Change search
Refine search result
1 - 25 of 25
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Birgisson, H
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Upper Abdominal Surgery.
    Tsimogiannis, Kostas
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Upper Abdominal Surgery.
    Freyhult, Eva
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Kamali-Moghaddam, Masood
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala Univ, Sci Life Lab, Dept Immunol Genet & Pathol, Uppsala, Sweden.
    Plasma Protein Profiling Reveal Osteoprotegerin as a Marker of Prognostic Impact for Colorectal Cancer2018In: Translational Oncology, ISSN 1944-7124, E-ISSN 1936-5233, Vol. 11, no 4, p. 1034-1043Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Due to difficulties in predicting recurrences in colorectal cancer stages II and III, reliable prognostic biomarkers could be a breakthrough for individualized treatment and follow-up. OBJECTIVE: To find potential prognostic protein biomarkers in colorectal cancer, using the proximity extension assays. METHODS: A panel of 92 oncology-related proteins was analyzed with proximity extension assays, in plasma from a cohort of 261 colorectal cancer patients with stage II-IV. The survival analyses were corrected for disease stage and age, and the recurrence analyses were corrected for disease stage. The significance threshold was adjusted for multiple comparisons. RESULTS: The plasma proteins expression levels had a greater prognostic relevance in disease stage III colorectal cancer than in disease stage II, and for overall survival than for time to recurrence. Osteoprotegerin was the only biomarker candidate in the protein panel that had a statistical significant association with overall survival (P = .00029). None of the proteins were statistically significantly associated with time to recurrence. CONCLUSIONS: Of the 92 analyzed plasma proteins, osteoprotegerin showed the strongest prognostic impact in patients with colorectal cancer, and therefore osteoprotegerin is a potential predictive marker, and it also could be a target for treatments.

  • 2.
    Chandran, Vineesh Indira
    et al.
    Lund Univ, Sect Oncol & Pathol, Dept Clin Sci, Barngatan 2 B, SE-22185 Lund, Sweden.
    Welinder, Charlotte
    Lund Univ, Sect Oncol & Pathol, Dept Clin Sci, Barngatan 2 B, SE-22185 Lund, Sweden;Lund Univ, CEBMMS, Lund, Sweden.
    Mansson, Ann-Sofie
    Lund Univ, Sect Oncol & Pathol, Dept Clin Sci, Barngatan 2 B, SE-22185 Lund, Sweden.
    Offer, Svenja
    Lund Univ, Sect Oncol & Pathol, Dept Clin Sci, Barngatan 2 B, SE-22185 Lund, Sweden.
    Freyhult, Eva
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Pernemalm, Maria
    Karolinska Inst, Dept Oncol & Pathol, Solna, Sweden.
    Lund, Sigrid M.
    Aalborg Univ Hosp, Dept Clin Biochem, Aalborg, Denmark.
    Pedersen, Shona
    Aalborg Univ Hosp, Dept Clin Biochem, Aalborg, Denmark;Aalborg Univ, Fac Clin Med, Aalborg, Denmark.
    Lehtio, Janne
    Karolinska Inst, Dept Oncol & Pathol, Solna, Sweden.
    Marko-Varga, Gyorgy
    Lund Univ, CEBMMS, Lund, Sweden;Lund Univ, Dept Biomed Engn, Biomed Ctr, Clin Prot Sci & Imaging, Lund, Sweden.
    Johansson, Maria C.
    Lund Univ, Sect Oncol & Pathol, Dept Clin Sci, Barngatan 2 B, SE-22185 Lund, Sweden.
    Englund, Elisabet
    Lund Univ, Sect Oncol & Pathol, Dept Clin Sci, Barngatan 2 B, SE-22185 Lund, Sweden.
    Sundgren, Pia C.
    Lund Univ, Sect Diagnost Radiol, Dept Clin Sci, Lund, Sweden;Lund Univ, Lund BioImaging Ctr, Lund, Sweden;Skane Univ Hosp, Dept Med Imaging & Funct, Lund, Sweden.
    Belting, Mattias
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology. Lund Univ, Sect Oncol & Pathol, Dept Clin Sci, Barngatan 2 B, SE-22185 Lund, Sweden;Skane Univ Hosp, Dept Hematol Oncol & Radiophys, Lund, Sweden.
    Ultrasensitive Immunoprofiling of Plasma Extracellular Vesicles Identifies Syndecan-1 as a Potential Tool for Minimally Invasive Diagnosis of Glioma2019In: Clinical Cancer Research, ISSN 1078-0432, E-ISSN 1557-3265, Vol. 25, no 10, p. 3115-3127Article in journal (Refereed)
    Abstract [en]

    Purpose: Liquid biopsy has great potential to improve the management of brain tumor patients at high risk of surgery-associated complications. Here, the aim was to explore plasma extracellular vesicle (plEV) immunoprofiling as a tool for noninvasive diagnosis of glioma. Experimental Design: PlEV isolation and analysis were optimized using advanced mass spectrometry, nanoparticle tracking analysis, and electron microscopy. We then established a new procedure that combines size exclusion chromatography isolation and proximity extension assay-based ultrasensitive immunoprofiling of plEV proteins that was applied on a well-defined glioma study cohort (n = 82). Results: Among potential candidates, we for the first time identify syndecan-1 (SDC1) as a plEV constituent that can discriminate between high-grade glioblastoma multiforme (GBM, WHO grade IV) and low-grade glioma [LGG, WHO grade II; area under the ROC curve (AUC): 0.81; sensitivity: 71%; specificity: 91%]. These findings were independently validated by ELISA. Tumor SDC1 mRNA expression similarly discriminated between GBM and LGG in an independent glioma patient population from The Cancer Genome Atlas cohort (AUC: 0.91; sensitivity: 79%; specificity: 91%). In experimental studies with GBM cells, we show that SDC1 is efficiently sorted to secreted EVs. Importantly, we found strong support of plEV(SDC1) originating from GBM tumors, as plEVSDC1 correlated with SDC1 protein expression in matched patient tumors, and plEV(SDC1) was decreased postoperatively depending on the extent of surgery. Conclusions: Our studies support the concept of circulating plEVs as a tool for noninvasive diagnosis and monitoring of gliomas and should move this field closer to the goal of improving the management of cancer patients.

