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  • 1.
    Ghosheh, Nidal
    et al.
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Bioscience. Institute of Biomedicine, Department of Clinical Chemistry and Transfusion Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Küppers-Munther, Barbara
    Takara Bio Europe Aktiebolaget, Gothenburg, Sweden.
    Asplund, Annika
    Takara Bio Europe Aktiebolaget, Gothenburg, Sweden.
    Edsbagge, Josefina
    Takara Bio Europe Aktiebolaget, Gothenburg, Sweden.
    Ulfenborg, Benjamin
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Andersson, Tommy B.
    Cardiovascular and Metabolic Diseases, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Mölndal, Sweden / Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, Stockholm, Sweden.
    Björquist, Petter
    NovaHep Aktiebolaget, Gothenburg, Sweden.
    Andersson, Christian X.
    Takara Bio Europe Aktiebolaget, Gothenburg, Sweden.
    Carén, Helena
    Sahlgrenska Cancer Center, Department of Pathology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Simonsson, Stina
    Institute of Biomedicine, Department of Clinical Chemistry and Transfusion Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Sartipy, Peter
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. AstraZeneca Research and Development, Global Medicines Development Cardiovascular and Metabolic Diseases Global Medicines Development Unit, Mölndal, Sweden.
    Synnergren, Jane
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Comparative transcriptomics of hepatic differentiation of human pluripotent stem cells and adult human liver tissue2017In: Physiological Genomics, ISSN 1094-8341, E-ISSN 1531-2267, Vol. 49, no 8, p. 430-446Article in journal (Refereed)
    Abstract [en]

    Hepatocytes derived from human pluripotent stem cells (hPSC-HEP) have the potential to replace presently used hepatocyte sources applied in liver disease treatment and models of drug discovery and development. Established hepatocyte differentiation protocols are effective and generate hepatocytes, which recapitulate some key features of their in vivo counterparts. However, generating mature hPSC-HEP remains a challenge. In this study, we applied transcriptomics to investigate the progress of in vitro hepatic differentiation of hPSCs at the developmental stages, definitive endoderm, hepatoblasts, early hPSC-HEP, and mature hPSC-HEP, to identify functional targets that enhance efficient hepatocyte differentiation. Using functional annotation, pathway and protein interaction network analyses, we observed the grouping of differentially expressed genes in specific clusters representing typical developmental stages of hepatic differentiation. In addition, we identified hub proteins and modules that were involved in the cell cycle process at early differentiation stages. We also identified hub proteins that differed in expression levels between hPSC-HEP and the liver tissue controls. Moreover, we identified a module of genes that were expressed at higher levels in the liver tissue samples than in the hPSC-HEP. Considering that hub proteins and modules generally are essential and have important roles in the protein-protein interactions, further investigation of these genes and their regulators may contribute to a better understanding of the differentiation process. This may suggest novel target pathways and molecules for improvement of hPSC-HEP functionality, having the potential to finally bring this technology to a wider use.

  • 2.
    Küppers-Munther, Barbara
    et al.
    Takara Bio Europe AB, Gothenburg, Sweden.
    Asplund, A.
    Takara Bio Europe AB, Gothenburg, Sweden.
    Ulfenborg, Benjamin
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Synnergren, Jane
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Abadie, A.
    Takara Bio Europe SAS, Paris, France.
    Novel human iPSC-derived hepatocytes with advanced functionality and long-term 2D cultures of human primary hepatocytes for metabolic disease studies2018In: Human Gene Therapy, ISSN 1043-0342, E-ISSN 1557-7422, Vol. 29, no 12, p. A146-A146, article id P406Article in journal (Refereed)
  • 3.
    Marcišauskas, Simonas
    et al.
    Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
    Ulfenborg, Benjamin
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Kristjansdottir, Björg
    Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Cancer Center, University of Gothenburg, Gothenburg, Sweden.
    Waldemarson, Sofia
    Department of Immunotechnology, Lund University, Medicon Village, Lund, Sweden.
    Sundfeldt, Karin
    Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Cancer Center, University of Gothenburg, Gothenburg, Sweden.
    Univariate and classification analysis reveals potential diagnostic biomarkers for early stage ovarian cancer Type 1 and Type 22019In: Journal of Proteomics, ISSN 1874-3919, E-ISSN 1876-7737, Vol. 196, p. 57-68Article in journal (Refereed)
    Abstract [en]

