Flexible, accessible & reproducible workflows for tandem proteogenomic and metaproteomic analysis using the GalaxyP platform.

Pratik Jagtap1; Brian Sandri2; Julie Yang2; Kevin Murray2; Joel Kooren2; James Johnson3; Getiria Onsongo3; Joel Rudney2; Christine Wendt2 and Tim Griffin11,2 1. Center for Mass Spectrometry and Proteomics, UMN, St. Paul, MN; 2. University of Minnesota, Minneapolis, MN; 3. Minnesota Supercomputing Institute, Minneapolis, MN.

• Salivary proteome was analyzed using search algorithms for high mass accuracy datasets to generate a confident list of proteins

and peptides.

FUTURE PLAN

• We plan to search the entire salivary proteome using ProteinPilot so as to analyze and compare it with MaxQuant results at

protein

ACKNOWLEDGEMENTS

Funding Sources : NIH/NIDCR R01 DE 017734 for Dr. Griffin and Dr. Bandhakavi. Multiple funding resources enlisted on

http://www.cbs.umn.edu/msp/about.shtml for Thomas McGowan.

REFERENCES

Salivary Proteome : Bandhakavi et al J. Proteome Res. 8, 5590 (2009)).

CONCLUSIONS

INTRODUCTION

GALAXYP

GalaxyP Workflow GalaxyP Tools

GalaxyP has multiple software tools

- some proteomics-specific - and

others from the genomics Galaxy

framework.

• Proteogenomics (for identifying unannotated proteoforms) and

metaproteomics (for characterizing non-host/multi-organism proteomes) are

research areas that extend discoveries beyond the reference proteome.

• For biomedically-relevant proteomics studies, tandem proteogenomic and

metaproteomic analysis offers great promise for new discoveries.

• We describe effective and accessible bioinformatic analytical workflows,

amenable to creative customization and sharing to foster collaborative

research efforts.

Tools can be used in a sequential manner to generate

workflows that can be reused, shared and creatively

modified for multiple studies.

Benefits of Galaxy / GalaxyP:

• Software accessibility and usability.

•  Share-ability of tools, workflows and histories.

• Reproducibility and ability to test and compare

results after using multiple parameters.

METHODS & DATASETS

• Analytical transparency

• Scalability of data

RAW files from multiple datasets (see below) were generated from Orbitrap Velos instrument.

The processed peak lists were searched using ProteinPilot ™ version 4.5 (AB Sciex) within GalaxyP (usegalaxyp.org). the datasets were searched against 3-frame translated cDNA database

and the human oral microbial database by using two-step method (Jagtap et al 2013). After

optimization & testing, multiple workflows were used in a sequential manner to generate inputs

for the subsequent workflow. Microbial peptides were identified after using metaproteomic

workflows & novel proteoforms were identified after using proteogenomic workflows.

UNLABELED SAMPLE:

• Oral pre-malignant lesion (OPML) dataset was collected as oral exudate using PerioPaper

strip method (Kooren et al 2011) from an individual with pre-malignant lesion & a matched

control sample from adjacent oral cavity.

4-plex iTRAQ LABELED SAMPLE:

• Brush biopsies were collected from patients diagnosed with OPML and from patients with

Oral Squamous Cell Carcinoma (OSCC). For each patient, brush biopsies from the lesion &

the healthy mucosa of corresponding contralateral area were collected (Yang et al 2014).

8-plex iTRAQ LABELED SAMPLE:

• Chronic Obstructive Pulmonary Disease (COPD) – linked lung cancer tissue samples were

collected & subjected to iTRAQ labeling and 2D LC-MS. Ten replicates of this dataset were

searched against the 3-frame translated cDNA database & human oral microbiome database

(HOMD) using the two-step method.

WORKFLOWS FOR TANDEM PROTEOGENOMIC AND METAPROTEOMIC ANALYSIS.

