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Dusart, P., Fagerberg, L., Perisic, L., Civelek, M., Struck, E., Hedin, U., . . . Butler, L. . (2018). A systems-approach reveals human nestin is an endothelial-enriched, angiogenesis-independent intermediate filament protein. Scientific Reports, 8(1), Article ID 14668.
Open this publication in new window or tab >>A systems-approach reveals human nestin is an endothelial-enriched, angiogenesis-independent intermediate filament protein
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2018 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, no 1, article id 14668Article in journal (Refereed) Published
Abstract [en]

The intermediate filament protein nestin is expressed during embryonic development, but considered largely restricted to areas of regeneration in the adult. Here, we perform a body-wide transcriptome and protein-profiling analysis to reveal that nestin is constitutively, and highly-selectively, expressed in adult human endothelial cells (EC), independent of proliferative status. Correspondingly, we demonstrate that it is not a marker for tumour EC in multiple malignancy types. Imaging of EC from different vascular beds reveals nestin subcellular distribution is shear-modulated. siRNA inhibition of nestin increases EC proliferation, and nestin expression is reduced in atherosclerotic plaque neovessels. eQTL analysis reveals an association between SNPs linked to cardiovascular disease and reduced aortic EC nestin mRNA expression. Our study challenges the dogma that nestin is a marker of proliferation, and provides insight into its regulation and function in EC. Furthermore, our systems-based approach can be applied to investigate body-wide expression profiles of any candidate protein. 

Place, publisher, year, edition, pages
Nature Publishing Group, 2018
National Category
Biological Sciences
Identifiers
urn:nbn:se:kth:diva-236564 (URN)10.1038/s41598-018-32859-4 (DOI)000446034000069 ()2-s2.0-85054173189 (Scopus ID)
Note

Export Date: 22 October 2018; Article; Correspondence Address: Butler, L.M.; Science for Life Laboratory, School of Biotechnology, Kungliga Tekniska Högskolan (KTH) Royal Institute of TechnologySweden; email: Lynn.Butler@ki.se. QC 20181127

Available from: 2018-11-27 Created: 2018-11-27 Last updated: 2018-11-27Bibliographically approved
Turanli, B., Grotli, M., Boren, J., Nielsen, J., Uhlén, M., Arga, K. Y. & Mardinoglu, A. (2018). Drug Repositioning for Effective Prostate Cancer Treatment. Frontiers in Physiology, 9, Article ID 500.
Open this publication in new window or tab >>Drug Repositioning for Effective Prostate Cancer Treatment
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2018 (English)In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 500Article, review/survey (Refereed) Published
Abstract [en]

Drug repositioning has gained attention from both academia and pharmaceutical companies as an auxiliary process to conventional drug discovery. Chemotherapeutic agents have notorious adverse effects that drastically reduce the life quality of cancer patients so drug repositioning is a promising strategy to identify non-cancer drugs which have anti-cancer activity as well as tolerable adverse effects for human health. There are various strategies for discovery and validation of repurposed drugs. In this review, 25 repurposed drug candidates are presented as result of different strategies, 15 of which are already under clinical investigation for treatment of prostate cancer (PCa). To date, zoledronic acid is the only repurposed, clinically used, and approved non-cancer drug for PCa. Anti-cancer activities of existing drugs presented in this review cover diverse and also known mechanisms such as inhibition of mTOR and VEGFR2 signaling, inhibition of PI3K/Akt signaling, COX and selective COX-2 inhibition, NF-kappa B inhibition, Wnt/beta - Catenin pathway inhibition, DNMT1 inhibition, and GSK-3 beta inhibition. In addition to monotherapy option, combination therapy with current anti-cancer drugs may also increase drug efficacy and reduce adverse effects. Thus, drug repositioning may become a key approach for drug discovery in terms of time- and cost-efficiency comparing to conventional drug discovery and development process.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2018
Keywords
prostate cancer, drug repositioning, non-cancer therapeutics, repurposing, approved drugs
National Category
Physiology
Identifiers
urn:nbn:se:kth:diva-229015 (URN)10.3389/fphys.2018.00500 (DOI)000432407100001 ()2-s2.0-85047004631 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceKnut and Alice Wallenberg Foundation
Note

