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Alzhemier’s
disease (AD), according to the amyloid cascade hypothesis, is caused by the
accumulation of amyloid beta peptide (Aβ), derived from the plasma membrane
bound amyloid precursor protein (APP), inside the neurons called as
neurofibrillary tangles or outside the neurons called as plaques. The
present study has been designed to understand the expression, interaction and dynamics of the hippocampal
proteins’ molecular networks that accompany the progression of AD in the rat
models compared with those of the controls. Compared with those of the
controls, the AD rats had been found altered expression and network of memory
and learning related protein (both interacting and functional pathways, as
revealed by the STRING and
Keywords: Alzhemier’s disease, Hippocampal proteins, Amyloid beta peptide, Rat
models
INTRODUCTION
Alzheimer’s
disease (AD), a neurodegenerative disorder and the most common form of
dementia, poses grave threat towards the ever-increasing lifespan of the
humanity. There are more than 40 million people worldwide suffering from AD
[1]. Although AD is an age-onset physical complication, manifested usually
after the age of 60, its initiation and progression occurs during early stages
of life. According to the amyloid cascade hypothesis, AD is caused by the
accumulation of amyloid beta peptide (Aβ), derived from the plasma membrane
bound amyloid precursor protein (APP), inside the neurons called as
neurofibrillary tangles or outside the neurons called as plaques [2]. Aβ
aggregates cause neuronal cell toxicity, cell function loss and/or cell death
leading to AD complications including memory loss and behavioral alteration. As
a consequence the affected person becomes unable to perform daily normal
activities, becomes confused about time and space, faces problem in planning
and executing even errands [3]. Advanced stage, the AD patients suffer from
difficulties in speaking and writing, sleeping and awakening and even cannot
recall their own names [3]. They become irritated and suspicious even about
their caregivers though they become dependent on their caregivers and family
members.
Prevalence of AD is highest in the Western Europe, followed very closely by the USA while lowest in the sub-Saharan Africa. AD is the prime cause of disability in later age of life. In 2016, global cost of AD is estimated to be 605 billion US dollars. Prevalence of AD increases up to 15 times during the age range of 65-80 years. AD prevalence is higher in the developed countries than those of the developing and least developed [4]. This might be due to the increased life span of the people living in the developed countries. As the developing countries are also harboring increased number of aged people, AD prevalence trend is also upward in those countries [5]. AD is posing threat to the global economic policy as it impacts world economy negatively. By 2050, the global figure of the centenarians (people aged 100 years) is expected to reach up to 2 million [1]. During their lifetime, one in every eight males and one in every four females has been predicted to develop AD [1]. Although AD is going to plague the aged humanity worldwide, till today, there is no specific drug for the treatment of AD; the available medico-strategies just delay the worsening of the symptoms. Therefore, to aid the ever-increasing global aged and AD prone populace, finding out of safe, less expensive and easy to achieve therapeutic agents has emerged as an urgent need. In this regard, identifying the novel mechanistic approaches governing AD pathogenesis seems apt for strategizing future therapeutic approaches against AD.
Proteomics
approach would aid much in high-throughput screening of the etiology and
therapeutic approaches for AD. Cellular proteomics studies might shed light on
AD biomarker development and elucidate the AD patho-mechanism. Mechanistically,
functions of proteins are mediated through protein-protein interaction (PPI)
and thus, identification of the PPI strategy remains elusive in AD brain
proteomics [6]. Therefore, the present study has been designed to understand
the expression, interaction and dynamics of the hippocampal proteins’ molecular
networks that accompany the progression of AD in the rat models compared with
those of the controls.
MATERIALS AND METHODS
Animals
Wistar male
rats (120 ± 5 g) were divided into two groups: Control (C) and AD (A), each
group containing 15 rats. AD model rats were prepared by infusing Aβ1-42
(ab120959, abcam, USA) into the cerebral ventricles following the method of
Fang et al. (2013) [7]. All the experimental protocols had been approved by the
ethical permission committee, University of Malaya Institutional Animal Care
and Use Committee (UMIACUC) [Ethics reference no. ISB/25/04/2013/NA (R)].
Brain sample preparation and protein quantification
Rats were
anaesthetized with intra-peritoneal injection of sodium pentobarbital (35
mg/kbw), sacrificed, head removed followed by collection of brain on ice bath.
Brains were frozen in liquid nitrogen and stored at -80°C. Protein extraction
from the brain samples was performed following homogenization of the brain
sample (50 mg) with lysis buffer (1 ml) using a homogenizer (Polytron PT 1200,
Kinematica). To avoid protein degradation, we added 10 μL of protease inhibitor
cocktail during homogenization followed by centrifugation at 10000× g at 4°C
for 10 min. The supernatant was collected and preceded towards delipidation
following specific methods [8]. Later, protein separation through SDS-PAGE and
protein quantification through LC-chip MS/MS Q-TOF was performed.
