1.
Ågren, Rasmus, 1982, et al.
(author)
Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling
2014
In: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 10:3
Journal article (peer-reviewed) abstract
Synopsis Personalized GEMs for six hepatocellular carcinoma patients are reconstructed using proteomics data and a task-driven model reconstruction algorithm. These GEMs are used to predict antimetabolites preventing tumor growth in all patients or in individual patients. The presence of proteins encoded by 15,841 genes in tumors from 27 HCC patients is evaluated by immunohistochemistry. Personalized GEMs for six HCC patients and GEMs for 83 healthy cell types are reconstructed based on HMR 2.0 and the tINIT algorithm for task-driven model reconstruction. 101 antimetabolites are predicted to inhibit tumor growth in all patients. Antimetabolite toxicity is tested using the 83 cell type-specific GEMs. Genome-scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype-phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task-driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type-specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty-two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.
2.
Gloriam, David E., et al.
(author)
A Community Standard Format for the Representation of Protein Affinity Reagents
2010
In: Molecular & Cellular Proteomics. - 1535-9476 .- 1535-9484. ; 9:1, s. 1-10
Journal article (peer-reviewed) 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.
3.
Robinson, Jonathan, 1986, et al.
(author)
An atlas of human metabolism
2020
In: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 13:624
Journal article (peer-reviewed) 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.
4.
Lindskog, Cecilia, et al.
(author)
The lung-specific proteome defined by integration of transcriptomics and antibody-based profiling
2014
In: The FASEB Journal. - : Wiley. - 0892-6638 .- 1530-6860. ; 28:12, s. 5184-5196
Journal article (peer-reviewed) abstract
The combined action of multiple cell types is essential for the physiological function of the lung, and increased awareness of the molecular constituents characterizing each cell type is likely to advance the understanding of lung biology and disease. In the current study, we used genome-wide RNA sequencing of normal lung parenchyma and 26 additional tissue types, combined with antibody-based protein profiling, to localize the expression to specific cell types. Altogether, 221 genes were found to be elevated in the lung compared with their expression in other analyzed tissues. Among the gene products were several well-known markers, but also several proteins previously not described in the context of the lung. To link the lungspecific molecular repertoire to human disease, survival associations of pneumocyte-specific genes were assessed by using transcriptomics data from 7 non-small-cell lung cancer (NSCLC) cohorts. Transcript levels of 10 genes (SFTPB, SFTPC, SFTPD, SLC34A2, LAMP3, CACNA2D2, AGER, EMP2, NKX2-1, and NAPSA) were significantly associated with survival in the adenocarcinoma subgroup, thus qualifying as promising biomarker candidates. In summary, based on an integrated omics approach, we identified genes with elevated expression in lung and localized corresponding protein expression to different cell types. As biomarker candidates, these proteins may represent intriguing starting points for further exploration in health and disease.-Lindskog, C., Fagerberg, L., Hallstrom, B., Edlund, K., Hellwig, B., Rahnenfuhrer, J., Kampf, C., Uhlen, M., Ponten, F., Micke, P. The lung-specific proteome defined by integration of transcriptomics and antibody-based profiling.
5.
Kampf, Caroline, et al.
(author)
The human liver-specific proteome defined by transcriptomics and antibody-based profiling
2014
In: FASEB Journal. - : Wiley. - 1530-6860 .- 0892-6638. ; 28:7, s. 2901-2914
Journal article (peer-reviewed) 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.
6.
Uhlén, Mathias, et al.
(author)
The human secretome
2019
In: Science Signaling. - : American Association for the Advancement of Science (AAAS). - 1945-0877 .- 1937-9145. ; 12:609
Journal article (peer-reviewed) 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.
7.
Lee, SangWook, et al.
(author)
Network analyses identify liver-specific targets for treating liver diseases
2017
In: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 13:8
Journal article (peer-reviewed) abstract
We performed integrative network analyses to identify targets that can be used for effectively treating liver diseases with minimal side effects. We first generated co-expression networks (CNs) for 46 human tissues and liver cancer to explore the functional relationships between genes and examined the overlap between functional and physical interactions. Since increased de novo lipogenesis is a characteristic of nonalcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC), we investigated the liver-specific genes co-expressed with fatty acid synthase (FASN). CN analyses predicted that inhibition of these liver-specific genes decreases FASN expression. Experiments in human cancer cell lines, mouse liver samples, and primary human hepatocytes validated our predictions by demonstrating functional relationships between these liver genes, and showing that their inhibition decreases cell growth and liver fat content. In conclusion, we identified liver-specific genes linked to NAFLD pathogenesis, such as pyruvate kinase liver and red blood cell (PKLR), or to HCC pathogenesis, such as PKLR, patatin-like phospholipase domain containing 3 (PNPLA3), and proprotein convertase subtilisin/kexin type 9 (PCSK9), all of which are potential targets for drug development.
