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  • 1.
    Ahlinder, Jon
    et al.
    Totalförsvarets Forskningsinstitut, FOI, Stockholm, Sweden.
    Nordgaard, Anders
    Swedish National Forensic Centre (NFC), Linköping, Sweden.
    Wiklund Lindström, Susanne
    Totalförsvarets Forskningsinstitut, FOI, Stockholm, Sweden.
    Chemometrics comes to court: evidence evaluation of chem–bio threat agent attacks2015In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 29, no 5, p. 267-276Article in journal (Refereed)
    Abstract [en]

    Forensic statistics is a well-established scientific field whose purpose is to statistically analyze evidence in order to support legal decisions. It traditionally relies on methods that assume small numbers of independent variables and multiple samples. Unfortunately, such methods are less applicable when dealing with highly correlated multivariate data sets such as those generated by emerging high throughput analytical technologies. Chemometrics is a field that has a wealth of methods for the analysis of such complex data sets, so it would be desirable to combine the two fields in order to identify best practices for forensic statistics in the future. This paper provides a brief introduction to forensic statistics and describes how chemometrics could be integrated with its established methods to improve the evaluation of evidence in court.

    The paper describes how statistics and chemometrics can be integrated, by analyzing a previous know forensic data set composed of bacterial communities from fingerprints. The presented strategy can be applied in cases where chemical and biological threat agents have been illegally disposed.

  • 2.
    Alexsson, Andrei
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics .
    Unsupervised hidden Markov model for automatic analysis of expressed sequence tags2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis provides an in-depth analyze of expressed sequence tags (EST) that represent pieces of eukaryotic mRNA by using unsupervised hidden Markov model (HMM). ESTs are short nucleotide sequences that are used primarily for rapid identificationof new genes with potential coding regions (CDS). ESTs are made by sequencing on double-stranded cDNA and the synthesizedESTs are stored in digital form, usually in FASTA format. Since sequencing is often randomized and that parts of mRNA contain non-coding regions, some ESTs will not represent CDS.It is desired to remove these unwanted ESTs if the purpose is to identifygenes associated with CDS. Application of stochastic HMM allow identification of region contents in a EST. Softwares like ESTScanuse HMM in which a training of the HMM is done by supervised learning with annotated data. However, because there are not always annotated data at hand this thesis focus on the ability to train an HMM with unsupervised learning on data containing ESTs, both with and without CDS. But the data used for training is not annotated, i.e. the regions that an EST consists of are unknown. In this thesis a new HMM is introduced where the parameters of the HMM are in focus so that they are reasonablyconsistent with biologically important regionsof an mRNA such as the Kozak sequence, poly(A)-signals and poly(A)-tails to guide the training and decoding correctly with ESTs to proper statesin the HMM. Transition probabilities in the HMMhas been adapted so that it represents the mean length and distribution of the different regions in mRNA. Testing of the HMM's specificity and sensitivityhave been performed via BLAST by blasting each EST and compare the BLAST results with the HMM prediction results.A regression analysis test shows that the length of ESTs used when training the HMM is significantly important, the longer the better. The final resultsshows that it is possible to train an HMM with unsupervised machine learning but to be comparable to supervised machine learning as ESTScan, further expansion of the HMM is necessary such as frame-shift correction of ESTs byimproving the HMM's ability to choose correctly positioned start codons or nucleotides. Usually the false positive results are because of incorrectly positioned start codons leadingto too short CDS lengths. Since no frame-shift correction is implemented, short predicted CDS lengths are not acceptable and is hence not counted as coding regionsduring prediction. However, when there is a lack of supervised models then unsupervised HMM is a potential replacement with stable performance and able to be adapted forany eukaryotic organism.

    Download full text (pdf)
    Master Thesis
  • 3.
    Araujo, Mario Jorge
    et al.
    Univ Porto, Portugal.
    Sousa, Maria Ligia
    Univ Porto, Portugal.
    Felpeto, Aldo Barreiro
    Univ Porto, Portugal.
    Turkina, Maria V
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences.
    Fonseca, Elza
    Univ Porto, Portugal.
    Martins, Jose Carlos
    Univ Porto, Portugal.
    Vasconcelos, Vitor
    Univ Porto, Portugal; Univ Porto, Portugal.
    Campos, Alexandre
    Univ Porto, Portugal.
    Comparison of Sample Preparation Methods for Shotgun Proteomic Studies in Aquaculture Species2021In: Proteomes, ISSN 2227-7382, Vol. 9, no 4, article id 46Article in journal (Refereed)
    Abstract [en]

    Proteomics has been recently introduced in aquaculture research, and more methodological studies are needed to improve the quality of proteomics studies. Therefore, this work aims to compare three sample preparation methods for shotgun LC-MS/MS proteomics using tissues of two aquaculture species: liver of turbot Scophthalmus maximus and hepatopancreas of Mediterranean mussel Mytilus galloprovincialis. We compared the three most common sample preparation workflows for shotgun analysis: filter-aided sample preparation (FASP), suspension-trapping (S-Trap), and solid-phase-enhanced sample preparations (SP3). FASP showed the highest number of protein identifications for turbot samples, and S-Trap outperformed other methods for mussel samples. Subsequent functional analysis revealed a large number of Gene Ontology (GO) terms in turbot liver proteins (nearly 300 GO terms), while fewer GOs were found in mussel proteins (nearly 150 GO terms for FASP and S-Trap and 107 for SP3). This result may reflect the poor annotation of the genomic information in this specific group of animals. FASP was confirmed as the most consistent method for shotgun proteomic studies; however, the use of the other two methods might be important in specific experimental conditions (e.g., when samples have a very low amount of protein).

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    fulltext
  • 4.
    Badam, Tejaswi
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. Univ Skovde, Sweden.
    de Weerd, Hendrik Arnold
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. Univ Skovde, Sweden.
    Martinez, David
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Olsson, Tomas
    Karolinska Inst, Sweden.
    Alfredsson, Lars
    Karolinska Inst, Sweden; Karolinska Inst, Sweden.
    Kockum, Ingrid
    Karolinska Inst, Sweden.
    Jagodic, Maja
    Karolinska Inst, Sweden.
    Lubovac-Pilav, Zelmina
    Univ Skovde, Sweden.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis2021In: BMC Genomics, E-ISSN 1471-2164, Vol. 22, no 1, article id 631Article in journal (Refereed)
    Abstract [en]

    Background There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10(- 47)) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.

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    fulltext
  • 5.
    Badam, Tejaswi Venkata Satya
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Omic Network Modules in Complex diseases2021Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Biological systems encompass various molecular entities such as genes, proteins, and other biological molecules, including interactions among those components. Understanding a given phenotype, the functioning of a cell or tissue, aetiology of disease, or cellular organization, requires accurate measurements of the abundance profiles of these molecular entities in the form of biomedical data. The analysis of the interplay between these different entities at various levels represented in the form of biological network provides a mechanistic understanding of the observed phenotype. In order to study this interplay, there is a requirement of a conceptual and intuitive framework which can model multiple omics such as genome, transcriptome, or a proteome. This can be addressed by application of network-based strategies.

    Translational bioinformatics deals with the development of analytic and interpretive methods to optimize the transformation of different omics and clinical data to understanding of complex diseases and improving human health. Complex diseases such as multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and non-small cell lung cancer (NSCLC) etc., are hypothesized to be a result of a disturbance in the omic networks rendering the healthy cells to be in a state of malfunction. Even though there are numerous methods to layout the relation of the interactions among omics in complex diseases, the output network modules were not clearly interpreted.

    In this PhD thesis, we showed how different omic data such as transcriptome and methylome can be mapped to the network of interactions to extract highly interconnected gene sets relevant to the disease, so called disease modules. First, we selected common module identification methods and assembled them into a unified framework of the methods implemented in an Rpackage MODifieR (Paper I). Secondly, we showed that the concept of the network modules can be applied on the whole genome sequencing data for developing a tested model for predicting myelosuppressive toxicity (Paper II).

    Furthermore, we demonstrated that network modules extracted using the methylome data helped identifying several genes that were associated with pregnancy-induced pathways and were enriched for disease-associated methylation changes that were also shared by three auto-immune and inflammatory diseases, namely MS, RA, and SLE (Paper III). Remarkably, those methylation changes correlated with the expected outcome from clinical experience in those diseases. Last, we benchmarked the omic network modules on 19 different complex diseases using both transcriptomic and methylomic data. This led to the identification of a multi-omic MS module that was highly enriched disease-associated genes identified by genome-wide association studies, but also genes associated with the most common environmental risk factors of MS (Paper IV).

    The application of the network modules concept on different omics is the centrepiece of the research presented in this PhD thesis. The thesis represents the application of omic network modules in complex diseases and how these modules should be integrated and interpreted. In particular, it aimed to show the importance of networks owing to the incomplete knowledge of the genes dysregulated in complex diseases and the contribution of this thesis that provides tools and benchmarks for the methods as well as insights into how a network module can be extracted and interpreted from the omic data in complex diseases.

    List of papers
    1. MODifieR: an Ensemble R Package for Inference of Disease Modules from Transcriptomics Networks
    Open this publication in new window or tab >>MODifieR: an Ensemble R Package for Inference of Disease Modules from Transcriptomics Networks
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    2020 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 36, no 12, p. 3918-3919Article in journal (Refereed) Published
    Abstract [en]

    Motivation: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form the so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best-performing method. Hence, there is a need for combining these methods to generate robust disease modules. Results: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.

    Place, publisher, year, edition, pages
    OXFORD UNIV PRESS, 2020
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-168277 (URN)10.1093/bioinformatics/btaa235 (DOI)000550127500051 ()32271876 (PubMedID)
    Note

    Funding Agencies|Knowledge Foundation; Swedish Research CouncilSwedish Research Council; Swedish foundation for strategic researchSwedish Foundation for Strategic Research

    Available from: 2020-08-21 Created: 2020-08-21 Last updated: 2023-01-19
    2. Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients
    Open this publication in new window or tab >>Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients
    Show others...
    2020 (English)In: npj Systems Biology and Applications, ISSN 2056-7189, Vol. 6, no 1, article id 25Article in journal (Refereed) Published
    Abstract [en]

    Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10−3 for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.

    Place, publisher, year, edition, pages
    Nature Publishing Group, 2020
    Keywords
    Cancer, Genetic interaction, Systems analysis
    National Category
    Medical Genetics Bioinformatics and Systems Biology Cancer and Oncology
    Identifiers
    urn:nbn:se:liu:diva-168465 (URN)10.1038/s41540-020-00146-6 (DOI)000568927100001 ()32839457 (PubMedID)2-s2.0-85089776223 (Scopus ID)
    Note

    Funding agencies: Swedish Cancer Society, the Swedish Research Council, Linköping University, ALF grants Region Östergötland, the Funds of Radiumhemmet, Marcus Borgströms stiftelse, Stiftelsen Assar Gabrielssons Fond

    Available from: 2020-08-24 Created: 2020-08-24 Last updated: 2021-01-15Bibliographically approved
    3. CD4(+) T-cell DNA methylation changes during pregnancy significantly correlate with disease-associated methylation changes in autoimmune diseases
    Open this publication in new window or tab >>CD4(+) T-cell DNA methylation changes during pregnancy significantly correlate with disease-associated methylation changes in autoimmune diseases
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    2022 (English)In: Epigenetics, ISSN 1559-2294, E-ISSN 1559-2308, Vol. 17, no 9, p. 1040-1055Article in journal (Refereed) Published
    Abstract [en]

    Epigenetics may play a central, yet unexplored, role in the profound changes that the maternal immune system undergoes during pregnancy and could be involved in the pregnancy-induced modulation of several autoimmune diseases. We investigated changes in the methylome in isolated circulating CD4(+) T-cells in non-pregnant and pregnant women, during the 1(st) and 2(nd) trimester, using the Illumina Infinium Human Methylation 450K array, and explored how these changes were related to autoimmune diseases that are known to be affected during pregnancy. Pregnancy was associated with several hundreds of methylation differences, particularly during the 2(nd) trimester. A network-based modular approach identified several genes, e.g., CD28, FYN, VAV1 and pathways related to T-cell signalling and activation, highlighting T-cell regulation as a central component of the observed methylation alterations. The identified pregnancy module was significantly enriched for disease-associated methylation changes related to multiple sclerosis, rheumatoid arthritis and systemic lupus erythematosus. A negative correlation between pregnancy-associated methylation changes and disease-associated changes was found for multiple sclerosis and rheumatoid arthritis, diseases that are known to improve during pregnancy whereas a positive correlation was found for systemic lupus erythematosus, a disease that instead worsens during pregnancy. Thus, the directionality of the observed changes is in line with the previously observed effect of pregnancy on disease activity. Our systems medicine approach supports the importance of the methylome in immune regulation of T-cells during pregnancy. Our findings highlight the relevance of using pregnancy as a model for understanding and identifying disease-related mechanisms involved in the modulation of autoimmune diseases.

    Place, publisher, year, edition, pages
    Taylor & Francis Inc, 2022
    Keywords
    Pregnancy; epigenetics; methylation; CD4(+) T cells; module; rheumatoid arthritis; multiple sclerosis; systemic lupus erythematosus
    National Category
    Cell and Molecular Biology
    Identifiers
    urn:nbn:se:liu:diva-180368 (URN)10.1080/15592294.2021.1982510 (DOI)000703400700001 ()34605719 (PubMedID)
    Note

    Funding Agencies|Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [SB16-0011]; Swedish Research CouncilSwedish Research CouncilEuropean Commission [K2013-61X-22310-01-4, 2015-030807, 2018-02776]; Lions research grant [Liu-2012-01948]

    Available from: 2021-10-18 Created: 2021-10-18 Last updated: 2023-12-22
    4. A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis
    Open this publication in new window or tab >>A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis
    Show others...
    2021 (English)In: BMC Genomics, E-ISSN 1471-2164, Vol. 22, no 1, article id 631Article in journal (Refereed) Published
    Abstract [en]

    Background There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10(- 47)) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.

    Place, publisher, year, edition, pages
    BMC, 2021
    Keywords
    Benchmark; Multi-omics; Network modules; Multiple sclerosis; Risk factors; Disease modules; Network analysis; Protein network analysis; Transcriptomics; Methylomics; Data integration; Genome-wide association analysis
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-179166 (URN)10.1186/s12864-021-07935-1 (DOI)000692402600002 ()34461822 (PubMedID)
    Note

    Funding Agencies|Swedish Research CouncilSwedish Research CouncilEuropean Commission [201503807, 2018-02638]; Swedish foundation for strategic researchSwedish Foundation for Strategic Research [SB16-0095]; Center for Industrial IT (CENIIT); European Union Horizon 2020/European Research Council Consolidator grant (Epi4MS) [818170]; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation [2019.0089]; Knowledge Foundation [20170298]; Linkoping University

    Available from: 2021-09-14 Created: 2021-09-14 Last updated: 2024-01-17
  • 6.
    Barrientos-Somarribas, Mauricio
    et al.
    Karolinska Inst, Sweden.
    Messina, David N.
    Stockholm Univ, Sweden.
    Pou, Christian
    Karolinska Inst, Sweden.
    Lysholm, Fredrik
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Bjerkner, Annelie
    Karolinska Univ Hosp, Sweden.
    Allander, Tobias
    Karolinska Univ Hosp, Sweden.
    Andersson, Björn
    Karolinska Inst, Sweden.
    Sonnhammer, Erik L. L.
    Stockholm Univ, Sweden.
    Discovering viral genomes in human metagenomic data by predicting unknown protein families2018In: Scientific Reports, E-ISSN 2045-2322, Vol. 8, article id 28Article in journal (Refereed)
    Abstract [en]

    Massive amounts of metagenomics data are currently being produced, and in all such projects a sizeable fraction of the resulting data shows no or little homology to known sequences. It is likely that this fraction contains novel viruses, but identification is challenging since they frequently lack homology to known viruses. To overcome this problem, we developed a strategy to detect ORFan protein families in shotgun metagenomics data, using similarity-based clustering and a set of filters to extract bona fide protein families. We applied this method to 17 virus-enriched libraries originating from human nasopharyngeal aspirates, serum, feces, and cerebrospinal fluid samples. This resulted in 32 predicted putative novel gene families. Some families showed detectable homology to sequences in metagenomics datasets and protein databases after reannotation. Notably, one predicted family matches an ORF from the highly variable Torque Teno virus (TTV). Furthermore, follow-up from a predicted ORFan resulted in the complete reconstruction of a novel circular genome. Its organisation suggests that it most likely corresponds to a novel bacteriophage in the microviridae family, hence it was named bacteriophage HFM.

    Download full text (pdf)
    fulltext
  • 7.
    Bartoszek, Krzysztof
    Department of Mathematics, Uppsala University, Uppsala, Sweden.
    Phylogenetic effective sample size2016In: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 407, p. 371-386Article in journal (Refereed)
    Abstract [en]

    In this paper I address the question—how large is a phylogenetic sample? I propose a definition of a phylogenetic effective sample size for Brownian motion and Ornstein-Uhlenbeck processes-the regression effective sample size. I discuss how mutual information can be used to define an effective sample size in the non-normal process case and compare these two definitions to an already present concept of effective sample size (the mean effective sample size). Through a simulation study I find that the AICc is robust if one corrects for the number of species or effective number of species. Lastly I discuss how the concept of the phylogenetic effective sample size can be useful for biodiversity quantification, identification of interesting clades and deciding on the importance of phylogenetic correlations.