QC 20180531

Available from: 2018-05-31 Created: 2018-05-31 Last updated: 2018-05-31Bibliographically approved
Edfors, F., Hober, A., Linderbäck, K., Maddalo, G., Azimi, A., Sivertsson, Å., . . . Uhlén, M. (2018). Enhanced validation of antibodies for research applications. Nature Communications, 9, Article ID 4130.
Open this publication in new window or tab >>Enhanced validation of antibodies for research applications
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2018 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 9, article id 4130Article in journal (Refereed) Published
Abstract [en]

There is a need for standardized validation methods for antibody specificity and selectivity. Recently, five alternative validation pillars were proposed to explore the specificity of research antibodies using methods with no need for prior knowledge about the protein target. Here, we show that these principles can be used in a streamlined manner for enhanced validation of research antibodies in Western blot applications. More than 6,000 antibodies were validated with at least one of these strategies involving orthogonal methods, genetic knockdown, recombinant expression, independent antibodies, and capture mass spectrometry analysis. The results show a path forward for efforts to validate antibodies in an application-specific manner suitable for both providers and users.

Place, publisher, year, edition, pages
Nature Publishing Group, 2018
National Category
Immunology in the medical area
Identifiers
urn:nbn:se:kth:diva-237096 (URN)10.1038/s41467-018-06642-y (DOI)000446566000016 ()30297845 (PubMedID)2-s2.0-85054574300 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceKnut and Alice Wallenberg Foundation
Note

QC 20181030

Available from: 2018-10-30 Created: 2018-10-30 Last updated: 2018-10-30Bibliographically approved
Jahn, M., Vialas, V., Karlsen, J., Maddalo, G., Edfors, F., Forsström, B., . . . Hudson, E. P. (2018). Growth of Cyanobacteria Is Constrained by the Abundance of Light and Carbon Assimilation Proteins. Cell reports, 25(2), 478-+
Open this publication in new window or tab >>Growth of Cyanobacteria Is Constrained by the Abundance of Light and Carbon Assimilation Proteins
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2018 (English)In: Cell reports, ISSN 2211-1247, E-ISSN 2211-1247, Vol. 25, no 2, p. 478-+Article in journal (Refereed) Published
Abstract [en]

Cyanobacteria must balance separate demands for energy generation, carbon assimilation, and biomass synthesis. We used shotgun proteomics to investigate proteome allocation strategies in the model cyanobacterium Synechocystis sp. PCC 6803 as it adapted to light and inorganic carbon (C-i) limitation. When partitioning the proteome into seven functional sectors, we find that sector sizes change linearly with growth rate. The sector encompassing ribosomes is significantly smaller than in E. coli, which may explain the lower maximum growth rate in Synechocystis. Limitation of light dramatically affects multiple proteome sectors, whereas the effect of C-i limitation is weak. Carbon assimilation proteins respond more strongly to changes in light intensity than to C-i. A coarse-grained cell economy model generally explains proteome trends. However, deviations from model predictions suggest that the large proteome sectors for carbon and light assimilation are not optimally utilized under some growth conditions and may constrain the proteome space available to ribosomes.

Place, publisher, year, edition, pages
et al., 2018
National Category
Physical Sciences
Identifiers
urn:nbn:se:kth:diva-237095 (URN)10.1016/j.celrep.2018.09.040 (DOI)000446691400020 ()30304686 (PubMedID)2-s2.0-85054193580& (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceSwedish Research Council Formas, 2015-939Swedish Research CouncilSwedish Foundation for Strategic Research , RBP14-0013
Note

QC 20181029

Available from: 2018-10-29 Created: 2018-10-29 Last updated: 2018-10-29Bibliographically approved
Zieba, A., Ponten, F., Uhlén, M. & Landegren, U. (2018). In situ protein detection with enhanced specificity using DNA-conjugated antibodies and proximity ligation. Modern Pathology, 31(2), 253-263
Open this publication in new window or tab >>In situ protein detection with enhanced specificity using DNA-conjugated antibodies and proximity ligation
2018 (English)In: Modern Pathology, ISSN 0893-3952, E-ISSN 1530-0285, Vol. 31, no 2, p. 253-263Article in journal (Refereed) Published
Abstract [en]

