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Goelen J, Farrell G, McGeehan J, Titman CM, J W Rattray N, Johnson TN, Horniblow RD, Batchelor HK. Quantification of drug metabolising enzymes and transporter proteins in the paediatric duodenum via LC-MS/MS proteomics using a QconCAT technique. Eur J Pharm Biopharm 2023; 191:68-77. [PMID: 37625656 DOI: 10.1016/j.ejpb.2023.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 08/13/2023] [Accepted: 08/18/2023] [Indexed: 08/27/2023]
Abstract
Characterising the small intestine absorptive membrane is essential to enable prediction of the systemic exposure of oral formulations. In particular, the ontogeny of key intestinal Drug Metabolising Enzymes and Transporter (DMET) proteins involved in drug disposition needs to be elucidated to allow for accurate prediction of the PK profile of drugs in the paediatric cohort. Using pinch biopsies from the paediatric duodenum (n = 36; aged 11 months to 15 years), the abundance of 21 DMET proteins and two enterocyte markers were quantified via LC-MS/MS. An established LCMS nanoflow method was translated to enable analysis on a microflow LC system, and a new stable-isotope-labelled QconCAT standard developed to enable quantification of these proteins. Villin-1 was used to standardise abundancy values. The observed abundancies and ontogeny profiles, agreed with adult LC-MS/MS-based data, and historic paediatric data obtained via western blotting. A linear trend with age was observed for duodenal CYP3A4 and CES2 only. As this work quantified peptides on a pinch biopsy coupled with a microflow method, future studies using a wider population range are very feasible. Furthermore, this DMET ontogeny data can be used to inform paediatric PBPK modelling and to enhance the understanding of oral drug absorption and gut bioavailability in paediatric populations.
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Affiliation(s)
- Jan Goelen
- School of Pharmacy, University of Birmingham, Birmingham B15 2TT, UK; Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow G4 0RE, UK
| | - Gillian Farrell
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow G4 0RE, UK
| | | | | | - Nicholas J W Rattray
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow G4 0RE, UK
| | | | - Richard D Horniblow
- School of Biomedical Science, University of Birmingham, Birmingham B15 2TT, UK
| | - Hannah K Batchelor
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow G4 0RE, UK.
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2
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Kempen T, Dahlseid T, Lauer T, Florea AC, Aase I, Cole-Dai N, Kaur S, Southworth C, Grube K, Bhandari J, Sylvester M, Schimek R, Pirok B, Rutan S, Stoll D. Characterization of a high throughput approach for large scale retention measurement in liquid chromatography. J Chromatogr A 2023; 1705:464182. [PMID: 37442072 DOI: 10.1016/j.chroma.2023.464182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/22/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023]
Abstract
Many contemporary challenges in liquid chromatography-such as the need for "smarter" method development tools, and deeper understanding of chromatographic phenomena-could be addressed more efficiently and effectively with larger volumes of experimental retention data than are available. The paucity of publicly accessible, high-quality measurements needed for the development of retention models and simulation tools has largely been due to the high cost in time and resources associated with traditional retention measurement approaches. Recently we described an approach to improve the throughput of such measurements by using very short columns (typically 5 mm), while maintaining measurement accuracy. In this paper we present a perspective on the characteristics of a dataset containing about 13,000 retention measurements obtained using this approach, and describe a different sample introduction method that is better suited to this application than the approach we used in prior work. The dataset comprises results for 35 different small molecules, nine different stationary phases, and several mobile phase compositions for each analyte/phase combination. During the acquisition of these data, we have interspersed repeated measurements of a small number of compounds for quality control purposes. The data from these measurements not only enable detection of outliers but also assessment of the repeatability and reproducibility of retention measurements over time. For retention factors greater than 1, the mean relative standard deviation (RSD) of replicate (typically n=5) measurements is 0.4%, and the standard deviation of RSDs is 0.4%. Most differences between selectivity values measured six months apart for 15 non-ionogenic compounds were in the range of +/- 1%, indicating good reproducibility. A critically important observation from these analyses is that selectivity defined as retention of a given analyte relative to the retention of a reference compound (kx/kref) is a much more consistent measure of retention over a time span of months compared to the retention factor alone. While this work and dataset also highlight the importance of stationary phase stability over time for achieving reliable retention measurements, we are nevertheless optimistic that this approach will enable the compilation of large databases (>> 10,000 measurements) of retention values over long time periods (years), which can in turn be leveraged to address some of the most important contemporary challenges in liquid chromatography. All the data discussed in the manuscript are provided as Supplemental Information.
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Affiliation(s)
- Trevor Kempen
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA
| | - Tina Dahlseid
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA
| | - Thomas Lauer
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA
| | | | - Isabella Aase
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA
| | - Nathan Cole-Dai
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA
| | - Simerjit Kaur
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA
| | | | - Kathleen Grube
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA
| | - Jos Bhandari
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA
| | - Maria Sylvester
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA
| | - Ryan Schimek
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA
| | - Bob Pirok
- Van 't Hoff Institute for Molecular Sciences, Analytical-Chemistry Group, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, the Netherlands
| | - Sarah Rutan
- Department of Chemistry, Virginia Commonwealth University, Richmond, VA 23284-2006, USA
| | - Dwight Stoll
- Gustavus Adolphus College, 800 W College Ave, St. Peter, MN 56082, USA.
