an approach to
analyze the metabolic network by integrating flux, network structure
and transcriptomic databases of neurotransmission and aging. Our
findings support that during brain aging: (1) The astrocyte undergoes a
metabolic switch from aerobic glycolysis to oxidative phosphorylation,
decreasing lactate supply to the neuron, while the neuron suffers
intrinsic energetic deficit by downregulation of Krebs cycle genes,
including mdh1 and mdh2 (Malate-Aspartate Shuttle); (2) Branched-chain
amino acid degradation genes were downregulated, identifying dld as a
central regulator; (3) Ketone body synthesis increases in the neuron,
while the astrocyte increases their utilization, in line with neuronal
energy deficit in favor of astrocytes. We identified candidates for
preclinical studies targeting energy metabolism to prevent
age-associated cognitive decline.
Keywords: astrocyte, neuron, brain aging, flux balance analysis,
network centrality
INTRODUCTION
Energy metabolism, essential for brain function, is one of the main
processes dysregulated during brain aging (reviewed in [[36]1, [37]2]).
Although the brain constitutes only 2% of total body mass, it
represents 20–25% of total body energy expenditure [[38]3, [39]4],
where most of it is used for re-establishing cation gradients after
neurotransmission [[40]5], a process mediated by
sodium/potassium-ATPase pumps [[41]6, [42]7]. To meet this high energy
demand, the neuron and astrocyte form a two-cell metabolic network (The
neuron-astrocyte metabolic network) with extensive metabolite exchange
[[43]4, [44]8]. One example of metabolic exchange is the
astrocyte-neuron lactate shuttle (ANLS) [[45]2]. The astrocyte performs
aerobic glycolysis, converts pyruvate into lactate, and then transports
it to the neuron to fuel ATP synthesis via oxidative phosphorylation
[[46]9, [47]10]. The neuron-astrocyte metabolic network also performs
the glutamate-glutamine cycle (GGC). In the GGC, astrocytes take up
glutamate -the main excitatory neurotransmitter in the central nervous
system- after neurotransmission. Inside the astrocyte, glutamate is
converted into glutamine, shuttled back to the neuron, and re-converted
into glutamate for a new neurotransmission cycle [[48]11–[49]14]. The
ANLS, GGC, and the exchange of sodium and potassium constitute
essential metabolic interactions between neurons and astrocytes, and
they are closely related to energy metabolism. Indeed, energy
availability is vital to ensure proper neurotransmission. However,
during human brain aging, metabolism becomes dysregulated in the brain.
Healthy aged human individuals display slower mitochondrial metabolism
and glutamate-glutamine cycle neuronal flux (−28%) when compared with
healthy young individuals. In comparison, astroglial mitochondrial flux
is 30% faster [[50]15]. In rats, adult primary astrocyte cultures also
display a higher mitochondrial oxidative metabolism when compared with
astrocytes derived from young rats [[51]16]. To date, the only
intervention demonstrated to extend lifespan in several model organisms
is caloric restriction, a metabolic intervention where animal models
are fed a diet consisting of 60–70% of the calorie intake in a regular
diet [[52]17]. This further supports the role of energy metabolism
during aging. Metabolic challenges like the ketogenic diet [[53]18,
[54]19] and intermittent fasting that aim to mimic the metabolic state
entered during caloric restriction have also been shown to extend
lifespan and health-span [[55]20]. Furthermore, a phase II clinical
trial using a fasting-mimicking diet improved metabolic health
[[56]21].
The complexity of brain aging is determined by the diversity and number
of metabolic pathways that contribute to energy balance. The molecular
mechanisms underlying age-associated dysregulation of brain energy
metabolism remain mostly unknown. Complex systems -particularly
metabolic pathways- are studied by modeling them as networks, which
allows to simulate and probe complex phenomena, such as aging, in a
computationally tractable and interpretable fashion [[57]22, [58]23].
Here, we present a novel network-wise approach mapping complex
interactions into a graph representation to discover energy-related
genes in the neuron-astrocyte metabolic network that may contribute to
brain aging. We used a genome-scale model of the neuron-astrocyte
metabolic network [[59]24] and analyzed it using complementary flux and
network-based methods. Flux-based methods allowed us to identify
reactions critical for maintaining optimal neurotransmission.
On the other hand, network-based methods (centrality) searched for
reactions that may modulate neurotransmission via network-wide effects.
This analysis provided us with a set of genes (metabolic hub genes)
that are key for neurotransmission in terms of flux distribution and
network structure. Next, we determined which metabolic hub genes showed
differential abundance associated with neurotransmission and/or brain
aging in the neuron and/or astrocyte, thus getting a final set of genes
called differential hub genes (DHG). These gene set represents a
validation of network analysis contrasting numerical predictions with
experimental data, including expected and novel results.
Functional annotation analysis of DHGs led to the following main
findings: (1) Gene expression changes in both the neuron and astrocyte
suggest an energetic deficit in the neuron, mainly by substantial
downregulation of tricarboxylic acid (TCA) cycle in the aging neuron;
(2) In line with the neuronal energy deficit, our results suggest that
the aging astrocyte undertakes a metabolic switch from aerobic
glycolysis to oxidative metabolism, where glucose is directed to CO[2]
instead of lactate; (3) Impaired branched-chain amino acid degradation
in both the neuron and astrocyte, mainly supported by downregulation of
the dld gene during aging. This gene encodes for a subunit of the
branched-chain amino acid (BCAA) dehydrogenase complex, which catalyzes
an early step in BCAA degradation; (4) Altered ketone body metabolism,
where gene expression changes in the neuron agree with an increased
synthesis during brain aging, while in the aging astrocyte bdh1 is
upregulated. This gene catalyzes the interconversion between
acetoacetate and β-hydroxybutyrate, the main two ketone bodies required
for ketone body utilization. These findings further support that energy
metabolism is favored in the astrocyte, in detriment of neuronal energy
supply; (5) Downregulation of genes associated with synaptic
transmission in the neuron, including downregulation of Na/K-ATPase
pumps in the aging neuron, and lower glutamate synthesis in both neuron
and astrocyte; (6) Our results suggest that the aging neuron
downregulates genes that supply the one carbon and tetrahydrofolate
(THF) pool, which is required for the synthesis of the methylation
precursor S-adenosylmethionine (SAM) and antioxidant glutathione
synthesis. Instead, the aging astrocyte displays expression changes
that agree with an increase in the THF pool available for glutathione
synthesis as an antioxidant strategy, which is also in line with the
metabolic switch into an oxidative metabolism in the astrocyte (which
requires more antioxidation).
The genes identified here are valuable candidates for future studies to
understand the molecular mechanisms of healthy brain aging and prevent
brain age-associated failure using energy metabolism as a target. We
also highlight how our approach provides a robust and tractable number
of final gene candidates for future studies, using an integrative
analysis of the two-cell neuron-astrocyte metabolic network, which may
be applied to other metabolic models.
RESULTS
Workflow overview
To facilitate reading of the following sections, we provide an overview
of the analyses performed in simple terms. We started by using a
previously available genome-scale neuron-astrocyte (N-A) metabolic
network model [[60]24], which included all metabolic reactions and
transport events (each representing a node in the network) that occur
in each cell across subcellular compartments, as well as transport
between cell types. Genome-scale metabolic models are constructed using
genome-wide gene expression data to only include nodes that are present
in neurons and/or astrocytes [[61]24] ([62]Figure 1A). We used this N-A
metabolic network to perform a Flux Balance Analysis (FBA) ([63]Figure
1B) and a Centrality Analysis ([64]Figure 1C). Broadly, the FBA
calculates the extent to which the flux through each node in the N-A
metabolic network should be modified for optimal achievement of the
metabolic objective, which we defined as glutamatergic
neurotransmission workload i.e., energy burden derived from
neurotransmission). This is defined as the optimal metabolic response.
The FBA identifies two types of nodes: flux nodes, which are those that
most contribute to optimally achieving the metabolic objective of
neurotransmission workload, and sensitive nodes, which are the key
nodes exerting control over neurotransmission workload. Merging these
two types of nodes yielded the list of optimal nodes, where each node
has been previously associated with specific genes. We defined optimal
genes as the list of genes associated with optimal nodes.
Figure 1.
[65]Figure 1
[66]Open in a new tab
Summary flowchart of network analyses depicting how optimal and central
genes were identified, which merged together form the hub genes group.
(A) A genome-scale metabolic model from Lewis et al., 2010 was used.
This network was analyzed first using. (B) Flux Balance Analysis, from
which Flux and Sensitive Nodes were identified. Merging these two node
lists yielded Optimal Nodes, from which Optimal Genes were identified.
Sensitive Nodes were then analyzed using. (C) Centrality Analysis,
which allowed identifying Central Nodes, from which Central Genes were
identified. Merging the list of Optimal and Central Genes produced the
Hub Genes list. (D) See boxes in dashed lines for the explanation of
each type of analysis.
The FBA was followed by a centrality analysis, which analyzes intrinsic
network structure, and is therefore independent of flux. In a
centrality analysis, each node in the network has a centrality score,
which, largely, represents the number of connections and pathways each
node participates in. We calculated how the removal of each individual
node in the network affected the centrality score of sensitive nodes
identified in the previous step, as those represent the ones that exert
control over the metabolic objective. Nodes significantly altering the
centrality of sensitive nodes were defined as central nodes, from which
the list of central genes was obtained. By merging optimal and central
node lists we obtained the list of hub genes ([67]Figure 1D), which
represent the genes that most affect glutamatergic neurotransmission
workload, and therefore play a key role in N-A metabolic network
function.
Having identified hub genes that play key roles in the N-A metabolic
network, we next determined which of these were differentially
expressed, i.e., up- or downregulated after neurotransmission and/or
brain aging in the neuron and/or astrocyte ([68]Figure 2A). To achieve
this, we used previously available transcriptomic databases for
neurotransmission ([69]Figure 2B) and brain aging ([70]Figure 2C)
[[71]12, [72]25]. The last step in gene selection identified hub genes
that were differentially expressed during neurotransmission and/or
brain aging (see shaded area in Venn diagram, [73]Figure 2D) defined as
differential hub genes (DHG) ([74]Figure 2E). This curated group of
genes represents those that most contribute to achieving glutamatergic
neurotransmission workload. The ultimate goal of this integrative
analysis was to identify genes and pathways important for
neurotransmission, which fail during brain aging, thus constituting
candidates to explain age-associated cognitive decline. To achieve
this, the final step was a KEGG pathway enrichment analysis of DHG,
which allowed us to identify the predominant metabolic pathways.
