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Sundling C, Yman V, Mousavian Z, Angenendt S, Foroogh F, von Horn E, Lautenbach MJ, Grunewald J, Färnert A, Sondén K. Disease-specific plasma protein profiles in patients with fever after traveling to tropical areas. Eur J Immunol 2024; 54:e2350784. [PMID: 38308504 DOI: 10.1002/eji.202350784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 02/04/2024]
Abstract
Fever is common among individuals seeking healthcare after traveling to tropical regions. Despite the association with potentially severe disease, the etiology is often not determined. Plasma protein patterns can be informative to understand the host response to infection and can potentially indicate the pathogen causing the disease. In this study, we measured 49 proteins in the plasma of 124 patients with fever after travel to tropical or subtropical regions. The patients had confirmed diagnoses of either malaria, dengue fever, influenza, bacterial respiratory tract infection, or bacterial gastroenteritis, representing the most common etiologies. We used multivariate and machine learning methods to identify combinations of proteins that contributed to distinguishing infected patients from healthy controls, and each other. Malaria displayed the most unique protein signature, indicating a strong immunoregulatory response with high levels of IL10, sTNFRI and II, and sCD25 but low levels of sCD40L. In contrast, bacterial gastroenteritis had high levels of sCD40L, APRIL, and IFN-γ, while dengue was the only infection with elevated IFN-α2. These results suggest that characterization of the inflammatory profile of individuals with fever can help to identify disease-specific host responses, which in turn can be used to guide future research on diagnostic strategies and therapeutic interventions.
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Affiliation(s)
- Christopher Sundling
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Victor Yman
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Stockholm South Hospital, Stockholm, Sweden
| | - Zaynab Mousavian
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Sina Angenendt
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Fariba Foroogh
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Ellen von Horn
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Maximilian Julius Lautenbach
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Johan Grunewald
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Respiratory Medicine Unit, Department of Medicine, Solna, Karolinska Institutet and Karolinska University Hospital Solna, Stockholm, Sweden
| | - Anna Färnert
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Klara Sondén
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
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Mousavian Z, Källenius G, Sundling C. From simple to complex: Protein-based biomarker discovery in tuberculosis. Eur J Immunol 2023; 53:e2350485. [PMID: 37740950 DOI: 10.1002/eji.202350485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/15/2023] [Accepted: 09/22/2023] [Indexed: 09/25/2023]
Abstract
Tuberculosis (TB) is a deadly infectious disease that affects millions of people globally. TB proteomics signature discovery has been a rapidly growing area of research that aims to identify protein biomarkers for the early detection, diagnosis, and treatment monitoring of TB. In this review, we have highlighted recent advances in this field and how it is moving from the study of single proteins to high-throughput profiling and from only using proteomics to include additional types of data in multi-omics studies. We have further covered the different sample types and experimental technologies used in TB proteomics signature discovery, focusing on studies of HIV-negative adults. The published signatures were defined as either coming from hypothesis-based protein targeting or from unbiased discovery approaches. The methodological approaches influenced the type of proteins identified and were associated with the circulating protein abundance. However, both approaches largely identified proteins involved in similar biological pathways, including acute-phase responses and T-helper type 1 and type 17 responses. By analysing the frequency of proteins in the different signatures, we could also highlight potential robust biomarker candidates. Finally, we discuss the potential value of integration of multi-omics data and the importance of control cohorts and signature validation.
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Affiliation(s)
- Zaynab Mousavian
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Gunilla Källenius
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Christopher Sundling
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
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Correia-Neves M, Nigou J, Mousavian Z, Sundling C, Källenius G. Immunological hyporesponsiveness in tuberculosis: The role of mycobacterial glycolipids. Front Immunol 2022; 13:1035122. [PMID: 36544778 PMCID: PMC9761185 DOI: 10.3389/fimmu.2022.1035122] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/25/2022] [Indexed: 12/09/2022] Open
Abstract
Glycolipids constitute a major part of the cell envelope of Mycobacterium tuberculosis (Mtb). They are potent immunomodulatory molecules recognized by several immune receptors like pattern recognition receptors such as TLR2, DC-SIGN and Dectin-2 on antigen-presenting cells and by T cell receptors on T lymphocytes. The Mtb glycolipids lipoarabinomannan (LAM) and its biosynthetic relatives, phosphatidylinositol mannosides (PIMs) and lipomannan (LM), as well as other Mtb glycolipids, such as phenolic glycolipids and sulfoglycolipids have the ability to modulate the immune response, stimulating or inhibiting a pro-inflammatory response. We explore here the downmodulating effect of Mtb glycolipids. A great proportion of the studies used in vitro approaches although in vivo infection with Mtb might also lead to a dampening of myeloid cell and T cell responses to Mtb glycolipids. This dampened response has been explored ex vivo with immune cells from peripheral blood from Mtb-infected individuals and in mouse models of infection. In addition to the dampening of the immune response caused by Mtb glycolipids, we discuss the hyporesponse to Mtb glycolipids caused by prolonged Mtb infection and/or exposure to Mtb antigens. Hyporesponse to LAM has been observed in myeloid cells from individuals with active and latent tuberculosis (TB). For some myeloid subsets, this effect is stronger in latent versus active TB. Since the immune response in individuals with latent TB represents a more protective profile compared to the one in patients with active TB, this suggests that downmodulation of myeloid cell functions by Mtb glycolipids may be beneficial for the host and protect against active TB disease. The mechanisms of this downmodulation, including tolerance through epigenetic modifications, are only partly explored.