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Gabriel W, Giurcoiu V, Lautenbacher L, Wilhelm M. Predicting fragment intensities and retention time of iTRAQ- and TMTPro-labeled peptides with Prosit-TMT. Proteomics 2022; 22:e2100257. [PMID: 35578405 DOI: 10.1002/pmic.202100257] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 04/22/2022] [Accepted: 05/05/2022] [Indexed: 11/08/2022]
Abstract
Isobaric labeling increases the throughput of proteomics by enabling the parallel identification and quantification of peptides and proteins. Over the past decades, a variety of isobaric tags have been developed allowing the multiplexed analysis of up to 18 samples. However, experiments utilizing such tags often exhibit reduced identification rates and thus show decreased analytical depth. Re-scoring has been shown to rescue otherwise missed identifications but was not yet systematically applied on isobarically labeled data. Because iTRAQ 4/8-plex and the recently released TMTpro 16/18-plex share similar characteristics with TMT 6/10/11-plex, we hypothesized that Prosit-TMT, trained exclusively on 6/10/11-plex labeled peptides, may be applicable to these isobaric labeling strategies as well. To investigate this, we re-analyzed nine publicly available datasets covering iTRAQ and TMTpro labeling for samples with human and mouse origin. We highlight that Prosit-TMT shows remarkably good performance when comparing experimentally acquired and predicted fragmentation spectra (R of 0.84 - 0.9) and retention times (ΔRT95% of 3 - 10% gradient time) of peptides. Furthermore, re-scoring substantially increases the number of confidently identified spectra, peptides, and proteins. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Victor Giurcoiu
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Ludwig Lautenbacher
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, Freising, Germany
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Gabriel W, The M, Zolg DP, Bayer FP, Shouman O, Lautenbacher L, Schnatbaum K, Zerweck J, Knaute T, Delanghe B, Huhmer A, Wenschuh H, Reimer U, Médard G, Kuster B, Wilhelm M. Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides. Anal Chem 2022; 94:7181-7190. [PMID: 35549156 DOI: 10.1021/acs.analchem.1c05435] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tandem mass tags (TMT) are chemical peptide labels that are coupled to free amine groups usually after protein digestion to enable the multiplexed analysis of multiple samples in bottom-up mass spectrometry. It is a standard workflow in proteomics ranging from single-cell to high-throughput proteomics. Particularly for TMT, increasing the number of confidently identified spectra is highly desirable as it provides identification and quantification information with every spectrum. Here, we report on the generation of an extensive resource of synthetic TMT-labeled peptides as part of the ProteomeTools project and present the extension of the deep learning model Prosit to accurately predict the retention time and fragment ion intensities of TMT-labeled peptides with high accuracy. Prosit-TMT supports CID and HCD fragmentation and ion trap and Orbitrap mass analyzers in a single model. Reanalysis of published TMT data sets show that this single model extracts substantial additional information. Applying Prosit-TMT, we discovered that the expression of many proteins in human breast milk follows a distinct daily cycle which may prime the newborn for nutritional or environmental cues.
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Affiliation(s)
- Wassim Gabriel
- Computational Mass Spectrometry, Technical University of Munich, 85354 Freising, Germany
| | - Matthew The
- Chair of Proteomics and Bioanalytics, Technical University of Munich, 85354 Freising, Germany
| | - Daniel P Zolg
- Chair of Proteomics and Bioanalytics, Technical University of Munich, 85354 Freising, Germany
| | - Florian P Bayer
- Chair of Proteomics and Bioanalytics, Technical University of Munich, 85354 Freising, Germany
| | - Omar Shouman
- Computational Mass Spectrometry, Technical University of Munich, 85354 Freising, Germany
| | - Ludwig Lautenbacher
- Computational Mass Spectrometry, Technical University of Munich, 85354 Freising, Germany
| | | | | | - Tobias Knaute
- JPT Peptide Technologies GmbH, 12489 Berlin, Germany
| | | | - Andreas Huhmer
- Thermo Fisher Scientific, San Jose, California 95134, United States
| | | | - Ulf Reimer
- JPT Peptide Technologies GmbH, 12489 Berlin, Germany
| | - Guillaume Médard
- Chair of Proteomics and Bioanalytics, Technical University of Munich, 85354 Freising, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, 85354 Freising, Germany.,Bavarian Center for Biomolecular Mass Spectrometry, 85354 Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich, 85354 Freising, Germany
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5
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Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat Methods 2019; 16:509-518. [DOI: 10.1038/s41592-019-0426-7] [Citation(s) in RCA: 340] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 04/18/2019] [Indexed: 11/08/2022]
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6
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Krokhin OV. Comparison of peptide retention prediction algorithm in reversed-phase chromatography. Comment on “Predictive chromatography of peptides and proteins as a complementary tool for proteomics”, by I. A. Tarasova, C. D. Masselon, A. V. Gorshkov and M. V. Gorshkov, Analyst, 2016, 141, 4816. Analyst 2017; 142:2050-2051. [DOI: 10.1039/c6an02510b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
In applied proteomics, correct use of peptide retention prediction models is vital.
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Affiliation(s)
- Oleg V. Krokhin
- Manitoba Centre for Proteomics and Systems Biology
- University of Manitoba
- Winnipeg
- Canada
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