Abstract

   Targeting modules or signalings may open a new path to understanding
   the complex pharmacological mechanisms of reversing disease processes.
   However, determining how to quantify the structural alteration of these
   signalings or modules in pharmacological networks poses a great
   challenge towards realizing rational drug use in clinical medicine.
   Here, we explore a novel approach for dynamic comparative and
   quantitative analysis of the topological structural variation of
   modules in molecular networks, proposing the concept of allosteric
   modules (AMs). Based on the ischemic brain of mice, we optimize module
   distribution in different compound-dependent modular networks by using
   the minimum entropy criterion and then calculate the variation in
   similarity values of AMs under various conditions using a novel method
   of SimiNEF. The diverse pharmacological dynamic stereo-scrolls of AMs
   with functional gradient alteration, which consist of five types of
   AMs, may robustly deconstruct modular networks under the same ischemic
   conditions. The concept of AMs can not only integrate the responsive
   mechanisms of different compounds based on topological cascading
   variation but also obtain valuable structural information about disease
   and pharmacological networks beyond pathway analysis. We thereby
   provide a new systemic quantitative strategy for rationally determining
   pharmacological mechanisms of altered modular networks based on
   topological variation.

Introduction

   The spatial structure of cell signaling systems [[42]1] represents a
   promising pathway for developing a better approach to studying the
   complexity of diseases and unraveling the mechanisms of pharmacological
   networks. Moreover, modularity is ubiquitous in biological networks
   [[43]2], and the exploration of modular structure has been proposed as
   a key factor for understanding the complexity of biological systems
   [[44]3] and disease networks [[45]4]. A cascade of network modules is
   used to define cancer progression, and modular structure plays a
   significant role in aiding the diagnosis, prevention, and therapeutic
   treatment of the disease [[46]5]. Because multiple drugs act within the
   context of the regulatory networks in which drug targets and disease
   gene products function, module-designed studies are becoming
   increasingly important for revealing the relationships between drug
   actions and disease outcomes in network pharmacology [[47]6]. Highly
   networked signaling hubs are often associated with disease, for
   example, the IKK-IkB-NFkB signaling module functions as a signaling hub
   for diverse inflammatory, immune, and developmental signals [[48]7].
   Modular pharmacology (MP) also suggests that the treatment of complex
   diseases requires a modular design to affect multiple targets [[49]8].
   To date, many methods or algorithms have been proposed for module
   identification [[50]3].

   From a network point of view, however, most intra-protein
   conformational changes may be dynamically transmitted across
   protein-protein interactions and signaling networks of the cell
   [[51]9]. Allostery exerts conformational control over cellular pathways
   and networks to determine cell responses; and if allostery is not at
   play, neither signal propagation nor pathway switching will take place
   [[52]10]. A previous study analyzed several methods for identifying
   allosteric pathways in intra-protein networks, including one method
   that employed the concept of modules and considered proteins as sets of
   modules [[53]9]. Allosteric networks have been characterized using a
   community network analysis approach previously applied to investigate
   allostery in tRNA-protein complexes [[54]11], protein dynamical network
   [[55]12] and innovative therapies [[56]13].

   Because allosteric conformational change involves the relative movement
   of both internal and external modules [[57]14], allosteric
   communication plays a crucial role in pharmacological cellular
   signaling processes. Positive and negative allosteric modulators of the
   type 5 metabotropic glutamate (mGlu5) receptor both have demonstrable
   therapeutic potential in neurological and psychiatric disorders
   [[58]15]. Therefore, by fusing the regulatory principles of protein
   allostery (a special and limited part in cellular networks) and dynamic
   network information, we propose the concept of allosteric modules (AMs)
   [[59]16]. Generally, the multi-potent functional changes in modular
   architecture are referred to as AMs. Allostery is an intrinsic property
   of many modules that is indispensable for molecular regulatory and
   feedback mechanisms. An AM may change its boundary in structural
   transformation based on different parametric variations (such as nodes
   and edges) [[60]16], which can be used to reflect the dynamics of
   modular networks and quantitatively analyze allosteric variations to
   reveal detailed allosteric pharmacological events in cellular networks.

   Our previous studies showed that baicalin (BA), cholic acid (CA), and
   jasminoidin (JA) could significantly reduce ischemic infarct volume
   [[61]17,[62]18]. Moreover, analysis of differentially expressed genes
   and signaling pathways indicated that the pharmacological mechanisms of
   these compounds showed both similarities and variations
   [[63]17,[64]19]. Based on these findings, we attempted to
   quantitatively determine the diversity of AMs in compound-related
   target networks fusing topological variation and functional alteration
   and to further reveal the comparative pharmacological mechanisms of
   different compound treatments toward cerebral ischemia according to the
   variability of allostery-of-function modules.

Materials and Methods

Animal model, compound treatment, microarray experiments and data preparation

   The animal model, compound treatment, microarray experiment and data
   preparation method used in this study have been previously described
   [[65]18,[66]20]. Animal use protocols were reviewed and approved by the
   Ethics Review Committee for Animal Experimentation, China Academy of
   Chinese Medical Sciences. All animal experiments were conducted in
   accordance with the Prevention of Cruelty to Animals Act 1986 and the
   National Institute of Health guidelines on the care and use of
   laboratory animals for experimental procedures. All surgery was
   performed under anesthesia, and all efforts were made to minimize
   suffering.

   Briefly, one hundred and ten adult male mice (3 months old, 38–48 g,
   Kunming strain, China) were purchased from the Experimental Animal
   Center of Peking University Health Science Center, and then randomly
   divided into five groups based on previous studies: sham, vehicle, BA,
   CA and JA, each consisting of 22 subjects. A cerebral
   ischemia–reperfusion mouse model was established based on methods
   described by Hara et al. [[67]21] and Himori et al. [[68]22]. Briefly,
   after being anesthetized with 2% pentobarbital (4 mg/kg, i.p.), the
   mice were subjected to middle cerebral artery occlusion, ligated with
   an intraluminal filament for 1.5 h and then reperfused for 24 h. In the
   sham- operated mice, the external carotid artery was surgically
   prepared for the insertion of the filament, but the filament was not
   inserted. Based on the infarction volume or behaviors of these mice
   [[69]23], we could determine whether the operations were successful.
   Mice in the experimental groups were injected with 2 ml/kg body weight
   BA (5 mg/ml), CA (7 mg/ml) and JA (25 mg/ml) via the tail vein 2 h
   after surgical occlusion. Mice in the sham-operated and vehicle groups
   underwent identical procedures, but were injected with vehicle (2 ml/kg
   body weight; 0.9% NaCl) rather than experimental compounds. During the
   experimental procedure, blood pressure, blood gas, and glucose levels
   were monitored, rectal temperature was maintained at 37.0–37.5°C with a
   heating pad, the body temperature was maintained at 37°C with a
   thermostatically controlled infrared lamp, and brain temperature
   (monitored with a 29-gauge thermocouple in the right corpus striatum)
   was maintained at 36–37°C with a temperature-regulating lamp.
   Electroencephalogram monitoring was performed to ensure isoelectricity
   during ischemia.