Antibodies are important tools in anatomical pathology and research, but the quality of in situ protein detection by immunohistochemistry greatly depends on the choice of antibodies and the abundance of the targeted proteins. Many antibodies used in scientific research do not meet requirements for specificity and sensitivity. Accordingly, methods that improve antibody performance and produce quantitative data can greatly advance both scientific investigations and clinical diagnostics based on protein expression and in situ localization. We demonstrate here protocols for antibody labeling that allow specific protein detection in tissues via bright-field in situ proximity ligation assays, where each protein molecule must be recognized by two antibodies. We further demonstrate that single polyclonal antibodies or purified serum preparations can be used for these dual recognition assays. The requirement for protein recognition by pairs of antibody conjugates can significantly improve specificity of protein detection over single-binder assays.

Place, publisher, year, edition, pages
Nature Publishing Group, 2018
Keywords
antibody conjugate, APEX 1 protein, calvasculin, DNA conjugated antibody, immunoglobulin G antibody, polyclonal antibody, protein, protein A, protein G, rabbit antiserum, reagent, trefoil factor 1, unclassified drug, animal tissue, antibody affinity, antibody labeling, antibody specificity, Article, assay, click chemistry, controlled study, DNA strand, genetic transcription, immunohistochemistry, in situ proximity ligation assay, limit of detection, mRNA expression level, nonhuman, priority journal, protein analysis, protein expression, protein expression level, protein purification, tissue microarray, tissue section
National Category
Biological Sciences
Identifiers
urn:nbn:se:kth:diva-227448 (URN)10.1038/modpathol.2017.102 (DOI)000424761400003 ()2-s2.0-85041805855 (Scopus ID)
Note

Export Date: 9 May 2018; Article; CODEN: MODPE; Correspondence Address: Landegren, U.; Department of Immunology, Genetics and Pathology, Science for Life Laboratory, BMC, Uppsala University, Husargatan 3, Sweden; email: Ulf.Landegren@igp.uu.se; Funding details: VINNOVA; Funding details: 222635; Funding details: 241481; Funding details: NCI, National Cancer Institute; Funding details: #2008:0143, Knut och Alice Wallenbergs Stiftelse; Funding details: FP5, Fifth Framework Programme; Funding details: FP/2007– 2013, FP7, Seventh Framework Programme; Funding details: ERC, European Research Council; Funding details: TRC, The Research Council; Funding details: 294409, ERC, European Research Council; Funding details: IngaBritt och Arne Lundbergs Forskningsstiftelse; Funding details: Uppsala Universitet; Funding text: This work was supported by the Knut and Alice Wallenberg Foundation (#2008:0143), the European Community's 7th Framework Program (FP7/2007–2013) under grant agreement n° 222635 (AffinityProteome) 241481 (Affinomics), The Swedish Research Council, Swedish Governmental Agency for Innovation Systems, IngaBritt and Arne Lundberg Foundation, the European Research Council under the European Union's Seventh Framework Programme (FP/2007– 2013) / ERC Grant Agreement n. 294409 (ProteinSeq), and Uppsala University. UL holds stock in Olink, having rights to the in situ proximity ligation assay technology. We would also like to thank Tara Hiltke at the National Cancer Institute for providing mAbs for in situ proximity ligation assay experiments. QC 20180528

Available from: 2018-05-28 Created: 2018-05-28 Last updated: 2018-05-28Bibliographically approved
Sjöstedt, E., Sivertsson, Å., Norradin, F. H., Katona, B., Näsström, Å., Vuu, J., . . . Lindskog, C. (2018). Integration of Transcriptomics and Antibody-Based Proteomics for Exploration of Proteins Expressed in Specialized Tissues. Journal of Proteome Research, 17(12), 4127-4137
Open this publication in new window or tab >>Integration of Transcriptomics and Antibody-Based Proteomics for Exploration of Proteins Expressed in Specialized Tissues
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2018 (English)In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 17, no 12, p. 4127-4137Article in journal (Refereed) Published
Abstract [en]

