1
|
Benchouaia M, Ripoche H, Sissoko M, Thiébaut A, Merhej J, Delaveau T, Fasseu L, Benaissa S, Lorieux G, Jourdren L, Le Crom S, Lelandais G, Corel E, Devaux F. Comparative Transcriptomics Highlights New Features of the Iron Starvation Response in the Human Pathogen Candida glabrata. Front Microbiol 2018; 9:2689. [PMID: 30505294 PMCID: PMC6250833 DOI: 10.3389/fmicb.2018.02689] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 10/22/2018] [Indexed: 11/21/2022] Open
Abstract
In this work, we used comparative transcriptomics to identify regulatory outliers (ROs) in the human pathogen Candida glabrata. ROs are genes that have very different expression patterns compared to their orthologs in other species. From comparative transcriptome analyses of the response of eight yeast species to toxic doses of selenite, a pleiotropic stress inducer, we identified 38 ROs in C. glabrata. Using transcriptome analyses of C. glabrata response to five different stresses, we pointed out five ROs which were more particularly responsive to iron starvation, a process which is very important for C. glabrata virulence. Global chromatin Immunoprecipitation and gene profiling analyses showed that four of these genes are actually new targets of the iron starvation responsive Aft2 transcription factor in C. glabrata. Two of them (HBS1 and DOM34b) are required for C. glabrata optimal growth in iron limited conditions. In S. cerevisiae, the orthologs of these two genes are involved in ribosome rescue by the NO GO decay (NGD) pathway. Hence, our results suggest a specific contribution of NGD co-factors to the C. glabrata adaptation to iron starvation.
Collapse
Affiliation(s)
- Médine Benchouaia
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Hugues Ripoche
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Mariam Sissoko
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Antonin Thiébaut
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Jawad Merhej
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Thierry Delaveau
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Laure Fasseu
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Sabrina Benaissa
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Geneviève Lorieux
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
| | - Laurent Jourdren
- École Normale Supérieure, PSL Research University, CNRS, Inserm U1024, Institut de Biologie de l’École Normale Supérieure, Plateforme Génomique, Paris, France
| | - Stéphane Le Crom
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7138, Évolution, Paris, France
| | - Gaëlle Lelandais
- UMR 9198, Institute for Integrative Biology of the Cell, CEA, CNRS, Université Paris-Sud, UPSay, Gif-sur-Yvette, France
| | - Eduardo Corel
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7138, Évolution, Paris, France
| | - Frédéric Devaux
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, UMR 7238, Laboratoire de Biologie Computationnelle et Quantitative, Paris, France
- *Correspondence: Frédéric Devaux,
| |
Collapse
|
2
|
Carapia-Minero N, Castelán-Vega JA, Pérez NO, Rodríguez-Tovar AV. The phosphorelay signal transduction system in Candida glabrata: an in silico analysis. J Mol Model 2017; 24:13. [PMID: 29248994 DOI: 10.1007/s00894-017-3545-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Accepted: 11/24/2017] [Indexed: 01/18/2023]
Abstract
Signaling systems allow microorganisms to sense and respond to different stimuli through the modification of gene expression. The phosphorelay signal transduction system in eukaryotes involves three proteins: a sensor protein, an intermediate protein and a response regulator, and requires the transfer of a phosphate group between two histidine-aspartic residues. The SLN1-YPD1-SSK1 system enables yeast to adapt to hyperosmotic stress through the activation of the HOG1-MAPK pathway. The genetic sequences available from Saccharomyces cerevisiae were used to identify orthologous sequences in Candida glabrata, and putative genes were identified and characterized by in silico assays. An interactome analysis was carried out with the complete genome of C. glabrata and the putative proteins of the phosphorelay signal transduction system. Next, we modeled the complex formed between the sensor protein CgSln1p and the intermediate CgYpd1p. Finally, phosphate transfer was examined by a molecular dynamic assay. Our in silico analysis showed that the putative proteins of the C. glabrata phosphorelay signal transduction system present the functional domains of histidine kinase, a downstream response regulator protein, and an intermediate histidine phosphotransfer protein. All the sequences are phylogenetically more related to S. cerevisiae than to C. albicans. The interactome suggests that the C. glabrata phosphorelay signal transduction system interacts with different proteins that regulate cell wall biosynthesis and responds to oxidative and osmotic stress the same way as similar systems in S. cerevisiae and C. albicans. Molecular dynamics simulations showed complex formation between the response regulator domain of histidine kinase CgSln1 and intermediate protein CgYpd1 in the presence of a phosphate group and interactions between the aspartic residue and the histidine residue. Overall, our research showed that C. glabrata harbors a functional SLN1-YPD1-SSK1 phosphorelay system.
Collapse
Affiliation(s)
- Natalee Carapia-Minero
- Laboratorio de Micología Médica, Depto. de Microbiología, Escuela Nacional de Ciencias Biológicas (ENCB) , Instituto Politécnico Nacional, Prolongación de Carpio y Plan de Ayala s/n, Col. Casco de Santo Tomás, Del. Miguel Hidalgo, CP 11340, Ciudad de México, Mexico
| | - Juan Arturo Castelán-Vega
- Laboratorio de Producción y Control de Biológicos ENCB, Instituto Politécnico Nacional, Carpio y Plan de Ayala s/n, Col. Casco de Santo Tomás, Del. Miguel Hidalgo, CP 11340, Ciudad de México, Mexico
| | - Néstor Octavio Pérez
- Unidad de investigación y Desarrollo, Probiomed, SA de CV, Cruce de Carreteras Acatzingo-Zumpahuacan S/N, CP 52400, Tenancingo, Edo de México, Mexico.
| | - Aída Verónica Rodríguez-Tovar
- Laboratorio de Micología Médica, Depto. de Microbiología, Escuela Nacional de Ciencias Biológicas (ENCB) , Instituto Politécnico Nacional, Prolongación de Carpio y Plan de Ayala s/n, Col. Casco de Santo Tomás, Del. Miguel Hidalgo, CP 11340, Ciudad de México, Mexico.
| |
Collapse
|