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\begin{document}

\title{The elusive pathways behind montelukast's repurposing for Alzheimer's
disease}



\author[1]{Cátia Marques}%
\author[2]{M. Matilde Marques}%
\author[1]{Goncalo Justino}%
\affil[1]{Universidade de Lisboa Centro de Química Estrutural}%
\affil[2]{Universidade de Lisboa Instituto Superior Técnico}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\selectlanguage{english}
\begin{abstract}
Background and purpose: Montelukast, an antagonist of the cysteinyl
leukotrienes receptor 1, has been proposed for repurposing for the
treatment of neurodegenerative disorders, including Alzheimer's disease.
Clinical trials are ongoing but the mechanisms supporting this
repurposing are still poorly understood. Experimental Approach: Taking
advantage of proteomics datasets deposited in public repositories, data
from mouse brain and a neuronal chicken model exposed to the drug were
reinterpreted in view of the repurposing proposal. Key Results:
Montelukast increases the levels of presenilin 1, nicastrin, neprilysin,
and insulin-degrading enzyme, all of which are involved in the amyloid
aggregation and clearance processes. Hexokinase 1, malate and isocitrate
dehydrogenate enzymes, from central metabolism pathways, are also
affected. Conclusions and Implications: The data suggest that
montelukast is a modulator of the amyloid clearance process, favouring
the removal of aggregates and counterbalancing the overall amyloidogenic
process. Montelukast also acts on energy supply pathways, compensating
the ageing-associated decrease of the basal cell metabolism. Taken
together, these actions of montelukast clearly support its repurposing
as a candidate for Alzheimer's disease management.%
\end{abstract}%



\sloppy


\section*{The elusive pathways behind montelukast's repurposing for
Alzheimer's
disease}

{\label{the-elusive-pathways-behind-montelukasts-repurposing-for-alzheimers-disease}}

C\selectlanguage{ngerman}átia F. Marques\textsuperscript{a}, M. Matilde
Marques\textsuperscript{a,b}, Gonçalo C. Justino\textsuperscript{a}

\textsuperscript{a}Centro de Química Estrutural, Institute of Molecular
Sciences, Instituto Superior Técnico, Universidade de Lisboa, Lisboa,
Portugal.

\textsuperscript{b}Departamento de Engenharia Química, Instituto
Superior Técnico, Universidade de Lisboa, Lisboa, Portugal.

\textbf{Address:} Centro de Química Estrutural, Instituto Superior
Técnico

Av. Rovisco Pais, 1

1049-001 Lisboa, Portugal

\textbf{Corresponding author:}

Cátia F. Marques

e-mail: catiafmarques@tecnico.ulisboa.pt

\section*{Abstract}

{\label{abstract}}

\textbf{Background and purpose:} Montelukast, an antagonist of the
cysteinyl leukotrienes receptor~1, has been proposed for repurposing for
the treatment of neurodegenerative disorders, including Alzheimer's
disease. Clinical trials are ongoing but the mechanisms supporting this
repurposing are still poorly understood.

\textbf{Experimental Approach:} Taking advantage of proteomics datasets
deposited in public repositories, data from mouse brain and a neuronal
chicken model exposed to the drug were reinterpreted in view of the
repurposing proposal.

\textbf{Key Results:} Montelukast increases the levels of presenilin 1,
nicastrin, neprilysin, and insulin-degrading enzyme, all of which are
involved in the amyloid aggregation and clearance processes. Hexokinase
1, malate and isocitrate dehydrogenate enzymes, from central metabolism
pathways, are also affected.

\textbf{Conclusions and Implications:} The data suggest that montelukast
is a modulator of the amyloid clearance process, favouring the removal
of aggregates and counterbalancing the overall amyloidogenic process.
Montelukast also acts on energy supply pathways, compensating the
ageing-associated decrease of the basal cell metabolism. Taken together,
these actions of montelukast clearly support its repurposing as a
candidate for Alzheimer's disease management.

\section*{Keywords}

{\label{keywords}}

Montelukast, repurposing, Alzheimer's disease, proteomics.

\section*{Bullet point summary}

{\label{bullet-point-summary}}

What is already known

Montelukast is an antagonist of the cysteinyl leukotrienes receptor~1,
widely used in asthma management.

Montelukast has been identified as an inhibitor of new targets involved
in neurodegenerative disorders.

What this study adds

Montelukast modulates the amyloid clearance pathway.

Central catabolic pathways are also modulated by montelukast.

Clinical significance

The identified pathways affected by montelukast support the drug's
repurposing for Alzheimer's disease management.

\section*{Introduction}

{\label{introduction}}

Montelukast (MTK, 1-({[}(1(\emph{R}
)-(3-(2-(7-chloro-2-quinolinyl)-(\emph{E}
)-ethenyl)phenyl)-3-(2-(1-hydroxy-1-methylethyl)phenyl)propyl)thio{]}methyl)
cyclopropylacetic acid) is an antagonist of the cysteinyl leukotrienes
receptor 1 (CysLTR\textsubscript{1}), widely used by patients with
asthma and allergic rhinitis to manage their symptoms. In addition to
CysLTR\textsubscript{1} inhibition, MTK has been identified as an
inhibitor of new targets, including 5-lipoxygenase (5-LOX)
(Ramires\emph{et al.} , 2004), as well as of the
CysLTR\textsubscript{2}, P2Y\textsubscript{12} (Trinh \emph{et al.} ,
2019), and GPR17 (Marschallinger \emph{et al.} , 2015) receptors,
suggesting that MTK can be exploited in other pathologies. In fact, MTK
has been proposed for repurposing in a number of other diseases,
particularly neurodegenerative disorders, including Alzheimer's disease
(AD) (Lai \emph{et al.} , 2014a; Lai \emph{et al.} , 2014b;
Marschallinger\emph{et al.} , 2015), Parkinson's disease (Jang \emph{et
al.} , 2017; Mansour \emph{et al.} , 2018; Nagarajan and Marathe, 2018;
Wallin and Svenningsson, 2021; Zhang \emph{et al.} , 2014) and
Huntington's disease (Kalonia \emph{et al.} , 2010). Recent studies have
identified MTK as being able to improve cognitive and neurological
functions due to its modulation role in the inflammatory and apoptotic
cascades involved in neurodegenerative features, particularly those
where TNF\selectlanguage{english}-\selectlanguage{greek}α, \selectlanguage{english}NF-\selectlanguage{greek}κ\selectlanguage{english}B, caspase-3, Bcl-2, MAPK, and IL-1\selectlanguage{greek}β \selectlanguage{english}participate.
Moreover, MTK appears to lead to a decrease in \selectlanguage{greek}α\selectlanguage{english}-\selectlanguage{english}synuclein load and in
A\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1-42}induced neurotoxicity, as well as to modulate the
oxidative stress associated with a dysregulation of the GSH/GSSG balance
or of superoxide dismutase activity - two key factors in the maintenance
of redox homeostasis (Grinde \emph{et al.} , 2021; Jang \emph{et al.} ,
2017; Kalonia \emph{et al.} , 2010; Lai \emph{et al.} , 2014a; Lai
\emph{et al.} , 2014b; Mansour \emph{et al.} , 2018; Marschallinger
\emph{et al.} , 2020; Marschallinger \emph{et al.} , 2015; Michael
\emph{et al.} , 2021; Nagarajan and Marathe, 2018; Zhang \emph{et al.} ,
2016).

