Abstract
Background
Functional modules in protein-protein interaction networks (PPIN) are
defined by maximal sets of functionally associated proteins and are
vital to understanding cellular mechanisms and identifying disease
associated proteins. Topological modules of the human proteome have
been shown to be related to functional modules of PPIN. However, the
effects of the weights of interactions between protein pairs and the
integration of physical (direct) interactions with functional (indirect
expression-based) interactions have not been investigated in the
detection of functional modules of the human proteome.
Results
We investigated functional homogeneity and specificity of topological
modules of the human proteome and validated them with known biological
and disease pathways. Specifically, we determined the effects on
functional homogeneity and heterogeneity of topological modules (i)
with both physical and functional protein-protein interactions; and
(ii) with incorporation of functional similarities between proteins as
weights of interactions. With functional enrichment analyses and a
novel measure for functional specificity, we evaluated functional
relevance and specificity of topological modules of the human proteome.
Conclusions
The topological modules ranked using specificity scores show high
enrichment with gene sets of known functions. Physical interactions in
PPIN contribute to high specificity of the topological modules of the
human proteome whereas functional interactions contribute to high
homogeneity of the modules. Weighted networks result in more number of
topological modules but did not affect their functional propensity.
Modules of human proteome are more homogeneous for molecular functions
than biological processes.
Electronic supplementary material
The online version of this article (10.1186/s12859-018-2549-8) contains
supplementary material, which is available to authorized users.
Keywords: Topological modules, Functional modules, Physical PPI,
Functional PPI, Functional enrichment analysis, Protein-protein
interaction networks
Background
Even after decades of research in the field of human genes, gene
products and functions, understanding of genotype-phenotype
relationship is far from complete. Biomolecules (genes, RNA, proteins,
metabolites) interact with each other and environmental factors in
order to accomplish various biological processes. Representing these
interactions as biological networks (metabolic, protein-protein
interactions, gene regulatory, co-expression) and their analyses
provide insights in finding genes associated with cellular processes
such as immune response, signalling pathways or with a complex disease
like cancer [[27]1].
Currently, 20,231 proteins of the human proteome have been identified
[[28]2] but the landscape of their interactions is only partially
known. Protein interactions may be physical when their amino acid
residues physically interact through electrostatic forces like
hydrophobic or functional interactions when a protein influences the
activity of another protein through regulation, co-expression, or some
other genetic interaction [[29]3, [30]4] (Fig. [31]1). Large scale
experiments like yeast two-hybrid and affinity purification coupled to
mass spectrometry identify physical protein interactions [[32]5, [33]6]
while high throughput expression techniques like microarray and RNA-seq
elucidate functional links between proteins [[34]7, [35]8].
Fig. 1.
Fig. 1
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Illustrations of physical, functional and combined protein-protein
interaction networks (PPIN)
Protein-protein interaction networks (PPIN) like most biological
networks are believed to be modular in nature [[37]4, [38]9, [39]10]
and detecting functional modules of PPIN are vital for understanding
gene-function associations and designing therapeutics. Topological
modules are sub-networks where nodes within a module have dense
connections as compared to the nodes of the other modules [[40]11].
Functional module, on the other hand, is a sub-network that contribute
to similar biological functions [[41]4, [42]9]. Computational methods
accurately inferring functional and disease modules of the human
proteome would be of paramount importance for studying cellular and
disease mechanisms.
Numerous computational algorithms have been attempted on biological
networks in order to identify modules by using networks’ topological
properties based on node neighbours [[43]12], edge weights [[44]13] and
modularity [[45]14, [46]15]. Other sub-network identifying algorithms
including those finding core and loop structures [[47]16, [48]17],
cliques [[49]18] and frequent graph patterns [[50]19] have also been
attempted to find topological modules in biological networks. However,
only a few studies have compared their functional properties and their
relevance to functional modules [[51]20–[52]22]. Usual approach to
evaluate the functional significance of topological modules is to
perform functional enrichment analysis and decide on the significantly
enriched biological functions [[53]21, [54]23, [55]24]. This approach
is however inconclusive of determining functional coherence and
specificity of topological modules [[56]25]. In present work, we
introduce a novel functional specificity measure that encompasses both
functional homogeneity and heterogeneity of the topological modules.
Top ranked topological modules are thereby identified and validated for
their functional specificity.
We combine functional interactions inferred from expression data
[[57]26, [58]27] and physical interactions of PPIN [[59]6, [60]16] to
provide holistic functional attributes to protein nodes and
interactions of the network for the determination of functional modules
[[61]28–[62]30]. Though several studies have reported characteristics
of resulting modules of different biological networks [[63]13, [64]17,
[65]21], there is a need of a systematic study elucidating the effects
of using both functional and physical interactions of PPIN on detecting
topological and functional modules. Previously, Theofilatos et al. and
Lubovac et al. have applied weighted PPIN to predict protein complexes
using a Markov clustering based approach and ranking measure on the
basis of weighted neighborhood property, respectively [[66]31, [67]32].
