1
|
Wei X, Pan C, Zhang X, Zhang W. Total network controllability analysis discovers explainable drugs for Covid-19 treatment. Biol Direct 2023; 18:55. [PMID: 37670359 PMCID: PMC10478273 DOI: 10.1186/s13062-023-00410-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/29/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND The active pursuit of network medicine for drug repurposing, particularly for combating Covid-19, has stimulated interest in the concept of structural controllability in cellular networks. We sought to extend this theory, focusing on the defense rather than control of the cell against viral infections. Accordingly, we extended structural controllability to total structural controllability and introduced the concept of control hubs. Perturbing any control hub may render the cell uncontrollable by exogenous stimuli like viral infections, so control hubs are ideal drug targets. RESULTS We developed an efficient algorithm to identify all control hubs, applying it to a largest homogeneous network of human protein interactions, including interactions between human and SARS-CoV-2 proteins. Our method recognized 65 druggable control hubs with enriched antiviral functions. Utilizing these hubs, we categorized potential drugs into four groups: antiviral and anti-inflammatory agents, drugs acting on the central nervous system, dietary supplements, and compounds enhancing immunity. An exemplification of our approach's effectiveness, Fostamatinib, a drug initially developed for chronic immune thrombocytopenia, is now in clinical trials for treating Covid-19. Preclinical trial data demonstrated that Fostamatinib could reduce mortality rates, ICU stay length, and disease severity in Covid-19 patients. CONCLUSIONS Our findings confirm the efficacy of our novel strategy that leverages control hubs as drug targets. This approach provides insights into the molecular mechanisms of potential therapeutics for Covid-19, making it a valuable tool for interpretable drug discovery. Our new approach is general and applicable to repurposing drugs for other diseases.
Collapse
Affiliation(s)
- Xinru Wei
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 210001, China
| | - Chunyu Pan
- School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Xizhe Zhang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210029, China.
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, 210001, China.
| | - Weixiong Zhang
- Department of Health Technology and Informatics, Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China.
| |
Collapse
|
2
|
Zhang X, Pan C, Wei X, Yu M, Liu S, An J, Yang J, Wei B, Hao W, Yao Y, Zhu Y, Zhang W. Cancer-keeper genes as therapeutic targets. iScience 2023; 26:107296. [PMID: 37520717 PMCID: PMC10382876 DOI: 10.1016/j.isci.2023.107296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/18/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023] Open
Abstract
Finding cancer-driver genes has been a central theme of cancer research. We took a different perspective; instead of considering normal cells, we focused on cancerous cells and genes that maintained abnormal cell growth, which we named cancer-keeper genes (CKGs). Intervening CKGs may rectify aberrant cell growth, making them potential cancer therapeutic targets. We introduced control-hub genes and developed an efficient algorithm by extending network controllability theory. Control hub are essential for maintaining cancerous states and thus can be taken as CKGs. We applied our CKG-based approach to bladder cancer (BLCA). All genes on the cell-cycle and p53 pathways in BLCA were identified as CKGs, showing their importance in cancer. We discovered that sensitive CKGs - genes easily altered by structural perturbation - were particularly suitable therapeutic targets. Experiments on cell lines and a mouse model confirmed that six sensitive CKGs effectively suppressed cancer cell growth, demonstrating the immense therapeutic potential of CKGs.
Collapse
Affiliation(s)
- Xizhe Zhang
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Chunyu Pan
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Xinru Wei
- Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Meng Yu
- Department of Laboratory Animal Science, China Medical University, Shenyang, China
- Key Laboratory of Transgenetic Animal Research, China Medical University, Shenyang, China
| | - Shuangjie Liu
- Department of Urology, First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jun An
- Department of Urology, First Affiliated Hospital of China Medical University, Shenyang, China
| | - Jieping Yang
- Department of Urology, First Affiliated Hospital of China Medical University, Shenyang, China
| | - Baojun Wei
- Department of Urology, First Affiliated Hospital of China Medical University, Shenyang, China
| | - Wenjun Hao
- Department of Urology, First Affiliated Hospital of China Medical University, Shenyang, China
| | - Yang Yao
- Department of Physiology, Shenyang Medical College, Shenyang, China
| | - Yuyan Zhu
- Department of Urology, First Affiliated Hospital of China Medical University, Shenyang, China
| | - Weixiong Zhang
- Department of Health Technology and Informatics, Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Computer Science and Engineering, Department of Genetics, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
3
|
Jia S, Xi Y, Li D, Shao H. Finding complete minimum driver node set with guaranteed control capacity. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.05.095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
4
|
Dilations and degeneracy in network controllability. Sci Rep 2021; 11:9568. [PMID: 33953239 PMCID: PMC8100115 DOI: 10.1038/s41598-021-88529-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/22/2021] [Indexed: 12/02/2022] Open
Abstract
Network controllability asserts a perspective that the structure—the location of edges that connect nodes—of the network contains important information about fundamental characteristics of our ability to change the behavior that evolves on these networks. It can be used, for example, to determine the parts of the system that when influenced by outside controlling signals, can ultimately steer the behavior of the entire network. One of the challenges in utilizing the ideas from network controllability on real systems is that there is typically more than one potential solution (often many) suggested by the topology of the graph that perform equally well. Picking a single candidate from this degenerate solution set over others should be properly motivated, however, to-date our understanding of how these different options are related has been limited. In this work, we operationalize the existing notion of a dilation into a framework that provides clarity on the source of this control degeneracy and further elucidates many of the existing results surrounding degeneracy in the literature.