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Affiliation(s)
- Margarida Correia-Neves
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal,Life and Health Sciences Research Institute/Biomaterials, Biodegradables and Biomimetics Research Group (ICVS/3B's), Portuguese (PT) Government Associate Laboratory, Braga, Portugal,Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | - Jérôme Nigou
- Institut de Pharmacologie et de Biologie Structurale, Université de Toulouse, Centre National de la Recherche Scientifique (CNRS), Université Paul Sabatier, Toulouse, France
| | - Zaynab Mousavian
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden,School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran,Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Christopher Sundling
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden,Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden,Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Gunilla Källenius
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden,Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden,*Correspondence: Gunilla Källenius,
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Mousavian Z, Folkesson E, Fröberg G, Foroogh F, Correia-Neves M, Bruchfeld J, Källenius G, Sundling C. A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis. iScience 2022; 25:105652. [PMID: 36561889 PMCID: PMC9763869 DOI: 10.1016/j.isci.2022.105652] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/19/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022] Open
Abstract
Annually, approximately 10 million people are diagnosed with active tuberculosis (TB), and 1.4 million die of the disease. If left untreated, each person with active TB will infect 10-15 new individuals. The lack of non-sputum-based diagnostic tests leads to delayed diagnoses of active pulmonary TB cases, contributing to continued disease transmission. In this exploratory study, we aimed to identify biomarkers associated with active TB. We assessed the plasma levels of 92 proteins associated with inflammation in individuals with active TB (n = 20), latent TB (n = 14), or healthy controls (n = 10). Using co-expression network analysis, we identified one module of proteins with strong association with active TB. We removed proteins from the module that had low abundance or were associated with non-TB diseases in published transcriptomic datasets, resulting in a 12-protein plasma signature that was highly enriched in individuals with pulmonary and extrapulmonary TB and was further associated with disease severity.
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Affiliation(s)
- Zaynab Mousavian
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Elin Folkesson
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Gabrielle Fröberg
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Microbiology, Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden
| | - Fariba Foroogh
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Margarida Correia-Neves
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s, PT Government Associate Laboratory, Braga, Portugal
| | - Judith Bruchfeld
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Gunilla Källenius
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Christopher Sundling
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
- Corresponding author
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Fathinavid A, Mousavian Z, Najafi A, Nematzadeh S, Salimi M, Masoudi-Nejad A. Identifying common signatures and potential therapeutic biomarkers in COPD and lung cancer using miRNA-mRNA co-expression networks. Informatics in Medicine Unlocked 2022. [DOI: 10.1016/j.imu.2022.101115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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Pournoor E, Mousavian Z, Nowzari-Dalini A, Masoudi-Nejad A. A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma. PLoS One 2021; 16:e0255718. [PMID: 34370784 PMCID: PMC8351981 DOI: 10.1371/journal.pone.0255718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 07/22/2021] [Indexed: 11/19/2022] Open
Abstract
Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.
