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Mukhtar MS, Mishra B, Athar M. Integrative systems biology framework discovers common gene regulatory signatures in multiple mechanistically distinct inflammatory skin diseases. RESEARCH SQUARE 2023:rs.3.rs-3611240. [PMID: 38014119 PMCID: PMC10680929 DOI: 10.21203/rs.3.rs-3611240/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
More than 20% of the population across the world is affected by non-communicable inflammatory skin diseases including psoriasis, atopic dermatitis, hidradenitis suppurativa, rosacea, etc. Many of these chronic diseases are painful and debilitating with limited effective therapeutic interventions. However, recent advances in psoriasis treatment have improved the effectiveness and provide better management of the disease. This study aims to identify common regulatory pathways and master regulators that regulate molecular pathogenesis. We designed an integrative systems biology framework to identify the significant regulators across several inflammatory skin diseases. With conventional transcriptome analysis, we identified 55 shared genes, which are enriched in several immune-associated pathways in eight inflammatory skin diseases. Next, we exploited the gene co-expression-, and protein-protein interaction-based networks to identify shared genes and protein components in different diseases with relevant functional implications. Additionally, the network analytics unravels 55 high-value proteins as significant regulators in molecular pathogenesis. We believe that these significant regulators should be explored with critical experimental approaches to identify the putative drug targets for more effective treatments. As an example, we identified IKZF1 as a shared significant master regulator in three inflammatory skin diseases, which can serve as a putative drug target with known disease-derived molecules for developing efficacious combinatorial treatments for hidradenitis suppurativa, atopic dermatitis, and rosacea. The proposed framework is very modular, which can indicate a significant path of molecular mechanism-based drug development from complex transcriptomics data and other multi-omics data.
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Frank ASJ, Larripa K, Ryu H, Röblitz S. Macrophage phenotype transitions in a stochastic gene-regulatory network model. J Theor Biol 2023; 575:111634. [PMID: 37839584 DOI: 10.1016/j.jtbi.2023.111634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/11/2023] [Accepted: 10/05/2023] [Indexed: 10/17/2023]
Abstract
Polarization is the process by which a macrophage cell commits to a phenotype based on external signal stimulation. To know how this process is affected by random fluctuations and events within a cell is of utmost importance to better understand the underlying dynamics and predict possible phenotype transitions. For this purpose, we develop a stochastic modeling approach for the macrophage polarization process. We classify phenotype states using the Robust Perron Cluster Analysis and quantify transition pathways and probabilities by applying Transition Path Theory. Depending on the model parameters, we identify four bistable and one tristable phenotype configuration. We find that bistable transitions are fast but their states less robust. In contrast, phenotype transitions in the tristable situation have a comparatively long time duration, which reflects the robustness of the states. The results indicate parallels in the overall transition behavior of macrophage cells with other heterogeneous and plastic cell types, such as cancer cells. Our approach allows for a probabilistic interpretation of macrophage phenotype transitions and biological inference on phenotype robustness. In general, the methodology can easily be adapted to other systems where random state switches are known to occur.
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Affiliation(s)
| | - Kamila Larripa
- Department of Mathematics, California State Polytechnic University Humboldt, Arcata, CA, USA.
| | - Hwayeon Ryu
- Department of Mathematics and Statistics, Elon University, Elon, NC, USA.
| | - Susanna Röblitz
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
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Kumar N, Mishra BK, Liu J, Mohan B, Thingujam D, Pajerowska-Mukhtar KM, Mukhtar MS. Network Biology Analyses and Dynamic Modeling of Gene Regulatory Networks under Drought Stress Reveal Major Transcriptional Regulators in Arabidopsis. Int J Mol Sci 2023; 24:ijms24087349. [PMID: 37108512 PMCID: PMC10139068 DOI: 10.3390/ijms24087349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 04/02/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of high-priority transcription factors (TFs), dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We identified distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficient nodes. Dynamic transcriptional regulatory modeling of integrated TF targets and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs, as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that could potentially be used in future genetic crop engineering programs.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Bharat K Mishra
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Jinbao Liu
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Binoop Mohan
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Doni Thingujam
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Karolina M Pajerowska-Mukhtar
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
- Nutrition Obesity Research Center, University of Alabama at Birmingham, 1675 University Boulevard, Birmingham, AL 35294, USA
- Department of Surgery, University of Alabama at Birmingham, 1808 7th Ave S, Birmingham, AL 35294, USA
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Kumar N, Mishra B, Mukhtar MS. A pipeline of integrating transcriptome and interactome to elucidate central nodes in host-pathogens interactions. STAR Protoc 2022; 3:101608. [PMID: 35990739 PMCID: PMC9386103 DOI: 10.1016/j.xpro.2022.101608] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Investigating the complexity of host-pathogen interactions is challenging. Here, we outline a pipeline to identify important proteins and signaling molecules in human-viral interactomes. Firstly, we curate a comprehensive human interactome. Subsequently, we infer viral targets and transcriptome-specific human interactomes (VTTSHI) for papillomavirus and herpes viruses by integrating viral targets and transcriptome data. Finally, we reveal the common and shared nodes and pathways in viral pathogenesis following network topology and pathway enrichment analyses. For complete details on the use and execution of this protocol, please refer to Kumar et al. (2020). Protocol for integrative analysis of transcriptome and proteome network data Network subgroup enrichment of two host-pathogen interaction networks Preprocessing of data and heterogeneous network integration
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
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