8.
Liu, Zhengtao, et al.
(author)
Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function
2019
In: Metabolic Engineering. - : Elsevier BV. - 1096-7176 .- 1096-7184. ; 52, s. 263-272
Journal article (peer-reviewed) abstract
The pathogenesis of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) has been associated with altered expression of liver-specific genes including pyruvate kinase liver and red blood cell (PKLR), patatin-like phospholipase domain containing 3 (PNPLA3) and proprotein convertase subtilisin/kexin type 9 (PCSK9). Here, we inhibited and overexpressed the expression of these three genes in HepG2 cells, generated RNA-seq data before and after perturbation and revealed the altered global biological functions with the modulation of these genes using integrated network (IN) analysis. We found that modulation of these genes effects the total triglycerides levels within the cells and viability of the cells. Next, we generated IN for HepG2 cells, identified reporter transcription factors based on IN and found that the modulation of these genes affects key metabolic pathways associated with lipid metabolism (steroid biosynthesis, PPAR signalling pathway, fatty acid synthesis and oxidation) and cancer development (DNA replication, cell cycle and p53 signalling) involved in the progression of NAFLD and HCC. Finally, we observed that inhibition of PKLR lead to decreased glucose uptake and decreased mitochondrial activity in HepG2 cells. Hence, our systems level analysis indicated that PKLR can be targeted for development efficient treatment strategy for NAFLD and HCC.
9.
Brennan, Donal J., et al.
(author)
Tumour-specific HMG-CoAR is an independent predictor of recurrence free survival in epithelial ovarian cancer
2010
In: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407 .- 1471-2407. ; 10, s. 125-
Journal article (peer-reviewed) abstract
Background: Our group previously reported that tumour-specific expression of the rate-limiting enzyme in the mevalonate pathway, 3-hydroxy-3-methylglutharyl-coenzyme A reductase (HMG-CoAR) is associated with more favourable tumour parameters and a good prognosis in breast cancer. In the present study, the prognostic value of HMG-CoAR expression was examined in tumours from a cohort of patients with primary epithelial ovarian cancer. Methods: HMG-CoAR expression was assessed using immunohistochemistry (IHC) on tissue microarrays (TMA) consisting of 76 ovarian cancer cases, analysed using automated algorithms to develop a quantitative scoring model. Kaplan Meier analysis and Cox proportional hazards modelling were used to estimate the risk of recurrence free survival (RFS). Results: Seventy-two tumours were suitable for analysis. Cytoplasmic HMG-CoAR expression was present in 65% (n = 46) of tumours. No relationship was seen between HMG-CoAR and age, histological subtype, grade, disease stage, estrogen receptor or Ki-67 status. Patients with tumours expressing HMG-CoAR had a significantly prolonged RFS (p = 0.012). Multivariate Cox regression analysis revealed that HMG-CoAR expression was an independent predictor of improved RFS (RR = 0.49, 95% CI (0.25-0.93); p = 0.03) when adjusted for established prognostic factors such as residual disease, tumour stage and grade. Conclusion: HMG-CoAR expression is an independent predictor of prolonged RFS in primary ovarian cancer. As HMG-CoAR inhibitors, also known as statins, have demonstrated anti-neoplastic effects in vitro, further studies are required to evaluate HMG-CoAR expression as a surrogate marker of response to statin treatment, especially in conjunction with current chemotherapeutic regimens.
10.
O'Hurley, Gillian, et al.
(author)
Analysis of the Human Prostate-Specific Proteome Defined by Transcriptomics and Antibody-Based Profiling Identifies TMEM79 and ACOXL as Two Putative, Diagnostic Markers in Prostate Cancer.
2015
In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 10:8
Journal article (peer-reviewed) abstract
To better understand prostate function and disease, it is important to define and explore the molecular constituents that signify the prostate gland. The aim of this study was to define the prostate specific transcriptome and proteome, in comparison to 26 other human tissues. Deep sequencing of mRNA (RNA-seq) and immunohistochemistry-based protein profiling were combined to identify prostate specific gene expression patterns and to explore tissue biomarkers for potential clinical use in prostate cancer diagnostics. We identified 203 genes with elevated expression in the prostate, 22 of which showed more than five-fold higher expression levels compared to all other tissue types. In addition to previously well-known proteins we identified two poorly characterized proteins, TMEM79 and ACOXL, with potential to differentiate between benign and cancerous prostatic glands in tissue biopsies. In conclusion, we have applied a genome-wide analysis to identify the prostate specific proteome using transcriptomics and antibody-based protein profiling to identify genes with elevated expression in the prostate. Our data provides a starting point for further functional studies to explore the molecular repertoire of normal and diseased prostate including potential prostate cancer markers such as TMEM79 and ACOXL.
