| 1. |
- Badam, Tejaswi V. S., et al.
(författare)
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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
- 2021
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Ingår i: BMC Genomics. - : BioMed Central. - 1471-2164. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- 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. |
- Badam, Tejaswi Venkata Satya, 1989-
(författare)
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Omic Network Modules in Complex diseases
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 3. |
- Björn, Niclas, 1990-, et al.
(författare)
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Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients
- 2020
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Ingår i: npj Systems Biology and Applications. - : Nature Publishing Group. - 2056-7189. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 4. |
- Borgmästars, Emmy, 1990-
(författare)
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In search of early biomarkers in pancreatic ductal adenocarcinoma using multi-omics and bioinformatics
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Background: Pancreatic ductal adenocarcinoma (PDAC) is a very aggressive malignancy with a 5-year survival of 10 %. Surgery is the only curative treatment. Unfortunately, few patients are eligible for surgery due to late detection. Thus, we need ways to detect the disease at an earlier stage and for that good screening biomarkers could be used. Previous studies have analyzed circulating analytes in prospective studies to identify early PDAC signals. One such class is microRNAs (miRNAs). MicroRNAs are non-coding RNAs of around 22 nucleotides that act as post- transcriptional regulators by interaction with messenger RNAs (mRNAs). The function of a miRNA can be elucidated by target prediction, to identify its potential targets, followed by enrichment analysis of the predicted targets. Challenges with this approach includes a lot of false positives being generated and that miRNAs can perform their role in a tissue- or disease-specific manner. Other classes of analytes that have previously been studied in prospective PDAC cohorts are metabolites and proteins. Aims: This thesis has three aims. First, to build a miRNA functional analysis pipeline with correlation support between miRNA and its predicted target genes. Second, to identify potential circulating biomarkers for early detection of PDAC using multi-omics. Third, to identify potential prognostic metabolites in a prospective PDAC cohort.Methods: We used publicly available data from the cancer genome atlas-pancreatic adenocarcinoma (TCGA-PAAD) and pre-diagnostic plasma samples from the Northern Sweden Health and Disease Study. We built a pipeline in R including miRNA, mRNA, and protein expression data from TCGA-PAAD for in silico miRNA functional analysis. Pre- diagnostic plasma samples from future PDAC patients as well as matched healthy controls were analyzed using multi- omics. Tissue polypeptide specific antigen (TPS) was analyzed by enzyme linked immunosorbent assay in 267 future PDAC samples and 320 healthy controls. Metabolomics and clinical biomarkers (carbohydrate antigen (CA) 19-9, carcinoembryonic antigen (CEA), and CA 15-3) were profiled in 100 future PDAC samples and 100 healthy controls using liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and multi-plex technology. Of these, a subset of 39 future PDAC patients and 39 healthy controls were profiled for 2083 microRNAs using targeted sequencing and 644 proteins using proximity extension assays. Circulating levels of multi-omics analytes were analyzed using conditional or unconditional logistic regression. Least absolute shrinkage and selection operator (LASSO) in combination with 500 bootstrap iterations identified the most informative variables. The prognostic value of metabolites was assessed using cox regression. Multi-omics factor analysis (MOFA) and data integration analysis for biomarker discovery using latent components (DIABLO) were used for multi-omics integration analyses.Results: An automated pipeline was built consisting of 1) miRNA target prediction, 2) correlation analyses between miRNA and its targets on mRNA and protein expression levels, and 3) functional enrichment of correlated targets to identify enriched Kyoto encyclopedia of genes and genomes (KEGG) pathways and gene ontology (GO) terms for a specific miRNA. The pipeline was run for all microRNAs (~700) detected in the TCGA-PAAD cohort. These results can be downloaded from a shiny app (https://emmbor.shinyapps.io/mirfa/). TPS was not altered in pre-diagnostic PDAC patients up to 24 years prior to diagnosis, but increased at diagnosis (OR = 1.03, 95 % CI: 1.01-1.05). Internal area under curves of 0.74, 0.80, and 0.88 were achieved for five metabolites, two proteins, and two miRNAs that were selected by LASSO and bootstrap iterations, in combination with CA 19-9. Neither MOFA nor DIABLO separated well between future PDAC cases and healthy controls. Conclusions: Our bioinformatics pipeline for in silico functional analysis of microRNAs successfully identifies enriched KEGG pathways and GO terms for miRNA isoforms. The investigated plasma samples are heterogeneous, but among the analyzed variables, we identified five metabolites, two proteins, and two microRNAs with highest potential for early PDAC detection. CA 19-9 levels increased closer to diagnosis. We identified five fatty acids that could be studied in a diagnostic PDAC cohort as prognostic biomarkers.