Clinical trials are ongoing to assess the effects of MTK in
neurodegenerative disorders, including Alzheimer's disease. However, the
mechanisms responsible for such effects are still poorly explored.

This work aims to interrogate proteomics data in order to identify
biological pathways affected by montelukast that may support its
repurposing for AD management. Toward this end, a reinterpretation
strategy based on proteomics datasets originated form our previous work
(Marques \emph{et al.} , 2022d) and deposited in public repositories
(Marques \emph{et al.} , 2022a; Marques \emph{et al.} , 2022b) was
followed. Thus, raw mass spectrometry-based proteomics data from mice
and from chicken neuron cultures treated with MTK were reinterpreted in
order to identify potential mechanisms supporting MTK's repurposing for
neurodegenerative diseases. One of the major features of omics studies
consists of the generation of big data sets; however, the huge
information contained in these data is systematically under-analysed. A
data-reinterpretation strategy not only allows extracting further
information from the same data sets, but also favours data dissemination
among the scientific community, reducing the costs associated with
research projects, as well as the laboratory resources and waste.
Besides, this strategy also contributes to the 3Rs principles of animal
research (Russell and Burch, 1959) since no additional animals were
employed.

\section*{Experimental procedures}

{\label{experimental-procedures}}

Mouse proteomics assays are described in detail in Marques \emph{et
al.}(2022d). Briefly, a group of healthy male C57BL/6J mice was treated
with 1~mg kg\textsuperscript{-1} MTK by oral gavage, once a day, for a
week. Brain, hippocampus, and prefrontal cortex were collected and used
for protein extraction. Samples were analysed by ultra-performance
liquid chromatography coupled to high resolution mass spectrometry with
electrospray ionization UPLC-HRMS-ESI and the raw data were deposited in
public data repositories (Marques \emph{et al.} , 2022b). The
well-characterized C57BL/6J mouse strain is the strain more commonly
used for safety and toxicity assessment, and is also refractory to the
development of many tumours, making it an ideal strain for metabolic and
omics studies on non-tumorigenic toxic effects (Kalueff and Nguyen,
2014; Mouse~Genome~Sequencing~Consortium, 2002; Sarsani \emph{et al.} ,
2019).

A detailed description of neuron proteomic assays is also available in
Marques \emph{et al.} (2022d). A chicken neuron-enriched cell model was
exposed to MTK and cells were collected for protein extraction. Samples
were analysed by UPLC-HRMS-ESI and raw data deposited in public data
repositories (Marques \emph{et al.} , 2022a). Brain-expressed chicken
amyloid-\selectlanguage{greek}β \selectlanguage{english}has a higher homology to the human form than that of mouse.
Since amyloid-\selectlanguage{greek}β \selectlanguage{english}is one the proteins involved in Alzheimer's disease, for
which the repurposing of MTK is being considered, we developed an
optimised chicken-based neuron model (Carrodeguas \emph{et al.} , 2005)
for assessment of MTK-driven proteomics alterations.

\subsection*{Data processing}

{\label{data-processing}}

Raw data were re-processed according to the parameters presented in
Marques \emph{et al.} (2022d). Mouse and chicken proteomics data were
analysed on MaxQuant software v2.0.3.0 (Cox and Mann, 2008;
Tyanova\emph{et al.} , 2016a) using the internal search engine Andromeda
(Cox\emph{et al.} , 2011), and Uniprot (UniProt~Consortium, 2020)
databases restricted to specific groups of proteins from \emph{Mus
musculus}(Proteome UP000000589; keywords ``\emph{ADRs (anxiety + stress
+ sleep + depression + suicide) + apoptosis + inflammation +
leukotrienes + metabolism + microglia + neurons + repurposing (amyloid,
synuclein, tau, APP, BACE, Psen1, Psen2, nicastrin, neprilysin, adam
17)'' + tissue name} '') or \emph{Gallus gallus} (Proteome UP000000539,
keywords ``\emph{ADRs (anxiety + stress + sleep) + inflammation +
leukotrienes + neurons + repurposing (amyloid, synuclein, tau, APP,
BACE, Psen1, Psen2, nicastrin, neprilysin, adam 17} )''). MaxQuant
output was processed using Perseus v1.6.15.0, with default settings
(Tyanova \emph{et al.} , 2016b). Significantly altered proteins
(\textbar{}fold change\textbar{}~[?]~1.5 and \emph{p} ~\textless{}~0.05)
were annotated using PANTHER v16.0 and\emph{Mus musculus} or
\emph{Gallus gallus} reference lists (Mi \emph{et al.} , 2021).

\section*{Results}

{\label{results}}

The present work reanalyses proteomics data available in public
repositories, focusing on elucidating the biological mechanisms that
support MTK's repurposing for AD management. With that purpose, 1) data
obtained from different tissues (brain, hippocampus, and prefrontal
cortex) collected from mice treated orally with daily doses of 1~mg
kg\textsuperscript{-1} MTK for one week (Marques \emph{et al.} , 2022b),
and 2) data obtained from chicken embryo neurons exposed to 1 and 5~\selectlanguage{greek}μ\selectlanguage{english}M
MTK for 48 h (Marques \emph{et al.} , 2022a) were thoroughly revisited.

