Conceived and designed the experiments: TS SM HK. Performed the experiments: TS. Analyzed the data: TS SM. Contributed reagents/materials/analysis tools: TS SM. Wrote the paper: TS HK.
The authors have declared that no competing interests exist.
Protein–protein interaction and gene regulatory networks are likely to be locked in a state corresponding to a disease by the behavior of one or more bistable circuits exhibiting switch-like behavior. Sets of genes could be over-expressed or repressed when anomalies due to disease appear, and the circuits responsible for this over- or under-expression might persist for as long as the disease state continues. This paper shows how a large-scale analysis of network bistability for various human cancers can identify genes that can potentially serve as drug targets or diagnosis biomarkers.
Since most disease states exhibit a certain level of resilience against therapeutic interventions, each disease state can be considered to be homeostatic to some extent. There must be one or more mechanisms that cause the gene-regulatory network to maintain a certain state, and one such mechanism is a bistable switch. In this work, bistable switch networks were constructed and their ON(upregulated)/OFF(downregulated) states were compared between human cancers and healthy control samples. Changes in the ON/OFF state with the progression of cancer were demonstrated. A series of genes that might serve as a drug target or diagnosis biomarker was identified. The approach presented here should provide useful insights into the states of biological networks, which may lead to the discovery of novel drug targets and therapeutic interventions.
Understanding diseases within the context of biological networks is one of the major challenges in systems biology. Diseases often persist and resist therapeutic intervention. The persistence of a disease in a system must be reflected in the ability of the system's networks to maintain the state underlying the disease. In other words, networks are “locked-in” to disease states and maintain their stability. Thus, it is important to understand how such multi-stable states are achieved within the context of network topology and to understand the dynamics of these states. A network robust against a range of perturbations can maintain a healthy state but can also, when affected by a disease, transition to a new steady state that is often also robust against perturbations, making the disease state persistent. A series of disease progressions may be the result of a sequence of state transitions in the network dynamics (
A: A network in a healthy state is robust against a range of perturbations, so it can continue to maintain a healthy state. With the onset of a disease, however, the network transitions to a new steady state that is also often robust against perturbations, making the disease state persistent. B: These state transitions might be driven by bistable switch networks. The nodes represent genes and the edges between them represent the pairing of bistable toggle switches. Red and blue nodes correspond to ON (upregulated) and OFF (downregulated) states.
Complex networks exhibiting such multi-stability must have a set of bi-stable or multi-stable circuits consisting of proteins and genes. The identification of circuits that exhibit bi- or multi-stability within large protein-interaction and gene-regulation networks would provide information useful for understanding the mechanism(s) of network bistability. Furthermore, circuits exhibiting bistability can be potential drug targets or biomarkers for classifying disease states.
Network dynamics are regulated by the structure of the network and the flow of information through feedforward and feedback loops. Mutual activation or mutual inhibition configurations can maintain the flow of biological information between two molecules and act as network memories or switches. Furthermore, an activation-inhibition configuration, in which one molecule stimulates the other while the latter inhibits the former, generates dynamics with periodicity like that seen in circadian rhythms and cell cycles
There are several important network motifs for system configurations
A robust toggle switch behaves as a signal memory unit by using a hysteresis mechanism
To identify circuits exhibiting bi- and multi-stability, we topologically analyzed activation and inhibition in proteins on a large scale by using various databases containing expression array data for various diseases. We compared the progression stages of these diseases with those of control samples by using data for healthy individuals taken from available databases, and we identified sets of switch circuits possibly responsible for maintaining the persistent disease states by using network topologies to analyze that data.
There are theoretically many system configurations that can lead to bistability
A type-1 bistable toggle switch (BTS) contains two genes with positive autoregulation. Each gene mutually inhibits the other's expression. The two genes in the type-2 BTS also suppress each other's expression. Each gene has double positive or negative feedback with the other gene, so the same function as a type-1 BTS may be exhibited. A type-3 BTS was constructed on the basis of a theoretical study on the modeling of genetic switches with positive feedback loops. The blue, green, and orange nodes respectively correspond to switch genes, mediators, and genes constituting a feedback loop. Positive (upregulated) interactions are indicated by green lines and negative (downregulated) interactions are indicated by red lines.