> ENST00000 cdna:

TACGGCCGTCGTGCCC

> ENST000000 cdna:

TCGTGCCGCTTACGGC

Two-step Database

Search method

translation

Peptide Summary from

Second-Step Search with

novel proteoform PSMs and

microbial peptides

Transcriptomic (cDNA or

RNASeq) or Genomic Database

Database

C

PTNTIALNEWPRTEFRM

PEPTIDESINFSTAFRMT

MICRPEPTIDES

ARCHAEALPEPTIDES

Identifying peptides from

microbial db

Data

Processing

Microbial

Peptides D

BLAST-P Analysis E

> ENST|Potential new Microbe1

PTNTIALNEWPRTEFRM

> ENST|Potential new Microbe2

PEPTIDESINFSTAFRMT

Peptides in

FASTA format

Submit for

MEGAN5 Anaysis

Submit to UniPept

for analysis

Peak list (MGF or mzml)

msconvert

MGF

Formatter

Mass spectra

Peak processing

A

> ENST00000 cdna:

TACGGCCGTCGTGCCC

> ENST000000 cdna:

TCGTGCCGCTTACGGC

> ENST00000 protein

ITSAPRTEINDATASET

> ENST000000 protein

INANTHERFRAMETHGH

> sp|Acc No 1|Human

MANPRTEINS

> sp|Acc No 2|Human

MANYHMANPRTEINS

> ENST00000 protein

ITSAPRTEINDATASET

> ENST000000 protein

INANTHERFRAMETHGH

> sp|Acc No 1|Human

MANPRTEINS

> sp|Acc No 2|Human

MANYHMANPRTEINS

Database generation B

translation

Merge

FASTA

Target

database

Host Protein

Database

Metagenomic

Database

Microbial

protein db

> ENST00000 protein

ITSAPRTEINDATASET

> ENST000000 protein

INANTHERFRAMETHGH

> sp|Acc No 1|Human

MANPRTEINS

> sp|Acc No 2|Human

MANYHMANPRTEINS

Translated

genomic db

Host Protein

Database

> ENST00000 protein

ITSAPRTEINDATASET

> ENST000000 protein

INANTHERFRAMETHGH

> sp|Acc No 1|Human

MANPRTEINS

> sp|Acc No 2|Human

MANYHMANPRTEINS

Merge

FASTA

Target

database

Peptide Summary

Peptide Summary

Data

Processing

Data

Processing

> ENST00000 protein

ITSAPRTEINDATASET

> ENST000000 protein

INANTHERFRAMETHGH

> sp|Acc No 1|Human

MANPRTEINS

> sp|Acc No 2|Human

MANYHMANPRTEINS

> ENST00000 protein (decoy)

TESATADNIIETRPASTI

> ENST000000 protein (decoy)

HGHTEMARFREHTNANI

> sp|Acc No 1|Human (decoy)

SNIETRPNAM

> sp|Acc No 2|Human (decoy)

SNIETRPNAMHYNAM

Target-Decoy database

from accession numbers

from first step search

Screenshot of a novel proteoform peptide within Integrated Genomic Viewer. Novel proteoform peptides

identified at 5% local FDR were used to generate GTF file so that the genomic localization of these peptides

can be viewed in genomic context. Proteoform corresponding to HNRNPA2B1 was identified to have an

alternative start site and was identified in 4 replicates.

RESULTS SUMMARY

Short peptides

(<30 aas) Long peptides

(<30 aas)

Spectral

Visualization

Filtering of Peptide Spectral

Matching Metrics

Identifying peptides from

translated genomic db

Data

Processing

F

PTNTIALNEWPRTEFRM

PEPTIDESINFSTAFRMT

MICRPEPTIDES

ARCHAEALPEPTIDES

Potential Novel

Proteoform

Peptides

> ENST|Potential new Microbe1

PTNTIALNEWPRTEFRM

> ENST|Potential new Microbe2

PEPTIDESINFSTAFRMT

Peptides in

FASTA format Short peptides

(<30 aas)

Long peptides

(<30 aas)

BLAST-P Analysis

Filter peptides

with mismatches

to human NCBI

database.

Peak list

(mzml)

Peptide Summary of new proteoform peptides.