    Biomarkers for early detection of ovarian tumors are urgently needed. Tumors of the ovary grow within cysts and most are benign. Surgical sampling is the only way to ensure accurate diagnosis, but often leads to morbidity and loss of female hormones. The present study explored the deep proteome in well-defined sets of ovarian tumors, FIGO stage I, Type 1 (low-grade serous, mucinous, endometrioid; n = 9), Type 2 (high-grade serous; n = 9), and benign serous (n = 9) using TMT–LC–MS/MS. Data are available via ProteomeXchange with identifier PXD010939. We evaluated new bioinformatics tools in the discovery phase. This innovative selection process involved different normalizations, a combination of univariate statistics, and logistic model tree and naive Bayes tree classifiers. We identified 142 proteins by this combined approach. One biomarker panel and nine individual proteins were verified in cyst fluid and serum: transaldolase-1, fructose-bisphosphate aldolase A (ALDOA), transketolase, ceruloplasmin, mesothelin, clusterin, tenascin-XB, laminin subunit gamma-1, and mucin-16. Six of the proteins were found significant (p <.05) in cyst fluid while ALDOA was the only protein significant in serum. The biomarker panel achieved ROC AUC 0.96 and 0.57 respectively. We conclude that classification algorithms complement traditional statistical methods by selecting combinations that may be missed by standard univariate tests. Significance: In the discovery phase, we performed deep proteome analyses of well-defined histology subgroups of ovarian tumor cyst fluids, highly specified for stage and type (histology and grade). We present an original approach to selecting candidate biomarkers combining several normalization strategies, univariate statistics, and machine learning algorithms. The results from validation of selected proteins strengthen our prior proteomic and genomic data suggesting that cyst fluids are better than sera in early stage ovarian cancer diagnostics. 

  • 4.
    Ulfenborg, Benjamin
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Bioinformatics tools for discovery and evaluation of biomarkers: Applications in clinical assessment of cancer2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Cancer is a disease characterized by abnormal proliferation of cells in the body and ranks as the second leading cause of death worldwide. In order to improve cancer patient care, a major focus of cancer research is to discover biomarkers. A biomarker is a biological molecule found in tissues or body fluids and can be used to predict or assess disease states. The aim of this thesis is to develop bioinformatics tools for discovery and evaluation of novel biomarkers from high-throughput datasets.

    MicroRNAs (miRNAs) are short non-coding RNAs that function as negative regulators of gene expression. Dysregulation of miRNAs in cancer is frequently reported, making them interesting as biomarker candidates. GenoScan was developed for genome-wide discovery of miRNA-coding genes, as a first step in the identification of novel mi-RNA biomarkers.

    High-throughput technologies such as microarrays allow researchers to measure the expression of thousands of genes or miRNAs simultaneously. The Decision Trunk Classifier (DTC) algorithm has been developed to screen datasets from these experiments for biomarker candidates. When applied to a miRNA expression dataset for endometrial cancer (EC) samples vs. controls, a two-marker model with 98 % accuracy was generated. These miRNAs (hsa-miR-183-5p and hsa-miRPlus-C1070) are promising as biomarkers for EC screening.

    The miREC database was developed to store gene and miRNA data from curated expression profiling studies of EC, as well as gene-miRNA regulatory connections. Using gene-miRNA interaction networks from miREC, the roles of miRNAs in cancer hallmark acquisition can be clarified. To further support exploratory analysis of expression data, DTC was extended with partial least squares regression models. The resulting PLS-DTC algorithm can be used to gain deeper insights into the perturbation of biological processes and pathways.

