In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta
- Karolinska Institute, Sweden
- KTH Royal Institute of Technology, Sweden
Abstract
Here we present a method for in-depth human plasma proteome analysis based on high-resolution isoelectric focusing HiRIEF LC-MS/MS, demonstrating high proteome coverage, reproducibility and the potential for liquid biopsy protein profiling. By integrating genomic sequence information to the MS-based plasma proteome analysis we enable detection of single amino acid variants and for the first time demonstrate transfer of multiple protein variants between mother and fetus across the placenta. We further show that our method has the ability to detect both low abundance tissue-annotated proteins and phosphorylated proteins in plasma, as well as quantitate differences in plasma proteomes between the mother and the newborn as well as changes related to pregnancy.
Data availability
MS raw data are available via ProteomeXchange with identifier PXD010899.
-
Plasma HiRIEFProteomeXchange, PXD010899.
Article and author information
Author details
Funding
Vetenskapsrådet
- Maria Pernemalm
- AnnSofi Sandberg
- Yafeng Zhu
- Jorrit Boekel
- Claes-Göran Östenson
- Janne Lehtiö
Stiftelsen Olle Engkvist Byggmästare
- Claes-Göran Östenson
Cancerfonden
- Maria Pernemalm
- Janne Lehtiö
Stiftelsen för Strategisk Forskning
- Maria Pernemalm
- AnnSofi Sandberg
- Yafeng Zhu
- Janne Lehtiö
Horizon 2020 Framework Programme
- Maria Pernemalm
- Janne Lehtiö
Familjen Erling-Perssons Stiftelse
- Janne Lehtiö
Barncancerfonden
- Janne Lehtiö
Stockholms Läns Landsting
- Claes-Göran Östenson
the Swedish Council for Working Life and Social Research
- Claes-Göran Östenson
Swedish Diabetes Foundation
- Claes-Göran Östenson
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: The plasma collections were approved by local ethics boards and all participants signed informed consent. The approval identifiers for the corresponding studies are as follows; Healthy normals Dnr 91:164 for men and Dnr 95:298 for women, mother/child 2014/1622-31/2 and female longitudinal 2008/915-31/4.
Copyright
© 2019, Pernemalm et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 11,155
- views
-
- 988
- downloads
-
- 62
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta
eLife 8:e41608.
https://doi.org/10.7554/eLife.41608
Further reading
-
- Computational and Systems Biology
Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.
-
- Computational and Systems Biology
- Microbiology and Infectious Disease
Antimicrobial peptides (AMPs) are attractive candidates to combat antibiotic resistance for their capability to target biomembranes and restrict a wide range of pathogens. It is a daunting challenge to discover novel AMPs due to their sparse distributions in a vast peptide universe, especially for peptides that demonstrate potencies for both bacterial membranes and viral envelopes. Here, we establish a de novo AMP design framework by bridging a deep generative module and a graph-encoding activity regressor. The generative module learns hidden ‘grammars’ of AMP features and produces candidates sequentially pass antimicrobial predictor and antiviral classifiers. We discovered 16 bifunctional AMPs and experimentally validated their abilities to inhibit a spectrum of pathogens in vitro and in animal models. Notably, P076 is a highly potent bactericide with the minimal inhibitory concentration of 0.21 μM against multidrug-resistant Acinetobacter baumannii, while P002 broadly inhibits five enveloped viruses. Our study provides feasible means to uncover the sequences that simultaneously encode antimicrobial and antiviral activities, thus bolstering the function spectra of AMPs to combat a wide range of drug-resistant infections.