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Affiliation(s)
- Ehsan Pournoor
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Zaynab Mousavian
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Abbas Nowzari-Dalini
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Mousavian Z, Khodabandeh M, Sharifi-Zarchi A, Nadafian A, Mahmoudi A. StrongestPath: a Cytoscape application for protein-protein interaction analysis. BMC Bioinformatics 2021; 22:352. [PMID: 34187355 PMCID: PMC8244221 DOI: 10.1186/s12859-021-04230-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 06/02/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND StrongestPath is a Cytoscape 3 application that enables the analysis of interactions between two proteins or groups of proteins in a collection of protein-protein interaction (PPI) network or signaling network databases. When there are different levels of confidence over the interactions, the application is able to process them and identify the cascade of interactions with the highest total confidence score. Given a set of proteins, StrongestPath can extract a set of possible interactions between the input proteins, and expand the network by adding new proteins that have the most interactions with highest total confidence to the current network of proteins. The application can also identify any activating or inhibitory regulatory paths between two distinct sets of transcription factors and target genes. This application can be used on the built-in human and mouse PPI or signaling databases, or any user-provided database for some organism. RESULTS Our results on 12 signaling pathways from the NetPath database demonstrate that the application can be used for indicating proteins which may play significant roles in a pathway by finding the strongest path(s) in the PPI or signaling network. CONCLUSION Easy access to multiple public large databases, generating output in a short time, addressing some key challenges in one platform, and providing a user-friendly graphical interface make StrongestPath an extremely useful application.
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Affiliation(s)
- Zaynab Mousavian
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.
| | - Mehran Khodabandeh
- School of Computing Science, Simon Fraser University, Burnaby, BC, Canada
| | - Ali Sharifi-Zarchi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.,Department of Stem cells and Developmental Biology at the Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
| | - Alireza Nadafian
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Alireza Mahmoudi
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
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Pournoor E, Mousavian Z, Dalini AN, Masoudi-Nejad A. Identification of Key Components in Colon Adenocarcinoma Using Transcriptome to Interactome Multilayer Framework. Sci Rep 2020; 10:4991. [PMID: 32193399 PMCID: PMC7081269 DOI: 10.1038/s41598-020-59605-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 01/31/2020] [Indexed: 12/21/2022] Open
Abstract
Complexity of cascading interrelations between molecular cell components at different levels from genome to metabolome ordains a massive difficulty in comprehending biological happenings. However, considering these complications in the systematic modelings will result in realistic and reliable outputs. The multilayer networks approach is a relatively innovative concept that could be applied for multiple omics datasets as an integrative methodology to overcome heterogeneity difficulties. Herein, we employed the multilayer framework to rehabilitate colon adenocarcinoma network by observing co-expression correlations, regulatory relations, and physical binding interactions. Hub nodes in this three-layer network were selected using a heterogeneous random walk with random jump procedure. We exploited local composite modules around the hub nodes having high overlay with cancer-specific pathways, and investigated their genes showing a different expressional pattern in the tumor progression. These genes were examined for survival effects on the patient's lifespan, and those with significant impacts were selected as potential candidate biomarkers. Results suggest that identified genes indicate noteworthy importance in the carcinogenesis of the colon.
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Affiliation(s)
- Ehsan Pournoor
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Zaynab Mousavian
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Abbas Nowzari Dalini
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Rayhan F, Ahmed S, Mousavian Z, Farid DM, Shatabda S. FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction. Heliyon 2020; 6:e03444. [PMID: 32154410 PMCID: PMC7052404 DOI: 10.1016/j.heliyon.2020.e03444] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 06/16/2019] [Accepted: 02/14/2020] [Indexed: 01/09/2023] Open
Abstract
The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/.
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Affiliation(s)
- Farshid Rayhan
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh
| | - Sajid Ahmed
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh
| | - Zaynab Mousavian
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Dewan Md Farid
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh
| | - Swakkhar Shatabda
- Department of Computer Science and Engineering, United International University, Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh
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Mousavian Z, Nowzari-Dalini A, Rahmatallah Y, Masoudi-Nejad A. Differential network analysis and protein-protein interaction study reveals active protein modules in glucocorticoid resistance for infant acute lymphoblastic leukemia. Mol Med 2019; 25:36. [PMID: 31370801 PMCID: PMC6676637 DOI: 10.1186/s10020-019-0106-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 07/24/2019] [Indexed: 01/24/2023] Open
Abstract
