Abstract
Amyotrophic Lateral Sclerosis (ALS) is a devastative neurodegenerative
disease characterized by selective loss of motoneurons. While several
breakthroughs have been made in identifying ALS genetic defects, the
detailed molecular mechanisms are still unclear. These genetic defects
involve in numerous biological processes, which converge to a common
destiny: motoneuron degeneration. In addition, the common comorbid
Frontotemporal Dementia (FTD) further complicates the investigation of
ALS etiology. In this study, we aimed to explore the protein-protein
interaction network built on known ALS-causative genes to identify
essential proteins and common downstream proteins between classical ALS
and ALS+FTD (classical ALS + ALS/FTD) groups. The results suggest that
classical ALS and ALS+FTD share similar essential protein set (VCP,
FUS, TDP-43 and hnRNPA1) but have distinctive functional enrichment
profiles. Thus, disruptions to these essential proteins might cause
motoneuron susceptible to cellular stresses and eventually vulnerable
to proteinopathies. Moreover, we identified a common downstream
protein, ubiquitin-C, extensively interconnected with ALS-causative
proteins (22 out of 24) which was not linked to ALS previously. Our in
silico approach provides the computational background for identifying
ALS therapeutic targets, and points out the potential downstream common
ground of ALS-causative mutations.
Introduction
Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder
characterized by progressive and selective loss of upper and lower
motoneurons with no effective treatment available[[32]1]. Clinical
symptoms includes tremor, muscle weak, spasticity and paralysis, and
patients usually die from respiratory failure within five years[[33]2].
The majority (90–95%) of ALS patients are sporadic form (sALS), only
small cohort of patients (5–10%) are associated to autosomal dominant
inheritance as familial cases (fALS)[[34]3, [35]4]. The incidence rate
is at 2.7/100,000 in a ten-year Ireland research[[36]5]. To date there
are 26 subtypes of ALS listed in the Online Mendelian Inheritance in
Man (OMIM) database with varied disease onset time and symptom onset
origin, and 15–52% ALS patients are comorbid with Frontotemporal
Dementia (FTD)[[37]6, [38]7], the second most common dementia
representing series of neurological symptoms involving frontotemporal
lobar degeneration. FTD can exist alone without developing ALS, while
certain forms of FTD and ALS shared some clinical and genetic features
[[39]8].
Although the pathogenic mechanisms of ALS are not fully clear, a number
of gene mutations linked to ALS were discovered over past 20 years,
such as superoxide dismutase 1 (SOD1), TAR DNA-binding protein
(TARDBP), fused in sarcoma (FUS), optinurin (OPTN), valosin-containing
protein (VCP), sequestosome-1 (SQSTM1), ubiquilin-2 (UBQLN2), C9ORF72,
heterogeneous nuclear ribonucleoproteins A1 and A2/B1 (HNRNPA1 and
HNRNPA2B1)[[40]9–[41]18]. In particular, SOD1, TDP-43, FUS, optinurin
and ubiquilin-2 proteins were identified in aggregates from the
autopsies of many patients[[42]19]. These ALS-causative genes encoded
proteins with divergent functions, many of which can be related to
several categories, such as cellular transport (axonal/vesicle
transport or whose aggregation would impede transport), RNA processing
and ubiquitin proteasome system. However, it remains unclear how these
minimally related proteins, once impaired, all result in motoneuron
degeneration which eventually lead to various ALS subtypes. One
explanation is that some of the ALS-causative proteins are “essential
proteins” that, when acted inappropriately on by other ALS-causative
mutations, would result in profound effects to motoneuron survival. An
alternative hypothesis is that there may be common downstream proteins,
which maximally connect with those ALS-causative proteins through
either direct or indirect interaction.
The original idea of “essential proteins” is that these proteins are
crucial for survival and that their deletion confers the lethal
phenotype[[43]20]. The conventional experimental approaches in
identifying essential proteins, such as gene knock-out or RNA
interference, are usually time-consuming and cost-intensive. Several
previous studies have shown the feasibility of computational approaches
to predict gene essentiality and morbidity[[44]21–[45]24]. For example,
topological properties of protein-protein interaction (PPI) have been
employed to identify essential proteins in various organisms[[46]25,
[47]26]. The main idea is the “centrality-lethality rule”, in which
highly connected hub proteins are more essential to survival in PPI
network[[48]22]. Although there is still significant debate regarding
the rule, several studies suggest a correlation between topological
centrality and protein essentiality[[49]27–[50]29]. To identify crucial
ALS-causative proteins, we applied this same concept to calculate the
essentiality of each protein in a PPI network. This approach also
allowed us to investigate the roles of ALS/FTD mutations such as
C9ORF72 in the PPI network, which had been shown to involve distinct
pathways from sALS in brain transcriptome[[51]30].
Given such divergent functional background of these ALS mutations, it
is tempting to hypothesize that there might be a common downstream
interaction, either via individual proteins or convergent pathways,
which would render motoneuron vulnerable to toxicity. To evaluate this
possibility, we fully explored the ALS PPI network to analyze those
proteins capable of interacting with ALS-causative proteins directly.
If such proteins do exist, they must maximally interact with
ALS-causative proteins and play vital roles in maintaining cellular
activities. Thus upstream ALS-causative mutations might impair or
disrupt the function of downstream proteins and cause long-term toxic
effects.