But here we investigate the role of edge weights incorporated from gene
functional similarities in the modular detection of PPIN.
Our contributions in this study are (i) evaluation of functional
coherence and specificity of the topological modules of the human
proteome by using novel measures, (ii) determination of the effect of
using both direct physical and indirect functional links of PPIN on
detection of functional modules, and (iii) systematic analysis of
incorporating functional context of interactions as edge weights using
functional similarities of genes. We have used three different PPIN
datasets of the human proteome and Louvain community detection
algorithm [[68]14] for modular detection. The weighted PPIN were
generated by calculating functional similarity between interacting
proteins by using molecular functions, biological processes and
cellular components of Gene Ontology (GO) [[69]33]. We also elaborate
on how physical and functional interactions between proteins affect
functional diversity of topological modules.
Results
Physical and functional PPIN
The present study considers three types of human PPIN based on
physical, functional, and combined interactions as given in
Table [70]1. The strengths or weights of protein-protein interactions
with respect to their functional context (MF, BP and CC) are calculated
from functional similarities of respective GO context, using Wang
measure [[71]34]. This led to nine sets of weighted PPIN and their
network properties are listed in Table [72]2.
Table 1.
Properties of different binary PPIN: physical (P), functional (F), and
combined (C)
Network Nodes Edges Avg. degree Avg. path length Diameter Edge density
Clustering coeff. Giant component size
P 13,269 98,013 14.73 6.95 11 0.0011 0.15 13,177
F 11,362 613,865 108.06 3.14 11 0.0095 0.26 11,271
C 15,562 700,640 90.04 6.10 11 0.0057 0.20 15,518
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Table 2.
Properties of weighted PPIN: physical (P), functional (F) and combined
(C) PPIN weighted by functional contexts: molecular function (MF),
biological process (BF) or cellular components (CC)
Network Nodes Edges Avg. degree Avg. path length Diameter Edge density
Clustering coeff.
P-MF 13,269 98,013 9.06 1.54 5.73 0.0007 0.133
P-BP 5.25 0.60 5.13 0.0004 0.136
P-CC 8.85 1.40 5.36 0.0007 0.135
F-MF 11,362 613,865 43.96 0.42 4.77 0.0038 0.223
F-BP 26.12 0.27 5.06 0.0023 0.222
F-CC 46.90 0.64 5.45 0.0040 0.226
C-MF 15,562 700,640 38.82 0.63 5.03 0.0025 0.176
C-BP 22.76 0.28 4.12 0.0015 0.178
C-CC 40.53 0.66 4.36 0.0026 0.180
[74]Open in a new tab
PPIN like other biological networks such as metabolic and
gene-regulatory networks are characterised by specific interactions
between proteins (nodes) and functions of proteins and therefore
demonstrate small world properties (i.e., short path length) and scale
free characteristics (i.e., few nodes with large number of neighbours)
(Tables [75]1 and [76]2).
Topological modules
Topological modules of binary and weighted PPIN were detected using
Louvain algorithm and analysed to investigate how (i) different
interactions (physical and functional) and (ii) different biological
contexts (i.e., MF, BP and CC ontologies) affect the functional
properties of the modules.
As shown in Table [77]3, the number of modules predicted for different
networks vary considerably although the modularity values remain almost
the same. We note that the number of modules predicted for weighted
networks (1586 to 2912) is much more than that of binary networks (34
to 64), but only 0.3 to 1.2% of these modules are mesoscale (size> 10)
as compared to 20–27% of binary networks. A closer inspection of
Figs. [78]2, [79]3 and [80]4 finds that most of the modules are of size
two, corresponding to isolated protein pairs whose interactions with
others is yet be known or weak.
Table 3.
Properties of topological modules of different PPIN
Network Modularity Number of Modules Mesoscale^a modules (%) Largest
Module Size Network edge density Module edge density
P 0.43 64 26.6 2150 0.0011 0.0014 ± 0.01
P-MF 0.46 1586 1.2 1658 0.0007 0.0006 ± 0.0003
P-BP 0.53 2213 1.0 1988 0.0004 0.0004 ± 0.0002
P-CC 0.45 1754 0.9 2159 0.0007 0.0006 ± 0.0003
F 0.52 54 20.4 2730 0.0095 0.007 ± 0.004
F-MF 0.52 1777 0.6 2367 0.0038 0.005 ± 0.003
F-BP 0.55 1999 0.7 1998 0.0023 0.002 ± 0.001
F-CC 0.50 1700 0.7 2565 0.0040 0.009 ± 0.015
C 0.51 34 23.5 6882 0.0057 0.004 ± 0.003
C-MF 0.50 2391 0.3 6484 0.0025 0.003 ± 0.003
C-BP 0.53 2912 0.4 4186 0.0015 0.002 ± 0.001
C-CC 0.48 2430 0.5 4869 0.0026 0.002 ± 0.001
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^aMesoscale modules refer to the modules with size more than 10
Fig. 2.
