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  • 1.
    Adori, Csaba
    et al.
    Karolinska Inst, Dept Neurosci, Retzius Lab, Retzius Vag 8, S-17177 Stockholm, Sweden.
    Barde, Swapnali
    Karolinska Inst, Dept Neurosci, Retzius Lab, Retzius Vag 8, S-17177 Stockholm, Sweden.
    Vas, Szilvia
    Semmelweis Univ, Dept Pharmacodynam, Nagyvarad Ter 4, H-1089 Budapest, Hungary; Hungarian Acad Sci, Neuropsychopharmacol & Neurochem Res Grp, Nagyvarad Ter 4, H-1089 Budapest, Hungary.
    Ebner, Karl
    Leopold Franzens Univ Innsbruck, CMBI, Inst Pharm, Dept Pharmacol & Toxicol, Innrain 80-82-3, A-6020 Innsbruck, Austria.
    Su, Jie
    Karolinska Inst, Dept Physiol & Pharmacol, Nanna Svartz Vag 2, S-17177 Stockholm, Sweden.
    Svensson, Camilla
    Karolinska Inst, Dept Physiol & Pharmacol, Nanna Svartz Vag 2, S-17177 Stockholm, Sweden.
    Mathé, Aleksander A
    Karolinska Inst, Sect Psychiat, Dept Clin Neurosci, Tomtebodavagen 18A, S-17177 Stockholm, Sweden.
    Singewald, Nicolas
    Leopold Franzens Univ Innsbruck, CMBI, Inst Pharm, Dept Pharmacol & Toxicol, Innrain 80-82-3, A-6020 Innsbruck, Austria.
    Reinscheid, Rainer R
    Univ Calif Irvine, Dept Pharmaceut Sci, Irvine, CA 92697 USA.
    Uhlén, Mathias
    Karolinska Inst, Dept Neurosci, Sci Life Lab, S-17165 Stockholm, Sweden; Royal Inst Technol, Albanova Univ Ctr, Sci Life Lab, S-17165 Stockholm, Sweden.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Bagdy, György
    Semmelweis Univ, Dept Pharmacodynam, Nagyvarad Ter 4, H-1089 Budapest, Hungary; Hungarian Acad Sci, Neuropsychopharmacol & Neurochem Res Grp, Nagyvarad Ter 4, H-1089 Budapest, Hungary.
    Hökfelt, Tomas
    Karolinska Inst, Dept Neurosci, Retzius Lab, Retzius Vag 8, S-17177 Stockholm, Sweden.
    Exploring the role of neuropeptide S in the regulation of arousal: a functional anatomical study.2016In: Brain Structure and Function, ISSN 1863-2653, E-ISSN 1863-2661, Vol. 221, no 7, p. 3521-3546Article in journal (Refereed)
    Abstract [en]

    Neuropeptide S (NPS) is a regulatory peptide expressed by limited number of neurons in the brainstem. The simultaneous anxiolytic and arousal-promoting effect of NPS suggests an involvement in mood control and vigilance, making the NPS-NPS receptor system an interesting potential drug target. Here we examined, in detail, the distribution of NPS-immunoreactive (IR) fiber arborizations in brain regions of rat known to be involved in the regulation of sleep and arousal. Such nerve terminals were frequently apposed to GABAergic/galaninergic neurons in the ventro-lateral preoptic area (VLPO) and to tyrosine hydroxylase-IR neurons in all hypothalamic/thalamic dopamine cell groups. Then we applied the single platform-on-water (mainly REM) sleep deprivation method to study the functional role of NPS in the regulation of arousal. Of the three pontine NPS cell clusters, the NPS transcript levels were increased only in the peri-coerulear group in sleep-deprived animals, but not in stress controls. The density of NPS-IR fibers was significantly decreased in the median preoptic nucleus-VLPO region after the sleep deprivation, while radioimmunoassay and mass spectrometry measurements showed a parallel increase of NPS in the anterior hypothalamus. The expression of the NPS receptor was, however, not altered in the VLPO-region. The present results suggest a selective activation of one of the three NPS-expressing neuron clusters as well as release of NPS in distinct forebrain regions after sleep deprivation. Taken together, our results emphasize a role of the peri-coerulear cluster in the modulation of arousal, and the importance of preoptic area for the action of NPS on arousal and sleep.

  • 2.
    Aftab, Obaid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Towards High-Throughput Phenotypic and Systemic Profiling of in vitro Growing Cell Populations using Label-Free Microscopy and Spectroscopy: Applications in Cancer Pharmacology2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Modern techniques like automated microscopy and spectroscopy now make it possible to study quantitatively, across multiple phenotypic and molecular parameters, how cell populations are affected by different treatments and/or environmental disturbances. As the technology development at the instrument level often is ahead of the data analytical tools and the scientific questions, there is a large and growing need for computational algorithms enabling desired data analysis. These algorithms must have capacity to extract and process quantitative dynamic information about how the cell population is affected by different stimuli with the final goal to transform this information into development of new powerful therapeutic strategies. In particular, there is a great need for automated systems that can facilitate the analysis of massive data streams for label-free methods such as phase contrast microscopy (PCM) imaging and spectroscopy (NMR). Therefore, in this thesis, algorithms for quantitative high-throughput phenotypic and systemic profiling of in vitro growing cell populations via label-free microscopy and spectroscopy are developed and evaluated. First a two-dimensional filter approach for high-throughput screening for drugs inducing autophagy and apoptosis from phase contrast time-lapse microscopy images is studied. Then new methods and applications are presented for label-free extraction and comparison of time-evolving morphological features in phase-contrast time-lapse microscopy images recorded from in vitro growing cell populations. Finally, the use of dynamic morphology and NMR/MS spectra for implementation of a reference database of drug induced changes, analogous to the outstanding mRNA gene expression based Connectivity Map database, is explored. In conclusion, relatively simple computational methods are useful for extraction of very valuable biological and pharmacological information from time-lapse microscopy images and NMR spectroscopy data offering great potential for biomedical applications in general and cancer pharmacology in particular.

    List of papers
    1. Label-free detection and dynamic monitoring of drug-induced intracellular vesicle formation enabled using a 2-dimensional matched filter
    Open this publication in new window or tab >>Label-free detection and dynamic monitoring of drug-induced intracellular vesicle formation enabled using a 2-dimensional matched filter
    Show others...
    2014 (English)In: Autophagy, ISSN 1554-8627, E-ISSN 1554-8635, Vol. 10, no 1, p. 57-69Article in journal (Refereed) Published
    Abstract [en]

    Analysis of vesicle formation and degradation is a central issue in autophagy research and microscopy imaging is revolutionizing the study of such dynamic events inside living cells. A limiting factor is the need for labeling techniques that are labor intensive, expensive, and not always completely reliable. To enable label-free analyses we introduced a generic computational algorithm, the label-free vesicle detector (LFVD), which relies on a matched filter designed to identify circular vesicles within cells using only phase-contrast microscopy images. First, the usefulness of the LFVD is illustrated by presenting successful detections of autophagy modulating drugs found by analyzing the human colorectal carcinoma cell line HCT116 exposed to each substance among 1266 pharmacologically active compounds. Some top hits were characterized with respect to their activity as autophagy modulators using independent in vitro labeling of acidic organelles, detection of LC3-II protein, and analysis of the autophagic flux. Selected detection results for 2 additional cell lines (DLD1 and RKO) demonstrate the generality of the method. In a second experiment, label-free monitoring of dose-dependent vesicle formation kinetics is demonstrated by recorded detection of vesicles over time at different drug concentrations. In conclusion, label-free detection and dynamic monitoring of vesicle formation during autophagy is enabled using the LFVD approach introduced.

