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  1. AU="Mathias Uhlen"
  2. AU="Torrens, Alexa"
  3. AU="Moloney, E"
  4. AU="Pulawski, S."
  5. AU="Ustuner, Mehmet Cengiz"
  6. AU="Solozhentseva, Kristina"
  7. AU="Sitar, Cristian"
  8. AU="Ahmed, Md Firoz"
  9. AU=Goldberg Elad
  10. AU="Moskowitz, Jeremy D"
  11. AU="Mugunthan, Susithra Priyadarshni"
  12. AU="Matini, Lawrence"
  13. AU="Pourova, Radka"
  14. AU="Saxena, Shweta"
  15. AU="McGovern, Sophie"
  16. AU="Shuai An"
  17. AU="Kirill S. Golokhvast"
  18. AU="Cho, Kwang-Hwi"
  19. AU="Davitoiu, Dragos"
  20. AU=Templeman Charles
  21. AU="Attaluri, Anilchandra"
  22. AU="Cox, Ryan M"
  23. AU="Barua, Melissa"
  24. AU=Wong Ngai-Sze
  25. AU="Salgotra, Romesh Kumar"
  26. AU="Rossano, Adam J"
  27. AU="Pfeiffer, Christian"
  28. AU="Klostermann, Cynthia E."
  29. AU="Ivory, Joannie M"
  30. AU="Sooltangos, Aisha"
  31. AU="Marcia Adriana Poll"
  32. AU="Wenzel, Ross"
  33. AU="Wang, Ruihan"
  34. AU=Qing Enya AU=Qing Enya
  35. AU=Xu Jian AU=Xu Jian

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  1. Artikel ; Online: Machine Learning Analysis Reveals Biomarkers for the Detection of Neurological Diseases

    Simon Lam / Muhammad Arif / Xiya Song / Mathias Uhlén / Adil Mardinoglu

    Frontiers in Molecular Neuroscience, Vol

    2022  Band 15

    Abstract: It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK ...

    Abstract It is critical to identify biomarkers for neurological diseases (NLDs) to accelerate drug discovery for effective treatment of patients of diseases that currently lack such treatments. In this work, we retrieved genotyping and clinical data from 1,223 UK Biobank participants to identify genetic and clinical biomarkers for NLDs, including Alzheimer's disease (AD), Parkinson's disease (PD), motor neuron disease (MND), and myasthenia gravis (MG). Using a machine learning modeling approach with Monte Carlo randomization, we identified a panel of informative diagnostic biomarkers for predicting AD, PD, MND, and MG, including classical liver disease markers such as alanine aminotransferase, alkaline phosphatase, and bilirubin. A multinomial model trained on accessible clinical markers could correctly predict an NLD diagnosis with an accuracy of 88.3%. We also explored genetic biomarkers. In a genome-wide association study of AD, PD, MND, and MG patients, we identified single nucleotide polymorphisms (SNPs) implicated in several craniofacial disorders such as apnoea and branchiootic syndrome. We found evidence for shared genetic risk loci among NLDs, including SNPs in cancer-related genes and SNPs known to be associated with non-brain cancers such as Wilms tumor, leukemia, and colon cancer. This indicates overlapping genetic characterizations among NLDs which challenges current clinical definitions of the neurological disorders. Taken together, this work demonstrates the value of data-driven approaches to identify novel biomarkers in the absence of any known or promising biomarkers.
    Schlagwörter systems biology ; machine learning ; neurodegeneration ; GWAS—genome-wide association study ; UK Biobank ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571
    Thema/Rubrik (Code) 610
    Sprache Englisch
    Erscheinungsdatum 2022-05-01T00:00:00Z
    Verlag Frontiers Media S.A.
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Artikel ; Online: The protein expression profile of ACE2 in human tissues

    Feria Hikmet / Loren Méar / Mathias Uhlén / Cecilia Lindskog

    Abstract: ABSTRACTThe international spread of the novel, pathogenic SARS-coronavirus 2 (SARS-CoV-2) poses a global challenge on both healthcare and society. A multitude of research efforts worldwide aim at characterizing the cellular factors involved in viral ... ...