   After 24 h reperfusion, 13 mice from each group were anesthetized with
   chloral hydrate (400 mg/kg) and decapitated rapidly. The cerebrum was
   removed and cut into five slices. The slices were transferred to 4% 2,
   3, 5-triphenyltetrazolium chloride solution and incubated for 30 min at
   37°C in darkness and then transferred into a 10% formalin solution. The
   area of the infarct region was calculated using a Pathology Image
   Analysis System, and the ratio of the infarct volume to the total slice
   was also calculated. 9 mice from each group were sacrificed by rapid
   decapitation under deep anesthesia with chloral hydrate (400 mg/kg).
   Hippocampal RNA from different treatment groups was homogenized in
   TRIzol Reagent and extracted according to the single-step method
   [[70]24]. RNA was further purified to remove genomic DNA contamination
   and concentrated using an RNeasy micro kit (Qiagen, Valencia, CA). RNA
   quality was assessed by determining the 26S/18S ratio using a
   Bioanalyzer microchip (Agilent, Palo Alto, CA). Microarrays were made
   from a collection of 16,463 mouse oligo chips provided by the Boao
   Biotech Company, Beijing.

   All experimental data were uploaded to the ArrayTrack system [US Food
   and Drug Administration (FDA), USA]. Experimental analysis was based on
   the Minimum Information About a Microarray Experiment (MIAME)
   guidelines and the MicroArray Quality Control (MAQC) project. The
   results were submitted to the Array Express database. All microarray
   data were normalized by locally weighted linear regression (Lowess) to
   reduce the experimental variability [[71]25] (smoothing factor: 0.2;
   robustness iterations: 3). A one-way ANOVA model and a significance
   analysis of microarrays (SAM) were used to compare the means of the
   altered genes between vehicle and sham, BA and vehicle, CA and vehicle,
   JA and vehicle groups. Genes with a P-value < 0.05 and a fold change
   >1.5 were selected for further analysis. After obtaining the P-values,
   Bonferroni correction was performed to select a list of significant
   genes for further analysis. In addition, an increase > 1.5-fold or a
   decrease < 0.5-fold of expression levels indicated up-regulation or
   down-regulation, respectively.

Constructing target networks in different groups

   We constructed global networks of different groups by integrating gene
   expression data and PPI information. A unique global mice gene and
   protein network was constructed by integrating protein interactions
   reported in the BIOGRID [[72]26], INTACT [[73]27], MINT [[74]28], and
   NIA Mouse Protein-Protein Interaction Databases [[75]29] and by
   deleting duplicated data and self-interactions.

   Gene expression data were adapted from our previous study of gene
   expression profiles of the hippocampus of ischemic mice treated with
   baicalin (BA), cholic acid (CA) and jasminoidin (JA) [[76]18,[77]20].
   Mean-centered normalization of expression data was performed. Genes
   with an expression value greater than one were defined to be
   significantly differentially expressed. These genes were then mapped to
   the interaction network, generating target networks for each group. The
   topological characteristics of related target networks were analyzed.

Identifying functional modules in different groups

   In related target networks of each group, functional modules were
   identified using Affinity propagation (AP) [[78]30], the Markov Cluster
   algorithm (MCL) [[79]31] and Molecular Complex Detection (MCODE)
   [[80]32], respectively. For the AP algorithm, we sampled the Preference
   parameters from 0.1 to 1.0 in steps of 0.1. For MCL, the range of
   possible Inflation parameter values (1.5 to 5.0) was sampled uniformly
   with a step size of 0.5. For MCODE, we tried all possible combinations
   of the following parameters (Include Loops: false; Degree Cutoff: 3;
   Node Score Cutoff: 0.2; Haircut: true or false; Fluff: true or false;
   K-Core: 2; Max. Depth from Seed: 100, 5, 4, 3).

Calculating minimal network entropy

   After identifying functional modules by three different methods, the
   next task was to determine the relative optimal module identification
   results. In this study, we attempted to assess the module
   identification results by incorporating the notion of entropy. The
   entropy of a random variable quantifies the uncertainty or randomness
   of that variable [[81]33]. Some researchers have provided definitions
   of the network structure entropy, which is based on node degree and
   indicates the homogeneity of node degree [[82]34,[83]35]. The
   importance of nodes is defined as follows:
   [MATH:
   <mrow><msub><mi>I</mi><mi>i</mi></msub><mo>=</mo><mrow><mrow><msub><mi>
   k</mi><mi>i</mi></msub></mrow><mo stretchy="true">/</mo><mrow><mstyle
   displaystyle="true"><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn
   >1</mn></mrow><mi>N</mi></munderover><mrow><msub><mi>k</mi><mi>i</mi></
   msub></mrow></mstyle></mrow></mrow></mrow> :MATH]
   (1)

   where I [i] is the importance of node i, N is the number of nodes in
   the network, and k [i] is the degree of node i. The network structure
   entropy is defined as follows:
   [MATH: <mrow><mi>E</mi><mo>=</mo><mo>−</mo><mstyle
   displaystyle="true"><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn
   >1</mn></mrow><mi>N</mi></munderover><mrow><msub><mi>I</mi><mi>i</mi></
   msub><mtext>ln</mtext><mspace
   width="0.15em"></mspace><msub><mi>I</mi><mi>i</mi></msub></mrow></mstyl
   e></mrow> :MATH]
   (2)

   In scale-free networks, a large number of low-degree peripheral nodes
   are linked to a few high-degree hubs; these networks are considered to
   be “ordered” [[84]34,[85]35]. The minimum entropy value is E[min],
   [MATH:
   <mrow><msub><mi>E</mi><mtext>min</mtext></msub><mo>=</mo><mo>−</mo><mfr
   ac><mn>1</mn><mn>2</mn></mfrac><mtext>ln</mtext><mfrac><mn>1</mn><mn>2<
   /mn></mfrac><mo>−</mo><mstyle
   displaystyle="true"><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn
   >2</mn></mrow><mi>N</mi></munderover><mrow><mfrac><mn>1</mn><mrow><mn>2
   </mn><mo stretchy="false">(</mo><mi>N</mi><mo>−</mo><mn>1</mn><mo
   stretchy="false">)</mo></mrow></mfrac></mrow></mstyle><mtext>ln</mtext>
   <mfrac><mn>1</mn><mrow><mn>2</mn><mo
   stretchy="false">(</mo><mi>N</mi><mo>−</mo><mn>1</mn><mo
   stretchy="false">)</mo></mrow></mfrac><mo>=</mo><mfrac><mrow><mtext>ln<
   /mtext><mspace width="0.1em"></mspace><mn>4</mn><mo
   stretchy="false">(</mo><mi>N</mi><mo>−</mo><mn>1</mn><mo
   stretchy="false">)</mo></mrow><mn>2</mn></mfrac></mrow> :MATH]
   (3)