Protein separation through SDS-PAGE
The mini-PROTEAN tetra cell (165-8000,
BIO-RAD, USA) was used according to the manufacturer’s instructions for running
SDS-PAGE in the current study. Coomassie brilliant blue (0.1%) was used for
staining the proteins with shaking for about 20 min. For destaining, 10% acetic
acid solution (aqueous) was used. The gel immersed into the destaining solution
contained in a covered box underwent occasional stirring until the entire gel
was fully destained. Then, individual bands were cut and if any gel plug still
contained stain, repeated shaking of the gel plugs in 50 µL of 50% acetonitrile
(ACN) in 50 mM ammonium bicarbonate was continued.
In-gel tryptic
digestion
For disrupting the tertiary structures of the
solubilized proteins, reduction and alkylation are applied to them so that the
disulfide linkages are broken and cannot be re-formed. For reduction, the gel
plugs were incubated in 150 µL of 10 mM dithiothreitol (DTT) in 100 mM ammonium
bicarbonate buffer at 60°C for 30 min. After cooling at room temperature, the
gel plugs were alkylated by incubating in 150 µL of 55 mM iodoacetamide (IAA)
in 100 mM ammonium bicarbonate for 20 min in the dark chamber. The gel plugs
were washed in triplicate with 500 µL of 50% ACN in 100 mM ammonium bicarbonate
for 20 min. For dehydration of the gel plugs, shaking in 50 µL of 100% ACN for
15 min was performed followed by drying the gel plugs in speed vacuum for 30
min at 4°C. For enzymatic digestion, the gel plugs were incubated with 25 µL of
6 ng/µL trypsin in 50 mM ammonium bicarbonate at 37°C overnight.
Extraction
After overnight
digestion, the digested products were spun down by vortexing and transferred
liquid to the fresh tubes. Adding 50 µL 50% ACN to the tubes, shaking was continued
for 15 min. Then, the gel plugs were incubated with 50 µL of 100% ACN and shook
for another 15 min. Transferring the liquid to the previous tubes, the digested
samples were completely dried using the speed vacuum at 1000 rpm. The dried
tubes were stored at -80°C and later on de-salting and zip tipping performed.
LC-Chip-MS/MS Q-TOF quantification
All the MS/MS
instruments and software used in the present section of the study were from
Agilent (Agilent, Santa Clara, CA, USA). Eluted sample obtained from zip tip
procedure was dried and 10 μL of the lyophilized samples was reconstituted in
the first LC mobile phase (0.1% formic acid) in triplicate. The peptides with a
Nano-LC 1260 linked directly with an Accurate Mass Q-TOF 6550 containing a
Chip-Cube interface Nano-ESI ion source. Polaris High Performance Chip was
utilized and enriched the peptides using 360 nl enrichment column followed by
their separation using the separation column (C18 reverse phase, 150mm × 75Âμm,
5 μm) with solvent A (0.2% formic acid in water) and a 5-80% gradient of
solvent B (0.1% formic acid in acetonitrile) for 34 min with a flow rate of
0.35 μL/min. Mass data acquisition was undertaken at 8 spectra/second in the
range of 100-200 m/z and subsequent collision induced dissociation (CID) of the
twenty most intense ions. Setting the mass-tolerance of precursor and product
ions at 20, MS/MS data acquisition was performed in the range of 200-3000 m/z.
In order to identify the proteins, the acquired MS/MS data was compared against
the UniProtKB/Swiss Prot rat (Rattus norvegicus) database using the Spectrum
Mill and X! Tandem. The differentially expressed proteins in the different
groups were identified using their canonical sequence and proteins having fold
change of at least 1.5 times were considered as the deregulated proteins. For
validation of the identified proteins, the data was exported to the Scaffold
database (version 4.5.1, Portland, USA). Proteins were grouped together if they
would share at least two peptides and maintained their threshold level at 95.0%
and <1% false discovery rate (FDR) by the Peptide Prophet algorithm with
Scaffold delta-mass correction for the matched peptide-spectra. Proteins that
contained similar peptides and could not be differentiated based on MS/MS
analysis alone were grouped to satisfy the principles of parsimony. Proteins
sharing significant peptide evidence were grouped into clusters. Proteins were
annotated with GO terms from NCBI.
Statistical
analysis
In the present experiment, label-free relative
quantification was performed depending on the regulation of the peptides. For
statistical analysis, the data was exported to the Mass Profiler Professional
(MPP) software that analyzed depending on the MPP entities, the intensity of
the total spectra of the proteins. Setting the baseline of the spectra to the
median of the samples, frequencies of the entities were filtered minimally at
all the replicates of each treatment. To overcome the complications of false
discovery associated with multiple test analyses, ANOVA (P<0.05) was
performed.