Figure 2.
[75]Figure 2
[76]Open in a new tab
Summary flowchart of integration of hub genes with transcriptomic data
generated during neurotransmission and brain aging. (A) Transcriptomic
data during neurotransmission (Hasel et al., 2017) and aging (Tabula
Muris Consortium, 2020), reporting differentially expressed genes
during each process in the neuron and/or astrocyte was obtained. This
allowed us to obtain a list of differentially expressed (DE) genes in
both cell types during. (B) neurotransmission and/or (C) brain aging.
(D) Venn diagram showing common genes: (1) Between DE genes during
neurotransmission and hub genes (pink and green sets); (2) Between DE
genes during brain aging and hub genes (yellow and green sets), and (3)
The intersection between all three gene groups (pink, yellow and green
sets). (E) The differential hub genes (DHG) list is shown in (D) in the
shaded area.
Flux-based analysis identifies optimal nodes in the neuron-astrocyte network
required for glutamatergic neurotransmission workload
Regarding the FBA ([77]Figure 1B), in this analysis we defined three
sub-objectives that represent key processes required for achieving
neurotransmission workload: (1) The astrocyte-neuron lactate shuttle
(ANLS), (2) The glutamate-glutamine cycle (GGC), and (3) Sodium removal
by Na/K-ATPase pumps ([78]Figure 3A–[79]3C). The FBA therefore
determined how to optimize flux through these three processes by
identifying flux and sensitive nodes ([80]Figure 1B). Furthermore, for
the results to be biologically coherent, we used experimentally
determined flux values during neurotransmission as constraints. These
were the neuronal and astrocytic glucose and oxygen consumption rates,
and neuronal ATP maintenance rate, reported by Fernandez-Moncada et al.
[[81]26] and Baeza-Lehnert et al. [[82]6]. Also, metabolite steady
state was imposed as a constraint. This means that intracellular
metabolite concentration levels remain constant under neurotransmission
(see [83]Supplementary Theoretical Framework Section 1.2 on how this is
relevant for the analysis).
Figure 3.
[84]Figure 3
[85]Open in a new tab
Identification of optimal nodes using flux balance analysis in the
neuron-astrocyte metabolic network suggests division of labor between
the neuron and astrocyte in response to neurotransmission workload.
(A–C) Reactions considered in the metabolic objective; here, metabolite
names correspond to the same as in the model reported by Lewis et al.
(2010). (A) Fluxes associated with the Astrocyte-Neuron Lactate Shuttle
(ANLS); left side: Lactate efflux from astrocyte to the interstitial
space (Lact-Ast); right side: Lactate from the interstitial space
entering neurons (Lact-Neu). (B) Fluxes related to the
Glutamate-Glutamine Cycle (GGC); left side: vesicle-exported glutamate
from neuron (GluVe-Neu); right side: glutamine excretion from astrocyte
(GlnEx-As). (C) Neuronal sodium efflux associated with its removal via
sodium ATPase pump. (D–G) Phenotypìc phase planes are shown as
two-dimensional color maps. Here, the Flux Balance Analysis (FBA)
solution is represented by the red-filled circle, while all fluxes
shown correspond to micromolar per second (uM/s). A white piece-wise
line depicts the specific contour level of the solution. (H) The
neuron-astrocyte metabolic network is represented as a bipartite
network; here, node shape (circle or square) denotes the partition
where it belongs, i.e., reaction or metabolite. (I) left side, flux
values distribution in each cell; right side: the bipartite network
presented in (H) showing node size proportional to absolute flux. (J)
left side, sensitivity values distribution in each cell; right side:
the bipartite network presented in (H) showing node size proportional
to absolute sensitivity. (K) Distribution of the Absolute Optimality
values in neuron and astrocyte, the 90 percentile is highlighted by a
red dashed line. This line depicts the cutoff over which a reaction was
classified as an optimal metabolic reaction. (L) Optimal metabolic
reactions (descending order) sorted by their Absolute Optimality and
presented alongside their flux and sensitivity.
[86]Figure 3D–[87]3G depict fluxes previously associated with the
metabolic sub-objectives ANLS, GGC and Na/K-ATPase pumps in phenotypic
phase planes (PhPPs), where non-zero slopes can be observed (see
Methods section Phenotypic Phase Plane Analysis for details). These
allowed validating that each sub-objective is dependent on oxygen and
glucose uptake rates, which is a hallmark of brain metabolism. The
optimal flux that maximizes each metabolic sub-objective is shown as a
red-filled circle in each PhPP ([88]Figure 3D–[89]3G). [90]Figure 3D
shows that the calculated optimal neuronal sodium efflux associated
with removal through Na/K-ATPase pumps was 350 uM/s. Also, [91]Figure
3E shows that lactate efflux from the astrocyte was 6.913 uM/s, and
[92]Figure 3F that vesicle-mediated export of glutamate from the neuron
was 4.138 uM/s (influx into the complementary cell and other relevant
fluxes are shown in [93]Supplementary Table 1). Furthermore, from
[94]Figure 3G it is possible to assume that the optimal solution is
unique since it is located on a vertex. In addition, the optimal
metabolic response was associated with complete (aerobic) glucose
oxidation. In this sense, six oxygen molecules oxidized one glucose
molecule ([95]Supplementary Figure 1A), while ATP yield was close to
27.5 ATP molecules per glucose molecule ([96]Supplementary Figure 1B).
Of note, it is possible that this last yield was lower than the
theoretical one due to flux to other pathways such as the pentose
phosphate pathway and reactions that exit the tricarboxylic acid cycle
(TCA), e.g., glutamate synthesis and the malate-aspartate shuttle
(MAS). Furthermore, in line with what Baeza-Lehnert et al. [[97]6]
reported, we observed flux coupling between ATP demand from the sodium
ATPase pump and ATP supply from oxidative phosphorylation in neurons
([98]Supplementary Figure 2). Overall, our model was mathematically
consistent and agreed with the biology of neurons and astrocytes
undergoing neurotransmission.
In addition to fluxes, the optimal metabolic response is shaped by
sensitivity, which is equally relevant to flux in the FBA [[99]27,
[100]28]. Sensitivity values inform the extent to which a change in any
given reaction modifies the optimal metabolic response. We calculated
sensitivities and, together with fluxes, determined how they
distributed throughout the neuron-astrocyte metabolic network.
Interestingly, high-flux reactions were mostly neuronal ([101]Figure
3I), while high-sensitivity reactions were mainly astrocytic
([102]Figure 3J). This cellular separation among flux and sensitivity
suggests neurotransmission sets up fluxes in neurons, and sensitivities
in astrocytes. Next, we combined the flux and sensitivity of each node
into a single quantity called Absolute Optimality (AO) (see Methods
section Absolute Optimality for details). The AO informed us about the
involvement any given node has in the achievement of the optimal
response. All nodes that had an AO above the significant threshold were
considered optimal nodes ([103]Figures 1B and [104]3K). [105]Figure 3L
shows fluxes and sensitivities of optimal nodes separated by cell type
and sorted in descending order for AO.
Taken together, the optimality analysis suggests a division of labor
between neurons and astrocytes in response to neurotransmission
workload. Here, the execution, represented by flux, is allocated to
neurons, while control, represented by sensitivity, is executed by
astrocytes.
Analysis of network structure based on sensitive nodes further supports the
division of labor between the neuron and astrocyte in the network
We further analyzed the N-A metabolic network to enrich our analysis,
by performing a centrality analysis ([106]Figure 1C). While part of
aging-derived damage to brain metabolism may reside in fast stationary
events such as those represented by FBA results, much of aging
deterioration may occur in non-steady state long-term events. Intrinsic
network structure allows identifying long-term phenomena beyond steady
state and short timescales (see Methods, Modeling rationale). As
mentioned before, the centrality score of a node represents how
connected the node is in the network. We calculated the extent to which
each node in the network, when removed, affected the centrality of
sensitive nodes identified in the previous step (see Methods, Absolute
Centrality Contribution). Four complementary centrality metrics were
employed to ensure analysis robustness; thus, each reaction was
associated with four quantities. These accounted for how much a given
reaction contributes to the centrality of the sensitive nodes and were
denominated centrality contributions. As can be observed in [107]Figure
4A, in astrocytes centrality contributions tended to be positive, while
in neurons it was mostly negative. This result indicates that
astrocytic nodes tend to increase the centrality of the sensitivity
set, while neuronal nodes tend to decrease it. This finding suggests
opposite and complementary roles between cells.
Figure 4.
[108]Figure 4
[109]Open in a new tab
Centrality-based analysis of the neuron-astrocyte metabolic network
further supports the division of labor between the neuron and
astrocyte. (A) Distributions, separated by cell, of the contributions
of each reaction to the centrality of the sensitivity set. (B)
Unsupervised hierarchical clustering of the pairwise correlations
between the contributions of each reaction to the centrality of the
sensitivity set. The Absolute Centrality Contribution per reaction
(ACC) is shown on the right-hand side of the heatmap. (C)
Dimensionality reduction via Principal Component Analysis (PCA) of the
pairwise correlations between the contributions of each reaction. (D)
Distribution of ACC in the neuron (top) and astrocyte (bottom), here,
the red dashed line by the 90% percentile indicates the cutoff over
which reactions were considered central metabolic reactions. (E) ACC
values for the central metabolic reaction separated by cell.