   It is believed that the ultimate aim of module identification is to
   find a stable modular state, which should have minimum uncertainty.
   Because the number of modules in a given network is uncertain in
   advance, the only task that can be completed is to minimize the
   uncertainty. Because minimum entropy indicates minimum uncertainty, we
   proposed to evaluate the results of module identification based on the
   minimum entropy criterion. To assess the statistical significance of
   the minimum network entropy of each network, an ensemble of randomized
   networks was constructed by randomly reshuffling all the edges of the
   original network [[86]36,[87]37]. With this type of randomization, each
   node preserved the same number of links as in the original network.

Enrichment analysis of gene ontology (GO) categories and KEGG pathways

   Significantly over-represented GO biological processes (BP) in modules
   were detected by the DAVID 6.7 functional annotation tool
   ([88]http://david.abcc.ncifcrf.gov/) [[89]38] (GOTERM_BP_ALL). The
   analysis was conducted using a modified Fisher's exact test, and we
   selected all GO terms that were significant with a P-value <0.05 after
   correcting for multiple-term testing by Benjamini. Enrichment analysis
   of KEGG pathways in modules was performed using a hypergeometric test,
   as implemented on the KOBAS 2.0 web server
   ([90]http://kobas.cbi.pku.edu.cn/) [[91]39].

Use of SimiNEF to calculate similarities of AMs

   A method that integrated the similarities of nodes, edges and GO
   functions of modules (SimiNEF) was proposed and applied to compare the
   degree of overlap between AMs, focusing particularly on node allosteric
   modules (^NAMs) and edge allosteric modules (^EAMs) in any two groups.
   In SimiNEF, we used similarity S[nef] to quantify the relative overlap
   between AMs m[i] and m[j], including the similarities of nodes (S[n]),
   edges (S[e]) and GO functions (S[f]) altogether. SimiNEF was based on
   Jaccard’s coefficient of similarity, which ranges from 0% (states have
   no nodes/edges/ GO functions in common) to 100% (states have identical
   nodes/edges/GO functions). The S[n] (m[i], m[j]), S[e] (m[i], m[j]) and
   S[f] (m[i], m[j]) are defined by Eqs [92]4, [93]5 and [94]6,
   respectively.
   [MATH: <mrow><msub><mi>S</mi><mi>n</mi></msub><mspace
   width="0.25em"></mspace><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>i</mi></msub><mo>,</mo
   ><msub><mi>m</mi><mi>j</mi></msub></mrow><mo
   stretchy="false">)</mo><mo>=</mo><mfrac><mrow><mrow><mo>|</mo><mrow><mi
   >N</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>i</mi></msub></mrow><m
   o stretchy="false">)</mo><mo>∩</mo><mi>N</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>j</mi></msub></mrow><m
   o
   stretchy="false">)</mo></mrow><mo>|</mo></mrow></mrow><mrow><mrow><mo>|
   </mo><mrow><mi>N</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>i</mi></msub></mrow><m
   o stretchy="false">)</mo><mo>∪</mo><mi>N</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>j</mi></msub></mrow><m
   o stretchy="false">)</mo></mrow><mo>|</mo></mrow></mrow></mfrac></mrow>
   :MATH]
   (4)
   [MATH: <mrow><msub><mi>S</mi><mi>e</mi></msub><mspace
   width="0.25em"></mspace><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>i</mi></msub><mo>,</mo
   ><msub><mi>m</mi><mi>j</mi></msub></mrow><mo
   stretchy="false">)</mo><mo>=</mo><mfrac><mrow><mrow><mo>|</mo><mrow><mi
   >E</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>i</mi></msub></mrow><m
   o stretchy="false">)</mo><mo>∩</mo><mi>E</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>j</mi></msub></mrow><m
   o
   stretchy="false">)</mo></mrow><mo>|</mo></mrow></mrow><mrow><mrow><mo>|
   </mo><mrow><mi>E</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>i</mi></msub></mrow><m
   o stretchy="false">)</mo><mo>∪</mo><mi>E</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>j</mi></msub></mrow><m
   o stretchy="false">)</mo></mrow><mo>|</mo></mrow></mrow></mfrac></mrow>
   :MATH]
   (5)
   [MATH: <mrow><msub><mi>S</mi><mi>f</mi></msub><mspace
   width="0.25em"></mspace><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>i</mi></msub><mo>,</mo
   ><msub><mi>m</mi><mi>j</mi></msub></mrow><mo
   stretchy="false">)</mo><mo>=</mo><mfrac><mrow><mrow><mo>|</mo><mrow><mi
   >F</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>i</mi></msub></mrow><m
   o stretchy="false">)</mo><mo>∩</mo><mi>F</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>j</mi></msub></mrow><m
   o
   stretchy="false">)</mo></mrow><mo>|</mo></mrow></mrow><mrow><mrow><mo>|
   </mo><mrow><mi>F</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>i</mi></msub></mrow><m
   o stretchy="false">)</mo><mo>∪</mo><mi>F</mi><mo
   stretchy="false">(</mo><mrow><msub><mi>m</mi><mi>j</mi></msub></mrow><m
   o stretchy="false">)</mo></mrow><mo>|</mo></mrow></mrow></mfrac></mrow>
   :MATH]
   (6)

   where |N(m[i]) ∩ N(m[j])|, |E(m[i]) ∩ E(m[j])|, and |F(m[i]) ∩ F(m[j])|
   are the numbers of overlapping nodes, edges, and GO functions in m[i]
   and m[j], and |N(m[i]) ∪ N(m[j])|, |E(m[i]) ∪ E(m[j])|, and |F(m[i]) ∪
   F(m[j])| are the numbers of nodes, edges, and GO functions in the union
   of m[i] and m[j] (Note: Here, GO functions refer to the GO biological
   processes mentioned above). If S[n], S[e], and S[f] are all greater
   than a certain value k simultaneously, then we establish that S[nef] is
   greater than k.