A large portion of human proteins are referred to as missing proteins, defined as protein-coding genes that lack experimental data on the protein level due to factors such as temporal expression, expression in tissues that are difficult to sample, or they actually do not encode functional proteins. In the present investigation, an integrated omics approach was used for identification and exploration of missing proteins. Transcriptomics data from three different sourcesthe Human Protein Atlas (HPA), the GTEx consortium, and the FANTOM5 consortiumwere used as a starting point to identify genes selectively expressed in specialized tissues. Complementing the analysis with profiling on more specific tissues based on immunohistochemistry allowed for further exploration of cell-type-specific expression patterns. More detailed tissue profiling was performed for >300 genes on complementing tissues. The analysis identified tissue-specific expression of nine proteins previously listed as missing proteins (POU4F1, FRMD1, ARHGEF33, GABRG1, KRTAP2-1, BHLHE22, SPRR4, AVPR1B, and DCLK3), as well as numerous proteins with evidence of existence on the protein level that previously lacked information on spatial resolution and cell-type- specific expression pattern. We here present a comprehensive strategy for identification of missing proteins by combining transcriptomics with antibody-based proteomics. The analyzed proteins provide interesting targets for organ-specific research in health and disease.

Place, publisher, year, edition, pages
American Chemical Society (ACS), 2018
Keywords
missing proteins, transcriptomics, proteomics, protein localization, immunohistochemistry, antibodies, tissue profiling
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy) Biological Sciences
Research subject
Biotechnology
Identifiers
urn:nbn:se:kth:diva-236474 (URN)10.1021/acs.jproteome.8b00406 (DOI)000452930000010 ()30272454 (PubMedID)2-s2.0-85055105364 (Scopus ID)
Note

QC 20181018

Available from: 2018-10-17 Created: 2018-10-17 Last updated: 2019-01-11Bibliographically approved
Reuterswärd, P., Bergström, S., Orikiiriza, J., Lindquist, E., Bergström, S., Svahn Andersson, H., . . . Nilsson, P. (2018). Levels of human proteins in plasma associated with acute paediatric malaria. Malaria Journal, 17, Article ID 426.
Open this publication in new window or tab >>Levels of human proteins in plasma associated with acute paediatric malaria
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2018 (English)In: Malaria Journal, ISSN 1475-2875, E-ISSN 1475-2875, Vol. 17, article id 426Article in journal (Refereed) Published
Abstract [en]

Background: The intimate interaction between the pathophysiology of the human host and the biology of the Plasmodium falciparum parasite results in a wide spectrum of disease outcomes in malaria. Development of severe disease is associated with a progressively augmented imbalance in pro- and anti-inflammatory responses to high parasite loads and sequestration of parasitized erythrocytes. Although these phenomena collectively constitute common denominators for the wide variety of discrete severe malaria manifestations, the mechanistic rationales behind discrepancies in outcome are poorly understood. Exploration of the human pathophysiological response by variations in protein profiles in plasma presents an excellent opportunity to increase the understanding. This is ultimately required for better prediction, prevention and treatment of malaria, which is essential for ongoing elimination and eradication efforts. Results: An affinity proteomics approach was used to analyse 541 paediatric plasma samples collected from community controls and patients with mild or severe malaria in Rwanda. Protein profiles were generated with an antibody-based suspension bead array containing 255 antibodies targetting 115 human proteins. Here, 57 proteins were identified with significantly altered levels (adjusted p-values<0.001) in patients with malaria compared to controls. From these, the 27 most significant proteins (adjusted p-values<10(-14)) were selected for a stringent analysis approach. Here, 24 proteins showed elevated levels in malaria patients and included proteins involved in acute inflammatory response as well as cell adhesion. The remaining three proteins, also implicated in immune regulation and cellular adhesivity, displayed lower abundance in malaria patients. In addition, 37 proteins (adjusted p-values<0.05) were identified with increased levels in patients with severe compared to mild malaria. This set includes, proteins involved in tissue remodelling and erythrocyte membrane proteins. Collectively, this approach has been successfully used to identify proteins both with known and unknown association with different stages of malaria. Conclusion: In this study, a high-throughput affinity proteomics approach was used to find protein profiles in plasma linked to P. falciparum infection and malaria disease progression. The proteins presented herein are mainly involved in inflammatory response, cellular adhesion and as constituents of erythrocyte membrane. These findings have a great potential to provide increased conceptual understanding of host-parasite interaction and malaria pathogenesis.