    Keywords
    phase-contrast microscopy, automated microscopy, vesicle detection, autophagy, image processing
    National Category
    Clinical Medicine
    Identifiers
    urn:nbn:se:uu:diva-216046 (URN)10.4161/auto.26678 (DOI)000328812400006 ()
    Conference
    High Content Anlaysis
    Available from: 2014-01-20 Created: 2014-01-17 Last updated: 2017-12-06Bibliographically approved
    2. Label free high throughput screening for apoptosis inducing chemicals using time-lapse microscopy signal processing
    Open this publication in new window or tab >>Label free high throughput screening for apoptosis inducing chemicals using time-lapse microscopy signal processing
    Show others...
    2014 (English)In: Apoptosis (London), ISSN 1360-8185, E-ISSN 1573-675X, Vol. 19, no 9, p. 1411-1418Article in journal (Refereed) Published
    Abstract [en]

    Label free time-lapse microscopy has opened a new avenue to the study of time evolving events in living cells. When combined with automated image analysis it provides a powerful tool that enables automated large-scale spatiotemporal quantification at the cell population level. Very few attempts, however, have been reported regarding the design of image analysis algorithms dedicated to the detection of apoptotic cells in such time-lapse microscopy images. In particular, none of the reported attempts is based on sufficiently fast signal processing algorithms to enable large-scale detection of apoptosis within hours/days without access to high-end computers. Here we show that it is indeed possible to successfully detect chemically induced apoptosis by applying a two-dimensional linear matched filter tailored to the detection of objects with the typical features of an apoptotic cell in phase-contrast images. First a set of recorded computational detections of apoptosis was validated by comparison with apoptosis specific caspase activity readouts obtained via a fluorescence based assay. Then a large screen encompassing 2,866 drug like compounds was performed using the human colorectal carcinoma cell line HCT116. In addition to many well known inducers (positive controls) the screening resulted in the detection of two compounds here reported for the first time to induce apoptosis.

    Keywords
    Apoptosis, high throughput screening, cancer
    National Category
    Cancer and Oncology
    Research subject
    Bioinformatics
    Identifiers
    urn:nbn:se:uu:diva-229069 (URN)10.1007/s10495-014-1009-9 (DOI)000340518000010 ()
    Funder
    Swedish Society for Medical Research (SSMF)
    Available from: 2014-07-29 Created: 2014-07-29 Last updated: 2018-01-09Bibliographically approved
    3. Detection of cell aggregation and altered cell viability by automated label-free video microscopy: A promising alternative to endpoint viability assays in high throughput screening
    Open this publication in new window or tab >>Detection of cell aggregation and altered cell viability by automated label-free video microscopy: A promising alternative to endpoint viability assays in high throughput screening
    Show others...
    2015 (English)In: Journal of Biomolecular Screening, ISSN 1087-0571, E-ISSN 1552-454X, Vol. 20, no 3, p. 372-381Article in journal (Refereed) Published
    Abstract [en]

    Automated phase-contrast video microscopy now makes it feasible to monitor a high-throughput (HT) screening experiment in a 384-well microtiter plate format by collecting one time-lapse video per well. Being a very cost-effective and label-free monitoring method, its potential as an alternative to cell viability assays was evaluated. Three simple morphology feature extraction and comparison algorithms were developed and implemented for analysis of differentially time-evolving morphologies (DTEMs) monitored in phase-contrast microscopy videos. The most promising layout, pixel histogram hierarchy comparison (PHHC), was able to detect several compounds that did not induce any significant change in cell viability, but made the cell population appear as spheroidal cell aggregates. According to recent reports, all these compounds seem to be involved in inhibition of platelet-derived growth factor receptor (PDGFR) signaling. Thus, automated quantification of DTEM (AQDTEM) holds strong promise as an alternative or complement to viability assays in HT in vitro screening of chemical compounds.

    Keywords
    time-lapse microscopy, video microscopy, phase contrast microscopy, differentially time evolving morphologies, high throughput screening (HTS), cell aggregation, PDGFR signalling.
    National Category
    Bioinformatics (Computational Biology) Social and Clinical Pharmacy
    Research subject
    Bioinformatics; Clinical Pharmacology
    Identifiers
    urn:nbn:se:uu:diva-234561 (URN)10.1177/1087057114562158 (DOI)000350310000007 ()25520371 (PubMedID)
    Available from: 2014-10-21 Created: 2014-10-21 Last updated: 2018-01-11Bibliographically approved
    4. Label free quantification of time evolving morphologies using time-lapse video microscopy enables identity control of cell lines and discovery of chemically induced differential activity in iso-genic cell line pairs
    Open this publication in new window or tab >>Label free quantification of time evolving morphologies using time-lapse video microscopy enables identity control of cell lines and discovery of chemically induced differential activity in iso-genic cell line pairs
    Show others...
    2015 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 141, p. 24-32Article in journal (Refereed) Published
    Abstract [en]

    Label free time-lapse video microscopy based monitoring of time evolving cell population morphology has potential to offer a simple and cost effective method for identity control of cell lines. Such morphology monitoring also has potential to offer discovery of chemically induced differential changes between pairs of cell lines of interest, for example where one in a pair of cell lines is normal/sensitive and the other malignant/resistant. A new simple algorithm, pixel histogram hierarchy comparison (PHHC), for comparison of time evolving morphologies (TEM) in phase contrast time-lapse microscopy movies was applied to a set of 10 different cell lines and three different iso-genic colon cancer cell line pairs, each pair being genetically identical except for a single mutation. PHHC quantifies differences in morphology by comparing pixel histogram intensities at six different resolutions. Unsupervised clustering and machine learning based classification methods were found to accurately identify cell lines, including their respective iso-genic variants, through time-evolving morphology. Using this experimental setting, drugs with differential activity in iso-genic cell line pairs were likewise identified. Thus, this is a cost effective and expedient alternative to conventional molecular profiling techniques and might be useful as part of the quality control in research incorporating cell line models, e.g. in any cell/tumor biology or toxicology project involving drug/agent differential activity in pairs of cell line models.