Collapse
|
5
|
Pan C, Zhu Y, Yu M, Zhao Y, Zhang C, Zhang X, Yao Y. Control Analysis of Protein-Protein Interaction Network Reveals Potential Regulatory Targets for MYCN. Front Oncol 2021; 11:633579. [PMID: 33968733 PMCID: PMC8096904 DOI: 10.3389/fonc.2021.633579] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 03/04/2021] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND MYCN is an oncogenic transcription factor of the MYC family and plays an important role in the formation of tissues and organs during development before birth. Due to the difficulty in drugging MYCN directly, revealing the molecules in MYCN regulatory networks will help to identify effective therapeutic targets. METHODS We utilized network controllability theory, a recent developed powerful tool, to identify the potential drug target around MYCN based on Protein-Protein interaction network of MYCN. First, we constructed a Protein-Protein interaction network of MYCN based on public databases. Second, network control analysis was applied on network to identify driver genes and indispensable genes of the MYCN regulatory network. Finally, we developed a novel integrated approach to identify potential drug targets for regulating the function of the MYCN regulatory network. RESULTS We constructed an MYCN regulatory network that has 79 genes and 129 interactions. Based on network controllability theory, we analyzed driver genes which capable to fully control the network. We found 10 indispensable genes whose alternation will significantly change the regulatory pathways of the MYCN network. We evaluated the stability and correlation analysis of these genes and found EGFR may be the potential drug target which closely associated with MYCN. CONCLUSION Together, our findings indicate that EGFR plays an important role in the regulatory network and pathways of MYCN and therefore may represent an attractive therapeutic target for cancer treatment.
Collapse
Affiliation(s)
- Chunyu Pan
- Northeastern University, Shenyang, China
- Joint Laboratory of Artificial Intelligence and Precision Medicine of China Medical University and Northeastern University, Shenyang, China
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yuyan Zhu
- Joint Laboratory of Artificial Intelligence and Precision Medicine of China Medical University and Northeastern University, Shenyang, China
- Department of Urology, The First Hospital of China Medical University, Shenyang, China
| | - Meng Yu
- Department of Reproductive Biology and Transgenic Animal, China Medical University, Shenyang, China
| | - Yongkang Zhao
- National Institute of Health and Medical Big Data, China Medical University, Shenyang, China
| | | | - Xizhe Zhang
- Joint Laboratory of Artificial Intelligence and Precision Medicine of China Medical University and Northeastern University, Shenyang, China
- Early Intervention Unit, Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Yang Yao
- Department of Physiology, Shenyang Medical College, Shenyang, China
| |
Collapse
|
6
|
Ackerman EE, Shoemaker JE. Network Controllability-Based Prioritization of Candidates for SARS-CoV-2 Drug Repositioning. Viruses 2020; 12:v12101087. [PMID: 32993136 PMCID: PMC7650805 DOI: 10.3390/v12101087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 09/22/2020] [Accepted: 09/23/2020] [Indexed: 12/12/2022] Open
Abstract
In a short time, the COVID-19 pandemic has left the world with over 25 million cases and staggering death tolls that are still rising. Treatments for SARS-CoV-2 infection are desperately needed as there are currently no approved drug therapies. With limited knowledge of viral mechanisms, a network controllability method of prioritizing existing drugs for repurposing efforts is optimal for quickly moving through the drug approval pipeline using limited, available, virus-specific data. Based on network topology and controllability, 16 proteins involved in translation, cellular transport, cellular stress, and host immune response are predicted as regulators of the SARS-CoV-2 infected cell. Of the 16, eight are prioritized as possible drug targets where two, PVR and SCARB1, are previously unexplored. Known compounds targeting these genes are suggested for viral inhibition study. Prioritized proteins in agreement with previous analysis and viral inhibition studies verify the ability of network controllability to predict biologically relevant candidates.
Collapse
Affiliation(s)
- Emily E. Ackerman
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA;
| | - Jason E. Shoemaker
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA;
- The McGowan Institute for Regenerative Medicine (MIRM), University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Correspondence:
| |
Collapse
|
7
|
Ackerman EE, Alcorn JF, Hase T, Shoemaker JE. A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery. BMC Bioinformatics 2019; 20:297. [PMID: 31159726 PMCID: PMC6545738 DOI: 10.1186/s12859-019-2917-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 05/28/2019] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Host factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cell's progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates. RESULTS Both topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. Functional analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets. CONCLUSIONS Seasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and controllability analyses may be valuable for future efforts in drug target discovery.