Bioinformatics and analysis of protein-protein interaction (PPI)
Most of the proteins
do not work singly rather they participate in complex network or scaffold and
interact with others. Thus, analysis of the relevant protein-protein networks
provides important information in deciphering any bio-molecular system. We
identified the functional interaction networks of the proteins using the STRING
(Search Tool for the Retrieval of Interacting Genes/Proteins) database (version
10.0; http://string-db.org/). STRING displays protein-protein interactions in a
large network of connectivity and protein hubs. Active prediction methods that
we used were experiments, neighborhood, databases, gene fusions, co-expression,
co-occurrence and text mining, using high confidence (0.7).
For further identifying over-representing pathways and biological functions, we used the ingenuity pathway analysis (IPA), build version: 389077M, content version: 27821452, (Release date: 2016-06-14) (https://www.ingenuity.com/wp-content/themes/ingenuity-qiagen). We uploaded the datasets (AD versus C, AD versus AE and C versus AE) of the proteins significantly expressed (p<0.05) and having log fold change of 1.5 and higher. Our analysis setup was as given in Table 1.
RESULTS AND DISCUSSION
Quantitative analysis of the identified proteins
Total 822 proteins with protein threshold at
95.0%, minimum peptide of 2 and peptide threshold at 0.1% FDR were identified
in the present study. Number of commonly expressed proteins was 361. Among all
the identified proteins (822), 329 were differentially expressed with
statistical significance (P<0.05). Among the significantly regulated
(P<0.05) 329 proteins, 289 met the criteria of fold change (LogFC of 1.5)
cut off value. Number of proteins linked with AD was 59. The highest amount of
proteins differentially expressed in the AD rats were those involved in
metabolic processes (26% increase in the rats) followed by those involved in
anti-oxidant activities.
Functional classification of the significantly regulated proteins
A. Proteins involved in
neuronal structure and function: We observed differential expression of the
neurotransmission, synaptic plasticity, neurogenesis, memory and learning
related proteins such as neurochondrin, synaptophysin, synapsin-1, synapsin-2,
synaptogyrin 3 (Syngr3), 4-aminobutyrate aminotransferase and 14-3-3 protein
gamma in the different rat groups. Associative learning and long-term memory
related proteins glutathione-S-transferase 3 and tenascin R were up regulated.
Synaptic plasticity promoting Ras related protein Rab 5a and nerve growth
factor (NGF) signaling Rap-1A were also among the significantly up-regulated
group. Similar was the case for the heat shock proteins (HSP) involved in the
regulation of neuronal migration (HSP 90-alpha) and apoptosis (HSp 60).
Up-regulation was also observed for the proteins involved in post-synaptic
excitatory potential (serine/threonine-protein phosphatase, syntaxin 1B), pre-
and post-synaptic density (isoform 2 of clathrin coat assembly protein AP180)
and tyrosine phosphorylation (hemopexin) in the AD versus C group.
Proteins involved in
synaptic organization (neurofascin) and synaptic vesicle budding
(ADP-ribosylation factor 1), vesicle mediated transport (syntaxin 1A), neuronal
differentiation and development (Dihydropyrimidinase-related protein 1 and 2),
axonogenesis (2’, 3’-cyclic-nucleotide 3’-phosphodiesterase), axonal choice
point recognition (neuromodulin) and axonal transport (neurofilament light
polypeptide) were also differentially expressed in the hippocampi of the three
rat groups. Beta-soluble NSF attachment protein (SNAP-β) involved in the
regulation of glutamatergic synaptic transmission, disassemble of SNARE complex
and synaptic vesicle priming was also up regulated. In addition to these
proteins, we observed differentially up regulated expression of glial
fibrillary acidic protein (GFAP). GFAP is involved in long-term synaptic
potentiation, neurotransmitter uptake, neurogenesis, glial and Schwan cell
proliferation.
We observed
down-regulated expression of memory and learning related proteins such as
clusterin (stimulator of Aβ and NFT), neuromodulin, neurofascin, NCAM1 and
proteins involved in dopamine decarboxylation in the AD versus control group.
Following are the AD related other proteins differentially expressed in the
present study:
Syntaxin-1A: Syntaxin-1A regulates vesicular trafficking during
exocytosis and trans-membranal protein insertion. Decreased expression of
syntaxin-1 A in the AD rats might have affected synaptic functions [8].
Synaptogyrin-1: Synaptogyrin-1 is involved in maintaining
short and long-term synaptic plasticity. Level of hippocampal syntaxin-1 A and
synaptogyrin-1 had been found to be reduced in line with AD progression [9].
Neuromodulin (GAP-3): Neuromodulin is a neuronal growth and neurite
forming protein whose level decreases in AD brains [10].
Neural cell adhesion molecule (NCAM): NCAM plays important
role in brain development and increased level of NCAM 1 in transgenic AD mouse
model (Tg2576) and of NCAM 2 in human AD patients have been reported [8].
