3.8 Article

Protamine Characterization by Top-Down Proteomics: Boosting Proteoform Identification with DBSCAN

Journal

PROTEOMES
Volume 9, Issue 2, Pages -

Publisher

MDPI
DOI: 10.3390/proteomes9020021

Keywords

male infertility; sperm; protamine; LC-MS/MS; top-down proteomics; proteoform; post-translational modifications; Bioinformatics; DBSCAN

Funding

  1. ISCIII-SGEFI/ERDF [IPT17/0019]
  2. Plataforma de Proteomica, Genotipado y Lineas celulares, PRB3 in the frame of the Plataforma de apoyo a la Investigacion en Ciencias y Tecnologia de la Salud de la convocatoria de 2017 de la Accion Estrategica en Salud 2013-2016 of Instituto de Salud Carl [PT17/0019/0022]
  3. European Regional Development Fund (ERDF) [IU16-015983]
  4. Spanish Ministerio de Ciencia e Innovacion
  5. fondos FEDER 'una manera de hacer Europa', Proyectos de Investigacion en Salud (ISCIII) [PI16/00346, PI20/00936]
  6. Sara Borrell Postdoctoral Fellowship, Accion Estrategica en Salud [CD17/00109]

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Protamines replace histones as the main nuclear protein in sperm cells and play a crucial role in compacting the paternal genome. Analysis of protamine proteoforms using top-down proteomics reveals potential associations with male infertility. Difficulties in data analysis can be overcome by combining different software packages and algorithms to decipher protamine proteoforms, providing new insights into personalized diagnosis of male infertility.
Protamines replace histones as the main nuclear protein in the sperm cells of many species and play a crucial role in compacting the paternal genome. Human spermatozoa contain protamine 1 (P1) and the family of protamine 2 (P2) proteins. Alterations in protamine PTMs or the P1/P2 ratio may be associated with male infertility. Top-down proteomics enables large-scale analysis of intact proteoforms derived from alternative splicing, missense or nonsense genetic variants or PTMs. In contrast to current gold standard techniques, top-down proteomics permits a more in-depth analysis of protamine PTMs and proteoforms, thereby opening up new perspectives to unravel their impact on male fertility. We report on the analysis of two normozoospermic semen samples by top-down proteomics. We discuss the difficulties encountered with the data analysis and propose solutions as this step is one of the current bottlenecks in top-down proteomics with the bioinformatics tools currently available. Our strategy for the data analysis combines two software packages, ProSight PD (PS) and TopPIC suite (TP), with a clustering algorithm to decipher protamine proteoforms. We identified up to 32 protamine proteoforms at different levels of characterization. This in-depth analysis of the protamine proteoform landscape of normozoospermic individuals represents the first step towards the future study of sperm pathological conditions opening up the potential personalized diagnosis of male infertility.

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