\subsection*{Brain proteomics from MTK-treated
mice}

{\label{brain-proteomics-from-mtk-treated-mice}}

Proteomics data from mouse brain tissue (Figure 1A) show that MTK is
able to up-regulate the neutral cholesterol ester hydrolase 1 (Nceh1,
3.33-fold higher in MTK-treated mice) and the transcription factor
HIVEP3 (3.32-fold higher in MTK-treated mice).

\textbf{FIGURE 1 HERE}

Figure 1. Altered proteins with an important role in Alzheimer's disease
identified in mouse tissues. This figure illustrates the heatmap of
identified altered proteins (\emph{p} \textless{} 0.05 and at least
1.5-fold change) in the (A) brain, (B) hippocampus, and (C) prefrontal
cortex of mice treated daily with 1 mg kg\textsuperscript{-1} MTK for
one week. The LFQ intensity scale represents the normalised relative
quantification across all samples. Adapted from Marques \emph{et al.}
(2022d), with permission from Elsevier.

\subsection*{Hippocampus proteomics of MTK-treated
mice}

{\label{hippocampus-proteomics-of-mtk-treated-mice}}

Regarding the hippocampus (Figure 1B), some identified proteins could be
interesting from a repurposing perspective, particularly presenilin 1
(Psen1). This protein, one of the components of the \selectlanguage{greek}γ\selectlanguage{english}-\selectlanguage{english}secretase complex
that is involved in the final cleavage of the \selectlanguage{greek}β-\selectlanguage{english}C-terminal fragment
(amyloidogenic pathway), originating the A\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1-40} and
A\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1-42} peptides (Chen \emph{et al.} , 2017; De
Strooper\emph{et al.} , 2010), was found to be up-regulated in the
hippocampus (2.28-fold higher) of MTK treated mice. High levels of
protrudin, a membrane protein that regulates polarized vesicular
trafficking in neurons (3.93-fold higher in MTK-treated mice), were also
found in hippocampus tissue. This protein has been associated with
beneficial effects on the development of axon growth, particularly in
regeneration (Petrova \emph{et al.} , 2020). Finally, polyunsaturated
fatty acid 5-lipoxygenase (5-LOX), a protein involved in the production
of leukotrienes, was found to be down-regulated in the hippocampus of
treated animals (1.81-fold lower in MTK treated mice).

\subsection*{Prefrontal cortex proteomics of MTK-treated
mice}

{\label{prefrontal-cortex-proteomics-of-mtk-treated-mice}}

Prefrontal cortex (PFC) proteomics (Figure 1C) also provided interesting
data regarding relevant proteins in Alzheimer's disease. The 14-3-3
protein family is among the most abundant proteins expressed in the
brain, binding specific phosphoserine- and phosphothreonine-containing
motifs from kinases, phosphatases, and transcription factors. These
protein-protein interactions regulate cellular processes such as cell
cycle, transcription, intracellular trafficking, apoptosis, and
autophagy (Gu \emph{et al.} , 2020); in neurons, these proteins are
involved in differentiation, migration, survival, neurite growth, and
ion channel regulation (Gu \emph{et al.} , 2020). From the seven 14-3-3
isoforms identified in the human frontal cortex, five {[}\selectlanguage{greek}η \selectlanguage{english}(4.72-fold),
\selectlanguage{greek}γ \selectlanguage{english}(5.68-fold), \selectlanguage{greek}ε \selectlanguage{english}(6.43-fold), \selectlanguage{greek}ζ/δ \selectlanguage{english}(7.38-fold), and \selectlanguage{greek}β/α \selectlanguage{english}(4.45-fold){]}
were found to be up-regulated in the PFC of treated mice, indicating an
up-regulation of this family of proteins upon MTK treatment.

Another significantly altered protein is hexokinase 1 (HK1, 7.17-fold
higher in MTK-treated mice). The brain is the organ that consumes the
greatest amount of energy, and neurons require large amounts of energy
to maintain their normal activity (Yan \emph{et al.} , 2020). Thus, HK1
is fundamental for glucose conversion to glucose-6-phosphate, the first
step in glycolysis. Moreover, the tricarboxylic acid cycle enzymes
malate dehydrogenase 1 (5.41-fold higher), and isocitrate dehydrogenase
subunit \selectlanguage{greek}α \selectlanguage{english}(3.92-fold higher) were also found to be up-regulated, likely
due to the feed-forward glycolytic stimulation.

Regarding proteins involved in A\selectlanguage{greek}β \selectlanguage{english}processing, the reticulon-3 protein, a
negative regulator of BACE1 (Deng \emph{et al.} , 2013; Kume \emph{et
al.} , 2009), was found to be 5.62-fold up-regulated in the PFC of
MTK-treated mice, similarly to the insulin-degrading enzyme (3.78-fold
higher in MTK-treated mice) and neprilysin (3.43-fold higher in
MTK-treated mice). Conversely, the subunit of the \selectlanguage{greek}γ\selectlanguage{english}-\selectlanguage{english}secretase,
nicastrin, was found to be decreased in MTK-treated mice (1.60-fold
lower).

The reinterpretation of proteomics results from brain, hippocampus and
PFC of MTK-treated mice shows that energy generating pathways and
amyloid clearance processes are the mechanisms whose modulation in the
brain would most likely be affected by MTK, providing a rationale for a
possible impact of MTK on the management of neurodegenerative disorders.

\subsection*{Altered proteins in chicken embryo neurons exposed to
MTK}

{\label{altered-proteins-in-chicken-embryo-neurons-exposed-to-mtk}}

To further explore MTK's effect on neuronal viability, mature isolated
embryonic chicken neurons treated with MTK were collected and their
proteome was analysed as before.

The data from mature neurons treated with 1~\selectlanguage{greek}μ\selectlanguage{english}M MTK for 48~h suggested
that MTK interferes with the \selectlanguage{greek}α\selectlanguage{english}-\selectlanguage{english}adrenergic signalling pathway
(Marques\emph{et al.} , 2022d). This pathway involves various G-protein
coupled receptors that are targeted by catecholamines such as adrenaline
and noradrenaline, modulating the synaptic transmission, as well as
learning and memory (Perez, 2020); however, no biological processes
specifically associated to the etiology or progression of AD were
identified as being dysregulated.