    Abstract ABSTRACTThe international spread of the novel, pathogenic SARS-coronavirus 2 (SARS-CoV-2) poses a global challenge on both healthcare and society. A multitude of research efforts worldwide aim at characterizing the cellular factors involved in viral transmission in order to reveal therapeutic targets. For a full understanding of the susceptibility for SARS-CoV-2 infection, the cell type-specific expression of the host cell surface receptor is necessary. The key protein suggested to be involved in host cell entry is Angiotensin I converting enzyme 2 (ACE2), and its expression has been reported in various human organs, in some cases with inconsistent or contradictory results. Here, we aim to verify a reliable expression profile of ACE2 in all major human tissues and cell types. Based on stringently validated immunohistochemical analysis and high-throughput mRNA sequencing from several datasets, we found that ACE2 expression is mainly localized to microvilli of the intestinal tract and renal proximal tubules, gallbladder epithelium, testicular Sertoli cells and Leydig cells, glandular cells of seminal vesicle and cardiomyocytes. The expression in several other previously reported locations, including alveolar type II (AT2) cells, could not be confirmed. Furthermore, ACE2 expression was absent in the AT2 lung carcinoma cell line A549, often used as a model for viral replication studies. Our analysis suggests that the expression of ACE2 in the human respiratory system appears to be limited, and the expression of the receptor in lung or respiratory epithelia on the protein level is yet to be confirmed. This raises questions regarding the role of ACE2 for infection of human lungs and highlights the need to further explore the route of transmission during SARS-CoV-2 infection.
    Schlagwörter covid19
    Verlag biorxiv
    Dokumenttyp Artikel ; Online
    DOI 10.1101/2020.03.31.016048
    Datenquelle COVID19

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  3. Artikel ; Online: Genome-Scale Metabolic Modeling of Glioblastoma Reveals Promising Targets for Drug Development

    Ida Larsson / Mathias Uhlén / Cheng Zhang / Adil Mardinoglu

    Frontiers in Genetics, Vol

    2020  Band 11

    Abstract: Glioblastoma (GBM) is an aggressive type of brain cancer with a poor prognosis for affected patients. The current line of treatment only gives the patients a survival time of on average 15 months. In this work, we use genome-scale metabolic models (GEMs) ...

    Abstract Glioblastoma (GBM) is an aggressive type of brain cancer with a poor prognosis for affected patients. The current line of treatment only gives the patients a survival time of on average 15 months. In this work, we use genome-scale metabolic models (GEMs) together with other systems biology tools to examine the global transcriptomics-data of GBM-patients obtained from The Cancer Genome Atlas (TCGA). We reveal the molecular mechanisms underlying GBM and identify potential therapeutic targets for effective treatment of patients. The work presented consists of two main parts. The first part stratifies the patients into two groups, high and low survival, and compares their gene expression. The second part uses GBM and healthy brain tissue GEMs to simulate gene knockout in a GBM cell model to find potential therapeutic targets and predict their side effect in healthy brain tissue. We (1) find that genes upregulated in the patients with low survival are linked to various stages of the glioma invasion process, and (2) identify five essential genes for GBM, whose inhibition is non-toxic to healthy brain tissue, therefore promising to investigate further as therapeutic targets.
    Schlagwörter glioblastoma ; GBM ; genome-scale metabolic models ; GEMs ; systems biology ; Genetics ; QH426-470
    Thema/Rubrik (Code) 616
    Sprache Englisch
    Erscheinungsdatum 2020-04-01T00:00:00Z
    Verlag Frontiers Media S.A.
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Artikel ; Online: Single-cell RNA-seq variant analysis for exploration of genetic heterogeneity in cancer

    Erik Fasterius / Mathias Uhlén / Cristina Al-Khalili Szigyarto

    Scientific Reports, Vol 9, Iss 1, Pp 1-

    2019  Band 11

    Abstract: Abstract Inter- and intra-tumour heterogeneity is caused by genetic and non-genetic factors, leading to severe clinical implications. High-throughput sequencing technologies provide unprecedented tools to analyse DNA and RNA in single cells and explore ... ...

    Abstract Abstract Inter- and intra-tumour heterogeneity is caused by genetic and non-genetic factors, leading to severe clinical implications. High-throughput sequencing technologies provide unprecedented tools to analyse DNA and RNA in single cells and explore both genetic heterogeneity and phenotypic variation between cells in tissues and tumours. Simultaneous analysis of both DNA and RNA in the same cell is, however, still in its infancy. We have thus developed a method to extract and analyse information regarding genetic heterogeneity that affects cellular biology from single-cell RNA-seq data. The method enables both comparisons and clustering of cells based on genetic variation in single nucleotide variants, revealing cellular subpopulations corroborated by gene expression-based methods. Furthermore, the results show that lymph node metastases have lower levels of genetic heterogeneity compared to their original tumours with respect to variants affecting protein function. The analysis also revealed three previously unknown variants common across cancer cells in glioblastoma patients. These results demonstrate the power and versatility of scRNA-seq variant analysis and highlight it as a useful complement to already existing methods, enabling simultaneous investigations of both gene expression and genetic variation.
    Schlagwörter Medicine ; R ; Science ; Q
    Thema/Rubrik (Code) 610
    Sprache Englisch
    Erscheinungsdatum 2019-07-01T00:00:00Z
    Verlag Nature Publishing Group
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Artikel ; Online: Affinity as a tool in life science

    Mathias Uhlén

    BioTechniques, Vol 44, Iss 5, Pp 649-

    2008  Band 654

    Abstract: The use of affinity-based tools has become invaluable as a platform for basic research and in the development of drugs and diagnostics. Applications include affinity chromatography and affinity tag fusions for efficient purification of proteins as well ... ...