Place, publisher, year, edition, pages
BMC, 2018
Keywords
Affinity proteomics, Human plasma profiling, Malaria, Plasmodium falciparum, suspension bead arrays, Sequestration, Cytoadhesion
National Category
Medical Biotechnology
Identifiers
urn:nbn:se:kth:diva-239769 (URN)10.1186/s12936-018-2576-y (DOI)000450509700002 ()30442134 (PubMedID)2-s2.0-85056636195 (Scopus ID)
Note

QC 20190109

Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2019-01-09Bibliographically approved
Bidkhori, G., Benfeitas, R., Elmas, E., Kararoudi, M. N., Arif, M., Uhlén, M., . . . Mardinoglu, A. (2018). Metabolic Network-Based Identification and Prioritization o f Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma. Frontiers in Physiology, 9, Article ID 916.
Open this publication in new window or tab >>Metabolic Network-Based Identification and Prioritization o f Anticancer Targets Based on Expression Data in Hepatocellular Carcinoma
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2018 (English)In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 9, article id 916Article in journal (Refereed) Published
Abstract [en]

Hepatocellular carcinoma (HCC) is a deadly form of liver cancer with high mortality worldwide. Unfortunately, the large heterogeneity of this disease makes it difficult to develop effective treatment strategies. Cellular network analyses have been employed to study heterogeneity in cancer, and to identify potential therapeutic targets. However, the existing approaches do not consider metabolic growth requirements, i.e., biological network functionality, to rank candidate targets while preventing toxicity to non-cancerous tissues. Here, we developed an algorithm to overcome these issues based on integration of gene expression data, genome-scale metabolic models, network controllability, and dispensability, as well as toxicity analysis. This method thus predicts and ranks potential anticancer non-toxic controlling metabolite and gene targets. Our algorithm encompasses both objective-driven and-independent tasks, and uses network topology to finally rank the predicted therapeutic targets. We employed this algorithm to the analysis of transcriptomic data for 50 HCC patients with both cancerous and non-cancerous samples. We identified several potential targets that would prevent cell growth, including 74 anticancer metabolites, and 3 gene targets (PRKACA, PGS1, and CRLS1). The predicted anticancer metabolites showed good agreement with existing FDA-approved cancer drugs, and the 3 genes were experimentally validated by performing experiments in HepG2 and Hep3B liver cancer cell lines. Our observations indicate that our novel approach successfully identifies therapeutic targets for effective treatment of cancer. This approach may also be applied to any cancer type that has tumor and non-tumor gene or protein expression data.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2018
Keywords
hepatocellular carcinoma, genome-scale metabolic model, network analysis, biological networks, cancer, gene expression, protein expression, systems biology and network biology
National Category
Physiology
Identifiers
urn:nbn:se:kth:diva-232768 (URN)10.3389/fphys.2018.00916 (DOI)000438974200001 ()2-s2.0-85050120958 (Scopus ID)
Funder
Knut and Alice Wallenberg FoundationScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20180807

Available from: 2018-08-06 Created: 2018-08-06 Last updated: 2018-11-23Bibliographically approved
Bidkhori, G., Benfeitas, R., Klevstig, M., Zhang, C., Nielsen, J., Uhlén, M., . . . Mardinoglu, A. (2018). Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes. Proceedings of the National Academy of Sciences of the United States of America, 115(50), E11874-E11883
Open this publication in new window or tab >>Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes
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2018 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 115, no 50, p. E11874-E11883Article in journal (Refereed) Published
Abstract [en]