[82]Fig. 2
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Size distributions for modules detected using Louvain algorithm in
physical networks of human proteome: x-axis represents the size of
modules while y-axis represents the count of meso-modules of size more
than 10 nodes. P denotes the binary physical network while P-MF, P-BP
and P-CC denote the weighted networks with edges scored according to
functional similarity based on molecular functions (MF), biological
process (BP) and cellular component (CC), respectively
Fig. 3.
[84]Fig. 3
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Size distributions of modules detected using Louvain algorithm in
functional networks of human proteome. x-axis represents the size of
modules while y-axis represents the count of meso-modules of size more
than 10 nodes. F denotes the binary functional network while F-MF, F-BP
and F-CC denote the weighted networks with edges scored according to
similarity based on molecular functions (MF), biological process (BP),
and cellular component (CC), respectively
Fig. 4.
[86]Fig. 4
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Size distributions for modules detected using Louvain algorithm in
combined networks (physical and functional) of human proteome. x-axis
represents the size of modules while y-axis represents the count of
meso-modules of size more than 10 nodes. C denotes the combined
physical network while C-MF, C-BP and C-CC denote the weighted networks
where edges scored according to similarity based on molecular
functions, biological process (BP), and cellular components,
respectively
Biological relevance of PPIN modules
More importantly, proteins in topological modules ought to share the
same functional profile. To study functional relevance of topological
modules in the human proteome, mesoscale modules from all networks were
tested for their biological relevance by using functional enrichment
analysis. The enriched function set F is given by the union of all
significantly enriched functions across topological modules and
functional specificities of the set of enriched functions were computed
for each PPIN. Figure [88]5 (and Additional file [89]1: Figure S2)
shows the distribution of significantly enriched biological functions
and size of topological modules of binary and weighted physical PPIN.
Fig. 5.
[90]Fig. 5
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Functional enrichment analyses of topological modules: (a) and (b) show
distributions of enriched molecular functions in topological modules of
PPIN networks. X-axis, Y-axis (left) and Y-axis (right) represent the
modules, number of statistically significant GO terms, and size of
modules, respectively. See Additional file [92]1: Figure S2 for the set
of enriched biological processes and cellular locations in the modules
Functional homogeneity and specificity of topological modules
Functional homogeneity of a module quantifies functional consistency of
a topological module as defined by the maximal fraction of proteins
associated with a biological function. The homogeneity ranges from 0 to
1 where a value of 1 indicates that all genes in the module exhibit
that function. A module’s heterogeneity value estimates how specific a
function is for a particular module.
A recent study of human proteome [[93]35] discussed how most of the
topological modules are functionally diverse despite high homogeneity
values. In our study, we further this observation by including
functional interactions and incorporating the weights to PPIN. As shown
in Table [94]4, the MF and BP homogeneity values are observed to be
higher (0.79 and 0.59) for physical networks than functional networks
(0.64 and 0.57) whereas cellular localizations (~ 0.7) do not vary much
across different networks. We conclude that functional interactions
lead to low homogeneity values in networks because they mostly
represent cross talks between modules with not much variations in
cellular localizations. For example, cross talks in TGF-beta signalling
is known to be involved in many developmental defects and cancer
[[95]36]. This observation concurs with homogeneity values derived in
gene-disease associations (a type of functional interactions) in
disease networks [[96]24, [97]37].
Table 4.
Functional homogeneity of mesoscale modules detected by Louvain
algorithm, evaluated using three ontologies: MF, BP, and CC
PPIN MF BP CC
max mean std max mean std max mean std
Physical Binary 0.81 0.79 0.01 0.85 0.59 0.24 0.78 0.75 0.04
Weighted 0.83 0.80 0.02 0.75 0.42 0.31 0.80 0.77 0.01
Functional Binary 0.71 0.64 0.12 0.72 0.57 0.25 0.8 0.72 0.25
Weighted 0.71 0.42 0.22 0.74 0.60 0.25 0.8 0.76 0.07
Combined Binary 0.74 0.73 0.01 0.70 0.58 0.25 0.77 0.75 0.01
Weighted 0.73 0.72 0.002 0.72 0.45 0.29 0.76 0.75 0.01
[98]Open in a new tab
Table [99]5 shows heterogeneity values for enriched functions of the
modules. On average, molecular function homogeneity was observed to be
higher than bioprocess homogeneity for physical (0.80 > 0.42) and
combined (0.72 > 0.45) networks except for functional networks
(0.42 < 0.60). But homogeneity and heterogeneity values are more varied
(high standard deviation) for functional PPIN than physical and
combined. Thus, it is advantageous to integrate physical protein
interactions with expression based networks for functional analyses as
attempted in some reported studies [[100]29, [101]38].
Table 5.