  • 3.
    Edvinsson, Åsa
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Research group (Dept. of women´s and children´s health), Reproductive Health.
    Bränn, Emma
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Research group (Dept. of women´s and children´s health), Reproductive Health. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Research group (Dept. of women´s and children´s health), Obstetrics and Reproductive Health Research.
    Hellgren, Charlotte
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health.
    Freyhult, Eva
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    White, Richard
    Norwegian Inst Publ Hlth, Oslo, Norway..
    Kamali-Moghaddam, Masood
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Olivier, Jocelien
    Univ Groningen, Groningen Inst Evolutionary Life Sci, Unit Behav Neurosci, Dept Neurobiol, Groningen, Netherlands..
    Bergquist, Jonas
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Analytical Chemistry.
    Boström, Adrian E.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Functional Pharmacology.
    Schiöth, Helgi B.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Functional Pharmacology.
    Skalkidou, Alkistis
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health.
    Cunningham, Janet
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Psychiatry, University Hospital.
    Sundström Poromaa, Inger
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health.
    Lower inflammatory markers in women with antenatal depression brings the M1/M2 balance into focus from a new direction2017In: Psychoneuroendocrinology, ISSN 0306-4530, E-ISSN 1873-3360, Vol. 80, p. 15-25Article in journal (Refereed)
    Abstract [en]

    Background: Antenatal depression and use of serotonin reuptake inhibitors (SSRI) in pregnancy have both been associated with an increased risk of poor pregnancy outcomes, such as preterm birth and impaired fetal growth. While the underlying biological pathways for these complications are poorly understood, it has been hypothesized that inflammation may be a common physiological pathway. The aim of the present study was to assess peripheral inflammatory markers in healthy women, women with antenatal depression, and in women using SSRI during pregnancy.

    Methods: 160 healthy pregnant controls, 59 women with antenatal depression and 39 women on treatment with SSRIs were included. The relative levels of 92 inflammatory proteins were analyzed by proximity extension assay technology.

    Results: Overall, 23 of the inflammatory markers were significantly lower in women with antenatal depression and in women on treatment with SSRIs in comparison with the healthy controls. No difference in any of the inflammatory markers was observed between women with antenatal depression and those who were using SSRI. Top three inflammatory markers that were down-regulated in women with antenatal depression were TNF-related apoptosis-inducing ligand (TRAIL), p = 0.000001, macrophage colony-stimulating factor 1 (CSF-1), p = 0.000004, and fractalkine (CX3CL1), p =0.000005. Corresponding inflammatory markers in SSRI users were CSF-1, p = 0.000011, vascular endothelial growth factor A (VEGF-A), p =0.000016, and IL-15 receptor subunit alpha (IL-15RA), p = 0.000027. The inflammatory markers were negatively correlated with cortisone serum concentrations in controls, but not in the cases. Differential DNA methylation of was found for seven of these inflammatory markers in an independent epigenetics cohort.

    Conclusion: Women with antenatal depression or on SSRI treatment have lower levels of a number of peripheral inflammatory markers than healthy pregnant controls. Hypothetically, this could be due to dysregulated switch to the pro-M2 milieu that characterizes normal third trimester pregnancy. However, longitudinal blood sampling is needed to elucidate whether the presumably dysregulated M2 shift is driving the development of antenatal depression or is a result of the depression.

  • 4.
    Freyhult, E
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Andersson, K
    Gustafsson, M G
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Technology, Department of Engineering Sciences. Signals and systems.
    Structural modeling extends QSAR analysis of antibody-lysozyme interactions to 3D-QSAR2003In: Biophysical Journal, Vol. 84, no 4, p. 2264-2272Article in journal (Refereed)
  • 5.
    Freyhult, Eva
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Mathematics.
    New techniques for analyzing RNA structure2004Licentiate thesis, monograph (Other scientific)
  • 6.
    Freyhult, Eva
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Cui, Yuanyuan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Nilsson, Olle
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Ardell, David H.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    New computational methods reveal tRNA identity element divergence between Proteobacteria and Cyanobacteria2007In: Biochimie, ISSN 0300-9084, E-ISSN 1638-6183, Vol. 89, no 10, p. 1276-1288Article in journal (Refereed)
    Abstract [en]

    There are at least 21 subfunctional classes of tRNAs in most cells that, despite a very highly conserved and compact common structure, must interact specifically with different cliques of proteins or cause grave organismal consequences. Protein recognition of specific tRNA substrates is achieved in part through class-restricted tRNA features called tRNA identity determinants. In earlier work we used TFAM, a statistical classifier of tRNA function, to show evidence of unexpectedly large diversity among bacteria in tRNA identity determinants. We also created a data reduction technique called function logos to visualize identity determinants for a given taxon. Here we show evidence that determinants for lysylated isoleucine tRNAs are not the same in Proteobacteria as in other bacterial groups including the Cyanobacteria. Consistent with this, the lysylating biosynthetic enzyme TilS lacks a C-terminal domain in Cyanobacteria that is present in Proteobacteria. We present here, using function logos, a map estimating all potential identity determinants generally operational in Cyanobacteria and Proteobacteria. To further isolate the differences in potential tRNA identity determinants between Proteobacteria and Cyanobacteria, we created two new data reduction visualizations to contrast sequence and function logos between two taxa. One, called Information Difference logos (ID logos), shows the evolutionary gain or retention of functional information associated to features in one lineage. The other, Kullback–Leibler divergence Difference logos (KLD logos), shows recruitments or shifts in the functional associations of features, especially those informative in both lineages. We used these new logos to specifically isolate and visualize the differences in potential tRNA identity determinants between Proteobacteria and Cyanobacteria. Our graphical results point to numerous differences in potential tRNA identity determinants between these groups. Although more differences in general are explained by shifts in functional association rather than gains or losses, the apparent identity differences in lysylated isoleucine tRNAs appear to have evolved through both mechanisms.