Hepatocellular carcinoma (HCC) is one of the most frequent forms of liver cancer, and effective treatment methods are limited due to tumor heterogeneity. There is a great need for comprehensive approaches to stratify HCC patients, gain biological insights into subtypes, and ultimately identify effective therapeutic targets. We stratified HCC patients and characterized each subtype using transcriptomics data, genome-scale metabolic networks and network topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differences in metabolic and signaling pathways reflecting at genomic, transcriptomic, and proteomic levels. These subtypes showed large differences in clinical survival associated with altered kynurenine metabolism, WNT/beta-catenin-associated lipid metabolism, and PI3K/AKT/mTOR signaling. Integrative analyses indicated that the three subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to catalyze the same reactions. Based on systems-level analysis, we identified 8 to 28 subtype-specific genes with pivotal roles in controlling the metabolic network and predicted that these genes may be targeted for development of treatment strategies for HCC subtypes by performing in silico analysis. To validate our predictions, we performed experiments using HepG2 cells under normoxic and hypoxic conditions and observed opposite expression patterns between genes expressed in high/moderate/low-survival tumor groups in response to hypoxia, reflecting activated hypoxic behavior in patients with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic network-driven approach, which may also be applied to other cancer types, and this stratification may have clinical implications to drive the development of precision medicine.

Place, publisher, year, edition, pages
National Academy of Sciences, 2018
Keywords
hepatocellular carcinoma, biological networks, personalized medicine, genome-scale metabolic models, systems biology
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kth:diva-240725 (URN)10.1073/pnas.1807305115 (DOI)000452866000035 ()30482855 (PubMedID)2-s2.0-85058364905 (Scopus ID)
Funder
Knut and Alice Wallenberg Foundation
Note

QC 20190109

Available from: 2019-01-09 Created: 2019-01-09 Last updated: 2019-01-09Bibliographically approved
Lee, C.-Y., Wang, D., Wilhelm, M., Zolg, D. P., Schmidt, T., Schnatbaum, K., . . . Kuster, B. (2018). Mining the Human Tissue Proteome for Protein Citrullination. Molecular & Cellular Proteomics, 17(7), 1378-1391
Open this publication in new window or tab >>Mining the Human Tissue Proteome for Protein Citrullination
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2018 (English)In: Molecular & Cellular Proteomics, ISSN 1535-9476, E-ISSN 1535-9484, Vol. 17, no 7, p. 1378-1391Article in journal (Refereed) Published
Abstract [en]

Citrullination is a posttranslational modification of arginine catalyzed by five peptidylarginine deiminases (PADs) in humans. The loss of a positive charge may cause structural or functional alterations, and while the modification has been linked to several diseases, including rheumatoid arthritis (RA) and cancer, its physiological or pathophysiological roles remain largely unclear. In part, this is owing to limitations in available methodology to robustly enrich, detect, and localize the modification. As a result, only a few citrullination sites have been identified on human proteins with high confidence. In this study, we mined data from mass-spectrometry-based deep proteomic profiling of 30 human tissues to identify citrullination sites on endogenous proteins. Database searching of similar to 70 million tandem mass spectra yielded similar to 13,000 candidate spectra, which were further triaged by spectrum quality metrics and the detection of the specific neutral loss of isocyanic acid from citrullinated peptides to reduce false positives. Because citrullination is easily confused with deamidation, we synthetized similar to 2,200 citrullinated and 1,300 deamidated peptides to build a library of reference spectra. This led to the validation of 375 citrullination sites on 209 human proteins. Further analysis showed that >80% of the identified modifications sites were new, and for 56% of the proteins, citrullination was detected for the first time. Sequence motif analysis revealed a strong preference for Asp and Gly, residues around the citrullination site. Interestingly, while the modification was detected in 26 human tissues with the highest levels found in the brain and lung, citrullination levels did not correlate well with protein expression of the PAD enzymes. Even though the current work represents the largest survey of protein citrullination to date, the modification was mostly detected on high abundant proteins, arguing that the development of specific enrichment methods would be required in order to study the full extent of cellular protein citrullination.

Place, publisher, year, edition, pages
AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC, 2018
Keywords
Post-translational modifications, Tissues, Data evaluation, Omics, Tandem Mass Spectrometry, citrullination, human proteome, peptidylarginine deiminase, synthetic peptides
National Category
Biochemistry and Molecular Biology
Identifiers
urn:nbn:se:kth:diva-232406 (URN)10.1074/mcp.RA118.000696 (DOI)000437410300010 ()29610271 (PubMedID)2-s2.0-85049241062 (Scopus ID)
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20170726

Available from: 2018-07-26 Created: 2018-07-26 Last updated: 2018-07-26Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-8993-048X

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