Functional heterogeneity of modules detected by Louvain algorithm,
calculated for all the enriched functions
PPIN MF BP CC
min mean std min mean std min mean std
Physical Binary 0.05 0.07 0.05 0.04 0.09 0.06 0.05 0.09 0.08
Weighted 0.04 0.08 0.12 0.04 0.09 0.05 0.05 0.21 0.20
Functional Binary 0.09 0.22 0.14 0.09 0.17 0.12 0.09 0.25 0.16
Weighted 0.08 0.20 0.14 0.07 0.16 0.15 0.09 0.24 0.16
Combined Binary 0.14 0.20 0.12 0.14 0.25 0.15 0.14 0.28 0.21
Weighted 0.13 0.21 0.18 0.07 0.10 0.05 0.09 0.31 0.19
[102]Open in a new tab
Effect of the resolution limit on module detection in PPIN
Modularity-based algorithms for module detection often suffer from
resolution limit [[103]39] as the scale of modularization depends upon
the inter-connectedness of the modules. This leads to the inability to
detect smaller modules in a given network. To study the effect of
resolution limit in detecting topological modules, we also implemented
the Incremental Louvain algorithm [[104]35], which first finds modules
by maximizing modularity while incrementally modularizing larger
modules into smaller sub-networks, thus converging the algorithm for
modules with size greater than a threshold size.
Here, we observed on average eight times more mesoscale modules as
compared to the Louvain algorithm, the majority of modules being
smaller in the size range of 10 to 200 (Additional file [105]1: Figure
S4). In case of smaller modules detected using Incremental Louvain
algorithm, an increase in the homogeneity values is observed when
indirect functional interactions are combined with physical PPI
(Tables [106]6 and [107]7). While functional homogeneities of modules
detected with the Louvain algorithm decreased when functional
interactions are introduced into PPI network. This phenomenon can be
simply attributed to difference in module sizes. When compared with
respect to three ontologies, the homogeneity of modules shows on
average 3.4% decrease for MF, 47.08% increase for BP and 4.6% decrease
in CC. And heterogeneity values showed large percentage of decrease for
these smaller modules (85.1, 78.9 and 87% decrease in MF, BP and CC,
respectively). Weighting interactions in PPI network improves
homogeneity of these modules but no change in heterogeneity values is
observed.
Table 6.
Functional homogeneity of mesoscale modules detected by Incremental
Louvain algorithm, evaluated using three ontologies: MF, BP, and CC
PPIN MF BP CC
max mean std max mean std max mean std
Physical Binary 1 0.57 0.28 1 0.67 0.29 1 0.65 0.28
Weighted 1 0.63 0.27 1 0.81 0.20 1 0.76 0.22
Functional Binary 1 0.59 0.25 0.97 0.73 0.22 1 0.72 0.23
Weighted 0.97 0.69 0.25 1 0.85 0.16 1 0.72 0.24
Combined Binary 1 0.60 0.26 1 0.72 0.26 1 0.70 0.24
Weighted 0.98 0.65 0.26 1 0.82 0.19 1 0.74 0.21
[108]Open in a new tab
Table 7.
Functional heterogeneity of the modules detected by Incremental Louvain
algorithm, calculated for all the enriched functions
PPIN MF BP CC
min mean std min mean std min mean std
Physical Binary 0.008 0.017 0.02 0.008 0.02 0.03 0.008 0.02 0.03
Weighted 0.007 0.017 0.02 0.008 0.03 0.04 0.008 0.03 0.04
Functional Binary 0.01 0.02 0.03 0.01 0.03 0.03 0.01 0.03 0.04
Weighted 0.01 0.03 0.03 0.01 0.04 0.04 0.01 0.03 0.05
Combined Binary 0.007 0.02 0.02 0.007 0.02 0.02 0.007 0.02 0.04
Weighted 0.008 0.02 0.02 0.007 0.02 0.03 0.01 0.03 0.05
[109]Open in a new tab
Functionally specific modules
The specificity of a particular function takes both its homogeneity
within the module and its diversity across the modules into account.
The normalized specificity scores for all significantly enriched
functions across modules are summarized in Fig. [110]6. As seen from
the patterns of homogeneity and heterogeneity values, physical PPIN
produce more functionally specific modules (highly homogenous and less
diverse) than functional and combined PPIN, underscoring the benefit of
including proteomics while analysing expression based networks in the
identification of functional modules.
Fig. 6.
[111]Fig. 6
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Functional specificity of significantly enriched molecular functions of
topological modules. See Additional file [113]1: Figure S3 for
specificity scores of topological modules for BP and CC enrichment
Topological modules were ranked using the specificity score and we
labelled the modules with normalized specificity greater than 0.90 as
functionally specific modules and the others as general modules.