In [110]Figure 4B, this behavior was confirmed via unsupervised
clustering of the correlations between the centrality contributions of
each node (see Methods section for details on this procedure). Here we
see that centrality contributions from the same cell are clustered
together. The latter was also confirmed via dimensionality reduction,
where the 2-dimensional distribution of the centrality contributions
also resembled the two-cell structure ([111]Figure 4C). Next, we
aggregated the four centrality contributions into a single index which
was a normalized and absolute value representing the capacity of a node
to change the centrality of sensitivity nodes. We called this index
Absolute Centrality Contribution (ACC). The ACC for each reaction is
shown on the right-hand side of the heatmap in [112]Figure 4B (see the
column with green bars). Finally, the nodes with the last tenth
percentile of the ACC values from each cell were categorized as central
nodes ([113]Figure 4D). Interestingly, the astrocyte concentrated the
highest ACC values ([114]Figure 4E). Merging optimal and central genes
resulted in the hub genes list, which represent the genes with the
highest probability to affect or control the N-A metabolic network in
achieving glutamatergic neurotransmission workload.
As a whole, positive centrality contributions in the astrocyte and
negative in the neuron, along with the predominantly high ACC of the
astrocyte suggest well-differentiated roles for the neuron and
astrocyte. These results are in the same line with those obtained by
the FBA supporting the division of labor between the two cells.
Identification of hub genes differentially regulated during neurotransmission
and/or brain aging
Previously identified hub genes represent the scaffolding required for
achieving glutamatergic neurotransmission, and among these, we sought
to identify which were also differentially expressed during
neurotransmission and/or brain aging. Disruption of these genes should
lead to subpar neurotransmission workload, and therefore provide
molecular insights into aging-associated brain functional decline. We
denominated this group differential hub genes (DHG). To achieve this,
we determined which of these were differentially expressed, i.e., up-
or downregulated after neurotransmission and/or brain aging in the
neuron and/or astrocyte ([115]Figure 2A). We used available
transcriptome databases for neurotransmission ([116]Figure 2B) and
brain aging ([117]Figure 2C) [[118]12, [119]25] (see shaded area in
Venn diagram, [120]Figure 2D and [121]2E).
On the one hand, the neurotransmission database reported transcriptomic
changes occurring in neurons and astrocytes grown in a mixed culture
setting, before and after neurostimulation, followed by RNA-seq
[[122]12]. The authors reported 4441 genes with differential abundance
in the neuron and 1307 in the astrocyte (fold-change, FC ≥1.3 or ≤0.77
and padj-SSS-value <0.05). On the other hand, the brain aging database
was generated using single-cell RNA sequencing to obtain the
age-coefficient for each gene, which is equivalent to the fold-change
of each gene when comparing neurons and astrocytes from aged and young
mouse brains [[123]25]. This study reported 5415 differentially
abundant genes in neurons and 1294 in astrocytes when comparing 1–3
months old with 18–30 months old mice (age-coefficient threshold at
0.005 reported by authors as equivalent to a 10%-fold change and an FDR
threshold of 0.01).
The differentially expressed genes reported in these databases were
then cross-referenced to the hub genes identified in the network
analyses, resulting in DHG ([124]Figure 2D and [125]2E). In response to
neurotransmission, we found 53 DHG in the neuron and 14 DHG in the
astrocyte. While for brain aging, we found 73 in the neuron and 26 in
the astrocyte.
Differential hub genes in the neuron suggest a metabolic deficit and impaired
synaptic transmission during brain aging
We performed a pathway enrichment analysis using the KEGG pathway
database, followed by manual curation to obtain a functional
characterization of DHG in neurotransmission and brain aging (see
Methods for manual curation criteria). [126]Figure 5 shows KEGG
pathways enriched in neuronal DHG during neurotransmission ([127]Figure
5A–[128]5F) and brain aging ([129]Figure 5A’–[130]5F’), where node
colors indicate up (red nodes) or downregulation (blue nodes) during
each process.
Figure 5.
[131]Figure 5
[132]Open in a new tab
KEGG pathway enrichment of differential hub genes reveals that the aged
neuron displays energetic deficit, dysfunctional neurotransmission,
decreased branched-chain amino acid degradation and utilization of
ketone bodies, and decreased one-carbon pool levels. KEGG pathway
enrichment of differential hub genes was followed by manual curation of
associated genes. The results are shown for neurotransmission (top
panel) and aging (bottom panel). Oxidative phosphorylation (OxPhos,
blue): high OxPhos levels during neurotransmission (A) but low OxPhos
levels during aging (A’). Synaptic transmission: upregulated
Na/K-ATPase pumps (orange) and glutamate synthesis (green) suggest
active re-establishment of cation gradients (B) and high glutamate
levels (C). The opposite was observed during aging (B’, C’). 3) Ketone
body metabolism (yellow): decreased synthesis and increased
degradation/utilization during neurotransmission (D), with the opposite
observed during aging (D’). 4) Branched-chain amino acid (BCAA)
degradation (purple): while differential hub genes involved in the
degradation of BCAA were found downregulated during both
neurotransmission (E) and aging (E’), dld, which encodes for a subunit
of BCAA-decarboxylase, an early step in the degradation of all three
BCAA was only downregulated during brain aging. 5) One carbon pool
(pink): differential hub gene expression associated with one-carbon
metabolism suggests high levels of one-carbon pool intermediates during
neurotransmission (F) but low during aging (F’). Created with
[133]https://www.biorender.com/.
We identified five main biological processes with different regulation
when comparing neurotransmission and brain aging. The first group
contained DHG associated with central energy metabolism associated with
KEGG pathways “Pyruvate metabolism”, “Citrate cycle (TCA cycle)” and
“Central carbon metabolism in cancer” ([134]Figure 5A and [135]5A’,
blue). This last pathway was included because metabolic changes
observed in cancer, such as the Warburg effect, also occur in the brain
[[136]29]. During neurotransmission, we observed upregulation of acss1,
a mitochondrial enzyme that synthesizes acetyl-CoA from acetate, and of
pdha1, which encodes for a subunit of the pyruvate dehydrogenase
complex (PDC) ([137]Figure 5A, blue). Upregulation of both enzymes
agrees with increased acetyl-CoA levels and therefore suggests
increased TCA flux, which would lead to high levels of oxidative
phosphorylation. Instead, during aging, we observed downregulation of
most genes involved in the three KEGG pathways mentioned above (except
for fh1, which was upregulated). Notably, most of these DHG
downregulated during neuronal aging participate in the TCA cycle. Plus,
we found downregulation of three genes encoding for PDC subunits:
pdha1, pdhb, and dld. These changes also suggest that acetyl-CoA entry
into the neuronal TCA cycle and TCA cycle activity are impaired in the
aged brain.
The second group was associated with synaptic activity, including a
cluster of Na/K-ATPase pumps ([138]Figure 5B and [139]5B’, orange) and
enzymes that catalyze glutamate synthesis ([140]Figure 5C and [141]5C’,
green). During neurotransmission, they were upregulated, while in brain
aging, they were downregulated except for atp1a2. Na/K-ATPase pumps are
required to re-establish ion gradients after neurotransmission to allow
the following cycle of synaptic activity. At the same time, glutamate
is the primary excitatory neurotransmitter, for which these results
agree with synaptic activity dysregulation during brain aging, with
got1/2 as DHG regulating glutamate levels.
The third group corresponds to the “Synthesis and degradation of ketone
bodies” pathway ([142]Figure 5D and [143]5D’, yellow). During
neurotransmission, hmgcs1, encoding for the cytosolic form of
3-hydroxy-3-methylglutaryl-CoA synthase 1 was upregulated while hmgcll1
(3-hydroxymethyl-3-methylglutaryl-CoA lyase like (1) was downregulated.
Hmgcs1 catalyzes the formation of HMG-CoA, which is further converted
into mevalonate for cholesterol synthesis (as opposed to the
mitochondrial isoform hmgcs2, which catalyzes the first irreversible
step in ketogenesis using the same substrates as hmgcs1). Instead,
hmgcll1, which catalyzes the second irreversible step in ketogenesis
and is downregulated, and oxct1, which catalyzes the interconversion
between acetoacetyl-CoA and acetoacetate and was upregulated. These
results suggest the downregulation of ketone body synthesis during
neurotransmission while favoring an increased degradation or
utilization ([144]Figure 5D). In contrast, during neuronal aging, it is
hmgcs2 which is upregulated (ketone body synthesis mitochondrial
isoform), while hmgcs1 is downregulated. In addition, oxct1 and bdh1
are also downregulated ([145]Figure 5D’). Bdh1 catalyzes the
interconversion between acetoacetate and beta-hydroxybutyrate, the two
main ketone bodies. Therefore, the downregulation of oxct1 and bdh1
suggest a decrease in ketone body turnover in the aged neuron.
The fourth group was associated with the “Valine, leucine, and
isoleucine degradation” pathway, and therefore refers to branched-chain
amino acid (BCAA) degradation ([146]Figure 5E and [147]5E’, purple).
While enzymes associated with BCAA degradation were downregulated
during both neurotransmission and brain aging in the neuron, dld, which
encodes for a subunit of the BCAA decarboxylase and thus catalyzes one
of the first steps of the degradation of all three BCAA was only
downregulated during brain aging ([148]Figure 5E’), supporting
downregulation of BCAA degradation during neuronal aging but possibly
not during neurotransmission.
Finally, the fifth group was associated with regulating one-carbon pool
levels ([149]Figure 5F and [150]5F’, pink), including pathways
“Glycine, serine and threonine metabolism” and “One carbon pool by
folate.” During neurotransmission, glycine degradation enzymes alas1
and amt were downregulated, while shmt2, which feeds the one-carbon
pool by producing 5,10-methylenetetrahydrofolate was upregulated. Also,
2 out of 3 enzymes involved in the metabolism of one-carbon pool
intermediates were upregulated ([151]Figure 5F, pink). However, all
enzymes (except for alas2) associated with these two pathways were
downregulated during neuronal aging, suggesting a decrease in the
one-carbon pool. We present a summary of all these changes in
[152]Table 1.
Table 1. Summary of the main biological processes and pathways identified
among differential hub genes during neurotransmission and aging in the
neuron.