  • 8.
    Bauer, Eva
    et al.
    Technical University of Munich, Germany.
    Schmutzer, Thomas
    Leibniz Institute Plant Genet and Crop Plant Research IPK Gat, Germany.
    Barilar, Ivan
    University of Hohenheim, Germany.
    Mascher, Martin
    Leibniz Institute Plant Genet and Crop Plant Research IPK Gat, Germany.
    Gundlach, Heidrun
    Helmholtz Zentrum Munchen, Germany.
    Martis, Mihaela-Maria
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences. Helmholtz Zentrum Munchen, Germany.
    Twardziok, Sven O.
    Helmholtz Zentrum Munchen, Germany.
    Hackauf, Bernd
    Julius Kuhn Institute, Germany.
    Gordillo, Andres
    KWS LOCHOW GMBH, Germany.
    Wilde, Peer
    KWS LOCHOW GMBH, Germany.
    Schmidt, Malthe
    KWS LOCHOW GMBH, Germany.
    Korzun, Viktor
    KWS LOCHOW GMBH, Germany.
    Mayer, Klaus F. X.
    Helmholtz Zentrum Munchen, Germany.
    Schmid, Karl
    University of Hohenheim, Germany.
    Schoen, Chris-Carolin
    Technical University of Munich, Germany.
    Scholz, Uwe
    Leibniz Institute Plant Genet and Crop Plant Research IPK Gat, Germany.
    Towards a whole-genome sequence for rye (Secale cereale L.)2017In: The Plant Journal, ISSN 0960-7412, E-ISSN 1365-313X, Vol. 89, no 5, p. 853-869Article in journal (Refereed)
    Abstract [en]

    We report on a whole-genome draft sequence of rye (Secale cereale L.). Rye is a diploid Triticeae species closely related to wheat and barley, and an important crop for food and feed in Central and Eastern Europe. Through whole-genome shotgun sequencing of the 7.9-Gbp genome of the winter rye inbred line Lo7 we obtained a de novo assembly represented by 1.29 million scaffolds covering a total length of 2.8 Gbp. Our reference sequence represents nearly the entire low-copy portion of the rye genome. This genome assembly was used to predict 27 784 rye gene models based on homology to sequenced grass genomes. Through resequencing of 10 rye inbred lines and one accession of the wild relative S. vavilovii, we discovered more than 90 million single nucleotide variants and short insertions/deletions in the rye genome. From these variants, we developed the high-density Rye600k genotyping array with 600 843 markers, which enabled anchoring the sequence contigs along a high-density genetic map and establishing a synteny-based virtual gene order. Genotyping data were used to characterize the diversity of rye breeding pools and genetic resources, and to obtain a genome-wide map of selection signals differentiating the divergent gene pools. This rye whole-genome sequence closes a gap in Triticeae genome research, and will be highly valuable for comparative genomics, functional studies and genome-based breeding in rye.

    Download full text (pdf)
    fulltext
  • 9.
    Bergamino, Maurizio
    et al.
    Laureate Institute for Brain Research, Tulsa, OK, USA.
    Farmer, Madison
    Roosevelt University, Department of Industrial and Organizational Psychology, Chicago, IL, USA.
    Yeh, Hung-Wen
    Laureate Institute for Brain Research, Tulsa, OK, USA.
    Paul, Elisabeth
    Linköping University, Department of Clinical and Experimental Medicine, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical and Clinical Sciences, Center for Social and Affective Neuroscience.
    Hamilton, Paul J.
    Linköping University, Department of Clinical and Experimental Medicine, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences.
    Statistical differences in the white matter tracts in subjects with depression by using different skeletonized voxel-wise analysis approaches and DTI fitting procedures2017In: Brain Research, ISSN 0006-8993, E-ISSN 1872-6240, Vol. 1669, p. 131-140Article in journal (Refereed)
    Abstract [en]

    Major depressive disorder (MDD) is one of the most significant contributors to the global burden of illness. Diffusion tensor imaging (DTI) is a procedure that has been used in several studies to characterize abnormalities in white matter (WM) microstructural integrity in MDD. These studies, however, have provided divergent findings, potentially due to the large variety of methodological alternatives available in conducting DTI research. In order to determine the importance of different approaches to coregistration of DTI-derived metrics to a standard space, we compared results from two different skeletonized voxel-wise analysis approaches: the standard TBBS pipeline and the Advanced Normalization Tools (ANTs) approach incorporating a symmetric image normalization (SyN) algorithm and a group-wise template (ANTs TBSS). We also assessed effects of applying twelve different fitting procedures for the diffusion tensor. For our dataset, lower fractional anisotropy (FA) and axial diffusivity (AD) in depressed subjects compared with healthy controls were found for both methods and for all fitting procedures. No group differences were found for radial and mean diffusivity indices. Importantly, for the AD metric, the normalization methods and fitting procedures showed reliable differences, both in the volume and in the number of significant between-groups difference clusters detected. Additionally, a significant voxel-based correlation, in the left inferior fronto-occipital fasciculus, between AD and self-reported stress was found only for one of the normalization procedure (ANTs TBSS). In conclusion, the sensitivity to detect group-level effects on DTI metrics might depend on the DTI normalization and/or tensor fitting procedures used.

  • 10.
    Bergenholm, Linnéa
    Linköping University, Department of Biomedical Engineering. Linköping University, Faculty of Health Sciences.
    Modeling as a Tool to Support Self-Management of Type 1 Diabetes2013Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Type 1 diabetes (T1D) is an auto-immune disease characterized by insulin-deficiency. Insulin is a metabolic hormone that is involved in lowering blood glucose (BG) levels in order to control BG level to a tight range. In T1D this glycemic control is lost, causing chronic hyperglycemia (excess glucose in blood stream). Chronic hyperglycemia damages vital tissues. Therefore, glycemic control must be restored.

    A common therapy for restoring glycemic control is intensive insulin therapy, where the missing insulin is replaced with regular insulin injections. When dosing this compensatory insulin many factors that affect glucose metabolism must be considered. Linkura is a company that has developed tools for monitoring the most important factors, which are meals and exercise. In the Linkura meal and exercise tools, the nutrition content in meals and the calorie consumption during exercise are estimated. Another tool designed to aid control of BG is the bolus calculator. Bolus calculators use input of BG level, carbohydrate intake, and insulin history to estimate insulin need. The accuracy of these insulin bolus calculations suffer from two problems. First, errors occur when users inaccurately estimate the carbohydrate content in meals. Second, exercise is not included in bolus calculations. To reduce these problems, it was suggested that the Linkura web tools could be utilized in combination with a bolus calculator.

    For this purpose, a bolus calculator was developed. The bolus calculator was based on existing models that utilize clinical parameters to relate changes in BG levels to meals, insulin, and exercise stimulations. The bolus calculator was evaluated using data collected from Linkura's web tools. The collected data showed some inconsistencies which cannot be explained by any model.  The performance of the bolus calculator in predicting BG levels using general equations to derive the clinical parameters was inadequate. Performance was increased by adopting an update-algorithm where the clinical parameters were updated daily using previous data. Still, better model performance is prefered for use in a bolus calculator.  

    The results show potential in developing bolus calculator tools combined with the Linkura tools. For such bolus calculator, further evaluation on modeling long-term exercise and additional safety features minimizing risk of hypoglycemia are required.

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    thesis_LinneaBergenholm
  • 11.
    Bergman Laurila, Jonas
    Linköping University. Linköping University, Department of Computer and Information Science.
    Ontology Slice Generation and Alignment for Enhanced Life Science Literature Search2009Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Query composition is an often complicated and cumbersome task for persons performing a literature search. This thesis is part of a project which aims to present possible queries to the user in form of natural language expressions. The thesis presents methods of ontology slice generation. Slices are parts of ontologies connecting two concepts along all possible paths between them. Those slices hence represent all relevant queries connecting the concepts and the paths can in a later step be translated into natural language expressions. Methods of slice alignment, connecting slices that originate from different ontologies, are also presented. The thesis concludes with some example scenarios and comparisons to related work.

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    FULLTEXT01
  • 12.
    Bergqvist, Jonathan
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics.
    Study of Protein Interfaces with Clustering2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Protein-protein interactions occur in nature and have different functions. The interacting surface between two interacting proteins contains the respective protein's interface residues.

    In this thesis, a series of Python scripts are presented which can perform interface-interface comparisons with the method InterComp, to obtain a distance matrix of different protein interfaces. The distance matrix can be studied with the use of clustering algorithms such as DBSCAN.

    The result from clustering using DBSCAN shows that for the 77,017 protein interfaces studied, a majority of the protein interfaces are part of a single cluster while most of the remaining interfaces are noise for the tested parameters Eps and MinPts.

    The conclusion of this thesis is the effect on the number of clusters for the tested parameters Eps and MinPts when performing DBSCAN.

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    fulltext
  • 13.
    Björklund, Emil
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Du Rietz, Anna
    Linköping University, Department of Physics, Chemistry and Biology, Molecular Surface Physics and Nano Science. Linköping University, Faculty of Science & Engineering.
    Lundström, Patrik
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Analysis of protein-ligand interactions from titrations and nuclear magnetic resonance relaxation dispersions2022In: Protein Science, ISSN 0961-8368, E-ISSN 1469-896X, Vol. 31, no 1, p. 301-307Article in journal (Refereed)
    Abstract [en]

    We present PLIS, a publicly available, open-source software for the determination of protein-ligand dissociation constants that can be used to characterize biological processes or to shed light on biophysical aspects of interactions. PLIS can analyze data from titration experiments monitored by for instance fluorescence spectroscopy or from nuclear magnetic resonance relaxation dispersion experiments. In addition to analysis of experimental data, PLIS includes functionality for generation of synthetic data, useful for understanding how different parameters effect the data in order to better analyze experiments.

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    fulltext
  • 14.
    Björn, Niclas
    et al.
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences.
    Badam, Tejaswi Venkata Satya
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. School of Bioscience, Systems Biology Research Centre, University of Skövde.
    Spalinskas, Rapolas
    Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology.
    Brandén, Eva
    Department of Respiratory Medicine, Gävle Hospital; Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle.
    Koyi, Hirsh
    Department of Respiratory Medicine, Gävle Hospital; Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle.
    Lewensohn, Rolf
    Thoracic Oncology Unit, Tema Cancer, Karolinska University Hospital; Department of Oncology-Pathology, Karolinska Institutet.
    De Petris, Luigi
    Thoracic Oncology Unit, Tema Cancer, Karolinska University Hospital; Department of Oncology-Pathology, Karolinska Institutet.
    Lubovac-Pilav, Zelmina
    School of Bioscience, Systems Biology Research Centre, University of Skövde.
    Sahlén, Pelin
    Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology.
    Lundeberg, Joakim
    Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Gréen, Henrik
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences. Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology; Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping.
    Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients2020In: npj Systems Biology and Applications, ISSN 2056-7189, Vol. 6, no 1, article id 25Article in journal (Refereed)
    Abstract [en]

    Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10−3 for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.

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    fulltext
  • 15.
    Borgmastars, Emmy
    et al.
    Umea Univ, Sweden.
    de Weerd, Hendrik Arnold
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. Univ Skovde, Sweden.
    Lubovac-Pilav, Zelmina
    Univ Skovde, Sweden.
    Sund, Malin
    Umea Univ, Sweden.
    miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer2019In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 20, article id 393Article in journal (Refereed)
    Abstract [en]

    BackgroundMicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA.The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used.ResultsFifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer.ConclusionsOur miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/.

