| 1. |
- Kampf, Caroline, et al.
(författare)
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The human liver-specific proteome defined by transcriptomics and antibody-based profiling
- 2014
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Ingår i: FASEB Journal. - : Wiley. - 1530-6860 .- 0892-6638. ; 28:7, s. 2901-2914
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Tidskriftsartikel (refereegranskat)abstract
- Human liver physiology and the genetic etiology of the liver diseases can potentially be elucidated through the identification of proteins with enriched expression in the liver. Here, we combined data from RNA sequencing (RNA-Seq) and antibody-based immunohistochemistry across all major human tissues to explore the human liver proteome with enriched expression, as well as the cell type-enriched expression in hepatocyte and bile duct cells. We identified in total 477 protein-coding genes with elevated expression in the liver: 179 genes have higher expression as compared to all the other analyzed tissues; 164 genes have elevated transcript levels in the liver shared with at least one other tissue type; and an additional 134 genes have a mild level of increased expression in the liver. We identified the precise localization of these proteins through antibody-based protein profiling and the subcellular localization of these proteins through immunofluorescent-based profiling. We also identified the biological processes and metabolic functions associated with these proteins, investigated their contribution in the occurrence of liver diseases, and identified potential targets for their treatment. Our study demonstrates the use of RNA-Seq and antibody-based immunohistochemistry for characterizing the human liver proteome, as well as the use of tissue-specific proteins in identification of novel drug targets and discovery of biomarkers.
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| 2. |
- Robinson, Jonathan, 1986, et al.
(författare)
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An atlas of human metabolism
- 2020
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Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 13:624
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Tidskriftsartikel (refereegranskat)abstract
- Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.
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| 3. |
- Uhlén, Mathias, et al.
(författare)
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The human secretome
- 2019
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Ingår i: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 12:609
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Tidskriftsartikel (refereegranskat)abstract
- The proteins secreted by human cells (collectively referred to as the secretome) are important not only for the basic understanding of human biology but also for the identification of potential targets for future diagnostics and therapies. Here, we present a comprehensive analysis of proteins predicted to be secreted in human cells, which provides information about their final localization in the human body, including the proteins actively secreted to peripheral blood. The analysis suggests that a large number of the proteins of the secretome are not secreted out of the cell, but instead are retained intracellularly, whereas another large group of proteins were identified that are predicted to be retained locally at the tissue of expression and not secreted into the blood. Proteins detected in the human blood by mass spectrometry-based proteomics and antibody-based immuno-assays are also presented with estimates of their concentrations in the blood. The results are presented in an updated version 19 of the Human Protein Atlas in which each gene encoding a secretome protein is annotated to provide an open-access knowledge resource of the human secretome, including body-wide expression data, spatial localization data down to the single-cell and subcellular levels, and data about the presence of proteins that are detectable in the blood.
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| 4. |
- Lam, S., et al.
(författare)
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Addressing the heterogeneity in liver diseases using biological networks
- 2021
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Ingår i: Briefings in Bioinformatics. - : Oxford University Press (OUP). - 1467-5463 .- 1477-4054. ; 22:2, s. 1751-1766
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Tidskriftsartikel (refereegranskat)abstract
- The abnormalities in human metabolism have been implicated in the progression of several complex human diseases, including certain cancers. Hence, deciphering the underlying molecular mechanisms associated with metabolic reprogramming in a disease state can greatly assist in elucidating the disease aetiology. An invaluable tool for establishing connections between global metabolic reprogramming and disease development is the genome-scale metabolic model (GEM). Here, we review recent work on the reconstruction of cell/tissue-type and cancer-specific GEMs and their use in identifying metabolic changes occurring in response to liver disease development, stratification of the heterogeneous disease population and discovery of novel drug targets and biomarkers. We also discuss how GEMs can be integrated with other biological networks for generating more comprehensive cell/tissue models. In addition, we review the various biological network analyses that have been employed for the development of efficient treatment strategies. Finally, we present three case studies in which independent studies converged on conclusions underlying liver disease.
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| 5. |
- Gloriam, David E., et al.
(författare)
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A Community Standard Format for the Representation of Protein Affinity Reagents
- 2010
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Ingår i: Molecular & Cellular Proteomics. - 1535-9476 .- 1535-9484. ; 9:1, s. 1-10
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Tidskriftsartikel (refereegranskat)abstract
- Protein affinity reagents (PARs), most commonly antibodies, are essential reagents for protein characterization in basic research, biotechnology, and diagnostics as well as the fastest growing class of therapeutics. Large numbers of PARs are available commercially; however, their quality is often uncertain. In addition, currently available PARs cover only a fraction of the human proteome, and their cost is prohibitive for proteome scale applications. This situation has triggered several initiatives involving large scale generation and validation of antibodies, for example the Swedish Human Protein Atlas and the German Antibody Factory. Antibodies targeting specific subproteomes are being pursued by members of Human Proteome Organisation (plasma and liver proteome projects) and the United States National Cancer Institute (cancer-associated antigens). ProteomeBinders, a European consortium, aims to set up a resource of consistently quality-controlled protein-binding reagents for the whole human proteome. An ultimate PAR database resource would allow consumers to visit one online warehouse and find all available affinity reagents from different providers together with documentation that facilitates easy comparison of their cost and quality. However, in contrast to, for example, nucleotide databases among which data are synchronized between the major data providers, current PAR producers, quality control centers, and commercial companies all use incompatible formats, hindering data exchange. Here we propose Proteomics Standards Initiative (PSI)-PAR as a global community standard format for the representation and exchange of protein affinity reagent data. The PSI-PAR format is maintained by the Human Proteome Organisation PSI and was developed within the context of ProteomeBinders by building on a mature proteomics standard format, PSI-molecular interaction, which is a widely accepted and established community standard for molecular interaction data. Further information and documentation are available on the PSI-PAR web site. Molecular & Cellular Proteomics 9: 1-10, 2010.