Process or gene(s) Associated KEGG pathways and biological processes
Differential hub genes upregulated after neurotransmission Differential
hub genes downregulated after neurotransmission Differential hub genes
upregulated in the aged brain Differential hub genes downregulated in
the aged brain
Central energy metabolism: oxidative phosphorylation (OxPhos) Pyruvate
metabolism; Citrate cycle (TCA cycle); Central carbon metabolism in
cancer Acetyl-CoA synthesis: acss1, pdha1 (pdha1 is the subunit
inhibited by phosphorylation by PDK regulating the activity of the
whole pyruvate dehydrogenase complex (PDC)); Glucose uptake
transporter: slc2a1; Lactate synthesis: ldha TCA cycle enzymes: idh2,
aco1 fh1: malate synthesis Pyruvate dehydrogenase complex (conversion
of pyruvate into acetyl-CoA for entry into the TCA cycle): dld, pdha1,
pdhb; dlst, dlat; Malate-aspartate shuttle for NADH transport into the
mitochondrial matrix: mdh1, mdh2; Synthesis of alpha-ketoglutarate
(which can exit TCA cycle as glutamate, connecting central metabolism
and neurotransmission): idh1, idh2; Lactate synthesis from pyruvate:
ldha, ldhb; Other TCA cycle enzymes: sdha, sdhb, aco2, hagh.
Synaptic activity and glutamate Na/K-ATPase pumps; glutamate synthesis
Na/K-ATPase pumps: atp1b1 (non-catalytic subunit, regulates
translocation to the plasma membrane), atp1a3, atp1a1, atp1b2;
Glutamate synthesis: got1 – Na/K-ATPase pumps: atp1a2 Na/K-ATPase
pumps: atp1b2, atp1b1, atp1b3, atp1a3; Glutamate synthesis: got1, got2
Ketone body metabolism Synthesis and degradation of ketone bodies
hmgcs1: diverts ketone body precursors into cholesterol synthesis;
oxct1 hmgcll1: second irreversible step in ketogenesis, but it’s a
cytosolic isoform. hmgcs2: first rate-limiting step in ketogenesis.
bdh1: catalyzes interconversion of the two main ketone bodies,
acetoacetate and beta-hydroxybutyrate (this conversion is required for
ketone body utilization); oxct1: catalyzes reversible reaction between
acetoacetyl-CoA and acetoacetate; hmgcs1
Branched-chain amino acid (BCAA) degradation Valine, leucine and
isoleucine degradation – aldh6a1: valine degradation; mccc2: leucine
degradation (deficiency is an autosomal recessive disorder). – dld:
subunit of the BCAA decarboxylase, catalyzes early step of the
degradation of all three BCAA; mccc1: leucine degradation; hibadh:
valine degradation.
One carbon pool Glycine, serine and threonine metabolism; One carbon
pool by folate shmt2: feeds the one carbon pool by increasing serine
and 5,10-methylenetetrahydrofolate; Folate metabolism: mthfd1l, mthfd2
Glycine degradation: alas1, amt; Folate metabolism: mthfd1 alas2 shmt2,
alas1, gcat, mthfd2, mthfd2l. An overall decrease in genomic DNA
methylation occurs during aging, and these changes agree with that.
[153]Open in a new tab
Differential hub gene abundance changes in the astrocyte suggest a metabolic
switch during brain aging
We next performed the same pathway enrichment of DHG in the astrocyte
during neurotransmission and brain aging, followed by manual curation.
The number of DHG in the astrocyte was lower than those found in the
neuron, leading also to a lower number of enriched pathways. All five
biological processes described for the neuron were also found during
astrocyte aging.
In the first group we identified DHG enriched in central energy
metabolism pathways ([154]Figure 6A and [155]6A’, blue). These were
“Pyruvate metabolism” during both astrocyte neurotransmission and
aging, while “Central metabolism in cancer” was only enriched during
neurotransmission, and “Citrate cycle (TCA cycle)” was only enriched
during astrocyte aging. The following changes were observed in the
astrocyte during neurotransmission ([156]Figure 6A). First, slc2a1,
encoding for the leading glucose uptake transporter in the blood-brain
barrier GLUT1 was upregulated. Second, ldha, which encodes for a
subunit of lactate dehydrogenase (LDH) that favors lactate levels in
the interconversion between pyruvate and lactate was also upregulated
[[157]30]. And third, pcx and acss1, which encode for enzymes that feed
substrates into the TCA cycle, were downregulated. These changes
suggest high glucose uptake during neurotransmission by the astrocyte,
elevated lactate synthesis, and low TCA cycle flux, which agrees with
an active astrocyte-neuron lactate shuttle (ANLS). In contrast, during
astrocyte aging, we observed the following changes ([158]Figure 6A’).
Instead of ldha, we observed upregulation of ldhb, which encodes for an
LDH subunit that favors pyruvate levels. Furthermore, mdh1 and mdh2,
which encode for the cytosolic and mitochondrial malate dehydrogenases,
respectively, were upregulated. These enzymes participate in the
malate-aspartate shuttle, which transports reducing equivalents into
mitochondria (NADH) therefore fueling the electron transport chain and
ATP synthesis by oxidative phosphorylation. These changes observed in
differential hub gene regulation suggest that oxidative metabolism is
favored in the aged astrocyte instead of flux through the ANLS,
affecting neuronal energy needs.
Figure 6.
[159]Figure 6
[160]Open in a new tab
KEGG pathway enrichment analysis of astrocyte differential hub genes
suggests a metabolic switch from aerobic glycolysis to oxidative
phosphorylation during aging. (A and A’) Metabolic switch (blue):
upregulation of ldha during neurotransmission but ldhb during aging.
Ldha/b genes encode for subunits of lactate dehydrogenase, which
catalyzes the interconversion of pyruvate into lactate. Ldha subunits
favor lactate levels and were upregulated during neurotransmission,
while ldhb favors pyruvate and is upregulated during aging. Also, the
major glucose uptake transporter in the blood-brain barrier, encoded by
slc2a1, was upregulated during neurotransmission only. Instead, during
aging, mdh1/2 encode for enzymes of the malate-aspartate shuttle, which
allows transport of NADH into the mitochondrial matrix to provide
electrons for the ETC. Both genes were upregulated during aging, in
agreement with a high OxPhos rate. (B and B’) Branched-chain amino acid
(BCAA) degradation (purple): during neurotransmission, upregulation of
slc7a5 was observed (amino acid transporter present in the cell surface
and lysosome; participates in leucine uptake into the lysosome for
degradation), while during aging, three enzymes involved in BCAA
degradation, including dld, were downregulated. (C) Ketone body
degradation/utilization (yellow): the enzyme encoded by bdh1 catalyzes
the interconversion of acetoacetate and β-hydroxybutyrate, the two main
ketone bodies, and was upregulated during aging only. (D) Synaptic
transmission (green): abat encodes for an enzyme that breaks down GABA
into glutamate and is downregulated during aging in the astrocyte. (E)
One carbon pool (pink): differential hub gene expression associated
with one-carbon metabolism suggests an increase in one-carbon pool
during astrocyte aging. Created with [161]https://www.biorender.com/.
The second group was related to branched-chain amino acid (BCAA)
degradation ([162]Figure 6B and [163]6B’, purple), where slc7a5, a cell
surface amino acid transporter that is also present in the lysosome for
leucine uptake for degradation, was upregulated during
neurotransmission ([164]Figure 6B, purple). In contrast, dld, aldh6a1,
and hibadh, all BCAA degradation genes, were downregulated during brain
aging ([165]Figure 6B’, purple). These results are in line with BCAA
accumulation in the astrocyte during brain aging.
The third group was associated with ketone body degradation/utilization
([166]Figure 6C, yellow), where bdh1 was upregulated during brain
aging. As mentioned previously, this gene encodes for the enzyme that
catalyzes interconversion of acetoacetate and β-hydroxybutyrate, thus
suggesting that the aged astrocyte favors ketone body degradation or
utilization. The fourth group was associated with glutamate levels
([167]Figure 6D, green), including abat, which encodes for an enzyme
that degrades GABA converting it into glutamate. Abat was found
downregulated during astrocyte aging. These results support a metabolic
switch in the astrocyte during brain aging that decreases ANLS flux,
which is required for meeting the high-energy neuronal demand,
promoting ATP synthesis for the astrocyte’s use. Finally, the fifth
group was related to one-carbon pool regulation ([168]Figure 6E), where
gcat was upregulated during aging, while gldc was downregulated. Gcat
activity feeds the one-carbon pool while gldc consumes one carbon
intermediates. These results strongly suggest that the aging astrocyte
favors the one-carbon pool. A summary of all changes observed in the
astrocyte is included in [169]Table 2.
Table 2. Summary of the main biological processes and pathways identified
among differential hub genes during neurotransmission and aging in the
astrocyte.
Process or gene(s) Associated KEGG pathways and biological processes
Differential hub genes upregulated after neurotransmission Differential
hub genes downregulated after neurotransmission Differential hub genes
upregulated in the aged brain Differential hub genes downregulated in
the aged brain
Central energy metabolism: switch from aerobic glycolysis
(neurotransmission) into oxidative phosphorylation metabolism Pyruvate
metabolism; Citrate cycle (TCA cycle); Central carbon metabolism in
cancer Aerobic glycolysis and astrocyte-neuron lactate shuttle (ANLS):
ldha, catalyzes interconversion between pyruvate and lactate, favoring
lactate; slc2a1 (a.k.a. GLUT1), the main glucose uptake transporter in
the blood brain barrier pcx, acss1: feed the TCA cycle with
intermediates (TCA anabolic reactions). Also in agreement with aerobic
glycolysis metabolic state Oxidative metabolism: ldhb favors pyruvate
levels instead of lactate (suggesting glycolytic flux in the aged
astrocyte is directed towards its own ATP synthesis); mdh1 and mdh2 are
involved in the malate-aspartate shuttle, which allows NADH transport
into the mitochondria to provide electrons for the electron transport
chain (also in agreement with favoring ATP synthesis in the aged
astrocyte instead of the ANLS). –
Synaptic activity and glutamate Na/K-ATPase pumps; glutamate synthesis
– – – abat: catalyzes GABA degradation, producing glutamate.