In current study, we constructed a PPI network based on ALS-causative
genes imported from the OMIM database. Genes linked only to classical
ALS were grouped with or without genes implicated in ALS/FTD, namely
ALS+FTD group vs. classical ALS group. Our integration of network
topological properties and protein cluster information revealed that
classical ALS and ALS+FTD groups showed similar essential protein sets
(VCP, FUS, TDP-43 and hnRNPA1) but distinct patterns in functional
enrichment analysis. These essential proteins might present a set of
proteins susceptible to disruptions. Moreover, we identified an
interconnected common protein, ubiquitin-C, which extensively interacts
with almost all ALS-causative proteins (22 out of 24 proteins). To the
best of our knowledge, ubiquitin-C has not been linked to ALS yet. Our
results, based on computational analyses, suggest potentials of novel
disease mechanisms that may underlie various forms of ALS and shed
light on new direction in ALS study
Materials and methods
The overall workflows used in this study are shown in [52]Fig 1. The
process involves four main steps: construction, processing,
identification and producing. Construction consisted of obtaining ALS
causal genes from OMIM database (labeled (1) in [53]Fig 1) and
constructing the protein-protein interaction network of ALS based on
I2D database (2). Processing consisted of detecting clusters by
analyzing PPI (3); assessing topological properties by measuring degree
centrality in the network based on global PPI (4); and finally,
analyzing functional enrichment and pathway (5). Identification
consisted of: finding essential proteins for ALS by adopted edge
clustering coefficient (ECC) method (6) and identifying the biological
functions and pathways by GO and KEGG pathway enrichment analysis (7).
Producing consisted of analyzing downstream common proteins by designed
DFloyd algorithm (8).
Fig 1. Overall work flow.
[54]Fig 1
[55]Open in a new tab
2.1 Genetic defects in ALS and construction of PPI network
OMIM database is a disease phenotype database and a catalogue of human
genes and genetic disorders[[56]31]. In OMIM, the list of hereditary
disease genes is described in the OMIM morbid map. The ALS-causative
genes were imported from the OMIM morbid map
([57]https://www.omim.org/phenotypicSeries/PS105400). Corresponding
protein names and IDs were obtained by investigating the mapping scheme
of UniProt database[[58]32]. Hence we only considered those loci with
known encoding protein profiles in UniProt, thus ALS3 and ALS7 were
excluded in this research. We referred to genes linked to ALS1-22 in
OMIM, except ALS3 and ALS7, as classical ALS (20 genes); FTDALS 1–4
were regarded as ALS/FTD (4 genes) ([59]Table 1). Total ALS-causative
genes were referred to ALS+FTD (20+4 genes) in this research. In order
to investigate the differences between classical ALS and ALS+FTD
groups, we explored experimentally validated interactions from the
Interologous Interaction Database (I2D) database
([60]http://ophid.utoronto.ca/ophidv2.204/)[[61]33, [62]34]. I2D is a
protein-protein interaction database and an integrated database of
known experimental and predicted human protein interaction data sets
(including HRPD, BIND, and BioGrid). The gene names were inputted into
I2D database with human as the only chosen target organism. I2D
contains more than 290,000 experimental interactions from 38 databases
of human source[[63]34]. Thus, choosing I2D instead of single database
might help minimizing the bias[[64]35]. All homologous predicted
protein interactions in I2D database were excluded to increase the
reliability of protein interaction data. The rest experiment-based PPIs
were then used to construct classical ALS and ALS+FTD PPI networks with
ALS-causative proteins/interacting partners as nodes and interactions
as edges. The comprehensive interaction data published in the I2D
database enabled our analysis on a complete network of disease
proteins. Identifiers of proteins were unified using the protein IDs
defined in the UniProt database[[65]32]. Some proteins were given
multiple names, to avoid the ambiguous referring, the results in tables
and figures were presented in the format of gene name and UniProt ID.
Our study considered the I2D database version 2.9 released in September
2015.
Table 1. ALS-causative genes from OMIM.
Subtype Uniprot ID Gene Subtype Uniprot ID Gene Subtype Uniprot ID Gene
ALS1 [66]P00441 SOD1 ALS10 [67]Q13148 TARDBP ALS19 [68]Q15303 ERBB4
ALS2 [69]Q96Q42 ALS2 ALS11 [70]Q92562 FIG4 ALS20 [71]P09651 HNRNPA1
ALS3 - - ALS12 [72]Q96CV9 OPTN ALS21 [73]P43243 MATR3
ALS4 [74]Q7Z333 SETX ALS13 [75]Q99700 ATXN2 ALS22 [76]P68366 TUBA4A
ALS5 [77]Q96J17 SPG11 ALS14 [78]P55072 VCP
-----------------------------------------
ALS6 [79]P35637 FUS ALS15 [80]Q9UHD9 UBQLN2 FTD-ALS1 [81]Q96LT7 C9ORF72
ALS7 - - ALS16 [82]Q99720 SIGMAR1 FTD-ALS2 [83]Q8WYQ3 CHCHD10
ALS8 [84]O95292 VAPB ALS17 [85]Q9UQN3 CHMP2B FTD-ALS3 [86]Q13501 SQSTM1
ALS9 [87]P03950 ANG ALS18 [88]P07737 PFN1 FTD-ALS4 [89]Q9UHD2 TBK1
[90]Open in a new tab
2.2 Computing topological properties of protein interaction network
We evaluated the centrality of proteins in PPI network to investigate
the essentiality of proteins. Many research works indicated that PPI
networks have characters of “small-world behavior” and
“centrality-lethality” [[91]22, [92]36]. Removal of nodes with high
centrality makes the PPI network collapse into isolated clusters which
might imply the collapse of biological system. Degree centrality of a
protein indicates how many interactions that protein has to other
proteins. For each node (protein), we applied the topological measures
to assess its role in the network: degree centrality (DC). A PPI
network is represented as an undirected graph G (V, E) with proteins as
nodes and interactions as edges. The degree centrality is calculated
by:
[MATH:
DC(v)=|e(u,v)|u,v∈V
:MATH]
(Eq 1)
v represents a node in PPI network where u is any node other than v in
the network. e(u, v) represents the interaction between v and u. If
such an interaction does exist, the value of e(u, v) is one. If not, is
e(u, v) zero. |e(u, v)| represents total interaction numbers between v
and u.