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  • 16.
    Botvinik-Nezer, Rotem
    et al.
    Tel Aviv Univ, Israel; Tel Aviv Univ, Israel; Dartmouth Coll, NH 03755 USA.
    Holzmeister, Felix
    Univ Innsbruck, Austria.
    Camerer, Colin F.
    CALTECH, CA 91125 USA.
    Dreber, Anna
    Stockholm Sch Econ, Sweden; Univ Innsbruck, Austria.
    Huber, Juergen
    Univ Innsbruck, Austria.
    Johannesson, Magnus
    Stockholm Sch Econ, Sweden.
    Kirchler, Michael
    Univ Innsbruck, Austria.
    Iwanir, Roni
    Tel Aviv Univ, Israel; Tel Aviv Univ, Israel.
    Mumford, Jeanette A.
    Univ Wisconsin, WI USA.
    Adcock, R. Alison
    Duke Univ, NC USA; Duke Univ, NC USA; Univ Ghent, Belgium; Karolinska Inst, Sweden.
    Avesani, Paolo
    Fdn Bruno Kessler, Italy; Univ Trento, Italy; Karolinska Inst, Sweden.
    Baczkowski, Blazej M.
    Max Planck Inst Human Cognit and Brain Sci, Germany.
    Bajracharya, Aahana
    Washington Univ, MO 63110 USA.
    Bakst, Leah
    Boston Univ, MA 02215 USA; Boston Univ, MA 02215 USA.
    Ball, Sheryl
    Virginia Tech, VA USA; Virginia Tech, VA USA.
    Barilari, Marco
    UCLouvain, Belgium.
    Bault, Nadege
    Univ Plymouth, England.
    Beaton, Derek
    Baycrest Hlth Sci Ctr, Canada.
    Beitner, Julia
    Univ Amsterdam, Netherlands; Goethe Univ, Germany.
    Benoit, Roland G.
    Max Planck Inst Human Cognit and Brain Sci, Germany.
    Berkers, Ruud M. W. J.
    Max Planck Inst Human Cognit and Brain Sci, Germany.
    Bhanji, Jamil P.
    Rutgers State Univ, NJ USA.
    Biswal, Bharat B.
    New Jersey Inst Technol, NJ 07102 USA; Univ Elect Sci and Technol China, Peoples R China.
    Bobadilla-Suarez, Sebastian
    New Jersey Inst Technol, NJ 07102 USA.
    Bortolini, Tiago
    D’Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil.
    Bottenhorn, Katherine L.
    Florida Int Univ, FL 33199 USA.
    Bowring, Alexander
    Univ Oxford, England.
    Braem, Senne
    Univ Ghent, Belgium; Vrije Univ Brussel, Belgium.
    Brooks, Hayley R.
    Univ Denver, CO 80208 USA.
    Brudner, Emily G.
    Rutgers State Univ, NJ USA.
    Calderon, Cristian B.
    Univ Ghent, Belgium.
    Camilleri, Julia A.
    Res Ctr Julich, Germany; Heinrich Heine Univ Dusseldorf, Germany.
    Castrellon, Jaime J.
    Duke Univ, NC USA; Duke Univ, NC USA.
    Cecchetti, Luca
    Univ Nebraska, NE 68588 USA.
    Cieslik, Edna C.
    Res Ctr Julich, Germany; Heinrich Heine Univ Dusseldorf, Germany.
    Cole, Zachary J.
    Univ Nebraska, NE 68588 USA.
    Collignon, Olivier
    Univ Trento, Italy; UCLouvain, Belgium.
    Cox, Robert W.
    NIMH, MD 20892 USA.
    Cunningham, William A.
    Univ Toronto, Canada.
    Czoschke, Stefan
    Goethe Univ, Germany.
    Dadi, Kamalaker
    Imperial Coll London, England; Univ Oxford, England.
    Davis, Charles P.
    Univ Connecticut, CT USA; Univ Connecticut, CT USA; Univ Connecticut, CT USA.
    Luca, Alberto De
    Univ Med Ctr Utrecht, Netherlands.
    Delgado, Mauricio R.
    New Jersey Inst Technol, NJ 07102 USA.
    Demetriou, Lysia
    Imperial Coll London, England; Univ Oxford, England.
    Dennison, Jeffrey B.
    Temple Univ, PA 19122 USA.
    Di, Xin
    New Jersey Inst Technol, NJ 07102 USA; Univ Elect Sci and Technol China, Peoples R China.
    Dickie, Erin W.
    Ctr Addict and Mental Hlth, Canada; Univ Toronto, Canada.
    Dobryakova, Ekaterina
    Kessler Fdn, NJ USA.
    Donnat, Claire L.
    Stanford Univ, CA 94305 USA.
    Dukart, Juergen
    Res Ctr Julich, Germany; Heinrich Heine Univ Dusseldorf, Germany.
    Duncan, Niall W.
    Taipei Med Univ, Taiwan; TMU ShuangHo Hosp, Taiwan.
    Durnez, Joke
    Stanford Univ, CA USA; Stanford Univ, CA 94305 USA.
    Eed, Amr
    CSIC UMH, Spain.
    Eickhoff, Simon B.
    Res Ctr Julich, Germany; Heinrich Heine Univ Dusseldorf, Germany.
    Erhart, Andrew
    Univ Denver, CO 80208 USA.
    Fontanesi, Laura
    Univ Basel, Switzerland.
    Fricke, G. Matthew
    Univ New Mexico, NM 87131 USA.
    Fu, Shiguang
    Zhejiang Univ Technol, Peoples R China; Zhejiang Univ Technol, Peoples R China.
    Galvan, Adriana
    Univ Calif Los Angeles, CA USA.
    Gau, Remi
    UCLouvain, Belgium.
    Genon, Sarah
    Res Ctr Julich, Germany; Heinrich Heine Univ Dusseldorf, Germany.
    Glatard, Tristan
    Concordia Univ, Canada.
    Glerean, Enrico
    Aalto Univ, Finland.
    Goeman, Jelle J.
    Leiden Univ, Netherlands.
    Golowin, Sergej A. E.
    Leiden Univ, Netherlands.
    Gonzalez-Garcia, Carlos
    Univ Ghent, Belgium.
    Gorgolewski, Krzysztof J.
    Stanford Univ, CA 94305 USA.
    Grady, Cheryl L.
    Baycrest Hlth Sci Ctr, Canada.
    Green, Mikella A.
    Duke Univ, NC USA; Duke Univ, NC USA.
    Guassi Moreira, Joao F.
    Univ Calif Los Angeles, CA USA.
    Guest, Olivia
    Res Ctr Interact Media Smart Syst and Emerging Tech, Cyprus.
    Hakimi, Shabnam
    Duke Univ, NC USA.
    Hamilton, Paul J.
    Linköping University, Department of Biomedical and Clinical Sciences, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences.
    Hancock, Roeland
    Univ Connecticut, CT USA; Univ Connecticut, CT USA.
    Handjaras, Giacomo
    IMT Sch Adv Studies Lucca, Italy.
    Harry, Bronson B.
    Western Sydney Univ, Australia.
    Hawco, Colin
    Ctr Addict and Mental Hlth, Canada.
    Herholz, Peer
    McGill Univ, Canada.
    Herman, Gabrielle
    Ctr Addict and Mental Hlth, Canada.
    Heunis, Stephan
    Eindhoven Univ Technol, Netherlands; Epilepsy Ctr Kempenhaeghe, Netherlands.
    Hoffstaedter, Felix
    Res Ctr Julich, Germany; Heinrich Heine Univ Dusseldorf, Germany.
    Hogeveen, Jeremy
    Univ New Mexico, NM 87131 USA; Univ New Mexico, NM 87131 USA.
    Holmes, Susan
    Stanford Univ, CA 94305 USA.
    Hu, Chuan-Peng
    LIR, Germany.
    Huettel, Scott A.
    Duke Univ, NC USA.
    Hughes, Matthew E.
    Swinburne Univ Technol, Australia.
    Iacovella, Vittorio
    Univ Trento, Italy.
    Iordan, Alexandru D.
    Univ Michigan, MI USA.
    Isager, Peder M.
    Eindhoven Univ Technol, Netherlands.
    Isik, Ayse I.
    Max Planck Inst Empir Aesthet, Germany.
    Jahn, Andrew
    Univ Michigan, MI 48109 USA.
    Johnson, Matthew R.
    Univ Nebraska, NE 68588 USA; Univ Nebraska, NE USA.
    Johnstone, Tom
    Swinburne Univ Technol, Australia.
    Joseph, Michael J. E.
    Ctr Addict and Mental Hlth, Canada.
    Juliano, Anthony C.
    Kessler Fdn, NJ USA.
    Kable, Joseph W.
    Univ Penn, PA 19104 USA; Univ Penn, PA 19104 USA.
    Kassinopoulos, Michalis
    McGill Univ, Canada.
    Koba, Cemal
    IMT Sch Adv Studies Lucca, Italy.
    Kong, Xiang-Zhen
    Max Planck Inst Psycholinguist, Netherlands.
    Koscik, Timothy R.
    Univ Iowa, IA 52242 USA.
    Kucukboyaci, Nuri Erkut
    Univ Nebraska, NE USA; Rutgers New Jersey Med Sch, NJ USA.
    Kuhl, Brice A.
    Univ Oregon, OR 97403 USA.
    Kupek, Sebastian
    Univ Innsbruck, Austria.
    Laird, Angela R.
    Florida Int Univ, FL 33199 USA.
    Lamm, Claus
    Univ Vienna, Austria; Univ Vienna, Austria.
    Langner, Robert
    Res Ctr Julich, Germany; Heinrich Heine Univ Dusseldorf, Germany.
    Lauharatanahirun, Nina
    US CCDC Army Res Lab, MD USA; Univ Penn, PA 19104 USA.
    Lee, Hongmi
    US CCDC Army Res Lab, MD USA.
    Lee, Sangil
    Univ Penn, PA 19104 USA.
    Leemans, Alexander
    Univ Med Ctr Utrecht, Netherlands.
    Leo, Andrea
    IMT Sch Adv Studies Lucca, Italy.
    Lesage, Elise
    Univ Ghent, Belgium.
    Li, Flora
    Fralin Biomed Res Inst, VA USA; Nanjing Audit Univ, Peoples R China.
    Li, Monica Y. C.
    Univ Connecticut, CT USA; Univ Connecticut, CT USA; Univ Connecticut, CT USA; Haskins Labs Inc, CT USA.
    Lim, Phui Cheng
    Univ Nebraska, NE 68588 USA; Univ Nebraska, NE USA.
    Lintz, Evan N.
    Univ Nebraska, NE 68588 USA.
    Liphardt, Schuyler W.
    Univ New Mexico, NM 87131 USA.
    Losecaat Vermeer, Annabel B.
    Univ Vienna, Austria.
    Love, Bradley C.
    Alan Turing Inst, England.
    Mack, Michael L.
    Univ Toronto, Canada.
    Malpica, Norberto
    Univ Rey Juan Carlos, Spain.
    Marins, Theo
    UCL, England; DOr Inst Res and Educ IDOR, Brazil.
    Maumet, Camille
    Univ Rennes, France.
    McDonald, Kelsey
    Duke Univ, NC USA.
    McGuire, Joseph T.
    Boston Univ, MA 02215 USA; Boston Univ, MA 02215 USA.
    Melero, Helena
    Univ Rey Juan Carlos, Spain; CES Cardenal Cisneros, Spain; Northeastern Univ, MA 02115 USA.
    Mendez Leal, Adriana S.
    Univ Calif Los Angeles, CA USA.
    Meyer, Benjamin
    LIR, Germany; Johannes Gutenberg Univ Mainz, Germany.
    Meyer, Kristin N.
    Univ N Carolina, NC 27515 USA.
    Mihai, Glad
    Max Planck Inst Human Cognit and Brain Sci, Germany; Tech Univ Dresden, Germany.
    Mitsis, Georgios D.
    McGill Univ, Canada.
    Moll, Jorge
    UCL, England; DOr Inst Res and Educ IDOR, Brazil; Stanford Univ, CA 94305 USA.
    Nielson, Dylan M.
    NIMH, MD 20892 USA.
    Nilsonne, Gustav
    Karolinska Inst, Sweden; Stockholm Univ, Sweden.
    Notter, Michael P.
    Univ Hosp Ctr, Switzerland; Univ Lausanne, Switzerland.
    Olivetti, Emanuele
    Fdn Bruno Kessler, Italy; Univ Trento, Italy.
    Onicas, Adrian I.
    IMT Sch Adv Studies Lucca, Italy.
    Papale, Paolo
    IMT Sch Adv Studies Lucca, Italy; Netherlands Inst Neurosci, Netherlands.
    Patil, Kaustubh R.
    Res Ctr Julich, Germany; Heinrich Heine Univ Dusseldorf, Germany.
    Peelle, Jonathan E.
    Washington Univ, MO 63110 USA.
    Perez, Alexandre
    McGill Univ, Canada.
    Pischedda, Doris
    Charite, Germany; Charite, Germany; Charite, Germany; Free Univ Berlin, Germany; Humboldt Univ, Germany; Berlin Inst Hlth, Germany; Tech Univ Berlin, Germany; Humboldt Univ, Germany; NeuroMI Milan Ctr Neurosci, Italy.
    Poline, Jean-Baptiste
    McGill Univ, Canada; Univ Calif Berkeley, CA 94720 USA.
    Prystauka, Yanina
    Univ Connecticut, CT USA; Univ Connecticut, CT USA; Univ Connecticut, CT USA.
    Ray, Shruti
    New Jersey Inst Technol, NJ 07102 USA.
    Reuter-Lorenz, Patricia A.
    Univ Michigan, MI USA.
    Reynolds, Richard C.
    NIMH, MD 20892 USA.
    Ricciardi, Emiliano
    IMT Sch Adv Studies Lucca, Italy.
    Rieck, Jenny R.
    Baycrest Hlth Sci Ctr, Canada.
    Rodriguez-Thompson, Anais M.
    Univ N Carolina, NC 27515 USA.
    Romyn, Anthony
    Univ Toronto, Canada.
    Salo, Taylor
    Florida Int Univ, FL 33199 USA.
    Samanez-Larkin, Gregory R.
    Duke Univ, NC USA; Duke Univ, NC USA.
    Sanz-Morales, Emilio
    Univ Rey Juan Carlos, Spain.
    Schlichting, Margaret L.
    Univ Toronto, Canada.
    Schultz, Douglas H.
    Dartmouth Coll, NH 03755 USA; Univ Nebraska, NE 68588 USA.
    Shen, Qiang
    Zhejiang Univ Technol, Peoples R China; Zhejiang Univ Technol, Peoples R China.
    Sheridan, Margaret A.
    Alan Turing Inst, England.
    Silvers, Jennifer A.
    Univ Calif Los Angeles, CA USA.
    Skagerlund, Kenny
    Linköping University, Department of Behavioural Sciences and Learning, Psychology. Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Biomedical and Clinical Sciences, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences.
    Smith, Alec
    Virginia Tech, VA USA; Virginia Tech, VA USA.
    Smith, David V.
    Temple Univ, PA 19122 USA.
    Sokol-Hessner, Peter
    Univ Denver, CO 80208 USA.
    Steinkamp, Simon R.
    Res Ctr Julich, Germany.
    Tashjian, Sarah M.
    Univ Calif Los Angeles, CA USA.
    Thirion, Bertrand
    Univ Paris Saclay, France.
    Thorp, John N.
    Columbia Univ, NY 10027 USA.
    Tinghög, Gustav
    Linköping University, Department of Management and Engineering, Economics. Linköping University, Faculty of Arts and Sciences. Linköping University, Department of Health, Medicine and Caring Sciences, Division of Society and Health. Linköping University, Faculty of Medicine and Health Sciences.
    Tisdall, Loreen
    Stanford Univ, CA 94305 USA; Univ Basel, Switzerland.
    Tompson, Steven H.
    US CCDC Army Res Lab, MD USA.
    Toro-Serey, Claudio
    Boston Univ, MA 02215 USA; Boston Univ, MA 02215 USA.
    Torre Tresols, Juan Jesus
    Univ Paris Saclay, France.
    Tozzi, Leonardo
    Stanford Univ, CA 94305 USA.
    Truong, Vuong
    Taipei Med Univ, Taiwan; TMU ShuangHo Hosp, Taiwan.
    Turella, Luca
    Univ Trento, Italy.
    van t Veer, Anna E.
    Leiden Univ, Netherlands.
    Verguts, Tom
    Univ Ghent, Belgium.
    Vettel, Jean M.
    US Combat Capabil Dev Command Army Res Lab, MD USA; Univ Calif Santa Barbara, CA 93106 USA; Univ Penn, PA 19104 USA.
    Vijayarajah, Sagana
    Univ Toronto, Canada.
    Vo, Khoi
    Duke Univ, NC USA; Duke Univ, NC USA.
    Wall, Matthew B.
    Invicro, England; Imperial Coll London, England; UCL, England.
    Weeda, Wouter D.
    Leiden Univ, Netherlands.
    Weis, Susanne
    Res Ctr Julich, Germany; Heinrich Heine Univ Dusseldorf, Germany.
    White, David J.
    Imperial Coll London, England.
    Wisniewski, David
    Univ Ghent, Belgium.
    Xifra-Porxas, Alba
    McGill Univ, Canada.
    Yearling, Emily A.
    Univ Connecticut, CT USA; Univ Connecticut, CT USA.
    Yoon, Sangsuk
    Univ Dayton, OH 45469 USA.
    Yuan, Rui
    Stanford Univ, CA 94305 USA.
    Yuen, Kenneth S. L.
    Duke Univ, NC USA; LIR, Germany; Johannes Gutenberg Univ Mainz, Germany.
    Zhang, Lei
    Univ Vienna, Austria.
    Zhang, Xu
    Univ Connecticut, CT USA; Univ Connecticut, CT USA; Univ Connecticut, CT USA.
    Zosky, Joshua E.
    Univ Nebraska, NE 68588 USA; Univ Nebraska, NE USA.
    Nichols, Thomas E.
    Univ Oxford, England.
    Poldrack, Russell A.
    Stanford Univ, CA 94305 USA.
    Schonberg, Tom
    Tel Aviv Univ, Israel; Tel Aviv Univ, Israel.
    Variability in the analysis of a single neuroimaging dataset by many teams2020In: Nature, ISSN 0028-0836, E-ISSN 1476-4687, Vol. 582, p. 84-88Article in journal (Refereed)
    Abstract [en]

    Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses(1). The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset(2-5). Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed. The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.

  • 17.
    Bzhalava, David
    et al.
    Karolinska Institutet and Karolinska University Hospital, Stockholm.
    Ekström, Johanna
    Lund University, Malmö.
    Lysholm, Fredrik
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Hultin, Emilie
    Karolinska Institutet and Karolinska University Hospital, Stockholm.
    Faust, Helena
    Lund University, Malmö.
    Persson, Bengt
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Lehtinen, Matti
    National Institute for Health and Welfare, Oulu, Finland.
    de Villiers, Ethel-Michele
    Deutsches Krebsforschungszentrum, Heidelberg, Germany.
    Dillner, Joakim
    Karolinska Institutet and Karolinska University Hospital, Stockholm.
    Phylogenetically diverse TT virus viremia among pregnant women2012In: Virology, ISSN 0042-6822, E-ISSN 1096-0341, Vol. 432, no 2, p. 427-434Article in journal (Refereed)
    Abstract [en]

    Infections during pregnancy have been suggested to be involved in childhood leukemias. We used high-throughput sequencing to describe the viruses most readily detectable in serum samples of pregnantwomen. Serum DNA of 112 mothers to leukemic children was amplified using whole genome amplification. Sequencing identified one TTvirus (TTV) isolate belonging to a known type and two putatively new TTVs. For 22 mothers, we also performed TTV amplification by general primer PCR before sequencing. This detected 39 TTVs, two of which were identical to the TTVs found after whole genome amplification.

    Altogether, we found 40 TTV isolates, 29 of which were putatively new types (similarities ranging from 89% to 69%). In conclusion, high throughput sequencing is useful to describe the known or unknown viruses that are present in serum samples of pregnantwomen.

  • 18. Order onlineBuy this publication >>
    Carlsson, Jonas
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Mutational effects on protein structure and function2009Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In this thesis several important proteins are investigated from a structural perspective. Some of the proteins are disease related while other have important but not completely characterised functions. The techniques used are general as demonstrated by applications on metabolic proteins (CYP21, CYP11B1, IAPP, ADH3), regulatory proteins (p53, GDNF) and a transporter protein (ANTR1).

    When the protein CYP21 (steroid 21-hydroxylase) is deficient it causes CAH (congenital adrenal hyperplasia). For this protein, there are about 60 known mutations with characterised clinical phenotypes. Using manual structural analysis we managed to explain the severity of all but one of the mutations. By observing the properties of these mutations we could perform good predictions on, at the time, not classified mutations.

    For the cancer suppressor protein p53, there are over thousand mutations with known activity. To be able to analyse such a large number of mutations we developed an automated method for evaluation of the mutation effect called PREDMUT. In this method we include twelve different prediction parameters including two energy parameters calculated using an energy minimization procedure. The method manages to differentiate severe mutations from non-severe mutations with 77% accuracy on all possible single base substitutions and with 88% on mutations found in breast cancer patients.

    The automated prediction was further applied to CYP11B1 (steroid 11-beta-hydroxylase), which in a similar way as CYP21 causes CAH when deficient. A generalized method applicable to any kind of globular protein was developed. The method was subsequently evaluated on nine additional proteins for which mutants were known with annotated disease phenotypes. This prediction achieved 84% accuracy on CYP11B1 and 81% accuracy in total on the evaluation proteins while leaving 8% as unclassified. By increasing the number of unclassified mutations the accuracy of the remaining mutations could be increased on the evaluation proteins and substantially increase the classification quality as measured by the Matthews correlation coefficient. Servers with predictions for all possible single based substitutions are provided for p53, CYP21 and CYP11B1.

    The amyloid formation of IAPP (islet amyloid polypeptide) is strongly connected to diabetes and has been studied using both molecular dynamics and Monte Carlo energy minimization. The effects of mutations on the amount and speed of amyloid formation were investigated using three approaches. Applying a consensus of the three methods on a number of interesting mutations, 94% of the mutations could be correctly classified as amyloid forming or not, evaluated with in vitro measurements.

    In the brain there are many proteins whose functions and interactions are largely unknown. GDNF (glial cell line-derived neurotrophic factor) and NCAM (neural cell adhesion molecule) are two such neuron connected proteins that are known to interact. The form of interaction was studied using protein--protein docking where a docking interface was found mediated by four oppositely charged residues in respective protein. This interface was subsequently confirmed by mutagenesis experiments. The NCAM dimer interface upon binding to the GDNF dimer was also mapped as well as an additional interacting protein, GFRα1, which was successfully added to the protein complex without any clashes.

    A large and well studied protein family is the alcohol dehydrogenase family, ADH. A class of this family is ADH3 (alcohol dehydrogenase class III) that has several known substrates and inhibitors. By using virtual screening we tried to characterize new ligands. As some ligands were already known we could incorporate this knowledge when the compound docking simulations were scored and thereby find two new substrates and two new inhibitors which were subsequently successfully tested in vitro.

    ANTR1 (anion transporter 1) is a membrane bound transporter important in the photosynthesis in plants. To be able to study the amino acid residues involved in inorganic phosphate transportation a homology model of the protein was created. Important residues were then mapped onto the structure using conservation analysis and we were in this way able to propose roles of amino acid residues involved in the transportation of inorganic phosphate. Key residues were subsequently mutated in vitro and a transportation process could be postulated.

    To conclude, we have used several molecular modelling techniques to find functional clues, interaction sites and new ligands. Furthermore, we have investigated the effect of muations on the function and structure of a multitude of disease related proteins.