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| 5. |
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| 6. |
- Borgmästars, Emmy, et al.
(författare)
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Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank
- 2024
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Ingår i: Journal of Gastrointestinal Oncology. - : AME Publishing Company. - 2078-6891 .- 2219-679X. ; 15:2, s. 755-767
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Tidskriftsartikel (refereegranskat)abstract
- Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.Methods: We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.Results: Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714–0.854] compared to 0.681 (95% CI: 0.597–0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].Conclusions: Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.
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| 7. |
- Borgmästars, Emmy, et al.
(författare)
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Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based biobank
- 2024
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Ingår i: Journal of Gastrointestinal Oncology. - : AME Publishing Company. - 2078-6891 .- 2219-679X. ; 15:2, s. 755-767
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Tidskriftsartikel (refereegranskat)abstract
- Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.Methods: We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.Results: Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714–0.854] compared to 0.681 (95% CI: 0.597–0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].Conclusions: Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.
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| 8. |
- Borgmästars, Emmy, et al.
(författare)
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miRFA : an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer
- 2019
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Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 20:1, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 9. |
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| 10. |
- Carlsson, Jessica, 1984-, et al.
(författare)
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A miRNA expression signature that separates between normal and malignant prostate tissues
- 2011
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Ingår i: Cancer Cell International. - : BioMed Central (BMC). - 1475-2867. ; :11, s. 14-
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundMicroRNAs (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.ResultsNine 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.ConclusionsWe 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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| 11. |
- Carlsson, Jessica, et al.
(författare)
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Validation of suitable endogenous control genes for expression studies of miRNA in prostate cancer tissues
- 2010
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Ingår i: Cancer Genetics and Cytogenetics. - : Elsevier Inc.. - 0165-4608 .- 1873-4456. ; 202:2, s. 71-75
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 12. |
- Carlsson, Jessica, et al.
(författare)
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Validation of suitable endogenous control genes for expression studies of miRNA in prostate cancer tissues
- 2010
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Ingår i: Cancer Genetics and Cytogenetics. - : Elsevier BV. - 0165-4608 .- 1873-4456. ; 202:2, s. 71-75
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Tidskriftsartikel (refereegranskat)abstract
- 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. (C) 2010 Elsevier Inc. All rights reserved.
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| 13. |
- de Weerd, Hendrik A., et al.
(författare)
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MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data
- 2022
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Ingår i: Bioinformatics Advances. - : Oxford University Press. - 2635-0041. ; 2:1
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 14. |
- de Weerd, Hendrik A., et al.
(författare)
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MODifieR : an ensemble R package for inference of disease modules from transcriptomics networks
- 2020
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 36:12, s. 3918-3919
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 15. |
- de Weerd, Hendrik Arnold, 1986-
(författare)
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Novel methods and software for disease module inference
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 16. |
- Jurcevic, Sanja, 1971-, et al.
(författare)
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Bioinformatics analysis of miRNAs in the neuroblastoma 11q-deleted region reveals a role of miR-548l in both 11q-deleted and MYCN amplified tumour cells
- 2022
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 17. |
- Linde, Jörg, et al.
(författare)
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Network Properties for Ranking Predicted miRNA Targets in Breast Cancer
- 2009
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Ingår i: Advances in Bioinformatics. - : Hindawi Publishing Corporation. - 1687-8027 .- 1687-8035. ; , s. Article ID 182689-
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 18. |
- Lindlöf, Angelica, et al.