2.3 Identification of clusters
A unique feature of the clique percolation clustering method (CPM) is
that it can uncover the overlapping community structure of complex
networks, i.e., one node can belong to several communities[[93]37]. In
order to detect the densely connected regions in network with possible
overlap and their functions in the network, the PPI information data
were imported into CFinder-2.0.6 (an open source software platform in
CPM method), and the clustering analysis was easily performed. The
clusters were identified to have the minimum k-cliques, which was
defined as the union of all k-cliques (complete sub-graph of size k)
that could be reached from each other.
2.4 Finding essential proteins
The use of global centrality measures based on network topology has
become an important method in identifying essential proteins. However
recent research pointed out that many essential proteins have low
connectivity and are difficult to be identified by centrality
measuring[[94]37–[95]41]. Hart et al. pointed out that clusters have
high correlation with essential proteins[[96]42]. A new method ECC was
proposed to identify essential proteins by integration of PPI network
topology and cluster information. The unabridged ECC method can be
found in Ren et al.[[97]43]. Its basic concepts are as follow.
In-degree K^in (i,c) of a protein i in a cluster C was defined as the
number of interactions which connect i to other proteins in C.
[MATH:
Kin(i,c)=
|{e(i,j<
mo>)|i,j∈v(v)}| :MATH]
(Eq 2)
The complex centrality of a protein i, Complex_C(i), was defined as the
sum of in-degree value of i in all clusters which included it.
Complex_C(i) could define the overlapping number of protein complexes.
[MATH:
Complex<
mo>_C(i)=∑Ci∈CS&
i∈CiKin(i,
Ci)<
/mrow> :MATH]
(Eq 3)
Where CS was the cluster set, and Ci was a cluster which included i.
The global centrality adopted subgraph centrality (DC) as it had better
performance in identifying essential proteins in centrality
methods[[98]29]. To integrate DC(i) and Complex_C(i), a harmonic
centrality (HC) of protein i was defined as follows:
[MATH:
HC(i)=α×DC(i)/DCma
x+(1−α)×Complex_C(i
)/Comple
x_Cm<
mi>ax :MATH]
(Eq 4)
Where α was a proportionality coefficient and took value in range of 0
to 1, generally set 0.5, DC[max] was the maximum DC value.
Complex_C[max] was the maximum Complex_C value.
2.5 Functional enrichment analysis
Functional enrichment analysis was performed to further study the
functions and enriched pathways of cluster based on GO database
(Version No.2010.09.03) ([99]http://www.genontology.org/)[[100]44] and
KEGG pathway ([101]http://www.genome.jp/kegg/)[[102]45], respectively.
In functional analysis, P<0.01 were considered statistically
significant. This analysis was performed by using the database for
annotation, visualization, and integrated discovery (DAVID,
[103]http://david.abcc.ncifcrf.gov/tools.jsp)[[104]46, [105]47], which
is an online platform providing functional annotation tools to analyze
biological meaning behind large list of genes.
2.6 Downstream common protein analysis
We carried out five steps to analyze the downstream common proteins by
designing a deformation Floyd (DFloyd) algorithm[[106]34] of the
shortest path. Give the source sets S and target T, S is the protein
set that composed of two sources: (1) k-clique cluster proteins under
highest possible k value excluding ALS-causative proteins; (2) proteins
presented in significant enrichment analysis GO term and KEGG pathway.
T is the ALS-causative protein set. The proteins in S and T are all
part of ALS PPI Network. The algorithm of DFloyd is shown below.
* Step1
Calculate the relation edge set W from source set S to target set T
by the enumerating method. W = {,,…,,…,,…,}
* Step 2
Computer all distance(s[i], t[i]), < s[i], t[i] > ∈W. If there is
not an edge between s[i] and t[i], then distance(s[i], t[i]) = ∞,
else distance(s[i], t[i]) = 1.
* Step 3
Remove all s[i], t[i] from W, s[i], t[i] ∈distance(s[i], t[i]) = 1
* Step 4
For any < s[i], t[i] > in W, Label = False.
If there is a vertax v, distance(s[i], ν)+distance(ν, t[i])<
distance(s[i], t[i]),
then distance(s[i], t[i]) = distance(s[i], ν)+distance(ν, t[i]).
Label = Ture
Endfor
* Step 5
Repeat step 4, until Label = False
2.7. Statistic
Thompson Tau test was performed to detect if the value was
significantly deviated from mean value. Modified Thompson Tau was
calculated as below:
[MATH: τ=t∙<
mo>(n−1)n∙n−2+t2 :MATH]
t = Student’s t value, α = 0.05, df = n-2; ** >mean ±τ SD; * >mean ±(τ
SD)/2
Results
3.1 Genes and PPI
Genetic deficits accounting for various classical ALS (ALS1-2, 4–6 and
8–22; 20 genes) and ALS/FTD phenotypes (ALS/FTD1-4; 4 genes) were
obtained from the OMIM database as shown in [107]Table 1. We studied
their interactions based on known protein-protein interactions by
exploring the I2D database. To investigate the roles of ALS/FTD
mutations in ALS, mutations linked to classical ALS phenotypes were
separated from those causing ALS/FTD. The PPI network was constructed
from proteins encoded by classical ALS (20) or ALS+FTD gene set (20+4).
The I2D contains more than 230,000 experiment-based interactions and
around 70,000 predicted interactions from human source. Our input of
ALS-causative proteins into I2D yielded 4,144 interactions in classical
ALS group and 5,454 interactions in ALS+FTD group. After removal of
homologous predicted interactions, 3,023 ([108]S1 Text) and 3,764
([109]S2 Text) interactions with 1,932 and 2,288 interacting nodes were
used to construct the PPI networks of classical ALS and ALS+FTD
respectively.
3.2 Network centrality degree analysis
The topological properties of protein interactions were calculated to
identify essential proteins in the network. The degree centrality of
each protein in PPI was ranked in [110]Table 2. The majority of
proteins with high degree centrality were proteins encoded by
ALS-causative genes listed in [111]Table 1, which was not surprising.