By contrast, when mature neurons were exposed to 5~\selectlanguage{greek}μ\selectlanguage{english}M MTK for 48~h
(Marques \emph{et al.} , 2022d), we were able to follow a
pathway-enrichment strategy, using the altered proteins (\textbf{Figure
2} ). The data indicate that MTK interferes with microtubule
polymerization and depolymerisation, as well as neurogenesis and the
development of the nervous system (not shown). Furthermore, pathway
enrichment also points to alterations in the AD presenilin-dependent
pathway. Five altered proteins were found in this enriched pathway:
amyloid-\selectlanguage{greek}β \selectlanguage{english}A4 (APP, 2.04-fold higher), Wnt-11 (1.84-fold higher), Wnt5a
(1.66-fold higher), histone acetyltransferase (KAT7) (1.56-fold higher),
and tumour necrosis factor \selectlanguage{greek}α \selectlanguage{english}converting enzyme (a subunit of the
\selectlanguage{greek}α\selectlanguage{english}-\selectlanguage{english}secretase complex known as ADAM17, 1.72-fold lower).

The peroxisome proliferator-activated receptor-\selectlanguage{greek}γ \selectlanguage{english}coactivator 1\selectlanguage{greek}α
\selectlanguage{english}(PGC-1\selectlanguage{greek}α\selectlanguage{english}), a regulator of liver gluconeogenesis, was also altered in
treated neurons (1.90-fold higher in mature neurons exposed to 5~\selectlanguage{greek}μ\selectlanguage{english}M
MTK). The glucagon-like peptide 1 receptor, involved in energy supply
(Athauda and Foltynie, 2016; Li \emph{et al.} , 2010), was also found to
be up-regulated in treated neurons (2.86-fold times higher).

\textbf{FIGURE 2 HERE}

Figure 2. Altered proteins with an important role in Alzheimer's disease
identified in chicken embryo neurons. This figure illustrates the
heatmap of identified altered proteins (\emph{p} \textless{} 0.05 and at
least 1.5-fold change) in mature isolated embryonic-chicken neurons
exposed to 5~\selectlanguage{greek}μ\selectlanguage{english}M MTK. The LFQ intensity scale represents the normalised
relative quantification across all samples. Image adapted from
Marques\emph{et al.} (2022d), with permission from Elsevier.

To conclude, a wide range of proteins were altered in both model systems
(mouse and chicken embryo neurons) and some of these have a direct
contribution to the processes involved in neurodegenerative disorders,
mainly in the aggregation of proteins associated with Alzheimer's and
Parkinson's diseases.

\section*{Discussion and conclusions}

{\label{discussion-and-conclusions}}

Montelukast has been associated with some neuropsychiatric side effects,
especially among children. Our previous work suggests that these adverse
effects may be associated with a dysregulation of the
hypothalamic-pituitary-adrenal axis and an increase of oxidative stress
in children's brain (Marques \emph{et al.} , 2022d). Since older people
have a mature brain, they should be less susceptible to these effects,
which could explain the decreased incidence of adverse drug reactions
(ADRs) in adults. Moreover, specific montelukast effects in the older
brain may underlie the proposed, yet mechanistically elusive,
repurposing of the drug for the management of neurodegenerative
disorders. In the current work, the same proteomics dataset generated in
our previous work was interrogated from a repurposing perspective, to
identify altered proteins known to be involved in the pathophysiology of
Alzheimer's disease.

The up-regulation of Nceh1 and HIVEP3 in the mouse brain suggests that
MTK contributes to AD management. Indeed, while Nceh1 has been
associated with a protective role against \selectlanguage{greek}α\selectlanguage{english}-\selectlanguage{english}synuclein toxicity
(Zhang\emph{et al.} , 2017) and has been found to have decreased levels
in the blood of AD patients (Mandas \emph{et al.} , 2012), HIVEP3 is
underexpressed in a double transgenic APP/PS1 mouse model of early-onset
AD (Islam \emph{et al.} , 2021). Likewise, the hippocampus up-regulation
of Psen1 mimics the effect observed upon \selectlanguage{greek}γ\selectlanguage{english}-\selectlanguage{english}secretase inhibition
(Campanari \emph{et al.} , 2014; Sogorb-Esteve \emph{et al.} , 2018).
This is in agreement with the observed up-regulation, in the PFC, of the
reticulon-3 protein, a known inhibitor of the anterograde transport of
the BACE1 subunit of \selectlanguage{greek}β\selectlanguage{english}-\selectlanguage{english}secretase, thus contributing to an improved
clearance of amyloid deposits (Deng \emph{et al.} , 2013; Kume \emph{et
al.} , 2009).

The insulin-degrading enzyme, up-regulated in mouse PFC, is involved in
the degradation of \selectlanguage{greek}α\selectlanguage{english}-\selectlanguage{english}synuclein aggregates, and its dysregulation has
been related to the occurrence of type 2 diabetes mellitus, whereby the
patients have a high risk of developing AD or PD (Sousa \emph{et al.} ,
2021). Neprilysin was also found to be up-regulated in the PFC of
MTK-treated mice. It has been proposed that the up-regulation of these
two enzymes could be a promising therapeutic strategy for
neurodegenerative disorders (Nalivaeva and Turner, 2019). It is
noteworthy that the glucagon-like peptide 1 receptor, which has been
associated with improved memory and learning in mice (During \emph{et
al.} , 2003), was also overexpressed in MTK-treated mature chicken
embryo neurons supporting the hypothesis of an improvement of the
amyloid clearance process caused by MTK.

The lower levels of nicastrin, coupled to the altered levels of Psen1
(another component of the \selectlanguage{greek}γ\selectlanguage{english}-\selectlanguage{english}secretase complex) in the PFC of MTK-treated
mice, also indicate that MTK may be involved in the inhibition of
\selectlanguage{greek}γ\selectlanguage{english}-\selectlanguage{english}secretase, leading to a decrease of amyloid production. Factoring in
the altered AD presenilin-dependent pathway, dominated by an
up-regulation of the Wnt pathways, observed in treated neurons, MTK
appears to have the potential to overcome the synaptic degeneration and
cognitive deficits characteristic of AD. It is noteworthy that Wnt
proteins are systematically found to be down-regulated in AD patients,
and these lower levels are associated to activation of the amyloidogenic
pathway (Palomer \emph{et al.} , 2019). Thus, the proteomic shifts
associated with MTK have the potential to counterbalance a number of
pro-amyloidogenic mechanisms involved in AD's etiology.