  • 7.
    Freyhult, Eva
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Gardner, Paul P
    Moulton, Vincent
    A comparison of RNA folding measures.2005In: BMC Bioinformatics, ISSN 1471-2105, Vol. 6, no 1, p. 241-Article in journal (Refereed)
  • 8.
    Freyhult, Eva
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Gustafsson, Mats G.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Strömbergsson, Helena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    A Machine Learning Approach to Explain Drug Selectivity to Soluble and Membrane Protein Targets2015In: Molecular Informatics, ISSN 1868-1751, Vol. 34, no 1, p. 44-52Article in journal (Refereed)
  • 9.
    Freyhult, Eva
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, Vincent
    Ardell, David
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Visualizing bacterial tRNA identity determinants and antideterminants using function logos and inverse function logos.2006In: Nucleic Acids Research, ISSN 1362-4962, Vol. 34, no 3, p. 905-916Article in journal (Refereed)
  • 10.
    Freyhult, Eva
    et al.
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Moulton, Vincent
    Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
    Gardner, Paul
    Predicting RNA structure using mutual information.2005In: Applied Bioinformatics, ISSN 1175-5636, Vol. 4, no 1, p. 53-59Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: With the ever-increasing number of sequenced RNAs and the establishment of new RNA databases, such as the Comparative RNA Web Site and Rfam, there is a growing need for accurately and automatically predicting RNA structures from multiple alignments. Since RNA secondary structure is often conserved in evolution, the well known, but underused, mutual information measure for identifying covarying sites in an alignment can be useful for identifying structural elements. This article presents MIfold, a MATLAB((R)) toolbox that employs mutual information, or a related covariation measure, to display and predict conserved RNA secondary structure (including pseudoknots) from an alignment. RESULTS: We show that MIfold can be used to predict simple pseudoknots, and that the performance can be adjusted to make it either more sensitive or more selective. We also demonstrate that the overall performance of MIfold improves with the number of aligned sequences for certain types of RNA sequences. In addition, we show that, for these sequences, MIfold is more sensitive but less selective than the related RNAalifold structure prediction program and is comparable with the COVE structure prediction package. CONCLUSION: MIfold provides a useful supplementary tool to programs such as RNA Structure Logo, RNAalifold and COVE, and should be useful for automatically generating structural predictions for databases such as Rfam. AVAILABILITY: MIfold is freely available from http://www.lcb.uu.se/~evaf/MIfold/

  • 11.
    Freyhult, Eva
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signal Processing.
    Prusis, Peteris
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signal Processing.
    Lapinsh, Maris
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signal Processing.
    Wikberg, Jarl E S
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signal Processing.
    Moulton, Vincent
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signal Processing.
    Gustafsson, Mats G
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences.
    Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling2005In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 6, p. 50-Article in journal (Refereed)
    Abstract [en]

    Background

    Proteochemometrics is a new methodology that allows prediction of protein function directly from real interaction measurement data without the need of 3D structure information. Several reported proteochemometric models of ligand-receptor interactions have already yielded significant insights into various forms of bio-molecular interactions. The proteochemometric models are multivariate regression models that predict binding affinity for a particular combination of features of the ligand and protein. Although proteochemometric models have already offered interesting results in various studies, no detailed statistical evaluation of their average predictive power has been performed. In particular, variable subset selection performed to date has always relied on using all available examples, a situation also encountered in microarray gene expression data analysis.

    Results

    A methodology for an unbiased evaluation of the predictive power of proteochemometric models was implemented and results from applying it to two of the largest proteochemometric data sets yet reported are presented. A double cross-validation loop procedure is used to estimate the expected performance of a given design method. The unbiased performance estimates (P2) obtained for the data sets that we consider confirm that properly designed single proteochemometric models have useful predictive power, but that a standard design based on cross validation may yield models with quite limited performance. The results also show that different commercial software packages employed for the design of proteochemometric models may yield very different and therefore misleading performance estimates. In addition, the differences in the models obtained in the double CV loop indicate that detailed chemical interpretation of a single proteochemometric model is uncertain when data sets are small.

    Conclusion

    The double CV loop employed offer unbiased performance estimates about a given proteochemometric modelling procedure, making it possible to identify cases where the proteochemometric design does not result in useful predictive models. Chemical interpretations of single proteochemometric models are uncertain and should instead be based on all the models selected in the double CV loop employed here.

  • 12.
    Hammerling, Ulf
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Edberg, Anna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Sand, Salomon
    Fagt, Sisse
    Knudsen, Vibeke Kildegaard
    Andersen, Lene Frost
    Lindroos, Anna Karin
    Soeria-Atmadja, Daniel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Gustafsson, Mats G.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Identifying Food Consumption Patterns among Young Consumers by Unsupervised and Supervised Multivariate Data Analysis2014In: European Journal of Nutrition & Food Safety, ISSN 2347-5641, Vol. 4, no 4, p. 392-403Article in journal (Refereed)
    Abstract [en]

    Although computational multivariate data analysis (MDA) already has been employed in the dietary survey area, the results reported are based mainly on classical exploratory (descriptive) techniques. Therefore, data of a Swedish and a Danish dietary survey on young consumers (4 to 5 years of age) were subjected not only to modern exploratory MDA, but also modern predictive MDA that via supervised learning yielded predictive classification models. The exploratory part, also encompassing Swedish 8 or 11-year old Swedish consumers, included new innovative forms of hierarchical clustering and bi-clustering. This resulted in several interesting multi-dimensional dietary patterns (dietary prototypes), including striking difference between those of the age-matched Danish and Swedish children. The predictive MDA disclosed additional multi-dimensional food consumption relationships. For instance, the consumption patterns associated with each of several key foods like bread, milk, potato and sweetened beverages, were found to differ markedly between the Danish and Swedish consumers. In conclusion, the joint application of modern descriptive and predictive MDA to dietary surveys may enable new levels of diet quality evaluation and perhaps also prototype-based toxicology risk assessment.