Type-1 BTS: A type-1 BTS uses a basic motif that has been identified in E. coli
Type-2 BTS: Only a small number of transcription factors with a positive autoregulation ability have been reported. From the viewpoint of dynamic properties, positive autoregulation has the same functional meaning that a positive feedback loop (double-positive feedback or double-negative feedback) does
Type-3 BTS: A theoretical study of modeling genetic switches with positive feedback loops
It is possible that double-negative feedback can be a bistable toggle switch when both nodes have positive feedback loops. Two BTSs can share their mutual inhibition configurations as positive feedback loops and can form network configurations.
Next, bistable toggle switches defined above was extracted from large-scale databases (ResNet 3.0, Ariadne Genomics Inc.) containing data for interaction networks. We detected 6585 pairs of bistable toggle switches, and these switch nodes formed a large network. Four-hundred and forty-two genes are involved in these BTS pairs, and the hubs of switch nodes in the network are clearly visible because of their high degree of connectivity (
Four-hundred and forty-two genes are involved in 6585 bistable toggle switch pairs. Nodes are shown in sizes proportional to their connectivity, making the hubs of switch nodes clearly visible. The Cytoscape session file for this network is available in
ArrayExpress microarray data were used to further examine the states of the BTS pairs. It is obvious that a BTS has four possible states: “ON/ON,” “ON/OFF,” “OFF/ON,” and “OFF/OFF.” Mathematical analysis of bistability for the chosen parameter condition demonstrated that the probability of “ON/OFF” and “OFF/ON” states is high, that of “ON/ON” is low, and that of “OFF/OFF” is extremely low
The ArrayExpress experimental categories and the mean number of corresponding BTS pairs with a significant ON/OFF change are shown in
There were few BTS pairs with significant changes for “lifestyle” and many with significant changes for “cancer.” Note the higher number of BTS pairs for iPS cells than for donor cells.
The switching of a molecule's function to the ON state generally means the molecule's intrinsic function related to intracellular molecular systems has become stronger, whereas switching to the OFF state means it has become weaker. The ON state of a molecule is produced not only by an increase in the absolute amount of that molecule but also by actions such as activation due to phosphorylation-induced transformation of the molecule's three-dimensional structure or to translocation of the molecule to an location where it can carry out its function properly.
In these studies using mRNA expression data from microarrays, the toggling of a BTS pair was defined as an instance in which a sample's mRNA level for one of that pair's molecules increased (relative to a control) and the mRNA level for the other of that pair's molecule decreased (relative to the same control).
A notable finding is that when mRNA levels were compared between induced pluripotent stem (iPS) cells and donor controls, more than 1000 BTS pairs demonstrated significant changes in the ON/OFF states. The high frequency of these changes in iPS cells is reasonable in that an iPS cell is in an undifferentiated state committed to differentiation to a particular lineage, in which many BTSs might be involved
Lung cancer is the leading cause of cancer-related deaths
A: Toggling inferred from time-dependent data (ArrayExpress ID: E-GEOD-10700 and E-GEOD-10718) for the mRNA expression of normal human bronchial epithelial cells exposed to cigarette smoke for 24 hours. B: 2 hrs after exposure start, C: 4 hrs after exposure start, D: 8 hrs after exposure start, E: 24 hrs after exposure start. The nodes represent genes and the edges between them represent the pairing of bistable toggle switches. The colors of nodes were automatically assigned as a continuous color gradient from red for ON (upregulated) to blue for OFF (downregulated) according to relative gene-expression levels of the nodes. In
SOCS3 inhibits cytokine signaling via the JAK(Janus kinase)/STAT(signal transducers and activators of transcription) pathway. Recent research has demonstrated that the activation of SOCS3 in the lung occurs during the acute inflammatory response
The state of BTS toggling determined by comparing mRNA expression data (ArrayExpress ID: E-GEOD-10072) for normal lung tissue with that for lung-cancer patients with a history of smoking (former smokers) (Fig. 6A) and that for lung-cancer patients still smoking (current smokers) (Fig. 6B). The nodes and genes surrounded by bold black frames are those also shown in
ON/OFF patterns of FN1-SPP1 (
In addition, although some EDN1(endothelin-1)-related BTS pairs and SHC1(Src homology 2 domain containing transforming protein)-related BTS pairs are shared in lung cancer tissue in current and former smokers, a considerable number of differing patterns are evident. This suggests that the mechanisms for carcinogenesis differ depending on the lengths of time that current and former smokers have smoked. EDN1, which is a hypoxia-inducible angiogenic growth factor for surrounding epithelial and endothelial cells, plays an important role in cancer-stromal interactions and tumor progression, and its expression is related to poor prognosis in NSCLC
Small molecules that can put these BTS pairs into normal ON/OFF states might be useful in preventing the progression of lung cancer in both current and former smokers.