Ketone body metabolism Synthesis and degradation of ketone bodies – –
bdh1: catalyzes interconversion of the two main ketone bodies,
acetoacetate and beta-hydroxybutyrate (this conversion is required for
ketone body utilization); hmgcs1: diverts ketone body precursors into
cholesterol synthesis –
Branched-chain amino acid (BCAA) degradation Valine, leucine and
isoleucine degradation slc7a5: cell surface amino acid transporter,
also present in the lysosomal membrane for leucine uptake into the
lysosome. – – dld: subunit of the BCAA decarboxylase, catalyzes early
step of the degradation of all three BCAA; aldh6a1 and hibadh: valine
degradation
One carbon pool levels Glycine, serine and threonine metabolism; One
carbon pool by folate – – gcat: threonine degradation into glycine and
acetyl-CoA gldc: glycine degradation
[170]Open in a new tab
Differential hub genes previously associated with aging
As a final step, we performed functional annotation for each individual
differential hub gene (see [171]Supplementary Tables 2–[172]7), to
determine which had been previously annotated to aging annotations.
Among annotated functional categories, we found the following
associated with aging: (1) From gene ontology, “aging” (GO:0007568),
“cell aging” (GO:0007569) and “multicellular organism aging”
(GO:0010259); (2) From BioCarta (database containing maps of metabolic
and signaling pathways) (see [173]Supplementary Table 8). We only found
six genes that had been previously annotated with these categories:
abat (4-aminobutyrate aminotransferase), dld (dihydrolipoamide
dehydrogenase), slc1a2 (solute carrier family 1 (glial high affinity
glutamate transporter, member 2) and superoxide dismutases sod1, sod2,
sod3 (see [174]Supplementary Table 9).
DISCUSSION
In the present work, we analyzed the neuron-astrocyte metabolic network
by integrating a flux-based approach (Flux Balance Analysis), and a
centrality analysis, which addresses the intrinsic structure of the
network. This network analysis was followed by cross-reference of the
identified hub genes, with gene expression data for both cell types
during neurotransmission and brain aging (see Workflow Overview in the
Results section, and [175]Figures 1 and [176]2). The integration of
these three approaches allowed the identification of differential hub
genes (DHG), which are a robust selection of gene candidates with a
high probability of playing pivotal roles in the neuron-astrocyte
metabolic network, and to explain the molecular mechanisms of
age-associated brain functional decline. DHG were further analyzed
using pathway enrichment analysis. This allowed identifying the main
biological processes in which DHG participate during neurotransmission
and/or brain aging.
Impaired central energy metabolism in the aged neuron
Brain energy metabolism dysfunction has been described as a hallmark of
brain aging [[177]1, [178]31], and metabolic deficit in the neuron
during human brain aging has been reported [[179]15]. This group
reported that flux through the tricarboxylic acid (TCA) cycle decreased
by 28% in the presynaptic neuron using in vivo magnetic resonance
spectroscopy. However, the genes involved in this deficit remain
largely unknown. Our analyses showed that the aging neuron
downregulated a high number of TCA cycle genes, including:
(1) Subunits of the pyruvate dehydrogenase complex, dld (EC:1.8.1.4),
pdha1 (EC:1.2.4.1), pdhb (EC:1.2.4.1), and dlat (EC:2.3.1.12), which
catalyzes the conversion of pyruvate into acetyl-CoA for entry into the
TCA cycle. Among these, it is worth highlighting that dld (EC:1.8.1.4)
is also a catalytic subunit of two other essential dehydrogenase
complexes: the α-ketoglutarate dehydrogenase complex (α-KGDH), which
catalyzes the conversion from α-KG into succinyl-CoA (reaction that
produces NADH in the mitochondrial matrix), and the branched-chain
amino acid (BCAA) dehydrogenase complex. Remarkably, downregulation of
dld severely affects overall metabolic function and causes the
hereditary disease dihydrolipoamide dehydrogenase deficiency (OMIM:
246900) [[180]32, [181]33].
(2) Two isoforms of malate dehydrogenase (mdh1 and mdh2) were
downregulated in aging neurons. These enzymes are involved in the
malate-aspartate shuttle (MAS), which allows the shuttling of NADH into
the mitochondrial matrix [[182]34], providing reducing equivalents for
the electron transport chain (ETC). Furthermore, it has been shown that
the expression of malate-aspartate shuttle enzymes decreases with
normal aging and can be reverted using dietary restriction [[183]35].
Also, loss-of-function mutations in the mdh2 gene are associated with
severe neurological deficits in children (Ait-El-Mkadem et al., 2017).
Notably, the NADH/NAD^+ ratio is one of the driving forces of the ANLS
together with pyruvate levels [[184]36], highlighting these two enzymes
as candidates to study age-associated brain functional decline.
(3) Downregulation of idh1 and idh2 (encoding for the enzymes
Isocitrate Dehydrogenases 1 and 2), which catalyze α-ketoglutarate
(α-KG) synthesis. This metabolite exits the TCA cycle and is converted
into glutamate, which is the main excitatory neurotransmitter, and
therefore it is central for metabolism since it connects energy
metabolism with neurotransmission via glutamate.
These changes agree with previous findings of metabolic deficit in the
neuron during aging and provide both previously reported genes
(validating our modeling method) and novel gene targets. An energetic
shortage in a cell with such high energy demand is critical and will
necessarily lead to dysfunction.
Astrocyte metabolic switch from aerobic glycolysis to oxidative
phosphorylation
Differential gene expression patterns in astrocytes indicate a
metabolic switch from aerobic glycolysis to oxidative metabolism. Since
astrocytes fuel neurons with lactate, this metabolic switch can lead to
neuronal energy deficit. This behavior has been described previously as
a selfish phenotype adopted by the astrocyte during aging [[185]16].
Here, astrocytes use pyruvate for their ATP synthesis instead of
shuttling it to neurons. In this regard, lactate dehydrogenase isoforms
dictate the fate of pyruvate, either by favoring its conversion into
lactate or by directing it into the TCA. Specifically, lactate
dehydrogenase isoform LDH-5 favors lactate production, while isoform
LDH-1 favors pyruvate production [[186]30]. Consistent with the ANLS,
the ldha1 gene (coding for polypeptides forming the LDH-5) increases in
astrocytes during neurotransmission. Remarkably, the ldhb gene, which
codes for the subunits of the polypeptides of LDH-1, was upregulated
during brain aging. These findings support the glycolytic-to-oxidative
metabolic switch in the astrocyte. Furthermore, while LDH-1 isoenzymes
localize in both neurons and astrocytes, the LDH-5 isoenzymes localize
exclusively in astrocytes [[187]30]. Hence, it is highly relevant that
ldhb increases in aged astrocytes.
Ldh upregulation occurs Drosophila melanogaster aging, where
loss-of-function in either neurons or astrocytes leads to an increase
in lifespan, while gain-of-function reduces lifespan [[188]37]. Ldh
overexpression also leads to increased neurodegeneration and motor
function decline, while downregulation is neuroprotective [[189]37].
However, specific studies on the ldha-to-ldhb switch in astrocytes have
not been performed and would be of great interest to understand the
mechanisms of functional brain decline during aging.
Returning to MAS, mdh1 and mdh2 were upregulated during astrocyte aging
(as opposed to downregulation in the aged neuron), supporting the
oxidative metabolism switch in the astrocyte [[190]34]. Of note, while
there were controversial reports of Aralar, the glutamate/aspartate
antiporter in the MAS not being expressed in astrocytes [[191]38],
later evidence showed the opposite [[192]39]. In fact, both
transcriptomic databases used here detected slc25a12 transcript
expression (which encodes for Aralar) in astrocytes, albeit not
differentially expressed [[193]12, [194]25].
Taken together, these results show that while the neuron displays an
intrinsic energetic deficit as demonstrated by its expression changes,
the astrocyte further contributes to this deficit by undergoing a
metabolic switch into a selfish phenotype during brain aging.
Role of mdh2 and ldhb in the metabolic switch of other cell types
In cancer cells, the “Warburg effect”, which is also known as aerobic
glycolysis, was first described. In the transition from
normal-to-tumoral cells, they undergo an oxidative-to-aerobic
glycolysis switch, favoring proliferation [[195]40]. However, exposure
of cancer cells to radiation induces a switch to oxidative metabolism
arresting proliferation [[196]41]. Notably, treatment of cancer cells
with an Mdh2 inhibitor induces downregulation of oxidative
phosphorylation [[197]42], which is in line with the role in the
metabolic switch of Mdh2.
Furthermore, it was recently reported that ldhb plays a role in
tumor-associated macrophages in breast carcinoma [[198]43]. These
macrophages express low levels of ldhb, perform aerobic glycolysis and
secrete high lactate levels. Yet, when the authors upregulate ldhb this
significantly decreases lactate production in these macrophages,
further supporting the role of ldhb upregulation in inducing an
oxidative phenotype.
Impaired branched-chain amino acid degradation
Valine, leucine, and isoleucine are the three branched-chain amino
acids (BCAA). Impairment in their degradation is detrimental to overall
metabolic health [[199]44–[200]46], and that a high consumption of BCAA
coupled with a high-fat diet increases tau neuropathology in the
3xTg-AD Alzheimer’s disease mouse model [[201]47]. We observed the
downregulation of genes involved in BCAA degradation during
neurotransmission and aging in the neuron. During neurotransmission,
aldh6a1, involved in valine degradation ([202]Figure 5E and [203]Table
1), and mccc2 involved in leucine degradation, while during aging,
mccc1 (also leucine degradation), hibadh (valine degradation), and dld,
which catalyzes an early step in the degradation of all three BCAA
([204]Figure 5E’). Importantly, a recent report showed that detrimental
effects of BCAA are mediated mainly through isoleucine, and to a lesser
extent, by valine [[205]46].
Significantly, dld was also downregulated in the astrocyte. In fact,
slc7a5 which transports leucine into the lysosome for degradation was
upregulated during neurotransmission in the astrocyte, while dld,
aldh6a1, and hibadh were downregulated in the aging astrocyte. Taken
together with the fact that dld encodes for a subunit in three central
dehydrogenases, its downregulation in both the aged neuron and the
astrocyte, plus its role in BCAA degradation, we propose dld as one of
the strongest candidates to target in the aging brain.