Background Acute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children and Glucocorticoids (GCs) form an essential component of the standard chemotherapy in most treatment regimens. The category of infant ALL patients carrying a translocation involving the mixed lineage leukemia (MLL) gene (gene KMT2A) is characterized by resistance to GCs and poor clinical outcome. Although some studies examined GC-resistance in infant ALL patients, the understanding of this phenomenon remains limited and impede the efforts to improve prognosis. Methods This study integrates differential co-expression (DC) and protein-protein interaction (PPI) networks to find active protein modules associated with GC-resistance in MLL-rearranged infant ALL patients. A network was constructed by linking differentially co-expressed gene pairs between GC-resistance and GC-sensitive samples and later integrated with PPI networks by keeping the links that are also present in the PPI network. The resulting network was decomposed into two sub-networks, specific to each phenotype. Finally, both sub-networks were clustered into modules using weighted gene co-expression network analysis (WGCNA) and further analyzed with functional enrichment analysis. Results Through the integration of DC analysis and PPI network, four protein modules were found active under the GC-resistance phenotype but not under the GC-sensitive. Functional enrichment analysis revealed that these modules are related to proteasome, electron transport chain, tRNA-aminoacyl biosynthesis, and peroxisome signaling pathways. These findings are in accordance with previous findings related to GC-resistance in other hematological malignancies such as pediatric ALL. Conclusions Differential co-expression analysis is a promising approach to incorporate the dynamic context of gene expression profiles into the well-documented protein interaction networks. The approach allows the detection of relevant protein modules that are highly enriched with DC gene pairs. Functional enrichment analysis of detected protein modules generates new biological hypotheses and may help in explaining the GC-resistance in MLL-rearranged infant ALL patients. Electronic supplementary material The online version of this article (10.1186/s10020-019-0106-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zaynab Mousavian
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran. .,Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Abbas Nowzari-Dalini
- School of Mathematics, Statistics, and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Yasir Rahmatallah
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Dehghanian F, Hojati Z, Hosseinkhan N, Mousavian Z, Masoudi-Nejad A. Reconstruction of the genome-scale co-expression network for the Hippo signaling pathway in colorectal cancer. Comput Biol Med 2018; 99:76-84. [PMID: 29890510 DOI: 10.1016/j.compbiomed.2018.05.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 05/24/2018] [Accepted: 05/24/2018] [Indexed: 01/22/2023]
Abstract
The Hippo signaling pathway (HSP) has been identified as an essential and complex signaling pathway for tumor suppression that coordinates proliferation, differentiation, cell death, cell growth and stemness. In the present study, we conducted a genome-scale co-expression analysis to reconstruct the HSP in colorectal cancer (CRC). Five key modules were detected through network clustering, and a detailed discussion of two modules containing respectively 18 and 13 over and down-regulated members of HSP was provided. Our results suggest new potential regulatory factors in the HSP. The detected modules also suggest novel genes contributing to CRC. Moreover, differential expression analysis confirmed the differential expression pattern of HSP members and new suggested regulatory factors between tumor and normal samples. These findings can further reveal the importance of HSP in CRC.
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Affiliation(s)
- Fariba Dehghanian
- Division of Genetics, Department of Biology, Faculty of Sciences, University of Isfahan, P.O. Box 81746-73441, Isfahan, Iran
| | - Zohreh Hojati
- Division of Genetics, Department of Biology, Faculty of Sciences, University of Isfahan, P.O. Box 81746-73441, Isfahan, Iran.
| | - Nazanin Hosseinkhan
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Zaynab Mousavian
- Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, Tehran, Iran; Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Hosseinkhan N, Mousavian Z, Masoudi-Nejad A. Comparison of gene co-expression networks in Pseudomonas aeruginosa and Staphylococcus aureus reveals conservation in some aspects of virulence. Gene 2017; 639:1-10. [PMID: 28987343 DOI: 10.1016/j.gene.2017.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Revised: 09/23/2017] [Accepted: 10/03/2017] [Indexed: 12/13/2022]
Abstract
Pseudomonas aeruginosa and Staphylococcus aureus are two evolutionary distant bacterial species that are frequently isolated from persistent infections such as chronic infectious wounds and severe lung infections in cystic fibrosis patients. To the best of our knowledge no comprehensive genome scale co-expression study has been already conducted on these two species and in most cases only the expression of very few genes has been the subject of investigation. In this study, in order to investigate the level of expressional conservation between these two species, using heterogeneous gene expression datasets the weighted gene co-expression network analysis (WGCNA) approach was applied to study both single and cross species genome scale co-expression patterns of these two species. Single species co-expression network analysis revealed that in P. aeruginosa, genes involved in quorum sensing (QS), iron uptake, nitrate respiration and type III secretion systems and in S. aureus, genes associated with the regulation of carbon metabolism, fatty acid-phospholipids metabolism and proteolysis represent considerable co-expression across a variety of experimental conditions. Moreover, the comparison of gene co-expression networks between P. aeruginosa and S. aureus was led to the identification of four co-expressed gene modules in both species totally consisting of 318 genes. Several genes related to two component signal transduction systems, small colony variants (SCVs) morphotype and protein complexes were found in the detected modules. We believe that targeting the key players among the identified co-expressed orthologous genes will be a potential intervention strategy to control refractory co-infections caused by these two bacterial species.