    Abstract The use of affinity-based tools has become invaluable as a platform for basic research and in the development of drugs and diagnostics. Applications include affinity chromatography and affinity tag fusions for efficient purification of proteins as well as methods to probe the protein network interactions on a whole-proteome level. A variety of selection systems has been described for in vitro evolution of affinity reagents using combinatorial libraries, which make it possible to create high-affinity reagents to virtually all biomolecules, as exemplified by generation of therapeutic antibodies and new protein scaffold binders. The strategies for high-throughput generation of affinity reagents have also opened up the possibility of generating specific protein probes on a whole-proteome level. Recently, such affinity proteomics have allowed the detailed analysis of human protein expression in a comprehensive manner both in normal and disease tissue using tissue microarrays and confocal microscopy.
    Schlagwörter Biology (General) ; QH301-705.5
    Sprache Englisch
    Erscheinungsdatum 2008-04-01T00:00:00Z
    Verlag Future Science Ltd
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  6. Artikel ; Online: Next generation plasma proteome profiling to monitor health and disease

    Wen Zhong / Fredrik Edfors / Anders Gummesson / Göran Bergström / Linn Fagerberg / Mathias Uhlén

    Nature Communications, Vol 12, Iss 1, Pp 1-

    2021  Band 12

    Abstract: The proximity extension assay (PEA) is a popular tool to measure plasma protein levels. Here, the authors extend the proteome coverage of PEA by combining it with next-generation sequencing, enabling the analysis of nearly 1500 proteins from minute ... ...

    Abstract The proximity extension assay (PEA) is a popular tool to measure plasma protein levels. Here, the authors extend the proteome coverage of PEA by combining it with next-generation sequencing, enabling the analysis of nearly 1500 proteins from minute amounts of plasma.
    Schlagwörter Science ; Q
    Sprache Englisch
    Erscheinungsdatum 2021-05-01T00:00:00Z
    Verlag Nature Portfolio
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Artikel ; Online: Targeted proteomics analysis of plasma proteins using recombinant protein standards for addition only workflows

    David Kotol / Andreas Hober / Linnéa Strandberg / Anne-Sophie Svensson / Mathias Uhlén / Fredrik Edfors

    BioTechniques, Vol 71, Iss 3, Pp 473-

    2021  Band 483

    Abstract: Targeted proteomics is an attractive approach for the analysis of blood proteins. Here, we describe a novel analytical platform based on isotope-labeled recombinant protein standards stored in a chaotropic agent and subsequently dried down to allow ... ...

    Abstract Targeted proteomics is an attractive approach for the analysis of blood proteins. Here, we describe a novel analytical platform based on isotope-labeled recombinant protein standards stored in a chaotropic agent and subsequently dried down to allow storage at ambient temperature. This enables a straightforward protocol suitable for robotic workstations. Plasma samples to be analyzed are simply added to the dried pellet followed by enzymatic treatment and mass spectrometry analysis. Here, we show that this approach can be used to precisely (coefficient of variation <10%) determine the absolute concentrations in human plasma of hundred clinically relevant protein targets, spanning four orders of magnitude, using simultaneous analysis of 292 peptides. The use of this next-generation analytical platform for high-throughput clinical proteome profiling is discussed.
    Schlagwörter blood plasma ; internal standards ; mass spectrometry ; multiplex analysis ; plasma profiling ; room temperature storage ; Biology (General) ; QH301-705.5
    Sprache Englisch
    Erscheinungsdatum 2021-09-01T00:00:00Z
    Verlag Future Science Ltd
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  8. Artikel ; Online: Genome-scale metabolic modelling of the human gut microbiome reveals changes in the glyoxylate and dicarboxylate metabolism in metabolic disorders

    Ceri Proffitt / Gholamreza Bidkhori / Sunjae Lee / Abdellah Tebani / Adil Mardinoglu / Mathias Uhlen / David L. Moyes / Saeed Shoaie

    iScience, Vol 25, Iss 7, Pp 104513- (2022)

    2022  

    Abstract: Summary: The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria ... ...

