Abstract
Background
Amyotrophic Lateral Sclerosis (ALS) is a rare progressive and chronic
motor neuron degenerative disease for which at present no cure is
available. In recent years, multiple genes encode kinases and other
causative agents for ALS have been identified. Kinases are enzymes that
show pleiotropic nature and regulate different signal transduction
processes and pathways. The dysregulation of kinase activity results in
dramatic changes in processes and causes many other human diseases
including cancers.
Methods
In this study, we have adopted a network-based system biology approach
to investigate the kinase-based molecular interplay between ALS and
other human disorders. A list of 62 ALS-associated-kinases was first
identified and then we identified the disease associated with them by
scanning multiple disease-gene interaction databases to understand the
link between the ALS-associated kinases and other disorders.
Results
An interaction network with 36 kinases and 381 different disorders
associated with them was prepared, which represents the complexity and
the comorbidity associated with the kinases. Further, we have
identified 5 miRNAs targeting the majority of the kinases in the
disease-causing network. The gene ontology and pathways enrichment
analysis of those miRNAs were performed to understand their biological
and molecular functions along with to identify the important pathways.
We also identified 3 drug molecules that can perturb the
disease-causing network by drug repurposing.
Conclusion
This network-based study presented hereby contributes to a better
knowledge of the molecular underpinning of comorbidities associated
with the kinases associated with the ALS disease and provides the
potential therapeutic targets to disrupt the highly complex
disease-causing network.
Keywords: kinases, ALS, cancer, network biology, miRNAs, drugs
Introduction
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative
disease, characterized by the progressive degeneration of upper and
lower motor neurons in the brain, in the brainstem, and in the spinal
region. This neuronal degeneration leads to progressive skeletal muscle
atrophy and death, by respiration failure within 2–5 years from the
onset of symptoms ([39]Alonso et al., 2009). ALS is a heterogeneous
disease where several pathophysiological processes have been
demonstrated to induce neuronal death, including oxidative stress,
mitochondria impairment, growth factor deficiency, neuro-inflammation,
defective axonal transport, RNA metabolism, aberrant stimulation of
kinase activity, impaired brain energy metabolism, autophagy, and
stress-induced cell death ([40]Taylor et al., 2016). Recent studies
also reported several other causative genes that encode for kinases and
are involved in ALS and other neurodegenerative diseases ([41]Guo et
al., 2020).
Kinases are enzymes that function as transferases to catalyze almost
every signal transduction process and pathway by adding a phosphate
group (PO[4]^3−) to hydroxyl groups of substrates such as amino acids,
nucleic acids, as well as lipids ([42]Rask-Andersen et al., 2014).
Based on their substrate binding, kinases are classified into protein,
lipid, and nucleotide kinases. Kinases are involved in several
biochemical reactions associated with proteins, lipids, and nucleotides
metabolism ([43]Higelin et al., 2018). The phosphorylation of protein
via kinases stimulates the majority of the cell life processes, while
the abnormal phosphorylation leads to the consequences of diseases,
such as human cancer initiation and progression. Apart from the
oncological issues, disruptive kinase activity has been demonstrated in
several other human diseases such as immune, neurological, and
infectious diseases ([44]Bhullar et al., 2018). Thus, the discovery of
kinases provides clarity to understand the cellular pathways, disease
mechanisms and to develop their therapeutic drugs.
In this study, we have used a network-based system biology approach to
investigate the kinase-based molecular interplay between ALS and other
human disorders. To date, multiple network-based analysis has been
reported to identify the target genes in the network ([45]Prasad et
al., 2020, [46]2021a,[47]b,[48]c,[49]d). Here, firstly we retrieved 62
ALS-associated kinases from several recent studies ([50]Guo et al.,
2020; [51]García-García et al., 2021; [52]Palomo et al., 2021;
[53]Sahana and Zhang, 2021) and databases including ALSoD ([54]Abel et
al., 2012) and Malacard ([55]Rappaport et al., 2017). The
protein–protein interaction (PPI) network of the identified 62 kinases
was prepared to understand the association between these kinases.
Further using these kinases, we identified their associated diseases by
scanning multiple disease-gene interaction databases to understand the
link between the ALS-associated kinases and other diseases. A
disease-kinase interaction network was prepared to have 36 kinases
associated with 381 different diseases and make a total of 603
disease-kinase interactions, which ultimately indicates the complexity
and comorbidities associated with the ALS-linked kinases. Next, we
explore the miRNAs as a potential therapeutic agent against the
identified disease-causing kinases. We have prepared a miRNA-kinase
interaction network and identified the top 5 miRNAs having interactions
with the majority of the kinases in the network suggesting a potential
therapeutic target. Similarly, we have screened multiple drug-gene
interaction databases to identify drug molecules interacting with the
kinases and finally identified 3 drug molecules having interactions
with the majority of kinases in the network. This study will thus lead
to the identification of potential drug candidates for disrupting the
disease-causing network related to the ALS-associated-kinases.