5-LOX is involved in the synthesis of cysteinyl leukotrienes, which have
a pro-inflammatory effect antagonized by MTK (Marques \emph{et al.} ,
2022c). Interestingly, while 5-LOX levels are found to be higher in AD
patients (Li \emph{et al.} , 2017), the observed lower 5-LOX levels in
the mouse hippocampus also suggest that MTK is able to, at least
partially, restore the altered phenotype of AD. This is further
corroborated by the observed up-regulation of the 14-3-3 protein family
in the prefrontal cortex of treated mice. While decreased levels of the
14-3-3 proteins are associated with several AD and PD hallmarks
(Gu\emph{et al.} , 2020), their MTK-driven overexpression is quite
promising, since the modulation of these isoforms has been described as
a potential target for neurodegenerative disorders (Pair and Yacoubian,
2021).

From a central metabolism standpoint, the ageing process is
characterized by a lowering of cell basal metabolic rate, particularly
in the case of neurons (Yan \emph{et al.} , 2020). HK1 levels are
consistently found to be decreased in AD patients (Yan \emph{et al.} ,
2020), whereby the observed increase of HK1 levels caused by MTK could
play a neuroprotective role in the brain. This HK1 increase was observed
in the PFC of treated mice, together with the up-regulation of the
tricarboxylic acid (TCA) cycle enzymes malate dehydrogenase and
isocitrate dehydrogenase. From a metabolic perspective, the observed
overexpression of the glucagon-like peptide 1 receptor in chicken embryo
neurons is a further link between the brain energy pathways and AD, as
this protein is also involved in the clearance of amyloid and synuclein
aggregates. Notably, the glucagon-like peptide 1 receptor has been
proposed as a therapeutic target for neurodegenerative disorders
(Athauda and Foltynie, 2016; Li \emph{et al.} , 2010). The MTK-induced
up-regulation of this receptor observed in our work, and its mechanistic
implications, may thus provide, at least in part, the missing rationale
for the repurposing of MTK in these diseases. It is also noteworthy that
the proteomic alterations of TCA cycle enzymes reported herein are in
agreement with the TCA changes we observed previously at a metabolomics
level (Marques \emph{et al.} , 2022d).

Taken together, the proteomics data reanalysed herein clearly indicate
that montelukast is associated with a shift towards amyloid clearance,
coupled to an up-regulation of specific enzymes involved in energy
production (\textbf{Figure 3} ). These are two fundamental biological
processes that sustain MTK's repurposing towards AD management, which is
currently being assessed in various clinical trials (for example,
ClinicalTrials.gov Identifiers NCT03991988 and NCT03402503). This wide
anti-AD activity of montelukast is reminiscent of reports from studies
on valproic acid, which has been shown to increase neprilysin expression
and activity in hypoxic rats and in model mice, and is speculated to
enhance amyloid clearance in AD patients (Nalivaeva \emph{et al.} ,
2012; Nalivaeva and Turner, 2019; Wang \emph{et al.} , 2014). Overall,
central nervous system-acting drugs of different classes offer new
potential strategies for the management of AD and other neurological
diseases. To be fulfilled, these promising approaches require further
investigation of the biochemical mechanisms underlying such beneficial
side effects.

\textbf{FIGURE 3 HERE}

Figure 3. Pathways identified that support montelukast repurposing.
\textbf{A.} Molecular basis of Alzheimer's disease. Amyloid precursor
protein (APP) can be processed through a non-amyloidogenic pathway,
where it is cleaved by \selectlanguage{greek}α- \selectlanguage{english}and \selectlanguage{greek}γ-\selectlanguage{english}secretases, or through an amyloidogenic
pathway, where it is cleaved by \selectlanguage{greek}β- \selectlanguage{english}and \selectlanguage{greek}γ-\selectlanguage{english}secretases, leading to the
formation of A\selectlanguage{greek}β \selectlanguage{english}peptides (Chen\emph{et al.} , 2017; De Strooper \emph{et
al.} , 2010). A\selectlanguage{greek}β \selectlanguage{english}peptides can then undergo clearance (or degradation)
processes within the brain or after transport from the brain to the
periphery (liver and kidney). These processes include proteolytic
pathways that depend on neprilysin (NE), insulin-degrading enzyme (IDE),
matrix metalloproteinases (MMPs), angiotensin-converting enzyme (ACE),
endothelin-converting enzyme (ECE), plasmin, the activity of the
ubiquitin-proteosome system, the autophagy-lysosome system, or
microglial phagocytosis (Nalivaeva and Turner, 2019; Xin \emph{et al.} ,
2018). Image adapted from Marques\emph{et al.} (2022c). \textbf{B.}
Energy producing glycolytic pathway and tricarboxylic acid cycle (Yan
\emph{et al.} , 2020). Enzymes marked in red represent the MTK-altered
proteins. Created with BioRender.com.

\section*{Author contributions}

{\label{author-contributions}}

C.F.M. conducted the experiments, analysed and interpreted the data;
M.M.M. and G.C.J. supervised the project; C.F.M. wrote the original
draft; M.M.M. and G.C.J. reviewed and edited the manuscript. All authors
have read and agreed to the published version of the manuscript.

\section*{Conflicts of interest}

{\label{conflicts-of-interest}}

All authors declare no conflict of interest.

\section*{Acknowledgment}

{\label{acknowledgment}}

This research was funded by Funda\selectlanguage{ngerman}ção para a Ciência e a Tecnologia
(FCT), grant numbers, PTDC/QUI-QAN/32242/2017, and
RNEM-LISBOA-01-0145-FEDER-022125 (Portuguese Mass Spectrometry Network).
C.F.M. acknowledges a PhD fellowship from Fundação para a Ciência e a
Tecnologia (PD/BD/143128/2019 and COVID/BD/152559/2022). Centro de
Química Estrutural is a Research Unit funded by FCT through projects
UIDB/00100/2020 and UIDP/00100/2020. Institute of Molecular Sciences is
an Associate Laboratory funded by FCT through project LA/P/0056/2020.

