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  • 1.
    Badam, Tejaswi V. S.
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Sweden.
    de Weerd, Hendrik A.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Sweden.
    Martínez-Enguita, David
    Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Sweden.
    Olsson, Tomas
    Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden.
    Alfredsson, Lars
    Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden ; Institute of Environmental Medicine, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden.
    Kockum, Ingrid
    Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden.
    Jagodic, Maja
    Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Gustafsson, Mika
    Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Sweden.
    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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  • 2.
    Björn, Niclas
    et al.
    Clinical Pharmacology, Division of Drug Research, Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
    Badam, Tejaswi
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Spalinskas, Rapolas
    Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden.
    Brandén, Eva
    Department of Respiratory Medicine, Gävle Hospital, Sweden / Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle, Sweden.
    Koyi, Hirsh
    Department of Respiratory Medicine, Gävle Hospital, Sweden / Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle, Sweden.
    Lewensohn, Rolf
    Thoracic Oncology Unit, Tema Cancer, Karolinska University Hospital, and Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
    De Petris, Luigi
    Thoracic Oncology Unit, Tema Cancer, Karolinska University Hospital, and Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Sahlén, Pelin
    Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden.
    Lundeberg,, Joakim
    Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden.
    Gustafsson, Mika
    Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Gréen, Henrik
    Clinical Pharmacology, Division of Drug Research, Department of Biomedical and Clinical Sciences, Linköping University, Sweden / Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden / Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden.
    Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients2020In: NPJ Systems Biology and Applications, E-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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  • 3.
    Borgmästars, Emmy
    et al.
    Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden.
    de Weerd, Hendrik Arnold
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Bioinformatics, Linköping University, Linköping, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Sund, Malin
    Department of Surgical and Perioperative Sciences, Umeå University, Umeå, 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, no 1, p. 1-17, article id 393Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: MicroRNAs (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.

    RESULTS: Fifteen 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.

    CONCLUSIONS: Our 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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  • 4.
    Carlsson, Jessica
    et al.
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Davidsson, Sabina
    Orebro Univ Hosp, Dept Urol, Orebro, Sweden / Univ Orebro, Sch Hlth & Med Sci, Orebro, Sweden.
    Helenius, Gisela
    Orebro Univ Hosp, Dept Lab Med, Orebro, Sweden.
    Karlsson, Mats
    Orebro Univ Hosp, Dept Lab Med, Orebro, Sweden.
    Lubovac, Zelmina
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Andren, Ove
    Orebro Univ Hosp, Dept Urol, Orebro, Sweden .
    Olsson, Björn
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Klinga-Levan, Karin
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    A miRNA expression signature that separates between normal and malignant prostate tissues2011In: Cancer Cell International, E-ISSN 1475-2867, Vol. 11, p. 14-Article in journal (Refereed)
    Abstract [en]

    Background: MicroRNAs (miRNAs) constitute a class of small non-coding RNAs that post-transcriptionally regulate genes involved in several key biological processes and thus are involved in various diseases, including cancer. In this study we aimed to identify a miRNA expression signature that could be used to separate between normal and malignant prostate tissues. Results: Nine miRNAs were found to be differentially expressed (p < 0.00001). With the exception of two samples, this expression signature could be used to separate between the normal and malignant tissues. A cross-validation procedure confirmed the generality of this expression signature. We also identified 16 miRNAs that possibly could be used as a complement to current methods for grading of prostate tumor tissues. Conclusions: We found an expression signature based on nine differentially expressed miRNAs that with high accuracy (85%) could classify the normal and malignant prostate tissues in patients from the Swedish Watchful Waiting cohort. The results show that there are significant differences in miRNA expression between normal and malignant prostate tissue, indicating that these small RNA molecules might be important in the biogenesis of prostate cancer and potentially useful for clinical diagnosis of the disease.