Endophilin A1: Endophilin A1 is a membrane bending protein
involved in CNS development, apoptosis, signal transduction and microtubule
based movement. AD rats’ hippocampi showed decreased expression while the
control rats experienced increased expression of endophilin A1 in the present
study. In the temporal neocortex of the AD patients, decreased level of
endophilin A1 has been observed [11].
Clathrin: Clathrin group of proteins are involved in neuronal
secretory functions and synaptic maintenance [12]. AD pathogenesis involves
altered clathrin-associated membrane trafficking resulting in neurodegeneration
[12]. Between the light and the heavy chains of clathrin, impaired distribution
of the former has been linked with the AD pathogenesis [12]. We observed
similar findings in the AD group of rats compared with others in the present
study.
Septin: Septins are GTP-binding proteins found to be
co-localized with the NFT in the AD brains [11]. We observed increased
expression of septins (septin-2, septin-3), NAD-dependent protein deacetylase
sirtuin-2 in different rat groups. Our findings are compatible with those of
[11].
UCH L1: Ubiquitin carboxyl-terminal hydrolase L1 (UCH L1) is
an important enzyme for maintenance of cognitive and synaptic [13]. Conflicting
information regarding its expression has been documented in different AD cases.
Soluble NSF-attachment protein beta (SNAP-β):
N-ethylmaleimide sensitive fusion proteins (NSF) are the part of APP and
overexpressed in AD [14]. Soluble NSF-attachment proteins are involved in
intracellular membrane fusion and vesicular trafficking. Among α-, β- and γ-
SNAPs, α - and γ- SNAPs are expressed in different tissues while the β-SNAP is
brain specific. In AD brain, differential expression and oxidized form of
SNAP-β had been detected through redox proteomics.
Neuropolypeptide h3: Neuropolypeptide h3 is a cholinergic
neuro-stimulating peptide that falls in the phosphatidyloethanolamine binding
protein group and is also known as Raf-kinase inhibitor protein (RKIP) and/or
hippocampal cholinergic neurostimulating peptide (HCNP) [15]. Our finding of
down regulated neuropolypeptide h3 is in agreement with other studies [8].
Oxidatively modified loss of function of neuropolypeptide h3 impairs
phospholipid asymmetry that might be involved in extrusion of phosphatidyl
serine to the outer membrane of neuron and signal for apoptosis and cause
neuronal death [16]. Also, neuropolypeptide h3 mediated stimulation of acetylcholine
esterase (AchE) becomes compromised and this effect is heightened when HNE
interacts with AChE in presence of Aβ (1-42) in synaptosome [17]. Thus, in AD
brains, neuropolypeptide h3 is linked with cholinergic abnormalities and
altered lipid metabolism that are the early events in AD pathogenesis [14].
Annexin: AD rats showed increased expression of annexin in the
hippocampi. Previous studies have linked increased plasma annexin5 with
increased AD risk [18].
Glycogen synthase kinase 3 β (GSK3β): Glycogen synthase
kinase 3 β (GSK3β) is a serine/threonine kinase having diversified regulatory
functions ranging from glycogen metabolism to gene transcription. Over activity
of GSK-3β has been linked with elevated Aβ production, tau hyper
phosphorylation and impaired memory and learning activities and “mitochondrial
traffic jam” [19].
Serine/Threonine protein phosphatase: Serine/Threonine
protein phosphatase negatively regulates memory and learning abilities by
impairing synaptic plasticity and LTP [20]. Up regulation of serine/threonine
protein phosphatase in the AD rats might contribute towards impaired memory and
learning performance in the present study.
Serine protease inhibitors (serpins): Serine protease
inhibitors (serpins) regulate proteolytic processing of proteins. Previous
studies indicated their increased level in plasma and CSF of AD patients [20].
We also observed increased expression of serpins (α1-antitrypsin) in the AD
rats’ hippocampi. Alpha 1-antitrypsin (A1AT) has been reported to be co-localizing
with Aβ plaques and NFTs [21].
B. Proteins involved in
Ca2+transportaion, homeostasis and signaling
Dysregulation of Ca2+
metabolism and signaling has been linked with neurodegeneration and AD
pathogenesis (Brawek and Garaschuk, 2014) [22]. We observed differential
expression of calmodulin, CamK2a and CamK2b involved in Ca2+transportation,
homeostasis and signaling.
a. Calmodulin: AD rats showed up regulated
expression of calmodulin. Calmodulin is a biomarker of AD whose increased
expression and its binding proteins are associated with AD pathogenesis
[23,24].