\section*{Declaration of transparency and scientific
rigour}

{\label{declaration-of-transparency-and-scientific-rigour}}

This Declaration acknowledges that this paper adheres to the principles
for transparent reporting and scientific rigour of preclinical research
as stated in the BJP guidelines for Design and Analysis, and as
recommended by funding agencies, publishers and other organisations
engaged with supporting research.

\section*{Data availability statement}

{\label{data-availability-statement}}

Data supporting the findings of this study are described in
Marques\emph{et al.} (2022d) and available through public data
repositories (Marques \emph{et al.} , 2022a; Marques \emph{et al.} ,
2022b).

\section*{References}

{\label{references}}

Athauda, D., Foltynie, T. (2016). The glucagon-like peptide 1 (GLP)
receptor as a therapeutic target in Parkinson's disease: mechanisms of
action. \emph{Drug Discov Today} \textbf{21} (5):802-18.

Campanari, M. L., García-Ayllón, M. S., Belbin, O., Galcerán, J., Lleó,
A., Sáez-Valero, J. (2014). Acetylcholinesterase modulates presenilin-1
levels and gamma-secretase activity. \emph{J Alzheimers Dis}\textbf{41}
(3):911-24.

Carrodeguas, J. A., Rodolosse, A., Garza, M. V., Sanz-Clemente, A.,
Pérez-Pé, R., Lacosta, A. M., Domínguez, L., Monleón, I., Sánchez-Díaz,
R., Sorribas, V., Sarasa, M. (2005). The chick embryo appears as a
natural model for research in beta-amyloid precursor protein
processing.\emph{Neuroscience} \textbf{134} (4):1285-1300.

Chen, G. F., Xu, T. H., Yan, Y., Zhou, Y. R., Jiang, Y., Melcher, K.,
Xu, H. E. (2017). Amyloid beta: structure, biology and structure-based
therapeutic development. \emph{Acta Pharmacol Sin}\textbf{38}
(9):1205-1235.

Cox, J., Mann, M. (2008). MaxQuant enables high peptide identification
rates, individualized p.p.b.-range mass accuracies and proteome-wide
protein quantification. \emph{Nat Biotechnol} \textbf{26} (12):1367-72.

Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V.,
Mann, M. (2011). Andromeda: a peptide search engine integrated into the
MaxQuant environment. \emph{J Proteome Res} \textbf{10} (4):1794-805.

De Strooper, B., Vassar, R., Golde, T. (2010). The secretases: enzymes
with therapeutic potential in Alzheimer disease. \emph{Nat Rev
Neurol}\textbf{6} (2):99-107.

Deng, M., He, W., Tan, Y., Han, H., Hu, X., Xia, K., Zhang, Z., Yan, R.
(2013). Increased expression of reticulon 3 in neurons leads to reduced
axonal transport of \selectlanguage{greek}β \selectlanguage{english}site amyloid precursor protein-cleaving enzyme
1.\emph{J Biol Chem} \textbf{288} (42):30236-30245.

During, M. J., Cao, L., Zuzga, D. S., Francis, J. S., Fitzsimons, H. L.,
Jiao, X., Bland, R. J., Klugmann, M., Banks, W. A., Drucker, D. J.,
Haile, C. N. (2003). Glucagon-like peptide-1 receptor is involved in
learning and neuroprotection. \emph{Nat Med} \textbf{9} (9):1173-9.

Grinde, B., Schirmer, H., Eggen, A. E., Aigner, L., Engdahl, B. (2021).
A possible effect of montelukast on neurological aging examined by the
use of register data. \emph{Int J Clin Pharm} \textbf{43} (3):541-548.

Gu, Q., Cuevas, E., Raymick, J., Kanungo, J., Sarkar, S. (2020).
Downregulation of 14-3-3 Proteins in Alzheimer's Disease. \emph{Mol
Neurobiol} \textbf{57} (1):32-40.

Islam, M. R., Kaurani, L., Berulava, T., Heilbronner, U., Budde, M.,
Centeno, T. P., Elerdashvili, V., Zafieriou, M. P., Benito, E., Sertel,
S. M., Goldberg, M., Senner, F., Kalman, J. L., Burkhardt, S., Oepen, A.
S., Sakib, M. S., Kerimoglu, C., Wirths, O., Bickeb\selectlanguage{ngerman}öller, H., Bartels,
C., Brosseron, F., Buerger, K., Cosma, N. C., Fliessbach, K., Heneka, M.
T., Janowitz, D., Kilimann, I., Kleinedam, L., Laske, C., Metzger, C.
D., Munk, M. H., Perneczky, R., Peters, O., Priller, J., Rauchmann, B.
S., Roy, N., Schneider, A., Spottke, A., Spruth, E. J., Teipel, S.,
Tscheuschler, M., Wagner, M., Wiltfang, J., Düzel, E., Jessen, F.,
Delcode Study, G., Rizzoli, S. O., Zimmermann, W. H., Schulze, T. G.,
Falkai, P., Sananbenesi, F., Fischer, A. (2021). A microRNA signature
that correlates with cognition and is a target against cognitive
decline. \emph{EMBO Mol Med} \textbf{13} (11):e13659.

Jang, H., Kim, S., Lee, J. M., Oh, Y.-S., Park, S. M., Kim, S. R.
(2017). Montelukast treatment protects nigral dopaminergic neurons
against microglial activation in the 6-hydroxydopamine mouse model of
Parkinson's disease. \emph{Neuroreport} \textbf{28} (5):242-249.

Kalonia, H., Kumar, P., Kumar, A., Nehru, B. (2010). Protective effect
of montelukast against quinolinic acid/malonic acid induced
neurotoxicity: possible behavioral, biochemical, mitochondrial and tumor
necrosis factor-\selectlanguage{greek}α \selectlanguage{english}level alterations in rats.
\emph{Neuroscience}\textbf{171} (1):284-99.

Kalueff, A. V., Nguyen, M. (2014). Testing anxiolytic drugs in the
C57BL/6J mouse strain. \emph{Journal of Pharmacological and
Toxicological Methods} \textbf{69} (2):205-207.

Kume, H., Konishi, Y., Murayama, K. S., Kametani, F., Araki, W. (2009).
Expression of reticulon 3 in Alzheimer's disease brain.\emph{Neuropathol
Appl Neurobiol} \textbf{35} (2):178-88.