     

    List of papers
    1. Molecular Model of Human CYP21 Based onMammalian CYP2C5: Structural Features Correlatewith Clinical Severity of Mutations CausingCongenital Adrenal Hyperplasia
    Open this publication in new window or tab >>Molecular Model of Human CYP21 Based onMammalian CYP2C5: Structural Features Correlatewith Clinical Severity of Mutations CausingCongenital Adrenal Hyperplasia
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    2006 (English)In: Molecular Endocrinology, ISSN 0888-8809, E-ISSN 1944-9917, Vol. 20, no 11, p. 2946-2964Article in journal (Refereed) Published
    Abstract [en]

    Enhanced understanding of structure-function relationshipsof human 21-hydroxylase, CYP21, is requiredto better understand the molecular causesof congenital adrenal hyperplasia. To this end, astructural model of human CYP21 was calculatedbased on the crystal structure of rabbit CYP2C5.All but two known allelic variants of missense type,a total of 60 disease-causing mutations and sixnormal variants, were analyzed using this model. Astructural explanation for the corresponding phenotypewas found for all but two mutants for whichavailable clinical data are also discrepant with invitro enzyme activity. Calculations of protein stabilityof modeled mutants were found to correlateinversely with the corresponding clinical severity.Putative structurally important residues were identifiedto be involved in heme and substrate binding,redox partner interaction, and enzyme catalysisusing docking calculations and analysis of structurallydetermined homologous cytochrome P450s(CYPs). Functional and structural consequences ofseven novel mutations, V139E, C147R, R233G,T295N, L308F, R366C, and M473I, detected inScandinavian patients with suspected congenitaladrenal hyperplasia of different severity, were predictedusing molecular modeling. Structural featuresdeduced from the models are in good correlationwith clinical severity of CYP21 mutants,which shows the applicability of a modeling approachin assessment of new CYP21 mutations.

    Place, publisher, year, edition, pages
    Stanford: The endocrin society, 2006
    Keywords
    Mutations, prediction, CAH, CYP21, homology model
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-21305 (URN)10.1210/me.2006-0172 (DOI)
    Available from: 2009-09-30 Created: 2009-09-30 Last updated: 2017-12-13Bibliographically approved
    2. Investigation and prediction of the severity of p53 mutants using parameters from structural calculations
    Open this publication in new window or tab >>Investigation and prediction of the severity of p53 mutants using parameters from structural calculations
    2009 (English)In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 276, no 15, p. 4142-4155Article in journal (Refereed) Published
    Abstract [en]

    A method has been developed to predict the effects of mutations in the p53 cancer suppressor gene. The new method uses novel parameters combined with previously established parameters. The most important parameter is the stability measure of the mutated structure calculated using molecular modelling. For each mutant, a severity score is reported, which can be used for classification into deleterious and nondeleterious. Both structural features and sequence properties are taken into account. The method has a prediction accuracy of 77% on all mutants and 88% on breast cancer mutations affecting WAF1 promoter binding. When compared with earlier methods, using the same dataset, our method clearly performs better. As a result of the severity score calculated for every mutant, valuable knowledge can be gained regarding p53, a protein that is believed to be involved in over 50% of all human cancers.

    Keywords
    Cancer; molecular modelling; mutations; p53; structural prediction
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-20141 (URN)10.1111/j.1742-4658.2009.07124.x (DOI)
    Available from: 2009-08-31 Created: 2009-08-31 Last updated: 2017-12-13Bibliographically approved
    3. A structural model of human steroid 11-betahydroxylase,CYP11B1, used to predict consequences of mutations
    Open this publication in new window or tab >>A structural model of human steroid 11-betahydroxylase,CYP11B1, used to predict consequences of mutations
    2009 (English)Article in journal (Other academic) Submitted
    Abstract [en]

    A prediction method has been developed to estimate the severity of amino acid residue exchanges in human steroid 11-beta-hydroxylase, CYP11B1, due to mutations in the corresponding gene. The prediction is based both on structural and on sequence dependent parameters. The method uses two approaches; one with general molecular property weights and one with a consensus voting strategy based upon distribution of molecular properties, which does not require any training. Both methods are tested on known mutations in CYP11B1 and result in 85% prediction accuracy. The consensus voting method is then further evaluated on 9 proteins with an average of 81% prediction accuracy. A server utilizing the results from the consensus voting on CYP11B1 is provided where the user can extract information about new mutants. A similar server is also provided for mutants in human steroid 21-hydroxylase (CYP21).

    Keywords
    CYP11B1, steroid 11-beta-hydroxylase, molecular modeling, structural prediction, mutations
    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:liu:diva-51118 (URN)
    Available from: 2009-10-19 Created: 2009-10-19 Last updated: 2009-10-19Bibliographically approved
    4. Disruption of the GDNF Binding Site in NCAM DissociatesLigand Binding and Homophilic Cell Adhesion
    Open this publication in new window or tab >>Disruption of the GDNF Binding Site in NCAM DissociatesLigand Binding and Homophilic Cell Adhesion
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    2007 (English)In: Journal of Biological Chemistry, ISSN 0021-9258, E-ISSN 1083-351X, Vol. 282, no 17, p. 12734-12740Article in journal (Refereed) Published
    Abstract [en]

    Most plasma membrane proteins are capable of sensing multiple cell-cell and cell-ligand interactions, but the extent towhich this functional versatility is founded on their modular design is less clear. We have identified the third immunoglobulin domain of the Neural Cell Adhesion Molecule (NCAM) as the necessary and sufficient determinant for its interaction with Glial Cell Line-derived Neurotrophic Factor (GDNF). Four charged contacts were identified by molecular modeling as the main contributors to binding energy. Their mutation abolished GDNF binding to NCAM but left intact the ability of NCAM tomediate cell adhesion, indicating that the two functions are genetically separable. The GDNF-NCAM interface allows complex formation with the GDNF family receptor α1, shedding light on the molecular architecture of a multicomponent GDNF receptor.

    Place, publisher, year, edition, pages
    Bethesda, MD: American Society for Biochemistry and Molecular Biology, 2007
    Keywords
    homology model, protein complex, interaction interface, mutagenesis
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-21306 (URN)10.1074/jbc.M701588200 (DOI)
    Available from: 2009-09-30 Created: 2009-09-30 Last updated: 2017-12-13Bibliographically approved
    5. Functionally Important Amino Acids in the Arabidopsis Thylakoid Phosphate Transporter: Homology Modeling and Site-directed Mutagenesis
    Open this publication in new window or tab >>Functionally Important Amino Acids in the Arabidopsis Thylakoid Phosphate Transporter: Homology Modeling and Site-directed Mutagenesis
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    2010 (English)In: Biochemistry, ISSN 0006-2960, E-ISSN 1520-4995, Vol. 49, no 30, p. 6430-6439Article in journal (Other academic) Published
    Abstract [en]

    The anion transporter 1 (ANTR1) from Arabidopsis thaliana, homologous to the mammalian SLC17 family, has recently been localized to the chloroplast thylakoid membrane. When expressed heterologously in Escherichia coli, ANTR1 mediates a Na+-dependent active transport of inorganic phosphate (Pi). The aim of this study was to identify amino acids involved in substrate binding/translocation by ANTR1 and in the Na+-dependence of its activity. A threedimensional structural model of ANTR1 was constructed using the crystal structure of glycerol-3-phosphate/phosphate antiporter (GlpT) from E.coli as a template. Based on this model and multiple sequence alignments, five highly conserved residues in plant ANTRs and mammalian SLC17 homologues have been selected for site-directed mutagenesis, namely Arg-120, Ser-124 and Arg-201 inside the putative translocation pathway, Arg-228 and Asp-382 exposed at the cytosolic surface of the protein. The activities of the wild type and mutant proteins have been analyzed using expression in E. coli and radioactive transport assays, and compared with bacterial cells carrying an empty plasmid. Based on Pi- and Na+-dependent kinetics, we propose that Arg-120, Arg-201 and Arg-228 are involved in binding and translocation of the substrate, Ser-124 functions as a periplasmic gate for Na+ ions, and finally Asp-382 participates in the turnover of the transporter via ionic interaction with either Arg-228 or Na+ ions. We also propose that the corresponding residues may have a similar function in other plant and mammalian SLC17 homologous transporters.

    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:liu:diva-51119 (URN)10.1021/bi100239j (DOI)
    Note
    On the day of the defence day the status of this article was ManuscriptAvailable from: 2009-10-19 Created: 2009-10-19 Last updated: 2017-12-12Bibliographically approved
    6. A folding study on IAPP (Islet Amyloid Polypeptide) using molecular dynamics simulations
    Open this publication in new window or tab >>A folding study on IAPP (Islet Amyloid Polypeptide) using molecular dynamics simulations
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    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Amyloidosis is the largest group among the protein misfolding diseases, and includes well known diseases such as Alzheimer’s disease and type 2 diabetes. In the latter, islet amyloid is present in the pancreas in almost all individuals. Today, more than 25 different proteins have been isolated from amyloid deposits in human. Even though these proteins differ in size, charge and sequence they all have the capacity to assemble in to fibrillar structures with inseparable morphological appearance. Therefore, it can be assumed that the fibril process is based upon principles that are general for all proteins and knowledge derived from one protein can be used for other amyloid proteins. In this paper, we study the process of amyloid formation in parts of islet amyloid polypeptide (residues 18-29 and 11-37) by analyzing mutations using three different in silico methods. Finally, we use the methods to predict the amyloidogenic properties of the native IAPP and 16 variants thereof and compare the result with in vitro measurements. Using a consensus prediction of the three methods we managed to correctly classify all but two peptides. We have also given further evidence to the importance of S28P for inhibiting amyloid fibre formation, found evidence for antiparallel stacking, and identified important regions for beta sheet stability.

    Keywords
    IAPP, molecular modeling, amyloid, prediction, molecular dynamics, Monte Carlo
    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:liu:diva-51120 (URN)
    Available from: 2009-10-19 Created: 2009-10-19 Last updated: 2010-01-14Bibliographically approved
    7. Virtual screening for ligands to human alcohol dehydrogenase 3
    Open this publication in new window or tab >>Virtual screening for ligands to human alcohol dehydrogenase 3
    Show others...
    (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Alcohol dehydrogenase 3 (ADH3) has been suggested a role in nitric oxide homeostasis due to its function as a S-nitrosoglutathione (GSNO) reductase. This has requested a modulator of the ADH3 activity for control of GSNO levels. Today virtual screenings are frequently used in drug discovery to dock and rank a large number of compounds. With molecular dockings of more than 40,000 compounds into the active site pocket of human ADH3 we ranked compounds with a novel method. Six top ranked compounds that were not known to interact with ADH3 were tested in vitro, where two showed substrate activity (9-decen-1-ol and dodecyltetraglycol), two showed inhibition capacity (deoxycholic acid and doxorubicin) and two did not have any detectable effect. For the substrates, site specific interactions and calculated binding scoring energies were determined with an extended docking simulation including flexible side chains of amino acids residues. The binding scoring energies correlated well with the logarithm of the substrates kcat over Km values. Furthermore, with these computational and experimental data three different lines for specific inhibitors for ADH3 are suggested: fatty acids, glutathione analogs and in addition deoxycholic acids.

    Keywords
    Alcohol dehydrogenase, Enzyme kinetics, Molecular docking, Virtual screening
    National Category
    Natural Sciences
    Identifiers
    urn:nbn:se:liu:diva-51121 (URN)
    Available from: 2009-10-19 Created: 2009-10-19 Last updated: 2010-01-14Bibliographically approved
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    Mutational effects on protein structure and function
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  • 19.
    Carlsson, Jonas
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Persson, Bengt
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, The Institute of Technology.
    Investigating protein variants using structural calculation techniques2012In: Homology Modeling: Methods and Protocols / [ed] Andrew J. W. Orry and Ruben Abagyan, Springer, 2012, Vol. 857, p. 313-330Chapter in book (Other academic)
    Abstract [en]

    Knowledge about protein tertiary structure can guide experiments, assist in the understanding of structure-function relationships, and aid the design of new therapeutics for disease. Homology modeling is an in silico method that predicts the tertiary structure of an amino acid sequence based on a homologous experimentally determined structure. In, Homology Modeling: Methods and Protocols experts in the field describe each homology modeling step from first principles, provide case studies for challenging modeling targets and describe methods for the prediction of how other molecules such as drugs can interact with the protein. Written in the highly successful Methods in Molecular Biology series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Homology Modeling: Methods and Protocols guides scientists in the available homology modeling methods.

  • 20.
    Cuello, Cristina
    et al.
    Univ Murcia, Spain.
    Martinez Serrano, Cristina
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Cambra, Josep M.
    Univ Murcia, Spain.
    Gonzalez-Plaza, Alejandro
    Univ Murcia, Spain.
    Parrilla, Inmaculada
    Univ Murcia, Spain.
    Rodriguez-Martinez, Heriberto
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Children's and Women's Health. Linköping University, Faculty of Medicine and Health Sciences.
    Gil, Maria A.
    Univ Murcia, Spain.
    Martinez, Emilio A.
    Univ Murcia, Spain.
    Vitrification Effects on the Transcriptome of in vivo-Derived Porcine Morulae2021In: Frontiers in Veterinary Science, E-ISSN 2297-1769, Vol. 8, article id 771996Article in journal (Refereed)
    Abstract [en]

    Despite the reported promising farrowing rates after non-surgical and surgical transfers of vitrified porcine morulae and blastocysts produced in vivo (range: 70-75%), the pregnancy loss is 5-15 fold higher with vitrified than with fresh embryos. The present study aimed to investigate whether vitrification affects the transcriptome of porcine morulae, using microarrays and RT-qPCR validation. Morulae were obtained surgically from weaned sows (n = 13) on day 6 (day 0 = estrus onset). A total of 60 morulae were vitrified (treatment group). After 1 week of storage, the vitrified morulae were warmed. Vitrified-warmed and non-vitrified fresh morulae (control; n = 40) were cultured for 24 h to assess embryo survival by stereomicroscopy after. A total of 30 vitrified/warmed embryos that were deemed viable and 30 fresh control embryos (three pools of 10 for each experimental group) were selected for microarray analysis. Gene expression was assessed with a GeneChip (R) Porcine Genome Array (Affymetrix). An ANOVA analysis p-unadjusted &lt;0.05 and a fold change cut-off of +/- 1.5 were set to identify differentially expressed genes (DEGs). Data analysis and biological interpretation were performed using the Partek Genomic Suite 7.0 software. The survival rate of morulae after vitrification and warming (92.0 +/- 8.3%) was similar to that of the control (100%). A total of 233 DEGs were identified in vitrified morulae (38 upregulated and 195 downregulated), compared to the control group. Nine pathways were significantly modified. Go-enrichment analysis revealed that DEGs were mainly related to the Biological Process functional group. Up-regulated DEGs were involved in glycosaminoglycan degradation, metabolic pathways and tryptophan metabolism KEGG pathways. The pathways related to the down-regulated DEGs were glycolysis/gluconeogenesis, protein export and fatty acid elongation. The disruption of metabolic pathways in morulae could be related to impaired embryo quality and developmental potential, despite the relatively high survival rates after warming observed in vitro. In conclusion, vitrification altered the gene expression pattern of porcine morulae produced in vivo, generating alterations in the transcriptome that may interfere with subsequent embryo development and pregnancy after embryo transfer.

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  • 21. Order onlineBuy this publication >>
    de Weerd, Hendrik Arnold
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Novel methods and software for disease module inference2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Cellular organization is believed to be modular, meaning cellular functions are carried out by modules composed of clusters of genes, proteins and metabolites that are interconnected, co-regulated or physically interacting. In turn, these modules interact together and thereby form complex networks that taken together is considered to be the interactome. 

    Modern high-throughput biological techniques have made high-scale accurate quantification of these biological molecules possible, the so called omics. The simultaneous measurement of these molecules enables a picture of the state of a cell at a resolution that was never before possible. Mapping these measurements aids greatly to elucidate a network structure of interactions. The ever growing size of public repositories for omics data has ushered in the advent of biology as a (big) data science and opens the door for data hungry machine learning approaches in biology. 

    Complex diseases are multi-factorial and arise from a combination of genetic, environmental and lifestyle factors. Additionally, diagnosis and treatment is complicated by the fact that these genetic, environmental and lifestyle factors can vary between patients and may or may not give rise to different disease phenotypes that still classify as the same disease. Genetically, there is substantial heterogeneity among patients and therefore the emergence of a disease phenotype cannot be attributed to a single genetic mutation but rather to a combination of various mutations that may vary from patient to patient. As complex diseases can have different root causes but give rise to a similar disease phenotype, the implication is that different root causes perturb similar components in the interactome. Most of the work in this thesis is aimed at developing methods and computational pipelines to identify, analyze and evaluate these perturbed disease specific sub-networks in the interactome, so called disease modules. 

    We started by collecting popular disease module inference methods and combined them in a unified framework, an R package called MODifieR (Paper I). The package uses standardized inputs and outputs, allowing for a more user-friendly way of running multiple disease module inference methods and the combining of modules. Next, we benchmarked the MODifieR methods on a compendium of transcriptomic and methylomic datasets and combined transcriptomic and methylomic disease modules for Multiple Sclerosis (MS) to a highly disease-relevant module greatly enriched with known risk factors for MS (Paper II). Subsequently, we extended the functionality of MODifieR with software for transcription factor hub detection in gene regulatory networks in a new framework with a graphical user interface, MODalyseR. We used MODalyseR to find upstream regulators and identified IKZF1 as an important upstream regulator for MS (Paper III). Lastly, we used the growing large-scale repositories of gene expression data to train a Variational Auto Encoder (VAE) to compress and decompress gene expression profiles with the aim of extracting disease modules from the latent space. Utilizing the continues nature of the latent space in VAE’s, we derived the differences in latent space representations between a compendium of complex disease gene expression profiles and matched healthy controls. We then derived disease modules from the decompressed latent space representation of this difference and found the modules highly enriched with disease-associated genes, generally outperforming the gold standard of transcriptomic analysis of diseases, top differentially expressed genes (Paper IV). 