Hepatocellular carcinoma (HCC) is a primary cancer that originates in hepatocytes and typically follows cirrhosis or chronic-hepatitis virus infections
BTS toggling graph comparing the mRNA expression data (ArrayExpress ID: E-GEOD-6764) of normal liver tissue with that of precancerous and cancerous liver tissue. The nodes and edges surrounded by the bold lines are BTSs for which toggling was observed when comparing dysplastic liver tissue, a precursor of liver cancer, with normal liver tissue. The nodes represent genes and the edges between them represent the pairing of bistable toggle switches. The colors of nodes were automatically assigned as a continuous color gradient from red for ON (upregulated) to blue for OFF (downregulated) according to relative gene-expression levels of the nodes.
When HCC tissue was compared with healthy liver tissue, toggling was most evident for CCNA2(cyclin A2)–related BTSs (
Fig. 8A: A network of CCNA2-related BTS pairs selected from the data used in
CCNA2 activates CDC2 or CDK2 kinases and regulates the cell cycle positively by promoting G1/S and G2/M transitions in both the G1 and G2 phases of the cell cycle
After the toggling of CCNA2-related BTSs but still in the early stage of carcinogenesis, the OFF state of IL6 is related to the ON states of PTK2 and SMAD3 (SMAD family member 3). PTK2 and SMAD3 play important roles in cell growth and the activation of intracellular signal transduction pathways, suggesting that cell proliferation might accelerate during this stage.
Toggling of PTK2(ON)-BCL2(OFF) was observed in advanced and very advanced stages. BCL2 (B-cell CLL/lymphoma 2) suppresses apoptosis, and the downregulation of BCL2 might be involved in the acceleration of apoptosis in cancer cells.
Notably, the ON/OFF state of the TP53-IGF1 BTS was changed from “OFF-OFF” to “ON(TP53)–OFF(IGF1)” in advanced HCC. And in very advanced HCC, almost all IGF1-related BTS pairs demonstrated “ON(other)–OFF(IGF1)” patterns.
In the very advanced stage, many IGF1(insulin-like growth factor-1)-related BTS pairs demonstrated significant ON/OFF changes. The liver is the main source of IGF1, and the development of HCC is accompanied by significantly reduced serum IGF1 levels
We constructed bistable switch networks, compared their ON/OFF states with those of control (healthy) samples, and found that their states changed with disease progression and differed between patient subtypes. Since most disease states exhibit a certain level of resilience against therapeutic intervention, each can be considered to be homeostatic to some extent. This homeostasis implies the robust status of a dynamical network and could not be maintained without mechanisms that drive a network to maintain a certain state. One such mechanism is a bistable switch, so we should look for sets of bistable switch circuits in large-scale protein interaction networks.
Our analysis revealed that BTS states change with disease progression, and the implications of this are far reaching. For example, it might be possible to prevent or delay disease progression by perturbing one or more such switches. Such switches may be novel drug-target candidates for controlling disease progression. Analysis of the ON/OFF states of genes constituting bistable circuits revealed similarities between disease subtypes.
While our analysis has provided insightful information, it has shortcomings. First, the network topologies were based on commercial databases created using a text-mining system. This means that the details of the molecular interactions were not verified. The development of a more accurate interaction database would enable more precise and accurate analysis of bistable network behaviors and of the contributions of switch circuits to those behaviors. Second, the analysis was based solely on network topologies—no parametric features were considered. Although topological analysis enabled us to identify circuits exhibiting bistable behavior, whether circuits exhibiting bistable behavior apparently exhibit bistable behavior depends on the kinetic parameters associated with each interaction
Using microarray data, we determined that the pairs of genes in the circuits we identified are polarized into ON and OFF states. Two mutually inhibitory nodes polarized into ON and OFF states do not function as a bistable switch if both genes are ON or OFF. This is why we focused on BTSs, which demonstrated “ON/OFF” or “OFF/ON” states. We should, however, note that the “ON/ON” states of some BTSs play important roles in mammalian embryogenesis
Cluster analysis of transcriptome data in microarrays is useful for classifying disease characteristics according to differences in expression patterns. Although several disease types that are difficult to classify morphologically have been classified using this approach, the rules underlying the cluster structure of the data are unclear, and the importance of each of the molecules in a cluster cannot be determined with a reasonable degree of certainty. The analysis of changes in gene-expression levels can also be used to create a list of molecules whose levels increase or decrease significantly over time or whose levels differ significantly between healthy and diseased tissues. Although examinations of gene interrelations using gene-ontology classification and analysis of the classification results using network diagrams have led to a greater degree of understanding of the changes in molecular networks, it is difficult to infer the meanings of biological interactions between molecules.