Interestingly ubiquitin-C (UBC) and YWHAE presented in ALS+FTD group,
as the only two proteins not encoded by ALS-causative genes. Both UBC
and YWHAE have not been linked to ALS or FTD yet. Among both groups,
VCP, hnRNPA1 and FUS were all in the top five with obviously higher
degree centrality. The results suggested the importance and extensive
involvement of VCP, hnRNPA1 and FUS in ALS pathogenesis. Moreover our
results revealed an intriguing role of UBC in ALS+FTD PPI networks, a
link that might have been otherwise overlooked. It is not surprising to
see similar results between two groups because degree centrality lack
of the topological information. Thus we performed cluster analysis to
further examine the PPI network.
Table 2. Degree centrality ranking in ALS PPI network.
Classical ALS ALS+FTD
Rank Uniprot Protein DC Rank Uniprot Protein DC Rank Uniprot Protein DC
1 [112]P55072 VCP 729[113]^** 1 [114]P55072 VCP 729[115]^** 15
[116]Q15303 ERBB4 68
2 [117]P35637 FUS 385[118]^* 2 [119]Q13501 SQSTM1 596[120]^** 16
[121]Q99700 ATXN2 55
3 [122]P09651 HNRNPA1 351 3 [123]P35637 FUS 385[124]^* 17 [125]Q9UQN3
CHMP2B 27
4 [126]Q13148 TARDBP 319 4 [127]P09651 HNRNPA1 351 18 [128]Q7Z333 SETX
25
5 [129]P68366 TUBA4A 223 5 [130]Q13148 TARDBP 319 19 [131]P0CG48 UBC 22
6 [132]P00441 SOD1 216 6 [133]P68366 TUBA4A 223 20 [134]Q96Q42 ALS2 14
7 [135]P43243 MATR3 149 7 [136]P00441 SOD1 216 21 [137]Q96LT7 C9orf72
13
8 [138]P07737 PFN1 110 8 [139]P43243 MATR3 149 22 [140]P62258 YWHAE 11
9 [141]Q96CV9 OPTN 101 9 [142]Q9UHD2 TBK1 144
10 [143]Q99720 SIGMAR1 90 10 [144]P07737 PFN1 110
11 [145]Q9UHD9 UBQLN2 78 11 [146]Q96CV9 OPTN 101
12 [147]O95292 VAPB 73 12 [148]Q99720 SIGMAR1 90
13 [149]Q15303 ERBB4 68 13 [150]Q9UHD9 UBQLN2 78
14 [151]Q99700 ATXN2 55 14 [152]O95292 VAPB 73
[153]Open in a new tab
** >mean ±τ SD;
* >mean ±(τ SD)/2
3.3 Cluster analysis
To further scrutinize the complex ALS PPI network, we conducted a
cluster analysis to get a clear picture of interactions between the
proteins in the PPI. The clusters (cliques) with densely connected
nodes in the PPI network were detected using the ClusterOne plug-in of
the CFinder 2.0.6 software. In the classical ALS group, 393 and 136
clusters were identified with parameters set to a minimum size of three
(k = 3) and four (k = 4) respectively, while in the ALS+FTD group, 578
clusters were detected when k = 3 and 59 clusters were found when k =
5. In current study VCP, FUS, hnRNPA1 and TDP-43 were the cores of
network in classical ALS (k = 4); ALS+FTD (k = 5) groups shared similar
feature but included SQSTM-1 instead of hnRNPA1 and FUS ([154]Fig 2A
and 2B). In the classical ALS group, however, a few proteins not
encoded by classical ALS gene set appeared in the list: UBC, YWHAE,
YWHAZ and sequestosome-1/p62 (SQSTM1). Mutations on SQSTM1 are known to
associate with ALS/FTD; In this research, SQSTM1 was categorized in
ALS+FTD group, but it also involved in some ALS without FTD
cases[[155]48], which directly validated our algorithm and highlighted
the importance of SQSTM1/p62 in pathology across ALS-FTD spectrum. More
importantly those surrounding proteins which interacted with the
cluster cores provided important clues to further investigate
downstream pathways of ALS pathogenesis. The involvement of each
protein was assessed by calculating the number of clusters each protein
participated in. VCP, TDP-43 and hnRNPA1 were at the top of the list in
both groups ([156]Table 3). Notably, UBC and YWHAE presented in both
groups again as proteins not encoded by ALS-causative genes.
Fig 2.
[157]Fig 2
[158]Open in a new tab
The clusters of (a) classical ALS, 136 clusters at k = 4; (b) ALS+FTD,
59 clusters at k = 5. The yellow highlights the core clusters
identified by significant involvement ranking calculated in [159]Table
3 based on Thompson Tau test.
Table 3. Protein involvement based on participated cluster number.
