Skip to main content
  • ASM
    • Antimicrobial Agents and Chemotherapy
    • Applied and Environmental Microbiology
    • Clinical Microbiology Reviews
    • Clinical and Vaccine Immunology
    • EcoSal Plus
    • Eukaryotic Cell
    • Infection and Immunity
    • Journal of Bacteriology
    • Journal of Clinical Microbiology
    • Journal of Microbiology & Biology Education
    • Journal of Virology
    • mBio
    • Microbiology and Molecular Biology Reviews
    • Microbiology Resource Announcements
    • Microbiology Spectrum
    • Molecular and Cellular Biology
    • mSphere
    • mSystems
  • Log in
  • My alerts
  • My Cart

Main menu

  • Home
  • Articles
    • Latest Articles
    • COVID-19 Special Collection
    • Archive
    • Minireviews
  • Topics
    • Applied and Environmental Science
    • Clinical Science and Epidemiology
    • Ecological and Evolutionary Science
    • Host-Microbe Biology
    • Molecular Biology and Physiology
    • Therapeutics and Prevention
  • For Authors
    • Submit a Manuscript
    • Scope
    • Editorial Policy
    • Submission, Review, & Publication Processes
    • Organization and Format
    • Errata, Author Corrections, Retractions
    • Illustrations and Tables
    • Nomenclature
    • Abbreviations and Conventions
    • Publication Fees
    • Ethics Resources and Policies
  • About the Journal
    • About mBio
    • Editor in Chief
    • Board of Editors
    • AAM Fellows
    • For Reviewers
    • For the Media
    • For Librarians
    • For Advertisers
    • Alerts
    • RSS
    • FAQ
  • ASM
    • Antimicrobial Agents and Chemotherapy
    • Applied and Environmental Microbiology
    • Clinical Microbiology Reviews
    • Clinical and Vaccine Immunology
    • EcoSal Plus
    • Eukaryotic Cell
    • Infection and Immunity
    • Journal of Bacteriology
    • Journal of Clinical Microbiology
    • Journal of Microbiology & Biology Education
    • Journal of Virology
    • mBio
    • Microbiology and Molecular Biology Reviews
    • Microbiology Resource Announcements
    • Microbiology Spectrum
    • Molecular and Cellular Biology
    • mSphere
    • mSystems

User menu

  • Log in
  • My alerts
  • My Cart

Search

  • Advanced search
mBio
publisher-logosite-logo

Advanced Search

  • Home
  • Articles
    • Latest Articles
    • COVID-19 Special Collection
    • Archive
    • Minireviews
  • Topics
    • Applied and Environmental Science
    • Clinical Science and Epidemiology
    • Ecological and Evolutionary Science
    • Host-Microbe Biology
    • Molecular Biology and Physiology
    • Therapeutics and Prevention
  • For Authors
    • Submit a Manuscript
    • Scope
    • Editorial Policy
    • Submission, Review, & Publication Processes
    • Organization and Format
    • Errata, Author Corrections, Retractions
    • Illustrations and Tables
    • Nomenclature
    • Abbreviations and Conventions
    • Publication Fees
    • Ethics Resources and Policies
  • About the Journal
    • About mBio
    • Editor in Chief
    • Board of Editors
    • AAM Fellows
    • For Reviewers
    • For the Media
    • For Librarians
    • For Advertisers
    • Alerts
    • RSS
    • FAQ
Research Article | Molecular Biology and Physiology

Combination of Proteogenomics with Peptide De Novo Sequencing Identifies New Genes and Hidden Posttranscriptional Modifications

B. Blank-Landeshammer, I. Teichert, R. Märker, M. Nowrousian, U. Kück, A. Sickmann
Joseph Heitman, Editor
B. Blank-Landeshammer
aLeibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Dortmund, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
I. Teichert
bAllgemeine und Molekulare Botanik, Ruhr-Universität, Bochum, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for I. Teichert
R. Märker
bAllgemeine und Molekulare Botanik, Ruhr-Universität, Bochum, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
M. Nowrousian
bAllgemeine und Molekulare Botanik, Ruhr-Universität, Bochum, Germany
cLehrstuhl für Molekulare und Zelluläre Botanik, Ruhr-Universität, Bochum, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for M. Nowrousian
U. Kück
bAllgemeine und Molekulare Botanik, Ruhr-Universität, Bochum, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
A. Sickmann
aLeibniz-Institut für Analytische Wissenschaften-ISAS-e.V., Dortmund, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Joseph Heitman
Duke University
Roles: Editor
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
DOI: 10.1128/mBio.02367-19
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Figures