Altered ketone body metabolism
Ketone bodies are produced during caloric restriction, which is the
only intervention known to extend lifespan across various organisms
[[206]17], and several metabolic challenges are being developed to
emulate the effects of caloric restriction, including the ketogenic
diet [[207]18, [208]19] and intermittent fasting [[209]20]. Our results
suggest that DHGs are regulated in the neuron such that during
neurotransmission, they suggest downregulation of ketogenesis by
upregulation of hmgcs1, the cytosolic isoform of hmgcs2. While Hmgcs2
(the mitochondrial isoform) catalyzes the first rate-limiting step in
ketogenesis [[210]48], Hmgcs1 catalyzes cholesterol biosynthesis in the
cytosol instead of ketone body synthesis. However, during neuronal
aging, hmgcs2 was upregulated while hmgcs1 downregulated, thus
suggesting upregulation of ketone body synthesis [[211]48]. Bdh1
(EC:1.1.1.30) and oxct1 (EC:2.8.3.5), which participate in the
utilization of ketone bodies were downregulated, suggesting
downregulation of ketone body degradation during neuronal aging.
In the astrocyte, bdh1 (EC:1.1.1.30) was upregulated. This gene encodes
for the enzyme that catalyzes the interconversion between
β-hydroxybutyrate and acetoacetate, the two main ketone bodies, a
reaction required for acetoacetate conversion into acetyl-CoA for
eventual ATP synthesis [[212]31]. Therefore, this suggests an
upregulation of ketone body degradation and utilization in the
astrocyte during aging. Taken together, our results suggest that ketone
body utilization increased during astrocyte aging while the aging
neuron upregulated ketogenesis. These results agree with those
mentioned above regarding astrocyte energy expenditure being favored
over the neuronal demand during brain aging.
Downregulation of genes associated with synaptic transmission in the aging
neuron
During synaptic transmission, we observed that the neuron upregulated
genes encoding for four sodium/potassium-ATPase (Na/K-ATPase) pumps
([213]Figure 5B orange), while four out of five Na/K-ATPase pumps were
downregulated during neuronal aging ([214]Figure 5B’, orange). These
pumps are required to re-establish neuronal ion gradients after
neurotransmission (Baeza-Lehnert et al., 2019; Erecinska and Silver,
1994). A downregulation of their expression during aging could
contribute to neuronal dysfunction. Furthermore, got1 (EC:2.6.1.1),
which synthesizes glutamate from TCA intermediate α-ketoglutarate, is
upregulated during neurotransmission, while got1 and got2 (EC:2.6.1.1)
were downregulated during brain aging. Since glutamate is the main
excitatory neurotransmitter [[215]49] and α-ketoglutarate a key
metabolic intermediate in the TCA cycle, these two enzymes, in
particular, got1 (the cytoplasmic isozyme), with opposite regulation
during neurotransmission and aging, provide a link between central
energy metabolism and synaptic activity. Remarkably, activity for the
enzyme encoded by got is increased in the brain of Alzheimer’s disease
individuals compared with healthy controls [[216]50]. However, further
characterization of the enzyme during pathological or healthy brain
aging is still lacking, making it an exciting target for future
studies.
Regarding glutamate levels, the enzyme abat (EC:2.6.1.19), which also
catalyzes the conversion of α-ketoglutarate into glutamate, is
downregulated during astrocyte aging. The reaction catalyzed by this
enzyme involves the degradation of ɣ-aminobutyric acid, or GABA, the
main inhibitory neurotransmitter.
As a whole, glutamate synthesized by got1, got2, and abat decreases
during both neuron and astrocyte aging. This has a possible detrimental
effect on neurotransmission and coupled with the downregulation of the
expression of Na/K-ATPase pumps in the aging neuron, provides valuable
future horizons for elucidating the molecular mechanisms of brain
aging.
Altered one-carbon pool for tetrahydrofolate (THF) synthesis
The final group we observed was defined by KEGG pathways “One carbon
pool by folate” (KEGG map00670) and “Glycine, serine and threonine
metabolism” (KEGG map00260). These pathways are of interest during
brain aging because THF is the precursor for S-adenosylmethionine
(SAM), the substrate required for methylation, including DNA and
histone methylation [[217]51–[218]53] linking central energy metabolism
with epigenetic modifications. Overall methylation levels decrease
during aging [[219]54–[220]56] and is one of the epigenetic clocks,
which can be modified by metabolic challenges such as caloric
restriction [[221]57]. Furthermore, glycine and serine degradation also
feed the one-carbon pool [[222]58].
In the neuron, we observed the following changes in one carbon pool
associated enzymes ([223]Figure 5F and [224]5F’). During
neurotransmission, shmt2 (EC:2.1.2.1), an enzyme that synthesizes
5,10-Methylene-THF (KEGG map00260), mthfd1l (EC: 6.3.4.3) and mthfd2
(EC: 3.5.4.9) were all upregulated. The enzymes encoded by mthd1l and
mthfd2 feed the THF pool (KEGG map00670). However, in the aged neuron,
alas1 was downregulated and this change is associated with decreased
THF levels [[225]58]. These changes suggest that during
neurotransmission, availability of THF increases, while during aging it
decreases, in line with the overall decrease in DNA methylation
reported during aging [[226]54–[227]56, [228]59]. Furthermore, THF is
required for glutathione synthesis (GSH), required to quench the high
reactive oxygen species levels produced from oxidative phosphorylation.
Therefore, high THF levels can be associated with the oxidative
metabolism during neurotransmission.
In the astrocyte, we observed differential expression of gcat and gldc.
The Gcat enzyme (EC:2.3.1.29) increases glycine levels from threonine
degradation, and therefore feeds the one carbon and THF pool was
upregulated during aging. In contrast, Gldc (EC:1.4.4.2) catalyzes
glycine degradation and therefore consumes THF, was downregulated.
These results suggest an overall increase in the one carbon pool during
astrocyte aging, which also suggest an increase in THF and a subsequent
increase in availability for GSH synthesis. This agrees with an
oxidative metabolic state that is also in line with the aerobic
glycolysis to oxidative phosphorylation switch we propose for the aged
astrocyte.
Pathways and genes previously associated with brain aging
The set of differential hub genes highlighted in [229]Figures 5 and
[230]6 had been previously associated with metabolic pathways that are
related to neurotransmission, e.g., glutamate metabolism and
Na/K-ATPase pumps, or are related to brain aging. Among these, we
found: (1) Glycolysis and oxidative phosphorylation [[231]1], which,
given their differential expression during aging, are associated with
metabolic dysregulation; (2) Ketone body metabolism, associated with
caloric restriction and other dietary interventions [[232]17–[233]19];
(3) Branched-chain amino acid degradation, described to play a role in
metabolic health and aging [[234]44–[235]46]; and (4) The one carbon
pool, which participates in glutathione synthesis (required during
neurotransmission and aging) as well as in SAM synthesis (the sole DNA
and histone methylation substrate), related with epigenetic changes
that occur during aging [[236]54–[237]56]. However, out of a total of
115 DHG, only 6 had been annotated in a functional annotation database
as associated with aging-related terms. These included: (1) Superoxide
dismutases sod1/2/3, which play a role in oxidative stress control; (2)
The glial glutamate transporter slc1a2; (3) abat, which catalyzes the
conversion of GABA and α-ketoglutarate into L-glutamate and succinate
semialdehyde; and (4) dld, which, as mentioned previously, encodes for
a subunit of the branched-chain amino acid, pyruvate, and
α-ketoglutarate dehydrogenase complexes, and was identified as a neuron
optimal gene, and a central gene for both the neuron and the astrocyte.
A lower Dld enzymatic activity has been observed in Alzheimer’s
disease, mainly associated with the α-ketoglutarate complex, which
converts α-ketoglutarate into succinyl-CoA and NADH in the TCA cycle
[[238]60, [239]61]. In physiological brain aging, Yan and collaborators
reported that mitochondrial Dld expression and activity (in
mitochondria isolated from whole rat brains) increases in the
progression into adulthood, with no further changes from 5 to 30 months
old [[240]62]. However, during caloric restriction, Dld levels are
higher in the hippocampus of rats subjected to caloric restriction
[[241]63]. Given that our results show that dld expression is lower in
aged astrocytes and neurons, and the beneficial anti-aging effects of
caloric restriction, we propose that restoring dld expression is an
interesting target to further address its role in brain aging.
Intriguingly, Dld has been reported to have a moonlighting proteolytic
activity [[242]64], which was more recently demonstrated to degrade the
NF-κB inhibitor IκBε in a context associated with Parkinson’s disease
[[243]65]. Taken together, the lower dld expression in aged astrocytes
and neurons, the decrease in Dld enzymatic activity in Alzheimer’s
disease, its proteolytic function in Parkinson’s disease, and that its
levels are partially restored during caloric restriction, suggest a
critical role for Dld in the neuron-astrocyte metabolic network. From a
geroscience standpoint, these results also propose Dld as an
aging-associated change that could increase the risk for
neurodegenerative disease. This supports that the method presented here
allows the identification of strong candidate genes for future
preclinical studies on brain aging and neurodegenerative disease.
Furthermore, differential hub genes involved in aging-associated
metabolic processes that have not been studied in brain aging represent
a set of robust candidates for future studies.
Labor division between the neuron and astrocyte
Differential hub gene expression in the aged astrocyte further
reinforces the notion of division of labor between the neuron and
astrocyte in the metabolic network shown by both flux balance analysis
and centrality analysis. The regulation of biological processes
associated with DHG suggests that the aged astrocyte fails to perform
its part in this division of labor, which is mainly providing lactate
to the neuron and recycling glutamate and glutamine. Instead, the
astrocyte switches into a selfish phenotype, where energy expenditure
reallocates to this cell during brain aging. Taken together,
differential hub gene regulation in both cell types strongly supports
neuronal metabolic deficit, which could contribute to the cognitive
deficit observed in the brain during aging.