Materials and methods
Data collection
The list of Genes associated with ALS was retrieved from ALSoD
([56]Abel et al., 2012) and Malacard ([57]Rappaport et al., 2017)
databases. The list of human kinases was retrieved from the kinome
database ([58]Manning et al., 2002). Next, those kinases that were
associated with the ALS, reported in ALSoD and Malacard databases were
used in the study. Apart from these two databases, we have also
retrieved recently reported kinases involved in ALS from many studies
including [59]Guo et al. (2020), [60]García-García et al. (2021),
[61]Sahana and Zhang (2021), and [62]Palomo et al. (2021).
Protein–protein and disease-gene interaction study of the selected kinases
After obtaining the list of kinases associated with ALS, we prepared a
PPI network of the kinases to identify how well connected these kinases
are with each other. Further, the kinases showing high-density
interaction with each other were used to scan the DisGeNET database
([63]Piñero et al., 2017) to find out the involvement of the kinases in
other diseases including cancer. The DisGeNET database includes the
information of human variant-disease associations and gene-disease
associations from several repositories such as environmental, complex,
and Mendelian diseases. The retrieved information was used to prepare
the disease-gene interaction network by using the Cytoscape tool
([64]Shannon et al., 2003).
Identification of hub gene among the kinases involved in ALS and other
diseases
Hub genes in the network are those genes that have the highest number
of direct interactions with other nodes in a network. For identifying
the hub genes in a network, a PPI network of the kinases involved in
ALS and other diseases was prepared using the STRING plugin of the
Cytoscape tool ([65]Shannon et al., 2003). The STRING plugin includes
direct and indirect association with gene fusion, text-mining,
co-expression, neighborhood, and experimental data for preparing the
PPI network. The network analyzer tool of the Cytoscape tool was used
to calculate the topological properties of the network such as degree
of connectivity and betweenness centrality values. Nodes having a
higher number of degrees of connectivity and betweenness centrality
score were considered as hub genes in the network. Briefly, degree (k)
signifies the number of interactions made by nodes in a network, and is
expressed as:
Degree centrality (k) =
[MATH: ∑aε
Kbw(a,b).
:MATH]
Where,
[MATH: Ka :MATH]
is the node-set containing all the neighbors of node u, and w (a, b) is
the edge weight connecting node a with node b.
Betweenness centrality (
[MATH: Cb :MATH]
) represents the degree to which nodes stand between each other based
on the shortest paths. A node with higher betweenness centrality
represents more control over the network. It is expressed as:
[MATH: Cb :MATH]
(u) =
[MATH: ∑k≠u<
/mi>≠fp(k,u,f)p(k,f) :MATH]
Where p (k, u, f) is the number of interactions from k to f that passes
through u, and p (k, f) denotes the total number of shortest
interactions between node k and f.
Gene ontology and pathway enrichment study
For a comprehensive analysis of the biological functions of kinases, we
have used the Enrichr web server ([66]Kuleshov et al., 2016) to study
the functional enrichment of the kinases. The Enrichr is an integrative
web-based software application that includes new gene set libraries for
analyzing gene sets generated by genome-wide experiments. The gene
ontology analysis included the annotation at the biological level,
cellular level, and molecular level. Kyoto encyclopedia of genes and
genomes (KEGG) database was used for the pathways enrichment analysis
of the concerned kinases. The pathways and functions with p < 0.05 were
considered significantly enriched.
miRNA-gene interaction analysis
Further, the microRNAs (miRNAs) were identified as a potential drug
target against the selected kinases, and the interacting miRNAs were
screened out from the miRNet database ([67]Fan and Xia, 2018). The
miRNet database integrates four well-annotated databases including
miRTarBase v8.0, TarBase v8.0, and miRecords, and provides miRNA
interaction with several hosts organism including humans. The
miRNA-Gene interaction network was created using the Cytoscape tool
([68]Shannon et al., 2003).
Drug-gene interaction analysis
Apart from miRNAs, the drug targets were also identified against the
selected kinases by screening DrugBank ([69]Wishart et al., 2006) and
DGIdb databases ([70]Cotto et al., 2018). DrugBank is a unique
bioinformatics/chemoinformatics resource that combines detailed drug
data with comprehensive drug target information. DGIdb is an
open-access database and web interface with open-source code The
predicted drugs were used to construct the drug-gene interaction
network by using the Cytoscape tool.