    Keywords
    Time evolving morphology, quality control, iso-genic cell line, cancer pharmacology, time-lapse microsopcy, video microscopy
    National Category
    Computer Sciences Cancer and Oncology
    Identifiers
    urn:nbn:se:uu:diva-234563 (URN)10.1103/PhysRevC.91.024602 (DOI)000350096200003 ()
    Available from: 2014-10-21 Created: 2014-10-21 Last updated: 2018-01-11Bibliographically approved
    5. NMR spectroscopy based metabolic profiling of drug induced changes in vitro can discriminate between pharmacological classes
    Open this publication in new window or tab >>NMR spectroscopy based metabolic profiling of drug induced changes in vitro can discriminate between pharmacological classes
    Show others...
    2014 (English)In: Journal of chemical information and modeling, ISSN 1549-9596, Vol. 54, no 11, p. 3251-3258Article in journal (Refereed) Published
    Abstract [en]

    Drug induced changes in mammalian cell line models have already been extensively profiled at the systemic mRNA level and subsequently used to suggest mechanisms of action for new substances as well as to support drug repurposing, i.e. identifying new potential indications for drugs already licensed for other pharmacotherapy settings. The seminal work in this field, which includes a large database and computational algorithms for pattern matching, is known as the “Connectivity Map” (CMap). The potential of similar exercises at the metabolite level is, however, still largely unexplored. Only recently the first high throughput metabolomic assay pilot study was published, involving screening of metabolic response to a set of 56 kinase inhibitors in a 96-well format. Here we report results from a separately developed metabolic profiling assay, which leverages 1H NMR spectroscopy to the quantification of metabolic changes in the HCT116 colorectal cancer cell line, in response to each of 26 compounds. These agents are distributed across 12 different pharmacological classes covering a broad spectrum of bioactivity. Differential metabolic profiles, inferred from multivariate spectral analysis of 18 spectral bins, allowed clustering of most tested drugs according to their respective pharmacological class. A more advanced supervised analysis, involving one multivariate scattering matrix per pharmacological class and using only 3 spectral bins (three metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this kind of relatively fast and inexpensive profiling seems to provide a promising alternative to that afforded by mRNA expression analysis, which is relatively slow and costly. As also indicated by the present pilot study, the resulting metabolic profiles do not seem to provide as information rich signatures as those obtained using systemic mRNA profiling, but the methodology holds strong promise for significant refinement.

    National Category
    Cancer and Oncology
    Identifiers
    urn:nbn:se:uu:diva-234564 (URN)10.1021/ci500502f (DOI)000345551000021 ()25321343 (PubMedID)
    Available from: 2014-10-21 Created: 2014-10-21 Last updated: 2015-02-03Bibliographically approved
  • 3.
    Aftab, Obaid
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Engskog, Mikael
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Haglöf, Jakob
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Elmsjö, Albert
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Arvidsson, Torbjörn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Pettersson, Curt
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry, Analytical Pharmaceutical Chemistry.
    Hammerling, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Gustafsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    NMR spectroscopy based metabolic profiling of drug induced changes in vitro can discriminate between pharmacological classes2014In: Journal of chemical information and modeling, ISSN 1549-9596, Vol. 54, no 11, p. 3251-3258Article in journal (Refereed)
    Abstract [en]

    Drug induced changes in mammalian cell line models have already been extensively profiled at the systemic mRNA level and subsequently used to suggest mechanisms of action for new substances as well as to support drug repurposing, i.e. identifying new potential indications for drugs already licensed for other pharmacotherapy settings. The seminal work in this field, which includes a large database and computational algorithms for pattern matching, is known as the “Connectivity Map” (CMap). The potential of similar exercises at the metabolite level is, however, still largely unexplored. Only recently the first high throughput metabolomic assay pilot study was published, involving screening of metabolic response to a set of 56 kinase inhibitors in a 96-well format. Here we report results from a separately developed metabolic profiling assay, which leverages 1H NMR spectroscopy to the quantification of metabolic changes in the HCT116 colorectal cancer cell line, in response to each of 26 compounds. These agents are distributed across 12 different pharmacological classes covering a broad spectrum of bioactivity. Differential metabolic profiles, inferred from multivariate spectral analysis of 18 spectral bins, allowed clustering of most tested drugs according to their respective pharmacological class. A more advanced supervised analysis, involving one multivariate scattering matrix per pharmacological class and using only 3 spectral bins (three metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this kind of relatively fast and inexpensive profiling seems to provide a promising alternative to that afforded by mRNA expression analysis, which is relatively slow and costly. As also indicated by the present pilot study, the resulting metabolic profiles do not seem to provide as information rich signatures as those obtained using systemic mRNA profiling, but the methodology holds strong promise for significant refinement.

  • 4.
    Aftab, Obaid
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Fryknäs, Mårten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Hammerling, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Gustafsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Detection of cell aggregation and altered cell viability by automated label-free video microscopy: A promising alternative to endpoint viability assays in high throughput screening2015In: Journal of Biomolecular Screening, ISSN 1087-0571, E-ISSN 1552-454X, Vol. 20, no 3, p. 372-381Article in journal (Refereed)
    Abstract [en]

    Automated phase-contrast video microscopy now makes it feasible to monitor a high-throughput (HT) screening experiment in a 384-well microtiter plate format by collecting one time-lapse video per well. Being a very cost-effective and label-free monitoring method, its potential as an alternative to cell viability assays was evaluated. Three simple morphology feature extraction and comparison algorithms were developed and implemented for analysis of differentially time-evolving morphologies (DTEMs) monitored in phase-contrast microscopy videos. The most promising layout, pixel histogram hierarchy comparison (PHHC), was able to detect several compounds that did not induce any significant change in cell viability, but made the cell population appear as spheroidal cell aggregates. According to recent reports, all these compounds seem to be involved in inhibition of platelet-derived growth factor receptor (PDGFR) signaling. Thus, automated quantification of DTEM (AQDTEM) holds strong promise as an alternative or complement to viability assays in HT in vitro screening of chemical compounds.

  • 5.
    Aftab, Obaid
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Fryknäs, Mårten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Hassan, Saadia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Nygren, Peter
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Oncology.
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Hammerling, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Gustafsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Label free quantification of time evolving morphologies using time-lapse video microscopy enables identity control of cell lines and discovery of chemically induced differential activity in iso-genic cell line pairs2015In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 141, p. 24-32Article in journal (Refereed)
    Abstract [en]

    Label free time-lapse video microscopy based monitoring of time evolving cell population morphology has potential to offer a simple and cost effective method for identity control of cell lines. Such morphology monitoring also has potential to offer discovery of chemically induced differential changes between pairs of cell lines of interest, for example where one in a pair of cell lines is normal/sensitive and the other malignant/resistant. A new simple algorithm, pixel histogram hierarchy comparison (PHHC), for comparison of time evolving morphologies (TEM) in phase contrast time-lapse microscopy movies was applied to a set of 10 different cell lines and three different iso-genic colon cancer cell line pairs, each pair being genetically identical except for a single mutation. PHHC quantifies differences in morphology by comparing pixel histogram intensities at six different resolutions. Unsupervised clustering and machine learning based classification methods were found to accurately identify cell lines, including their respective iso-genic variants, through time-evolving morphology. Using this experimental setting, drugs with differential activity in iso-genic cell line pairs were likewise identified. Thus, this is a cost effective and expedient alternative to conventional molecular profiling techniques and might be useful as part of the quality control in research incorporating cell line models, e.g. in any cell/tumor biology or toxicology project involving drug/agent differential activity in pairs of cell line models.