CONCLUSIONS
The work reported here integrated two network-based approaches combined
with bioinformatics analyses of transcriptomics data, through which we
identified differential hub genes. These constitute a selection of
genes that play an important role in the neuron-astrocyte metabolic
network in terms of metabolite flux, intrinsic network structure, and
are also regulated during neurotransmission and/or brain aging. Our
findings suggest that the astrocyte undergoes a metabolic switch from
aerobic glycolysis to oxidative metabolism, with a concomitant
upregulation of THF precursor synthesis required for glutathione
synthesis, to control the increased oxidative stress caused by this
metabolic switch. Additionally, differential hub genes in the neuron
suggest substantial metabolic impairment and downregulation of genes
required for synaptic transmission.
The proposed integrative computational analysis is a versatile approach
that can be applied to other biological questions, ranging from brain
function in neurodevelopmental disorders to neurodegenerative diseases.
In fact, available metabolic network models for other cell-types and
tissues are available [[244]66], for which the applicability is not
limited to the brain. However, it is important to note that the method
does have limitations. First, enzyme gene expression changes may not
correlate with metabolite abundance, given the different levels of
regulation of metabolic enzyme expression, such as negative feedback
from metabolite levels and/or post-translational modifications. Second,
high-throughput databases like the transcriptomics data used here will
have an intrinsic heterogeneity, as they were generated by different
research groups under different conditions. For example, the
transcriptomics neurotransmission database used here [[245]12] was
obtained from a mixed culture of primary rat postnatal astrocytes with
primary mouse embryonic neurons. This allowed obtaining cell-type
specific data using bioinformatics analyses to separate reads
corresponding to each species. Instead, the transcriptomics aging
database [[246]25] used brain samples from adult young and aged mice
and obtained cell-type specific gene expression data by performing
single-cell RNA-sequencing. Each approach was appropriate for the
question the study was addressing. On the one hand, obtaining primary
neurons from adult brains is a technically difficult procedure making
the single-cell RNA-seq approach more appropriate for the aging study.
On the other hand, for the neurotransmission study, in order to dissect
transcriptomic changes specific to neurotransmission in neurons and
astrocytes, that result from their interaction, a primary cell culture
approach is required.
In spite of these limitations, our analysis identified a small group of
genes that had been previously reported to play a role in brain aging,
and a larger set of genes that participate in metabolic pathways
associated in brain aging, but their specific role has not been
addressed yet. We propose that this second group includes genes that
have a high probability of mediating functional changes in the
neuron-astrocyte metabolic network during brain aging and are candidate
targets for future studies to prevent age-associated cognitive changes.
We also highlight the value of using of integrative computational
approaches, from the integration of network analyses to the integration
of multi-omics databases as powerful tools to make an unbiased
selection of pathways and genes of interest, saving valuable resources
and time before starting experimental studies.
METHODS
Modeling rationale
Our modeling approach tackled three aspects of the metabolic network
conformed by neurons and astrocytes: (i) fast response to
glutamatergic-neurotransmission workload [[247]67], (ii) constant
energy availability, i.e., invariant neuronal concentrations of
cytosolic ATP and ADP [[248]6], and (iii) long-term impairment upon
aging [[249]1]. We addressed the former aspect (i) by employing a
genome-scale constraint-based model of the neuron-astrocyte metabolic
network [[250]24]; henceforth, the neuron-astrocyte model. To simulate
the response to neurotransmission workload (i), we coupled and
maximized three critical fluxes. These fluxes were those that are
activated under glutamatergic neurotransmission and comprised neuronal
ATP consumption derived from sodium removal, the ANLS, and the GGC.
These three events were combined into a single flux denoted as the
metabolic objective. The second aspect (ii), constant energy
availability, was managed by subjecting the maximization of the
metabolic objective to a steady-state constraint. This
optimization-based procedure is known as FBA [[251]68] and simulates an
optimal metabolic response to neurotransmission. The essentials of FBA
can be found in the [252]Supplementary Theoretical Framework Section 1.
The FBA allowed us to identify the optimal metabolic reactions, which
were the reactions responsible for achieving a proper response to
neurotransmission. Up to this point, our model encoded the stationary
and optimal nature of the response to neurotransmission workload.
Notably, metabolic states computed via FBA simulate events that are
required to be reproducible for the cell [[253]69]. Consistently, the
brain must maintain a reproducible outcome, namely a proper response to
energy workload, particularly in the face of aging. Even though part of
aging-derived damage to brain metabolism may reside in fast stationary
events, much of aging deterioration may relate to non-stationary
long-term events. Network topology can encode wide-spectrum phenomena
beyond steady-state and short timescales since it can encode the row
space of the stoichiometric matrix (see [254]Supplementary Theoretical
Framework Section 4). Therefore, we identified a group of reactions
that modulate the optimal metabolic response via topological effects to
analyze aging-derived phenomena (theoretical details on the
topology-based analysis are exposed in the [255]Supplementary
Theoretical Framework section 2). Since we employed centrality analysis
[[256]70], these modulators were called central metabolic reactions
and, along with the optimal metabolic reactions, were used to identify
aging-affected genes.
Neuron-astrocyte metabolic network
We used a genome-scale metabolic network reconstruction [[257]71,
[258]72] of the glutamatergic synapse comprising neurons and astrocytes
[[259]24]. This model is available at
[260]https://systemsbiology.ucsd.edu/InSilicoOrganisms/Brain.
Flux constraints
The theory behind constraint-based modeling and flux constraints is
briefly presented in the [261]Supplementary Theoretical Framework
Sections 1.1 to 1.4. Neuronal flux constraints were derived from
measurements taken in primary cultures reported by [[262]6]. In this
study, they used genetically encoded fluorescence resonance energy
transfer (FRET) reporters [[263]73, [264]74] along with ion-sensitive
dyes to make real-time measurements of intracellular fluxes in neurons
co-cultured with astrocytes. Baeza-Lehnert et al. [[265]6] investigated
how the neuronal ATP pool is maintained upon acute energy demands
derived from the activity of the Na+/K+ ATPase pump induced by neuronal
stimulation. They were able to estimate that sodium ions are extruded
at a rate of 350 μM/s after neuronal stimulation. This sodium efflux
rate corresponds to 116.6 μM/s of ATP consumption since 1 molecule of
ATP is spent to export 3 ions of sodium. Also, Baeza-Lehnert et al.
[[266]5] estimated a housekeeping ATP demand of 38 μM/s. Adding the ATP
spent during stimuli-associated sodium removal and the housekeeping
demand, the same authors estimated a total ATP demand of 155 μM/s to
re-establish ions gradient after neurotransmision. Additionally, they
reported that at resting conditions neuronal glucose consumption was
near 0.9 μM/s (in the presence of lactate) and that neuronal glycolytic
rate increases 2.353 times after stimulation. This yields a glycolytic
rate of 2.1177 μM/s in stimulated neurons. Overall, a stimulated neuron
must cope with an ATP demand of 155 μM/s having a glycolytic rate of
2.1177 μM/s. Considering this glycolytic rate of 2.1177 μM/s and an
energy yield of 31 molecules of ATP per glucose [[267]6], the neuronal
metabolism roughly produces 66 μM/s of ATP. The rest of the required
ATP is achieved via lactate uptake, where lactate is supplied by
astrocytes [[268]75]. Such lactate production in astrocytes is
associated with an astrocytic glycolic flux that is triggered under
neuronal stimulation [[269]26]. We used Flux Balance Analysis (FBA) to
fit the astrocytic glycolytic flux to meet the neuronal ATP demand of
155 μM/s, and hence the sodium efflux of 350 μM/s. Astrocytic oxygen
uptake was fixed at 0.01666 μM/s as reported in experiments where
astrocytes are co-cultured with neurons that undergo stimulation
[[270]22]. Hence, we computed the optimal metabolic state using the
latter astrocytic oxygen uptake rate along with the fitted astrocytic
glycolytic rate, the neuronal glycolytic rate, and the housekeeping ATP
demand as flux constraints.
Phenotypic phase plane analysis
In this analysis, a non-zero slope of the planes means that the optimal
state depends on the given substrates [[271]76]. We computed the
phenotypic phase planes as follows:
[MATH: For (
boxygen, i) in (<
/mtext>ratesoxygen1… n): For
(bglucose, i) in
(ratesglucose1… m):<
/mtr>
Maximize z = cT
υ Subject toSυ =
0Lb≤ υ ≤
Ubboxygen≤ υoxyge
n≤ b
oxygenbglucose≤ υgluc
ose≤ bglucose Phpp (<
/mtext>i,j, :
) := υ EndEnd<
mrow>(Eq. 1)
:MATH]
here, rates[oxygen] and rates[glucose] are ranges of uptake rates which
may be of any length. For details, the theoretical basics of the Flux
Balance Analysis (FBA) are presented in the [272]Supplementary
Theoretical Framework Sections 1.2–[273]1.4. The term z = c^Tv
correspond to the metabolic objective of the FBA, which correspond to a
linear combination of the fluxes v weighted by c^T Specifically, the
vector c has ones for the reactions shown in [274]Figure 2A–[275]2C,
and zero for the rest. The stoichiometric matrix is denoted as S, and
the equality constraint Sv = 0 corresponds to the mass-balance at
steady state. The vectors L[b], U[b], b[oxygen], b[glucose] are bounds
for the inequality constraints, respectively, these correspond to the
full-length lower bounds, full-length upper bounds, lower bound for
oxygen uptake rate, and lower bound for glucose uptake rate. The flux
variables v[oxygen] and v[glucose] correspond to oxygen uptake rate,
and glucose uptake rate. The term Phpp is a tensor where the first two
dimensions are of the corresponding lengths of the uptake ranges. The
third dimension of the tensor Phpp is the number of fluxes in the
model. From this tensor, we extracted the phenotypic phase planes shown
in [276]Figure 3D–[277]3G.
Sensitivity analysis of the FBA
The sensitivity analysis was carried out over the solution of the FBA.
This was done via calculation of what is known as the reduced cost
vector [[278]77, [279]78]. Each value of this vector indicates the
amount by which the objective function changes upon an increase in a
given flux. Thus, a reduced cost (δ[i]) is the sensitivity of the
objective function z with respect to a change in the ith flux value
(v[i]).
[MATH: δi = ∂z∂υi
(Eq. 2) :MATH]
Hence, a group of reactions able to perturb the optimal response may be
identified. This group comprised of reactions having non-zero δ[i],
being named as the sensitivity set. This group acts as an “interface”
able to send fast perturbations to the optimal state.