Survival analysis of hub kinases from TCGA database
The correlation of hub kinases expression and overall survival from
upregulated and downregulated PPI networks was accessed using the
UALCAN database ([71]Chandrashekar et al., 2017). UALCAN database is an
interactive web portal that provides the survival analysis of TCGA data
by using Kalpen-Meier analysis. The Kalpen Meier analysis determines
the survival rate and hazard using expression and available clinical
data of the patients. Two expression groups, i.e., high expression and
low expression were defined using median kinase value as a cutoff
threshold. Analysis having a value of p < 0.1 was considered
statistically significant.
Results
Identification of ALS specific human kinases
For identifying the kinases specific to ALS disease, several kinase
databases, and disease-gene interaction databases were screened. A list
of 650 human kinases was retrieved from the Kinome database, whereas a
list of 474 genes reported with ALS diseases was retrieved from the
MalaCard database. Out of these 650 and 474 genes, a total of 21
ALS-associated kinases were identified and further used in the study.
Apart from databases, several literatures were also screened to
identify the ALS-associated kinases ([72]Chang et al., 2020;
[73]García-García et al., 2021; [74]Palomo et al., 2021; [75]Sahana and
Zhang, 2021). A total of 41 kinases associated with the ALS disease
were identified from the literature search. Finally, a list of 62
(21 + 41) kinases was prepared ([76]Supplementary Table S1). Further, a
PPI network between the selected kinases was prepared using the STRING
plugin of the Cytoscape tool to identify the interaction among the
kinases. Out of 62 kinases, 56 kinases were preparing the interaction
network ([77]Supplementary Figure S1). A network having 56 nodes and
196 protein–protein interactions was prepared. Those 56 kinases were
further used in the study.
Generation of disease-kinase and kinase-kinase interaction network specific
to ALS-associated kinases
After identifying the list of 56 ALS-associated highly interacting
human kinases, the DisGeNet database was scanned to identify the link
between the selected kinases and other diseases. After screening the
database, a total of 36 kinases were identified showing their role in
other diseases such as lung cancer, breast cancer, melanoma, and others
along with ALS. A disease-kinase interaction network was prepared using
the Cytoscape tool having 36 kinases associated with 381 different
diseases and making a total of 603 disease-kinase interactions
([78]Figure 1A; [79]Supplementary Table S2). The disease-kinases
interaction network showed that several disorders were connected with
more than one kinase in the network such as schizophrenia (n = 11),
bipolar-disorder (n = 8), depression (n = 8), melanoma (n = 8),
adenocarcinoma of lungs (n = 7) and adenocarcinoma of large intestine
(n = 5; [80]Figure 1B).
Figure 1.
[81]Figure 1
[82]Open in a new tab
Disease-kinase interaction network. (A) ALS-associated 36 Kinases (Red)
show interaction with the human diseases along with ALS (Green). (B)
Bar plot representing the highest number of genes associated with the
disease. (C) Bar plot representing the highest number of diseases
associated with the genes.
Similarly, the disease-kinase interaction network also reveals that
many of the disorders share a common genotype. For example, AKT1
(n = 96), MAPK3 (n = 67), mTOR (n = 63), NTRK2 (n = 53), GSK3B (n = 36)
and ATR (n = 29) kinases are linked to the multiple disorders
([83]Figure 1C). These molecular overlapping represents a
highly-clustered-high-density disease network and suggests patients
having altered forms of ALS-associated kinases are more prone to the
other diseases.
Further, we have prepared a PPI network to identify the association
between the kinases. The selected 36 kinases show very high interaction
among each other. The network topological properties such as degree of
connectivity and the betweenness centrality values of the nodes in the
network were calculated using the network analyzer tool to identify the
hub genes in the network. The hub genes in the network are those genes
that are highly connected with the other nodes in the network on a
direct basis ([84]Figure 2A). Any change in the expression of the hub
genes in the network can influence the major part of the network. It is
also suggested to target the hub genes in the network to disrupt the
disease-causing network. The top hub genes in the PPI network are MTOR
(k = 20), AKT1 (k = 17), SRC (k = 15), FYN (k = 13), and GNB2L1
(k = 13; [85]Figure 2B; [86]Supplementary Table S3).
Figure 2.
[87]Figure 2
[88]Open in a new tab
(A) Interaction network of kinases associated with ALS and other
diseases in humans. The size of the node depends on its degree of
connectivity. (B) Bar plot showing the degree of connectivity value of
top 5 hub genes.
Gene ontology and pathway enrichment analysis of selected kinases
Gene ontology analysis helps in identifying the roles of the genes at
biological, cellular, and molecular levels. The Gene ontology analysis
of kinases was performed using the Enrichr tool. The selected kinases
were mainly enriched in biological processes such as protein
phosphorylation and auto-phosphorylation, peptidyl-serine and
peptidyl-threonine phosphorylation and modification, regulation of
protein phosphorylation, and cellular protein modification process. In
cellular components, the kinases were mainly enriched in neuron
projection, axon, dendrite, early endosome, membrane raft,
intercellular membrane-bounded organelle, caveola, nucleus, endosome
membrane, and glial cell projection. Whereas, the molecular functions
were enriched in kinase activity, protein serine/threonine kinase
activity, protein tyrosine kinase activity, tau-protein kinase
activity, and binding, transmembrane receptor protein kinase activity,
transmembrane receptor protein tyrosine kinase activity, phosphatase
binding, protein homodimerization activity, and SH2 domain binding
([89]Figure 3A; [90]Supplementary Table S4). We also performed the
pathways enrichment analysis of kinases using KEGG from the Enrichr
webserver. The kinases were mainly enriched in axon guidance, ErbB
signaling pathways, human cytomegalovirus infection, chemokine
signaling pathway, lipid and atherosclerosis, yersinia infection,
PI3K-Akt signaling pathway, and focal adhesion pathways ([91]Figure 3B;
[92]Supplementary Table S4).
Figure 3.
[93]Figure 3
[94]Open in a new tab
(A) Gene Ontology enrichment analysis of the identified kinases. (B)
KEGG pathway enrichment analysis of the identified kinases.
Identification of miRNA and drug molecules as a potential therapeutic target
Micro-RNAs are the small non-coding RNAs, involved in the expression of
genes by interacting with mRNAs. For identifying the miRNAs interacting
with the selected kinases, several miRNA-gene interaction databases
such as miRNet and miRTarBase were screened. A total of 788 miRNAs were
identified showing the interaction with the selected kinases. A
miRNA-kinase interaction network having 788 miRNAs, 36 kinases, and
1891 miRNA-kinase interaction was prepared using the Cytoscape tool
([95]Figure 4A; [96]Supplementary Table S5). Further, we have
calculated the topological properties of the miRNA-kinase network and
selected the top 5 highly interactive miRNAs in the network based on
their degree value. These top 5 miRNAs were interacting with 29 kinases
out of 36 kinases, suggesting that these miRNAs can be potentially used
as a therapeutic target. Out of 5 miRNAs, has-miR-16-5p hsa-miR-124-3p
were showing the highest interaction with a maximum 17 number of
kinases, hsa-miR-27a-3p, hsa-miR-1-3p and hsa-miR-34a-5p were showing
interaction with 16, 15, and 14 kinases, respectively ([97]Figure 4B).
Figure 4.
[98]Figure 4
[99]Open in a new tab
miRNA-kinases interaction network. (A) The kinases (Green) show
interaction with the miRNAs from several databases including miRNet,
miRTarBase, and TarBase. (B) Interaction of the top 5 miRNAs, selected
based on a high degree of connectivity with 29 kinases.
Further, the enrichment analysis of top identified miRNAs reveals their
role in biological processes such as positive regulation of the
cellular metabolic process, regulation of RNA metabolic process,
regulation of signal transduction, regulation of signaling, regulation
of vasculature development, and others. In molecular functions, the
miRNAs are mainly enriched in RNA binding, mRNA binding, nucleic acid
binding, and organic cyclic compound binding. These miRNAs showed
enrichment in the focal adhesion-PI3K-Akt–mTOR-signaling pathway
([100]Figure 5; [101]Supplementary Table S6).
Figure 5.
[102]Figure 5
[103]Open in a new tab
Gene ontology of top 5 miRNAs interacting with kinases.
Apart from miRNAs, we have also identified the potential drug target
against the selected kinases. Several drug-gene interaction databases
were screened to identify the drugs interacting with selected kinases.
We have identified a total of 467 drug molecules showing interactions
with 36 kinases ([104]Figure 6A; [105]Supplementary Table S7). Further
based on interactions, we have identified 3 drugs molecules namely,
PF-00562271, Cenisertib, and Vandetanib interacting with 13, 11, and 8
kinases, respectively. The result thus suggests the therapeutic
potential of the identified drugs either in an individual manner or in
combination as well ([106]Figure 6B).
Figure 6.
[107]Figure 6
[108]Open in a new tab
Drug-kinase interaction network. (A) The 36 kinases (Green) showed
interaction with 468 drugs (Orange) from the DGI database. (B)
Drug-kinase interaction of the top 3 selected drugs having association
with the majority of kinases in the network.