    To conclude, the main scientific contribution of this thesis lies in the development of software and methods for improving disease module inference, the evaluation of existing inference methods, the creation of new analysis workflows for multi-omics modules, and the introduction of a deep learning-based approach to the disease module inference toolkit. 

    List of papers
    1. MODifieR: an Ensemble R Package for Inference of Disease Modules from Transcriptomics Networks
    Open this publication in new window or tab >>MODifieR: an Ensemble R Package for Inference of Disease Modules from Transcriptomics Networks
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    2020 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 36, no 12, p. 3918-3919Article in journal (Refereed) Published
    Abstract [en]

    Motivation: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form the so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best-performing method. Hence, there is a need for combining these methods to generate robust disease modules. Results: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.

    Place, publisher, year, edition, pages
    OXFORD UNIV PRESS, 2020
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-168277 (URN)10.1093/bioinformatics/btaa235 (DOI)000550127500051 ()32271876 (PubMedID)
    Note

    Funding Agencies|Knowledge Foundation; Swedish Research CouncilSwedish Research Council; Swedish foundation for strategic researchSwedish Foundation for Strategic Research

    Available from: 2020-08-21 Created: 2020-08-21 Last updated: 2023-01-19
    2. A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis
    Open this publication in new window or tab >>A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis
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    2021 (English)In: BMC Genomics, E-ISSN 1471-2164, Vol. 22, no 1, article id 631Article in journal (Refereed) Published
    Abstract [en]

    Background There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10(- 47)) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases.

    Place, publisher, year, edition, pages
    BMC, 2021
    Keywords
    Benchmark; Multi-omics; Network modules; Multiple sclerosis; Risk factors; Disease modules; Network analysis; Protein network analysis; Transcriptomics; Methylomics; Data integration; Genome-wide association analysis
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-179166 (URN)10.1186/s12864-021-07935-1 (DOI)000692402600002 ()34461822 (PubMedID)
    Note

    Funding Agencies|Swedish Research CouncilSwedish Research CouncilEuropean Commission [201503807, 2018-02638]; Swedish foundation for strategic researchSwedish Foundation for Strategic Research [SB16-0095]; Center for Industrial IT (CENIIT); European Union Horizon 2020/European Research Council Consolidator grant (Epi4MS) [818170]; Knut and Alice Wallenberg FoundationKnut & Alice Wallenberg Foundation [2019.0089]; Knowledge Foundation [20170298]; Linkoping University

    Available from: 2021-09-14 Created: 2021-09-14 Last updated: 2024-01-17
    3. MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data
    Open this publication in new window or tab >>MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data
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    2022 (English)In: Bioinformatics Advances, ISSN 2635-0041, Vol. 2, no 1Article in journal (Refereed) Published
    Abstract [en]

    Network-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.We developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.MODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.Supplementary data are available at Bioinformatics Advances online.

    Place, publisher, year, edition, pages
    Oxford University Press, 2022
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-191117 (URN)10.1093/bioadv/vbac006 (DOI)
    Note

    Funding agencies: This work was supported by the Knowledge Foundation [dnr HSK219/26]; Swedish Foundation for Strategic Research [SB16-0011]; and Swedish Research Council [grant 2019-04193].

    Available from: 2023-01-19 Created: 2023-01-19 Last updated: 2023-11-16Bibliographically approved
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  • 22.
    de Weerd, Hendrik Arnold
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. Syst Biol Res Ctr, Sweden.
    Badam, Tejaswi
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. Syst Biol Res Ctr, Sweden.
    Martinez, David
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Akesson, Julia
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. Syst Biol Res Ctr, Sweden.
    Muthas, Daniel
    AstraZeneca, Sweden.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Lubovac-Pilav, Zelmina
    Syst Biol Res Ctr, Sweden.
    MODifieR: an Ensemble R Package for Inference of Disease Modules from Transcriptomics Networks2020In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 36, no 12, p. 3918-3919Article in journal (Refereed)
    Abstract [en]

    Motivation: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form the so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best-performing method. Hence, there is a need for combining these methods to generate robust disease modules. Results: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.

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  • 23.
    de Weerd, Hendrik Arnold
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden.
    Åkesson, Julia
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden.
    Guala, Dimitri
    Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden, Merck AB, Solna, Sweden.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Lubovac-Pilav, Zelmina
    School of Bioscience, Systems Biology Research Center, University of Skövde, Skövde, Sweden.
    MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data2022In: Bioinformatics Advances, ISSN 2635-0041, Vol. 2, no 1Article in journal (Refereed)
    Abstract [en]

    Network-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.We developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.MODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.Supplementary data are available at Bioinformatics Advances online.

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  • 24.
    Durrieu, Lucía
    et al.
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Johansson, Rikard
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Bush, Alan
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Janzén, David
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences.
    Gollvik, Martin
    Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Cedersund, Gunnar
    Linköping University, Faculty of Science & Engineering. Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering.
    Colman-Lerner, Alejandro
    IFIByNE, DFBMC, FCEN, UBA, Buenos Aires, Argentine.
    Quantification of nuclear transport in single cells2014Other (Other academic)
    Abstract [en]

    Regulation of nuclear transport is a key cellular function involved in many central processes, such as gene expression regulation and signal transduction. Rates of protein movement between cellular compartments can be measured by FRAP. However, no standard and reliable methods to calculate transport rates exist. Here we introduce a method to extract import and export rates, suitable for noisy single cell data. This method consists of microscope procedures, routines for data processing, an ODE model to fit to the data, and algorithms for parameter optimization and error estimation. Using this method, we successfully measured import and export rates in individual yeast. For YFP, average transport rates were 0.15 sec-1. We estimated confidence intervals for these parameters through likelihood profile analysis. We found large cell-to-cell variation (CV = 0.79) in these rates, suggesting a hitherto unknown source of cellular heterogeneity. Given the passive nature of YFP diffusion, we attribute this variation to large differences among cells in the number or quality of nuclear pores. Owing to its broad applicability and sensitivity, this method will allow deeper mechanistic insight into nuclear transport processes and into the largely unstudied cell-to-cell variation in kinetic rates.

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    Quantification of nuclear transport in single cells
  • 25.
    Dwivedi, Sanjiv
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Tjärnberg, Andreas
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering. NYU, NY 10003 USA.
    Tegner, Jesper
    King Abdullah Univ Sci & Technol KAUST, Saudi Arabia; Karolinska Inst, Sweden; Sci Life Lab, Sweden.
    Gustafsson, Mika
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder2020In: Nature Communications, E-ISSN 2041-1723, Vol. 11, no 1, article id 856Article in journal (Refereed)
    Abstract [en]

    Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein-protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes. The study of disease modules facilitates insight into complex diseases, but their identification relies on knowledge of molecular networks. Here, the authors show that disease modules and genes can also be discovered in deep autoencoder representations of large human gene expression datasets.

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  • 26.
    Ekhtiari, Hamed
    et al.
    Laureate Inst Brain Res, OK 74136 USA; Univ Minnesota, MN 55455 USA.
    Zare-Bidoky, Mehran
    Univ Tehran Med Sci, Iran; Shahid Sadoughi Univ Med Sci, Iran.
    Sangchooli, Arshiya
    Univ Tehran Med Sci, Iran.
    Janes, Amy C.
    Harvard Med Sch, MA USA.
    Kaufman, Marc J.
    Harvard Med Sch, MA USA.
    Oliver, Jason A.
    Denglande Univ, NC USA; Stephenson Canc Ctr, OK USA; Oklahoma State Univ, OK USA.
    Prisciandaro, James J.
    Med Univ South Carolina, SC 29425 USA.
    Wustenberg, Torsten
    Charite Univ Med Berlin, Germany.
    Anton, Raymond F.
    Med Univ South Carolina, SC 29425 USA.
    Bach, Patrick
    Heidelberg Univ, Germany.
    Baldacchino, Alex
    Univ St Andrews, Scotland.
    Beck, Anne
    Charite Univ Med Berlin, Germany; Hlth & Med Univ, Germany.
    Bjork, James M.
    Virginia Commonwealth Univ, VA USA.
    Brewer, Judson
    Brown Univ, RI 02912 USA.
    Childress, Anna Rose
    Univ Penn, PA 19104 USA.
    Claus, Eric D.
    Penn State Univ, PA 16802 USA.
    Courtney, Kelly E.
    Univ Calif San Diego, CA 92093 USA.
    Ebrahimi, Mohsen
    Univ Tehran Med Sci, Iran.
    Filbey, Francesca M.
    Univ Texas Dallas, TX USA.
    Ghahremani, Dara G.
    Univ Calif Los Angeles, CA 90024 USA.
    Azbari, Peyman Ghobadi
    Univ Tehran Med Sci, Iran; Shahed Univ, Iran.
    Goldstein, Rita Z.
    Icahn Sch Med Mt Sinai, NY 10029 USA.
    Goudriaan, Anna E.
    Univ Amsterdam, Netherlands; Amsterdam Neurosci, Netherlands.
    Grodin, Erica N.
    Univ Calif Los Angeles, CA 90024 USA.
    Hamilton, Paul
    Linköping University, Department of Biomedical and Clinical Sciences, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Hanlon, Colleen A.
    Wake Forest Sch Med, NC 27101 USA.
    Hassani-Abharian, Peyman
    Inst Cognit Sci Studies, Iran.
    Heinz, Andreas
    Charite Univ Med Berlin, Germany.
    Joseph, Jane E.
    Med Univ South Carolina, SC 29425 USA.
    Kiefer, Falk
    Heidelberg Univ, Germany.
    Zonoozi, Arash Khojasteh
    Univ Tehran Med Sci, Iran; Mashhad Univ Med Sci, Iran.
    Kober, Hedy
    Yale Sch Med, CT USA.
    Kuplicki, Rayus
    Laureate Inst Brain Res, OK 74136 USA.
    Li, Qiang
    Fourth Mil Med Univ, Peoples R China.
    London, Edythe D.
    Univ Calif Los Angeles, CA 90024 USA.
    McClernon, Joseph
    Denglande Univ, NC USA.
    Noori, Hamid R.
    Chinese Acad Sci, Peoples R China; MIT, MA 02139 USA.
    Owens, Max M.
    Univ Vermont, VT USA.
    Paulus, Martin
    Laureate Inst Brain Res, OK 74136 USA.
    Perini, Irene
    Linköping University, Department of Biomedical and Clinical Sciences, Center for Social and Affective Neuroscience. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Potenza, Marc
    Yale Sch Med, CT USA; Connecticut Mental Hlth Ctr, CT USA; Connecticut Council Problem Gambling, CT USA; Yale Sch Med, CT USA; Yale Sch Med, CT USA.
    Potvin, Stephane
    Univ Montreal, Canada.
    Ray, Lara
    Univ Calif Los Angeles, CA 90024 USA.
    Schacht, Joseph P.
    Univ Colorado, CO USA.
    Seo, Dongju
    Yale Sch Med, CT USA.
    Sinha, Rajita
    Yale Sch Med, CT USA.
    Smolka, Michael N.
    Tech Univ Dresden, Germany.
    Spanagel, Rainer
    Cent Inst Mental Hlth, Germany.
    Steele, Vaughn R.
    Yale Sch Med, CT USA.
    Stein, Elliot A.
    NIDA, MD USA.
    Steins-Loeber, Sabine
    Otto Friedrich Univ Bamberg, Germany.
    Tapert, Susan F.
    Univ Calif San Diego, CA 92093 USA.
    Verdejo-Garcia, Antonio
    Monash Univ, Australia.
    Vollstaedt-Klein, Sabine
    Heidelberg Univ, Germany.
    Wetherill, Reagan R.
    Univ Penn, PA 19104 USA.
    Wilson, Stephen J.
    Penn State Univ, PA 16802 USA.
    Witkiewitz, Katie
    Univ New Mexico, NM 87131 USA.
    Yuan, Kai
    Xidian Univ, Peoples R China.
    Zhang, Xiaochu
    Univ Sci & Technol China, Peoples R China; Univ Sci & Technol China, Peoples R China; Univ Sci & Technol China, Peoples R China.
    Zilverstand, Anna
    Univ Minnesota, MN 55455 USA.
    A methodological checklist for fMRI drug cue reactivity studies: development and expert consensus2022In: Nature Protocols, ISSN 1754-2189, E-ISSN 1750-2799, Vol. 17, no 3, p. 567-595Article in journal (Refereed)
    Abstract [en]

    Cue reactivity measured by functional magnetic resonance imaging is used in studies of substance-use disorders. This Consensus Statement is the result of a Delphi process to arrive at parameters that should be reported in describing these studies. Cue reactivity is one of the most frequently used paradigms in functional magnetic resonance imaging (fMRI) studies of substance use disorders (SUDs). Although there have been promising results elucidating the neurocognitive mechanisms of SUDs and SUD treatments, the interpretability and reproducibility of these studies is limited by incomplete reporting of participants characteristics, task design, craving assessment, scanning preparation and analysis decisions in fMRI drug cue reactivity (FDCR) experiments. This hampers clinical translation, not least because systematic review and meta-analysis of published work are difficult. This consensus paper and Delphi study aims to outline the important methodological aspects of FDCR research, present structured recommendations for more comprehensive methods reporting and review the FDCR literature to assess the reporting of items that are deemed important. Forty-five FDCR scientists from around the world participated in this study. First, an initial checklist of items deemed important in FDCR studies was developed by several members of the Enhanced NeuroImaging Genetics through Meta-Analyses (ENIGMA) Addiction working group on the basis of a systematic review. Using a modified Delphi consensus method, all experts were asked to comment on, revise or add items to the initial checklist, and then to rate the importance of each item in subsequent rounds. The reporting status of the items in the final checklist was investigated in 108 recently published FDCR studies identified through a systematic review. By the final round, 38 items reached the consensus threshold and were classified under seven major categories: Participants Characteristics, General fMRI Information, General Task Information, Cue Information, Craving Assessment Inside Scanner, Craving Assessment Outside Scanner and Pre- and Post-Scanning Considerations. The review of the 108 FDCR papers revealed significant gaps in the reporting of the items considered important by the experts. For instance, whereas items in the General fMRI Information category were reported in 90.5% of the reviewed papers, items in the Pre- and Post-Scanning Considerations category were reported by only 44.7% of reviewed FDCR studies. Considering the notable and sometimes unexpected gaps in the reporting of items deemed to be important by experts in any FDCR study, the protocols could benefit from the adoption of reporting standards. This checklist, a living document to be updated as the field and its methods advance, can help improve experimental design, reporting and the widespread understanding of the FDCR protocols. This checklist can also provide a sample for developing consensus statements for protocols in other areas of task-based fMRI.

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  • 27.
    Eles, My
    Linköping University, Department of Physics, Chemistry and Biology.
    A comparison between two computational tools estimating tumor purity using NGS data2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In 2020, cancer accounted for almost 20% of all deaths in the United States. Cancer is highly individual, and individualized treatments are essential in the battle against the disease. The tumor microenvironment is complex, and the cancer genome contains mutations driving the cancer. Identification and inference of mutations in the cancer genome are important for individualized diagnosis, prognosis, and treatment decisions. With NGS techniques, getting information about a tumor on the DNA level is possible. However, the data must be analyzed to reveal information from the NGS analysis. A tumor consists of both cancer and normal cells. When analyzing a tumor, DNA from cancer and normal cells is intermixed, and the information of which DNA comes from which cell is lost. The analysis is complicated since the fraction of cancer cells is unknown. Tumor purity is defined as the fraction of cancer cells in a tumor. Traditionally a pathologist decides the tumor purity by visually inspecting a tumor sample. As NGS techniques have developed, computational tools distinguishing between cancer and normal cells, including the fraction, have arisen. The purpose of this master’s thesis was to study how precise computational tools can estimate tumor purity using NGS data compared to a purity estimate made by a pathologist. To study the subject, a search was done for computational tools estimating tumor purity using NGS data. The software code had to be open, and the tools should focus on one tumor specimen from a patient, and papers using a normal sample from the patient were excluded. The search resulted in eight computational tools estimating tumor purity. Further, the two tools, ABSOLUTE and PureCN, were selected for comparison. An open access data set was used containing seven specimens. The data was filtered to imitate panel data targeting 250 genes. For some specimens, ABSOLUTE and PureCN performed consistent estimates with the pathologist’s estimates. However, for most specimens, the estimated purity by the tools was not in agreement with the ones made by the pathologist. PureCN performed more consistently with the pathologist estimates than ABSOLUTE, but it cannot be concluded with certainty. The study in this master’s thesis could not prove that the computational tools, ABSOLUTE and PureCN, are good enough at estimating tumor pu- rity on the imitated panel data to be used in the clinic. The study included data from only seven tumors. Therefore, significant conclusions could not be drawn from it.

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  • 28.
    Elfving, Eric
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics .
    Automated annotation of protein families2011Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Introduction: The great challenge in bioinformatics is data integration. The amount of available data is always increasing and there are no common unified standards of where, or how, the data should be stored. The aim of this workis to build an automated tool to annotate the different member families within the protein superfamily of medium-chain dehydrogenases/reductases (MDR), by finding common properties among the member proteins. The goal is to increase the understanding of the MDR superfamily as well as the different member families.This will add to the amount of knowledge gained for free when a new, unannotated, protein is matched as a member to a specific MDR member family.

    Method: The different types of data available all needed different handling. Textual data was mainly compared as strings while numeric data needed some special handling such as statistical calculations. Ontological data was handled as tree nodes where ancestry between terms had to be considered. This was implemented as a plugin-based system to make the tool easy to extend with additional data sources of different types.