  • 13.
    Landegren, Nils
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Autoimmunity. Uppsala University, Science for Life Laboratory, SciLifeLab. Karolinska Inst, Karolinska Univ Hosp, Dept Med Solna, Stockholm, Sweden.
    Rosen, Lindsey B.
    NIAID, Lab Clin Immunol & Microbiol, NIH, 9000 Rockville Pike, Bethesda, MD 20892 USA.
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab. Natl Bioinformat Infrastruct, Dept Med Sci, Uppsala, Sweden.
    Eriksson, Daniel
    Karolinska Inst, Karolinska Univ Hosp, Dept Med Solna, Stockholm, Sweden;Karolinska Univ Hosp, Dept Endocrinol Metab & Diabet, Stockholm, Sweden.
    Fall, Tove
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular epidemiology.
    Smith, Gustav
    Lund Univ, Skane Univ Hosp, Dept Cardiol, Clin Sci, Lund, Sweden;MIT, Broad Inst Harvard, Program Med & Populat Genet, Cambridge, MA 02139 USA;Lund Univ, Ctr Diabet, Wallenberg Ctr Mol Med, Lund, Sweden.
    Ferre, Elise M. N.
    NIAID, Lab Clin Immunol & Microbiol, NIH, 9000 Rockville Pike, Bethesda, MD 20892 USA.
    Brodin, Petter
    Lund Univ, Skane Univ Hosp, Dept Cardiol, Clin Sci, Lund, Sweden;Karolinska Inst, Dept Womens & Childrens Hlth, Sci Life Lab, Stockholm, Sweden;Karolinska Univ Hosp, Dept Newborn Med, Stockholm, Sweden.
    Sharon, Donald
    Stanford Univ, Dept Genet, Sch Med, Stanford, CA 94305 USA.
    Snyder, Michael
    Lund Univ, Ctr Diabet, Wallenberg Ctr Mol Med, Lund, Sweden;Stanford Univ, Dept Genet, Sch Med, Stanford, CA 94305 USA.
    Lionakis, Michail
    NIAID, Lab Clin Immunol & Microbiol, NIH, 9000 Rockville Pike, Bethesda, MD 20892 USA.
    Anderson, Mark
    Univ Calif San Francisco, Ctr Diabet, San Francisco, CA 94143 USA.
    Kampe, Olle
    Karolinska Inst, Karolinska Univ Hosp, Dept Med Solna, Stockholm, Sweden;Karolinska Univ Hosp, Dept Endocrinol Metab & Diabet, Stockholm, Sweden;Univ Bergen, KG Jebsen Ctr Autoimmune Dis, Bergen, Norway.
    Comment on 'AIRE-deficient patients harbor unique high-affinity disease-ameliorating autoantibodies'2019In: eLIFE, E-ISSN 2050-084X, Vol. 8, article id e43578Article in journal (Other academic)
    Abstract [en]

    The AIRE gene plays a key role in the development of central immune tolerance by promoting thymic presentation of tissue-specific molecules. Patients with AIRE-deficiency develop multiple autoimmune manifestations and display autoantibodies against the affected tissues. In 2016 it was reported that: i) the spectrum of autoantibodies in patients with AIRE-deficiency is much broader than previously appreciated; ii) neutralizing autoantibodies to type I interferons (IFNs) could provide protection against type 1 diabetes in these patients (Meyer et al., 2016). We attempted to replicate these new findings using a similar experimental approach in an independent patient cohort, and found no evidence for either conclusion.

  • 14.
    Landegren, Nils
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Autoimmunity. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sharon, Donald
    Freyhult, Eva
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Hallgren, Åsa
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Eriksson, Daniel
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Edqvist, Per-Henrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Bensing, Sophie
    Wahlberg, Jeanette
    Nelson, Lawrence M
    Gustafsson, Jan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Pediatrics.
    Husebye, Eystein S
    Anderson, Mark S
    Snyder, Michael
    Kämpe, Olle
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Autoimmunity.
    Proteome-wide survey of the autoimmune target repertoire in autoimmune polyendocrine syndrome type 12016In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 6, article id 20104Article in journal (Refereed)
    Abstract [en]

    Autoimmune polyendocrine syndrome type 1 (APS1) is a monogenic disorder that features multiple autoimmune disease manifestations. It is caused by mutations in the Autoimmune regulator (AIRE) gene, which promote thymic display of thousands of peripheral tissue antigens in a process critical for establishing central immune tolerance. We here used proteome arrays to perform a comprehensive study of autoimmune targets in APS1. Interrogation of established autoantigens revealed highly reliable detection of autoantibodies, and by exploring the full panel of more than 9000 proteins we further identified MAGEB2 and PDILT as novel major autoantigens in APS1. Our proteome-wide assessment revealed a marked enrichment for tissue-specific immune targets, mirroring AIRE's selectiveness for this category of genes. Our findings also suggest that only a very limited portion of the proteome becomes targeted by the immune system in APS1, which contrasts the broad defect of thymic presentation associated with AIRE-deficiency and raises novel questions what other factors are needed for break of tolerance.

  • 15.
    Landegren, Nils
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Autoimmunity.
    Sharon, Donald
    Shum, Anthony K.
    Khan, Imran S.
    Fasano, Kayla J.
    Hallgren, Åsa
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Autoimmunity. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kampf, Caroline
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Ardesjo-Lundgren, Brita
    Alimohammadi, Mohammad
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Dermatology and Venereology.
    Rathsman, Sandra
    Ludvigsson, Jonas F.
    Lundh, Dan
    Motrich, Ruben
    Rivero, Virginia
    Fong, Lawrence
    Giwercman, Aleksander
    Gustafsson, Jan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Pediatrics.
    Perheentupa, Jaakko
    Husebye, Eystein S.
    Anderson, Mark S.
    Snyder, Michael
    Kämpe, Olle
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Autoimmunity.
    Transglutaminase 4 as a prostate autoantigen in male subfertility2015In: Science Translational Medicine, ISSN 1946-6234, E-ISSN 1946-6242, Vol. 7, no 292, article id 292ra101Article in journal (Refereed)
    Abstract [en]

    Autoimmune polyendocrine syndrome type 1 (APS1), a monogenic disorder caused by AIRE gene mutations, features multiple autoimmune disease components. Infertility is common in both males and females with APS1. Although female infertility can be explained by autoimmune ovarian failure, the mechanisms underlying male infertility have remained poorly understood. We performed a proteome-wide autoantibody screen in APS1 patient sera to assess the autoimmune response against the male reproductive organs. By screening human protein arrays with male and female patient sera and by selecting for gender-imbalanced autoantibody signals, we identified transglutaminase 4 (TGM4) as a male-specific autoantigen. Notably, TGM4 is a prostatic secretory molecule with critical role in male reproduction. TGM4 autoantibodies were detected in most of the adult male APS1 patients but were absent in all the young males. Consecutive serum samples further revealed that TGM4 autoantibodies first presented during pubertal age and subsequent to prostate maturation. We assessed the animal model for APS1, the Aire-deficient mouse, and found spontaneous development of TGM4 autoantibodies specifically in males. Aire-deficient mice failed to present TGM4 in the thymus, consistent with a defect in central tolerance for TGM4. In the mouse, we further link TGM4 immunity with a destructive prostatitis and compromised secretion of TGM4. Collectively, our findings in APS1 patients and Aire-deficient mice reveal prostate autoimmunity as a major manifestation of APS1 with potential role in male subfertility.