Biological validation for AMs

   Two AMs Mrm1-Guk1-Hrsp12 and Fos-Cebpg-Atf2 were selected to validate
   the relationship between their mRNA or protein expressions and cerebral
   ischemia, as well as the effects of different compound interventions on
   the expression of genes or proteins using RT-PCR and western blotting,
   respectively. The experiment, including animal model and compound
   treatment, was performed as described previously (see section 1 in
   [95]Materials and Methods).

Real-time reverse transcription-polymerase chain reaction (RT-PCR)

   Eight animals from each group were anesthetized with chloral hydrate
   (400 mg/kg). Euthanasia was performed by rapid decapitation under deep
   anesthesia. Total RNA was extracted using TRIzol Reagent (Invitrogen,
   Carlsbad, CA). cDNA was synthesized using a First Strand cDNA Synthesis
   Kit (Fermentas MBI) according to the manufacturer's instructions.
   Expressions of Guk1, Hrsp12 and Mrm1 were determined by real-time PCR,
   and the following primer sequences were used: Guk1,
   5’-TATGGGACAAGCAAGGAAGC-3’ (forward) and 5’-GGCTTCATCCAGGTTGTCAT-3’
   (reverse); Hrsp12, 5’-CCAAGCTGTGCTAGTGGACA-3’ (forward) and
   5’-GCAGCCTTCAGAATCTCACC-3’ (reverse); Mrm1, 5’-CAATCTTGGGGCTGTGATG-3’
   (forward) and 5’-TGGCCTTGCTGACTACTGG-3’ (reverse); GAPDH,
   5’-CAAAGTTGTCATGGATGACC-3’ (forward) and 5’-CCATGGAGAAGGCTGGG-3’
   (reverse). Real-time PCR was performed in a 7900HT Fast Real-Time PCR
   System (Applied Biosystems) using 2× SYBR Green PCR Master Mix (Applied
   Biosystems). The data were quantified using the standard curve method
   after normalizing with GAPDH gene expression.

Western blotting

   Three animals were sacrificed 24 h after ischemia by rapid decapitation
   under deep anesthesia. Brain tissues were prepared for western
   blotting. Protein concentration was determined by the Bradford assay
   (Tiangen Biotech Co., Ltd., Beijing, China). Protein samples (50 μg per
   lane) were electrophoresed in 10% SDS-polyacrylamide gels and
   transferred to polyvinylidene fluoride (PVDF) membranes (Millipore,
   Billerica, MA, USA) at 60 V for 2 hours at 4°C in a transfer buffer
   containing 48 mmol L^-1 Tris-base, 39 mmol L^-1 glycine, and 20%
   methanol. The blots were blocked in fresh blocking buffer
   (Tris-buffered saline with 0.05% Tween 20 [TBS-T] plus 5% non-fat dry
   milk) for 1 h at room temperature. The blots were then incubated at 4°C
   overnight with anti-Atf2 antibody (sc-164978), anti-c-Fos antibody
   (sc-52), or anti-C/EBPγ antibody (sc-25769) (all at 1:1000 dilution;
   Santa Cruz Biotechnology, Inc., Santa Cruz, CA, USA) and anti-β-actin
   antibody (1: 10000, Santa Cruz Biotechnology). A secondary antibody
   conjugated with horseradish peroxidase (HRP, 1:5000, Bio-Rad) was used.
   Immunoblots were visualized on X-ray film by a chemiluminescence
   reaction (Pierce, Rockford, IL). Image analysis of the blots was
   performed on optical density-calibrated images using AlphaEase Stand
   Alone software (Alpha Innotech Corp., San Leandro, CA).

Results

   The protocols followed in conducting the experiment and data analysis
   are shown in [96]Fig 1. In previous studies, we demonstrated that BA,
   JA and CA all exerted a significant pharmacological effect in reducing
   infarction volume and neurological scores [[97]17,[98]18,[99]20].

Fig 1. Flow diagram.

   [100]Fig 1
   [101]Open in a new tab

   Drug-target networks were constructed by integrating gene expression
   data and protein interaction data, and then functional modules were
   identified using the AP, MCL and MCODE algorithms, respectively. The
   results of module identification were then optimized based on the
   minimum entropy criterion. Enrichment analysis of GO biological
   processes and KEGG pathways was performed with the DAVID 6.7 software
   program. The similarity or overlap between modules was calculated using
   SimiNEF. Then, five different types of modular allostery were
   identified. We defined the five types of AMs as follows. (1) AMs. Most
   modules showed partial overlap (0<S[nef] <100%) between vehicle-treated
   and compound-treated groups, as well as between various
   compound-treated groups, and thus they were referred to as AMs. (2)
   Conserved allosteric modules (CAMs, AM^C). If the similarity between
   vehicle-treated group and a compound-treated group reached 100% (S[nef]
   = 100%), these modules were referred to as CAMs. (3) Generated
   allosteric modules (GAMs, AM^G). If a module was not found in the
   vehicle-treated group but could be identified in compound-treated
   groups, we defined it as a GAM. (4) Disappeared allosteric modules
   (DAMs, AM^D). If a module was found in the vehicle-treated group but
   could not be identified in compound-treated groups, we defined it as a
   DAM. (5) Watershed allosteric module (WAM, AM^W). 0<S[nef] <100%, the
   first overlapped module between vehicle-treated and compound-treated
   groups, as well as between various compound-treated groups, was
   referred to as the WAM. V = Vehicle, C = Compound. ‘√’ or ‘×’
   represents its appearance ‘yes’ or ‘no’ in the group, respectively.
   Finally, two modules could be validated using RT-PCR and Western
   blotting, respectively.

Comparing topological attributes of compound-dependent ischemic networks

   We constructed a mice protein interaction network containing 65,850
   edges by integrating multiple protein interaction databases. After the
   preprocessing of gene expression data, different microarray
   experimental data from ischemic mice were mapped to this network, and
   differential target networks of vehicle (vehicle vs. sham), BA (BA vs.
   vehicle), CA (CA vs. vehicle), and JA (JA vs. vehicle) groups were
   constructed ([102]Fig 2). The topological attributes of these four
   networks were similar to each other, although there was a small
   difference in network size ([103]S1 Table). Therefore, analysis of the
   entire networks might still not be sufficient to uncover the diverse
   pharmacological protective mechanisms among these compounds.

Fig 2. Global networks of vehicle, BA, CA, and JA groups.

   [104]Fig 2
   [105]Open in a new tab

   The degree distribution of the four networks can be approximated by a
   power law distribution, which appears as a straight line on a
   logarithmic plot.