Classical ALS ALS+FTD
Rank Uniprot Protein Clusters Rank Uniprot Protein Clusters
1 [160]P55072 VCP 296[161]^** 1 [162]Q13501 SQSTM1 460[163]^**
2 [164]Q13148 TDP-43 269[165]^** 2 [166]P55072 VCP 351[167]^*
3 [168]P09651 HNRNPA1 214[169]^* 3 [170]Q13148 TDP-43 294[171]^*
4 [172]P35637 FUS 185[173]^* 4 [174]P09651 HNRNPA1 241
5 [175]P43243 MATR3 81 5 [176]P35637 FUS 228
6 [177]P07737 PFN1 65 6 [178]P43243 MATR3 88
7 [179]Q96CV9 OPTN 25 7 [180]P07737 PFN1 65
8 [181]O95292 VAPB 20 8 [182]P68366 TUBA4A 62
9 [183]Q99700 ATXN2 16 9 [184]P00441 SOD1 47
10 [185]P0CG48 UBC 11 10 [186]Q96CV9 OPTN 47
11 [187]Q96Q42 ALS2 9 11 [188]O95292 VAPB 20
12 [189]P00441 SOD1 8 12 [190]Q99700 ATXN2 16
13 [191]Q13501 SQSTM1 8 13 [192]P0CG48 UBC 14
14 [193]Q9UHD9 UBQLN2 6 14 [194]Q9UHD2 TBK1 12
15 [195]P62258 YWHAE 6 15 [196]Q96Q42 ALS2 8
16 [197]P63104 YWHAZ 6 16 [198]P62258 YWHAE 8
[199]Open in a new tab
** >mean ±τ SD;
* >mean ±(τ SD)/2
3.4 Essential proteins
All above analysis were based on network topological properties. In
order to take the relevance between interactions and protein
essentiality into account, we used the ECC method to identify essential
proteins in network. The result of harmonic centrality are shown in
[200]Fig 3 in which VCP, hnRNPA1, FUS and TDP-43 were the top-ranking
hub proteins in both the classical ALS and ALS+FTD PPI networks, based
on Thompson Tau test. In addition SQSTM1/p62 had the highest harmonic
centrality in ALS+FTD group, and UBC presented in both groups as the
only none ALS-causative protein. Interestingly the widely presented
C9ORF72 mutation (34% fALS and 7% sALS[[201]48, [202]49]) was not top
ranked in either group. However, its interactive protein profile was
unique from other ALS-causative proteins ([203]Fig 4A).
Fig 3.
[204]Fig 3
[205]Open in a new tab
Essential proteins ranked by HC (a) classical ALS; (b) ALS+FTD. **
>mean ±τ SD; * >mean ±(τ SD)/2
Fig 4. Profiles of the interacting proteins.
[206]Fig 4
[207]Open in a new tab
A) Profile of C9orf72 with only direct interaction B) The profiles
include both direct and indirect interactions of important downstream
proteins. Red, blue and green denote direct, secondary and tertiary
contact proteins respectively. Yellow highlights ALS/FTD proteins.
3.5 Functional enrichment analysis
GO term and KEGG pathway enrichment analysis were performed on the 393
clusters, k = 3 in classical ALS group as well as the 578 clusters, k =
3 in ALS+FTD group. GO term analysis was carried out in three
categories, including biological processes (BP), molecular function
(MF), and cellular components (CC). Tables [208]4 and [209]5 describe
the top five GO terms of BP, MF, and CC as well as top five pathways in
KEGG (GO terms with p values <0.01 were discarded). In classical ALS
group the most significant terms in BP and CC were all RNA processing
related; the translation control was also highlighted in BP ([210]Table
4). In accordance with the results from GO terms analysis, ribosme and
spliceosome pathways were all significant in KEGG pathway analysis
([211]Table 5). Consistenly, the ALS+FTD group showed high functional
enrichment in translation control, ribosome and RNA processing
([212]Table 4). Autophagy, ribosome, spliceosome and neurotrophin
signaling pathways were shared by classical ALS and ALS+FTD groups in
KEGG enrichment analysis ([213]Table 5).
Table 4. Most significantly enriched GO terms.
Classical ALS ALS+FTD
Terms P value Terms P value
Biological mRNA metabolic process 7.12E-23 Translational elongation
9.54E-45
Process mRNA processing 5.40E-21 Translation 8.27E-27
RNA processing 5.50E-21 mRNA metabolic process 5.95E-25
Translational elongation 1.00E-19 RNA processing 5.80E-24
RNA splicing 1.17E-17 mRNA processing 2.17E-23
Cellular Ribonucleoprotein complex 6.71E-45 Ribonucleoprotein complex
4.76E-62
Component Organelle lumen 4.56E-28 Cytosol 1.74E-54
Intracellular organelle lumen 1.15E-27 Cytosolic part 3.09E-38
Membrane-enclosed lumen 2.60E-27 Intracellular organelle lumen 3.82E-34
Cytosol 7.40E-27 Organelle lumen 4.39E-34
Molecular RNA binding 1.06E-38 RNA binding 3.40E-48
Function Nucleotide binding 9.80E-16 Structural constituent of ribosome
1.34E-29
Enzyme binding 3.33E-15 Nucleotide binding 6.21E-22
Structural constituent of ribosome 2.57E-12 Structural molecule
activity 2.23E-20
Unfolded protein binding 6.83E-11 Enzyme binding 4.20E-18
[214]Open in a new tab
Table 5. Most significantly enriched KEGG pathways.
Classical ALS ALS+FTD
Terms P value Terms P value
1. Ribosome
2. Regulation of autophagy
3. Spliceosome
4. mTOR signaling pathway
5. Neurotrophin signaling pathway 3.94E-14
1.33E-07
1.62E-07
6.97E-06
6.39E-05 1. Ribosome
2. Neurotrophin signaling pathway
3. Prostate cancer
4. Regulation of autophagy
5. Spliceosome 2.81E-29
4.73E-10
9.69E-09
1.91E-08
1.26E-06
[215]Open in a new tab
3.6 Common proteins analysis
To investigate how these functionally diverse pathogenic proteins all
led to motoneuron degeneration, we analyzed their PPI profiles with the
aim to identify common ground downstream of ALS-causative proteins. For
the classical ALS group, in addition to the 78 interacting proteins at
k = 4 ([216]Fig 2A), we included proteins involved in significant GO
terms and KEGG pathways as protein set S1. For the ALS+FTD group, 27
proteins from k = 5 ([217]Fig 2B) along with proteins from significant
GO terms and KEGG pathways formed protein set S2. The DFloyd algorithm
was employed to investigate the interaction between ALS-causative
protein set (T) and downstream protein sets (S1 and S2). The results
suggested that UBC was the most interconnected protein in both
classical ALS and ALS+FTD groups. Other top ranking common proteins
shared by both groups were YWHAZ, PARK2, GBAS and GABARAP ([218]Table
6). The detailed interactive profiles of selective proteins were shown
in [219]Fig 4B, in which UBC was connected to all ALS-causative
proteins except ANG and CHCHD10.