  • 16. Larssen, Pia
    et al.
    Wik, Lotta
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Czarnewski, Paulo
    Eldh, Maria
    Löf, Liza
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Ronquist, Göran
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Biochemial structure and function.
    Dubois, Louise
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Biochemial structure and function.
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Gallant, Caroline
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Oelrich, Johan
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Larsson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Biochemial structure and function.
    Ronquist, Gunnar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Biochemial structure and function.
    Villablanca, Eduardo
    Landegren, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Gabrielsson, Susanne
    Kamali-Moghaddam, Masood
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Tracing Cellular Origin of Human Exosomes Using Multiplex Proximity Extension Assay2017In: Molecular & cellular proteomics (online), ISSN 1535-9476, E-ISSN 1535-9484, Vol. 16, no 3, p. 502-511Article in journal (Refereed)
    Abstract [en]

    Extracellular vesicles (EVs) are membrane-coated objects such as exosomes and microvesicles, released by many cell-types. Their presence in body fluids and the variable surface composition and content render them attractive potential biomarkers. The ability to determine their cellular origin could greatly move the field forward. We used multiplex proximity extension assays (PEA) to identify with high specificity and sensitivity the protein profiles of exosomes of different origins, including seven cell lines and two different body fluids. By comparing cells and exosomes, we successfully identified the cells originating the exosomes. Furthermore, by principal component analysis of protein patterns human milk EVs and prostasomes released from prostate acinar cells clustered with cell lines from breast and prostate tissues, respectively. Milk exosomes uniquely expressed CXCL5, MIA and KLK6, while prostasomes carried NKX31, GSTP1 and SRC, highlighting that EVs originating from different origins express distinct proteins. In conclusion, PEA provides a powerful protein screening tool in exosome research, for purposes of identifying the cell source of exosomes, or new biomarkers in diseases such as cancer and inflammation.

  • 17.
    Lind, Alexander
    et al.
    Lund Univ, Skåne Univ Hosp SUS, Dept Clin Sci, Clin Res Ctr.
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Ramelius, Anita
    Lund Univ, Skåne Univ Hosp SUS, Dept Clin Sci, Clin Res Ctr.
    Olsson, Tomas
    Karolinska Inst, Dept Clin Neurosci.
    Arnheim-Dahlström, Lisen
    Karolinska Inst, Dept Med Epidemiol & Biostat.
    Lamb, Favelle
    Karolinska Inst, Dept Med Epidemiol & Biostat.
    Khademi, Mohsen
    Karolinska Inst, Dept Clin Neurosci.
    Ambati, Aditya
    Karolinska Inst, Dept Med.
    Maeurer, Markus
    Karolinska Inst, LabMed, TIM.; Karolinska Univ Hosp, CAST.
    Bomfim, Izaura Lima
    Karolinska Inst, Dept Clin Neurosci.
    Fink, Katharina
    Karolinska Inst, Dept Clin Neurosci.; Karolinska Univ Hosp, Dept Neurol.
    Fex, Malin
    Lund Univ, Skåne Univ Hosp SUS, Dept Clin Sci, Clin Res Ctr.
    Torn, Carina
    Lund Univ, Skåne Univ Hosp SUS, Dept Clin Sci, Clin Res Ctr.
    Elding Larsson, Helena
    Lund Univ, Skåne Univ Hosp SUS, Dept Clin Sci, Clin Res Ctr.
    Lernmark, Åke
    Lund Univ, Skåne Univ Hosp SUS, Dept Clin Sci, Clin Res Ctr.
    Antibody Affinity Against 2009 A/H1N1 Influenza and Pandemrix Vaccine Nucleoproteins Differs Between Childhood Narcolepsy Patients and Controls2017In: Viral immunology, ISSN 0882-8245, E-ISSN 1557-8976, Vol. 30, no 8, p. 590-600Article in journal (Refereed)
    Abstract [en]

    Increased narcolepsy incidence was observed in Sweden following the 2009 influenza vaccination with Pandemrix((R)). A substitution of the 2009 nucleoprotein for the 1934 variant has been implicated in narcolepsy development. The aims were to determine (a) antibody levels toward wild-type A/H1N1-2009[A/California/04/2009(H1N1)] (NP-CA2009) and Pandemrix-[A/Puerto Rico/8/1934(H1N1)] (NP-PR1934) nucleoproteins in 43 patients and 64 age-matched controls; (b) antibody affinity in reciprocal competitive assays in 11 childhood narcolepsy patients compared with 21 age-matched controls; and (c) antibody levels toward wild-type A/H1N1-2009[A/California/04/2009(H1N1)] (H1N1 NS1), not a component of the Pandemrix vaccine. In vitro transcribed and translated S-35-methionine-labeled H1N1 influenza A virus proteins were used in radiobinding reciprocal competition assays to estimate antibody levels and affinity (Kd). Childhood patients had higher NP-CA2009 (p=0.0339) and NP-PR1934 (p=0.0246) antibody levels compared with age-matched controls. These childhood controls had lower NP-CA2009 (p=0.0221) and NP-PR1934 (p=0.00619) antibodies compared with controls 13 years or older. In contrast, in patients 13 years or older, the levels of NP-PR1934 (p=0.279) and NP-CA2009 (p=0.0644) antibodies did not differ from the older controls. Childhood antibody affinity (Kd) against NP-CA2009 was comparable between controls (68ng/mL) and patients (74ng/mL; p=0.21) with NP-CA2009 and NP-PR1934 displacement (controls: 165ng/mL; patients: 199ng/mL; p=0.48). In contrast, antibody affinity against NP-PR1934 was higher in controls with either NP-PR1934 (controls: 9ng/mL; patients: 20ng/mL; p=0.0031) or NP-CA2009 (controls: 14ng/mL; patients: 23ng/mL; p=0.0048). A/H1N1-NS1 antibodies were detected in 0/43 of the narcolepsy patients compared with 3/64 (4.7%) controls (p=0.272). Similarly, none (0/11) of the childhood patients and 1/21 (4.8%) of the childhood controls had A/H1N1-NS1 antibodies. The higher antibody affinities against NP-PR1934 in controls suggest better protection against wild-type virus. In contrast, the reduced NP-PR1934 antibody affinities among childhood narcolepsy patients suggest poor protection from the wild-type A/H1N1 virus and possibly increased risk for viral damage.

