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Zhang S, Xie L, Zheng S, Lu B, Tao W, Wang X, Kocher TD, Zhou L, Wang D. Identification, Expression and Evolution of Short-Chain Dehydrogenases/Reductases in Nile Tilapia ( Oreochromis niloticus). Int J Mol Sci 2021; 22:ijms22084201. [PMID: 33919636 PMCID: PMC8073704 DOI: 10.3390/ijms22084201] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/14/2021] [Accepted: 04/15/2021] [Indexed: 01/31/2023] Open
Abstract
The short-chain dehydrogenases/reductases (SDR) superfamily is involved in multiple physiological processes. In this study, genome-wide identification and comprehensive analysis of SDR superfamily were carried out in 29 animal species based on the latest genome databases. Overall, the number of SDR genes in animals increased with whole genome duplication (WGD), suggesting the expansion of SDRs during evolution, especially in 3R-WGD and polyploidization of teleosts. Phylogenetic analysis indicated that vertebrates SDRs were clustered into five categories: classical, extended, undefined, atypical, and complex. Moreover, tandem duplication of hpgd-a, rdh8b and dhrs13 was observed in teleosts analyzed. Additionally, tandem duplications of dhrs11-a, dhrs7a, hsd11b1b, and cbr1-a were observed in all cichlids analyzed, and tandem duplication of rdh10-b was observed in tilapiines. Transcriptome analysis of adult fish revealed that 93 SDRs were expressed in more than one tissue and 5 in one tissue only. Transcriptome analysis of gonads from different developmental stages showed that expression of 17 SDRs were sexually dimorphic with 11 higher in ovary and 6 higher in testis. The sexually dimorphic expressions of these SDRs were confirmed by in situ hybridization (ISH) and qPCR, indicating their possible roles in steroidogenesis and gonadal differentiation. Taken together, the identification and the expression data obtained in this study contribute to a better understanding of SDR superfamily evolution and functions in teleosts.
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Affiliation(s)
- Shuai Zhang
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Key Laboratory of Aquatic Science of Chongqing, School of Life Sciences, Southwest University, Chongqing 400715, China; (S.Z.); (L.X.); (S.Z.); (B.L.); (W.T.); (X.W.)
| | - Lang Xie
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Key Laboratory of Aquatic Science of Chongqing, School of Life Sciences, Southwest University, Chongqing 400715, China; (S.Z.); (L.X.); (S.Z.); (B.L.); (W.T.); (X.W.)
| | - Shuqing Zheng
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Key Laboratory of Aquatic Science of Chongqing, School of Life Sciences, Southwest University, Chongqing 400715, China; (S.Z.); (L.X.); (S.Z.); (B.L.); (W.T.); (X.W.)
| | - Baoyue Lu
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Key Laboratory of Aquatic Science of Chongqing, School of Life Sciences, Southwest University, Chongqing 400715, China; (S.Z.); (L.X.); (S.Z.); (B.L.); (W.T.); (X.W.)
| | - Wenjing Tao
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Key Laboratory of Aquatic Science of Chongqing, School of Life Sciences, Southwest University, Chongqing 400715, China; (S.Z.); (L.X.); (S.Z.); (B.L.); (W.T.); (X.W.)
| | - Xiaoshuang Wang
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Key Laboratory of Aquatic Science of Chongqing, School of Life Sciences, Southwest University, Chongqing 400715, China; (S.Z.); (L.X.); (S.Z.); (B.L.); (W.T.); (X.W.)
| | - Thomas D Kocher
- Department of Biology, University of Maryland, College Park, MD 20742, USA;
| | - Linyan Zhou
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Key Laboratory of Aquatic Science of Chongqing, School of Life Sciences, Southwest University, Chongqing 400715, China; (S.Z.); (L.X.); (S.Z.); (B.L.); (W.T.); (X.W.)
- Correspondence: (L.Z.); (D.W.); Tel.: +86-23-68253702 (D.W.)
| | - Deshou Wang
- Key Laboratory of Freshwater Fish Reproduction and Development (Ministry of Education), Key Laboratory of Aquatic Science of Chongqing, School of Life Sciences, Southwest University, Chongqing 400715, China; (S.Z.); (L.X.); (S.Z.); (B.L.); (W.T.); (X.W.)
- Correspondence: (L.Z.); (D.W.); Tel.: +86-23-68253702 (D.W.)
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Identification of significantly mutated subnetworks in the breast cancer genome. Sci Rep 2021; 11:642. [PMID: 33436820 PMCID: PMC7804148 DOI: 10.1038/s41598-020-80204-5] [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: 06/14/2018] [Accepted: 12/17/2020] [Indexed: 11/24/2022] Open
Abstract
Recent studies showed that somatic cancer mutations target genes that are in specific signaling and cellular pathways. However, in each patient only a few of the pathway genes are mutated. Current approaches consider only existing pathways and ignore the topology of the pathways. For this reason, new efforts have been focused on identifying significantly mutated subnetworks and associating them with cancer characteristics. We applied two well-established network analysis approaches to identify significantly mutated subnetworks in the breast cancer genome. We took network topology into account for measuring the mutation similarity of a gene-pair to allow us to infer the significantly mutated subnetworks. Our goals are to evaluate whether the identified subnetworks can be used as biomarkers for predicting breast cancer patient survival and provide the potential mechanisms of the pathways enriched in the subnetworks, with the aim of improving breast cancer treatment. Using the copy number alteration (CNA) datasets from the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study, we identified a significantly mutated yet clinically and functionally relevant subnetwork using two graph-based clustering algorithms. The mutational pattern of the subnetwork is significantly associated with breast cancer survival. The genes in the subnetwork are significantly enriched in retinol metabolism KEGG pathway. Our results show that breast cancer treatment with retinoids may be a potential personalized therapy for breast cancer patients since the CNA patterns of the breast cancer patients can imply whether the retinoids pathway is altered. We also showed that applying multiple bioinformatics algorithms at the same time has the potential to identify new network-based biomarkers, which may be useful for stratifying cancer patients for choosing optimal treatments.