    Abstract Summary: The human gut microbiome has been associated with metabolic disorders including obesity, type 2 diabetes, and atherosclerosis. Understanding the contribution of microbiome metabolic changes is important for elucidating the role of gut bacteria in regulating metabolism. We used available metagenomics data from these metabolic disorders, together with genome-scale metabolic modeling of key bacteria in the individual and community-level to investigate the mechanistic role of the gut microbiome in metabolic diseases. Modeling predicted increased levels of glutamate consumption along with the production of ammonia, arginine, and proline in gut bacteria common across the disorders. Abundance profiles and network-dependent analysis identified the enrichment of tartrate dehydrogenase in the disorders. Moreover, independent plasma metabolite levels showed associations between metabolites including proline and tyrosine and an increased tartrate metabolism in healthy obese individuals. We, therefore, propose that an increased tartrate metabolism could be a significant mediator of the microbiome metabolic changes in metabolic disorders.
    Schlagwörter Microbiome ; Systems biology ; Metabolomics ; Omics ; Science ; Q
    Thema/Rubrik (Code) 570
    Sprache Englisch
    Erscheinungsdatum 2022-07-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  9. Artikel ; Online: A Systematic Investigation of the Malignant Functions and Diagnostic Potential of the Cancer Secretome

    Jonathan L. Robinson / Amir Feizi / Mathias Uhlén / Jens Nielsen

    Cell Reports, Vol 26, Iss 10, Pp 2622-2635.e

    2019  Band 5

    Abstract: Summary: The collection of proteins secreted from a cell—the secretome—is of particular interest in cancer pathophysiology due to its diagnostic potential and role in tumorigenesis. However, cancer secretome studies are often limited to one tissue or ... ...

    Abstract Summary: The collection of proteins secreted from a cell—the secretome—is of particular interest in cancer pathophysiology due to its diagnostic potential and role in tumorigenesis. However, cancer secretome studies are often limited to one tissue or cancer type or focus on biomarker prediction without exploring the associated functions. We therefore conducted a pan-cancer analysis of secretome gene expression changes to identify candidate diagnostic biomarkers and to investigate the underlying biological function of these changes. Using transcriptomic data spanning 32 cancer types and 30 healthy tissues, we quantified the relative diagnostic potential of secretome proteins for each cancer. Furthermore, we offer a potential mechanism by which cancer cells relieve secretory pathway stress by decreasing the expression of tissue-specific genes, thereby facilitating the secretion of proteins promoting invasion and proliferation. These results provide a more systematic understanding of the cancer secretome, facilitating its use in diagnostics and its targeting for therapeutic development. : Robinson et al. compare secreted protein expression changes across different cancer types and healthy tissues to identify candidate biomarkers likely to be detectable in biological fluids. Functional analyses reveal a pattern whereby cancers decrease the expression of secreted proteins responsible for tissue of origin function in favor of those supporting proliferation and invasion. Keywords: secretome, protein secretion, cancer biomarkers, unfolded protein response, pan-cancer, systems biology
    Schlagwörter Biology (General) ; QH301-705.5
    Thema/Rubrik (Code) 610 ; 616
    Sprache Englisch
    Erscheinungsdatum 2019-03-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Artikel ; Online: Systems biology perspective for studying the gut microbiota in human physiology and liver diseases

    Ozlem Altay / Jens Nielsen / Mathias Uhlen / Jan Boren / Adil Mardinoglu

    EBioMedicine, Vol 49, Iss , Pp 364-

    2019  Band 373

    Abstract: The advancement in high-throughput sequencing technologies and systems biology approaches have revolutionized our understanding of biological systems and opened a new path to investigate unacknowledged biological phenomena. In parallel, the field of ... ...

    Abstract The advancement in high-throughput sequencing technologies and systems biology approaches have revolutionized our understanding of biological systems and opened a new path to investigate unacknowledged biological phenomena. In parallel, the field of human microbiome research has greatly evolved and the relative contribution of the gut microbiome to health and disease have been systematically explored. This review provides an overview of the network-based and translational systems biology-based studies focusing on the function and composition of gut microbiota. We also discussed the association between the gut microbiome and the overall human physiology, as well as hepatic diseases and other metabolic disorders. Keywords: Gut microbiome, Liver diseases, Host-microbiome interactions, Systems biology, Personalized medicine, Meta-omics, Biomarker, Metabolic models
    Schlagwörter Medicine ; R ; Medicine (General) ; R5-920
    Sprache Englisch
    Erscheinungsdatum 2019-11-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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