  • 18.
    Lind, Anne-Li
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care.
    Yu, Di
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Bodolea, Constantin
    Department of Anaesthesia and Intensive Care, University of Medicine and Pharmacy, Cluj, Napoca, Romania..
    Ekegren, Titti
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Materials Sciences.
    Larsson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Biochemial structure and function.
    Gustafsson, Mats G
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Katila, Lenka
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care.
    Bergquist, Jonas
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Analytical Chemistry.
    Gordh, Torsten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Anaesthesiology and Intensive Care.
    Landegren, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Kamali-Moghaddam, Masood
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    A Multiplex Protein Panel Applied to Cerebrospinal Fluid Reveals Three New Biomarker Candidates in ALS but None in Neuropathic Pain Patients2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 2, article id e0149821Article in journal (Refereed)
    Abstract [en]

    The objective of this study was to develop and apply a novel multiplex panel of solid-phase proximity ligation assays (SP-PLA) requiring only 20 μL of samples, as a tool for discovering protein biomarkers for neurological disease and treatment thereof in cerebrospinal fluid (CSF). We applied the SP-PLA to samples from two sets of patients with poorly understood nervous system pathologies amyotrophic lateral sclerosis (ALS) and neuropathic pain, where patients were treated with spinal cord stimulation (SCS). Forty-seven inflammatory and neurotrophic proteins were measured in samples from 20 ALS patients and 15 neuropathic pain patients, and compared to normal concentrations in CSF from control individuals. Nineteen of the 47 proteins were detectable in more than 95% of the 72 controls. None of the 21 proteins detectable in CSF from neuropathic pain patients were significantly altered by SCS. The levels of the three proteins, follistatin, interleukin-1 alpha, and kallikrein-5 were all significantly reduced in the ALS group compared to age-matched controls. These results demonstrate the utility of purpose designed multiplex SP-PLA panels in CSF biomarker research for understanding neuropathological and neurotherapeutic mechanisms. The protein changes found in the CSF of ALS patients may be of diagnostic interest.

  • 19.
    Lindqvist, C. Mårten
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Lundmark, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Nordlund, Jessica
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    Ekman, Diana
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Almlöf, Jonas Carlsson
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Raine, Amanda
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Övernäs, Elin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Abrahamsson, Jonas
    Queen Silvia Childrens Hosp, Dept Pediat, Gothenburg, Sweden..
    Frost, Britt-Marie
    Univ Childrens Hosp, Dept Womens & Childrens Hlth, Uppsala, Sweden..
    Grander, Dan
    Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden..
    Heyman, Mats
    Karolinska Univ Hosp, Astrid Lindgren Childrens Hosp, Dept Women & Child Hlth, Childhood Canc Res Unit, Stockholm, Sweden..
    Palle, Josefine
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Pediatrics. Uppsala Univ, Dept Med Sci, Mol Med & Sci Life Lab, Uppsala, Sweden.;Univ Childrens Hosp, Dept Womens & Childrens Hlth, Uppsala, Sweden..
    Forestier, Erik
    Umea Univ, Dept Med Biosci, Umea, Sweden..
    Lönnerholm, Gudmar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Pediatrics.
    Berglund, Eva C.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Syvänen, Ann-Christine
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Deep targeted sequencing in pediatric acute lymphoblastic leukemia unveils distinct mutational patterns between genetic subtypes and novel relapse-associated genes2016In: OncoTarget, ISSN 1949-2553, E-ISSN 1949-2553, Vol. 7, no 39, p. 64071-64088Article in journal (Refereed)
    Abstract [en]

    To characterize the mutational patterns of acute lymphoblastic leukemia (ALL) we performed deep next generation sequencing of 872 cancer genes in 172 diagnostic and 24 relapse samples from 172 pediatric ALL patients. We found an overall greater mutational burden and more driver mutations in T-cell ALL (T-ALL) patients compared to B-cell precursor ALL (BCP-ALL) patients. In addition, the majority of the mutations in T-ALL had occurred in the original leukemic clone, while most of the mutations in BCP-ALL were subclonal. BCP-ALL patients carrying any of the recurrent translocations ETV6-RUNX1, BCR-ABL or TCF3-PBX1 harbored few mutations in driver genes compared to other BCP-ALL patients. Specifically in BCP-ALL, we identified ATRX as a novel putative driver gene and uncovered an association between somatic mutations in the Notch signaling pathway at ALL diagnosis and increased risk of relapse. Furthermore, we identified EP300, ARID1A and SH2B3 as relapse-associated genes. The genes highlighted in our study were frequently involved in epigenetic regulation, associated with germline susceptibility to ALL, and present in minor subclones at diagnosis that became dominant at relapse. We observed a high degree of clonal heterogeneity and evolution between diagnosis and relapse in both BCP-ALL and T-ALL, which could have implications for the treatment efficiency.

  • 20.
    Salami, Falastin
    et al.
    Lund Univ, Clin Res Ctr, Skåne Univ Hosp, Dept Clin Sci, Malmö, Sweden.
    Lee, Hye-Seung
    Univ S Florida, Hlth Informat Inst, Dept Pediat, Tampa, FL USA.
    Freyhult, Eva
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Larsson, Helena Elding
    Lund Univ, Clin Res Ctr, Skåne Univ Hosp, Dept Clin Sci, Malmö, Sweden.
    Lernmark, Åke
    Lund Univ, Clin Res Ctr, Skåne Univ Hosp, Dept Clin Sci, Malmö, Sweden.
    Törn, Carina
    Lund Univ, Clin Res Ctr, Skåne Univ Hosp, Dept Clin Sci, Malmö, Sweden.
    Reduction in White Blood Cell, Neutrophil and Red Blood Cell counts Related to Gender, HLA and Islet Autoantibodies in Swedish TEDDY Children at Increased Risk for Type 1 Diabetes2018In: Diabetes, ISSN 0012-1797, E-ISSN 1939-327X, Vol. 67, no 11, p. 2329--2336, article id db180355Article in journal (Refereed)
    Abstract [en]

