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

ABSTRACT: 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.

SUBMITTER: Arauz-Garofalo G

PROVIDER: S-EPMC8162566 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Protamine characterization by top-down proteomics: Boosting proteoform identification with DBSCAN

Project description:Protamines replace histones as the main nuclear protein in sperm cells having a crucial role in compacting the paternal genome. Human spermatozoa contain the protamine 1 (P1) and the family of protamine 2 (P2) proteins. Alterations in protamine PTMs or on the P1/P2 ratio could be associated with male infertility. Top-down proteomics enables large-scale analysis of intact proteforms derived from alternative splicing, missense or nonsense genetic variants or PTMs. In contrast to current gold standard techniques, top-down allows a more in-depth analysis of protamine PTMs and proteoforms, opening up new perspectives to unravel their impact on male fertility. We analyzed two normozoospermic semen samples by top-down and discussed in detail the difficulties we found in the data analysis and the suggested solutions, as this step is one of the current bottlenecks in top-down proteomics with currently available bioinformatic tools. Our strategy for the data analysis combines two different software, 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 an individual boosts personalized diagnosis of male infertility.

2021-05-13 | PXD024405 | Pride

SPECTRUM - A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data.

Project description:Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose 'SPECTRUM', an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/.

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PERCEPTRON: an open-source GPU-accelerated proteoform identification pipeline for top-down proteomics.

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TopPIC: a software tool for top-down mass spectrometry-based proteoform identification and characterization.

Project description:Top-down mass spectrometry enables the observation of whole complex proteoforms in biological samples and provides crucial information complementary to bottom-up mass spectrometry. Because of the complexity of top-down mass spectra and proteoforms, it is a challenging problem to efficiently interpret top-down tandem mass spectra in high-throughput proteome-level proteomics studies. We present TopPIC, a tool that efficiently identifies and characterizes complex proteoforms with unknown primary structure alterations, such as amino acid mutations and post-translational modifications, by searching top-down tandem mass spectra against a protein database.Availability and implementationhttp://proteomics.informatics.iupui.edu/software/toppic/ CONTACT: xwliu@iupui.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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Top-Down Proteomics Reveals Myofilament Proteoform Heterogeneity among Various Rat Skeletal Muscle Tissues.

Project description:Heterogeneity in skeletal muscle contraction time, peak power output, and resistance to fatigue, among others, is necessary to accommodate the wide range of functional demands imposed on the body. Underlying this functional heterogeneity are a myriad of differences in the myofilament protein isoform expression and post-translational modifications; yet, characterizing this heterogeneity remains challenging. Herein, we have utilized top-down liquid chromatography (LC)-mass spectrometry (MS)-based proteomics to characterize myofilament proteoform heterogeneity in seven rat skeletal muscle tissues including vastus lateralis, vastus medialis, vastus intermedius, rectus femoris, soleus, gastrocnemius, and plantaris. Top-down proteomics revealed that myofilament proteoforms varied greatly across the seven different rat skeletal muscle tissues. Subsequently, we quantified and characterized myofilament proteoforms using online LC-MS. We have comprehensively characterized the fast and slow skeletal troponin I isoforms, which demonstrates the ability of top-down MS to decipher isoforms with high sequence homology. Taken together, we have shown that top-down proteomics can be used as a robust and high-throughput method to characterize the molecular heterogeneity of myofilament proteoforms from various skeletal muscle tissues.

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Proteoform Identification by Combining RNA-Seq and Top-Down Mass Spectrometry.

Project description:In proteogenomic studies, genomic and transcriptomic variants are incorporated into customized protein databases for the identification of proteoforms, especially proteoforms with sample-specific variants. Most proteogenomic research has been focused on combining genomic or transcriptomic data with bottom-up mass spectrometry data. In the last decade, top-down mass spectrometry has attracted increasing attention because of its capacity to identify various proteoforms with alterations. However, top-down proteogenomics, in which genomic or transcriptomic data are combined with top-down mass spectrometry data, has not been widely adopted, and there is still a lack of software tools for top-down proteogenomic data analysis. In this paper, we introduce TopPG, a proteogenomic tool for generating proteoform sequence databases with genetic alterations and alternative splicing events. Experiments on top-down proteogenomic data of DLD-1 colorectal cancer cells showed that TopPG coupled with database search confidently identified proteoforms with sample-specific alterations.

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Project description:Hypertrophic cardiomyopathy (HCM) is the most common heritable heart disease. Although the genetic cause of HCM has been linked to mutations in genes encoding sarcomeric proteins, the ability to predict clinical outcomes based on specific mutations in HCM patients is limited. Moreover, how mutations in different sarcomeric proteins can result in highly similar clinical phenotypes remains unknown. Posttranslational modifications (PTMs) and alternative splicing regulate the function of sarcomeric proteins; hence, it is critical to study HCM at the level of proteoforms to gain insights into the mechanisms underlying HCM. Herein, we employed high-resolution mass spectrometry-based top-down proteomics to comprehensively characterize sarcomeric proteoforms in septal myectomy tissues from HCM patients exhibiting severe outflow track obstruction (n = 16) compared to nonfailing donor hearts (n = 16). We observed a complex landscape of sarcomeric proteoforms arising from combinatorial PTMs, alternative splicing, and genetic variation in HCM. A coordinated decrease of phosphorylation in important myofilament and Z-disk proteins with a linear correlation suggests PTM cross-talk in the sarcomere and dysregulation of protein kinase A pathways in HCM. Strikingly, we discovered that the sarcomeric proteoform alterations in the myocardium of HCM patients undergoing septal myectomy were remarkably consistent, regardless of the underlying HCM-causing mutations. This study suggests that the manifestation of severe HCM coalesces at the proteoform level despite distinct genotype, which underscores the importance of molecular characterization of HCM phenotype and presents an opportunity to identify broad-spectrum treatments to mitigate the most severe manifestations of this genetically heterogenous disease.

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