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Aamir M, Singh VK, Dubey MK, Meena M, Kashyap SP, Katari SK, Upadhyay RS, Umamaheswari A, Singh S. In silico Prediction, Characterization, Molecular Docking, and Dynamic Studies on Fungal SDRs as Novel Targets for Searching Potential Fungicides Against Fusarium Wilt in Tomato. Front Pharmacol 2018; 9:1038. [PMID: 30405403 PMCID: PMC6204350 DOI: 10.3389/fphar.2018.01038] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Accepted: 08/27/2018] [Indexed: 12/31/2022] Open
Abstract
Vascular wilt of tomato caused by Fusarium oxysporum f.sp. lycopersici (FOL) is one of the most devastating diseases, that delimits the tomato production worldwide. Fungal short-chain dehydrogenases/reductases (SDRs) are NADP(H) dependent oxidoreductases, having shared motifs and common functional mechanism, have been demonstrated as biochemical targets for commercial fungicides. The 1,3,6,8 tetra hydroxynaphthalene reductase (T4HNR) protein, a member of SDRs family, catalyzes the naphthol reduction reaction in fungal melanin biosynthesis. We retrieved an orthologous member of T4HNR, (complexed with NADP(H) and pyroquilon from Magnaporthe grisea) in the FOL (namely; FOXG_04696) based on homology search, percent identity and sequence similarity (93% query cover; 49% identity). The hypothetical protein FOXG_04696 (T4HNR like) had conserved T-G-X-X-X-G-X-G motif (cofactor binding site) at N-terminus, similar to M. grisea (1JA9) and Y-X-X-X-K motif, as a part of the active site, bearing homologies with two fungal keto reductases T4HNR (M. grisea) and 17-β-hydroxysteroid dehydrogenase from Curvularia lunata (teleomorph: Cochliobolus lunatus PDB ID: 3IS3). The catalytic tetrad of T4HNR was replaced with ASN115, SER141, TYR154, and LYS158 in the FOXG_04696. The structural alignment and superposition of FOXG_04696 over the template proteins (3IS3 and 1JA9) revealed minimum RMSD deviations of the C alpha atomic coordinates, and therefore, had structural conservation. The best protein model (FOXG_04696) was docked with 37 fungicides, to evaluate their binding affinities. The Glide XP and YASARA docked complexes showed discrepancies in results, for scoring and ranking the binding affinities of fungicides. The docked complexes were further refined and rescored from their docked poses through 50 ns long MD simulations, and binding free energies (ΔGbind) calculations, using MM/GBSA analysis, revealed Oxathiapiprolin and Famoxadone as better fungicides among the selected one. However, Famoxadone had better interaction of the docked residues, with best protein ligand contacts, minimum RMSD (high accuracy of the docking pose) and RMSF (structural integrity and conformational flexibility of docking) at the specified docking site. The Famoxadone was found to be acceptable based on in silico toxicity and in vitro growth inhibition assessment. We conclude that the FOXG_04696, could be employed as a novel candidate protein, for structure-based design, and screening of target fungicides against the FOL pathogen.
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Affiliation(s)
- Mohd Aamir
- Laboratory of Mycopathology and Microbial Technology, Centre of Advanced Study in Botany, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Vinay Kumar Singh
- Centre for Bioinformatics, School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Manish Kumar Dubey
- Laboratory of Mycopathology and Microbial Technology, Centre of Advanced Study in Botany, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Mukesh Meena
- Laboratory of Mycopathology and Microbial Technology, Centre of Advanced Study in Botany, Institute of Science, Banaras Hindu University, Varanasi, India.,Department of Botany, University College of Science, Mohanlal Sukhadia University, Udaipur, India
| | - Sarvesh Pratap Kashyap
- Division of Crop Improvement and Biotechnology, Indian Institute of Vegetable Research, Indian Council of Agricultural Research (ICAR), Varanasi, India
| | - Sudheer Kumar Katari
- Bioinformatics Centre, Department of Bioinformatics, Sri Venkateswara Institute of Medical Sciences University, Tirupati, India
| | - Ram Sanmukh Upadhyay
- Laboratory of Mycopathology and Microbial Technology, Centre of Advanced Study in Botany, Institute of Science, Banaras Hindu University, Varanasi, India
| | - Amineni Umamaheswari
- Bioinformatics Centre, Department of Bioinformatics, Sri Venkateswara Institute of Medical Sciences University, Tirupati, India
| | - Surendra Singh
- Laboratory of Mycopathology and Microbial Technology, Centre of Advanced Study in Botany, Institute of Science, Banaras Hindu University, Varanasi, India
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