    Results: The biggest challenge was data incompleteness yielding little (or no) results for some families and thus decreasing the statistical significance of the results. Results show that all the human and mouse MDR members have a Pfam ADH domain (ADH_N and/or ADH_zinc_N) and takes part in an oxidation-reduction process, often with NAD or NADP as cofactor. Many of the proteins contain zinc and are expressed in liver tissue.

    Conclusions: A python based tool for automatic annotation has been created to annotate the different MDR member families. The tool is easily extendable to be used with new databases and much of the results agrees with information found in literature. The utility and necessity of this system, as well as the quality of its produced results, are expected to only increase over time, even if no additional extensions are produced, as the system itself is able to make further and more detailed inferences as more and more data become available.

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  • 29.
    Elmsjö, Albert
    et al.
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, S-58758 Linkoping, Sweden.
    Söderberg, Carl
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, S-58758 Linkoping, Sweden.
    Jakobsson, Gerd
    Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, S-58758 Linkoping, Sweden.
    Green, Henrik
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Clinical Chemistry and Pharmacology. Linköping University, Faculty of Medicine and Health Sciences. Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, S-58758 Linkoping, Sweden.
    Kronstrand, Robert
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Clinical Chemistry and Pharmacology. Linköping University, Faculty of Medicine and Health Sciences. Natl Board Forens Med, Dept Forens Genet & Forens Toxicol, S-58758 Linkoping, Sweden.
    Postmortem Metabolomics Reveal Acylcarnitines as Potential Biomarkers for Fatal Oxycodone-Related Intoxication2022In: Metabolites, ISSN 2218-1989, E-ISSN 2218-1989, Vol. 12, no 2, article id 109Article in journal (Refereed)
    Abstract [en]

    Postmortem metabolomics has recently been suggested as a potential tool for discovering new biological markers able to assist in death investigations. Interpretation of oxycodone concentrations in postmortem cases is complicated, as oxycodone tolerance leads to overlapping concentrations for oxycodone intoxications versus non-intoxications. The primary aim of this study was to use postmortem metabolomics to identify potential endogenous biomarkers that discriminate between oxycodone-related intoxications and non-intoxications. Ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry data from 934 postmortem femoral blood samples, including oxycodone intoxications and controls positive and negative for oxycodone, were used in this study. Data were processed and evaluated with XCMS and SIMCA. A clear trend in group separation was observed between intoxications and controls, with a model sensitivity and specificity of 80% and 76%. Approximately halved levels of short-, medium-, and long-chain acylcarnitines were observed for oxycodone intoxications in comparison with controls (p &lt; 0.001). These biochemical changes seem to relate to the toxicological effects of oxycodone and potentially acylcarnitines constituting a biologically relevant biomarker for opioid poisonings. More studies are needed in order to elucidate the potential of acylcarnitines as biomarker for oxycodone toxicity and their relation to CNS-depressant effects.

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  • 30.
    Fallahshahroudi, Amir
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, Faculty of Science & Engineering.
    de Kock, Nick
    Department of Chemistry - Biomedical Center, Analytical Chemistry and Science for Life Laboratory, Uppsala University, Sweden.
    Johnsson, Martin
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, Faculty of Science & Engineering.
    Ubhayasekera, S.J. Kumari A.
    Department of Chemistry - Biomedical Center, Analytical Chemistry and Science for Life Laboratory, Uppsala University, Sweden.
    Bergqvist, Jonas
    Department of Chemistry - Biomedical Center, Analytical Chemistry and Science for Life Laboratory, Uppsala University, Sweden.
    Wright, Dominic
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, Faculty of Science & Engineering.
    Jensen, Per
    Linköping University, Department of Physics, Chemistry and Biology, Biology. Linköping University, Faculty of Science & Engineering.
    Domestication Effects on Stress Induced Steroid Secretion and Adrenal Gene Expression in Chickens2015In: Scientific Reports, E-ISSN 2045-2322, Vol. 5, p. 1-10, article id 15345Article in journal (Refereed)
    Abstract [en]

    Understanding the genetic basis of phenotypic diversity is a challenge in contemporary biology. Domestication provides a model for unravelling aspects of the genetic basis of stress sensitivity. The ancestral Red Junglefowl (RJF) exhibits greater fear-related behaviour and a more pronounced HPA-axis reactivity than its domesticated counterpart, the White Leghorn (WL). By comparing hormones (plasmatic) and adrenal global gene transcription profiles between WL and RJF in response to an acute stress event, we investigated the molecular basis for the altered physiological stress responsiveness in domesticated chickens. Basal levels of pregnenolone and dehydroepiandrosterone as well as corticosterone response were lower in WL. Microarray analysis of gene expression in adrenal glands showed a significant breed effect in a large number of transcripts with over-representation of genes in the channel activity pathway. The expression of the best-known steroidogenesis genes were similar across the breeds used. Transcription levels of acute stress response genes such as StAR, CH25 and POMC were upregulated in response to acute stress. Dampened HPA reactivity in domesticated chickens was associated with changes in the expression of several genes that presents potentially minor regulatory effects rather than by means of change in expression of critical steroidogenic genes in the adrenal.

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  • 31.
    Forsgren, Mikael Fredrik
    Linköping University, Department of Medical and Health Sciences, Radiation Physics. Linköping University, Faculty of Health Sciences.
    Human Whole Body Pharmacokinetic Minimal Model for the Liver Specific Contrast Agent Gd-EOB-DTPA2011Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Magnetic resonance imaging (MRI) of the liver is an important non-invasive tool for diagnosing liver disease. A key application is dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). With the use of the hepatocyte specific contrast agent (CA) Gd-EOB-DTPA it is now possible to evaluate the liver function. Beyond traditional qualitative evaluation of the DCE-MRI images, parametric quantitative techniques are on the rise which yields more objective evaluations. Systems biology is a gradually expanding field using mathematical modeling to gain deeper mechanistic understanding in complex biological systems. The aim of this thesis to combine these two fields in order to derive a physiologically accurate minimal whole body model that can be used to quantitatively evaluate liver function using clinical DCE-MRI examinations. 

    The work is based on two previously published sources of data using Gd-EOB-DTPA in healthy humans; i) a region of interest analysis of the liver using DCE-MRI ii) a pre-clinical evaluation of the contrast agent using blood sampling.  The modeling framework consists of a system of ordinary differential equations for the contrast agent dynamics and non-linear models for conversion of contrast agent concentrations to relaxivity values in the DCE-MRI image volumes.

    Using a χ2-test I have shown that the model, with high probability, can fit the experimental data for doses up to twenty times the clinically used one, using the same parameters for all doses. The results also show that some of the parameters governing the hepatocyte flux of CA can be numerically identifiable. Future applications with the model might be as a basis for regional liver function assessment. This can lead to disease diagnosis and progression evaluation for physicians as well as support for surgeons planning liver resection.

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    M.Sc. thesis
  • 32.
    Fujiwara, Takanori
    et al.
    Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
    Liu, Tzu-Ping
    Univ Taipei, Taiwan.
    Contrastive multiple correspondence analysis (cMCA): Using contrastive learning to identify latent subgroups in political parties2023In: PLOS ONE, E-ISSN 1932-6203, Vol. 18, no 7Article in journal (Refereed)
    Abstract [en]

    Scaling methods have long been utilized to simplify and cluster high-dimensional data. However, the general latent spaces across all predefined groups derived from these methods sometimes do not fall into researchers interest regarding specific patterns within groups. To tackle this issue, we adopt an emerging analysis approach called contrastive learning. We contribute to this growing field by extending its ideas to multiple correspondence analysis (MCA) in order to enable an analysis of data often encountered by social scientists-containing binary, ordinal, and nominal variables. We demonstrate the utility of contrastive MCA (cMCA) by analyzing two different surveys of voters in the U.S. and U.K. Our results suggest that, first, cMCA can identify substantively important dimensions and divisions among subgroups that are overlooked by traditional methods; second, for other cases, cMCA can derive latent traits that emphasize subgroups seen moderately in those derived by traditional methods.

  • 33.
    Ge, Yue
    et al.
    National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency.
    Wang, Da-Zhi
    State Key Laboratory of Marine Environmental Science, College of the Environment and Ecology, Xiamen University, China .
    Chiu, Jen-Fu
    University of Hong Kong and Shantou University College of Medicine, China.
    Cristobal, Susana
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
    Sheehan, David
    Department of Biochemistry, University College Cork, Ireland.
    Silvestre, Frédéric
    Research Unit in Environmental and Evolutionary Biology, University of Namur, Belgium.
    Peng, Xianxuan
    Center for Proteomics, State Key Laboratory of Bio-Control, School of Life Sciences, Sun Yat-Sen University, China.
    Li, Hui
    Center for Proteomics, State Key Laboratory of Bio-Control, School of Life Sciences, Sun Yat-Sen University, China.
    Gong, Zhiyuan
    Department of Biological Sciences, National University of Singapore, Singapore.
    Lam, Siew Hong
    Department of Biological Sciences, National University of Singapore, Singapore.
    Wentao, Hu
    Department of Biochemistry, University College Cork, Ireland.
    Iwahashi, Hitoshi
    Department of Applied Biological Sciences, Gifu University, Japan.
    Liu, Jianjun
    Shenzhen Center for Disease Control and Prevention, China.
    Mei, Nan
    National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
    Shi, Leming
    National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
    Bruno, Maribel
    National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency.
    Foth, Heidi
    Institute for Environmental Toxicology, Martin Luther University, Halle/Saale, Germany.
    Teichman, Kevin
    Office of Research and Development, U.S. Environmental Protection Agency, Washington D.C., USA.
    Environmental OMICS: Current Status and Future Directions2013In: JOURNAL OF INTEGRATED OMICS, ISSN 2182-0287, Vol. 3, no 2, p. 75-87Article in journal (Refereed)
    Abstract [en]

    Applications of OMICS to high throughput studies of changes of genes, RNAs, proteins, metabolites, and their associated functionsin cells or organisms exposed to environmental chemicals has led to the emergence of a very active research field: environmental OMICS.This developing field holds an important key for improving the scientific basis for understanding the potential impacts of environmentalchemicals on both health and the environment. Here we describe the state of environmental OMICS with an emphasis on its recent accomplishmentsand its problems and potential solutions to facilitate the incorporation of OMICS into mainstream environmental and healthresearch.Data sources: We reviewed relevant and recently published studies on the applicability and usefulness of OMICS technologies to the identificationof toxicity pathways, mechanisms, and biomarkers of environmental chemicals for environmental and health risk monitoring andassessment, including recent presentations and discussions on these issues at The First International Conference on Environmental OMICS(ICEO), held in Guangzhou, China during November 8-12, 2011. This paper summarizes our review.Synthesis: Environmental OMICS aims to take advantage of powerful genomics, transcriptomics, proteomics, and metabolomics tools toidentify novel toxicity pathways/signatures/biomarkers so as to better understand toxicity mechanisms/modes of action, to identify/categorize/prioritize/screen environmental chemicals, and to monitor and predict the risks associated with exposure to environmental chemicalson human health and the environment. To improve the field, some lessons learned from previous studies need to be summarized, aresearch agenda and guidelines for future studies need to be established, and a focus for the field needs to be developed.Conclusions: OMICS technologies for identification of RNA, protein, and metabolic profiles and endpoints have already significantly improvedour understanding of how environmental chemicals affect our ecosystem and human health. OMICS breakthroughs are empoweringthe fields of environmental toxicology, chemical toxicity characterization, and health risk assessment. However, environmental OMICS is stillin the data generation and collection stage. Important data gaps in linking and/or integrating toxicity data with OMICS endpoints/profilesneed to be filled to enable understanding of the potential impacts of chemicals on human health and the environment. It is expected thatfuture environmental OMICS will focus more on real environmental issues and challenges such as the characterization of chemical mixturetoxicity, the identification of environmental and health biomarkers, and the development of innovative environmental OMICS approachesand assays. These innovative approaches and assays will inform chemical toxicity testing and prediction, ecological and health risk monitoringand assessment, and natural resource utilization in ways that maintain human health and protects the environment in a sustainable manner.

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  • 34.
    Gonzalez Bosca, Alejandra
    et al.
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Jafari, Shadi
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences.
    Zenere, Alberto
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Alenius, Mattias
    Linköping University, Department of Clinical and Experimental Medicine, Divison of Neurobiology. Linköping University, Faculty of Medicine and Health Sciences.
    Altafini, Claudio
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
    Thermodynamic model of gene regulation for the Or59b olfactory receptor in Drosophila2019In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, no 1, article id e1006709Article in journal (Refereed)
    Abstract [en]

    Complex eukaryotic promoters normally contain multiple cis-regulatory sequences for different transcription factors (TFs). The binding patterns of the TFs to these sites, as well as the way the TFs interact with each other and with the RNA polymerase (RNAp), lead to combinatorial problems rarely understood in detail, especially under varying epigenetic conditions. The aim of this paper is to build a model describing how the main regulatory cluster of the olfactory receptor Or59b drives transcription of this gene in Drosophila. The cluster-driven expression of this gene is represented as the equilibrium probability of RNAp being bound to the promoter region, using a statistical thermodynamic approach. The RNAp equilibrium probability is computed in terms of the occupancy probabilities of the single TFs of the cluster to the corresponding binding sites, and of the interaction rules among TFs and RNAp, using experimental data of Or59b expression to tune the model parameters. The model reproduces correctly the changes in RNAp binding probability induced by various mutation of specific sites and epigenetic modifications. Some of its predictions have also been validated in novel experiments.

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  • 35.
    Gouveia, Duarte
    et al.
    Laboratoire Innovations technologiques pour la Détection et le Diagnostic (Li2D), Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, F-30207 Bagnols-sur-Cèze, France.
    Almunia, Christine
    Laboratoire Innovations technologiques pour la Détection et le Diagnostic (Li2D), Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, F-30207 Bagnols-sur-Cèze, France.
    Cogne, Yannick
    Laboratoire Innovations technologiques pour la Détection et le Diagnostic (Li2D), Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, F-30207 Bagnols-sur-Cèze, France.
    Pible, Olivier
    Laboratoire Innovations technologiques pour la Détection et le Diagnostic (Li2D), Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, F-30207 Bagnols-sur-Cèze, France.
    Degli-Esposti, Davide
    Irstea, UR Riverly Laboratoire d'écotoxicologie, Centre de Lyon-Villeurbanne, F-69625 Villeurbanne, France.
    Salvador, Arnaud
    Université Claude Bernard Lyon 1, CNRS, ENS de Lyon, Institut des Sciences Analytiques, UMR 5280, 5 rue de la Doua, F-69100 Villeurbanne, France.
    Cristobal, Susana
    Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Medicine and Health Sciences. Department of Physiology, Ikerbasque, Faculty of Medicine and Dentistry, University of the Basque Country, Spain.
    Sheehan, David
    College of Arts and Sciences, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, United Arab Emirates.
    Chaumot, Arnaud
    Irstea, UR Riverly Laboratoire d'écotoxicologie, Centre de Lyon-Villeurbanne, F-69625 Villeurbanne, France.
    Geffard, Olivier
    Irstea, UR Riverly Laboratoire d'écotoxicologie, Centre de Lyon-Villeurbanne, F-69625 Villeurbanne, France.
    Armengaud, Jean
    Laboratoire Innovations technologiques pour la Détection et le Diagnostic (Li2D), Service de Pharmacologie et Immunoanalyse (SPI), CEA, INRA, F-30207 Bagnols-sur-Cèze, France.
    Ecotoxicoproteomics: A decade of progress in our understanding of anthropogenic impact on the environment2019In: Journal of Proteomics, ISSN 1874-3919, E-ISSN 1876-7737, Vol. 198, p. 66-77, article id S1874-3919(18)30423-8Article in journal (Refereed)
    Abstract [en]

    Anthropogenic pollutants are found worldwide. Their fate and effects on human and ecosystem health must be appropriately monitored. Today, ecotoxicology is focused on the development of new methods to assess the impact of pollutant toxicity on living organisms and ecosystems. In situ biomonitoring often uses sentinel animals for which, ideally, molecular biomarkers have been defined thanks to which environmental quality can be assessed. In this context, high-throughput proteomics methods offer an attractive approach to study the early molecular responses of organisms to environmental stressors. This approach can be used to identify toxicity pathways, to quantify more precisely novel biomarkers, and to draw the possible adverse outcome pathways. In this review, we discuss the major advances in ecotoxicoproteomics made over the last decade and present the current state of knowledge, emphasizing the technological and conceptual advancements that allowed major breakthroughs in this field, which aims to “make our planet great again”.

    Significance

    Ecotoxicoproteomics is a protein-centric methodology that is useful for ecotoxicology and could have future applications as part of chemical risk assessment and environmental monitoring. Ecotoxicology employing non-model sentinel organisms with highly divergent phylogenetic backgrounds aims to preserve the functioning of ecosystems and the overall range of biological species supporting them. The classical proteomics workflow involves protein identification, functional annotation, and extrapolation of toxicity across species. Thus, it is essential to develop multi-omics approaches in order to unravel molecular information and construct the most suitable databases for protein identification and pathway analysis in non-model species. Current instrumentation and available software allow relevant combined transcriptomic/proteomic studies to be performed for almost any species. This review summarizes these approaches and illustrates how they can be implemented in ecotoxicology for routine biomonitoring.