Table 6. Downstream proteins ranked by number of direct interactions with
ALS-causative proteins.
Classical ALS ALS+FTD
Rank Uniprot Protein Interacting # Rank Uniprot Protein Interacting #
1 POCG48 UBC 19[220]^** 1 POCG48 UBC 22[221]^**
2 [222]Q13501 SQSTM1 11[223]^* 2 [224]P63104 YWHAZ 10
3 [225]P63104 YWHAZ 9 3 [226]O60260 PARK2 9
4 [227]Q00987 MDM2 8 4 [228]O75323 GBAS 9
5 [229]O00443 PIK3C2A 8 5 [230]O95166 GABARAP 9
6 [231]O60260 PARK2 8 6 [232]P11021 GRP78 9
7 [233]O75323 GBAS 8 7 [234]P55854 SUMO3 9
8 [235]O95166 GABARAP 8 8 [236]Q13618 CUL3 9
9 [237]P55854 SUMO3 8 9 [238]Q9H492 MAP1LC3A 9
10 [239]Q13618 CUL3 8 10 [240]Q00987 MDM2 9
11 [241]P60520 GABARAPL2 8 11 [242]Q9Y4P8 WIPI2 8
12 [243]Q86VP9 PRKAG2 7 12 [244]Q676U5 ATG16L1 8
13 [245]Q9BSB4 ATG101 7 13 [246]Q14999 CUL7 8
14 [247]Q9H1Y0 ATG5 7 14 [248]Q15843 NEDD8 8
15 [249]Q9H492 MAP1LC3A 7 15 [250]P60520 GABARAPL2 8
16 [251]P11021 GRP78 7 16 [252]Q9H0R8 GABARAPL1 8
17 [253]Q676U5 ATG16L1 7 17 [254]O00443 PIK3C2A 8
18 [255]Q9H0R8 GABARAPL1 7 18 [256]Q9H1Y0 ATG5 8
19 [257]P61956 SUMO2 7 19 [258]P61956 SUMO2 8
20 [259]Q14999 CUL7 7 20 [260]Q13616 CUL1 8
21 [261]Q15843 NEDD8 7 21 [262]Q9BSB4 ATG101 7
22 [263]Q9Y4P8 WIPI2 7 22 [264]Q9UGJ0 PRKAG1 7
23 [265]Q13573 SNW1 6 23 [266]P07437 TUBB 7
24 [267]Q99459 CDC5L 6 24 [268]P07900 HSP90AA1 7
25 [269]P15297. NOF 6 25 [270]Q13573 SNW1 7
26 [271]P54619 PRKAG1 6 26 [272]Q86VP9 PRKAG2 7
27 [273]Q13286 CLN3 6 27 [274]P54619 PRKAG1 7
28 [275]Q9GZZ9 UBA5 6 28 [276]Q13286 CLN3 7
29 [277]Q9NR46 SH3GLB2 6 29 [278]Q9GZZ9 UBA5 7
30 [279]Q9Y484 WDR45 6 30 [280]Q9NR46 SH3GLB2 7
31 [281]Q9UGJ0 PRKAG2 6 31 [282]Q99459 CDC5L 6
32 [283]O75147 OBSL1 5 32 [284]P15297 NOF 6
33 [285]O75530 EED 5 33 [286]Q9Y484 WDR45 6
34 [287]P02751 FN1 5 34 [288]O75147 OBSL1 6
35 [289]P13612 ITGA4 5 35 [290]O75530 EED 6
36 [291]P19320 VCAM1 5 36 [292]P02751 FN1 6
37 [293]P27694 RPA1 5 37 [294]P13612 ITGA4 6
38 [295]P35244 RPA3 5 38 [296]P19320 VCAM1 6
39 [297]P35638 DDIT3 5 39 [298]P35638 DDIT3 6
40 [299]P54646 PRKAA2 5 40 [300]P54646 PRKAA2 6
41 [301]Q15831 STK11 5 41 [302]Q15831 STK11 6
42 [303]Q13616 CUL1 5 42 [304]P62993 GRB2 6
43 [305]O15530 PDPK1 5 43 [306]P27694 RPA1 6
44 [307]P62993 GRB2 5 44 [308]P27361 MAPK3 6
[309]Open in a new tab
** >mean ± τ ∙ SD.
* >mean ± (τ ∙ SD)/2.
Discussion
Essential proteins
The ECC method was used to identify essential proteins in PPI network
that integrated global topological properties and cluster
information[[310]43]. Our results showed that VCP, SQSTM1/p62, hnRNPA1,
FUS and TDP-43 were proteins with significantly high harmonic
centrality in ECC method. VCP is known to associate with some forms of
FTD, Paget's disease of the bone (PDB) and inclusion body myopathy
(IBM) before the discovery that mutations in these same genes account
for approximately 1–2% fALS patients[[311]50, [312]51]. The finding of
VCP mutations in ALS gives rise to an interesting phenomenon that the
mutations on single gene can affect multiple tissues and result in
distinctive diseases. VCP is associated with nucleocytoplasmic
transport and putative ATP binding protein[[313]52]. Bartolome et al.