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  • 5.
    Carlsson, Jessica
    et al.
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Helenius, Gisela
    Örebro University Hospital.
    Karlsson, Mats
    Örebro University Hospital.
    Lubovac, Zelmina
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Andrén, Ove
    Örebro University Hospital.
    Olsson, Björn
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Klinga-Levan, Karin
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Validation of suitable endogenous control genes for expression studies of miRNA in prostate cancer tissues2010In: Cancer Genetics and Cytogenetics, ISSN 0165-4608, E-ISSN 1873-4456, Vol. 202, no 2, p. 71-75Article in journal (Refereed)
    Abstract [en]

    When performing quantitative polymerase chain reaction analysis, there is a need for correction of technical variation between experiments. This correction is most commonly performed by using endogenous control genes, which are stably expressed across samples, as reference genes for normal expression in a specific tissue. In microRNA (miRNA) studies, two types of control genes are commonly used: small nuclear RNAs and small nucleolar RNAs. In this study, six different endogenous control genes for miRNA studies were investigated in prostate tissue material from the Swedish Watchful Waiting cohort. The stability of the controls was investigated using two different software applications, NormFinder and BestKeeper. RNU24 was the most suitable endogenous control gene for miRNA studies in prostate tissue materials.

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  • 6.
    de Weerd, Hendrik A.
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Badam, Tejaswi V. S.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Martínez-Enguita, David
    Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Åkesson, Julia
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Muthas, Daniel
    Translational Science & Experimental Medicine, Early Respiratory, Inflammation and Autoimmunity, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
    Gustafsson, Mika
    Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    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 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.

    AVAILABILITY: MODifieR is available under the GNU GPL license and can be freely downloaded from https://gitlab.com/Gustafsson-lab/MODifieR and as a Docker image from https://hub.docker.com/r/ddeweerd/modifier.

    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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  • 7.
    de Weerd, Hendrik A.
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Åkesson, Julia
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Guala, Dimitri
    Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden ; Merck AB, Solna, Sweden.
    Gustafsson, Mika
    Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data2022In: Bioinformatics Advances, E-ISSN 2635-0041, Vol. 2, no 1, article id vbac006Article in journal (Refereed)
    Abstract [en]

    MotivationNetwork-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.

    ResultsWe 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.

    Availability and implementationMODalyseR 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 informationSupplementary data are available at Bioinformatics Advances online.

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  • 8.
    Jurcevic, Sanja
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Keane, Simon
    University of Skövde, School of Health Sciences. University of Skövde, Digital Health Research (DHEAR).
    Borgmästars, Emmy
    Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Ejeskär, Katarina
    University of Skövde, School of Health Sciences. University of Skövde, Digital Health Research (DHEAR). University of Skövde, School of Bioscience.
    Bioinformatics analysis of miRNAs in the neuroblastoma 11q-deleted region reveals a role of miR-548l in both 11q-deleted and MYCN amplified tumour cells2022In: Scientific Reports, E-ISSN 2045-2322, Vol. 12, no 1, article id 19729Article in journal (Refereed)
    Abstract [en]

    Neuroblastoma is a childhood tumour that is responsible for approximately 15% of all childhood cancer deaths. Neuroblastoma tumours with amplification of the oncogene MYCN are aggressive, however, another aggressive subgroup without MYCN amplification also exists; rather, they have a deleted region at chromosome arm 11q. Twenty-six miRNAs are located within the breakpoint region of chromosome 11q and have been checked for a possible involvement in development of neuroblastoma due to the genomic alteration. Target genes of these miRNAs are involved in pathways associated with cancer, including proliferation, apoptosis and DNA repair. We could show that miR-548l found within the 11q region is downregulated in neuroblastoma cell lines with 11q deletion or MYCN amplification. In addition, we showed that the restoration of miR-548l level in a neuroblastoma cell line led to a decreased proliferation of these cells as well as a decrease in the percentage of cells in the S phase. We also found that miR-548l overexpression suppressed cell viability and promoted apoptosis, while miR-548l knockdown promoted cell viability and inhibited apoptosis in neuroblastoma cells. Our results indicate that 11q-deleted neuroblastoma and MYCN amplified neuroblastoma coalesce by downregulating miR-548l.

