1
|
Wang Q, Wu Y, Wang X, Zhang J, Li L, Wu J, Lu Y, Han L. Genomic correlation, shared loci, and causal relationship between insomnia and psoriasis: a large-scale genome-wide cross-trait analysis. Arch Dermatol Res 2024; 316:425. [PMID: 38904754 DOI: 10.1007/s00403-024-03178-8] [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: 05/07/2024] [Revised: 06/08/2024] [Accepted: 06/08/2024] [Indexed: 06/22/2024]
Abstract
Psoriasis and insomnia have co-morbidities, however, their common genetic basis is still unclear. We analyzed psoriasis and insomnia with summary statistics from genome-wide association studies. We first quantified overall genetic correlations, then ascertained multiple effector loci and expression-trait associations, and lastly, we analyzed the causal effects between psoriasis and insomnia. A prevalent genetic link between psoriasis and insomnia was found, four pleiotropic loci affecting psoriasis and insomnia were identified, and 154 genes were shared, indicating a genetic link between psoriasis and insomnia. Yet, there is no causal relationship between psoriasis and insomnia by two-sample Mendelian randomization. We discovered a genetic connection between insomnia and psoriasis driven by biological pleiotropy and unrelated to causation. Cross-trait analysis indicates a common genetic basis for psoriasis and insomnia. The results of this study highlight the importance of sleep management in the pathogenesis of psoriasis.
Collapse
Affiliation(s)
- Qing Wang
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Research Team of bio-molecular and system biology of Chinese medicine, Guangdong Academy of Traditional Chinese Medicine, Guangzhou, China
| | - Yuan Wu
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Xuehua Wang
- School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Junhong Zhang
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Research Team of bio-molecular and system biology of Chinese medicine, Guangdong Academy of Traditional Chinese Medicine, Guangzhou, China
| | - Li Li
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
- Research Team of bio-molecular and system biology of Chinese medicine, Guangdong Academy of Traditional Chinese Medicine, Guangzhou, China
| | - Jingjing Wu
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
- Research Team of bio-molecular and system biology of Chinese medicine, Guangdong Academy of Traditional Chinese Medicine, Guangzhou, China.
| | - Yue Lu
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
- Research Team of bio-molecular and system biology of Chinese medicine, Guangdong Academy of Traditional Chinese Medicine, Guangzhou, China.
| | - Ling Han
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
- Research Team of bio-molecular and system biology of Chinese medicine, Guangdong Academy of Traditional Chinese Medicine, Guangzhou, China.
| |
Collapse
|
2
|
Corneillie L, Lemmens I, Weening K, De Meyer A, Van Houtte F, Tavernier J, Meuleman P. Virus-Host Protein Interaction Network of the Hepatitis E Virus ORF2-4 by Mammalian Two-Hybrid Assays. Viruses 2023; 15:2412. [PMID: 38140653 PMCID: PMC10748205 DOI: 10.3390/v15122412] [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: 09/14/2023] [Revised: 12/01/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023] Open
Abstract
Throughout their life cycle, viruses interact with cellular host factors, thereby influencing propagation, host range, cell tropism and pathogenesis. The hepatitis E virus (HEV) is an underestimated RNA virus in which knowledge of the virus-host interaction network to date is limited. Here, two related high-throughput mammalian two-hybrid approaches (MAPPIT and KISS) were used to screen for HEV-interacting host proteins. Promising hits were examined on protein function, involved pathway(s), and their relation to other viruses. We identified 37 ORF2 hits, 187 for ORF3 and 91 for ORF4. Several hits had functions in the life cycle of distinct viruses. We focused on SHARPIN and RNF5 as candidate hits for ORF3, as they are involved in the RLR-MAVS pathway and interferon (IFN) induction during viral infections. Knocking out (KO) SHARPIN and RNF5 resulted in a different IFN response upon ORF3 transfection, compared to wild-type cells. Moreover, infection was increased in SHARPIN KO cells and decreased in RNF5 KO cells. In conclusion, MAPPIT and KISS are valuable tools to study virus-host interactions, providing insights into the poorly understood HEV life cycle. We further provide evidence for two identified hits as new host factors in the HEV life cycle.