  • 5.
    Ulfenborg, Benjamin
    et al.
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Jurcevic, Sanja
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Lindelöf, Angelica
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Klinga-Levan, Karin
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Olsson, Björn
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    miREC: a database of miRNAs involved in the development of endometrial cancer2015In: BMC Research Notes, ISSN 1756-0500, E-ISSN 1756-0500, Vol. 8, no 1, article id 104Article in journal (Refereed)
    Abstract [en]

    Background

    Endometrial cancer (EC) is the most frequently diagnosed gynecological malignancy and the fourth most common cancer diagnosis overall among women. As with many other forms of cancer, it has been shown that certain miRNAs are differentially expressed in EC and these miRNAs are believed to play important roles as regulators of processes involved in the development of the disease. With the rapidly growing number of studies of miRNA expression in EC, there is a need to organize the data, combine the findings from experimental studies of EC with information from various miRNA databases, and make the integrated information easily accessible for the EC research community.

    Findings

    The miREC database is an organized collection of data and information about miRNAs shown to be differentially expressed in EC. The database can be used to map connections between miRNAs and their target genes in order to identify specific miRNAs that are potentially important for the development of EC. The aim of the miREC database is to integrate all available information about miRNAs and target genes involved in the development of endometrial cancer, and to provide a comprehensive, up-to-date, and easily accessible source of knowledge regarding the role of miRNAs in the development of EC. Database URL: http://www.mirecdb.orgwebcite.

    Conclusions

    Several databases have been published that store information about all miRNA targets that have been predicted or experimentally verified to date. It would be a time-consuming task to navigate between these different data sources and literature to gather information about a specific disease, such as endometrial cancer. The miREC database is a specialized data repository that, in addition to miRNA target information, keeps track of the differential expression of genes and miRNAs potentially involved in endometrial cancer development. By providing flexible search functions it becomes easy to search for EC-associated genes and miRNAs from different starting points, such as differential expression and genomic loci (based on genomic aberrations).

  • 6.
    Ulfenborg, Benjamin
    et al.
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Karlsson, Alexander
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Riveiro, Maria
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Améen, Caroline
    Takara Bio Europe AB, Gothenburg, Sweden.
    Åkesson, Karolina
    Takara Bio Europe AB, Gothenburg, Sweden.
    Andersson, Christian X.
    Takara Bio Europe AB, Gothenburg, Sweden.
    Sartipy, Peter
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre. Cardiovascular and Metabolic Disease Global Medicines Development Unit, AstraZeneca, Mölndal, Sweden.
    Synnergren, Jane
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    A data analysis framework for biomedical big data: Application on mesoderm differentiation of human pluripotent stem cells2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 6, article id e0179613Article in journal (Refereed)
    Abstract [en]

    The development of high-throughput biomolecular technologies has resulted in generation of vast omics data at an unprecedented rate. This is transforming biomedical research into a big data discipline, where the main challenges relate to the analysis and interpretation of data into new biological knowledge. The aim of this study was to develop a framework for biomedical big data analytics, and apply it for analyzing transcriptomics time series data from early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. To this end, transcriptome profiling by microarray was performed on differentiating human pluripotent stem cells sampled at eleven consecutive days. The gene expression data was analyzed using the five-stage analysis framework proposed in this study, including data preparation, exploratory data analysis, confirmatory analysis, biological knowledge discovery, and visualization of the results. Clustering analysis revealed several distinct expression profiles during differentiation. Genes with an early transient response were strongly related to embryonic-and mesendoderm development, for example CER1 and NODAL. Pluripotency genes, such as NANOG and SOX2, exhibited substantial downregulation shortly after onset of differentiation. Rapid induction of genes related to metal ion response, cardiac tissue development, and muscle contraction were observed around day five and six. Several transcription factors were identified as potential regulators of these processes, e.g. POU1F1, TCF4 and TBP for muscle contraction genes. Pathway analysis revealed temporal activity of several signaling pathways, for example the inhibition of WNT signaling on day 2 and its reactivation on day 4. This study provides a comprehensive characterization of biological events and key regulators of the early differentiation of human pluripotent stem cells towards the mesoderm and cardiac lineages. The proposed analysis framework can be used to structure data analysis in future research, both in stem cell differentiation, and more generally, in biomedical big data analytics.