Our proposed method (i.e., focusing on BTS ON/OFF changes) takes as the starting point the interactions between molecules. This makes it easy to infer biological meaning and makes it possible to analyze time-dependent data for time periods corresponding to that of disease progression (from hours to years). In addition, while conventional methods sometimes neglect molecules that are downregulated, our method places equal importance on both increases and decreases in expression.
DNA microarray technology makes it possible to study the expression of thousands of genes at the same time, but much of the microarray data consists of low signal intensities that can produce erroneous gene expression ratios between control and experimental samples
We used the transcriptome of normal tissue as the control in our analyses. This means that the identification of the molecular ON/OFF states inherent to normal tissue was unclear. Even if the ON/OFF state of a molecular pair for a certain switch is important for a particular tissue, if this state is retained in the diseased tissue, we would be unable to detect it in the present study because the ON and OFF states are not mutually exclusive. Therefore, molecules exhibiting even the slightest change are emphasized while those showing no change are ignored. We aim to overcome this drawback by identifying what types of ON/OFF changes occur in switches when embryonic stem (ES) cells or iPS cells undergo differentiation.
Since proteins are responsible for cell function, the ON/OFF state of a molecule must be determined at the protein level when searching for molecular-network structures mediating cell functions. Because there are more than 20 control steps along the way from mRNA to functional proteins
Despite its shortcomings, the approach presented here provides useful insights into the states of biological networks, insights that may lead to discovery of novel drug targets and therapeutic interventions.
The lists of molecular interactions were constructed using the Ariadne Genomics ResNet human protein interaction database (ver. 3.0) compiled, using MedScan
The interactions can be divided into two major classes: direct physical interactions (binding, protein modifications, and promoter binding) and indirect regulatory interactions (regulation, expression regulation, direct regulation, molecular transport regulation, and molecular synthesis regulation). MedScan also extracted information on the relation direction and the effect on the target molecule. The “Effect” attribute has three possible values: “positive,” “negative,” and “unknown.” The BTS pairs were extracted from the database on the basis of five rules.
Nodes are limited to genes and proteins only.
Edges are limited to “Regulation,” “Expression,” and “DirectRegulation.”
“Unknown” edges in the “Effect” attribute are omitted.
Edges extracted from fewer than three references are omitted.
If there is a positive and negative attribute in the same direction, the edge is extracted from additional references.
We extracted 19,178 relationships involving 3,682 genes (basic interaction datasets).
Using basic interaction datasets, we extracted possible network motifs for toggle switches. We defined these motifs as follows.
The type-1 BTS contains two genes that have positive autoregulation and inhibit each other's expression. The type-2 BTS also contains two genes that suppress each other's expression, but each gene also has a positive or negative loop with the other gene. One of the four subtypes of type-2 BTSs (corresponding to the four possible combinations of double positive and/or negative feedback) shows the same function as the type-1 BTS. The type-3 BTS was based on a theoretical study of the modeling of genetic switches with positive feedback loops
We used mRNA microarray data to examine the changes in the ON/OFF states of BTS candidates. CEL format files or tab-limited text files were downloaded via ArrayExpress (
The toggling of a BTS pair was defined as instances in which the mRNA levels of a sample increased for one molecule of the pair and decreased for the other. To remove background noise, we calculated the toggling score using
where SW1 and SW2 are the two molecules in alphabetical order. Changes in the ON/OFF states were considered significant when the toggling score was more than two standard deviations greater than the mean of all the toggling scores.
For pathway visualization, we used Cytoscape (Version 2.6.3), which is widely used open-source software for visualization and analysis of networks
List of BTS pairs SW1 and SW2 are the two molecules comprising a BTS pair in alphabetical order.
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Cytoscape session file for
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We are grateful to Dr. Y. Hamada (Sony Corporation) for preparing the basic interaction datasets, Mr. J. Suzuki (Tokyo Institute of Technology) for assisting us with the data extraction, and Dr. S. Ueda (Otsu Municipal Hospital) for providing us with the microarray data. We also thank Dr. K. Tabuchi for his useful comments and discussion.