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  • 9.
    Linde, Jörg
    et al.
    Leibniz-Institute for Natural Product Research and Infection Biology.
    Olsson, Björn
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Lubovac, Zelmina
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Network Properties for Ranking Predicted miRNA Targets in Breast Cancer2009In: Advances in Bioinformatics, ISSN 1687-8027, E-ISSN 1687-8035, p. Article ID 182689-Article in journal (Refereed)
    Abstract [en]

    MicroRNAs control the expression of their target genes by translational repression and transcriptional cleavage. They are involved in various biological processes including development and progression of cancer. To uncover the biological role of miRNAs it is important to identify their target genes. The small number of experimentally validated target genes makes computer prediction methods very important. However, state-of-the-art prediction tools result in a great number of putative targets with an unpredictable number of false positives. In this paper, we propose and evaluate two approaches for ranking the biological relevance of putative targets of miRNAs which are associated with breast cancer.

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  • 10.
    Lindlöf, Angelica
    et al.
    University of Skövde, School of Humanities and Informatics.
    Lubovac, Zelmina
    University of Skövde, School of Humanities and Informatics.
    Simulations of simple artificial genetic networks reveal features in the use of Relevance Networks2005In: In Silico Biology, ISSN 1386-6338, Vol. 5, no 3, p. 239-249Article in journal (Refereed)
    Abstract [en]

    Recent research on large scale microarray analysis has explored the use of Relevance Networks to find networks of genes that are associated to each other in gene expression data. In this work, we compare Relevance Networks with other types of clustering methods to test some of the stated advantages of this method. The dataset we used consists of artificial time series of Boolean gene expression values, with the aim of mimicking microarray data, generated from simple artificial genetic networks. By using this dataset, we could not confirm that Relevance Networks based on mutual information perform better than Relevance Networks based on Pearson correlation, partitional clustering or hierarchical clustering, since the results from all methods were very similar. However, all three methods successfully revealed the subsets of co-expressed genes, which is a valuable step in identifying co-regulation.

  • 11.
    Lubovac, Zelmina
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Investigating Topological and Functional Features of Multimodular Proteins2009In: Journal of Biomedicine and Biotechnology, ISSN 1110-7243, E-ISSN 1110-7251, p. Article Number: 472415-Article in journal (Refereed)
    Abstract [en]

    To generate functional modules as functionally and structurally cohesive formations in protein interaction networks (PINs) constitutes an important step towards understanding how modules communicate on a higher level of the PIN organisation that underlies cell functionality. However, we need to understand how individual modules communicate and are organized into the higher-order structure(s) of the PIN organization that underlies cell functionality. In an attempt to contribute to this understanding, we make an assumption that the proteins reappearing in several modules, termed here as multimodular proteins (MMPs), may be useful in building higher-order structure(s) as they may constitute communication points between different modules. In this paper, we investigate common properties shared by these proteins and compare them with the properties of so called single-modular proteins (SMPs) by analyzing three aspects: functional aspect, that is, annotation of the proteins, topological aspect that is betweenness centrality of the proteins, and lethality. Furthermore, we investigate the interconnectivity role of some proteins that are identified as functionally and topologically important.

  • 12.
    Lubovac, Zelmina
    University of Skövde, School of Humanities and Informatics.
    Thesis Materials: Knowledge-based Methods for Identification of Functional Modules in Protein Interaction Networks2006Report (Other academic)
    Abstract [en]

    The majority of the current methods for identifying modules in protein interaction networks are based solely on analysing topological features of the networks. In contrast, the main idea that underpins the planned thesis is that combining topological information with knowledge about protein function will result in more biologically plausible modules than using approaches based solely on topology. We here propose approaches that use a combination of domain-specific knowledge, derived from Gene Ontology, and topological properties, to generate functional modules from protein interaction networks. By using yeast two hybrid (Y2H) interactions from /S/. /Cerevisiae/ and knowledge in terms of Gene Ontology (GO) annotations, we have elucidated functional modules of interacting proteins.

    In this report, a summary of the proposed approaches is presented. The methods with the same rationale but slightly different designs have been implemented, tested and evaluated. The first approach, where we combine clusters of proteins based on their mutual neighbours profiles with the corresponding clusters based on GO semantic similarity profiles, treats each of the aspects (functional knowledge and topology) separately to obtain functional clusters, and thereafter merges the clusters into one single structure. In contrast, the other approaches integrate both aspects from the beginning. The two other approaches are two versions of a method named SWEMODE (Semantic WEights for MODule Elucidation), which uses knowledge-based clustering coefficient to identify network modules. The first one is uses the original protein interaction graph, and the second one is a recently designed extension of SWEMODE where the /k/-cores of the graph are emphasised. We demonstrate that all three methods are able to identify the key functional modules in protein interaction networks.