  • 6.
    Aftab, Obaid
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Fryknäs, Mårten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Zhang, Xiaonan
    De Milito, Angelo
    Hammerling, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Linder, Stig
    Larsson, Rolf
    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.
    Label-free detection and dynamic monitoring of drug-induced intracellular vesicle formation enabled using a 2-dimensional matched filter2014In: Autophagy, ISSN 1554-8627, E-ISSN 1554-8635, Vol. 10, no 1, p. 57-69Article in journal (Refereed)
    Abstract [en]

    Analysis of vesicle formation and degradation is a central issue in autophagy research and microscopy imaging is revolutionizing the study of such dynamic events inside living cells. A limiting factor is the need for labeling techniques that are labor intensive, expensive, and not always completely reliable. To enable label-free analyses we introduced a generic computational algorithm, the label-free vesicle detector (LFVD), which relies on a matched filter designed to identify circular vesicles within cells using only phase-contrast microscopy images. First, the usefulness of the LFVD is illustrated by presenting successful detections of autophagy modulating drugs found by analyzing the human colorectal carcinoma cell line HCT116 exposed to each substance among 1266 pharmacologically active compounds. Some top hits were characterized with respect to their activity as autophagy modulators using independent in vitro labeling of acidic organelles, detection of LC3-II protein, and analysis of the autophagic flux. Selected detection results for 2 additional cell lines (DLD1 and RKO) demonstrate the generality of the method. In a second experiment, label-free monitoring of dose-dependent vesicle formation kinetics is demonstrated by recorded detection of vesicles over time at different drug concentrations. In conclusion, label-free detection and dynamic monitoring of vesicle formation during autophagy is enabled using the LFVD approach introduced.

  • 7.
    Aftab, Obaid
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Nazir, Madiha
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Fryknäs, Mårten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Hammerling, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Larsson, Rolf
    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.
    Label free high throughput screening for apoptosis inducing chemicals using time-lapse microscopy signal processing2014In: Apoptosis (London), ISSN 1360-8185, E-ISSN 1573-675X, Vol. 19, no 9, p. 1411-1418Article in journal (Refereed)
    Abstract [en]

    Label free time-lapse microscopy has opened a new avenue to the study of time evolving events in living cells. When combined with automated image analysis it provides a powerful tool that enables automated large-scale spatiotemporal quantification at the cell population level. Very few attempts, however, have been reported regarding the design of image analysis algorithms dedicated to the detection of apoptotic cells in such time-lapse microscopy images. In particular, none of the reported attempts is based on sufficiently fast signal processing algorithms to enable large-scale detection of apoptosis within hours/days without access to high-end computers. Here we show that it is indeed possible to successfully detect chemically induced apoptosis by applying a two-dimensional linear matched filter tailored to the detection of objects with the typical features of an apoptotic cell in phase-contrast images. First a set of recorded computational detections of apoptosis was validated by comparison with apoptosis specific caspase activity readouts obtained via a fluorescence based assay. Then a large screen encompassing 2,866 drug like compounds was performed using the human colorectal carcinoma cell line HCT116. In addition to many well known inducers (positive controls) the screening resulted in the detection of two compounds here reported for the first time to induce apoptosis.

  • 8.
    Ahnoff, Martin
    et al.
    Univ Gothenburg, Dept Chem Mol Biol, SE-41296 Gothenburg, Sweden.;Denator AB, Gothenburg, Sweden..
    Cazares, Lisa H.
    US Army Med Res Inst Infect Dis, Mol & Translat Sci, Frederick, MD 21702 USA.;US Army Med Res & Mat Command, DoD Biotechnol High Performance Comp Software App, Telemed & Adv Technol Res Ctr, Ft Detrick, MD 21702 USA..
    Sköld, Karl
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Denator AB, Gothenburg, Sweden.;Uppsala Univ, Dept Med Sci Canc Pharmacol & Computat Med, Uppsala, Sweden..
    Thermal inactivation of enzymes and pathogens in biosamples for MS analysis2015In: Bioanalysis, ISSN 1757-6180, E-ISSN 1757-6199, Vol. 7, no 15, p. 1885-1899Article, review/survey (Refereed)
    Abstract [en]

    Protein denaturation is the common basis for enzyme inactivation and inactivation of pathogens, necessary for preservation and safe handling of biosamples for downstream analysis. While heat-stabilization technology has been used in proteomic and peptidomic research since its introduction in 2009, the advantages of using the technique for simultaneous pathogen inactivation have only recently been addressed. The time required for enzyme inactivation by heat (approximate to 1 min) is short compared with chemical treatments, and inactivation is irreversible in contrast to freezing. Heat stabilization thus facilitates mass spectrometric studies of biomolecules with a fast conversion rate, and expands the chemical space of potential biomarkers to include more short-lived entities, such as phosphorylated proteins, in tissue samples as well as whole-blood (dried blood sample) samples.

  • 9.
    Almandoz-Gil, Leire
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Welander, Hedvig
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Ihse, Elisabet
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Khoonsari, Payam Emami
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Musunuri, Sravani
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Analytical Chemistry.
    Lendel, Christofer
    KTH Royal Inst Technol, Dept Chem, Stockholm, Sweden.
    Sigvardson, Jessica
    BioArctic AB, Stockholm, Sweden.
    Karlsson, Mikael
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Materials Sciences.
    Ingelsson, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Bergström, Joakim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Corrigendum to “Low molar excess of 4-oxo-2-nonenal and 4-hydroxy-2-nonenal promote oligomerization of alpha-synuclein through different pathways” [Free Rad. Biol. Med. (2017) 421–431]2018In: Free Radical Biology & Medicine, ISSN 0891-5849, E-ISSN 1873-4596, Vol. 117, p. 258-258Article in journal (Refereed)
  • 10.
    Almandoz-Gil, Leire
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Welander, Hedvig
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Ihse, Elisabet
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Khoonsari, Payam Emami
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Musunuri, Sravani
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Analytical Chemistry.
    Lendel, Christofer
    KTH, Royal Institute of Technology, Sweden.
    Sigvardson, Jessica
    BioArctic AB, Sweden.
    Karlsson, Mikael
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Materials Sciences.
    Ingelsson, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Bergström, Joakim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Public Health and Caring Sciences, Geriatrics.
    Low molar excess of 4-oxo-2-nonenal and 4-hydroxy-2-nonenal promote oligomerization of alpha-synuclein through different pathways2017In: Free Radical Biology & Medicine, ISSN 0891-5849, E-ISSN 1873-4596, Vol. 110, p. 421-431Article in journal (Refereed)
    Abstract [en]

    Aggregated alpha-synuclein is the main component of Lewy bodies, intraneuronal inclusions found in brains with Parkinson's disease and dementia with Lewy bodies. A body of evidence implicates oxidative stress in the pathogenesis of these diseases. For example, a large excess (30:1, aldehyde:protein) of the lipid peroxidation end products 4-oxo-2-nonenal (ONE) or 4-hydroxy-2-nonenal (HNE) can induce alpha-synuclein oligomer formation. The objective of the study was to investigate the effect of these reactive aldehydes on alpha-synuclein at a lower molar excess (3:1) at both physiological (7.4) and acidic (5.4) pH. As observed by size-exclusion chromatography, ONE rapidly induced the formation of alpha-synuclein oligomers at both pH values, but the effect was less pronounced under the acidic condition. In contrast, only a small proportion of alpha-synuclein oligomers were formed with low excess HNE-treatment at physiological pH and no oligomers at all under the acidic condition. With prolonged incubation times (up to 96 h), more alpha-synuclein was oligomerized at physiological pH for both ONE and HNE. As determined by Western blot, ONE-oligomers were more SDS-stable and to a higher-degree cross-linked as compared to the HNE-induced oligomers. However, as shown by their greater sensitivity to proteinase K treatment, ONE-oligomers, exhibited a less compact structure than HNE-oligomers. As indicated by mass spectrometry, ONE modified most Lys residues, whereas HNE primarily modified the His50 residue and fewer Lys residues, albeit to a higher degree than ONE. Taken together, our data show that the aldehydes ONE and HNE can modify alpha-synuclein and induce oligomerization, even at low molar excess, but to a higher degree at physiological pH and seemingly through different pathways.