Table [114]8 summarizes the biological functions and Table [115]9
enlists enriched biological pathways of specific modules. Main
functions specific to the modules were enzymatic activities like
kinase, hydrolase, and transferase; and protein and nucleotide binding
activities. About 36 to 55% of topological modules in binary and 14 to
32% of in weighted networks were classified as specific modules
according to above mentioned criteria. More number of modules are found
to be functionally specific (55%) in physical PPIN as compared to
functional and combined PPIN (Table [116]8). This is in agreement with
the effect of heterogeneity and homogeneity values of physical
networks. This maybe imparted to the fact that direct interaction
between proteins which are elucidated through high throughput screening
experiments [[117]6, [118]40] are more often studied and more popularly
annotated with functions and that gene-function associations as
annotation based functional enrichment analysis are affected by missing
annotations. See Additional file [119]1: Tables S1 and S2 for specific
modules enriched in biological processes and cellular locations.
Table 8.
The percentage (%), mean size, and summary of molecular functions of
the specific modules of physical (P), functional (F) and combined (C)
PPIN
Network % Mean Size (std.) Specific molecular functions of modules
P 0.55 1523 (390) Module1: cytoskeletal protein binding; Module2:
receptor activity; Module3: cation binding, dimerization/transcription
factor activity; Module4: cyclic compound/RNA/chromatin binding,
Wnt-activated receptor activity; Module5: DNA binding; Module6:
pyruvate dehydrogenase (acetyl-transferring) kinase activity
P-weighted 0.32 1215 (262) Module1: deacytylase activity; Module2:
cyclic compound/nucleotide/ATP/nucleoside binding, kinase/transferase
activity; Module3: amide/peptide binding; Module4: transferase
activity; Module5: protein domain specific binding; Module6: DNA
binding
F 0.36 2298(223) Module1: RNA binding; Module2:ssDNA/
nucleotide/nucleoside/GTP/Mg ion binding binding,
oxidoreductase/transferase/kinase/ activity, transmembrane transporter
activity; Module3: ATPase/DNA helicase activity, chromatin binding
F-weighted 0.42 1036 (978) Module1: hydrolase/transferase activity,
TF/transcription regulator/transcription coactivator/transcription
cofactor/transmembrane transporter activity, CCR5 chemokine receptor
binding; Module2: ATPase/hydrolase activity, transmembrane transporter
activity; Module3: kinase activity, DNA binding; Module3: ATPase
activity; Module4: alcohol binding
C 0.38 4011 (2495) Module1: lamin binding; Module2: cyclic
compound/ion/DNA/enzyme/small molecule/ATP/chromatin/protein
kinase/nucleotide/nucleoside binding, transcription regulator/protein
dimerization/kinase/nucleoside-triphosphatase/phosphotransferase
activity; Module3: macrolide/FK506 binding
C-weighted 0.14 6484(0) Module1: ion/cyclic
compound/DNA/cation/enzyme/identical protein binding,
catalytic/transferase activity
[120]Open in a new tab
Table 9.
The top enriched protein pathways in the specific modules of physical
(P), functional (F) and combined (C) PPIN. Pathways are mapped using
PANTHER Pathway database ([121]http://www.pantherdb.org/pathway/)
Network Specific pathways of modules
P Inflammation mediated by chemokine and cytokine signalling pathway;
gonadotropin-releasing hormone receptor pathway; Wnt signalling
pathway; Integrin signalling pathway; CCKR signalling map;
Heterotrimeric G-protein signalling pathway; Angiogenesis; PDGF
signalling pathway; Apoptosis signalling pathway; EGF receptor
signalling pathway
P-weighted Inflammation mediated by chemokine and cytokine signalling
pathway; gonadotropin-releasing hormone receptor pathway; Wnt
signalling pathway; Integrin signalling pathway; PDGF signalling
pathway; Heterotrimeric G-protein signalling pathway; Angiogenesis;
Apoptosis signalling pathway; CCKR signalling map; Angiogenesis; EGF
receptor signalling pathway; FGF signalling pathway; Huntington
disease; Cadherin signalling pathway; Alzheimer disease-presenilin
pathway
F Inflammation mediated by chemokine and cytokine signalling pathway;
gonadotropin-releasing hormone receptor pathway; Wnt signalling
pathway; Integrin signalling pathway, CCKR signalling map;
Angiogenesis; EGF receptor signalling pathway; Huntington disease;
Alzheimer disease-presenilin pathway; TGF-beta signalling pathway; PDGF
signalling pathway; Heterotrimeric G-protein signalling pathway;
Nicotinic acetylcholine receptor signalling pathway
F-weighted Inflammation mediated by chemokine and cytokine signalling
pathway; gonadotropin-releasing hormone receptor pathway; Wnt
signalling pathway; Integrin signalling pathway, CCKR signalling map;
Angiogenesis; EGF receptor signalling pathway; FGF signalling pathway;
Heterotrimeric G-protein signalling pathway; PDGF signalling pathway;
Huntington disease; Alzheimer disease-presenilin pathway; B-cell
activation; Parkinson disease; Insulin/IGF pathway; Interleukin
signalling pathway; Ionotropic glutamate receptor pathway; Mannose
metabolism; Pyridoxal-5-phosphate biosynthesis; PDGF signalling pathway
C Inflammation mediated by chemokine and cytokine signalling pathway;
gonadotropin-releasing hormone receptor pathway; Wnt signalling
pathway; Integrin signalling pathway, CCKR signalling map;
Angiogenesis; Heterotrimeric G-protein signalling pathway; EGF receptor
signalling pathway; PDGF signalling pathway; Huntington disease; FGF
signalling pathway; Apoptosis signalling pathway
C-weighted Inflammation mediated by chemokine and cytokine signalling
pathway; gonadotropin-releasing hormone receptor pathway; Wnt
signalling pathway; Integrin signalling pathway, CCKR signalling map;
Angiogenesis; Heterotrimeric G-protein signalling pathway; EGF receptor
signalling pathway; FGF signalling pathway; Cadherin signalling pathway
[122]Open in a new tab
Biological validation by pathway enrichment analysis
To validate biological relevance of top ranked specific modules, their
enrichment with genes from experimentally known biological pathways was
computed. Four gene sets of known pathways were considered: glycolysis,
transcriptional regulation, lung cancer and breast cancer, and their
details [[123]40–[124]42] are given in Table [125]10. Breast and lung
cancer pathway set has a total 363 and 300 genes, out of which 347 and
286 are present in the physical, 260 and 219 in the functional and 349
and 288 in the combined PPIN. Out of 244 genes from glycolysis pathway,
158 are present in the physical, 187 in the functional and 226 in the
combined PPIN.