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| 6. |
- Mardinoglu, Adil, 1982, et al.
(författare)
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Personal model-assisted identification of NAD(+) and glutathione metabolism as intervention target in NAFLD
- 2017
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 13:3
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Tidskriftsartikel (refereegranskat)abstract
- To elucidate the molecular mechanisms underlying non-alcoholic fatty liver disease (NAFLD), we recruited 86 subjects with varying degrees of hepatic steatosis (HS). We obtained experimental data on lipoprotein fluxes and used these individual measurements as personalized constraints of a hepatocyte genome-scale metabolic model to investigate metabolic differences in liver, taking into account its interactions with other tissues. Our systems level analysis predicted an altered demand for NAD(+) and glutathione (GSH) in subjects with high HS. Our analysis and metabolomic measurements showed that plasma levels of glycine, serine, and associated metabolites are negatively correlated with HS, suggesting that these GSH metabolism precursors might be limiting. Quantification of the hepatic expression levels of the associated enzymes further pointed to altered de novo GSH synthesis. To assess the effect of GSH and NAD(+) repletion on the development of NAFLD, we added precursors for GSH and NAD(+) biosynthesis to the Western diet and demonstrated that supplementation prevents HS in mice. In a proof-of-concept human study, we found improved liver function and decreased HS after supplementation with serine (a precursor to glycine) and hereby propose a strategy for NAFLD treatment.
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| 7. |
- Lee, Sunjae, et al.
(författare)
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TCSBN: a database of tissue and cancer specific biological networks
- 2018
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 46:D1
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Tidskriftsartikel (refereegranskat)abstract
- Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integratingmetabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.
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| 8. |
- Zhang, C., et al.
(författare)
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The acute effect of metabolic cofactor supplementation: a potential therapeutic strategy against non-alcoholic fatty liver disease
- 2020
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 16:4
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Tidskriftsartikel (refereegranskat)abstract
- The prevalence of non-alcoholic fatty liver disease (NAFLD) continues to increase dramatically, and there is no approved medication for its treatment. Recently, we predicted the underlying molecular mechanisms involved in the progression of NAFLD using network analysis and identified metabolic cofactors that might be beneficial as supplements to decrease human liver fat. Here, we first assessed the tolerability of the combined metabolic cofactors including l-serine, N-acetyl-l-cysteine (NAC), nicotinamide riboside (NR), and l-carnitine by performing a 7-day rat toxicology study. Second, we performed a human calibration study by supplementing combined metabolic cofactors and a control study to study the kinetics of these metabolites in the plasma of healthy subjects with and without supplementation. We measured clinical parameters and observed no immediate side effects. Next, we generated plasma metabolomics and inflammatory protein markers data to reveal the acute changes associated with the supplementation of the metabolic cofactors. We also integrated metabolomics data using personalized genome-scale metabolic modeling and observed that such supplementation significantly affects the global human lipid, amino acid, and antioxidant metabolism. Finally, we predicted blood concentrations of these compounds during daily long-term supplementation by generating an ordinary differential equation model and liver concentrations of serine by generating a pharmacokinetic model and finally adjusted the doses of individual metabolic cofactors for future human clinical trials.
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| 9. |
- Fagerberg, Linn, et al.
(författare)
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Prediction of the human membrane proteome
- 2010
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Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 10:6, s. 1141-1149
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Tidskriftsartikel (refereegranskat)abstract
- Membrane proteins are key molecules in the cell, and are important targets for pharmaceutical drugs. Few three-dimensional structures of membrane proteins have been obtained, which makes computational prediction of membrane proteins crucial for studies of these key molecules. Here, seven membrane protein topology prediction methods based on different underlying algorithms, such as hidden Markov models, neural networks and support vector machines, have been used for analysis of the protein sequences from the 21 416 annotated genes in the human genome. The number of genes coding for a protein with predicted cc-helical transmembrane region(s) ranged from 5508 to 7651, depending on the method used. Based on a majority decision method, we estimate 5539 human genes to code for membrane proteins, corresponding to approximately 26% of the human protein-coding genes. The largest fraction of these proteins has only one predicted transmembrane region, but there are also many proteins with seven predicted transmembrane regions, including the G-protein coupled receptors. A visualization tool displaying the topologies suggested by the eight prediction methods, for all predicted membrane proteins, is available on the public Human Protein Atlas portal (www.proteinatlas.org).
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| 10. |
- Uhlén, Mathias, et al.
(författare)
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Towards a knowledge-based Human Protein Atlas
- 2010
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Ingår i: Nature Biotechnology. - : Springer Science and Business Media LLC. - 1087-0156 .- 1546-1696. ; 28:12, s. 1248-1250
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Tidskriftsartikel (refereegranskat)
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