Collapse
Affiliation(s)
- Laura Corneillie
- Laboratory of Liver Infectious Diseases, Department of Diagnostic Sciences, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Irma Lemmens
- VIB-UGent Center for Medical Biotechnology, Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Karin Weening
- Laboratory of Liver Infectious Diseases, Department of Diagnostic Sciences, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Amse De Meyer
- Laboratory of Liver Infectious Diseases, Department of Diagnostic Sciences, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Freya Van Houtte
- Laboratory of Liver Infectious Diseases, Department of Diagnostic Sciences, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Jan Tavernier
- VIB-UGent Center for Medical Biotechnology, Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Philip Meuleman
- Laboratory of Liver Infectious Diseases, Department of Diagnostic Sciences, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| |
Collapse
|
3
|
Forst CV, Zeng L, Wang Q, Zhou X, Vatansever S, Xu P, Song W, Tu Z, Zhang B. Multiscale network analysis identifies potential receptors for SARS-CoV-2 and reveals their tissue-specific and age-dependent expression. FEBS Lett 2023; 597:1384-1402. [PMID: 36951513 PMCID: PMC10294276 DOI: 10.1002/1873-3468.14613] [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: 07/11/2022] [Revised: 02/13/2023] [Accepted: 02/27/2023] [Indexed: 03/24/2023]
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has affected tens of millions of individuals and caused hundreds of thousands of deaths worldwide. Here, we present a comprehensive, multiscale network analysis of the transcriptional response to the virus. In particular, we focused on key regulators, cell receptors, and host processes that were hijacked by the virus for its advantage. ACE2-controlled processes involved CD300e (a TYROBP receptor) as a key regulator and the activation of IL-2 pro-inflammatory cytokine signaling. We further investigated the age dependency of such receptors in different tissues. In summary, this study provides novel insights into the gene regulatory organization during the SARS-CoV-2 infection and the tissue-specific, age-dependent expression of the cell receptors involved in COVID-19.
Collapse
Affiliation(s)
- Christian V. Forst
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of MicrobiologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Lu Zeng
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Qian Wang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Xianxiao Zhou
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Peng Xu
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Won‐Min Song
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Zhidong Tu
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNYUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNYUSA
| |
Collapse
|
4
|
Gerardi V, Rohaim MA, Naggar RFE, Atasoy MO, Munir M. Deep Structural Analysis of Myriads of Omicron Sub-Variants Revealed Hotspot for Vaccine Escape Immunity. Vaccines (Basel) 2023; 11:vaccines11030668. [PMID: 36992252 DOI: 10.3390/vaccines11030668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 03/18/2023] Open
Abstract
The emergence of the Omicron variant has reinforced the importance of continued SARS-CoV-2 evolution and its possible impact on vaccine effectiveness. Specifically, mutations in the receptor-binding domain (RBD) are critical to comprehend the flexibility and dynamicity of the viral interaction with the human agniotensin-converting enzyme 2 (hACE2) receptor. To this end, we have applied a string of deep structural and genetic analysis tools to map the substitution patterns in the S protein of major Omicron sub-variants (n = 51) with a primary focus on the RBD mutations. This head-to-head comparison of Omicron sub-variants revealed multiple simultaneous mutations that are attributed to antibody escape, and increased affinity and binding to hACE2. Our deep mapping of the substitution matrix indicated a high level of diversity at the N-terminal and RBD domains compared with other regions of the S protein, highlighting the importance of these two domains in a matched vaccination approach. Structural mapping identified highly variable mutations in the up confirmation of the S protein and at sites that critically define the function of the S protein in the virus pathobiology. These substitutional trends offer support in tracking mutations along the evolutionary trajectories of SAR-CoV-2. Collectively, the findings highlight critical areas of mutations across the major Omicron sub-variants and propose several hotspots in the S proteins of SARS-CoV-2 sub-variants to train the future design and development of COVID-19 vaccines.
Collapse
Affiliation(s)
- Valeria Gerardi
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster LA1 4YG, UK
| | - Mohammed A Rohaim
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster LA1 4YG, UK
- Department of Virology, Faculty of Veterinary Medicine, Cairo University, Giza 12211, Egypt
| | - Rania F El Naggar
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster LA1 4YG, UK
- Department of Virology, Faculty of Veterinary Medicine, University of Sadat City, Sadat 32897, Egypt
| | - Mustafa O Atasoy
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster LA1 4YG, UK
| | - Muhammad Munir
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster LA1 4YG, UK
| |
Collapse
|