  • Tables
  • Supplemental Material
  • FIG 1
    • Open in new tab
    • Download powerpoint
    FIG 1

    Schematic representation of the data analysis pipeline. Generated MS/MS spectra were subjected to subsequent database searches against the known S. macrospora protein sequences and a 6-frame translation of the S. macrospora genome as well as an independent de novo peptide sequencing method. Putative novel peptide identifications were clustered, filtered, converted to a genome browser readable format, and analyzed in conjunction with RNA-Seq data. Final curation of the genome annotation was performed manually.

  • FIG 2
    • Open in new tab
    • Download powerpoint
    FIG 2

    Evaluation of peptide identifications, classified as known and novel, in the 2-day data set. All peptide spectrum matches (PSMs) belonging to the respective class were compared to known false-positive decoy hits. (A) Normalized density plot of observed precursor mass deviation indicates no difference in distribution of identified known and novel PSMs. (B) Distribution of posterior error probability (PEP) shows clear distinction between decoy PSMs and known PSMs but almost overlapping distribution of known and novel hits. (C) Length of identified peptides of both classes, but not between classes, differs from decoy identifications. AA, amino acids. (D) Observed retention time shows tight correlation to predicted hydrophobicity index (HI) by SSRCalc for both classes, while retention times of known false positives only weakly correlate. In all cases, all decoy PSMs with a q value of <0.01 were plotted as a reference. See Fig. S1 and S2 for additional data sets.

  • FIG 3
    • Open in new tab
    • Download powerpoint
    FIG 3

    Alternative splicing in the pheromone pathway-specific kinase gene mek2. (A) Graphical representation of the canonical gene structure, including observed peptides (green bars), covering three splice junctions. The mek2 gene comprises 5 exons and was identified with a total of 50 peptides, covering 75% of the sequence. (B) Graphical representation of the alternatively spliced variant. Retention of intron 4 leads to translation into an alternative protein C terminus, identified by 6 novel peptides (orange bars). (C) Label-free quantification of both MEK2 isoforms throughout six growth conditions of S. macrospora reveals downregulation of the newly identified variant (orange, SMAC_06526.3_t2) during sexual development (BMM_5d and BMM_7d). (D) Sashimi plot visualizing the RNA-Seq coverage of both splice variants in vegetative and sexual mycelium.

  • FIG 4
    • Open in new tab
    • Download powerpoint
    FIG 4

    Peptide-level evidence for a recoding RNA editing event. (A) Parallel reaction monitoring (PRM) transitions of peptide EDDAVFFNYR originating from a putative RNA editing event in SMAC_03693, leading to the exchange of Thr to Ala at position 255. (B) PRM transitions of the genome-encoded peptide EDDTVFFNYR. (C) Peptides were monitored in fungal cells grown for 2, 3, 5, and 7 days, with the peptide originating from edited RNA only being identified in the latter two cases.

  • FIG 5
    • Open in new tab
    • Download powerpoint
    FIG 5

    Validation of a stop-loss editing site in the transcript for the white collar 1 protein. (A) Primary amino acid sequence of the white collar 1 protein. Editing results in the conversion of a stop codon into a tryptophan codon and extends the amino acid sequence by 131 amino acids. As a consequence, the protein carries a C-terminal histone deacetylase domain (HDAC). Peptides encoded by the canonical gene are shown in red, while the blue peptide is unique to the novel C-terminal sequence. (B) Annotated MS/MS spectrum of the editing-specific peptide observed in the 7-day data set. (C) Domain structure of the white collar 1 protein, including the HDAC domain (green box) in the extended C terminus (blue box). PAS, Per-Arnt-Sim domain; GATA, GATA-type zinc finger transcription factor domain.

  • FIG 6
    • Open in new tab
    • Download powerpoint
    FIG 6

    Phylostratigraphic map of all S. macrospora proteins. (A) Division into known and identified detected proteins, novel proteins identified by the proteogenomics analysis, and known and unidentified proteins not found in this study. Relative protein occurrence (i.e., the number of proteins assigned to a PS relative to the total number of proteins) describes the share of proteins assigned to a given phylostratum (PS) within its aforementioned class. (B) Detailed phylostratigraphic map of all novel, completely annotated class I proteins, displaying the BLAST E value of the top 5 hits in every PS. Proteins are hierarchically clustered (Ward’s method) to show similarities in PS distribution.