b. CamK2a: Ca2+/calmodulin-dependent
serine/threonine protein kinase (Camk2a) is highly important for maintenance of
the glutametargic synaptic plasticity [25]. Its role in spatial learning becomes
also evident from its supporting role towards NMDAR-dependent LTP in the
hippocampus [26]. Through Ca2+/calmodulin kinase II signaling,
Camk2a also regulates neurotrophin-3 and BDNF secretion from the hippocampal
post-synaptic neurons [26,27]. In the present study, the CamK2a showed down
regulated pattern compared with their control and mushroom-treated
counterparts. Our findings are in line with those [28,24]. Its deficiency has
been shown to hamper neuronal development and dentate gyrus formation as well
as behavioral alteration [28]. On the other hand, up-regulated expression of
CamK2a has been linked with improved cognitive performance of the SMP8 mice
[23,28].
c. Annexins (Annexin-1, -5 and -6)
Our findings of increased annexin expression
in the AD rats’ hippocampi are compatible with those of [18,25,29]. Annexins
are intracellular Ca2+-respondents capable of binding with membrane
phospholipids and participate in membrane trafficking, endo- and exo-cytosis [25].
Aβ-induced Ca2+dysregulation
(increased intracellular level) hyper activates c-Jun N-terminal kinase (JNK),
cyclin-dependent kinase 5 (CDK5), tau phosphorylation and disrupts microtubule
network [19]. It leads towards mitochondrial trafficking defects that impair
the normal movement of mitochondria across the microtubules and thus cause
“mitochondrial traffic jam” in the AD neurons and affects neuronal functions [20].
Thus, intracellular Ca2+ dyshomeostasis might be among different
mechanisms involved in disrupted neuronal activity and corresponding impaired
memory of the AD rats in the present study.
C. Proteins involved in
signal transduction
a. 14-3-3 proteins
(Ywhag): We found up regulated expression of the 14-3-3
proteins (ζ/δ, θ, η, γ, β/α) in the AD rat brains. They constitute about 1% of
total soluble proteins of the normal brain. 14-3-3 proteins participate in
signaling through binding with the phospho-serine containing proteins and can
regulate the activities of the kinases, phosphatases and trans-membrane
proteins [29]. Thus, they mediate diversified activities involving neuronal
plasticity, neurotransmission, neurite outgrowth generation and neurogenesis
[30]. In AD brains, they have been found to be closely associated with tau and
aid in the formation of the NFTs [29]. Differential expression of 14-3-3
proteins β/α, ζ/δ and ε had been observed in the temporal neocortex and other
parts of the brains of the AD patients [11]. In the AD hippocampi, both intra-
and extra-cellular expression of the 14-3-3 proteins have been detected. Among
different isoforms, the highest immunoreactivity towards NFT has been observed
for the 14-3-3ζ [31]. 14-3-3ζ-mediated tau phosphorylation involves protein
kinases such as glycogen synthase kinase-3 beta (GSK3β). GSK-3β hyperactivity
impairs mitochondrial intra-neuronal anterograde movement and causes
“mitochondrial traffic jam” and disrupts neuronal activities [19]. 14-3-3ζ also
binds with δ-catenin and disrupts the formation of the adherens junction
complex that compromises the neural structure and cognitive performance [19].
Also, interaction of δ-catenin with pre-senilin 1 is an important stimulator of
the wnt signaling and thus of AD pathogenesis and neuronal under-development
[24].
b. VDAC
(voltage-dependent anion selective channel 1): Voltage-dependent
anion selective channels (VDAC1 and VDAC2) are mitochondrial porins involved in
transportation of ATP and Ca2+ and in apoptotic signaling [32]. Its
altered expression has been noticed in AD and other neurodegenerative diseases
and in mitochondrial dysfunctions.
c. SLC12A5: SLC12A5
are neuronal K+/Cl- symporter involved in maintaining
intra-neuronal low Cl- concentration [33]. It mediates neuronal
excitotoxicity and synaptic inhibition [34]. Its increased expression is linked
with the glutamate transporter Slc17a7 that actively participates in GABAergic
neurotransmission [33].
D. Proteins involved in
apoptosis
Apoptosis is an
important feature of neuronal and synaptic cell losses. Decreased levels of the
apoptosis regulatory enzymes (peptidyl prolyl cis-trans isomerase, protein
phosphatase) found in the present study are distinct hallmarks of AD
pathogenesis [35].
E. Proteins involved in
neuronal cytoskeleton maintenance
Derangement of
neuronal cytoskeleton through microtubule disassembly is an important feature
of neurodegeneration [36]. STRING analysis revealed strong networks among
microtubule assembling the cytoskeletal proteins such as tubulin, β-actin and
keratin.
a. Tubulin: AD neurons
suffer from disrupted microtubule structure and functioning [37]. Tubulin is
the main component of microtubule and consists of dimers imparted by the alpha
and beta chains. Differential expression of tubulin α -1c, -4a, β -2a, -2B, -3
and -5 chains were observed in the present study. Molecular function based sub-network
analysis showed different tubulin chains to be clustered together and deranged
in AD. Both animal and human studies have linked decreased level of α and β
tubulin with human AD [38,39]. In AD brain, β tubulin becomes abnormally hyper
phosphorylated and modified tubulin fails to assemble microtubules.