Collapse
Affiliation(s)
- Emily E Ackerman
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - John F Alcorn
- Division of Pulmonary Medicine, Allergy, and Immunology, Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
| | - Takeshi Hase
- The Systems Biology Institute, Saisei Ikedayama Bldg. 5-10-25 Higashi Gotanda, Shinagawa, Tokyo, 141-0022, Japan
- Medical Data Sciences Office, Tokyo Medical and Dental University, M&D Tower 20F, 1-5-45 Yushima, Bunkyo, Tokyo, 113-8510, Japan
| | - Jason E Shoemaker
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- The McGowan Institute for Regenerative Medicine (MIRM), University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
| |
Collapse
|
8
|
Zhang X. Altering Indispensable Proteins in Controlling Directed Human Protein Interaction Network. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:2074-2078. [PMID: 29994604 DOI: 10.1109/tcbb.2018.2796572] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The numerous interconnections within complex systems enable us to control networks towards a desired state through a few suitable selected nodes, which are called driver nodes. Recent works analyzed directed human Protein-Protein Interaction (PPI) network based on structural control theory. They found that indispensable proteins, whose removal increase the number of driver nodes, are the primary targets of human viruses and drugs. However, the human PPI network is usually incomplete and may include many false-positive or false-negative interactions. That prompts us to ask whether these indispensable proteins are stable to possible structural changes. Here, we present a method to alter the type of indispensable proteins and thereby investigate the stability of indispensable proteins. By comparing the sets of indispensable proteins before and after structural changes to the network, we find that very few added or removed interactions can change the type of many indispensable nodes. Furthermore, some indispensable proteins are very sensitive to structural changes and have significantly lower interactions than the other indispensable proteins. The results indicate that indispensable proteins are sensitive to structural changes. Therefore, approaches based on structural control theory should be used with caution because of the incomplete nature of these networks.
Collapse
|
9
|
Zhang X, Han J, Zhang W. An efficient algorithm for finding all possible input nodes for controlling complex networks. Sci Rep 2017; 7:10677. [PMID: 28878394 PMCID: PMC5587595 DOI: 10.1038/s41598-017-10744-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 08/14/2017] [Indexed: 11/12/2022] Open
Abstract
Understanding structural controllability of a complex network requires to identify a Minimum Input nodes Set (MIS) of the network. Finding an MIS is known to be equivalent to computing a maximum matching of the network, where the unmatched nodes constitute an MIS. However, maximum matching is often not unique for a network, and finding all possible input nodes, the union of all MISs, may provide deep insights to the controllability of the network. Here we present an efficient enumerative algorithm for the problem. The main idea is to modify a maximum matching algorithm to make it efficient for finding all possible input nodes by computing only one MIS. The algorithm can also output a set of substituting nodes for each input node in the MIS, so that any node in the set can replace the latter. We rigorously proved the correctness of the new algorithm and evaluated its performance on synthetic and large real networks. The experimental results showed that the new algorithm ran several orders of magnitude faster than an existing method on large real networks.
Collapse
Affiliation(s)
- Xizhe Zhang
- Key Laboratory of Medical Image Computing of Northeastern University, Ministry of Education, Shenyang, China. .,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
| | - Jianfei Han
- School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Weixiong Zhang
- College of Math and Computer Science, Institute for Systems Biology, Jianghan University, Wuhan, 430056, China.,Department of Computer Science and Engineering, Washington University, Saint Louis, Missouri, USA
| |
Collapse
|
10
|
Zhang X, Wang H, Lv T. Efficient target control of complex networks based on preferential matching. PLoS One 2017; 12:e0175375. [PMID: 28384303 PMCID: PMC5383299 DOI: 10.1371/journal.pone.0175375] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Accepted: 03/06/2017] [Indexed: 11/18/2022] Open
Abstract
Controlling a complex network towards a desired state is of great importance in many applications. Existing works present an approximate algorithm to find the input nodes used to control partial nodes of the network. However, the input nodes obtained by this algorithm depend on the node matching order and cannot achieve optimum results. Here we present a novel algorithm to find the input nodes for target control based on preferential matching. The algorithm elaborately arranges the matching order of the nodes to reduce the size of the input node set. The results on both synthetic and real networks indicate that the proposed algorithm outperforms the previous algorithm.
Collapse
Affiliation(s)
- Xizhe Zhang
- Key Laboratory of Medical Image Computing of Northeastern University, Ministry of education, Shenyang, Liaoning, China
- School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
- * E-mail:
| | - Huaizhen Wang
- School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Tianyang Lv
- College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China
- IT Center, National Audit Office, Beijing, China
| |
Collapse
|