    Islet autoantibodies (IAs) precede the clinical onset of type 1 diabetes (T1D); however, the knowledge is limited about whether the prodrome affects complete blood counts (CBCs) in 4- to 12-year-old children with increased genetic risk for T1D. This study tested whether CBCs were altered in 4- to 12-year-old children without (n = 376) or with one or several IAs against insulin, GAD65, or IA-2 (n = 72). CBC was analyzed during longitudinal follow-up in 448 Swedish children enrolled in The Environmental Determinants of Diabetes in the Young (TEDDY) study. A linear mixed-effects model was used to assess potential association between IA and CBC measurements over time. The white blood cell and neutrophil counts were reduced in children with IAs, primarily in boys. In contrast, girls had lower levels of hemoglobin and hematocrit. Positivity for multiple IAs showed the lowest counts in white blood cells and neutrophils in boys and red blood cells, hemoglobin, and hematocrit in girls. These associations were primarily observed in children with the HLA-DR3-DQ2/DR4-DQ8 genotype. We conclude that the reduction in neutrophils and red blood cells in children with multiple IAs and HLA-DR3-DQ2/DR4-DQ8 genotype may signal a sex-dependent islet autoimmunity detected in longitudinal CBCs.

  • 21.
    Skalkidou, Alkistis
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Research group (Dept. of women´s and children´s health), Obstetrics and Reproductive Health Research.
    Sundström Poromaa, Inger
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Research group (Dept. of women´s and children´s health), Reproductive Health.
    Iliadis, Stavros I.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Research group (Dept. of women´s and children´s health), Obstetrics and Reproductive Health Research.
    Huizink, Anja C.
    Hellgren, Charlotte
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Research group (Dept. of women´s and children´s health), Reproductive Health.
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Comasco, Erika
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Stress-related genetic polymorphisms in association with peripartum depression symptoms and stress hormones: A longitudinal population-based study2019In: Psychoneuroendocrinology, ISSN 0306-4530, E-ISSN 1873-3360, Vol. 103, p. 296-305Article in journal (Refereed)
    Abstract [en]

    Individual differences in the response of the stress system to hormonal changes during pregnancy and the postpartum period render some women susceptible to developing depression. The present study sought to investigate peripartum depression and stress hormones in relation to stress-related genotypes. The Edinburgh Postnatal Depression Scale was used to assess peripartum depressive symptoms in a sample of 1629 women, followed from pregnancy week seventeen to six months postpartum. Genotypes of ninety-four haplotype-tag single nucleotide polymorphisms (SNPs) in sixteen genes of the hypothalamus-pituitary-adrenal axis pathway were analyzed and data on psychosocial and demographic factors was collected. In sub-studies, salivary cortisol awakening response in gestational week 35-39, salivary evening cortisol levels in gestational week 36 and postpartum week 6, and blood cortisol and cortisone levels in gestational week 35-39 were analyzed. SNP-set kernel association tests were performed at the gene-level, considering psychosocial and demographic factors, followed by post-hoc analyses of SNPs of significant genes. Statistically significant findings at the 0.05 p-level included SNPs in the hydroxysteroid 11-beta dehydrogenase 1 (HSD11B1) gene in relation to self-rated depression scores in postpartum week six among all participants, and serpin family A member 6 (SERPINA6) gene at the same time-point among women with de novo onset of postpartum depression. SNPs in these genes also associated with stress hormone levels during pregnancy. The present study adds knowledge to the neurobiological basis of peripartum depression by systematically assessing SNPs in stress-regulatory genes and stress-hormone levels in a population-based sample of women.

  • 22.
    Sooman, Linda
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Oncology.
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Jaiswal, Archita
    Navani, Sanjay
    Edqvist, Per-Henrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Pontén, Fredrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Tchougounova, Elena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Cancer and Vascular Biology.
    Smits, Anja
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Elsir, Tamador
    Cancer Center Karolinska, Karolinska University Hospital Solna, Stockholm, Sweden.
    Gullbo, Joachim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Oncology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Lennartsson, Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Ludwig Institute for Cancer Research.
    Bergqvist, Michael
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Oncology.
    Ekman, Simon
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Oncology.
    FGF2 as a potential prognostic biomarker for proneural glioma patients2015In: Acta Oncologica, ISSN 0284-186X, E-ISSN 1651-226X, Vol. 54, no 3, p. 385-394Article in journal (Refereed)
    Abstract [en]

    Background. The survival of high-grade glioma patients is poor and the treatment of these patients can cause severe side effects. This fosters the necessity to identify prognostic biomarkers, in order to optimize treatment and diminish unnecessary suffering of patients. The aim of this study was to identify prognostic biomarkers for high-grade glioma patients.

    Methods. Eleven proteins were selected for analysis due to their suggested importance for survival of patients with other types of cancers and due to a high variation in protein levels between glioma patients (according to the Human Protein Atlas, www.proteinatlas.org). Protein expression patterns of these 11 proteins were analyzed by immunohistochemistry in tumor samples from 97 high-grade glioma patients. The prognostic values of the proteins were analyzed with univariate and multivariate Cox regression analyses for the high-grade glioma patients, including subgroup analyses of histological subtypes and immunohistochemically defined molecular subtypes.

    Results. The proteins with the most significant (univariate and multivariate p < 0.05) correlations were analyzed further with cross-validated Kaplan-Meier analyses for the possibility of predicting survival based on the protein expression pattern of the corresponding candidate. Random Forest classification with variable subset selection was used to analyze if a protein signature consisting of any combination of the 11 proteins could predict survival for the high-grade glioma patients and the subgroup with glioblastoma patients. The proteins which correlated most significantly (univariate and multivariate p < 0.05) to survival in the Cox regression analyses were Myc for all high-grade gliomas and FGF2, CA9 and CD44 for the subgroup of proneural gliomas, with FGF2 having a strong negative predictive value for survival. No prognostic signature of the proteins could be found.

    Conclusion. FGF2 is a potential prognostic biomarker for proneural glioma patients, and warrants further investigation.