Lai, J., Hu, M., Wang, H., Hu, M., Long, Y., Miao, M. X., Li, J. C.,
Wang, X. B., Kong, L. Y., Hong, H. (2014a). Montelukast targeting the
cysteinyl leukotriene receptor 1 ameliorates
A\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1-42}-induced memory impairment and neuroinflammatory
and apoptotic responses in mice. \emph{Neuropharmacology}\textbf{79}
:707-14.

Lai, J., Mei, Z. L., Wang, H., Hu, M., Long, Y., Miao, M. X., Li, N.,
Hong, H. (2014b). Montelukast rescues primary neurons against
A\selectlanguage{greek}β\selectlanguage{english}\textsubscript{1--42}-induced toxicity through inhibiting
CysLT\textsubscript{1}R-mediated NF-\selectlanguage{greek}κ\selectlanguage{english}B signaling. \emph{Neurochem
Int}\textbf{75} :26-31.

Li, J. G., Barrero, C., Merali, S., Pratic\selectlanguage{ngerman}ò, D. (2017). Five
lipoxygenase hypomethylation mediates the homocysteine effect on
Alzheimer's phenotype. \emph{Sci Rep} \textbf{7} :46002.

Li, Y., Duffy, K. B., Ottinger, M. A., Ray, B., Bailey, J. A., Holloway,
H. W., Tweedie, D., Perry, T., Mattson, M. P., Kapogiannis, D.,
Sambamurti, K., Lahiri, D. K., Greig, N. H. (2010). GLP-1 receptor
stimulation reduces amyloid-beta peptide accumulation and cytotoxicity
in cellular and animal models of Alzheimer's disease. \emph{J Alzheimers
Dis} \textbf{19} (4):1205-19.

Mandas, A., Abete, C., Putzu, P. F., la Colla, P., Dessì, S., Pani, A.
(2012). Changes in cholesterol metabolism-related gene expression in
peripheral blood mononuclear cells from Alzheimer patients. \emph{Lipids
Health Dis} \textbf{11} :39.

Mansour, R. M., Ahmed, M. A. E., El-Sahar, A. E., El Sayed, N. S.
(2018). Montelukast attenuates rotenone-induced microglial
activation/p38 MAPK expression in rats: Possible role of its
antioxidant, anti-inflammatory and antiapoptotic effects. \emph{Toxicol
Appl Pharmacol} \textbf{358} :76-85.

Marques, C., Marques, M. M., Justino, G. C. (2022a). Montelukast effect
on chicken neuron proteomics. \emph{Mendeley Data} , V1,
doi:10.17632/rn8yvtmtxx.1.

Marques, C., Marques, M. M., Justino, G. C. (2022b). Montelukast effect
on mouse proteomics. \emph{Mendeley Data} , V1,
doi:10.17632/6jphhgnrt8.1.

Marques, C. F., Marques, M. M., Justino, G. C. (2022c). Leukotrienes vs.
Montelukast-Activity, Metabolism, and Toxicity Hints for
Repurposing.\emph{Pharmaceuticals} \textbf{15} (9):1039.

Marques, C. F., Marques, M. M., Justino, G. C. (2022d). The mechanisms
underlying montelukast's neuropsychiatric effects - new insights from a
combined metabolic and multiomics approach. \emph{Life
Sciences}\textbf{310} :121056.

Marschallinger, J., Altendorfer, B., Rockenstein, E., Holztrattner, M.,
Garnweidner-Raith, J., Pillichshammer, N., Leister, I., Hutter-Paier,
B., Strempfl, K., Unger, M. S., Chishty, M., Felder, T., Johnson, M.,
Attems, J., Masliah, E., Aigner, L. (2020). The Leukotriene Receptor
Antagonist Montelukast Reduces Alpha-Synuclein Load and Restores Memory
in an Animal Model of Dementia with Lewy Bodies.\emph{Neurotherapeutics}
\textbf{17} (3):1061-1074.

Marschallinger, J., Schäffner, I., Klein, B., Gelfert, R., Rivera, F.
J., Illes, S., Grassner, L., Janssen, M., Rotheneichner, P.,
Schmuckermair, C., Coras, R., Boccazzi, M., Chishty, M., Lagler, F. B.,
Renic, M., Bauer, H.-C., Singewald, N., Blümcke, I., Bogdahn, U.,
Couillard-Despres, S., Lie, D. C., Abbracchio, M. P., Aigner, L. (2015).
Structural and functional rejuvenation of the aged brain by an approved
anti-asthmatic drug. \emph{Nat Commun} \textbf{6} :8466-8466.

Mi, H., Ebert, D., Muruganujan, A., Mills, C., Albou, L.-P.,
Mushayamaha, T., Thomas, P. D. (2021). PANTHER version 16: a revised
family classification, tree-based classification tool, enhancer regions
and extensive API. \emph{Nucleic Acids Res} \textbf{49} (D1):D394-D403.

Michael, J., Zirknitzer, J., Unger, M. S., Poupardin, R., Rieß , T.,
Paiement, N., Zerbe, H., Hutter-Paier, B., Reitsamer, H., Aigner, L.
(2021). The Leukotriene Receptor Antagonist Montelukast Attenuates
Neuroinflammation and Affects Cognition in Transgenic 5xFAD
Mice.\emph{Int J Mol Sci} \textbf{22} (5):2782.

Mouse~Genome~Sequencing~Consortium (2002). Initial sequencing and
comparative analysis of the mouse genome. \emph{Nature}\textbf{420}
(6915):520-562.

Nagarajan, V. B., Marathe, P. A. (2018). Effect of montelukast in
experimental model of Parkinson's disease. \emph{Neurosci
Lett}\textbf{682} :100-105.

Nalivaeva, N. N., Belyaev, N. D., Lewis, D. I., Pickles, A. R., Makova,
N. Z., Bagrova, D. I., Dubrovskaya, N. M., Plesneva, S. A., Zhuravin, I.
A., Turner, A. J. (2012). Effect of Sodium Valproate Administration on
Brain Neprilysin Expression and Memory in Rats. \emph{Journal of
Molecular Neuroscience} \textbf{46} (3):569-577.