  • 11.
    Alvarsson, Jonathan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Andersson, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Carlsson, Lars
    AstraZeneca R&D.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wikberg, Jarl E. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines2014In: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 54, no 11, p. 3211-3217Article in journal (Refereed)
    Abstract [en]

    QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: C, ?, and signature height. C is a penalty parameter that limits overfitting, ? controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using C in the range of 1 to 100 and gamma in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected.

  • 12.
    Alvarsson, Jonathan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Schaal, Wesley
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Andersson, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Wikberg, Jarl E. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Large-scale ligand-based predictive modelling using support vector machines2016In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 8, article id 39Article in journal (Refereed)
    Abstract [en]

    The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

  • 13.
    Banduseela, Varuna
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology.
    Chen, Yi-wen
    Göransson Kultima, Hanna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Norman, Holly
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology. Department of Medicine, University of Wisconsin, Madison, Wisconsin.
    Aare, Sudhakar
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology.
    Radell, Peter
    Eriksson, Lars
    Hoffman, Eric
    Larsson, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Clinical Neurophysiology. Department of Biobehavioral Health, The Pennsylvania State University, University Park, Pennsylvania.
    Impaired autophagy, chaperone expression, and protein synthesis in response to critical illness interventions in porcine skeletal muscle2013In: Physiological Genomics, ISSN 1094-8341, E-ISSN 1531-2267, Vol. 45, no 12, p. 477-486Article in journal (Refereed)
    Abstract [en]

    Critical illness myopathy (CIM) is characterized by a preferential loss of the motor protein myosin, muscle wasting, and impaired muscle function in critically ill intensive care unit (ICU) patients. CIM is associated with severe morbidity and mortality and has a significant negative socioeconomic effect. Neuromuscular blocking agents, corticosteroids, sepsis, mechanical ventilation, and immobilization have been implicated as important risk factors, but the causal relationship between CIM and the risk factors has not been established. A porcine ICU model has been used to determine the immediate molecular and cellular cascades that may contribute to the pathogenesis prior to myosin loss and extensive muscle wasting. Expression profiles have been compared between pigs exposed to the ICU interventions, i.e., mechanically ventilated, sedated, and immobilized for 5 days, with pigs exposed to critical illness interventions, i.e., neuromuscular blocking agents, corticosteroids, and induced sepsis in addition to the ICU interventions for 5 days. Impaired autophagy as well as impaired chaperone expression and protein synthesis were observed in the skeletal muscle in response to critical illness interventions. A novel finding in this study is impaired core autophagy machinery in response to critical illness interventions, which when in concert with downregulated chaperone expression and protein synthesis may collectively affect the proteostasis in skeletal muscle and may exacerbate the disease progression in CIM.

  • 14.
    Baskaran, Sathishkumar
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Mayrhofer, Markus
    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.
    Göransson Kultima, Hanna
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Bergström, Tobias
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Elfineh, Lioudmila
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Cavelier, Lucia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik.
    Isaksson, Anders
    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.
    Nelander, Sven
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Primary glioblastoma cells for precision medicine: a quantitative portrait of genomic (in)stability during the first 30 passages2018In: Neuro-Oncology, ISSN 1522-8517, E-ISSN 1523-5866, Vol. 20, no 8, p. 1080-1091Article in journal (Refereed)
    Abstract [en]

    Background: Primary glioblastoma cell (GC) cultures have emerged as a key model in brain tumor research, with the potential to uncover patient-specific differences in therapy response. However, there is limited quantitative information about the stability of such cells during the initial 20-30 passages of culture.

    Methods: We interrogated 3 patient-derived GC cultures at dense time intervals during the first 30 passages of culture. Combining state-of-the-art signal processing methods with a mathematical model of growth, we estimated clonal composition, rates of change, affected pathways, and correlations between altered gene dosage and transcription.

    Results: We demonstrate that GC cultures undergo sequential clonal takeovers, observed through variable proportions of specific subchromosomal lesions, variations in aneuploid cell content, and variations in subpopulation cell cycling times. The GC cultures also show significant transcriptional drift in several metabolic and signaling pathways, including ribosomal synthesis, telomere packaging and signaling via the mammalian target of rapamycin, Wnt, and interferon pathways, to a high degree explained by changes in gene dosage. In addition to these adaptations, the cultured GCs showed signs of shifting transcriptional subtype. Compared with chromosomal aberrations and gene expression, DNA methylations remained comparatively stable during passaging, and may be favorable as a biomarker.

    Conclusion: Taken together, GC cultures undergo significant genomic and transcriptional changes that need to be considered in functional experiments and biomarker studies that involve primary glioblastoma cells.

  • 15. Berenjian, Saideh
    et al.
    Hu, Kefei
    Abedi-Valugerdi, Manuchehr
    Hassan, Moustapha
    Hassan, Sadia Bashir
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Morein, Bror
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Clinical Microbiology and Infectious Medicine.
    The nanoparticulate Quillaja saponin KGI exerts anti-proliferative eff ects by down-regulation of cell cycle molecules in U937 and HL-60 human leukemia cells2014In: Leukemia and Lymphoma, ISSN 1042-8194, E-ISSN 1029-2403, Vol. 55, no 7, p. 1618-1624Article in journal (Refereed)
    Abstract [en]

    Cancer cells are characterized by uncontrolled replication involving loss of control of cyclin dependent kinases (CDKs) and cyclins, and by abolished differentiation. In this study we introduce KGI, which is a nanoparticle with a Quillaja saponin as an active molecule. By the use of RNA array analysis and confirmation at the protein level, we show that KGI affects myeloid leukemia cells (in particular, the U937 monoblast cancer cell) by the following mechanisms: (A) ceasing cell replication via proteasome degradation, (B) down-regulation of key molecules at check points between G1/S and G2/M phases, (C) reduction of thymidine kinase activity, followed by (D) exit to differentiation and production of interleukin-8 (IL-8), eventually leading to apoptosis. Leukemia cell lines (U937 and HL-60 cells) were exposed to KGI for 8 h, after which the drug was removed. The cancer cells did not revert to replication over the following 10 days. Thus our findings suggest that the nanoparticle KGI inhibits proliferation and promotes differentiation in leukemic cells by interfering with the cell cycle process.

  • 16.
    Berg, L.
    et al.
    Karolinska Inst, S-10401 Stockholm, Sweden..
    Sundström, Y.
    Karolinska Inst, S-10401 Stockholm, Sweden..
    Aftab, Obaid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Bergqvist, F.
    Karolinska Inst, S-10401 Stockholm, Sweden..
    Kultima, Kim
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Gustafsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Sundström, M.
    Karolinska Inst, S-10401 Stockholm, Sweden..
    Ossipova, E.
    Karolinska Inst, S-10401 Stockholm, Sweden..
    Lengqvist, J.
    Karolinska Inst, S-10401 Stockholm, Sweden..
    Jakobsson, P-J
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Rubin, Jenny
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Biochemial structure and function.
    Characterizing the effects of epigenetic regulation in assays using peripheral blood mononuclear cells from patients with inflammatory diseases2016In: Scandinavian Journal of Rheumatology, ISSN 0300-9742, E-ISSN 1502-7732, Vol. 45, p. 44-45Article in journal (Other academic)
  • 17.
    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.