Identifying functional modules from the entire ischemic networks

   The results identified by AP, MCL, and MCODE are shown in
   [106]S2–[107]S4 Tables. Considering the effect of different parameters
   on the clustering results, we tested multiple parameter settings for
   each method. The results indicated that there were large differences in
   the number of modules, size, modularity and entropy among modules
   obtained from the three methods in each group ([108]S2–[109]S4 Tables,
   [110]Fig 3A).

Fig 3. Changes in network structure entropy in different treatment groups.

   [111]Fig 3
   [112]Open in a new tab

   (A) The changes in network structure entropy under different parameter
   settings in MCODE, AP, and MCL. The red, green, blue, and violet lines
   denote the entropies in the BA, JA, CA and vehicle groups,
   respectively. E[Veh] (violet dotted line), E[CA] (blue short dashed
   line), E[BA] (red long dashed line) and E[JA] (green solid line) denote
   the mean minimal entropy in randomized networks corresponding to the
   vehicle, CA, BA and JA groups. (B and D) Distributions of overlapping
   and non-overlapping GO biological processes and KEGG pathways among
   groups. Only significant functions or pathways are shown in this figure
   based on a corrected P-value <0.05. (C) The 14 categories of 218
   significantly enriched GO biological processes. (E-G) Examples of
   modular overlaps between BA and CA, BA and JA, CA and JA groups,
   respectively. Each column of numbers indicates the number of each
   module in each group. For example, if the overlap of nodes, edges or GO
   functions (any one of them) between two modules is greater than 1%, 50%
   or 75%, these two modules are connected (See also [113]S1 Fig).

Optimizing functional modules using MCODE approach

   According to the minimum entropy criterion, results identified by
   MCODE, which had minimal network entropy, were selected for further
   analysis ([114]Fig 3A). To evaluate the statistical significance of the
   minimum network entropy of each target network, we constructed an
   ensemble of 50 randomized networks with the same degree as the original
   networks in each group, and the same method was used to identify
   modules in randomized networks. The entropy of randomized networks was
   then calculated. The means of the minimum entropy in randomized
   networks corresponding to the vehicle, BA, CA, and JA groups were
   5.60826, 5.51223, 5.52381, 5.50189, respectively ([115]Fig 3A). The
   values of the minimum network entropy in the original networks were
   significantly lower than those in the randomized networks (P<0.05).
   After the optimization of the minimum entropy, 50, 49, 41, and 42
   modules (nodes ≥3) were identified from relevant target networks using
   MCODE ([116]S5 Table). The average sizes of those modules ranged from
   5.327 to 6.171, and the entropy values of the four networks were
   similar after module optimization ([117]S5 Table).

Distribution of GO biological processes and KEGG pathways

   GO functional enrichment analysis revealed 218 significantly enriched
   biological processes ([118]S6 Table), which could be largely divided
   into 14 categories, including immune response and inflammatory response
   ([119]Fig 3C). A total of 185, 153, 147, and 192 GO biological
   processes were enriched in the vehicle, BA, CA and JA groups,
   respectively ([120]Fig 3B, [121]S7 Table). Pathway analysis revealed
   74, 65, 62, and 68 significantly enriched KEGG pathways in modules from
   the vehicle, BA, CA, and JA groups, respectively ([122]Fig 3D, [123]S8
   Table). Based on modular analysis, we unexpectedly expanded traditional
   pathway analysis not only by discovering many unknown pathways in
   different groups but also by developing an enrichment strategy based on
   the variation of modular dynamics beyond known pathways.

Associations between GO functions and topological structures of AMs

   Within the same group, there were no significant linear correlations
   between the changes in the number of GO biological processes and nodes
   or edges in modules. We calculated the connecting percentage of each
   node (i.e., the degree of each node/the total degree of the module it
   belongs to) to indicate the importance of each node in the module. A
   significant linear correlation was observed between the connecting
   percentage of each node and the changes in the number of GO biological
   processes in the same module, and the correlation coefficient value was
   only 0.18. These results suggested that local topological variations,
   such as changes in nodes or edges, might not lead to sufficient
   functional alteration of the network.

Similarity gradient among AMs using SimiNEF in different groups

   By calculating the similarities between modules using SimiNEF, we found
   that there were different degrees of overlap between AMs of these
   groups ([124]Fig 3E–3G, [125]S1 Fig). We used similarity S[nef]>1%,
   >25%, >50%, >70%, >75%, and = 100% to define the different degrees of
   overlap between two AMs ([126]Fig 4A–4F). Results showed that, from
   S[nef]>1% to S[nef] = 100%, the numbers of overlapping modules among
   the vehicle, BA, CA and JA groups were 8, 5, 3, 1, 0, and 0,
   respectively, showing a gradually decreasing trend ([127]Fig 4G), and
   the numbers of overlapping GO functions among the four groups were 192,
   123, 83, 37, 0, and 0, respectively, also showing a decreasing trend.
   When S[nef]>75% and S[nef] = 100%, there were no overlapping modules
   among the four groups (Fig [128]4E and 4F). With changes in similarity,
   the changing trends of the number of overlapping and non-overlapping
   modules between groups were as shown in [129]Fig 4G–4I. Clearly, the
   number of non-overlapping AMs (special modules in each group, which we
   defined as new allosteric modules) gradually increased with an increase
   in similarity (from 1% to 100%) in all four groups ([130]Fig 4I).
   Linear regression analysis showed significant associations between the
   number of non-overlapping AMs and the similarities of the vehicle
   (regression coefficient, 0.063; SE, 0.011; P = 0.005), BA (regression
   coefficient, 0.092; SE, 0.017; P = 0.006), CA (regression coefficient,
   0.092; SE, 0.025; P = 0.02) and JA groups (regression coefficient,
   0.081; SE, 0.005; P<0.001).

Fig 4. Different degrees of overlaps between modules of different treatment
groups.

   [131]Fig 4
   [132]Open in a new tab

   (A-F) Six levels of similarity reflect the degree of overlaps between
   modules of different treatment groups, including S[nef] >1%, >25%,
   >50%, >70%, >75%, and = 100%. (D) The WAM identified among the four
   groups. When S[nef]>70%, AM^W[BCJV] was first identified among the four
   groups. The green dashed area indicates the AM 10 in the BA group
   (AM[BA10]). The areas within the blue, orange and red squares represent
   AM 6 in the CA group (AM[CA6]), AM 6 in the JA group (AM[JA6]) and AM 7
   in the vehicle group (AM[V7]), respectively. (F) Five CAMs were
   identified, namely AM^C[BA8-V11], AM^C[JA36-V36], AM^C[JA39-V45],
   AM^C[CA6-JA6-V7], and AM^C[CA36-JA37-V38]. (G and H) The changing
   trends of the number of overlapping modules between groups. (I) The
   changing trends of the number of non-overlapping modules between
   groups.