Tables

  • Figures
  • Supplemental Material
  • TABLE 1

    Classification of all annotation refinements performed in this study

    TABLE 1
  • TABLE 2

    Classification of identified SAAVs by typea

    TABLE 2
    • ↵a Total number of observed single-amino-acid variation (SAAV) putatively caused by mRNA editing events in the 7-day sample. Respective theoretical mass shifts of each amino acid exchange are given. Additionally, percentages of sites found for both the edited as well as the nonedited variant are given. PROVEAN prediction score was retrieved for each individual SAAV via the PROVEAN web interface (70), and the median score for each class of SAAV was calculated. A default threshold of −2.5 was used to estimate the extent of potentially deleterious variants.

    • ↵b N→D events were not considered, as they can be caused by RNA editing as well as by asparagine deamidation on the protein/peptide level.

Supplemental Material

  • Figures
  • Tables
  • TABLE S1

    Overview of all spectra, PSMs, and identified known and novel peptides in this study Download Table S1, PDF file, 0.04 MB.

    Copyright © 2019 Blank-Landeshammer et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S1

    Comparison of known and novel peptide identifications with regard to PSM mass deviation (A) and PEP (B). Known identifications referred to peptide hits already identified in genome annotation v3, while novel identifications are those additionally found through proteogenomics efforts. Precursor mass deviation of all identified PSMs of all 8 datasets show tight correlation for both classes. Mass deviation of known false-positive decoy hits are plotted for reference. PEP calculated by Percolator is plotted for all 8 datasets and shows clear distinction between decoy PSMs and known PSMs but almost overlapping distribution of known and novel hits. Download FIG S1, PDF file, 1.8 MB.

    Copyright © 2019 Blank-Landeshammer et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S2

    Comparison of known and novel peptide identifications with regard to peptide length (A) and peptide hydrophobicity index (HI) (B). Known identifications refer to peptide hits already identified in genome annotation v3, while novel identifications are those additionally found through proteogenomics efforts. Length distribution of all known and novel identified peptides and all decoy hits are shown for the 8 analyzed datasets. In all cases, known and novel peptides are distinctly longer than the false-positive decoy hits plotted for reference. Peptide HI was calculated by SSRCalc Q and plotted against the measured retention time (RT) for known, novel, and decoy hits. Both known and novel peptides show high correlation coefficients, while for decoy peptides, predicted HI and observed RT only weakly correlate. Download FIG S2, PDF file, 1.9 MB.

    Copyright © 2019 Blank-Landeshammer et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • DATA SET S1

    Summary of all gene refinement procedures performed in this study with BLAST2GO results of all identified hidden genes given in detail. Download Data Set S1, XLSX file, 0.1 MB.

    Copyright © 2019 Blank-Landeshammer et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S3

    Overview of BLAST2GO and subsequent GO enrichment analysis. (A) Overall tag distribution as a result of the BLAST2GO analysis of the S. macrospora v3.1 protein sequence database. The total number of sequences (10,015) is indicated by a dotted vertical line. (B) Distribution of the top BLAST E values of all proteins identified with an E value cutoff of 1e−10 (9,752). (C) Result of GO enrichment analysis of proteins uniquely identified in the 7-day sample (n = 410), performed with the Ontologizer 2.0 command line tool. Data are sorted by total number of proteins matching the respective GO term within the three domains, with the top 8 GO terms (sorted by adjusted P value, threshold of 0.05) of every domain being displayed. (D) Result of GO enrichment analysis of proteins uniquely identified in the 2-day sample. All GO terms below a threshold of 0.05 are displayed. Download FIG S3, PDF file, 0.1 MB.

    Copyright © 2019 Blank-Landeshammer et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S4

    Expression profiles of proteins displayed as a log2-transformed ratio to their respective median abundance. Only proteins with module membership correlation to the respective module eigenprotein greater than 0.6 and a P value of <0.05 are displayed. Proteins that were identified in our proteogenomics approach are shown in red. Download FIG S4, PDF file, 0.1 MB.

    Copyright © 2019 Blank-Landeshammer et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • Text S1

    Detailed description of the LC-MS instrumentation and parameters, de novo and database search strategies, and label-free quantification software and parameters used in this study, as well as description of the functional coexpression analysis workflow. Download Text S1, DOCX file, 1.5 MB.