Consequently, microtubule disassembles leads towards cytoskeletal
vulnerability. Recently, micro tubular disassembly has been implicated in
causing “mitochondrial traffic jam” in the AD neurons as mitochondrial shifting
across the “rail-road of microtubule” becomes impeded in the AD brain [19].
b. β-Actin: Normally,
β-actin is involved in maintenance of cytoskeleton, internal cell motility,
neuronal network integrity and aids in memory and learning performances. Its
altered expression and oxidized form had been linked with AD pathogenesis [15].
Impaired expression of actin is in agreement with the synaptic dysregulation
associated with AD and age-related altered cytoskeletal structure, axonal
dystrophy, reduced dendritic spines and impaired transport across membranes
[40]. Enhanced accumulation of actin enhances tau-governed neurotoxicity [41].
c. Dihydropyrimidinase-related protein 2
(DRP-2): Dihydropyrimidinase related protein 2 (DRP2) is involved in regulation
of axonal outgrowth and becomes hyper phosphorylated in NFT and its increased
level had been observed in AD model animals [42]. Compared with the normal
neurons, AD neurons possess shortened dendrites that are a characteristic of
their lowered communication with neighboring neurons [15]. Oxidative
modification of DRP2 might have caused reduced length of the dendrites and
communication leading to lowered cognitive performance of the AD rats in the
present study [15].
d. Glial fibrillary acidic protein (GFAP):
GFAP provides structural support to the astrocytes and its elevated level in AD
model animals and in human subjects [42]. Our findings are conforming to those
[42].
e. RhoA proteins: Ras
homolog gene family, member A (RhoA) proteins are involved in neuronal
cytoskeleton regulatory processes such as dendrite development, axonal
extension and protrusion [43]. They also stabilize the Aβ-disrupted
microtubules [43]. Aβ increases RhoA-GTPases and decreases neuronal spine
production and neural connection both in the cell lines and also in the brains
of the transgenic AD models [14].
f. Septin: Septins
are microtubule associated, filament-forming and GTP-binding proteins that
participate in dendritic spine formation and in neurotransmitter release [44].
Like that of we found increased expression of septin-2 and -3 in the AD rats’
hippocampi. Its increased expression might be involved in disrupting micro
tubular filament formation and associated cytoskeletal derangement in the AD
rats.
g. Cofilin: Brain
cofilin activity reduces with age and in the AD subjects, it goes down
aberrantly [45]. As cofilin is a regulator of actin, decreased cofilin
expression in AD rats points towards decreased actin turnover and lowered
depolymerization of actin filament.
h. Dynamin: Dynamin
is a neuronal GTPase capable of free entry into and release from the synaptic
vesicles [45]. Our finding of its decreased expression in the AD rats is
consistent with [12,48] Aβ-induced depleted dynamin1 level had been found to
impair memory in the AD model rats [47].
i. Gelsolin: Gelsolin
is a member of the actin-binding proteins having anti-oxidative, Aβ binding and
fibrillation inhibitory potentiality [48]. Its overproduction and/or
administration showed Aβ lowering effect and thus, gelsolin has been regarded
as an AD therapeutic agent [47]. We found decreased level of gelsolin
expression in the AD rats’ hippocampi that is in par with those of [49].
Protein-protein interaction (PPI) findings
In addition to
functional, modular and pathway-related insights, PPI maps provide disease
specific information. As identification of the target protein in any disease
pathogenesis is an important aspect, PPI analyses shed light towards
understanding the complex connectivity and identifying the protein of interest
for further evaluation and management [50].
Based on the analysis
of the PPI networks and pathways of the differentially expressed proteins, it
is obvious that AD causes a disturbed protein expression affecting the global
protein-protein interactive networks and the relevant biological pathways. We
categorized them into functional framework of metabolic process, intracellular
signaling cascade, signal transduction, oxidation reduction, cell
communication, molecular transport, regulation of biological processes,
regulation of cellular processes and apoptosis.
PPI among the up-regulated proteins
PPI among up-regulated proteins of AD vs. control group: PPI interaction among the up-regulated proteins in the AD versus control group can be divided into several network clusters (Figure 1). Among them, the first two consists mainly of the cytoskeletal proteins such as 14-3-3 proteins along with tubulin, actin, Ras-related proteins, cofilin, sirtuin, actin, myosin and the Ras-like proteins. The heat shock proteins (HSP) of different molecular weight and function formed another important network cluster (Figure 1). Calmodulin, Ca2+/calmodulin-dependent protein kinases, serine/threonine-protein phosphatase and synapsin-1 formed another functional interaction (Figure 1). Enzymatic proteins involved in metabolism and energy generation formed functional interactions among themselves (Figure 1). There was strong interaction among the proteins (at high confidence score of 0.700). As some of the proteins had been involved in different functions, functional overlap of the proteins led them towards extended integration and interaction beyond any single class (Figure 1).