Distribution of AMs, CAMs, GAMs, and DAMs

   Compound interventions had different effects on modules in the modular
   networks based on ischemic mice, which resulted in topological
   variations or changes of compound-associated modules under different
   conditions. By calculating the relative overlap (i.e. similarity)
   between different states of the same module, we could quantitatively
   analyze topological structural variations of modules (based on the
   changes in nodes and edges primarily) in different states and explore
   the dynamics of allosteric modular networks. Therefore, by calculating
   the variation in similarity values of AMs under various conditions,
   five types of modular allostery (AMs, CAMs, GAMs, DAMs and WAMs) were
   identified in ischemic modular networks before and after compound
   intervention. We illustrated and defined AMs, CAMs, GAMs, DAMs and WAMs
   in [133]Fig 1 in advance. As shown in [134]Fig 5, we presented in
   detail several examples. (1) AMs. Most modules showed partial overlap
   (0<S[nef] <100%) between various groups. In anti-ischemic modular
   networks, a given compound only attacked part of the disease-associated
   modules, namely, only parts of nodes in the disease-associated modules
   were affected by the compound. For example, BA acted on three nodes in
   AM[V21] (Prkcd, Ppp2ca and Ppp2r5c), JA affected only one node (Raf1),
   and all four nodes were affected by CA ([135]Fig 5A), indicating that
   the intervention of CA on AM[V21] might be greater than that of BA or
   JA. (2) CAMs. Five CAMs were identified between vehicle vs. different
   treatment groups, e.g., AM^C[CA6 -JA6-V7] ([136]Fig 4F), and
   AM^C[CA36-JA37-V38] ([137]Fig 5B). In anti-ischemic modular networks,
   compounds affected the overall ischemia-associated modules, i.e., all
   nodes in the ischemia-associated module were affected by the compound.
   For instance, both CA and JA affected all nodes in AM^C[V38] ([138]Fig
   5B). CAMs were not significantly altered after compound intervention;
   therefore, these modules might not be particularly useful from a
   therapeutic standpoint. (3) GAMs. A GAM indicated that the module
   appeared after compound intervention (birth). When S[nef] = 100%, a
   total of 115 GAMs were identified ([139]Fig 4F). For example,
   AM^G[BA48]and AM^G[CA40] (Mrm1-Guk1-Hrsp12) were not found in the
   vehicle group but appeared in the BA and CA groups ([140]Fig 5C);
   therefore, we assumed they were excitatory modules. (4) DAMs. A DAM
   indicated that the module in the vehicle group disappeared after
   compound intervention (death). When S[nef] = 100%, 45 DAMs were found
   ([141]Fig 4F). For example, AM^D[V33] (Fos-Cebpg-Atf2) was included in
   the vehicle group but disappeared in the BA, CA, or JA groups ([142]Fig
   5D); therefore, we assumed it was an inhibitory module.

Fig 5. Examples of AMs, CAMs, GAMs, and DAMs.

   [143]Fig 5
   [144]Open in a new tab

   (A) AM [BA33- CA16- JA34]. The pink dashed area, green dotted area,
   area in the yellow solid line indicate AM[BA33], AM[CA16], AM[JA34],
   respectively. Blue nodes and dark gray edges represent AM[V21]. (B)
   AM^C[CA36-JA37-V37]. AM[CA36] and AM[JA37] (represented by green and
   yellow circles) were completely overlapped with AM[V38]. (C)
   AM^G[BA48-CA40]. Green nodes and dark gray edges denote AM[BA48] or
   AM[CA40], and the blue dashed square indicates that the module did not
   appear in the vehicle group. (D) AM^D[V33]. Blue nodes and dark gray
   edges denote AM[V33], and the purple dashed square indicates that the
   AM did not appear in the BA, CA or JA group.

Distribution of WAMs and enriched KEGG pathways

   When S[nef]>70%, the first overlapping module AM^W[BCJV] (including
   AM[BA10], AM[CA6], AM[JA6] and AM[V7]) was identified among the
   vehicle, BA, CA and JA groups ([145]Fig 4D).

   According to the results of KEGG pathway enrichment analysis, three
   significantly enriched pathways (nucleotide excision repair, basal
   transcription factors, and viral carcinogenesis) were identified in
   AM^W[V7] ([146]Table 1). The three pathways have been reported to be
   associated with BA in the literature, but the possible relationships
   between CA and nucleotide excision repair, JA and nucleotide excision
   repair, JA and basal transcription factors have not been reported
   ([147]S9 Table). Additionally, AM^W[BC] (S[nef]>1%), AM^W[CJ]
   (S[nef]>1%), and AM^W[BJ] (S[nef]>25%) were identified between the BA
   and CA, CA and JA, BA and JA groups, respectively ([148]Table 1). The
   AMs between the BA and JA groups held a higher similarity than those
   between the BA and CA or the JA and CA groups. All WAMs enriched two or
   three KEGG pathways, except AM^W[CJ]. The WAMs might provide another
   approach to revealing complex pharmacological networks beyond pathways
   analysis.

Table 1. Distribution of the watershed allosteric modules and significantly
enriched pathways.

   Watershed allosteric modules S[nef] Enriched terms Sample Background
   P-Value Corrected Genes IDs (Entrez Gene
   AM IDs KEGG Pathways number number P-Value ID)
   AM^W[BCJV] (BA vs. CA vs. JA vs. V) >70% AM[BA10] Nucleotide excision
   repair 5 44 1.94E-11 4.10E-11 66467|209357|13872|17420|12572
   Basal transcription factors 5 47 2.74E-11 4.10E-11
   66467|209357|13872|17420|12572
   AM[CA6] Nucleotide excision repair 6 44 1.41E-13 3.22E-13
   12572|13872|14884|209357|66467|17420
   Basal transcription factors 6 47 2.15E-13 3.22E-13
   12572|13872|14884|209357|66467|17420
   AM[JA6] Nucleotide excision repair 6 44 1.41E-13 3.22E-13
   12572|13872|14884|209357|66467|17420
   Basal transcription factors 6 47 2.15E-13 3.22E-13
   12572|13872|14884|209357|66467|17420
   AM[V7] Nucleotide excision repair 6 45 4.15E-13 9.37E-13
   12572|13872|14884|66467|209357|17420
   Basal transcription factors 6 46 4.68E-13 9.37E-13
   12572|13872|14884|66467|209357|17420
   Viral carcinogenesis 2 236 0.01011 0.01348 209357|14884
   AM^W[BC] (BA vs. CA) >1% AM[BA23], AM[CA18] Oxidative phosphorylation 3
   142 0.00013 0.0003882 225887|226646|75406
   Parkinson's disease 3 143 0.00013 0.0003882 225887|226646|75406
   Alzheimer's disease 3 183 0.00026 0.0004351 225887|226646|75406
   Huntington's disease 3 189 0.00029 0.0004351 225887|226646|75406
   Sulfur relay system 1 10 0.00757 0.009089 69372
   AM^W[CJ] (CA vs. JA) >1% AM[CA28] — — — — — —
   AM[JA35] — — — — — —
   AM^W[BJ] (BA vs. JA) >25% AM[BA49] RNA transport 4 172 1.11E-07
   2.22E-07 54364|74097|117109|67676
   Ribosome biogenesis in eukaryotes 3 88 2.03E-06 2.03E-06
   54364|74097|117109
   AM[JA42] Ribosome biogenesis in eukaryotes 4 88 1.61E-08 3.23E-08
   54364|74097|117109|66161
   RNA transport 4 172 2.21E-07 2.21E-07 54364|74097|117109|66161
   [149]Open in a new tab

