ORCID Profile
0000-0002-3015-5850
Current Organisation
Philipps-Universität Marburg
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Publisher: American Chemical Society (ACS)
Date: 25-06-2013
DOI: 10.1021/PR400099J
Abstract: Metabolic labeling with stable isotopes is a prominent technique for comparative quantitative proteomics, and stable isotope labeling with amino acids in cell culture (SILAC) is the most commonly used approach. SILAC is, however, traditionally limited to simple tissue culture regimens and only rarely employed in the context of complex culturing conditions as those required for human embryonic stem cells (hESCs). Classic hESC culture is based on the use of mouse embryonic fibroblasts (MEFs) as a feeder layer, and as a result, possible xenogeneic contamination, contribution of unlabeled amino acids by the feeders, interlaboratory variability of MEF preparation, and the overall complexity of the culture system are all of concern in conjunction with SILAC. We demonstrate a feeder-free SILAC culture system based on a customized version of a commonly used, chemically defined hESC medium developed by Ludwig et al. and commercially available as mTeSR1 [mTeSR1 is a trade mark of WiCell (Madison, WI) licensed to STEMCELL Technologies (Vancouver, Canada)]. This medium, together with adjustments to the culturing protocol, facilitates reproducible labeling that is easily scalable to the protein amounts required by proteomic work flows. It greatly enhances the usability of quantitative proteomics as a tool for the study of mechanisms underlying hESCs differentiation and self-renewal. Associated data have been deposited to the ProteomeXchange with the identifier PXD000151.
Publisher: Elsevier BV
Date: 10-2019
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 07-2021
Abstract: Studies on the plasma proteome of renal function have identified several biomarkers, but have lacked replication, were limited to European populations, and/or did not investigate causality with eGFR. Among four cohorts in a transethnic cross-sectional study, 57 plasma proteins were associated with eGFR, 23 of them also with CKD. Furthermore, Mendelian randomization and gene expression analyses in kidney tissue highlighted testican-2 as a physiological marker of kidney disease progression with potential clinical relevance, and identified a few additional proteins warranting further investigation. Studies on the relationship between renal function and the human plasma proteome have identified several potential biomarkers. However, investigations have been conducted largely in European populations, and causality of the associations between plasma proteins and kidney function has never been addressed. A cross-sectional study of 993 plasma proteins among 2882 participants in four studies of European and admixed ancestries (KORA, INTERVAL, HUNT, QMDiab) identified transethnic associations between eGFR/CKD and proteomic biomarkers. For the replicated associations, two-s le bidirectional Mendelian randomization (MR) was used to investigate potential causal relationships. Publicly available datasets and transcriptomic data from independent studies were used to examine the association between gene expression in kidney tissue and eGFR. In total, 57 plasma proteins were associated with eGFR, including one novel protein. Of these, 23 were additionally associated with CKD. The strongest inferred causal effect was the positive effect of eGFR on testican-2, in line with the known biological role of this protein and the expression of its protein-coding gene ( SPOCK2 ) in renal tissue. We also observed suggestive evidence of an effect of melanoma inhibitory activity (MIA), carbonic anhydrase III, and cystatin-M on eGFR. In a discovery-replication setting, we identified 57 proteins transethnically associated with eGFR. The revealed causal relationships are an important stepping stone in establishing testican-2 as a clinically relevant physiological marker of kidney disease progression, and point to additional proteins warranting further investigation.
Publisher: eLife Sciences Publications, Ltd
Date: 13-01-2022
DOI: 10.7554/ELIFE.71802
Abstract: Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent s le (Generation Scotland n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.
Publisher: Cold Spring Harbor Laboratory
Date: 02-12-2020
DOI: 10.1101/2020.12.01.404681
Abstract: Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNAm signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent s le, (Generation Scotland n=9,537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore – disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.
Location: United States of America
Location: United States of America
No related grants have been discovered for Johannes Graumann.