Nalivaeva, N. N., Turner, A. J. (2019). Targeting amyloid clearance in
Alzheimer's disease as a therapeutic strategy. \emph{Br J
Pharmacol}\textbf{176} (18):3447-3463.

Pair, F. S., Yacoubian, T. A. (2021). 14-3-3 Proteins: Novel
Pharmacological Targets in Neurodegenerative Diseases. \emph{Trends
Pharmacol Sci} \textbf{42} (4):226-238.

Palomer, E., Buechler, J., Salinas, P. C. (2019). Wnt Signaling
Deregulation in the Aging and Alzheimer's Brain. \emph{Front Cell
Neurosci} \textbf{13} :227.

Perez, D. M. (2020). \selectlanguage{greek}α\selectlanguage{english}1-Adrenergic Receptors in Neurotransmission,
Synaptic Plasticity, and Cognition. \emph{Front Pharmacol}\textbf{11}
:581098.

Petrova, V., Pearson, C. S., Ching, J., Tribble, J. R., Solano, A. G.,
Yang, Y., Love, F. M., Watt, R. J., Osborne, A., Reid, E., Williams, P.
A., Martin, K. R., Geller, H. M., Eva, R., Fawcett, J. W. (2020).
Protrudin functions from the endoplasmic reticulum to support axon
regeneration in the adult CNS. \emph{Nat Commun} \textbf{11} (1):5614.

Ramires, R., Caiaffa, M. F., Tursi, A., Haeggstr\selectlanguage{ngerman}öm, J. Z., Macchia, L.
(2004). Novel inhibitory effect on 5-lipoxygenase activity by the
anti-asthma drug montelukast. \emph{Biochem Biophys Res
Commun}\textbf{324} (2):815-821.

Russell, W. M. S., Burch, R. L. (1959). \emph{The principles of humane
experimental technique} . Methuen \& Co. Limited.: London, UK.

Sarsani, V. K., Raghupathy, N., Fiddes, I. T., Armstrong, J.,
Thibaud-Nissen, F., Zinder, O., Bolisetty, M., Howe, K., Hinerfeld, D.,
Ruan, X., Rowe, L., Barter, M., Ananda, G., Paten, B., Weinstock, G. M.,
Churchill, G. A., Wiles, M. V., Schneider, V. A., Srivastava, A.,
Reinholdt, L. G. (2019). The Genome of C57BL/6J ``Eve'', the Mother of
the Laboratory Mouse Genome Reference Strain. \emph{G3
Genes\textbar{}Genomes\textbar{}Genetics} \textbf{9} (6):1795-1805.

Sogorb-Esteve, A., Garcia-Ayllón, M. S., Llansola, M., Felipo, V.,
Blennow, K., Sáez-Valero, J. (2018). Inhibition of gamma-Secretase Leads
to an Increase in Presenilin-1. \emph{Mol Neurobiol}\textbf{55}
(6):5047-5058.

Sousa, L., Guarda, M., Meneses, M. J., Macedo, M. P., Vicente Miranda,
H. (2021). Insulin-degrading enzyme: an ally against metabolic and
neurodegenerative diseases. \emph{J Pathol} \textbf{255} (4):346-361.

Trinh, H. K. T., Suh, D. H., Nguyen, T. V. T., Choi, Y., Park, H. S.,
Shin, Y. S. (2019). Characterization of cysteinyl leukotriene-related
receptors and their interactions in a mouse model of
asthma.\emph{Prostaglandins Leukot Essent Fatty Acids} \textbf{141}
:17-23.

Tyanova, S., Temu, T., Cox, J. (2016a). The MaxQuant computational
platform for mass spectrometry-based shotgun proteomics. \emph{Nat
Protoc} \textbf{11} (12):2301-2319.

Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger,
T., Mann, M., Cox, J. (2016b). The Perseus computational platform for
comprehensive analysis of (prote)omics data. \emph{Nat
Methods}\textbf{13} (9):731-740.

UniProt~Consortium (2020). UniProt: the universal protein knowledgebase
in 2021. \emph{Nucleic Acids Research} \textbf{49} (D1):D480-D489.

Wallin, J., Svenningsson, P. (2021). Potential Effects of Leukotriene
Receptor Antagonist Montelukast in Treatment of Neuroinflammation in
Parkinson's Disease. \emph{Int J Mol Sci} \textbf{22} (11):5606.

Wang, Z., Zhang, X.-J., Li, T., Li, J., Tang, Y., Le, W. (2014).
Valproic Acid Reduces Neuritic Plaque Formation and Improves Learning
Deficits in APPSwe/PS1A246E Transgenic Mice via Preventing the Prenatal
Hypoxia-Induced Down-Regulation of Neprilysin. \emph{CNS Neuroscience \&
Therapeutics} \textbf{20} (3):209-217.

Xin, S. H., Tan, L., Cao, X., Yu, J. T., Tan, L. (2018). Clearance of
Amyloid Beta and Tau in Alzheimer's Disease: from Mechanisms to
Therapy.\emph{Neurotox Res} \textbf{34} (3):733-748.

Yan, X., Hu, Y., Wang, B., Wang, S., Zhang, X. (2020). Metabolic
Dysregulation Contributes to the Progression of Alzheimer's
Disease.\emph{Front Neurosci} \textbf{14} :530219.

Zhang, C. T., Lin, J. R., Wu, F., Ghosh, A., Tang, S. S., Hu, M., Long,
Y., Sun, H. B., Hong, H. (2016). Montelukast ameliorates
streptozotocin-induced cognitive impairment and neurotoxicity in
mice.\emph{Neurotoxicology} \textbf{57} :214-222.

Zhang, S., Glukhova, S. A., Caldwell, K. A., Caldwell, G. A. (2017).
NCEH-1 modulates cholesterol metabolism and protects against
alpha-synuclein toxicity in a C. elegans model of Parkinson's
disease.\emph{Hum Mol Genet} \textbf{26} (19):3823-3836.

Zhang, X. Y., Chen, L., Yang, Y., Xu, D. M., Zhang, S. R., Li, C. T.,
Zheng, W., Yu, S. Y., Wei, E. Q., Zhang, L. H. (2014). Regulation of
rotenone-induced microglial activation by 5-lipoxygenase and cysteinyl
leukotriene receptor 1. \emph{Brain Res} \textbf{1572} :59-71.\selectlanguage{english}
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