    The first method was applied to smaller well-studied networks, that are known to contain modules of signalling pathways, while SWEMODE was applied on a large network containing 2 231 proteins and 6 379 interactions. The methods were also used to study intermodule connections, which is a step towards revealing a higher order hierarchy between modules.

    In this report, we describe and discuss the proposed approaches, along with their strengths and weaknesses. We also propose further extensions and improvements of the proposed methods, some of which may be attempted as the final steps in the implementation phase of the dissertation

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    FULLTEXT01
  • 13.
    Lubovac, Zelmina
    et al.
    University of Skövde, School of Humanities and Informatics. Heriot-Watt University, School of Mathematical and Computer Sciences, Edinburgh, United Kingdom.
    Corne, David
    Heriot-Watt University, School of Mathematical and Computer Sciences, Edinburgh, United Kingdom.
    Gamalielsson, Jonas
    University of Skövde, School of Humanities and Informatics.
    Olsson, Björn
    University of Skövde, School of Humanities and Informatics.
    Weighted Cohesiveness for Identification of Functional Modules and their Interconnectivity2007In: Bioinformatics Research and Development: First International Conference, BIRD 2007 Berlin, Germany, March 12-14, 2007 Proceedings / [ed] Sepp Hochreiter, Roland Wagner, Springer, 2007, p. 185-198Conference paper (Refereed)
    Abstract [en]

    Systems biology offers a holistic perspective where individual proteins are viewed as elements in a network of protein-protein interactions (PPI), in which the proteins have contextual functions within functional modules. In order to facilitate the identification and analysis of such modules, we have previously proposed a Gene Ontology-weighted clustering coefficient for identification of modules in PPI networks and a method, named SWEMODE (Semantic WEights for MODule Elucidation), where this measure is used to identify network modules. Here, we introduce novel aspects of the method that are tested and evaluated. One of the aspects that we consider is to use the k-core graph instead of the original protein-protein interaction graph.Also, by taking the spatial aspect into account, by using the GO cellular component annotation when calculating weighted cohesiveness, we are able to improve the results compared to previous work where only two of the GO aspects (molecular function and biological process) were combined. We here evaluate the predicted modules by calculating their overlap with MIPS functional complexes. In addition, we identify the “most frequent” proteins, i.e. the proteins that most frequently participate in overlapping modules. We also investigate the role of these proteins in the interconnectivity between modules. We find that the majority of identified proteins are involved in the assembly and arrangement of cell structures, such as the cell wall and cell envelope.

  • 14.
    Lubovac, Zelmina
    et al.
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Gamalielsson, Jonas
    University of Skövde, School of Humanities and Informatics.
    Olsson, Björn
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Combining functional and topological properties to identify core modules in protein interaction networks2006In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 64, no 4, p. 948-959Article in journal (Refereed)
    Abstract [en]

    Advances in large-scale technologies in proteomics, such as yeast two-hybrid screening and mass spectrometry, have made it possible to generate large Protein Interaction Networks (PINs). Recent methods for identifying dense sub-graphs in such networks have been based solely on graph theoretic properties. Therefore, there is a need for an approach that will allow us to combine domain-specific knowledge with topological properties to generate functionally relevant sub-graphs from large networks. This article describes two alternative network measures for analysis of PINs, which combine functional information with topological properties of the networks. These measures, called weighted clustering coefficient and weighted average nearest-neighbors degree, use weights representing the strengths of interactions between the proteins, calculated according to their semantic similarity, which is based on the Gene Ontology terms of the proteins. We perform a global analysis of the yeast PIN by systematically comparing the weighted measures with their topological counterparts. To show the usefulness of the weighted measures, we develop an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub-graphs containing functionally similar proteins. The proposed method is based on the ranking of nodes, i.e., proteins, according to their weighted neighborhood cohesiveness. The highest ranked nodes are considered as seeds for candidate modules. The algorithm then iterates through the neighborhood of each seed protein, to identify densely connected proteins with high functional similarity, according to the chosen parameters. Using a yeast two-hybrid data set of experimentally determined protein-protein interactions, we demonstrate that SWEMODE is able to identify dense clusters containing proteins that are functionally similar. Many of the identified modules correspond to known complexes or subunits of these complexes.