Survival analysis of hub kinases
Survival analysis of hub kinases from upregulated and downregulated PPI
networks was performed by Kalpen Meier analysis using the UALCAN web
portal. A threshold of value of p < 0.1 was applied to identify a
statistically significant prognostic marker. Out of the 5 hub kinases
namely AKT1, GNB2L1, FYN, SRC, and mTOR from the PPI network, only 2
kinases namely AKT1 and mTOR in LGG, and only one kinase GNB2L1 in GBM
had a value of p < 0.1 and was considered a probable potential
biomarker for prognosis. The survival analysis chart of the AKT1 gene
in LGG cancer patients reveals that patients had higher expression of
the AKT1 gene and had more survival probability and living time (in
days) as compared to patients who had lower AKT1 gene expression
([109]Figure 7A). However, in LGG cancer patients, the lower expression
of the mTOR gene represents a higher survival rate in patients
([110]Figure 7B). Whereas, in GBM cancer patients, the higher
expression of the GNB2L1 gene represents a higher rate of survival in
patients as compared to patients having a lower expression of the
GNB2L1 gene ([111]Figure 7C).
Figure 7.
[112]Figure 7
[113]Open in a new tab
Survival Analysis. The Kalpen Meier survival analysis of (A) AKT1 and
(B) MTOR gene in LGG cancer patients. (C) The Kalpen Meier survival
analysis of GNB2L1 gene in GBM cancer patients.
Discussion
Kinases are the dynamically signaling proteins that act as a switch in
the cell by phosphorylating target proteins. Several studies have been
reported that the abruption in kinases activity or disturbances in the
kinome network may cause various neurodegenerative diseases and cancers
in humans ([114]Freedman et al., 2013). In this study, we have adopted
a network-based system biology approach to investigate the kinase-based
molecular interplay between ALS and other human disorders.
Here, we analyzed the disease-kinase network of ALS-associated kinases
that resulted in a disproportionately large number of disease-kinase
associations. It includes diseases such as schizophrenia, bipolar
disorder, depression, melanoma, cardiac diseases, adenocarcinoma of the
lung, adenocarcinoma of the large intestine, malignant neoplasm of
prostate, and prostatic neoplasms, which represents the higher
connection with kinases.
Kinases commonly involved in ALS and cancers
The significant finding of this study is the identification of 28
kinases that are commonly linked to ALS as well as various type of
human cancers. Among the common kinases, the mTOR, AKT1, SRC, FYN, and
GNB2L1 are the kinases that are common in ALS and cancers and also
identified as hub genes from the PPI network. The mechanistic/mammalian
target of rapamycin (mTOR) is a Serine/Threonine kinase, that plays a
central role in regulating human physiological activities including
tissue regeneration, regulatory T cell differentiation, and function,
and various types of cancers ([115]Sabatini, 2017). The interruption of
mTOR signaling results in several disorders including cancers,
diabetes, obesity, and neurodegenerative diseases ([116]Huang, 2020).
In certain human diseases, it is considered promising to target mTOR
pathways according to their physiological role. Another hub gene, the
AKT1 is a serine/threonine kinase, involved in the stimulation of
several cellular functions, including cell proliferation, migration,
growth, and cell survival. It also play important role in the
initiation of protein synthesis, cell metabolism, and immune cell
activity ([117]Henderson et al., 2015). It is reported that it
influences all aspects of cancer biology and has clinical relevance to
the outcome of cancer therapy ([118]Szymonowicz et al., 2018). Whereas,
a decrease in AKT1 activity is associated with ALS ([119]Wang et al.,
2019). Another hub kinase RACK1 (GNB2L1), is a highly conserved
intracellular adaptor protein and involved in several biological
processes including virus infection, cell migration, neural
development, and angiogenesis. It also functions as an anchoring
protein for the activation of protein kinase C (PKC; [120]Li and Xie,
2015). The SRC family of protein tyrosine kinases (SFKs) is one of the
kinases associated with ALS and cancer. It plays a central role in the
activation of signal transduction via an extensive set of cell surface
receptors in the context of several cellular environments. The SFKs are
also involved in several cellular processes such as cell growth, shape,
differentiation, migration, specialized cell signals, and survival
([121]Parsons and Parsons, 2004). The FYN kinase is also involved in
neurodegenerative diseases and cancers. It belongs to the SRC family
and plays important role in several signal transduction pathways such
as axon guidance, myelination, synaptic transmission, and
oligodendrocyte formation in the central nervous system ([122]Matrone
et al., 2020). Recent studies report its role in molecular signaling
pathways underlying neurodevelopment as well as neuropathological
events ([123]Matrone et al., 2020).