Absolute optimality
We constructed an index of reaction importance in the context of the
optimal metabolic response. This index was called Absolute Optimality
(AO) and corresponds to the L2 norm of a vector composed by normalized
flux and normalized sensitivity. We normalized flux and sensitivity in
order to get standardized positive values. Such a normalization
consisted in applying the Signed Pseudo Logarithm and rescaling the
values to a zero-one range (scaler):
[MATH: N(z) = scaler asinh
(z/2)2/log210<
mrow>(Eq. 3) :MATH]
where z corresponds to any given flux or sensitivity. Then, the AO for
the i reaction is:
[MATH: AOi =[N (υi), N (δi)]2(Eq. 4) :MATH]
where v[i] and δ[i] corresponds to the flux and sensitivity of the i
reaction, respectively.
Absolute centrality contribution
We carried out centrality analysis over the reaction projection of the
stoichiometric matrix. The projection of the stoichiometric matrix is
explained in detail in the [280]Supplementary Theoretical Framework
Section 2.2. It is worth noting that we did not only assess the
centrality of the reactions involved in the sensitivity set. Rather, we
determined how other nodes contribute to the centrality of the
reactions involved in the sensitivity set. In this sense, we build from
the concept of induced centrality, which views a node’s centrality as a
measure of its contribution to another node’s centrality [[281]79].
Formally, induced centrality accounts for the contribution of any node
to the network's cohesiveness, where cohesiveness is defined as the
aggregation of all nodes’ centrality scores. Induced centrality is
computed by taking any centrality metric and aggregating all node
scores (averaging them, for instance) to get a baseline measure of
network cohesiveness, and then recalculating the aggregation without
the node of interest. The difference between the baseline and the
recalculation yields the induced centrality of the node of interest. We
adapted this procedure to our ends. Instead of taking the centrality
scores of all nodes, we only took sensitivity nodes and aggregated them
via arithmetic mean. Also, instead of using the difference, we used the
fold change between the baseline and the recalculation. Formally, our
implementation of the node induced centrality defines the basal
centrality of the sensitivity set as the mean of its node centralities:
[MATH: Cbasal<
/mtext>= 1<
mi>k∑i = 0kci ∀i∈ s(Eq. 5) :MATH]
Where C is any given centrality metric (eigenvector, closeness or
information), and k is the numbers of members of the sensitivity set
(s), while C[i] is the centrality of a member of the sensitivity set.
Next, we defined the perturbed centrality of the sensitivity set as the
same mean but recalculated without node x,
[MATH: C−x = <
mtext>1k∑i = 0kCi −x ∀i∈<
/mo>s(Eq. 6) :MATH]
here, c[i] – {x} refers to the recalculated centrality (centrality
without x) of a member of sensitivity set, and C[−x] is the perturbed
centrality. Then, we computed the induced centrality of node x as,
[MATH: Icx = log2Cbasal
C−x(Eq. 7) :MATH]
where centrality C can be eigenvector, closeness or information
centrality. I[c](x) is the contribution of node x to the centrality of
sensitivity set. Next, we normalized these data in order to get
standardized positive values. To such an end, we employed [282]Eq. 3.
Induced centrality may be calculated by using centrality metrics that
inform on the probability of getting an interaction (eigenvector) or
calculated via centralities associated with the cost of such
interaction (closeness or information centrality). Details on the
concept of probability and cost-associated centralities can be found in
the [283]Supplementary Theoretical Section 3. Finally, we added the
normalized cost-associated induced centralities into one quantity (C[S]
(x)), and for consistency, normalized eigenvector induced-centrality
was renamed as P[s](x).
[MATH: Ps
x=NI
eigenve
ctorxCsx=NIclosenessx+N<
mrow>Iinformati
onx<
/mfenced>(Eq. 8) :MATH]
here, subscripts s highlight the fact that induced centralities are
defined regarding the sensitivity set. Finally, we computed the
Absolute Centrality Contribution (ACC) as the L2 norm of a vector
compose by the probability and the cost,
[MATH: ACCsx= Ps(
x), C
s(x)
2(Eq. 9) :MATH]
here, ACC[s](x) encodes the contribution of node x to the availability
of the sensitivity set (s) to have interactions with the rest of the
network.
Pairwise correlations between nodal contributions
Correlations were calculated using Pearson’s coefficient via its
implementation in the R language. Non-parametric coefficients were not
necessary as each node was related only to four data points, each one
corresponding a different I[c](x).
Hierarchical clustering
We used unsupervised hierarchical clustering to verify the opposite
regulation found between neurons and astrocytes regarding their induced
centralities. In this sense, we determined if the clusterization of
nodal contributions resembles the two-cell structure (neuron-astrocyte)
of the network. To this end, each reaction was regarded as a variable
while its four induced centralities were regarded as samples. Hence, we
computed the correlation matrix between reactions. If there is opposite
regulation, the neuron-astrocyte structure should emerge from
unsupervised clusterization of the correlation matrix. Hierarchical
clustering was done by using euclidean norm to compute distances, and
complete-linkage as agglomeration method. The PCA was applied according
to standard implementation.
Genes associated with reactions
Each reaction (enzyme or transporter) is associated with some gene or
group of genes. We manually annotated those genes by using the Virtual
Metabolic Human website ([284]https://www.vmh.life), which is a
database based on information provided by constraint-based
stoichiometric models of human metabolism [[285]80].
Software, programming languages and libraries
Pathway visualizations shown in [286]Figure 2A–[287]2C were done using
Escher ([288]https://escher.github.io/). Phenotypic phase planes
(PhPPs) were computed in the Python language using CobraPy
([289]https://opencobra.github.io/cobrapy/). All statistical tests
(wilcoxon) were carried out employing the ggplot2 built-in function
stat_compare_means. All plots shown in [290]Figures 3 and [291]4 were
composed and rendered using the R language
([292]https://www.r-project.org/) employing the library ggplot2
([293]https://ggplot2.tidyverse.org/), except for the network
visualizations shown in [294]Figure 3H, [295]3I (left-side), and
[296]3J (left-side) which were made in Python using graph-tool
([297]https://graph-tool.skewed.de/). Hierarchical clustering and
heatmap were done using the R library ComplexHeatmap
([298]https://jokergoo.github.io/ComplexHeatmap-reference/). In the
same manner, PCA was carried out in R by using the library PCAtools
([299]https://github.com/kevinblighe/PCAtools).
Code availability
The code to replicate the results presented in [300]Figures 3 and
[301]4 is available under prior solicitation to the corresponding
author.
High-performance computing software and infrastructure
This research was partially supported by the supercomputing
infrastructure of the National Laboratory for High Performance
Computing (NLHPC) of Chile (ECM-02). Distributed computing was
implemented by using Python package Ray ([302]https://docs.ray.io/).
Extracting differential gene expression values from databases
Genes displaying differential abundance in response to glutamatergic
neurotransmission were extracted from [303]Supplementary Material
reported in [[304]12], using the following threshold reported for the
astrocyte: fold-change (stimulated/basal) ≥1.3 or ≤0.77 and
p-adjusted-SSS-value <0.05. Differentially abundant genes reported with
or without TBOA treatment were merged into a single gene set. For the
neuron, the same parameters were used for comparable results. The same
procedure was used to extract genes showing differential abundance in
response to brain aging in the astrocyte and neuron [[305]25]. We used
the threshold reported by the authors at: age coefficient threshold at
0.005 reported by authors as equivalent to a 10%-fold change and an FDR
cutoff of 0.01. Given that the abovementioned studies used different
RNA-seq approaches (Hasel and et al. [[306]12] performed RNA-Seq of
whole cell samples, while the Tabula Muris Consortium [[307]25] used
single-cell RNA-Seq), we used the fold-change reported by the authors
as significant differential expression and separated each group into up
or downregulated after glutamatergic neurotransmission or brain aging,
in each cell type.
Mouse ortholog search for hub genes
Hub associated genes; denominated hub genes were originally linked to a
human entrez gene ID (see above). We used the g:Profiler tool [[308]81,
[309]82] at [310]https://biit.cs.ut.ee/gprofiler/gost, and used the
g:Orth Orthology search tool to transform human entrez gene IDs into
mouse orthologs. This tool delivers the official gene symbol and
Ensembl mouse IDs. The resulting mouse ortholog set was cross
referenced with the hub genes set, and the intersection resulted in the
four differential hub gene sets: (1) Differential hub genes after
glutamatergic neurotransmission in the: (a) Neuron, (b) Astrocyte; and
(2) Differential hub genes regulated during brain aging (aged/young) in
the: (a) Neuron, (b) Astrocyte.
KEGG pathway enrichment analysis
The ClueGO [[311]83] plugin in Cytoscape [[312]84] was used. Mus
musculus (10090) was selected, and for each subset mentioned above (1a,
1b, 2a, 2b), a separate analysis was performed, using two clusters: one
for upregulated genes and the second for downregulated genes. The KEGG
database from 13 May 2021 was used, the minimum number of genes per
cluster was set as 2, and all other parameters were left as default.
Resulting enriched KEGG pathways were manually curated to exclude terms
that were unrelated to the nervous systems (see [313]Supplementary
Figures 3–[314]6 for uncurated files).
Gene-by-gene functional annotation and identification of aging-associated
terms and genes
Functional annotation for all differential hub genes was obtained from
the DAVID Bioinformatics Resources database [[315]85]. The annotated
differential hub gene list was then searched for the terms “aging”,
“senescence” and “longevity”, and only terms including the words
“aging” and “longevity” were found.
Supplementary Materials
Supplementary Theoretical Framework
[316]aging-15-204663-s001.pdf^ (1.2MB, pdf)
Supplementary Figures
[317]aging-15-204663-s002.pdf^ (977KB, pdf)
Supplementary Tables 1 and 9
[318]aging-15-204663-s003.pdf^ (181.7KB, pdf)
Supplementary Tables 2-7
[319]aging-15-204663-s004.xlsx^ (11.6MB, xlsx)
Supplementary Table 8
[320]aging-15-204663-s005.xlsx^ (304.6KB, xlsx)
ACKNOWLEDGMENTS