  • 15.
    Lubovac, Zelmina
    et al.
    University of Skövde, School of Humanities and Informatics.
    Gamalielsson, Jonas
    University of Skövde, School of Humanities and Informatics.
    Olsson, Björn
    University of Skövde, School of Humanities and Informatics.
    Lindlöf, Angelica
    University of Skövde, School of Humanities and Informatics.
    Exploring protein networks with a semantic similarity measure across Gene Ontology2005In: Proceedings of the 8th Joint Conference on Information Sciences, Vols 1-3 / [ed] S. Blair, Durham, NC: Joint Conference on Information Sciences , 2005, p. 1203-1208Conference paper (Refereed)
  • 16.
    Lubovac, Zelmina
    et al.
    University of Skövde, School of Humanities and Informatics.
    Olsson, Björn
    University of Skövde, School of Humanities and Informatics.
    Towards Reverse Engineering of Genetic Regulatory Networks2003Report (Other academic)
    Abstract [en]

    The major goal of computational biology is to derive regulatory interactions between genes from large-scale gene expression data and other biological sources. There have been many attempts to reach this goal, but the field needs more research before we can claim that we have reached a complete understanding of reverse engineering of regulatory networks. One of the aspects that have not been considered to a great extent in the development of reverse engineering approaches is combinatorial regulation. Combinatorial regulation can be obtained by the presence of modular architectures in regulation, where multiple binding sites for multiple transcription factors are combined into modular units.

    When modelling regulatory networks, genes are often considered as "black boxes", where gene expression level is an input signal and changed level of expression is the output. We need to shed light on reverse engineering of regulatory networks by modelling the gene "boxes" at a more detailed level of information, e.g., by using regulatory elements as input to gene boxes as a complement to expression levels. Another problem in the context of inferring regulatory networks is the difficulty of validating inferred interactions because it is practically impossible to test and experimentally confirm hundreds to thousands of predicted interactions. Therefore, we need to develop an artificial network to evaluate the developed method for reverse engineering. One of the major research questions that will be proposed in this work is: Can we reverse engineer the cis-regulatory logic controlling the network organised by modular units?

    This work is aiming to give an overview of possible research directions in this field as well as the chosen direction for the future work where more research is needed. It also gives a theoretical foundation for the reverse engineering problem, where key aspects are reviewed.

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  • 17.
    Lubovac, Zelmina
    et al.
    University of Skövde, School of Humanities and Informatics.
    Olsson, Björn
    University of Skövde, School of Humanities and Informatics.
    Gamalielsson, Jonas
    University of Skövde, School of Humanities and Informatics.
    Combining topological characteristics and domain knowledge reveals functional modules in protein interaction networks2005In: Proceedings of CompBioNets 2005: Algorithms and Computational Methods for Biochemical and Evolutionary Networks / [ed] Marie-France Sagot and Katia S. Guimaraes, College Publications, 2005, p. 93-106Conference paper (Refereed)
  • 18.
    Lubovac, Zelmina
    et al.
    University of Skövde, School of Humanities and Informatics.
    Olsson, Björn
    University of Skövde, School of Humanities and Informatics.
    Gamalielsson, Jonas
    University of Skövde, School of Humanities and Informatics.
    Weighted Clustering Coefficient for Identifying Modular Formations in Protein-Protein Interaction Networks2006In: Proceedings of World Academy of Science, Engineering and Technology, Vol 14 / [ed] C. Ardil, World Academy of Science, Engineering and Technology , 2006, p. 122-127Conference paper (Refereed)
    Abstract [en]

    This paper describes a novel approach for deriving modules from protein-protein interaction networks, which combines functional information with topological properties of the network. This approach is based on weighted clustering coefficient, which uses weights representing the functional similarities between the proteins. These weights are calculated according to the semantic similarity between the proteins, which is based on their Gene Ontology terms. We recently proposed an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub-graphs containing functionally similar proteins. The rational underlying this approach is that each module can be reduced to a set of triangles (protein triplets connected to each other). Here, we propose considering semantic similarity weights of all triangle-forming edges between proteins. We also apply varying semantic similarity thresholds between neighbours of each node that are not neighbours to each other (and hereby do not form a triangle), to derive new potential triangles to include in module-defining procedure. The results show an improvement of pure topological approach, in terms of number of predicted modules that match known complexes.