In addition, the other identified common kinases in ALS and human
cancers, including the homeodomain-interacting protein kinase 2
(HIPK2), is a serine–threonine kinase, that participates in the
regulation of gene expression, signal transduction, and apoptosis
regulations. It is well known for its pathological role in human
cancers. Whereas, the HIPK2 has also been reported in neurodegenerative
diseases via endoplasmic reticulum (ER) stress ([124]Feng et al.,
2017). Further, the cyclin-dependent kinase (CDK5) is a
serine/threonine kinase, belongs to the mitotic cyclic-dependent
kinases family. It is characterized by its role in the central system
for axon elongation, neuronal migration, and differentiation rather
than in the cell cycle ([125]Pozo and Bibb, 2016). It is also involved
in the microtubular arrangement, sorting of axodendritic cargos,
([126]Klinman et al., 2017) and phosphorylation of NF-H subunit to
stimulate axonal transport in neurons ([127]Shea et al., 2004). The
CDK5 has been reported in the development of various types of human
cancers including breast, colon, lung, pancreatic, and brain tumors
([128]Pozo and Bibb, 2016). In addition, we also identified EPHA4 as a
common kinase in ALS and cancers. EPHA4 is a tyrosine kinase that
belongs to the Ephrin receptor subfamily. It stimulates axonal guidance
in the corticospinal tract, and also functions as a mediator of
inflammation in spinal cord injury ([129]Goldshmit et al., 2004;
[130]Zhao et al., 2018). The EPHA4 influences motor neuron degeneration
and disease progression in ALS ([131]Van Hoecke et al., 2012), while,
in another study, it is reported that downregulation of EPHA4 signaling
enhances the functionality and motor neuronal survival ([132]Zhao et
al., 2018). Even though these results indicate that EPHA4 receptor
tyrosine kinase may serve as a therapeutic target for ALS. Another
kinase, the ERBB4 that encodes Erb-B4 receptor tyrosine kinase 4, is
involved in important cellular processes, including neurodevelopment
([133]Takahashi et al., 2013). It also activates multiple signal
transduction proteins such as mTORC1, mitogen-activated protein kinase
(MAPK), STAT, and Agrin/MuSK pathways. Several studies reported that
the abnormal expression and activation of ERBB4 could lead to human
cancers ([134]Hynes and Lane, 2005; [135]Qiu et al., 2008; [136]Segers
et al., 2020) and the loss of function due to mutations also associated
with autosomal-dominant ALS ([137]Takahashi et al., 2013). Even the
TANK-binding kinase 1 (TBK1) a serine/threonine kinase, is also
associated with both ALS and human cancers. It is known for its
involvement in the regulation of innate immunity and autophagy through
interaction with their proteins ([138]Pottier et al., 2015). It
phosphorylates p62/SQSTM1 and optineurin (OPTN) to stimulate its
binding to cargo proteins and to efficiently bring them to
autophagosomes for degradation ([139]Matsumoto et al., 2015). The
inhibition of TBK1 activity resulted in dendritic swellings, abnormally
shaped astrocytes, cargos, and p62-and ubiquitin-positive aggregates in
the cerebellum ([140]Duan et al., 2019). Whereas, its impaired function
causes suppression of cargo proteins clearance by autophagy, and
contributes to the ALS ([141]Chang et al., 2020). These mechanisms may
act alone or in combination with other affected processes,
therapeutically stimulating the kinase function of TBK1 may be
beneficial.
The GSK3B is also a serine/threonine kinase, involved in the initiation
of dynein-dependent axonal transport ([142]Duan et al., 2019). Its
activation is reported in ALS-associated disruptions in the
ER/mitochondrial communication ([143]Stoica et al., 2016), which may
also moderate axonal transport indirectly via disrupting the
ER-mitochondrial interactions. The GSK3B also phosphorylates TDP-43,
while the knockout of the GSK3B gene protected against TDP-43 induced
toxicity ([144]Sreedharan et al., 2008). Although extensive research
identified a direct and indirect involvement of GSK3B in ALS pathology,
the real therapeutic potential in ALS patients is not yet clear.