Peptide Spectral

Match Evaluation

Dataset

RAW

Files

Distinct peptides

of microbial

origin

Number of

unique

peptides

(Species

Identified )

Novel proteoform

peptides

OPML Control

(unlabeled) 7 637 136 (6) 50

OPML Lesion

(non-labeled) 7 688 136 (3) 47

Brush Biopsy OSCC

(4-plex iTRAQ) 15 1118 166 (6) 6

COPD

(10 replicates)

(8-plex iTRAQ) 150 87 9 (9) 7

RESULTS

G

H

Peptide to GTF

conversion

Peptide Summary of new proteoform peptides with

quality peptide spectral matching characteristics.

General Transfer Format

file for Human genome

Peptide to

GTF

I

Visualization in

genomic context

In COPD dataset, five lung-infecting organisms were identified. Actinomyces viscosus – a bacterium that causes

Actinomycosis (granulomatous infection with the formation of abscesses) in the lungs was identified in five

replicates.

EFFECT OF SEARCH DATABASES ON MICROBIAL &

NOVEL PROTEOFORM IDENTIFICATIONS.

598 30 39

627 33 61

22 6 28

30 11 17

OPML dataset

(CONTROL)

OPML dataset

(LESION)

MICROBIAL PEPTIDES PROTEOFORM PEPTIDES

Search against microbial db

Search against microbial and

translated cDNA db

Search against microbial and

translated cDNA db

Search against translated cDNA db

METAPROTEOMICS

PROTEOGENOMICS

0

1

2

3

4

5

CONTROL

LESION

M1 M2

Microbial peptides identified at 5% local FDR were analyzed using UniPept and MEGAN5.

Fig M1: Organisms were identified at species level only when assigned 3 unique peptides or more.

Fig M2: Functional groups were assigned using SEED program within MEGAN5.

# o

f Id

enti

fied

Pep

tid

es

# o

f Id

enti

fied

Pep

tid

es

Workflow Link:

z.umn.edu/peaklistconversion

Workflow Link: z.umn.edu/dbgenmp

Workflow Link:

z.umn.edu/mn2stp

Workflow Link: z.umn.edu/dbgen

Workflow Link:

z.umn.edu/pepfrommicrobialdb

Workflow Link: z.umn.edu/blastp Workflow Link: z.umn.edu/pep2gtf

Workflow Link: z.umn.edu/blastp

Workflow Link:

z.umn.edu/peptidesfromcdnadb

Workflow Link:

z.umn.edu/psme

Using correct search databases that contribute to the proteome under study (both metaproteomic &

proteogenomic databases in tandem) help in confident identification of microbial peptides & novel proteoforms.

PSM EVALUATION & GENOME VISUALIZATION PSME Input Parameters

HTML

Output

Spectral

Visualization

Tabular Output

PSME (Peptide-Spectral-Match Evaluator)

uses spectral summary as an input to parse

mzml data and generates a tabular format

output with customized spectral features.

PSME also generates HTML links that are

used to visualize spectral assignments.

Interactive boxes can be used to change ion

assignments and other parameters.

GENOME VISUALIZATION

• We demonstrate the use of a complete platform for tandem

metaproteomic / proteogenomic analysis. Workflow for each

module/step have been shared for use within Galaxy environment.

• Using both metaproteomic & proteogenomic databases in tandem help

in confident identification of microbial peptides & novel proteoforms.

• Using this platform, we have identified microbial peptides & novel

proteoforms from both labeled & unlabeled datasets. For example for

COPD datasets, the number of identifications from both

proteogenomic & metaproteomic databases is limited yet consistent

across replicates..

• For metaproteomics studies, identified microbial peptides were used

for taxonomic classification (Unipept and MEGAN5) for functional

classification (MEGAN5).

• For proteogenomic studies, identified novel proteoforms were

validated using PSM evaluation tool and visualizing peptides against

the genome.

ACKNOWLEDGEMENTS: GalaxyP is supported through the National Science Foundation Grant

1147079. Many thanks to John Chilton (PennState) for GalaxyP development. Also thanks to LeeAnn

Higgins, Todd Markowski (CMSP, UMN), Bart Gottschalk and Anne Lamblin (MSI), Katie Vermillion

(UMD-Duluth) and Gloria Shenykman (UW-Madison) for helpful discussions.