Table 10.
Details of biological pathways used for validation of functional
modules
Biological Pathway No. of genes Overlap with Source
Physical PPIN Functional PPIN Combined PPIN
Glycolysis 262 203 187 229 KEGG [[126]41], MSigDB [[127]42]
Transcriptional regulation 1705 1554 1243 1640 Rolland et al. [[128]40]
Lung cancer 300 286 219 288 Rolland et al. [[129]40]
Breast cancer 363 347 260 349 Rolland et al. [[130]40]
[131]Open in a new tab
The overlapped fractions of genes of known pathways to those in
specific and general modules were calculated in order to estimate
validity of the topological modules. As shown in Fig. [132]7, specific
modules from binary combined PPIN retrieved ~ 79% of breast and lung
cancer genes as compared to 43–45% by modules of weighted PPIN. In a
similar fashion, for physical and functional PPIN, specific modules of
binary PPIN were enriched with more cancer pathway genes (69 and 89%
for breast cancer, 69 and 85% for lung cancer) than respective modules
from weighted PPIN (56 and 45% for breast cancer, ~ 49% for lung
cancer). Specific modules of binary networks were also highly enriched
with 70, 90, and 76% of glycolysis genes and 71, 87 and 77% of
transcriptional regulation genes in physical, functional and combined
networks, respectively.
Fig. 7.
[133]Fig. 7
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Overlap of specific topological modules (specificity score > 0.9) and
general topological modules (specificity score < 0.9) with
experimentally known biological pathways: glycolysis, transcriptional
regulation, lung cancer and breast cancer. Bars represent overlap of
genes involved in biologically validated pathways with specific modules
(brown colour) and general modules (green colour). Topological modules
are detected via molecular function enrichment for binary and weighted
physical(P), functional(F) and combined (C) PPINs
Discussion
The three different PPIN (physical, functional and combined) were
modularized and their functional relevance was analysed using
functional enrichment analysis. As observed from Table [135]1, physical
PPIN are sparser (have high average path length and low edge density)
than functional PPIN, resulting due to high number of functional
interactions and noise in the gene expression experiments. For weighted
networks (Table [136]2), the edges with low functional similarity
between proteins reduce the average path length to lower values than
binary PPIN (ranges from 0.2 to 1.5 as compared to 3.1 to 6.9). There
is a high overlap between functional and physical PPIN with 9069 common
nodes between the two, underlining that most physical interactions also
exert functional interactions. However, small amount of non-overlapping
edges between physical and functional PPIN suffices to cause changes in
edge density and clustering coefficient for the combined network.
When modularized using Louvain algorithm, size distribution of
topological modules in three PPIN (Figs. [137]2, [138]3, [139]4 and
Table [140]3) shows that weighting interactions with functional
similarities of proteins removes weak protein-protein interactions in
PPIN and leads to higher number of compact modules.
Figure [141]5 (and Additional file [142]1: Figure S2) shows the
functionally enriched GO terms in the PPIN modules. The number of
enriched cellular functions and processes are observed to be higher for
the weighted PPIN despite the smaller size of the modules. The number
of cellular locations decreases however with the inclusion of weights
of protein interactions. Overall, combined PPIN are enriched by more GO
terms, with biological processes approximately 1.5 to 3 times more,
molecular functions up to 3 times more and cellular locations
approximately 1.4 times more, than those in physical and functional
binary PPIN.