  • 18.
    Birgisson, Helgi
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Colorectal Surgery.
    Edlund, Karolina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Wallin, Ulrik
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Colorectal Surgery.
    Påhlman, Lars
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Colorectal Surgery.
    Kultima, Hanna Göransson
    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.
    Mayrhofer, Markus
    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.
    Micke, Patrick
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Isaksson, Anders
    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.
    Botling, Johan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Glimelius, Bengt
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology.
    Sundström, Magnus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology.
    Microsatellite instability and mutations in BRAF and KRAS are significant predictors of disseminated disease in colon cancer2015In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 15, article id 125Article in journal (Refereed)
    Abstract [en]

    Background: Molecular alterations are well studied in colon cancer, however there is still need for an improved understanding of their prognostic impact. This study aims to characterize colon cancer with regard to KRAS, BRAF, and PIK3CA mutations, microsatellite instability (MSI), and average DNA copy number, in connection with tumour dissemination and recurrence in patients with colon cancer. Methods: Disease stage II-IV colon cancer patients (n = 121) were selected. KRAS, BRAF, and PIK3CA mutation status was assessed by pyrosequencing and MSI was determined by analysis of mononucleotide repeat markers. Genome-wide average DNA copy number and allelic imbalance was evaluated by SNP array analysis. Results: Patients with mutated KRAS were more likely to experience disease dissemination (OR 2.75; 95% CI 1.28-6.04), whereas the opposite was observed for patients with BRAF mutation (OR 0.34; 95% 0.14-0.81) or MSI (OR 0.24; 95% 0.09-0.64). Also in the subset of patients with stage II-III disease, both MSI (OR 0.29; 95% 0.10-0.86) and BRAF mutation (OR 0.32; 95% 0.16-0.91) were related to lower risk of distant recurrence. However, average DNA copy number and PIK3CA mutations were not associated with disease dissemination. Conclusions: The present study revealed that tumour dissemination is less likely to occur in colon cancer patients with MSI and BRAF mutation, whereas the presence of a KRAS mutation increases the likelihood of disseminated disease.

  • 19.
    Blom, Kristin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Biochemial structure and function.
    Nygren, Peter
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Andersson, Claes R.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Ex Vivo Assessment of Drug Activity in Patient Tumor Cells as a Basis for Tailored Cancer Therapy2016In: JALA, ISSN 2211-0682, Vol. 21, no 1, p. 178-187Article in journal (Refereed)
    Abstract [en]

    Although medical cancer treatment has improved during the past decades, it is difficult to choose between several first-line treatments supposed to be equally active in the diagnostic group. It is even more difficult to select a treatment after the standard protocols have failed. Any guidance for selection of the most effective treatment is valuable at these critical stages. We describe the principles and procedures for ex vivo assessment of drug activity in tumor cells from patients as a basis for tailored cancer treatment. Patient tumor cells are assayed for cytotoxicity with a panel of drugs. Acoustic drug dispensing provides great flexibility in the selection of drugs for testing; currently, up to 80 compounds and/or combinations thereof may be tested for each patient. Drug response predictions are obtained by classification using an empirical model based on historical responses for the diagnosis. The laboratory workflow is supported by an integrated system that enables rapid analysis and automatic generation of the clinical referral response.

  • 20.
    Blom, Kristin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Nygren, Peter
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology.
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Andersson, Claes R.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Predictive Value of Ex Vivo Chemosensitivity Assays for Individualized Cancer Chemotherapy: A Meta-Analysis2017In: SLAS TECHNOLOGY, ISSN 2472-6303, Vol. 22, no 3, p. 306-314Article in journal (Refereed)
    Abstract [en]

    Current treatment strategies for chemotherapy of cancer patients were developed to benefit groups of patients with similar clinical characteristics. In practice, response is very heterogeneous between individual patients within these groups. Precision medicine can be viewed as the development toward a more fine-grained treatment stratification than what is currently in use. Cell-based drug sensitivity testing is one of several options for individualized cancer treatment available today, although it has not yet reached widespread clinical use. We present an up-to-date literature meta-analysis on the predictive value of ex vivo chemosensitivity assays for individualized cancer chemotherapy and discuss their current clinical value and possible future developments.

  • 21.
    Blom, Kristin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Senkowski, Wojciech
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Jarvius, Malin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Berglund, Malin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Rubin, Jenny
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Lenhammar, Lena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Parrow, Vendela
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Andersson, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Loskog, Angelica
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology.
    Fryknäs, Mårten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Nygren, Peter
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology.
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    The anticancer effect of mebendazole may be due to M1 monocyte/macrophage activation via ERK1/2 and TLR8-dependent inflammasome activation2017In: Immunopharmacology and immunotoxicology, ISSN 0892-3973, E-ISSN 1532-2513, Vol. 39, no 4, p. 199-210Article in journal (Refereed)
    Abstract [en]

    Mebendazole (MBZ), a drug commonly used for helminitic infections, has recently gained substantial attention as a repositioning candidate for cancer treatment. However, the mechanism of action behind its anticancer activity remains unclear. To address this problem, we took advantage of the curated MBZ-induced gene expression signatures in the LINCS Connectivity Map (CMap) database. The analysis revealed strong negative correlation with MEK/ERK1/2 inhibitors. Moreover, several of the most upregulated genes in response to MBZ exposure were related to monocyte/macrophage activation. The MBZ-induced gene expression signature in the promyeloblastic HL-60 cell line was strongly enriched in genes involved in monocyte/macrophage pro-inflammatory (M1) activation. This was subsequently validated using MBZ-treated THP-1 monocytoid cells that demonstrated gene expression, surface markers and cytokine release characteristic of the M1 phenotype. At high concentrations MBZ substantially induced the release of IL-1 beta and this was further potentiated by lipopolysaccharide (LPS). At low MBZ concentrations, cotreatment with LPS was required for MBZ-stimulated IL-1 beta secretion to occur. Furthermore, we show that the activation of protein kinase C, ERK1/2 and NF-kappaB were required for MBZ-induced IL-1 release. MBZ-induced IL-1 release was found to be dependent on NLRP3 inflammasome activation and to involve TLR8 stimulation. Finally, MBZ induced tumor-suppressive effects in a coculture model with differentiated THP-1 macrophages and HT29 colon cancer cells. In summary, we report that MBZ induced a pro-inflammatory (M1) phenotype of monocytoid cells, which may, at least partly, explain MBZ's anticancer activity observed in animal tumor models and in the clinic.