showed that VCP mutations were likely resulted in reduced ATP level in
energy production due to dysregulated mitochondria[[314]53], which
might partially explain the profound effects of mutations in diverse
tissues. The idea of “multisystem proteinopathy” is further reinforced
by the discovery of HNRNPA1/HNRNPA2B1 mutations in some forms of ALS
patients[[315]54]. Firstly the involvement of hnRNPA1 brings up the
importance of RNA processing in motoneuron degeneration that was
previously highlighted by discovery of mutations of FUS and TDP-43
being implicated in both ALS and FTD[[316]55–[317]58]. Secondly the
results suggest that certain ALS subtypes indeed have wide-spreading
disease effects on not only motoneuron but also on muscles, brain and
bones. These wide-spread effects imply that motoneuron degeneration in
ALS, at least in some subtypes, might be the final outcome of a series
of genetic deficits causing multisystem dysregulations instead of a
single disease. Given above rationale that ALS might not be a single
disease as well as the complexity of motoneuron degeneration etiology,
it is reasonable to integrate diverse causative proteins to identify a
hub network in which essential proteins strongly interact with each
other and downstream proteins ([318]Fig 2A and 2B). Such a hub network
would ideally incorporate as many causative proteins as possible. Thus,
essential proteins in the network provide important clues to understand
mechanisms underlying motoneuron degeneration. SQSTM1/p62 was
identified as having involvement in ALS, FTD and PDB[[319]18, [320]59,
[321]60]. P62, SQSTM1 encoding protein, has been reported to be
strongly associated with ubiquitination and involve in autophagy,
oxidative stress and NF-ƙB pathway[[322]59, [323]61, [324]62]. In ALS
and FTD, it is frequently found in inclusion bodies containing
polyubiquitinated proteins along with UBQLN2[[325]63]. The common
pathological features of ALS and FTD strongly imply the overlapping
between diseases spectrum[[326]64]. TARDBP encoded protein, TDP-43, is
found in the common pathological hallmark, ubiquitin-positive inclusion
bodies, in both ALS and FTD[[327]56]. TDP-43 is an important regulator
of RNA metabolism and its association with ALS evokes major interest in
the role of RNA processing in ALS[[328]65]. Soon after the discovery,
FUS mutations were also found in small cohort of fALS patients
(~4%)[[329]55, [330]58]. More importantly the majority of mutants are
clustered at RNA-binding domain rich C-terminus, a feature similar to
TDP-43 in ALS. However, the FUS mutation induced ALS seems to lack of
TDP-43 positive inclusions. Further evidence showed that overexpressed
wild-type FUS rescued TARDBP knockdown, but not vice versa, suggesting
TDP-43 might be upstream of FUS[[331]66]. In current work, we
calculated the essential proteins based on PPI network built from ALS
causative proteins. hnRNPA1, TDP-43 and FUS are all associated with
RNA-processing while VCP and SQSTM1/p62 are involved in
nucleocytoplasmic transport and autophagy respectively. It seems that
we have a chain of essential proteins involved in cellular activities
ranging from nucleus/RNA level to cytoplasmic/protein level. It is
clear that this chain tightly regulates protein turnover activities
through upstream RNA metabolism to downstream protein chaperoning and
clearance. Conceivably, disruption in any part of the chain would lead
to catastrophic results. However, certain mutations only affect very
small amount of ALS patients, which does not to fit in the so called
“centrality-lethality” theory in PPI network analysis field. The
contradiction is likely due to redundancies of regulatory proteins,
compensation pathways or feedback regulations. Besides it is unclear
why the same mutation would cause different levels of damage in
tissues, and resulted in distinctive phenotypes, onset time and disease
severity. One possible explanation is that each tissue has its own
protein homeostasis/turnover profile, such that each mutation is likely
to cause different level of impacts onto the PPI network. Thus,
identifying essential proteins and hub network, and a detailed
profiling of interested tissue are worthy further investigation to
precisely assess the level of damage in specific tissue due to certain
mutations. In addition, mutations of essential proteins with various
functions all led to motoneuron degeneration in ALS suggests that there
might be common downstream proteins where different pathogenic
mechanisms converge.
Downstream common proteins
In present work, we analyzed both classical ALS and ALS+FTD PPI
networks, and identified the aforementioned essential proteins. Further
calculation revealed a set of common downstream proteins with strong
interactivities toward causative proteins. In the classical ALS group,
UBC and SQSTM1/p62 stood out as the most interconnected downstream
proteins. SQSTM1 as a causative gene is categorized in the ALS+FTD
group but not the classical ALS group; the result validates our
algorithm in identifying potential targets associated to motoneuron
degeneration. There are a set of widely-interconnected downstream
proteins shared by both groups: UBC, YWHAZ, PARK2, GBAS and GABARAP
([332]Table 6). It seems reasonable to consider UBC as the most
interconnected downstream protein in both classical ALS and ALS+FTD
groups since it is a polyubiquitin precursor protein. However, it is
intriguing that ubiquitin A-52 (UBA-52) and polyubiquitin B (UBB) are
not on top of the list; especially as UBB is involved in several
neurodegenerative diseases. UBB frame shifting mutation (UBB+1) impedes
proteasomal proteolysis, and has been extensively identified in
Alzheimer disease (AD), FTD and Huntington disease (HD) as pathological
hallmark[[333]67]. However, UBB+1 transgenic mice with mutant
huntingtin showed no aberrant phenotype except increased inclusion
bodies, albeit they were more sensitive to the toxicity[[334]68]. In
contrast to the well-studied role of UBB in neurodegeneration, to the
best of our knowledge, there is no strong molecular evidence to link
UBC and neurodegeneration to date. However, the same observation, high
interactivities between UBC and causative proteins in neuronal
degeneration, was also made in an in silico network analysis of
AD[[335]69]. UBC, not to be confused with ubiquitin-conjugating enzyme
(also called UBC or more frequently E2), along with UBB encode
polyubiquitin precursor proteins with nine and three ubiquitin tandem
repeats respectively. Both UBB and UBC transcription is shown to be
induced and upregulated in response to various cellular
stresses[[336]70–[337]74], in addition to the constitutive expression
under normal condition[[338]75]. The presumed redundancy of both genes
seems to be insufficient to compensate for the loss of each other. In
the case of Ubc knockout mice, it is lethal to transgenic mice at
embryonic stage due to the disrupted fetal liver development[[339]75,
[340]76]. On the other hand, Ubb-null mice lead to damaged neurons
within the arcuate nucleus of the hypothalamus[[341]77]. The results
strongly suggest that UBC and UBB are not functionally redundant. Since
UBB and UBC are functionally and structurally similar, the role and
importance of UBB regulation in AD might reveal the importance of UBC
in neurodegeneration. UBB+1 and UCHL1 are both important regulators,
likely to have opposite effects, of beta-amyloid production and amyloid
precursor protein processing in AD [[342]78]. Although UCHL1 is not
known to directly cause ALS, UCHL1 null mice showed upper motoneuron
vulnerability [[343]79]. Thus it stressed the importance of protein
quality control which involved both UCHL1 and polyubiquitin precursor.