Functional homogeneity analysis (Tables [143]4 and [144]5) shows
Physical PPIN modules to be more specific than functional networks, in
case of molecular functions as compared to bioprocesses and cellular
localizations. Overall, homogeneity and heterogeneity values are not
much different when weighted interactions are considered, indicating
that topological modules are more resilient to edge weights when
functional annotations are considered. We also conclude that
topological modules in PPIN are more homogeneous and specific in
molecular functions, and less homogeneous (diverse) in terms of
biological processes. This is in agreement with the fact that a
biological process may involve multiple sets of molecular functions and
thus functional modules map to a number of molecular functions but less
number of biological processes. Most importantly, the results indicate
that the functional modules are observed to be more homogenous and
specific when direct interactions in PPIN are also considered (as seen
in the combined network), a fact to kept in mind when identifying
biologically relevant modules by using computational methods. To study
the effect of resolution limit on functional properties of modules,
three PPIN were modularized using Incremental Louvain Algorithm that
resulted in modules, eight times more in number but smaller in size
than Louvain (Additional file [145]1: Figure S4). Despite the
differences, enrichment analyses of modules from both type of
algorithms show that physical networks are more specific than
functional ones (see Tables [146]6 and [147]7). Thus topological
modules are more specific and homogeneous when direct interactions are
considered with indirect functional associations (such as derived from
co-expression or microarray based experiments).
A specificity score is introduced in this study that considers both
functional homogeneity and heterogeneity of a module. Topological
modules with specificity score greater than 0.90 were labelled
functionally specific modules and the others as general modules. Table
[148]8 shows that physical PPIN modules are more enriched in specific
modules than functional PPIN. As seen in Fig. [149]6 and Additional
file [150]1: Figure S3, the modules appear to become smaller and the
biological functions re-distributed into more number of highly specific
modules when edge weights are introduced to physical and functional
protein interaction networks. However, combining functional
interactions with physical interactions led to formation of few and
larger specific modules. This limitation due to increasing module size
can be handled by optimizing modularizing algorithm for detecting
smaller modules of high functional specificity in future and is beyond
the scope of present study.
Biological relevance of top ranked specific modules in physical,
functional and combined PPIN was evaluated on the basis of their
enrichment with genes from experimentally known biological pathways
such as glycolysis, transcriptional regulation, lung cancer and breast
cancer. As shown in Fig. [151]7, specific modules are overall found to
be more enriched than general modules for all four biological pathways,
but the specific modules from binary PPIN were observed to be highly
enriched than those of weighted PPIN. This indicates that the specific
modules obtained by using specificity scores of enriched functions are
highly enriched with known functional and disease pathways. However,
inclusion of weights did not improve the enrichment of biological and
disease pathways in physical and functional networks.
Conclusions
We systematically analysed functional properties of topological modules
in human proteome and investigated the effect of physical and
functional interactions in PPIN on functional specificity of modules.
We also studied the contribution of weighting edges with functional
similarities on topological modules. A specificity score was introduced
to identify more accurate biologically relevant and specific modules.
Functionally homogeneity was earlier used to evaluate functional value
of topological modules detected in biological networks [[152]24,
[153]25] but failed to consider the heterogeneity of functional modules
of biological networks due to a protein or gene mapping to a number of
cellular processes. Thus, a set of proteins (in a module) are involved
in more than one function and also a biological function is mapped to
more than one module. In order to handle this, functional specificity
was introduced which considers both functional homogeneity within the
module and functional heterogeneity across the modules. The function
specificity helps in identifying functional modules or specific
functions of topological modules and one may use our methods to
confidently map specific functions to topological modules of PPIN.
The topological modules detected using physical, functional and
combined PPIN are found to be homogeneous, highly specific, and
enriched in a number of significant biological functions, processes and
cellular localizations (Fig. [154]5 and Additional file [155]1: Figure
S2). Though weighted edges do not affect the homogeneity and
heterogeneity of the modules, incorporating functional similarities of
edge do help in identifying compact and highly specific functional
modules based on topological properties.
Functional or indirect interactions are generally noisy as they are
determined using statistical inferences from gene expressions based
experiments and vary on tissue and patient sample basis [[156]40]. But
functional interactions encompass the whole interaction profile of
genes involved in a cellular function or a disease and thus important
for systematic analysis and prediction of functional modules. Present
study provides a first hand insight into the effect of these different
type of protein-protein interactions on topological modules of human
proteome. We conclude that instead of using only co-expression based
networks in identifying functionally relevant topological modules, one
should combine the accuracy of physical interactions with the larger
coverage of interactome landscape by functional networks. Though our
methodology provides an edge over usual methods (like homogeneity, GO
enrichment) for functional validation of topological modules and helps
in identifying specific functions of these modules, it does not
identify core components of a biological pathway. One limitation of our
study is that our methods do not handle the overlapping modules and
consider overlapping properties of functional modules. It would be
interesting to study overlapping sub-modules, core modules, and the
hierarchical organization of functionally specific topological modules
as future work of this study.