   Notes: AM^W[BCJV] denotes the watershed allosteric module identified
   among the BA, CA, JA and vehicle groups. AM^W[BC] denotes the watershed
   allosteric module identified between the BA and CA groups. AM^W[CJ]
   denotes the watershed allosteric module identified between the CA and
   JA groups. AM^W[BJ] denotes the watershed allosteric module identified
   between the BA and JA groups. The fourth column “Sample number” lists
   the number of input genes mapped to the particular pathway. The fifth
   column “Background number” lists the number of background genes mapped
   to the particular pathway.

   “—” indicates that no KEGG pathway was enriched from the AM.

Five types of AMs make up dynamic stereo-scroll

   To systemically reveal the complex interaction of AMs in modular
   networks, we must define, in detail, the contributions of diverse AMs
   under different treatments or at different time points. AM^C represents
   the baseline of AMs under different treatments or at different time
   points, whereas AM^W splits the different conditions into an
   allostery-of-function gradient. These two types of AMs provide evidence
   of the stability of pharmacological systems, which may be used to
   bridge different compounds in clinical medicine. Based on biological
   and pharmacological disturbances in inter-and intra-AMs, the numbers of
   AM^G and AM^D may respond to reach a dynamic balance from the pool of
   AMs. To summarize the relationships among these AMs, we proposed a
   scheme of AM dynamic stereo-scroll to integrate all AMs ([150]Fig 6A).

Fig 6. Schematic representation of AM dynamic stereo-scroll and biological
verification for AMGBA48 of Mrm1-Guk1-Hrsp12 and AMDV33 of Fos-Cebpg-Atf2.

   [151]Fig 6
   [152]Open in a new tab

   (A) Five types of AMs can structure a multiple-dimensional map in
   diverse dynamic directions. (B-D) The mRNA levels of Guk1, Hrsp12 and
   Mrm1 among different treatment groups. ^#P<0.05, ^##P<0.01, compared
   with the sham group; ^*P<0.05, ^**P<0.01, compared with the vehicle
   group. (E) Representative RT-PCR bands of Guk1, Hrsp12, Mrm1 and GAPDH.
   (F) Representative immunoblots of Atf2, Fos, Cebpg and β-actin. (G-I)
   The protein expression levels of Atf2, Fos, and Cebpg among different
   treatment groups. ^#P<0.05, ^##P<0.01, compared with the sham group;
   ^*P<0.05, ^**P<0.01, compared with the vehicle group.

Validating functional alteration of AM^G[BA48] and AM^D[V33]

   We selected a GAM and a DAM from two ends of AMs to validate that
   topological variations were associated with ischemia and compound
   treatment. Although the mRNA levels of Guk1 and Hrsp12 were not
   significantly different among groups (P>0.05), the mRNA level of Mrm1
   in AM^G[BA48] was indeed significantly different among these treatment
   groups ([153]Fig 6B–6E). Compared with the vehicle group, Mrm1 mRNA was
   significantly up-regulated by BA and down-regulated by CA (P<0.01),
   while the associations between Mrm1 and cerebral ischemia have not been
   previously reported.

   Compared with the sham group, the protein expression levels of Atf2,
   Fos, and Cebpg were significantly up-regulated in the vehicle group
   (P<0.01) ([154]Fig 6F–6I), which was also consistent with our previous
   findings [[155]40,[156]41]. Fos and Cebpg protein expression levels in
   AM^D[V33] were both significantly down-regulated by JA, BA and CA
   relative to the levels observed for the vehicle group (P<0.05).

Discussion

   We performed a comparative modular analysis of ischemic targeted
   networks based on different identification methods of AMs. After
   developing a novel similarity approach for analyzing
   allostery-of-function, we defined five types of AMs and established a
   dynamic stereo-scroll of different allosteric variations. This
   exploration offers a powerful strategy for reflecting the
   characteristic precision and robustness of allostery-mediated modular
   pattern transformation.

SimiNEF is a novel tool for modular functional analysis

   Modularity has become a fundamental concept for building disease
   network and drug-target networks [[157]6]. Different modules may
   contribute different functions to outcome variations. Therefore, an
   important step of network-based approaches to disease is to identify
   the disease module for the pathophenotype of interest, which in turn
   can guide further experimental work and influence drug development
   [[158]1]. Moreover, understanding AMs that contribute to pharmaceutics
   might provide novel signatures that can be used as endpoints to define
   disease processes or the effects of drugs under healthy or diseased
   conditions [[159]6]. Although traditional pathways have served as
   conceptual frameworks in biological research [[160]42], at the module
   layer, the synthetic biologist uses a diverse library of biological
   devices to assemble complex pathways that function like integrated
   circuits. However, the exact definition of a functional module still
   varies [[161]6,[162]43,[163]44], and modular analysis is becoming a
   powerful approach for deconstrusting complex biological networks.

   AMs display different degrees of flexibility [[164]16]. Recently,
   several approaches to parameterizations and generative rules of
   different parameters such as path length, node, size, and modularity
   similarity as well as pertinent models have been developed according to
   a modules-within-modules perspective [[165]45,[166]46]. However, more
   fundamental tools are required to anticipate phenomena by
   quantitatively decomposing, reconstituting and optimizing modular
   structure from a topological perspective [[167]47]. In this case, by
   fusing topological variation and functional alteration, SimiNEF is a
   powerful approach for modular functional analysis, which may help
   explore, in detail, the effect of modular network on the quality and
   stability of dynamic communities when different compounds are
   administered. In addition, we may also use known pathways to improve
   and predict unknown functions of modules [[168]48].