Moreover UBC demonstrates a tissue-specific manner in coping with
various cellular stresses instead of a generalized response[[344]72,
[345]80]. UBC is shown to be positively regulated by Sp1, MEK1 and
FOXO3a in rat L6 muscle cell[[346]80–[347]82], and FOXO3a is
neuroprotective in models of motoneuron diseases[[348]83]. Additionally
TDP-43 regulates protein quality control through FOXO-dependent
pathway[[349]84]. Thus it is highly plausible that FOXO-mediated UBC
expression contributes to motoneuron proteasome regulation which
eventually affects ubiquitin-proteasome system. Given that UBC is able
to interact with almost all ALS causative proteins (22 out of 24),
further investigation is needed to profile UBC in motoneuron to better
understand the behavior of this common downstream protein in ALS.
Another interesting finding is the presence of 14-3-3 protein family
member (YWHAZ) in downstream protein analysis. It has been known that
14-3-3 protein interacts with TDP-43 and SOD1 in ALS to modulate
neurofilament light chain mRNA stability in G93A and A4T mSOD1
mice[[350]85]; 14-3-3 protein was also found in lewy body in
sALS[[351]86]. YWHAZ also interacts with FUS in ALS whereas its isoform
YWHAQ was reported to have significantly elevated mRNA level in sALS
patients[[352]86, [353]87]. 14-3-3 protein extensively involves in
apoptosis, and protein and mRNA stabilization, however, much remains
unknown about its role in ALS pathology. Notably its isoform, YWHAE,
also presents in the results of cluster analysis and ECC, which
suggests a deep involvement of this protein family in ALS.
C9ORF72
Although considerable progression has been made in identifying ALS
causative genes over past few years, TARDBP, FUS, VCP, UBQLN2, SQSTM1
and C9ORF72 for example, underlying mechanisms remain elusive, as does
the clinical spectrum of phenotypes. C9ORF72 not only associates with a
large portion of ALS patients (7% sALS/ 34% fALS) but also implicates
in FTD (25%). The functions of C9ORF72 encoded protein are not fully
clear, however, it has been shown capable of interacting with hnRNPA1,
hnRNPA2/B1, ubiquilin-2 in immunoprecipitation from cell line
models[[354]88]. The GGGGCC repeats in C9ORF72 translate to dipeptide
repeat protein (DPR) in repeat-associated non-ATG manner which could
impair RNA processing and lead to cell death[[355]89, [356]90]. Recent
study showed that C9orf72 mutation led to SQSTM-1 (p62) pathology as
seen in ALS/FTD patients through Rab1a and ULK1 autophagy initiation
complex [[357]91]. Our analysis of C9ORF72 interacting profile is based
on I2D database which does not reflect the DPR proteins and PPI
described above. This should be taken as caveat in interpreting our
analysis results. Nevertheless, the functional enrichment analysis
suggests that ALS+FTD is different from classical ALS in GO terms and
KEGG pathway. Specifically, unfolded protein response (UPR), membrane
transport, splicing and RNA metabolism pathway are unique in ALS+FTD
group compared to classical ALS, which highly enriches with autophagy,
survival and nutrient-sensing pathways ([358]Table 6). The result is
consistent with brain transcriptome study in ALS conducted by Prudencio
et al., in which C9ALS mainly affected intracellular
transport/localization and UPR pathways, and sALS involved cytoskeleton
organization, defense response and synaptic transmission while
alternative splicing and RNA processing defects were found in
both[[359]30]. In addition, the surprisingly low ranking in centrality
and topology analysis of C9ORF72 suggests that certain forms of ALS
associated with FTD might not experience exactly the same molecular
mechanism as classical ALS, although they share many pathological
hallmarks and final destiny. If this holds true, future ALS therapeutic
development might take into consideration individual genetic profile to
tailor treatment.
Conclusion
The PPI network analysis highlights a set of ALS causative proteins as
essential proteins, which form a complete regulatory chain of protein
turnover. The result emphasizes on the importance of protein turnover
in motoneuron degeneration. More importantly the hub network formed by
essential proteins provides a converging point connecting other ALS
causative proteins to downstream common proteins. It might help to
explain why these functionally diverse ALS mutations all led to
motoneuron degeneration. Our in silico analysis suggests a more active
role of UBC in motoneuron degeneration which has been overlooked.
Considering its active regulatory roles in ubiquitination and
transcription under various conditions, UBC is likely the common
protein connecting most causative proteins to proteasome regulation.
UBC itself might not be sufficient to cause motoneuron degeneration,
but it surely can serve as a useful start point to explore further
UBC-related pathways that might shed light on common mechanism
underlying motoneuron degeneration.
Supporting information
S1 Text
(TXT)
[360]Click here for additional data file.^ (44.3KB, txt)
S2 Text
(TXT)
[361]Click here for additional data file.^ (55.1KB, txt)
Data Availability
All relevant data are within the paper and its Supporting Information
files.
Funding Statement
This work was supported Northwestern University, Grant #T32
([362]http://www.nuin.northwestern.edu/inside-nuin/training-grants/trai
ning-in-the-neurobiology-of-movement-and-rehabilitation-sciences/) to
SWK; the National Institute of Neurological Disorders and Stroke, Grant
#NS07786303 ([363]http://www.ninds.nih.gov/) to CJH and the ALS
Association, Grant #277 ([364]http://www.alsa.org/) to MJ. The funders
had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
References