  • 19.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Integrative Approach for Detection of Functional Modules from Protein-Protein Interaction Networks2012In: Protein-Protein Interactions: Computational and Experimental Tools / [ed] Weibo Cai; Hao Hong, INTECH, 2012, p. 97-112Chapter in book (Refereed)
  • 20.
    Lubovac-Pilav, Zelmina
    et al.
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Borràs, Daniel M.
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Ponce, Esmeralda
    Dominican University of California, United States of America.
    Louie, Maggie C.
    Dominican University of California, United States of America / College of Pharmacy, Touro University of California, United States of America .
    Using expression profiling to understand the effects of chronic cadmium exposure on mcf-7 breast cancer cells2013In: PLOS ONE, E-ISSN 1932-6203, Vol. 8, no 12, article id e84646Article in journal (Refereed)
    Abstract [en]

    Cadmium is a metalloestrogen known to activate the estrogen receptor and promote breast cancer cell growth. Previous studies have implicated cadmium in the development of more malignant tumors; however the molecular mechanisms behind this cadmium-induced malignancy remain elusive. Using clonal cell lines derived from exposing breast cancer cells to cadmium for over 6 months (MCF-7-Cd4, -Cd6, -Cd7, -Cd8 and -Cd12), this study aims to identify gene expression signatures associated with chronic cadmium exposure. Our results demonstrate that prolonged cadmium exposure does not merely result in the deregulation of genes but actually leads to a distinctive expression profile. The genes deregulated in cadmium-exposed cells are involved in multiple biological processes (i.e. cell growth, apoptosis, etc.) and molecular functions (i.e. cadmium/metal ion binding, transcription factor activity, etc.). Hierarchical clustering demonstrates that the five clonal cadmium cell lines share a common gene expression signature of breast cancer associated genes, clearly differentiating control cells from cadmium exposed cells. The results presented in this study offer insights into the cellular and molecular impacts of cadmium on breast cancer and emphasize the importance of studying chronic cadmium exposure as one possible mechanism of promoting breast cancer progression.

  • 21.
    Magnusson, Rasmus
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    TFTenricher: a python toolbox for annotation enrichment analysis of transcription factor target genes2021In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 22, no 1, article id 440Article in journal (Refereed)
    Abstract [en]

    Background: Transcription factors (TFs) are the upstream regulators that orchestrate gene expression, and therefore a centrepiece in bioinformatics studies. While a core strategy to understand the biological context of genes and proteins includes annotation enrichment analysis, such as Gene Ontology term enrichment, these methods are not well suited for analysing groups of TFs. This is particularly true since such methods do not aim to include downstream processes, and given a set of TFs, the expected top ontologies would revolve around transcription processes.

    Results: We present the TFTenricher, a Python toolbox that focuses specifically at identifying gene ontology terms, cellular pathways, and diseases that are over-represented among genes downstream of user-defined sets of human TFs. We evaluated the inference of downstream gene targets with respect to false positive annotations, and found an inference based on co-expression to best predict downstream processes. Based on these downstream genes, the TFTenricher uses some of the most common databases for gene functionalities, including GO, KEGG and Reactome, to calculate functional enrichments. By applying the TFTenricher to differential expression of TFs in 21 diseases, we found significant terms associated with disease mechanism, while the gene set enrichment analysis on the same dataset predominantly identified processes related to transcription.

    Conclusions and availability: The TFTenricher package enables users to search for biological context in any set of TFs and their downstream genes. The TFTenricher is available as a Python 3 toolbox at https://github.com/rasma774/Tftenricher, under a GNU GPL license and with minimal dependencies.

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  • 22.
    Riquelme Medina, Ignacio
    et al.
    University of Skövde, The Systems Biology Research Centre.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes2016In: PLOS ONE, E-ISSN 1932-6203, Vol. 11, no 6, article id e0156006Article in journal (Refereed)
    Abstract [en]

    Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body?s inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D.

