Sophia Hober

Bio: Sophia Hober is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Medicine & Affinity chromatography. The author has an hindex of 43, co-authored 179 publications receiving 18585 citations. Previous affiliations of Sophia Hober include Science for Life Laboratory & Chiron Corporation.

Tissue-based map of the human proteome

Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

A pathology atlas of the human cancer transcriptome

Abstract: Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

Towards a knowledge-based Human Protein Atlas

Abstract: 1. Anonymous. Nat. Biotechnol. 28, 987 (2010). 2. Plosila, W.H. Econ. Dev. Q. 18, 113–126 (2004). 3. Stayn, S. BNA Med. Law Pol. Rep. 5, 718–725 (2006). 4. Lomax, G. & Stayn, S. BNA Med. Law Pol. Rep. 7, 695–698 (2008). 5. Levine, A.D. Public Adm. Rev. 68, 681–694 (2008). 6. Levine, A.D. Nat. Biotechnol. 24, 865–866 (2006). 7. McCormick, J.B., Owen-Smith, J. & Scott, C.T. Cell Stem Cell 4, 107–110 (2009). 8. Fossett, J.W., Ouellette, A.R., Philpott, S., Magnus, D. & Mcgee, G. Hastings Cent. Rep. 37, 24–35 (2007). 9. Mintrom, M. Publius 39, 606–631 (2009). 10. Scott, C.T., McCormick, J.B. & Owen-Smith, J. Nat. Biotechnol. 27, 696–697 (2009). 11. Takahashi, K. & Yamanaka, S. Cell 126, 663–676 (2006). Foundation and the Georgia Research Alliance, and Georgia Tech. They thank J. Walsh at Georgia Tech for helpful comments on an earlier version of this manuscript. They also appreciate the assistance they received with data collection from officials in various state stem cell agencies. A.D.L. would also like to thank A. Jakimo, whose comment at a meeting of the Interstate Alliance on Stem Cell Research inspired collection of these data. stem cell programs, as well as similar state programs supporting other areas of science, is uncertain. The analysis here suggests that state stem cell funding programs are sufficiently large and established that simply ending the programs, at least in the absence of substantial investment in the field by other funding sources, could have deleterious effects. Such action would fail to capitalize on the initial efforts of scientists who have been drawn to the field of stem cell research by state programs and leave many stem cell scientists suddenly searching for funding to continue their research. Large-scale state funding for basic research is a relatively new phenomenon, and many questions remain about the impact of these programs on the development of scientific fields and the careers of scientists. The influence of state funding programs on the distribution of research publications, the acquisition of future external funding, the creation of new companies and the translation of basic research into medical practice, for instance, are important unanswered questions. Similarly, comparing state funding programs with federal funding programs as well as foundations could offer new insight into the relative priorities of different funding bodies and the extent to which their funding portfolios overlap or are distinct. We hope the analysis presented here and the public release of the underlying database will inspire additional analysis of state science funding programs generally and state-funded stem cell science in particular.

A subcellular map of the human proteome

Abstract: Resolving the spatial distribution of the human proteome at a subcellular level can greatly increase our understanding of human biology and disease. Here we present a comprehensive image-based map ...

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.

Abstract: Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein-protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein-protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.

Tissue-based map of the human proteome

Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses.

Abstract: Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.