  • 7.
    Ulfenborg, Benjamin
    et al.
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Klinga-Levan, Karin
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Olsson, Björn
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Classification of tumor samples from expression data using decision trunks2013In: Cancer Informatics, ISSN 1176-9351, E-ISSN 1176-9351, Vol. 12, p. 53-66Article in journal (Refereed)
    Abstract [en]

    We present a novel machine learning approach for the classification of cancer samples using expression data. We refer to the method as "decision trunks," since it is loosely based on decision trees, but contains several modifications designed to achieve an algorithm that: (1) produces smaller and more easily interpretable classifiers than decision trees; (2) is more robust in varying application scenarios; and (3) achieves higher classification accuracy. The decision trunk algorithm has been implemented and tested on 26 classification tasks, covering a wide range of cancer forms, experimental methods, and classification scenarios. This comprehensive evaluation indicates that the proposed algorithm performs at least as well as the current state of the art algorithms in terms of accuracy, while producing classifiers that include on average only 2-3 markers. We suggest that the resulting decision trunks have clear advantages over other classifiers due to their transparency, interpretability, and their correspondence with human decision-making and clinical testing practices. © the author(s), publisher and licensee Libertas Academica Ltd.

  • 8.
    Ulfenborg, Benjamin
    et al.
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Klinga-Levan, Karin
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Olsson, Björn
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Bioscience.
    Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression2015In: International Journal of Data Mining and Bioinformatics, ISSN 1748-5681, Vol. 13, no 4, p. 338-359Article in journal (Refereed)
    Abstract [en]

    In silico prediction of novel miRNAs from genomic sequences remains a challenging problem. This study presents a genome-wide miRNA discovery software package called GenoScan and evaluates two hairpin classification methods. These methods, one ensemble-based and one using logistic regression were benchmarked along with 15 published methods. In addition, the sequence-folding step is addressed by investigating the impact of secondary structure prediction methods and the choice of input sequence length on prediction performance. Both the accuracy of secondary structure predictions and the miRNA prediction are evaluated. In the benchmark of hairpin classification methods, the regression model achieved highest classification accuracy. Of the structure prediction methods evaluated, ContextFold achieved the highest agreement between predicted and experimentally determined structures. However, both the choice of secondary structure prediction method and input sequence length had limited impact on hairpin classification performance.

  • 9.
    Ulfenborg, Benjamin
    et al.
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Klinga-Levan, Karin
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Olsson, Björn
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    GenoScan: Genomic Scanner for Putative miRNA Precursors2014In: Bioinformatics Research and Applications: 10th International Symposium, ISBRA 2014, Zhangjiajie, China, June 28-30, 2014. Proceedings / [ed] Mitra Basu, Yi Pan, Jianxin Wang, Springer, 2014, p. 266-277Conference paper (Refereed)
    Abstract [en]

    The significance of miRNAs has been clarified over the last decade as thousands of these small non-coding RNAs have been found in a wide variety of species. By binding to specific target mRNAs, miRNAs act as negative regulators of gene expression in many different biological processes. Computational approaches for discovery of miRNAs in genomes usually take the form of an algorithm that scans sequences for miRNA-characteristic hairpins, followed by classification of those hairpins as miRNAs or nonmiRNAs. In this study, two new approaches to genome-scale miRNA discovery are presented and evaluated. These methods, one ensemble-based and one using logistic regression, have been designed to detect miRNA candidates without relying on conservation or transcriptome data, and to achieve high-confidence predictions in reasonable computational time. GenoScan achieves high accuracy with a good balance between sensitivity and specificity. In a benchmark evaluation including 15 previously published methods, the regression-based approach in GenoScan achieved the highest classification accuracy.

1 - 9 of 9
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