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  • 23.
    Tilevik, Diana
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Saxenborn, Patricia
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Tilevik, Andreas
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Fagerlind, Magnus
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Pernestig, Anna-Karin
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Enroth, Helena
    Department of Clinical Microbiology, Unilabs AB, Skövde, Sweden.
    Using next-generation sequencing to study biodiversity in Klebsiella spp. isolated from patients with suspected sepsis2019Conference paper (Refereed)
  • 24.
    Tina, Elisabet
    et al.
    Clinical Research Centre, Örebro University Hospital, Sweden ; School of Health and Medical Sciences, Örebro University, Sweden.
    Malakkaran Lindqvist, Breezy
    School of Health and Medical Sciences, Örebro University, Sweden.
    Gabrielson, Marike
    School of Health and Medical Sciences, Örebro University, Sweden.
    Lubovac, Zelmina
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Wegman, Pia
    School of Health and Medical Sciences, Örebro University, Sweden.
    Wingren, Sten
    School of Health and Medical Sciences, Örebro University, Sweden.
    The mitochondrial transporter SLC25A43 is frequently deleted and may influence cell proliferation in HER2-positive breast tumors2012In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 12, article id 350Article in journal (Refereed)
    Abstract [en]

    Background: Overexpression of the human epidermal growth factor receptor (HER) 2 is associated with poor prognosis and shortened survival in breast cancer patients. HER2 is a potent activator of several signaling pathways that support cell survival, proliferation and metabolism. In HER2- positive breast cancer there are most likely unexplored proteins that act directly or indirectly downstream of well established pathways and take part in tumor development and treatment response.

    Methods: In order to identify novel copy number variations (CNVs) in HER2-positive breast cancer whole-genome single nucleotide polymorphism (SNP) arrays were used. A PCR-based loss of heterozygosis (LOH) assay was conducted to verify presence of deletion in HER2-positive breast cancer cases but also in HER2 negative breast cancers, cervical cancers and lung cancers. Screening for mutations was performed using single-strand conformation polymorphism (SSCP) followed by PCR sequencing. Protein expression was evaluated with immunohistochemistry (IHC).

    Results: A common deletion at chromosome Xq24 was found in 80% of the cases. This locus harbors the gene solute carrier (SLC) family 25A member 43 (SLC25A43) encoding for a mitochondrial transport protein. The LOH assay revealed presence of SLC25A43 deletion in HER2-positive (48%), HER2-negative (9%), cervical (42%) and lung (67%) cancers. HER2- positive tumors with negative or low SLC25A43 protein expression had significantly lower S-phase fraction compared to tumors with medium or high expression (P = 0.024).

    Conclusions: We have found deletion in the SLC25A43 gene to be a common event in HER2-positive breast cancer as well as in other cancers. In addition, the SLC25A43 protein expression was shown to be related to S-phase fraction in HER2-positive breast cancer. Our results indicate a possible role of SLC25A43 in HER2-positive breast cancer and support the hypothesis of altered mitochondrial function in cancer.

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  • 25.
    Weishaupt, Holger
    et al.
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
    Johansson, Patrik
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
    Sundström, Anders
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Olsson, Björn
    University of Skövde, School of Bioscience. University of Skövde, The Systems Biology Research Centre.
    Nelander, Sven
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
    Swartling, Fredrik J.
    Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden.
    Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes2019In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, no 18, p. 3357-3364Article in journal (Refereed)
    Abstract [en]

    Motivation: Medulloblastoma (MB) is a brain cancer predominantly arising in children. Roughly 70% of patients are cured today, but survivors often suffer from severe sequelae. MB has been extensively studied by molecular profiling, but often in small and scattered cohorts. To improve cure rates and reduce treatment side effects, accurate integration of such data to increase analytical power will be important, if not essential.