    Copyright © 2019 Blank-Landeshammer et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S5

    Log10-transformed areas under the concentration-time curve (AUC) of PRM measurements of peptides representing 17 selected amino acid changes as well as their nonedited counterparts and a further proteotypic peptide not affected by RNA editing. Spectra were acquired in S. macrospora samples harvested after growth for 2, 3, 5, or 7 days, and only AUC values of identifications meeting the minimum QC requirements are displayed. The top 4 to 6 transitions were selected to monitor the putative RNA editing-derived variant peptides. At least one diagnostic transition with respect to the SAAV was included for each peptide. Dot product was >0.8 for all monitored peptides with respect to the initially identified MS/MS spectrum, and mass deviation was not greater than ±1.3 ppm for any transition. The only edited peptide present under all four conditions is associated with SMAC_02363, which represents histone 3A. Download FIG S5, PDF file, 0.02 MB.

    Copyright © 2019 Blank-Landeshammer et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • DATA SET S2

    List of all identified RNA editing-related peptides classified as SAAV or stop-loss peptide and inclusion list for the PRM measurements of 17 selected SAAV peptides and 43 corresponding nonedited peptides. Download Data Set S2, XLSX file, 0.03 MB.

    Copyright © 2019 Blank-Landeshammer et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

  • FIG S6

    Phylogram of known GCN5-related N-acetyltransferase (GNAT) family proteins and novel protein SMAC_12930.3. Analysis was performed using the phylogeny.fr web server in “one-click” mode. Alignment was done with MUSCLE, the phylogenetic tree was constructed based on the maximum-likelihood principle by PhyML, and TreeDyn was used for rendering. Branch support values are shown in red. Download FIG S6, PDF file, 0.1 MB.

    Copyright © 2019 Blank-Landeshammer et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license.

PreviousNext
Back to top
Download PDF
Citation Tools
Combination of Proteogenomics with Peptide De Novo Sequencing Identifies New Genes and Hidden Posttranscriptional Modifications
B. Blank-Landeshammer, I. Teichert, R. Märker, M. Nowrousian, U. Kück, A. Sickmann
mBio Oct 2019, 10 (5) e02367-19; DOI: 10.1128/mBio.02367-19

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Print

Alerts
Sign In to Email Alerts with your Email Address
Email

Thank you for sharing this mBio article.

NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. We do not retain these email addresses.

Enter multiple addresses on separate lines or separate them with commas.
Combination of Proteogenomics with Peptide De Novo Sequencing Identifies New Genes and Hidden Posttranscriptional Modifications
(Your Name) has forwarded a page to you from mBio
(Your Name) thought you would be interested in this article in mBio.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Combination of Proteogenomics with Peptide De Novo Sequencing Identifies New Genes and Hidden Posttranscriptional Modifications
B. Blank-Landeshammer, I. Teichert, R. Märker, M. Nowrousian, U. Kück, A. Sickmann
mBio Oct 2019, 10 (5) e02367-19; DOI: 10.1128/mBio.02367-19
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Top
  • Article
    • ABSTRACT
    • INTRODUCTION
    • RESULTS
    • DISCUSSION
    • MATERIALS AND METHODS
    • ACKNOWLEDGMENTS
    • FOOTNOTES
    • REFERENCES
  • Figures & Data
  • Info & Metrics
  • PDF

KEYWORDS

proteogenomics
peptide de novo sequencing
RNA editing
alternative splicing
phylostratigraphy
gene ontology
alternative splice sites
fungal genome
genomics
proteomics

Related Articles

Cited By...

About

  • About mBio
  • Editor in Chief
  • Board of Editors
  • AAM Fellows
  • Policies
  • For Reviewers
  • For the Media
  • For Librarians
  • For Advertisers
  • Alerts
  • RSS
  • FAQ
  • Permissions
  • Journal Announcements

Authors

  • ASM Author Center
  • Submit a Manuscript
  • Author Warranty
  • Article Types
  • Ethics
  • Contact Us

Follow #mBio

@ASMicrobiology

       

ASM Journals

ASM journals are the most prominent publications in the field, delivering up-to-date and authoritative coverage of both basic and clinical microbiology.

About ASM | Contact Us | Press Room

 

ASM is a member of

Scientific Society Publisher Alliance

 

American Society for Microbiology
1752 N St. NW
Washington, DC 20036
Phone: (202) 737-3600

Copyright © 2021 American Society for Microbiology | Privacy Policy | Website feedback

Online ISSN: 2150-7511