A panoramagram of AMs sufficiently reveals complex disease networks

   The allosteric theory of signal transduction has been applied to
   signaling molecules as diverse as regulatory enzymes, nuclear
   receptors, and various classes of membrane receptors [[169]49]. The
   concept of allosteric modulation in drug targeting has attracted
   considerable interest in recent years and may become a promising
   therapeutic principle [[170]50]. Allostery appears to play a key
   unifying role by specifying the conformational barcode. Dysfunctional
   conformational barcodes in disease states can be (partially) restored
   to their “healthy” barcode ensemble states by allosteric drugs
   [[171]2,[172]51]. In this study, three different effective compounds
   acted on the same ischemic AM network. Modular overlaps might reveal
   the simultaneous involvement of nodes in multiple modules, which were
   determined by assigning proteins to multiple modules [[173]52]. Not
   merely addressing topological overlaps (e.g., overlaps of nodes or
   edges), SimiNEF took into account the similarities of nodes, edges and
   GO functions of AMs altogether to reveal the fusing alteration of
   topological and functional similarities between AMs. Different
   effective compounds attacked diverse nodes in the same ischemic AMs
   network, and five types of modular allostery (AMs, CAMs, GAMs, and DAMs
   and WAMs) were identified, which reflected the structural and
   functional diversity of AMs before and after compound intervention.
   Because allosteric propagation can occur via large cellular assemblies
   over large distances [[174]51] or within the protein matrix to
   eventually reach the substrate site [[175]53], in our study most AMs
   were partially overlapped (0< S[nef] <100%) and more GAMs were observed
   than DAMs, whereas only five CAMs were identified in the vehicle group.
   Thus, allostery could help explain how different compounds perturbed
   the ischemic modular network. For example, as shown in [176]Fig 5A,
   AM[BA33], AM[CA16], and AM[JA34] all affected AM[V21], which was
   enriched for the function “phosphate metabolic process”; then, BA, CA,
   and JA all intervened in the function, which reflected the similarity
   of their pharmacological mechanisms. Specifically, however, BA, CA, and
   JA affected different genes or proteins in AM[V21], e.g., the number of
   genes affected by CA was the sum of that of BA and JA, which reflected
   the diversity of their pharmacological mechanisms. Moreover, based on
   different levels of similarity, WAMs were identified among the vehicle,
   BA, CA and JA groups, as well as between any two of the
   compound-treated groups. Such a WAM contributed to reveal the
   demarcation point of common and diverse pharmacological mechanisms
   between different effective compounds.

   Indirectly using specific inter-protein network pathways can affect the
   pharmacological target protein [[177]9,[178]51]. Thus, drugs do not
   target the actual disease-associated proteins but bind to their 3rd or
   4th neighbors. The distance between drug targets and disease-associated
   proteins is regarded as a sign of palliative drug action
   [[179]4,[180]9]. In this study, we suppose that BA, CA or JA might
   indirectly and specifically affect pharmacological targets or key
   proteins by targeting “by-stander” proteins (e.g., Cebpg, Mrm1). Our
   findings indicate that Cebpg protein expression was down-regulated by
   JA, BA and CA. Although the association between Cebpg and CA has not
   been previously reported, it was demonstrated that chenodeoxycholic
   acid (CDCA), one of the primary bile acids, induced antioxidant and
   xenobiotic-metabolizing enzymes by activating C/EBPβthrough
   phosphorylation [[181]54]. With the assurance of different allosteric
   modulators of diverse functions and dynamics [[182]55,[183]56], an
   allosteric modulated approach may be achieved from disease molecular
   insights into therapeutic perspectives [[184]50]. Although this is the
   first time AMs have been analyzed, the allostery of targeted systems is
   anticipated to provide effective solutions to challenges that include
   variations in nodes (^NAM), edges (^EAM) and related functions. Our
   ability to reveal, in detail, the transformational information from
   disease network systems and to process information inside AMs is
   critical to advancing the topological alteration and functional
   complexity with which we can engineer, predict, and probe
   pharmacological systems. Thus, we developed a novel paradigm of
   assembling AMs that allows for the quantitative analysis of gradient
   mechanisms of targeted network variations.

Supporting Information

   S1 Fig. The degree of overlap between modules of different groups.

   (PDF)
   [185]Click here for additional data file.^ (231.8KB, pdf)
   S1 Table. Topological attributes of global networks in different
   groups.

   (DOCX)
   [186]Click here for additional data file.^ (16.8KB, docx)
   S2 Table. Affinity propagation (AP) results for all parameters tested.

   (DOCX)
   [187]Click here for additional data file.^ (20.6KB, docx)
   S3 Table. MCL results for all parameters tested.

   (DOCX)
   [188]Click here for additional data file.^ (20.6KB, docx)
   S4 Table. MCODE results for all parameters tested.

   (DOCX)
   [189]Click here for additional data file.^ (26KB, docx)
   S5 Table. Functional modules identified by MCODE in different groups.

   (DOCX)
   [190]Click here for additional data file.^ (16.5KB, docx)
   S6 Table. 218 significantly enriched GO biological processes.

   (DOCX)
   [191]Click here for additional data file.^ (33.6KB, docx)
   S7 Table. Overlapping and non-overlapping GO biological processes.

   (DOCX)
   [192]Click here for additional data file.^ (16.9KB, docx)
   S8 Table. Overlapping and non-overlapping KEGG pathways.

   (DOCX)
   [193]Click here for additional data file.^ (18.9KB, docx)
   S9 Table. Relationship between compounds and KEGG pathways in the
   watershed allosteric modules supported by previous literature.

   (DOCX)
   [194]Click here for additional data file.^ (27.2KB, docx)

Abbreviations

   BA
          baicalin

   CA
          cholic acid

   JA
          jasminoidin

   AMs
          allosteric modules

   WAM
          watershed allosteric module

   CAMs
          conserved allosteric modules

   GAMs
          generated allosteric modules

   DAMs
          disappeared allosteric modules

Data Availability

   The microarray data are available from the Array Express database
   (accession number: E-TABM-662). All other relevant data are within the
   paper and its Supporting Information files.

Funding Statement

   Our research was largely supported by the Hi-Tech Research and
   Development Program of China (863), the “Eleventh Five-Year” National
   Key Technologies R&D Program (2006BAI08B04-06), and the National Major
   Scientific and Technological Special Project for “Significant New Drugs
   Development” (2013ZX09303301). The funders had no role in study design,
   data collection and analysis, decision to publish, or preparation of
   the manuscript.

References