    Results: We have integrated 23 transcription datasets, spanning 1350 MB and 291 normal brain samples. To remove batch effects, we combined the Removal of Unwanted Variation (RUV) method with a novel pipeline for determining empirical negative control genes and a panel of metrics to evaluate normalization performance. The documented approach enabled the removal of a majority of batch effects, producing a large-scale, integrative dataset of MB and cerebellar expression data. The proposed strategy will be broadly applicable for accurate integration of data and incorporation of normal reference samples for studies of various diseases. We hope that the integrated dataset will improve current research in the field of MB by allowing more large-scale gene expression analyses.

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  • 26.
    Zichner, Thomas
    et al.
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre. Institut für Informatik, Friedrich-Schiller-Universität Jena, Germany.
    Lubovac, Zelmina
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Olsson, Björn
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Temporal analysis of oncogenesis using microRNA expression data2008In: German Conference on Bioinformatics, GCB 2008: Proceedings / [ed] Andreas Beyer, Michael Schroeder, Bonn: Gesellschaft für Informatik , 2008, p. 128-137Conference paper (Refereed)
    Abstract [en]

    MicroRNAs (miRNAs) have rapidly become the focus of many cancer research studies. These small non-coding RNAs have been shown to play important roles in the regulation of oncogenes and tumor suppressors. It has also been demonstrated that miRNA expression profiles differ significantly between normal and cancerous cells, which indicates the possibility of using miRNAs as markers for cancer diagnosis and prognosis. However, not much is known about the regulation of miRNA expression. One of the issues worth investigating is whether deregulations of miRNA expression in cancer cells occur according to some pattern or in a random order. We therefore selected two approaches, previously used to derive graph models of oncogenesis using chromosomal imbalance data, and adapted them to miRNA expression data. Applying the adapted algorithms to a breast cancer data set, we obtained results indicating the temporal order of miRNA deregulations during tumor development. When analyzing the specific deregulations appearing at different time points in the derived model, we found that several of the deregulations identified as early events could be supported through literature studies.

  • 27.
    Åkesson, Julia
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Hojjati, Sara
    Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
    Hellberg, Sandra
    Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden ; Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
    Raffetseder, Johanna
    Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
    Khademi, Mohsen
    Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, Stockholm, Sweden.
    Rynkowski, Robert
    Department of Neurology, and Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
    Kockum, Ingrid
    Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, Stockholm, Sweden.
    Altafini, Claudio
    Division of Automatic Control, Department of Electrical Engineering, Linköping University, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Mellergård, Johan
    Department of Neurology, and Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
    Jenmalm, Maria C.
    Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
    Piehl, Fredrik
    Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, Stockholm, Sweden.
    Olsson, Tomas
    Neuroimmunology Unit, Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska University Hospital, Karolinska Institute, Stockholm, Sweden.
    Ernerudh, Jan
    Department of Clinical Immunology and Transfusion Medicine, and Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
    Gustafsson, Mika
    Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis2023In: Nature Communications, E-ISSN 2041-1723, Vol. 14, no 1, article id 6903Article in journal (Refereed)
    Abstract [en]

    Sensitive and reliable protein biomarkers are needed to predict disease trajectory and personalize treatment strategies for multiple sclerosis (MS). Here, we use the highly sensitive proximity-extension assay combined with next-generation sequencing (Olink Explore) to quantify 1463 proteins in cerebrospinal fluid (CSF) and plasma from 143 people with early-stage MS and 43 healthy controls. With longitudinally followed discovery and replication cohorts, we identify CSF proteins that consistently predicted both short- and long-term disease progression. Lower levels of neurofilament light chain (NfL) in CSF is superior in predicting the absence of disease activity two years after sampling (replication AUC = 0.77) compared to all other tested proteins. Importantly, we also identify a combination of 11 CSF proteins (CXCL13, LTA, FCN2, ICAM3, LY9, SLAMF7, TYMP, CHI3L1, FYB1, TNFRSF1B and NfL) that predict the severity of disability worsening according to the normalized age-related MS severity score (replication AUC = 0.90). The identification of these proteins may help elucidate pathogenetic processes and might aid decisions on treatment strategies for persons with MS.

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  • 28.
    Åkesson, Julia
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Magnusson, Rasmus
    Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Gustafsson, Mika
    Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    ComHub: Community predictions of hubs in gene regulatory networks2021In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 22, no 1, article id 58Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs.

    RESULTS: We have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets.

    CONCLUSIONS: In contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable.

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