Methods
Datasets
In a cellular machinery, proteins function as enzymes, transcription
factors, receptors or structural proteins, and interact with other
biomolecules. Protein interactions are either physical (direct) or
functional (indirect). For studying the role of these two types of
interactions on detection of modules of PPIN, three datasets were used:
Physical, Functional and Combined (see Fig. [157]1, Table [158]1).
These three datasets were prepared from HPRD (Human Protein Reference
Database) (version Release9) [[159]43] and STRING database (version 10)
[[160]44]; and include experimental information from other well-known
databases like BIND, DIP, GRID, HPRD, IntAct, MINT and PID (updated
till 14 May 2017). All the proteins were mapped to their Entrez gene
ids. Details of data pre-processing are provided in Additional file
[161]1.
1. Physical PPIN enlists curated binary interactions of proteins,
representing physical or direct interactions that are determined
using in vivo (e.g. co-immunoprecipitation), in vitro (e.g. GST
pull-down assays) or yeast two-hybrid experiments.
2. Functional PPIN represents functional interactions of proteins,
i.e., these proteins may or may not physically interact but they do
participate in a biological function by influencing each other
genetically through co-regulation or co-expression, which are
determined using experimental techniques like microarray expression
data analysis or double mutant analysis.
3. Combined PPIN is the inclusive set of both the physical and
functional networks mentioned above.
Weights for protein-protein interactions
Weighted PPIN are obtained by assigning functional similarities between
proteins as edge weights, considering different GO domains: molecular
function (MF), biological process (BP) and cellular component (CC). We
used popular Wang’s semantic similarity measure [[162]34, [163]45] to
evaluate the functional similarity between genes (i.e., weights of
protein-protein interactions).
Module detection
Functional modules of PPIN correspond to communities or sub-networks of
proteins having specific and similar biological functions [[164]4,
[165]46]. We chose the Louvain algorithm modular detection algorithm to
find topological modules of PPIN because it has demonstrated excellent
performance and low computational complexity on benchmark networks
[[166]20] (Lancichinetti & Fortunato, 2009). The Louvain algorithm
finds the community or modular structure by optimizing the modularity Q
(the quality function) of the network:
[MATH: Q=∑ijeij−ai2 :MATH]
1
where e[ij] is fraction of edges between modules i and j, and a[i] is
the fraction of edges connected to the nodes in module i. The modular
structure is found by maximizing the modularity in an iterative manner.
All the nodes in the network are assigned to independent modules in the
beginning and the algorithm progressively merges two communities that
best increase the modularity of the resulting network structure.
Merging of nodes and modules continues until there is no further
increase in the modularity of the network.
Functional enrichment analysis
The functional enrichment analysis was performed in order to find the
GO terms in MF, BP, and CC contexts, which are significantly
represented (enriched) by the proteins in the predicted topological
modules. The functional enrichment analysis was implemented using R
package BioStats [[167]47]. The statistical significance of a GO term
in a module was estimated by evaluating its overrepresentation using a
hypergeometric test. A functionally enriched module signifies that the
number of genes observed to be annotated with a function (i.e., the GO
term) is more than the expected number of genes annotated to that
function. The ‘expected value’ for a function is the number of genes
having that specific function in the given module, with respect to the
reference list (whole list of human genes).
Functional homogeneity and specificity of topological modules
In this section, we introduce measures to quantify functional
homogeneity and heterogeneity of topological modules of PPIN. First,
functional enrichment analysis is performed on the modules to identify
biological functions (GO terms) that are significantly enriched
(p-value < 0.0001) in the modules and the functions are ranked
according to their significance values. Systematic estimation of
p-value is done using a set of detailed experiments explained in
Additional file [168]1. We selected the enriched functions for each
module and identified the set F of enriched functions in all the
modules.
Homogeneity of a module with respect to a particular function is
computed by the proportion of genes annotated by the function. That is,
the homogeneity of a function f ∈ F within a module is given by
[MATH: homogeneity=nfN :MATH]
where n[f] is the number of genes annotated by the function and N is
the total number of genes in the module. The functional homogeneity (H)
of a module is defined as the homogeneity of maximally enriched
function in the module. The heterogeneity of a function is defined as
the proportion of the modules where the function f ∈ F is enriched.
That is,
[MATH: heterogeneity=k<
/mi>fK :MATH]
where k[f] is the number of modules enriched with function f and K is
the total number of modules detected in PPIN.
Functional homogeneity measures functional coherence of the modules
while functional heterogeneity indicates how exclusive the modules are
for the function across all predicted modules. To combine functional
homogeneity and heterogeneity of a module, functional specificity for
an enriched function is defined as follows:
[MATH: specificity=homogeneity+1heterogeneity :MATH]
2
The values of specificity scores across all enriched functions are
normalized to a range between 0 and 1. The functional specificity value
measures how exclusively the module is enriched by the specific
biological function. Modules are ranked using the functional
specificity score and the top ranked modules are considered as highly
specific modules.
Additional file
[169]Additional file 1:^ (1.5MB, pdf)
Supplementary information. (PDF 1521 kb)
Acknowledgements