a. Pathway analysis of
the up regulated proteins in the AD vs. control group
Through KEGG (Kyoto
Encyclopedia of Genes and Genomes) pathway analysis, we found 65 pathways to be
significantly enriched (P<0.05) in the AD versus control group. Among them
the most notable were the AD pathway (pathway ID 05010) involving the genes
Camk2a, Camk2b, Ndufs1, Ndufv2 and Atp5o (FDR 0.0176); the LTP pathway (pathway
ID 04720) involving the genes Calm1, Camk2a, Camk2b and Rap1a (FDR 0.0037) and
the neurotrophin signaling pathway (pathway ID 04722, FDR 0.0246) involving the
genes Calm1, Camk2b, Rap1a and Ywhae. The P13K-Akt signaling pathway (pathway
ID 04151, FDR 0.0161) involving the genes Hsp90aa1, Ywhab, Ywhae, Ywhag, Ywhah
and Ywhaq; Ca2+signaling pathway (ID 04020, FDR 0.0176) with the
genes Calm1, Camk2a, Camk2b, Ppp3ca, Slc25a4 and Slc25a5; cGMP-PKG signaling
pathway (ID 04022, FDR 0.0032) involving the genes Camk2a, Camk2b, Atp1a1,
Atp1b1 and Slc25a5 were also among the interacted signaling pathways. PFAM and
INTERPRO protein domain analyses identified 14-3-3 (PF0024) as the most
significantly enriched protein in the AD versus control group.
PPI among the down-regulated proteins
PPI among the down-regulated proteins of the AD vs. control group: Among the down regulated AD versus control group, the most notable interaction had been observed among the cytoskeletal proteins such as keratin isoforms (Krt1, Krt2, Neflh, Krt5, Krt8, Krt10, Krt13, Krt15, Krt14, GFAP, Krt17, Krt42, Krt73), junction plakoglobin (Jup), neurofilament heavy chain (Neflh) and glial fibrillary acidic protein (GFAP) (Figure 2). Down regulated anti-oxidant proteins peroxidredoxin 5, superoxide dismutase 1, protein disulfide isomerases (Prdx5, Sod1, P4hb, Hspe1 and Cct3), oxoglutarate/malate carrier protein and alcohol dehydrogenase also formed a sub-network (Figure 2). Besides, metabolic and ATP (Atp5c1, Atp6v1d and Atp6v1e1) generating proteins were also interacted with each other (Figure 2).
Identification of functional network interaction through integrated pathway analysis (IPA)
Based on IPA Knowledge Base (IPAKB), genes are transformed into relevant networks. In the network, relationships among the genes are expressed as the “edges” and genes become connected with each other only if there is any path among them in the global network. In this case, molecules from the dataset that are uploaded are called the “focus molecules”. We performed core analysis through IPA so that we could interpret our datasets in the form of their functional networks. In the IPA Knowledge Base (IPAKB), corresponding objects were mapped with the protein identifiers [51]. Depending on the physical interaction (direct relationship) among the eligible proteins, IPAKB generated the networks and the score (probability value) of the networks [51]. Higher the network connectivity greater is the representation of significant biological functions of the relevant genes [51]. Statistical justification of the network connectivity is performed through measuring “p scores (-log10 p value, Fisher’s exact test)” and “network score”. Network score is also measured through Fisher’s exact test that is based on the focus protein and biological functions and thus shows the relevancy of the analysis.
Functional networks in AD vs C
IPA of the AD vs C
identified 18 networks of which the top-most one had score of 90 having 93
focus molecules among 140 total selected molecules (Figure 3). The top-most network has been associated with cell
death and survival, neurological diseases and psychological disorders.
The top upstream
regulators with corresponding p value of overlapping were PPARG (6.50E-05),
NFE2L2 (3.34E-04), STAT3 (1.36E-03), CEBPA (5.25E-03), GATA4 (5.25E-03).
Peroxisome proliferator activated receptors (PPARs) are the groups of nuclear
hormone receptors that regulate lipid metabolism, energy production, metabolic
balance between lipid and carbohydrates by acting as the lipid sensors [52]. AD
ameliorating effect of the non-steroidal anti-inflammatory drugs (NSAIDs) has
been associated with PPAR stimulating effect [53].
Neurological diseases
were among the top diseases and bio-functions with 14 molecules and p value of
4.74E-02 - 5.26E-04. Among the top toxicity lists were OS (22.2%, 7.95E-11),
mitochondrial dysfunction (12.7%, p 2.30E-12), LXR/RXR activation (14.4%,
5.73E-11), positive acute phase response proteins (34.6%, 2.43E-10) and FXR/RXR
activation (13.0%, 9.72E-10).
The top-most
up-regulated molecules with their log ratios were (THY1 18.403), ATP5F1
(18.175), GSTP1 (17.998), VAMP2 (17.922), Igh-6 (17.867), SLC1A3 (17.808), SOD2
(17.784), HSPA9 (17.354), TPPP (17.347) and RAB11B (17.254).