  • 23.
    Srivastava, Vaibhav
    et al.
    Umeå University, KTH.
    Obudulu, Ogonna
    Umeå University.
    Bygdell, Joakim
    Löfstedt, Tommy
    Rydén, Patrik
    Nilsson, Robert
    Ahnlund, Maria
    Johansson, Annika
    Jonsson, Pär
    Freyhult, Eva
    Computational life science cluster (CLiC), Department of Chemistry, Umeå University, Umeå, Sweden.
    Qvarnström, Johanna
    Karlsson, Jan
    Melzer, Michael
    Moritz, Thomas
    Trygg, Johan
    Hvidsten, Torgeir R
    Wingsle, Gunnar
    OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI- superoxide dismutase Populus plants2013In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 14, p. 893-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Reactive oxygen species (ROS) are involved in the regulation of diverse physiological processes in plants, including various biotic and abiotic stress responses. Thus, oxidative stress tolerance mechanisms in plants are complex, and diverse responses at multiple levels need to be characterized in order to understand them. Here we present system responses to oxidative stress in Populus by integrating data from analyses of the cambial region of wild-type controls and plants expressing high-isoelectric-point superoxide dismutase (hipI-SOD) transcripts in antisense orientation showing a higher production of superoxide. The cambium, a thin cell layer, generates cells that differentiate to form either phloem or xylem and is hypothesized to be a major reason for phenotypic perturbations in the transgenic plants. Data from multiple platforms including transcriptomics (microarray analysis), proteomics (UPLC/QTOF-MS), and metabolomics (GC-TOF/MS, UPLC/MS, and UHPLC-LTQ/MS) were integrated using the most recent development of orthogonal projections to latent structures called OnPLS. OnPLS is a symmetrical multi-block method that does not depend on the order of analysis when more than two blocks are analysed. Significantly affected genes, proteins and metabolites were then visualized in painted pathway diagrams.

    RESULTS: The main categories that appear to be significantly influenced in the transgenic plants were pathways related to redox regulation, carbon metabolism and protein degradation, e.g. the glycolysis and pentose phosphate pathways (PPP). The results provide system-level information on ROS metabolism and responses to oxidative stress, and indicate that some initial responses to oxidative stress may share common pathways.

    CONCLUSION: The proposed data evaluation strategy shows an efficient way of compiling complex, multi-platform datasets to obtain significant biological information.

  • 24.
    Thuring, Camilla
    et al.
    Lund Univ, Immunol Sect, Dept Expt Med Sci, SE-22184 Lund, Sweden..
    Follin, Elna
    Lund Univ, Immunol Sect, Dept Expt Med Sci, SE-22184 Lund, Sweden..
    Geironson, Linda
    Lund Univ, Immunol Sect, Dept Expt Med Sci, SE-22184 Lund, Sweden..
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Junghans, Victoria
    Lund Univ, Immunol Sect, Dept Expt Med Sci, SE-22184 Lund, Sweden..
    Harndahl, Mikkel
    Inst Int Hlth Immunol & Microbiol, Dept Expt Immunol, DK-2200 Copenhagen, Denmark..
    Buus, Soren
    Inst Int Hlth Immunol & Microbiol, Dept Expt Immunol, DK-2200 Copenhagen, Denmark..
    Paulsson, Kajsa M.
    Lund Univ, Immunol Sect, Dept Expt Med Sci, SE-22184 Lund, Sweden..
    HLA class I is most tightly linked to levels of tapasin compared with other antigen-processing proteins in glioblastoma2015In: British Journal of Cancer, ISSN 0007-0920, E-ISSN 1532-1827, Vol. 113, no 6, p. 952-962Article in journal (Refereed)
    Abstract [en]

    Background: Tumour cells can evade the immune system by dysregulation of human leukocyte antigens (HLA-I). Low quantity and/or altered quality of HLA-I cell surface expression is the result of either HLA-I alterations or dysregulations of proteins of the antigen-processing machinery (APM). Tapasin is an APM protein dedicated to the maturation of HLA-I and dysregulation of tapasin has been linked to higher malignancy in several different tumours. Methods: We studied the expression of APM components and HLA-I, as well as HLA-I tapasin-dependency profiles in glioblastoma tissues and corresponding cell lines. Results: Tapasin displayed the strongest correlation to HLA-I heavy chain but also clustered with beta(2)-microglobulin, transporter associated with antigen processing (TAP) and LMP. Moreover, tapasin also correlated to survival of glioblastoma patients. Some APM components, for example, TAP1/TAP2 and LMP2/LMP7, showed variable but coordinated expression, whereas ERAP1/ERAP2 displayed an imbalanced expression pattern. Furthermore, analysis of HLA-I profiles revealed variable tapasin dependence of HLA-I allomorphs in glioblastoma patients. Conclusions: Expression of APM proteins is highly variable between glioblastomas. Tapasin stands out as the APM component strongest correlated to HLA-I expression and we proved that HLA-I profiles in glioblastoma patients include tapasin-dependent allomorphs. The level of tapasin was also correlated with patient survival time. Our results support the need for individualisation of immunotherapy protocols.

  • 25. Önskog, Jenny
    et al.
    Freyhult, Eva
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Landfors, Mattias
    Ryden, Patrik
    Hvidsten, Torgeir R.
    Classification of microarrays: synergistic effects between normalization, gene selection and machine learning2011In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 12, p. 390-Article in journal (Refereed)
    Abstract [en]

    Background: Machine learning is a powerful approach for describing and predicting classes in microarray data. Although several comparative studies have investigated the relative performance of various machine learning methods, these often do not account for the fact that performance (e. g. error rate) is a result of a series of analysis steps of which the most important are data normalization, gene selection and machine learning. Results: In this study, we used seven previously published cancer-related microarray data sets to compare the effects on classification performance of five normalization methods, three gene selection methods with 21 different numbers of selected genes and eight machine learning methods. Performance in term of error rate was rigorously estimated by repeatedly employing a double cross validation approach. Since performance varies greatly between data sets, we devised an analysis method that first compares methods within individual data sets and then visualizes the comparisons across data sets. We discovered both well performing individual methods and synergies between different methods. Conclusion: Support Vector Machines with a radial basis kernel, linear kernel or polynomial kernel of degree 2 all performed consistently well across data sets. We show that there is a synergistic relationship between these methods and gene selection based on the T-test and the selection of a relatively high number of genes. Also, we find that these methods benefit significantly from using normalized data, although it is hard to draw general conclusions about the relative performance of different normalization procedures.

1 - 25 of 25
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf