| 11. |
|
|
| 12. |
- Berglund, Lisa, 1978-
(författare)
-
Selection of antigens for antibody-based proteomics
- 2008
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The human genome is predicted to contain ~20,500 protein-coding genes. The encoded proteins are the key players in the body, but the functions and localizations of most proteins are still unknown. Antibody-based proteomics has great potential for exploration of the protein complement of the human genome, but there are antibodies only to a very limited set of proteins. The Human Proteome Resource (HPR) project was launched in August 2003, with the aim to generate high-quality specific antibodies towards the human proteome, and to use these antibodies for large-scale protein profiling in human tissues and cells. The goal of the work presented in this thesis was to evaluate if antigens can be selected, in a high-throughput manner, to enable generation of specific antibodies towards one protein from every human gene. A computationally intensive analysis of potential epitopes in the human proteome was performed and showed that it should be possible to find unique epitopes for most human proteins. The result from this analysis was implemented in a new web-based visualization tool for antigen selection. Predicted protein features important for antigen selection, such as transmembrane regions and signal peptides, are also displayed in the tool. The antigens used in HPR are named protein epitope signature tags (PrESTs). A genome-wide analysis combining different protein features revealed that it should be possible to select unique, 50 amino acids long PrESTs for ~80% of the human protein-coding genes. The PrESTs are transferred from the computer to the laboratory by design of PrEST-specific PCR primers. A study of the success rate in PCR cloning of the selected fragments demonstrated the importance of controlled GC-content in the primers for specific amplification. The PrEST protein is produced in bacteria and used for immunization and subsequent affinity purification of the resulting sera to generate mono-specific antibodies. The antibodies are tested for specificity and approved antibodies are used for tissue profiling in normal and cancer tissues. A large-scale analysis of the success rates for different PrESTs in the experimental pipeline of the HPR project showed that the total success rate from PrEST selection to an approved antibody is 31%, and that this rate is dependent on PrEST length. A second PrEST on a target protein is somewhat less likely to succeed in the HPR pipeline if the first PrEST is unsuccessful, but the analysis shows that it is valuable to select several PrESTs for each protein, to enable generation of at least two antibodies, which can be used to validate each other.
|
|
| 13. |
- Berglund, Lisa, et al.
(författare)
-
The epitope space of the human proteome
- 2008
-
Ingår i: Protein Science. - : Wiley. - 0961-8368 .- 1469-896X. ; 17:4, s. 606-613
-
Tidskriftsartikel (refereegranskat)abstract
- In the post-genome era, there is a great need for protein-specific affinity reagents to explore the human proteome. Antibodies are suitable as reagents, but generation of antibodies with low cross-reactivity to other human proteins requires careful selection of antigens. Here we show the results from a proteomewide effort to map linear epitopes based on uniqueness relative to the entire human proteome. The analysis was based on a sliding window sequence similarity search using short windows (8, 10, and 12 amino acid residues). A comparison of exact string matching (Hamming distance) and a heuristic method (BLAST) was performed, showing that the heuristic method combined with a grid strategy allows for whole proteome analysis with high accuracy and feasible run times. The analysis shows that it is possible to find unique antigens for a majority of the human proteins, with relatively strict rules involving low sequence identity of the possible linear epitopes. The implications for human antibody-based proteomics efforts are discussed.
|
|
| 14. |
- Bjorling, E., et al.
(författare)
-
Human protein atlas, version 2
- 2006
-
Ingår i: Molecular & Cellular Proteomics. - : AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC. - 1535-9476 .- 1535-9484. ; 5:10, s. S328-S328
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
|
|
| 15. |
- Bjorling, E., et al.
(författare)
-
The creation and usage of a human protein atlas database
- 2005
-
Ingår i: Molecular & Cellular Proteomics. - : AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC. - 1535-9476 .- 1535-9484. ; 4:8, s. S18-S18
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
|
|
| 16. |
- Björling, Erik, et al.
(författare)
-
A web-based tool for in silico biomarker discovery based on tissue-specific protein profiles in normal and cancer tissues
- 2008
-
Ingår i: Molecular & Cellular Proteomics. - 1535-9476 .- 1535-9484. ; 7:5, s. 825-844
-
Tidskriftsartikel (refereegranskat)abstract
- Here we report the development of a publicly available Web-based analysis tool for exploring proteins expressed in a tissue- or cancer-specific manner. The search queries are based on the human tissue profiles in normal and cancer cells in the Human Protein Atlas portal and rely on the individual annotation performed by pathologists of images representing immunohistochemically stained tissue sections. Approximately 1.8 million images representing more than 3000 antibodies directed toward human proteins were used in the study. The search tool allows for the systematic exploration of the protein atlas to discover potential protein biomarkers. Such biomarkers include tissue-specific markers, cell type-specific markers, tumor type-specific markers, markers of malignancy, and prognostic or predictive markers of cancers. Here we show examples of database queries to generate sets of candidate biomarker proteins for several of these different categories. Expression profiles of candidate proteins can then subsequently be validated by examination of the underlying high resolution images. The present study shows examples of search strategies revealing several potential protein biomarkers, including proteins specifically expressed in normal cells and in cancer cells from specified tumor types. The lists of candidate proteins can be used as a starting point for further validation in larger patient cohorts using both immunological approaches and technologies utilizing more classical proteomics tools.
|
|
| 17. |
- Björling, Erik, et al.
(författare)
-
Antibodypedia : a portal for sharing antibody and antigen validation data
- 2008
-
Ingår i: Molecular & Cellular Proteomics. - 1535-9476. ; 7:10, s. 2028-2037
-
Tidskriftsartikel (refereegranskat)abstract
- Antibodies are useful tools to characterize the components of the human proteome and to validate potential protein biomarkers discovered through various clinical proteomics efforts. The lack of validation results across various applications for most antibodies often makes it necessary to perform cumbersome investigations to ensure specificity of a particular antibody in a certain application. A need therefore exists for a standardized system for sharing validation data about publicly available antibodies and to allow antibody providers as well as users to contribute and edit experimental evidence data, including data also on the antigen. Here we describe a new publicly available portal called Antibodypedia, which has been developed to allow sharing of information regarding validation of antibodies in which providers can submit their own validation results and reliability scores. We report standardized validation criteria and submission rules for applications such as Western blots, protein arrays, immunohistochemistry, and immunofluorescence. The contributor is expected to provide experimental evidence and a validation score for each antibody, and the users can subsequently provide feedback and comments on the use of the antibody. The database thus provides a virtual resource of publicly available antibodies toward human proteins with accompanying experimental evidence supporting an individual validation score for each antibody in an application-specific manner.
|
|
| 18. |
- Björling, Erik, 1973-
(författare)
-
Databases for antibody-based proteomics
- 2008
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Humans are believed to have ~20,500 protein-coding genes andmuch effort has over the last years been put into the characterizationand localization of the encoded proteins in order to understand theirfunctions. One such effort is the Human Proteome Resource (HPR)project, started in Sweden 2003 with the aim to generate specificantibodies to each human protein and to use those antibodies toanalyze the human proteome by screening human tissues and cells.The work reported in this thesis deals with structuring of data fromantibody-based proteomics assays, with focus on the importance ofaggregating and presenting data in a way that is easy to apprehend.The goals were to model and build databases for collecting, searchingand analyzing data coming out of the large-scale HPR project and tomake all collected data publicly available. A public website, theHuman Protein Atlas, was developed giving all end-users in thescientific community access to the HPR database with proteinexpression data. In 2008, the Human Protein Atlas was released in its4th version containing more than 6000 antibodies, covering more than25% of the human proteins. All the collected protein expression datais searchable on the public website. End-users can query for proteinsthat show high expression in one tissue and no expression in anotherand possibly find tissue specific biomarkers. Queries can also beconstructed to find proteins with different expression levels in normalvs. cancer tissues. The proteins found by such a query could identifypotential biomarkers for cancer that could be used as diagnosticmarkers and maybe even be involved in cancer therapy in the future.Validation of antibodies is important in order to get reliable resultsfrom different assays. It has been noted that some antibodies arereliable in certain assays but not in others and therefore anotherpublicly available database, the Antibodypedia, has been createdwhere any antibody producer can submit their binders together withthe validation data in order for end users to purchase the bestantibody for their protein target and their intended assay.
|
|
| 19. |
- Bolander, Å., et al.
(författare)
-
The protein expression of TRP-1 and galectin-1 in cutaneous malignant melanomas
- 2008
-
Ingår i: Cancer Genomics & Proteomics. - 1109-6535 .- 1790-6245. ; 5:6, s. 293-300
-
Tidskriftsartikel (refereegranskat)abstract
- Background: Patients with metastazing malignant melanoma have a poor outcome and determination of thickness of the primary tumor remains as the most important prognostic predictor. The aim of this study was to use an antibody-based proteomics strategy to search for new molecular markers associated with melanoma progression. Two proteins, TRP-1 and galectin-1, were identified as proteins with enhanced expression in cells from the melanocytic lineage. Patients and Methods: Protein profiling of TRP-1 and galectin-1 together with proliferation marker Ki-67 and melanocyte marker Melan-A was performed in normal tissues from 144 individuals and in 216 different tumors using tissue microarrays and immunohistochemistry. The protein expression pattern was further analyzed in a defined cohort of 157 patients diagnosed with invasive cutaneous malignant melanoma. Results: Both TRP-1 and galectin-1 were highly expressed in normal melanocytes and melanoma. The expression of TRP-1 was inversely correlated with tumor stage (p=0.002, (R=-0.28)). Neither TRP-1 or galectin-1 was associated with overall or disease free survival (p>0.14, p>0.46 respectively). Ki-67 was associated with tumor stage and survival (p<0.001). Conclusion: TRP-1 and galectin-1 protein expression patterns were determined in normal and cancer tissues and both proteins were expressed in the majority of the malignant melanomas. There was no correlation between TRP-1 or galectin-1 expression and survival.
|
|
| 20. |
|
|