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  • 36.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Ernerudh, Jan
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Inflammation and Infection. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Clinical Immunology and Transfusion Medicine.
    Olsson, Tomas
    Department of Clinical Neuroscience, Karolinska Institute.
    Data for: Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis2023Data set
    Abstract [en]

    Protein levels were measured in cerebrospinal fluid samples (CSF; n = 186) and plasma samples (n = 165) from persons with multiple sclerosis and healthy controls. CSF samples and plasma samples were taken from 92 persons with CIS or RRMS at Linköping University Hospital, Sweden and 51 persons with CIS or RRMS at the Karolinska University Hospital, Sweden. Everyone fulfilled the revised McDonald criteria from 2010 and 2017 for CIS or Multiple sclerosis (MS). Age-matched healthy controls (HC) were recruited from healthy blood donors (23 at the Linköping University hospital and 20 at the Karolinska University Hospital). The concentration of 1463 proteins were measured using the Olink Explore platform which uses Proximity Extension Assay (PEA) technology. The proteins were preselected from four Olink panels: Explore 384 Cardiometabolic, Explore 384 Inflammation, Explore 384 Neurology, and Explore 384 Oncology. The protein concentrations are given as Olink’s relative protein quantification unit on log2 scale: Normalized Protein Expression (NPX). The NPX values were intensity normalized by Olink.

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  • 37.
    Gustafsson, Mika
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Hörnquist, Michael
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gene Expression Prediction by Soft Integration and the Elastic Net: Best Performance of the DREAM3 Gene Expression Challenge2010In: PLoS ONE, ISSN 1932-6203, Vol. 5, no 2, p. e9134-Article in journal (Refereed)
    Abstract [en]

    Background: To predict gene expressions is an important endeavour within computational systems biology. It can both be a way to explore how drugs affect the system, as well as providing a framework for finding which genes are interrelated in a certain process. A practical problem, however, is how to assess and discriminate among the various algorithms which have been developed for this purpose. Therefore, the DREAM project invited the year 2008 to a challenge for predicting gene expression values, and here we present the algorithm with best performance.

    Methodology/Principal Findings: We develop an algorithm by exploring various regression schemes with different model selection procedures. It turns out that the most effective scheme is based on least squares, with a penalty term of a recently developed form called the “elastic net”. Key components in the algorithm are the integration of expression data from other experimental conditions than those presented for the challenge and the utilization of transcription factor binding data for guiding the inference process towards known interactions. Of importance is also a cross-validation procedure where each form of external data is used only to the extent it increases the expected performance.

    Conclusions/Significance: Our algorithm proves both the possibility to extract information from large-scale expression data concerning prediction of gene levels, as well as the benefits of integrating different data sources for improving the inference. We believe the former is an important message to those still hesitating on the possibilities for computational approaches, while the latter is part of an important way forward for the future development of the field of computational systems biology.

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    Gene Expression Prediction by Soft Integration and the Elastic Net—Best Performance of the DREAM3 Gene Expression Challenge
  • 38.
    Haddad, Tariq Sami
    et al.
    Radboud Univ Nijmegen, Netherlands.
    Friedl, Peter
    Radboud Univ Nijmegen, Netherlands; Univ Texas MD Anderson Canc Ctr, TX 77030 USA; Canc Genom Nl CGC Nl, Netherlands.
    Farahani, Navid
    Strateos, CA USA.
    Treanor, Darren
    Linköping University, Department of Biomedical and Clinical Sciences, Division of Inflammation and Infection. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Clinical pathology. Leeds Teaching Hosp NHS Trust, England; Univ Leeds, England.
    Zlobec, Inti
    Univ Bern, Switzerland.
    Nagtegaal, Iris
    Radboud Univ Nijmegen, Netherlands.
    Tutorial: methods for three-dimensional visualization of archival tissue material2021In: Nature Protocols, ISSN 1754-2189, E-ISSN 1750-2799, Vol. 16, no 11, p. 4945-4962Article, review/survey (Refereed)
    Abstract [en]

    The authors describe three-dimensional imaging pipelines available to analyze archival patient specimens. The pipelines facilitate the visualization of both large and small volumes of tissue with subcellular resolution. Analysis of three-dimensional patient specimens is gaining increasing relevance for understanding the principles of tissue structure as well as the biology and mechanisms underlying disease. New technologies are improving our ability to visualize large volume of tissues with subcellular resolution. One resource often overlooked is archival tissue maintained for decades in hospitals and research archives around the world. Accessing the wealth of information stored within these samples requires the use of appropriate methods. This tutorial introduces the range of sample preparation and microscopy approaches available for three-dimensional visualization of archival tissue. We summarize key aspects of the relevant techniques and common issues encountered when using archival tissue, including registration and antibody penetration. We also discuss analysis pipelines required to process, visualize and analyze the data and criteria to guide decision-making. The methods outlined in this tutorial provide an important and sustainable avenue for validating three-dimensional tissue organization and mechanisms of disease.

  • 39.
    Hafstao, Volundur
    et al.
    Lund Univ, Sweden.
    Hakkinen, Jari
    Lund Univ, Sweden.
    Larsson, Malin
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
    Staaf, Johan
    Lund Univ, Sweden.
    Vallon-Christersson, Johan
    Lund Univ, Sweden.
    Persson, Helena
    Lund Univ, Sweden.
    Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification2023In: BMC Genomics, E-ISSN 1471-2164, Vol. 24, no 1, article id 783Article in journal (Refereed)
    Abstract [en]

    BackgroundGenomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts.ResultsWe developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining.ConclusionsA classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.

  • 40.
    Hedberg, Lilia
    Linköping University, Department of Clinical and Experimental Medicine.
    Identification of obesity-associated SNPs in the human genome: Method development and implementation for SOLiD sequencing data analysis2010Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Over the last few years, genome-wide association studies (GWAS) have been used to identify numerous obesity associated SNPs in the human genome. By using linkage studies, candidate obesity genes have been identified. When SNPs in the first intron of FTO were found to be associated to BMI, it became the first gene to be linked to common obesity. In order to look for causative explanations behind the associated SNPs, a re-sequencing of FTO had been performed on the SOLiD sequencing platform. In-house candidate gene, SLCX, was also sequenced in order to evaluate a potential obesity association. The purpose of this project was to analyse the sequences and also to evaluate the quality of the SOLiD sequencing. A part of the project consisted in performing PCRs and selecting genomic regions for future sequencing projects. I developed and implemented a sequence analysis strategy to identify obesity associated SNPs. I found 39 obesity-linked SNPs in FTO, a majority of which were located in introns 1 and 8. I also identified 3 associated intronic SNPs in SLCX. I found that the SOLiD sequencing coverage varies between non-repetitive and repetitive genomic regions, and that it is highest near amplicon ends. Interestingly, coverage varies significantly between different amplicons even after repetitive sequences have been removed, which indicates that it is affected by features inherent to the sequence. Still, the observed allele frequencies for known SNPs were highly correlated with the SNP frequencies documented in HapMap. In conclusion, I verify that SNPs in FTO are associated with obesity and also identify a previously unassociated gene, SLCX, as a potential obesity gene. Re-sequencing of genomic regions on the SOLiD platform was proven to be successful for SNP identification, although the difference in sequencing coverage might be problematic.

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    Lilia Hedberg Master's Thesis
  • 41.
    Hyvönen, Martin
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics.
    Protein-Protein Docking Using Starting Points Based On Structural Homology2015Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Protein-protein interactions build large networks which are essential in understanding complex diseases. Due to limitations of experimental methodology there are problems with large amounts of false negative and positive interactions; and a large gap in the amount of known interactions and structurally determined interactions. By using computational methods these problems can be alleviated.

    In this thesis the quality of a newly developed pipeline (InterPred) were investigated for its ability to generate coarse interaction models and score them. This ability was investigated by performing docking experiments in Rosetta on models generated in InterPred.

    The results suggest that InterPred is highly successful in generating good starting points for docking proteins in silico and to distinguish the quality of models.

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    Protein-Protein Docking Using Starting Points Based On Structural Homology
  • 42.
    Hörnquist, Michael
    et al.
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Gustafsson, Mika
    Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, The Institute of Technology.
    Stability and Flexibility from a System Analysis of Gene RegulatoryNetworks Based on Ordinary Differential Equations2011In: The Open Bioinformatics Journal, E-ISSN 1875-0362, Vol. 5, p. 26-33Article in journal (Refereed)
    Abstract [en]

    The inference of large-scale gene regulatory networks from high-throughput data sets has revealed a diverse picture of only partially overlapping descriptions. Nevertheless, several properties in the organization of these networks are recurrent, such as hubs, a modular structure and certain motifs. Several authors have recently claimed cell systems to be stable against perturbations and random errors, but still able to rapidly switch between different states from specific stimuli. Since inferred mathematical models of large-scale systems need to be extremely simple to avoid overfitting, these two features are hard to attain simultaneously for a model. Here we review and discuss possible measures of how system stability and flexibility may be manifested and measured for linearized models based on systems of ordinary differential equations. Furthermore, we review how the network properties mentioned above together with the nature of the interactions contribute to these systems level properties. It turns out that the presence of repressed hubs, together with other phenomena of topological nature such as motifs and modules, contribute to the overall stability and/or flexibility of the model.

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    FULLTEXT01
  • 43.
    Iversen, Alexandra
    et al.
    Linköping University, Department of Physics, Chemistry and Biology.
    Nordén, Ebba
    Linköping University, Department of Physics, Chemistry and Biology.
    Bjers, Julia
    Linköping University, Department of Physics, Chemistry and Biology.
    Wickström, Filippa
    Linköping University, Department of Physics, Chemistry and Biology.
    Zhou, Martin
    Linköping University, Department of Physics, Chemistry and Biology.
    Hassan, Mohamed
    Linköping University, Department of Physics, Chemistry and Biology.
    Characterisation of Potential Inhibitors of Calmodulin from Plasmodium falciparum2020Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Each year countless lives are affected and about half a million people die from malaria, a disease caused by parasites originating from the Plasmodium family. The most virulent species of the parasite is Plasmodium falciparum (P. falciparum).

     

    Calmodulin (CaM) is a small, 148 amino acid long, highly preserved and essential protein in all eukaryotic cells. Previous studies have determined that CaM is important for the reproduction and invasion of P. falciparum in host cells. The primary structure of human CaM (CaMhum) and CaM from P. falciparum (CaMpf) differ in merely 16 positions, making differences in their structures and ligand affinity interesting to study. Especially since possible inhibitors of CaMpf in favor of CaMhum, in extension, could give rise to new malaria treatments.

     

    Some antagonists, functioning as inhibitors of CaM, have already been analysed in previous studies. However, there are also compounds that have not yet been studied in regards to being possible antagonists of CaM. This study regards three known antagonists; trifluoperazine (TFP), calmidazolium (CMZ) and artemisinin (ART) and also three recently created fentanyl derivatives; 3-OH-4-OMe-cyclopropylfentanyl (ligand 1), 4-OH-3OMe-4F-isobutyrylfentanyl (ligand 2) and 3-OH-4-OMe-isobutyrylfentanyl (ligand 3).

     

    Bioinformatic methods, such as modelling and docking, were used to compare the structures of CaMhum and CaMpf as well as observe the interaction of the six ligands to CaM from both species. In addition to the differences in primary structure, distinguished with ClustalW, disparities in tertiary structure were observed. Structure analysis of CaMhum and CaMpf in PyMOL disclosed a more open conformation as well as a larger, more defined, hydrophobic cleft in CaMhum compared to CaMpf. Simulated binding of the six ligands to CaM from both species, using Autodock 4.2, indicated that TFP and ART bind with higher affinity to CaMhum which is expected. Ligand 2 and ligand 3 also bound with higher affinity and facilitated stronger binding to CaMhum, which is reasonable since their docking is based on how TFP binds to CaM. However, ligand 1 as well as CMZ both bound to CaMpf with higher affinity. Despite promising results for ligand 1 and CMZ, no decisive conclusion can be made solely based on bioinformatic studies. 

     

    To gain a better understanding on the protein-ligand interactions of the six ligands to CaMhum and CaMpf, further studies using e.g. circular dichroism and fluorescence would be advantageous. Based on the results from this study, future studies on the binding of CMZ and ligand 1 to CaM as well as ligands with similar characteristics would be especially valuable. This is because they, based on the results from this study, possibly are better inhibitors of CaMpf than CaMhum and thereby could function as possible antimalarial drugs.

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  • 44.
    Jabeen, Rakhshanda
    Linköping University, Department of Computer and Information Science.
    Text Mining Methods for Biomedical Data Analysis2021Independent thesis Advanced level (degree of Master (One Year)), 80 credits / 120 HE creditsStudent thesis
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  • 45.
    Janzén, David
    Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
    Standard two-stage and Nonlinear mixed effect modelling for determination of cell-to-cell variation of transport parameters in Saccharomyces cerevisiae2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The interest for cell-to-cell variation has in recent years increased in a steady pace. Several studies have shown that a large portion of the observed variation in the nature originates from the fact that all biochemical reactions are in some respect stochastic. Interestingly, nature has evolved highly advanced frameworks specialized in dealing with stochasticity in order to still be able to produce the delicate signalling pathways that are present in even very simple single-cell organisms.

    Such a simple organism is Saccharomyces cerevisiae, which is the organism that has been studied in this thesis. More particulary, the distribution of the transport rate in S. cerevisiae has been studied by a mathematical modelling approach. It is shown that a two-compartment model can adequately describe the flow of a yellow fluorescent protein (YFP) between the cytosol and the nucleus. A profile likelihood (PLH) analysis shows that the parameters in the two-compartment model are identifiable and well-defined under the experimental data of YFP. Furthermore, the result from this model shows that the distribution of the transport rates in the 80 studied cells is lognormal. Also, in contradiction to prior beliefs, no significant difference between recently divided mother and daughter cells in terms of transport rates of YFP is to be seen. The modelling is performed by using both standard two-stage(STS) and nonlinear mixed effect model (NONMEM).

    A methodological comparison between the two very different mathematical STS and NONMEM is also presented. STS is today the conventional approach in studies of cell-to-cell variation. However, in this thesis it is shown that NONMEM, which has originally been developed for population pharmacokinetic/ pharmacodynamic (PK/PD) studies, is at least as good, or in some cases even a better approach than STS in studies of cell-to-cell variation.

    Finally, a new approach in studies of cell-to-cell variation is suggested that involves a combination of STS, NONMEM and PLH. In particular, it is shown that this combination of different methods would be especially useful if the data is sparse. By applying this combination of methods, the uncertainty in the estimation of the variability could be greatly reduced.

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  • 46.
    Johansson, Joakim
    Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics.
    Modifying a Protein-Protein Interaction Identifier with a Topology and Sequence-Order Independent Structural Comparison Method2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Using computational methods to identify protein-protein interactions (PPIs) supports experimental techniques by using less time and less resources. Identifying PPIs can be made through a template-based approach that describes how unstudied proteins interact by aligning a common structural template that exists in both interacting proteins. A pipeline that uses this is InterPred, that combines homology modelling and massive template comparison to construct coarse interaction models. These models are reviewed by a machine learning classifier that classifies models that shows traits of being true, which can be further refined with a docking technique. However, InterPred is dependent on using complex structural information, that might not be available from unstudied proteins, while it is suggested that PPIs are dependent of the shape and interface of proteins. A method that aligns structures based on the interface attributes is InterComp, which uses topological and sequence-order independent structural comparison. Implementing this method into InterPred will lead to restricting structural information to the interface of proteins, which could lead to discovery of undetected PPI models. The result showed that the modified pipeline was not comparable based on the receiver operating characteristic (ROC) performance. However, the modified pipeline could identify new potential PPIs that were undetected by InterPred.

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  • 47. Order onlineBuy this publication >>
    Johansson, Mikaela
    Linköping University, Department of Physics, Chemistry and Biology, Chemistry. Linköping University, Faculty of Science & Engineering.
    Metaproteogenomics-guided enzyme discovery: Targeted identification of novel proteases in microbial communities2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Industrial biotechnology is a large and growing industry as it is part of establishing a “greener” and more sustainable bioeconomy-based society. Using enzymes as biocatalysts is a viable alternative to chemicals and energy intense industrial processes and is en route to a more sustainable industry. Enzymes have been used in different areas for ages and are today used in many industrial processes such as biofuels production, food industry, tanning, chemical synthesis, pharmaceuticals etc. Enzymes are today a billion-dollar industry in itself and the demand for novel catalysts for various present and future processes of renewable resources are high and perfectly in line with converting to a more sustainable society.

    Most enzymes used in industry today have been identified from isolated and pure cultured microorganisms with identified desirable traits and enzymatic capacities. However, it is known that less than 1% of all microorganisms can be can be obtained in pure cultures. Thus, if we were to rely solely on pure culturing, this would leave the 99% of the microorganisms that constitutes the “microbial dark matter” uninvestigated for their potential in coding for and producing valuable novel enzymes. Therefore, to investigate these “unculturable” microorganisms for novel and valuable enzymes, pure-culture independent methods are needed.

    During the last two decades there has been a fast and extensive development in techniques and methods applicable for this purpose. Especially important has been the advancements made in mass spectrometry for protein identification and next generation sequencing of DNA. With these technical developments new research fields of proteomics and genomics have been developed, by which the complete protein complement of cells (the proteome) and all genes (the genome) of organisms can be investigated. When these techniques are applied to microbial communities these fields of research are known as meta-proteomics and meta-genomics.

    However, when applied to complex microbial communities, difficulties different from those encountered in their original usage for analysis of single multicellular organisms or cell linages arises, and when used independently both methods have their own limitations and bottlenecks. In addition, both metaproteomics and metagenomics are largely non-targeting techniques. Thus, if the purpose is still to - somewhat contradictory – use these non-targeting methods for targeted identification of novel enzymes with certain desired activities and properties from within microbial communities, special measures need to be taken.

