In-Depth Quantitative Proteomic Analysis of Trophozoites and Pseudocysts of Trichomonas vaginalis.
Geovane Dias-LopesJacek R WiśniewskiNathalia Pinho de SouzaVítor Ennes VidalGabriel PadrónConstança BrittoPatricia CuervoJosé Batista De JesusPublished in: Journal of proteome research (2018)
Trichomonas vaginalis is a sexually transmitted anaerobic parasite that infects humans causing trichomoniasis, a common and ubiquitous sexually transmitted disease. The life cycle of this parasite possesses a trophozoite form without a cystic stage. However, the presence of nonproliferative and nonmotile, yet viable and reversible spherical forms with internalized flagella, denominated pseudocysts, has been commonly observed for this parasite. To understand the mechanisms involved in the formation of pseudocysts, we performed a mass spectrometry-based high-throughput quantitative proteomics study using a label-free approach and functional assays by biochemical and flow cytometric methods. We observed that the morphological transformation of trophozoite to pseudocysts is coupled to (i) a metabolic shift toward a less glycolytic phenotype; (ii) alterations in the abundance of hydrogenosomal iron-sulfur cluster (ISC) assembly machinery; (iii) increased abundance of regulatory particles of the ubiquitin-proteasome system; (iv) significant alterations in proteins involved in adhesion and cytoskeleton reorganization; and (v) arrest in G2/M phase associated with alterations in the abundance of regulatory proteins of the cell cycle. These data demonstrate that pseudocysts experience important physiological and structural alterations for survival under unfavorable environmental conditions.
Keyphrases
- life cycle
- cell cycle
- label free
- high throughput
- mass spectrometry
- antibiotic resistance genes
- high resolution
- cell proliferation
- microbial community
- toxoplasma gondii
- transcription factor
- wastewater treatment
- trypanosoma cruzi
- electronic health record
- small molecule
- liquid chromatography
- big data
- cystic fibrosis
- gas chromatography
- machine learning
- single cell
- biofilm formation
- ms ms
- deep learning
- capillary electrophoresis
- staphylococcus aureus
- simultaneous determination
- cell adhesion