  • 22. Brnjic, Slavica
    et al.
    Mazurkiewicz, Magdalena
    Fryknäs, Mårten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Sun, Chao
    Zhang, Xiaonan
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    D'Arcy, Padraig
    Linder, Stig
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences.
    Induction of Tumor Cell Apoptosis by a Proteasome Deubiquitinase Inhibitor Is Associated with Oxidative Stress2014In: Antioxidants and Redox Signaling, ISSN 1523-0864, E-ISSN 1557-7716, Vol. 21, no 17, p. 2271-2285Article in journal (Refereed)
    Abstract [en]

    Aims: b-AP15 is a recently described inhibitor of the USP14/UCHL5 deubiquitinases (DUBs) of the 19S proteasome. Exposure to b-AP15 results in blocking of proteasome function and accumulation of polyubiquitinated protein substrates in cells. This novel mechanism of proteasome inhibition may potentially be exploited for cancer therapy, in particular for treatment of malignancies resistant to currently used proteasome inhibitors. The aim of the present study was to characterize the cellular response to b-AP15-mediated proteasome DUB inhibition. Results: We report that b-AP15 elicits a similar, but yet distinct, cellular response as the clinically used proteasome inhibitor bortezomib. b-AP15 induces a rapid apoptotic response, associated with enhanced induction of oxidative stress and rapid activation of Jun-N-terminal kinase 1/2 (JNK)/activating protein-1 signaling. Scavenging of reactive oxygen species and pharmacological inhibition of JNK reduced b-AP15-induced apoptosis. We further report that endoplasmic reticulum (ER) stress is induced by b-AP15 and is involved in apoptosis induction. In contrast to bortezomib, ER stress is associated with induction of alpha-subunit of eukaryotic initiation factor 2 phosphorylation. Innovation: The findings establish that different modes of proteasome inhibition result in distinct cellular responses, a finding of potential therapeutic importance. Conclusion: Our data show that enhanced oxidative stress and ER stress are major determinants of the strong apoptotic response elicited by the 19S DUB inhibitor b-AP15. Antioxid. Redox Signal. 21, 2271-2285.

  • 23. Brunberg, E.
    et al.
    Jensen, P.
    Isaksson, Anders
    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.
    Keeling, L. J.
    Brain gene expression differences are associated with abnormal tail biting behavior in pigs2013In: Genes, Brain and Behavior, ISSN 1601-1848, E-ISSN 1601-183X, Vol. 12, no 2, p. 275-281Article in journal (Refereed)
    Abstract [en]

    Knowledge about gene expression in animals involved in abnormal behaviors can contribute to the understanding of underlying biological mechanisms. This study aimed to explore the motivational background to tail biting, an abnormal injurious behavior and severe welfare problem in pig production. Affymetrix microarrays were used to investigate gene expression differences in the hypothalamus and prefrontal cortex of pigs performing tail biting, pigs receiving bites to the tail and neutral pigs who were not involved in the behavior. In the hypothalamus, 32 transcripts were differentially expressed (P<0.05) when tail biters were compared with neutral pigs, 130 when comparing receiver pigs with neutrals, and two when tail biters were compared with receivers. In the prefrontal cortex, seven transcripts were differently expressed in tail biters when compared with neutrals, seven in receivers vs. neutrals and none in the tail biters vs. receivers. In total, 19 genes showed a different expression pattern in neutral pigs when compared with both performers and receivers. This implies that the functions of these may provide knowledge about why the neutral pigs are not involved in tail biting behavior as performers or receivers. Among these 19 transcripts were genes associated with production traits in pigs (PDK4), sociality in humans and mice (GTF2I) and novelty seeking in humans (EGF). These are in line with hypotheses linking tail biting with reduced back fat thickness and explorative behavior.

  • 24. Brunberg, Emma
    et al.
    Jensen, Per
    Isaksson, Anders
    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.
    Keeling, Linda J.
    Behavioural and Brain Gene Expression Profiling in Pigs during Tail Biting Outbreaks - Evidence of a Tail Biting Resistant Phenotype2013In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 6, p. e66513-Article in journal (Refereed)
    Abstract [en]

    Abnormal tail biting behaviour is a major welfare problem for pigs receiving the behaviour, as well as an indication of decreased welfare in the pigs performing it. However, not all pigs in a pen perform or receive tail biting behaviour and it has recently been shown that these 'neutral' pigs not only differ in their behaviour, but also in their gene expression compared to performers and receivers of tail biting in the same pen. To investigate whether this difference was linked to the cause or a consequence of them not being involved in the outbreak of tail biting, behaviour and brain gene expression was compared with 'control' pigs housed in pens with no tail biting. It was shown that the pigs housed in control pens performed a wider variety of pig-directed abnormal behaviour (belly nosing 0.95 +/- 1.59, tail in mouth 0.31 +/- 0.60 and 'other' abnormal 1.53 +/- 4.26; mean +/- S.D) compared to the neutral pigs (belly nosing 0.30 +/- 0.62, tail in mouth 0.13 +/- 0.50 and "other" abnormal 0.42 +/- 1.06). With Affymetrix gene expression arrays, 107 transcripts were identified as differently expressed (p < 0.05) between these two categories of pigs. Several of these transcripts had already been shown to be differently expressed in the neutral pigs when they were compared to performers and receivers of tail biting in the same pen in an earlier study. Hence, the different expression of these genes cannot be a consequence of the neutral pigs not being involved in tail biting behaviour, but rather linked to the cause contributing to why they were not involved in tail biting interactions. These neutral pigs seem to have a genetic and behavioural profile that somehow contributes to them being resistant to performing or receiving pig-directed abnormal behaviour, such as tail biting, even when housed in an environment that elicits that behaviour in other pigs.

  • 25.
    Bäcklin, Christofer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Machine Learning Based Analysis of DNA Methylation Patterns in Pediatric Acute Leukemia2015Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Acute lymphoblastic leukemia (ALL) is the most common pediatric cancer in the Nordic countries. Recent evidence indicate that DNA methylation (DNAm) play a central role in the development and progression of the disease.

    DNAm profiles of a collection of ALL patient samples and a panel of non-leukemic reference samples were analyzed using the Infinium 450k methylation assay. State-of-the-art machine learning algorithms were used to search the large amounts of data produced for patterns predictive of future relapses, in vitro drug resistance, and cytogenetic subtypes, aiming at improving our understanding of the disease and ultimately improving treatment.

    In paper I, the predictive modeling framework developed to perform the analyses of DNAm dataset was presented. It focused on uncompromising statistical rigor and computational efficiency, while allowing a high level of modeling flexibility and usability. In paper II, the DNAm landscape of ALL was comprehensively characterized, discovering widespread aberrant methylation at diagnosis strongly influenced by cytogenetic subtype. The aberrantly methylated regions were enriched for genes repressed by polycomb group proteins, repressively marked histones in healthy cells, and genes associated with embryonic development. A consistent trend of hypermethylation at relapse was also discovered. In paper III, a tool for DNAm-based subtyping was presented, validated using blinded samples and used to re-classify samples with incomplete phenotypic information. Using RNA-sequencing, previously undetected non-canonical aberrations were found in many re-classified samples. In paper IV, the relationship between DNAm and in vitro drug resistance was investigated and predictive signatures were obtained for seven of the eight therapeutic drugs studied. Interpretation was challenging due to poor correlation between DNAm and gene expression, further complicated by the discovery that random subsets of the array can yield comparable classification accuracy. Paper V presents a novel Bayesian method for multivariate density estimation with variable bandwidths. Simulations showed comparable performance to the current state-of-the-art methods and an advantage on skewed distributions.

    In conclusion, the studies characterize the information contained in the aberrant DNAm patterns of ALL and assess its predictive capabilities for future relapses, in vitro drug sensitivity and subtyping. They also present three publicly available tools for the scientific community to use.