    The work presented in this thesis describes the development of a method that combines

    metaproteomics and metagenomics (i.e. metaproteogenomics) for the targeted discovery of novel enzymes with desired activities, and their correct coding genes, from within microbial communities. Thus, what is described is a method that can be used to circumvent the pure-culturing problem so that a much larger fraction of the microbial dark matter can be specifically investigated for the identification of novel valuable enzymes.

    List of papers
    1. Applying theories of microbial metabolism for induction of targeted enzyme activity in a methanogenic microbial community at a metabolic steady state
    Open this publication in new window or tab >>Applying theories of microbial metabolism for induction of targeted enzyme activity in a methanogenic microbial community at a metabolic steady state
    2016 (English)In: Applied Microbiology and Biotechnology, ISSN 0175-7598, E-ISSN 1432-0614, Vol. 100, no 18, p. 7989-8002Article in journal (Refereed) Published
    Abstract [en]

    Novel enzymes that are stable in diverse conditions are intensively sought because they offer major potential advantages in industrial biotechnology, and microorganisms in extreme environments are key sources of such enzymes. However, most potentially valuable enzymes are currently inaccessible due to the pure culturing problem of microorganisms. Novel metagenomic and metaproteomic techniques that circumvent the need for pure cultures have theoretically provided possibilities to identify all genes and all proteins in microbial communities, but these techniques have not been widely used to directly identify specific enzymes because they generate vast amounts of extraneous data. In a first step towards developing a metaproteomic approach to pinpoint targeted extracellular hydrolytic enzymes of choice in microbial communities, we have generated and analyzed the necessary conditions for such an approach by the use of a methanogenic microbial community maintained on a chemically defined medium. The results show that a metabolic steady state of the microbial community could be reached, at which the expression of the targeted hydrolytic enzymes were suppressed, and that upon enzyme induction a distinct increase in the targeted enzyme expression was obtained. Furthermore, no cross talk in expression was detected between the two focal types of enzyme activities under their respective inductive conditions. Thus, the described approach should be useful to generate ideal samples, collected before and after selective induction, in controlled microbial communities to clearly discriminate between constituently expressed proteins and extracellular hydrolytic enzymes that are specifically induced, thereby reducing the analysis to only those proteins that are distinctively up-regulated.

    Place, publisher, year, edition, pages
    Springer, 2016
    Keywords
    Microbial community; Enzyme discovery; Metaproteomics; Biogas; Cellulase; Protease
    National Category
    Microbiology
    Identifiers
    urn:nbn:se:liu:diva-131888 (URN)10.1007/s00253-016-7547-z (DOI)000382008000017 ()27115757 (PubMedID)
    Note

    Funding Agencies|Swedish Research Council [621-2009-4150]; InZymes Biotech AB

    Available from: 2016-10-13 Created: 2016-10-11 Last updated: 2018-05-15
    2. Assessment of sample preparation methods for metaproteomics of extracellular proteins
    Open this publication in new window or tab >>Assessment of sample preparation methods for metaproteomics of extracellular proteins
    2017 (English)In: Analytical Biochemistry, ISSN 0003-2697, E-ISSN 1096-0309, Vol. 516, p. 23-36Article in journal (Refereed) Published
    Abstract [en]

    Enzyme discovery in individual strains of microorganisms is compromised by the limitations of pure culturing. In principle, metaproteomics allows for fractionation and study of different parts of the protein complement but has hitherto mainly been used to identify intracellular proteins. However, the extracellular environment is also expected to comprise a wealth of information regarding important proteins. An absolute requirement for metaproteomic studies of protein expression, and irrespective of downstream methods for analysis, is that sample preparation methods provide clean, concentrated and representative samples of the protein complement. A battery of methods for concentration, extraction, precipitation and resolubilization of proteins in the extracellular environment of a constructed microbial community was assessed by means of 2D gel electrophoresis and image analysis to elucidate whether it is possible to make the extracellular protein complement available for metaproteomic analysis. Most methods failed to provide pure samples and therefore negatively influenced protein gel migration and gel background clarity. However, one direct precipitation method (TCA-DOC/acetone) and one extraction/precipitation method (phenol/methanol) provided complementary high quality 2D gels that allowed for high spot detection ability and thereby also spot detection of less abundant extracellular proteins.

    Place, publisher, year, edition, pages
    Elsevier, 2017
    Keywords
    Enzyme discovery, Microbial community, Metaproteome, Extracellular, Sample preparation, 2D gel electrophoresis
    National Category
    Analytical Chemistry Biocatalysis and Enzyme Technology
    Identifiers
    urn:nbn:se:liu:diva-132902 (URN)10.1016/j.ab.2016.10.008 (DOI)000388056800005 ()27742212 (PubMedID)
    Funder
    Swedish Research Council, 621-2009-4150
    Note

    Funding agencies: Swedish Research Council [621-2009-4150]; Tekniska Verken i Linkoping AB; InZymes Biotech AB

    Available from: 2016-12-01 Created: 2016-12-01 Last updated: 2018-05-15Bibliographically approved
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  • 48. Order onlineBuy this publication >>
    Johansson, Rikard
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Model-Based Hypothesis Testing in Biomedicine: How Systems Biology Can Drive the Growth of Scientific Knowledge2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The utilization of mathematical tools within biology and medicine has traditionally been less widespread compared to other hard sciences, such as physics and chemistry. However, an increased need for tools such as data processing, bioinformatics, statistics, and mathematical modeling, have emerged due to advancements during the last decades. These advancements are partly due to the development of high-throughput experimental procedures and techniques, which produce ever increasing amounts of data. For all aspects of biology and medicine, these data reveal a high level of inter-connectivity between components, which operate on many levels of control, and with multiple feedbacks both between and within each level of control. However, the availability of these large-scale data is not synonymous to a detailed mechanistic understanding of the underlying system. Rather, a mechanistic understanding is gained first when we construct a hypothesis, and test its predictions experimentally. Identifying interesting predictions that are quantitative in nature, generally requires mathematical modeling. This, in turn, requires that the studied system can be formulated into a mathematical model, such as a series of ordinary differential equations, where different hypotheses can be expressed as precise mathematical expressions that influence the output of the model.

    Within specific sub-domains of biology, the utilization of mathematical models have had a long tradition, such as the modeling done on electrophysiology by Hodgkin and Huxley in the 1950s. However, it is only in recent years, with the arrival of the field known as systems biology that mathematical modeling has become more commonplace. The somewhat slow adaptation of mathematical modeling in biology is partly due to historical differences in training and terminology, as well as in a lack of awareness of showcases illustrating how modeling can make a difference, or even be required, for a correct analysis of the experimental data.

    In this work, I provide such showcases by demonstrating the universality and applicability of mathematical modeling and hypothesis testing in three disparate biological systems. In Paper II, we demonstrate how mathematical modeling is necessary for the correct interpretation and analysis of dominant negative inhibition data in insulin signaling in primary human adipocytes. In Paper III, we use modeling to determine transport rates across the nuclear membrane in yeast cells, and we show how this technique is superior to traditional curve-fitting methods. We also demonstrate the issue of population heterogeneity and the need to account for individual differences between cells and the population at large. In Paper IV, we use mathematical modeling to reject three hypotheses concerning the phenomenon of facilitation in pyramidal nerve cells in rats and mice. We also show how one surviving hypothesis can explain all data and adequately describe independent validation data. Finally, in Paper I, we develop a method for model selection and discrimination using parametric bootstrapping and the combination of several different empirical distributions of traditional statistical tests. We show how the empirical log-likelihood ratio test is the best combination of two tests and how this can be used, not only for model selection, but also for model discrimination.

    In conclusion, mathematical modeling is a valuable tool for analyzing data and testing biological hypotheses, regardless of the underlying biological system. Further development of modeling methods and applications are therefore important since these will in all likelihood play a crucial role in all future aspects of biology and medicine, especially in dealing with the burden of increasing amounts of data that is made available with new experimental techniques.

    List of papers
    1. Combining test statistics and models in bootstrapped model rejection: it is a balancing act
    Open this publication in new window or tab >>Combining test statistics and models in bootstrapped model rejection: it is a balancing act
    2014 (English)In: BMC Systems Biology, E-ISSN 1752-0509, Vol. 8, no 46Article in journal (Refereed) Published
    Abstract [en]

    Background: Model rejections lie at the heart of systems biology, since they provide conclusive statements: that the corresponding mechanistic assumptions do not serve as valid explanations for the experimental data. Rejections are usually done usinge.g. the chi-square test (χ2) or the Durbin-Watson test (DW). Analytical formulas for the corresponding distributions rely on assumptions that typically are not fulfilled. This problem is partly alleviated by the usage of bootstrapping, a computationally heavy approach to calculate an empirical distribution. Bootstrapping also allows for a natural extension to estimation of joint distributions, but this feature has so far been little exploited.

    Results: We herein show that simplistic combinations of bootstrapped tests, like the max or min of the individual p-values, give inconsistent, i.e. overly conservative or liberal, results. A new two-dimensional (2D) approach based on parametric bootstrapping, on the other hand, is found both consistent and with a higher power than the individual tests, when tested on static and dynamic examples where the truth is known. In the same examples, the most superior test is a 2D χ2 vs χ2, where the second χ2-value comes from an additional help model, and its ability to describe bootstraps from the tested model. This superiority is lost if the help model is too simple, or too flexible. If a useful help model is found, the most powerful approach is the bootstrapped log-likelihood ratio (LHR). We show that this is because the LHR is one-dimensional, because the second dimension comes at a cost, and because LHR has retained most of the crucial information in the 2D distribution. These approaches statistically resolve a previously published rejection example for the first time.

    Conclusions: We have shown how to, and how not to, combine tests in a bootstrap setting, when the combinatio is advantageous, and when it is advantageous to include a second model. These results also provide a deeper insight into the original motivation for formulating the LHR, for the more general setting of nonlinear and non-nested models. These insights are valuable in cases when accuracy and power, rather than computational speed, are prioritized.

    Place, publisher, year, edition, pages
    BioMed Central (BMC), 2014
    Keywords
    Model rejection, Bootstrapping, Combining information, 2D, Insulin signaling, Model Mimicry, Likelihood ratio
    National Category
    Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
    Identifiers
    urn:nbn:se:liu:diva-106427 (URN)10.1186/1752-0509-8-46 (DOI)000335472800001 ()24742065 (PubMedID)
    Available from: 2014-05-07 Created: 2014-05-07 Last updated: 2023-10-20Bibliographically approved
    2. Dominant negative inhibition data should be analyzed using mathematical modeling - re-interpreting data from insulin signaling.
    Open this publication in new window or tab >>Dominant negative inhibition data should be analyzed using mathematical modeling - re-interpreting data from insulin signaling.
    Show others...
    2015 (English)In: The FEBS Journal, ISSN 1742-464X, E-ISSN 1742-4658, Vol. 282, no 4, p. 788-802Article in journal (Refereed) Published
    Abstract [en]

    As our ability to measure the complexity of intracellular networks has evolved, it has become increasingly clear that we need new methods for data analysis: methods involving mathematical modeling. Nevertheless, it is still uncontroversial to publish and interpret experimental results without a model-based proof that the reasoning is correct. In the present study, we argue that this attitude probably needs to change in the future. We illustrate this need for modeling by considering the common experimental technique of using dominant-negative constructs. More specifically, we consider published time-series and dose-response data which previously have been used to argue that the protein S6 kinase does not phosphorylate insulin receptor substrate-1 at a specific serine residue. Using a presented general approach to interpret such data, we now demonstrate that the given dominant-negative data are not conclusive (i.e. that in the absence of other proofs, S6 kinase still may be the kinase). Using simulations with uncertainty analysis and analytical solutions, we show that an alternative explanation is centered around depletion of substrate, which can be tested experimentally. This analysis thus illustrates both the necessity and the benefits of using mathematical modeling to fully understand the implications of biological data, even for a small system and relatively simple data.

    Keywords
    insulin signalling, dominant negative data, mathematical modelling
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-115805 (URN)10.1111/febs.13182 (DOI)000350288300011 ()25546185 (PubMedID)
    Funder
    Swedish Research Council
    Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2020-08-14
    3. Quantification of nuclear transport in single cells
    Open this publication in new window or tab >>Quantification of nuclear transport in single cells
    Show others...
    2014 (English)Other (Other academic)
    Abstract [en]

    Regulation of nuclear transport is a key cellular function involved in many central processes, such as gene expression regulation and signal transduction. Rates of protein movement between cellular compartments can be measured by FRAP. However, no standard and reliable methods to calculate transport rates exist. Here we introduce a method to extract import and export rates, suitable for noisy single cell data. This method consists of microscope procedures, routines for data processing, an ODE model to fit to the data, and algorithms for parameter optimization and error estimation. Using this method, we successfully measured import and export rates in individual yeast. For YFP, average transport rates were 0.15 sec-1. We estimated confidence intervals for these parameters through likelihood profile analysis. We found large cell-to-cell variation (CV = 0.79) in these rates, suggesting a hitherto unknown source of cellular heterogeneity. Given the passive nature of YFP diffusion, we attribute this variation to large differences among cells in the number or quality of nuclear pores. Owing to its broad applicability and sensitivity, this method will allow deeper mechanistic insight into nuclear transport processes and into the largely unstudied cell-to-cell variation in kinetic rates.

    Place, publisher, year, pages
    New York, USA: Cold Spring Harbor Laboratory Press (CSHL), 2014. p. 23
    Series
    bioRxiv ; 001768
    National Category
    Bioinformatics and Systems Biology
    Identifiers
    urn:nbn:se:liu:diva-141807 (URN)10.1101/001768 (DOI)
    Note

    Article id 001768.

    Available from: 2017-10-06 Created: 2017-10-06 Last updated: 2020-08-14Bibliographically approved
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    Model-Based Hypothesis Testing in Biomedicine: How Systems Biology Can Drive the Growth of Scientific Knowledge
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  • 49.
    Johansson, Rikard
    et al.
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Kreutz, Clemens
    University of Freiburg, Department of Physics.
    Bartolomé Rodríguez, M. M.
    University of Freiburg, Department of Medicine.
    Strålfors, Peter
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Timmer, Jens
    University of Freiburg, Department of Physics.
    Cedersund, Gunnar
    Linköping University, Department of Clinical and Experimental Medicine, Cell Biology. Linköping University, Faculty of Health Sciences.
    Elucidating mechanisms of early insulin signaling in primary adipocytes and hepatocytes: a joint systems biology effort2009Conference paper (Other academic)
    Abstract [en]

    Type II diabetes is one of the most common diseases afflicting people today. Understanding how this disease works, not only on a cellular level and between different organs and tissues, but also how it affects whole body level homeostasis is crucial for enhancement of its treatment. We use model-bases analysis as a tool for distinguishing different biological hypothesis on the system behavior.

    The Insulin Receptor (IR), is located in the cell membrane as a dimer, and thus has the potential two bind two different insulin molecules. It can also undergo a series of phosphorylations, as well as having the ability to become internalized, and thus be removed from the cell’s censing area. However, it can then be recycled back to the membrane again. The major target of IR is the Insulin Receptor Substrate 1 (IRS1). IRS1 in turn mediates the signal further downstream through Protein Kinase B (PBK) and mammalian Target of Rapamycin (mTOR).  In adipocytes the end result is the translocation of internal vesicles containing Glucose Transporters (GLUT4) to the membrane, thus increasing the uptake of glucose. The liver, on the other hand, responds by down regulating the endogenous glucose production.

    The activity of IRS1 is determined by its phospho-tyrosine composition. This in turn is regulated by at least two serine-phosphorylations, on ser307 and ser312. The serine levels of this protein are regulated by downstream kinases, of which only one is known, S6K. The ser307 phosphorylation appears to allow for a short term positive feedback while the ser312 phosphorylation has the dynamics of a more long term negative feedback.

    The overall dynamics of the IRS1 tyrosine phosphorylation is a mirror of that of the Insulin Receptor. They both have a quick response to insulin within minutes, manifested as a high overshoot before declining to a steady state level. The overshoot behavior of this system can be explained either by a downstream negative feedback, or by having an advanced internalization and recycling model. Several hypotheses of the negative feedback mechanisms necessary to allow for the receptor to adopt such a behavior have previously been rejected by us. So has the hypothesis of internalization (unpublished data). The internalized Insulin Receptors can account for only a small fraction of the total amount of receptors, it however seems to be necessary for its own down regulation, since without it the overshoot behavior disappears.

    The complexity of this system is immense and hence we keep to as minimal models as possible, only considering adding complexity to the system when data indicates so, or when a simpler model structure has been rejected. We model the system with a series of Ordinary Differential Equations (ODEs), optimize and estimate the parameters of a given model structure with the Systems Biology Toolbox (SBTB) and reject, or fail to reject, models based on their statistical agreement with our data. We search the entire approximated parameter space for a sample of all acceptable parameter values for any given parameter. We then look for commonalities shared between model simulations of all parameter sets in the sample. That is, a behavior of e.g. a state in the model that has to be above a certain threshold for it to be able to explain the data, while other states might be of arbitrary sizes. If we find such a commonality, we call it a core prediction. Assuming your data is correct and your analysis thorough, a Core Prediction has the same strength as a model rejection. The common aspect, shared between all acceptable parameter etc, is something that has to be true, no matter how much more data you acquire. One such core prediction, which led to the rejection of the internalization hypothesis, was that the amount of internalized IR had to be above 80% of the total receptor pool.  We subsequently rejected this experimentally.

  • 50.
    Johnsson, Anna
    Linköping University, Department of Physics, Chemistry and Biology, Biotechnology .
    Mining for Lung Cancer Biomarkers in Plasma Metabolomics Data2010Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Lung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.NyckelordLung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.Nyckelord

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