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Affiliation(s)
- Nazanin Hosseinkhan
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Zaynab Mousavian
- Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, Tehran, Iran; Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Mousavian Z, Nowzari-Dalini A, Stam RW, Rahmatallah Y, Masoudi-Nejad A. Network-based expression analysis reveals key genes related to glucocorticoid resistance in infant acute lymphoblastic leukemia. Cell Oncol (Dordr) 2016; 40:33-45. [PMID: 27798768 DOI: 10.1007/s13402-016-0303-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/19/2016] [Indexed: 12/20/2022] Open
Abstract
PURPOSE Despite vast improvements that have been made in the treatment of children with acute lymphoblastic leukemia (ALL), the majority of infant ALL patients (~80 %, < 1 year of age) that carry a chromosomal translocation involving the mixed lineage leukemia (MLL) gene shows a poor response to chemotherapeutic drugs, especially glucocorticoids (GCs), which are essential components of all current treatment regimens. Although addressed in several studies, the mechanism(s) underlying this phenomenon have remained largely unknown. A major drawback of most previous studies is their primary focus on individual genes, thereby neglecting the putative significance of inter-gene correlations. Here, we aimed at studying GC resistance in MLL-rearranged infant ALL patients by inferring an associated module of genes using co-expression network analysis. The implications of newly identified candidate genes with associations to other well-known relevant genes from the same module, or with associations to known transcription factor or microRNA interactions, were substantiated using literature data. METHODS A weighted gene co-expression network was constructed to identify gene modules associated with GC resistance in MLL-rearranged infant ALL patients. Significant gene ontology (GO) terms and signaling pathways enriched in relevant modules were used to provide guidance towards which module(s) consisted of promising candidates suitable for further analysis. RESULTS Through gene co-expression network analysis a novel set of genes (module) related to GC-resistance was identified. The presence in this module of the S100 and ANXA genes, both well-known biomarkers for GC resistance in MLL-rearranged infant ALL, supports its validity. Subsequent gene set net correlation analyses of the novel module provided further support for its validity by showing that the S100 and ANXA genes act as 'hub' genes with potentially major regulatory roles in GC sensitivity, but having lost this role in the GC resistant phenotype. The detected module implicates new genes as being candidates for further analysis through associations with known GC resistance-related genes. CONCLUSIONS From our data we conclude that available systems biology approaches can be employed to detect new candidate genes that may provide further insights into drug resistance of MLL-rearranged infant ALL cases. Such approaches complement conventional gene-wise approaches by taking putative functional interactions between genes into account.
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Affiliation(s)
- Zaynab Mousavian
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | | | - Ronald W Stam
- Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Yasir Rahmatallah
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Mousavian Z, Díaz J, Masoudi-Nejad A. Information theory in systems biology. Part II: protein-protein interaction and signaling networks. Semin Cell Dev Biol 2015; 51:14-23. [PMID: 26691180 DOI: 10.1016/j.semcdb.2015.12.006] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Accepted: 12/07/2015] [Indexed: 12/25/2022]
Abstract
By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein-protein interaction and signaling networks will be surveyed.
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Affiliation(s)
- Zaynab Mousavian
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - José Díaz
- Grupo de Biología Teórica y Computacional, Centro de Investigación en Dinámica Celular, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, Mexico
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Abstract
INTRODUCTION Identification of the interaction between drugs and target proteins is a crucial task in genomic drug discovery. The in silico prediction is an appropriate alternative for the laborious and costly experimental process of drug-target interaction prediction. Developing a variety of computational methods opens a new direction in analyzing and detecting new drug-target pairs. AREAS COVERED In this review, we will focus on chemogenomic methods which have established a learning framework for predicting drug-target interactions. Learning-based methods are classified into supervised and semi-supervised, and the supervised learning methods are studied as two separate parts including similarity-based methods and feature-based methods. EXPERT OPINION In spite of many improvements for pharmacology applications by learning-based methods, there are many over simplification settings in construction of predictive models that may lead to over-optimistic results on drug-target interaction prediction.
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Affiliation(s)
- Zaynab Mousavian
- University of Tehran, Institute of Biochemistry and Biophysics, Laboratory of Systems Biology and Bioinformatics (LBB) , Tehran , Iran +98 21 6695 9256 ; +98 21 6640 4680 ;
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