Furthermore, The JAK3 tyrosine kinase is mainly associated with the
regulation of gene expression. The dysregulation of the JAK–STAT
pathway occurs in inflammation and neurodegenerative disease, such as
ALS ([145]Nicolas et al., 2013). The constitutive initiation of the
JAK–STAT signaling is a characteristic feature of several hematological
neoplasms ([146]Walters et al., 2006). On the other hand, the protein
tyrosine kinase 2 (PTK2) also known as focal adhesion kinase (FAK), is
involved in several cellular adhesion and spreading processes. The
pathological role of PTK2 was reported in several advanced-stage solid
cancers, recently it is identified in ALS neurodegenerative disease
also ([147]Sulzmaier et al., 2014). The leucine-rich repeat kinase 2
(LRRK2) is a large, extensively expressed, multi-domain protein,
involved in several functions ([148]Marín, 2008). The LRRK2 pathogenic
mutations as well as overexpression, enhance its kinase activity and
lead to cause Parkinson’s disease ([149]Tolosa et al., 2020). While the
decrease in the expression of LRRK2 has been reported to cause lung
adenocarcinoma (LUAD). In patients, reduced LRRK2 was significantly
associated with ongoing smoking and worse survival, as well as
signatures of less differentiated LUAD, altered surfactant metabolism,
and immunosuppression ([150]Lebovitz et al., 2021). The LRRK2 is also
involved in the tumorigenesis and progression of clear cell renal cell
carcinoma ([151]Yang et al., 2021). The ROCK2 (Rho-associated kinase)
is a serine/threonine kinase, involved in various cellular activities
such as cell adhesion and motility, actin cytoskeleton organization,
smooth muscle cell contraction, remodeling of the extracellular matrix,
proliferation, and apoptosis. Moreover, Rock signaling can affect
differently in cellular function, depending on their regulation,
subcellular localization, and other environmental factors
([152]Hartmann et al., 2015).
Identification of miRNAs and drug repurposing
Further, we have mapped miRNAs targeting kinases and identified 5
miRNAs including hsa-miR-16-5p, hsa-miR-124-3p, hsa-miR-27a-3p,
hsa-miR-1-3p, and hsa-miR-34a-5p as the most interacting miRNAs. These
miRNAs showed interaction with a maximum of 29 kinases from a total of
36 kinases. One of the identified miRNA, hsa-miR-27a-3p, is an
important stimulator of adipogenesis, where it becomes downregulated
during the adipogenic differentiation of Simpson-Golabi-Behmel syndrome
cells, human multipotent adipose-derived cells, human primary
adipose-derived stromal cells ([153]Wu et al., 2021). The
hsa-miR-27a-3p showed disruption in adipogenesis via inhibiting
peroxisome proliferator-activated receptor γ ([154]Wu et al., 2021).
Another miRNA, the hsa-miR-1-3p has been involved in several biological
functions. Its downregulation causes stimulation of proliferation and
invasion in many cancers, including oral squamous cell carcinoma,
colorectal carcinoma, prostate cancer, bladder cancer, and lung cancer
([155]Zhang et al., 2019). Even, in liver injury, the hsa-miR-1-3p is
upregulated and functions as a biomarker for hepatocellular injury
([156]Kagawa et al., 2018). On the other hand, the hsa-miR-34a-5p miRNA
are from the miRNA-34 family, with potential therapeutic properties.
Its expression is associated with the survival of patients in
colorectal cancers and is considered a marker of prognosis in earlier
cancer stages ([157]Hasakova et al., 2019). It is also reported that
the overexpression of hsa-miR-34a-5p inhibits the growth of drug
resistance tumors ([158]Deng et al., 2021). Further, we identified the
hsa-miR-124-3p, which significantly downregulates the plectin (PLEC)
protein which connects junctions with the cytoskeleton components
([159]Deng et al., 2021). In lung cancer, it showed the downregulation
of other cellular cytoskeleton proteins including beta-1, vimentin,
talin 1, cadherin 2 or N-cadherin, IQ motif containing GTPase
activating protein1, and junctional adhesion molecule A (JAMA or F11R
or JAM1) resulting in remodeling of cytoskeletons that causes
interruption of cell–cell junctions ([160]Deng et al., 2021). Moreover,
miR-124-3p also decreases the cell adhesion capacity by directly
inhibiting the formation of focal adhesion plaques. In breast cancer,
it controls the NF-κB pathway by inhibiting AKT3, and moderated
migration, proliferation, invasion, and inducing apoptosis ([161]Wang
et al., 2021).
Moreover, we have also identified three potential drugs including the
PF-00562271, Cenisertib, and Vandetanib, targeting the kinases. These
drugs result in high interaction with about a total of 18 kinases out
of 36. The PF-00562271 is one of the potent dual and reversible
ATP-competitive inhibitors of FAK and PYK2 ([162]Roberts et al., 2008)
and has shown interaction with about 13 kinases out of 36 kinases. The
FAK is a cytoplasmic protein tyrosine kinase, that showed upregulation
in several cancers such as breast, thyroid, liver, esophageal, colon,
prostate, head, and neck ([163]Stokes et al., 2011). In pancreatic
ductal adenocarcinoma (PDA), the elevated expression of FAK showed a
correlation with poor survival rates ([164]Miyazaki et al., 2003;
[165]Itoh et al., 2004) and tumor size ([166]Furuyama et al., 2006).