The top-most
down-regulated molecules with their log ratios were DDT (-18.544), LOC259246
(-18.299), CYB5A (-18.203), SOD1 (-17.609), HIST1H3E (-17.581), DSG1 (-17.409),
FABP1 (-17.253), HSPE1 (-17.230), LOC100911847 (-16.892) and JUP (-16.852).
Top canonical
pathways with their corresponding overlap and p values were phagosome
maturation (21.5%, 2.98E-19), 14-3-3-mediated signaling (16.1%, 2.37E-14),
remodeling of epithelial adherens junctions (24.6%, 6.49E-14), epithelial
adherens junction signaling (14.0%, 1.55E-12) and mitochondrial dysfunction
(12.9%, 1.80E-12). 14-3-3-mediated signaling and the network associated
affected proteins have been depicted in Figure
5. Besides, pathways associated with glucose metabolism (glycolysis and
gluconeogenesis), TCA cycle, oxidative phosphorylation, unfolded protein response,
HIPPO signaling and xenobiotic metabolism signaling were also among the notable
functionally interacted ones. The HIPPO signaling entails the protein kinase
“Hippo (HPO)” and is evolutionarily conserved for developing the mammalian
nervous system. Its emerging role in AD pathogenesis has intrigued the current
AD research field [54].
Consistent with our
quantitative proteomics findings and interaction analysis by STRING, the
proteins altered in their expression are also functionally related with each
other. The most prominent proteins in this regard are those involved in nervous
system development and function (YWHAE; NCAM1, NEFH, SIRT2, VSNL1; NCAM1, NEFH,
SIRT2, VSNL1; NME2; UGT1A1; SEPT1; CAMK2A; 2, SEPT5, SNCA, SOD1; KNG1, TUB1A1).
Proteins involved in anti-oxidative defense through free radical scavenging
(GPX1, GPX3, PRDX1, PRDX2, PRDX6, SNCA, SOD1 and SOD2) also were functionally
related. Similar trend was observed for the proteins involved in synaptic
functions, cytoskeletal arrangements, microtubule-associated proteins, cellular
stress response (especially the HSPs) and calcium binding. Our findings closely
resemble those of [25].
Besides, proteins
involved in mitochondrial structure and functions were functionally closed.
Deranged expression pattern of metabolism, energy generation and
mitochondria-associated proteins had been observed. The IPA analysis tally with
those and indicate that the AD mitochondria suffer from disrupted expression
and functionality of the proteins involved in metabolism, energy generation, OS
regulation and Ca2+ homeostasis [51]. Another important aspect of neuronal
mitochondria is maintenance of cellular dynamics so mitochondrial “traffic jam”
is overcome through proper translocation of mitochondria through the neuronal
cytoskeleton [19]. Present findings of the affected proteins linked together in
the AD subjects are “red signal” across the neuronal cytoskeletal
“cross-roads”. In line with the mitochondrial derangement, the functional
interaction among the affected cytoskeleton and microtubule-associated proteins
(especially tubulins) reinforce the observed jumble of the AD hippocampal
proteins.
Proteins involved in
metabolism of almost all the biomolecules have been affected in the AD groups.
The most notable alteration was those metabolizing glucose and lipids (APOA1,
HSD11B1, PHB, SERPINA1, SIRT2; Akr1c14, COMT, HSD11B1, Sult1a1; ACSL1, ALB,
FABP1; ABAT, ACSL1, ALB, APOA4, APOE, CS, DLAT, DLD, F2, FABP1, KNG1, MDH1,
MDH2, PDHB, RAC1, RGN, STX1A, SUCLA2). Top-ranking proteins involved in protein
metabolism and interacted together were ACADM, ALDOC, ANXA5, CRP, EEF1A1,
Gnmt/LOC100911564, HBA1/HBA2, HBB, HSD17B10, IVD, JUP, MAT1A, NEFL, NME2, SOD2,
VCP and YWHAB. Metabolism-related proteins were linked in the peripheral nodes
of the IPA.
CONCLUSION
Compared with those
of the controls, the AD rats had been found altered expression and network of
memory and learning related protein (both interacting and functional pathways,
as revealed by the STRING and IPA analysis, respectively). Observed findings
might be attributed to the infused Aβ in the model animals. The altered
proteins, functional networks and pathways might be targeted as an AD
withstanding stratagey. Thus, the present study paves a new vista in the realm
of AD therapeutics.
HIGHLIGHTS
• Control
and Alzheimer’s disease (ad) model rats’ hippocampal proteomics has been
compared.
• Ad
model rats showed altered expression of proteins and protein-protein
interaction networks and functional pathways.
Proteins and networks
involved in neuronal structure maintenance and regulation had been found to be
mostly affected in the ad rats’ hippocampi.
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