    List of papers
    1. Developer-Friendly and Computationally Efficient Predictive Modeling without Information Leakage: The emil Package for R
    Open this publication in new window or tab >>Developer-Friendly and Computationally Efficient Predictive Modeling without Information Leakage: The emil Package for R
    2018 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 85, no 13, p. 1-30Article in journal (Refereed) Published
    Abstract [en]

    Data driven machine learning for predictive modeling problems (classification, regression, or survival analysis) typically involves a number of steps beginning with data preprocessing and ending with performance evaluation. A large number of packages providing tools for the individual steps are available for R, but there is a lack of tools for facilitating rigorous performance evaluation of the complete procedures assembled from them by means of cross-validation, bootstrap, or similar methods. Such a tool should strictly prevent test set observations from influencing model training and meta- parameter tuning, so- called information leakage, in order to not produce overly optimistic performance estimates. Here we present a new package for R denoted emil (evaluation of modeling without information leakage) that offers this form of performance evaluation. It provides a transparent and highly customizable framework for facilitating the assembly, execution, performance evaluation, and interpretation of complete procedures for classification, regression, and survival analysis. The components of package emil have been designed to be as modular and general as possible to allow users to combine, replace, and extend them if needed. Package emil was also developed with scalability in mind and has a small computational overhead, which is a key requirement for analyzing the very big data sets now available in fields like medicine, physics, and finance. First package emil's functionality and usage is explained. Then three specific application examples are presented to show its potential in terms of parallelization, customization for survival analysis, and development of ensemble models. Finally a brief comparison to similar software is provided.

    Place, publisher, year, edition, pages
    JOURNAL STATISTICAL SOFTWARE, 2018
    Keywords
    predictive modeling, machine learning, performance evaluation, resampling, high performance computing
    National Category
    Computer Sciences Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-362159 (URN)10.18637/jss.v085.i13 (DOI)000440230100001 ()
    Funder
    Swedish Foundation for Strategic Research , RBc08-008Swedish Research Council, 621-2008-5854
    Available from: 2018-10-19 Created: 2018-10-19 Last updated: 2018-10-19Bibliographically approved
    2. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia
    Open this publication in new window or tab >>Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia
    Show others...
    2013 (English)In: Genome Biology, ISSN 1465-6906, E-ISSN 1474-760X, Vol. 14, no 9, p. r105-Article in journal (Refereed) Published
    Abstract [en]

    BACKGROUND:

    Although aberrant DNA methylation has been observed previously in acute lymphoblastic leukemia (ALL), the patterns of differential methylation have not been comprehensively determined in all subtypes of ALL on a genome-wide scale. The relationship between DNA methylation, cytogenetic background, drug resistance and relapse in ALL is poorly understood.

    RESULTS:

    We surveyed the DNA methylation levels of 435,941 CpG sites in samples from 764 children at diagnosis of ALL and from 27 children at relapse. This survey uncovered four characteristic methylation signatures. First, compared with control blood cells, the methylomes of ALL cells shared 9,406 predominantly hypermethylated CpG sites, independent of cytogenetic background. Second, each cytogenetic subtype of ALL displayed a unique set of hyper- and hypomethylated CpG sites. The CpG sites that constituted these two signatures differed in their functional genomic enrichment to regions with marks of active or repressed chromatin. Third, we identified subtype-specific differential methylation in promoter and enhancer regions that were strongly correlated with gene expression. Fourth, a set of 6,612 CpG sites was predominantly hypermethylated in ALL cells at relapse, compared with matched samples at diagnosis. Analysis of relapse-free survival identified CpG sites with subtype-specific differential methylation that divided the patients into different risk groups, depending on their methylation status.

    CONCLUSIONS:

    Our results suggest an important biological role for DNA methylation in the differences between ALL subtypes and in their clinical outcome after treatment.

    National Category
    Medical Genetics
    Identifiers
    urn:nbn:se:uu:diva-208296 (URN)10.1186/gb-2013-14-9-r105 (DOI)000328195700011 ()24063430 (PubMedID)
    Note

    De två första författarna delar förstaförfattarskapet.

    Available from: 2013-09-27 Created: 2013-09-27 Last updated: 2018-01-11Bibliographically approved
    3. DNA methylation-based subtype prediction for pediatric acute lymphoblastic leukemia
    Open this publication in new window or tab >>DNA methylation-based subtype prediction for pediatric acute lymphoblastic leukemia
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    2015 (English)In: Clinical Epigenetics, E-ISSN 1868-7083, Vol. 7, article id 11Article in journal (Refereed) Published
    Abstract [en]

    Background

    We present a method that utilizes DNA methylation profiling for prediction of the cytogenetic subtypes of acute lymphoblastic leukemia (ALL) cells from pediatric ALL patients. The primary aim of our study was to improve risk stratification of ALL patients into treatment groups using DNA methylation as a complement to current diagnostic methods. A secondary aim was to gain insight into the functional role of DNA methylation in ALL.

    Results

    We used the methylation status of ~450,000 CpG sites in 546 well-characterized patients with T-ALL or seven recurrent B-cell precursor ALL subtypes to design and validate sensitive and accurate DNA methylation classifiers. After repeated cross-validation, a final classifier was derived that consisted of only 246 CpG sites. The mean sensitivity and specificity of the classifier across the known subtypes was 0.90 and 0.99, respectively. We then used DNA methylation classification to screen for subtype membership of 210 patients with undefined karyotype (normal or no result) or non-recurrent cytogenetic aberrations (‘other’ subtype). Nearly half (n = 106) of the patients lacking cytogenetic subgrouping displayed highly similar methylation profiles as the patients in the known recurrent groups. We verified the subtype of 20% of the newly classified patients by examination of diagnostic karyotypes, array-based copy number analysis, and detection of fusion genes by quantitative polymerase chain reaction (PCR) and RNA-sequencing (RNA-seq). Using RNA-seq data from ALL patients where cytogenetic subtype and DNA methylation classification did not agree, we discovered several novel fusion genes involving ETV6, RUNX1, and PAX5.

    Conclusions

    Our findings indicate that DNA methylation profiling contributes to the clarification of the heterogeneity in cytogenetically undefined ALL patient groups and could be implemented as a complementary method for diagnosis of ALL. The results of our study provide clues to the origin and development of leukemic transformation. The methylation status of the CpG sites constituting the classifiers also highlight relevant biological characteristics in otherwise unclassified ALL patients.

    National Category
    Hematology
    Identifiers
    urn:nbn:se:uu:diva-242351 (URN)10.1186/s13148-014-0039-z (DOI)000350260800001 ()25729447 (PubMedID)
    Funder
    Swedish Foundation for Strategic Research , RBc08-008
    Note

    De två sista författarna delar sistaförfattarskapet.

    Available from: 2015-01-25 Created: 2015-01-25 Last updated: 2017-12-05Bibliographically approved
    4. DNA methylation-based prediction of in vitro drug resistance in primary pediatric acute lymphoblastic leukemia patient samples
    Open this publication in new window or tab >>DNA methylation-based prediction of in vitro drug resistance in primary pediatric acute lymphoblastic leukemia patient samples
    Show others...
    (English)Manuscript (preprint) (Other academic)
    National Category
    Cancer and Oncology Hematology Bioinformatics (Computational Biology)
    Identifiers
    urn:nbn:se:uu:diva-242543 (URN)
    Funder
    Swedish Foundation for Strategic Research , RBc08-008
    Available from: 2015-01-27 Created: 2015-01-27 Last updated: 2018-01-11
    5. Bayesian model averaging of adaptive bandwidth kernel density estimators yields state-of-the-art performance
    Open this publication in new window or tab >>Bayesian model averaging of adaptive bandwidth kernel density estimators yields state-of-the-art performance