The PF-00562271 inhibits phosphorylation of FAK in epidermal squamous
cell carcinoma ([167]Roberts et al., 2008) and Ewing sarcoma cell lines
([168]Crompton et al., 2013), which results in the suppression of
downstream pathways. Moreover, it is also involved in impairment of T
cell proliferation, adhesion to intercellular adhesion molecule-1
(ICAM-1), and interactions with antigen-presenting cells ([169]Wiemer
et al., 2013). In preclinical studies, the combination of PF-00562271
with Sunitinib (multi-targeted RTK inhibitor (RTKi)), results in the
suppression of proliferation and angiogenesis in the liver and
epithelial ovarian cancers ([170]Bagi et al., 2009; [171]Stone et al.,
2014). Furthermore, the PF-00562271 phase1 clinical trial
([172]NCT00666926) was also conducted to evaluate the safety profile
([173]Infante et al., 2012). Another identified drug target, CENISERTIB
is a highly potent inhibitor of Aurora kinases. The aurora kinases are
serine/threonine kinases, involved in the cell cycle via stimulation of
mitotic spindles, while its overexpression is associated with several
human cancers and its suppression by Cenisertib disrupt cell division
and induce apoptosis ([174]Mou et al., 2021). Cenisertib inhibits the
kinase activity of AKT as well as FLT3, VEGFR2, LYN, BTK, and KIT and
promotes growth inhibition, cell cycle arrest, and apoptosis in many
cancer cell lines ([175]McLaughlin et al., 2010). The phase-I trial
([176]NCT00391521) of cenisertib in advance solid tumors and
hematological malignancies reported early evidence of tolerance in
patients with leukemia ([177]Sonet et al., 2008). The other drug,
Vandetanib is a multifunctional tyrosine kinase inhibitor
([178]Commander et al., 2011). It acts as an orally active antagonist
of EGFR/HER1, VEGFR-2 and is rearranged during transfection (RET)
kinase ([179]Commander et al., 2011). The PubChem data showed several
studies reporting clinical trials of Vandetanib for many human diseases
and around 23 of them completed either phase I or phase I/II trials
([180]Supplementary Table S8). The Vandetanib results in a promising
candidate for the treatment of progressive medullary thyroid cancer,
and biliary tract cancers ([181]Bianco et al., 2006; [182]Yoshikawa et
al., 2009), while in metastatic pancreatic cancer it reduced primary
pancreatic tumor growth and decrease lymph node. Whereas, for liver
metastasis vandetanib showed inhibition of tumor growth with
gemcitabine in combination ([183]Conrad et al., 2007).
We also performed survival analysis and found 2 two kinases that are
associated with survival in LGG patients.
Conclusion
In this study, we have used a network-based system biology approach to
investigate the kinase-based molecular interplay between ALS and other
human diseases including cancer. We constructed the disease-kinase
interactome that demonstrates the significant involvement of kinases in
several human diseases including ALS, schizophrenia, bipolar disorder,
depression, and different cancers. Here, from the PPI network, the
resulting hub genes including AKT1, GNB2L1, SRC, FYN, and mTOR show a
high degree of interactions between the kinases. Moreover, we also
identified 28 kinases including hub genes, that are involved in ALS as
well as various human cancers.
Owing to its pleiotropic nature, kinases have been considered as the
potential target for human diseases. We further, identified 5 miRNAs
and 3 potential drug candidates by drug repurposing approach. We
believe our results will help to understand the molecular interplay
between ALS and other diseases by targeting kinases and this
understanding of the association between ALS and other human diseases
may provide a new insight for future therapeutic strategies.
Data availability statement
The original contributions presented in the study are included in the
article/[184]Supplementary material, further inquiries can be directed
to the corresponding author.
Author contributions
FK: data curation, visualization, investigation. ShH: data curation,
software. AH: data curation, visualization. AM and HT: investigation.
DM: writing-original draft preparation. StH and BA: reviewing and
editing. VK: conceptualization, supervision, writing, reviewing and
editing. All authors contributed to the article and approved the
submitted version.
Funding
This research work was funded by the Institutional Fund projects under
grant no. (IFPIP:1867-141-1443). Therefore, the authors gratefully
acknowledge technical and financial support from the Ministry of
Education and King Abdulaziz University, Deanship of Scientific
Research, Jeddah, Saudi Arabia.
Conflict of interest
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary material
The Supplementary material for this article can be found online at:
[185]https://www.frontiersin.org/articles/10.3389/fnmol.2022.1023286/fu
ll#supplementary-material
[186]Click here for additional data file.^ (126.1KB, xlsx)
[187]Click here for additional data file.^ (153.3KB, pdf)
References