(författare)
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Simulations of simple artificial genetic networks reveal features in the use of Relevance Networks
- 2005
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Ingår i: In Silico Biology. - : IOS Press. - 1386-6338. ; 5:3, s. 239-249
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 19. |
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| 20. |
- Lubovac-Pilav, Zelmina, et al.
(författare)
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Using expression profiling to understand the effects of chronic cadmium exposure on mcf-7 breast cancer cells
- 2013
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 8:12
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 21. |
- Lubovac, Zelmina, et al.
(författare)
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Combining functional and topological properties to identify core modules in protein interaction networks
- 2006
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Ingår i: Proteins. - : John Wiley & Sons. - 0887-3585 .- 1097-0134. ; 64:4, s. 948-959
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 22. |
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| 23. |
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| 24. |
- Lubovac, Zelmina
(författare)
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Investigating Topological and Functional Features of Multimodular Proteins
- 2009
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Ingår i: Journal of Biomedicine and Biotechnology. - : Hindawi Publishing Corporation. - 1110-7243 .- 1110-7251. ; , s. Article Number: 472415-
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 25. |
- Lubovac, Zelmina
(författare)
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Thesis Materials : Knowledge-based Methods for Identification of Functional Modules in Protein Interaction Networks
- 2006
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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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| 26. |
- Lubovac, Zelmina, et al.
(författare)
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Towards Reverse Engineering of Genetic Regulatory Networks
- 2003
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- 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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| 27. |
- Lubovac, Zelmina, et al.
(författare)
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Weighted Clustering Coefficient for Identifying Modular Formations in Protein-Protein Interaction Networks
- 2006
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Ingår i: Proceedings of World Academy of Science, Engineering and Technology, Vol 14. - : World Academy of Science, Engineering and Technology. ; , s. 122-127
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 28. |
- Lubovac, Zelmina, et al.
(författare)
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Weighted Cohesiveness for Identification of Functional Modules and their Interconnectivity
- 2007
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Ingår i: Bioinformatics Research and Development. - Berlin, Heidelberg : Springer. - 9783540712329 - 9783540712336 ; , s. 185-198
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 29. |
- Magnusson, Rasmus, et al.
(författare)
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TFTenricher : a python toolbox for annotation enrichment analysis of transcription factor target genes
- 2021
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Ingår i: BMC Bioinformatics. - : Springer Nature. - 1471-2105. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 30. |
- Riquelme Medina, Ignacio, et al.
(författare)
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Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes
- 2016
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Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 11:6
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 31. |
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| 32. |
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| 33. |
- Tina, Elisabet, 1975-, et al.
(författare)
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The mitochondrial transporter SLC25A43 is frequently deleted and may influence cell proliferation in HER2-positive breast tumors
- 2012
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Ingår i: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 34. |
- Weishaupt, Holger, et al.
(författare)
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Batch-normalization of cerebellar and medulloblastoma gene expression datasets utilizing empirically defined negative control genes
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811. ; 35:18, s. 3357-3364
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 35. |
- Zichner, Thomas, et al.
(författare)
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Temporal analysis of oncogenesis using microRNA expression data
- 2008
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Ingår i: German Conference on Bioinformatics, GCB 2008. - Bonn : Gesellschaft für Informatik. - 9783885792260 ; , s. 128-137
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 36. |
- Åkesson, Julia, et al.
(författare)
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ComHub : Community predictions of hubs in gene regulatory networks
- 2021
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Ingår i: BMC Bioinformatics. - : Springer Nature. - 1471-2105. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 37. |
- Åkesson, Julia, 1994-
(författare)
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Network-based biomarker discovery for multiple sclerosis
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Complex autoimmune diseases, such as multiple sclerosis (MS), develop as a result of perturbations in the regulatory system controlling the function of immune cells. The disease course of MS is heterogeneous but is characterised by chronic inflammation in the central nervous system causing neurodegeneration resulting in gradual disability worsening. Disease biomarkers which are present at early stages of a disease can help clinicians to tailor treatment strategies to the expected disease course of individual persons. Gene products, i.e. RNA and proteins, serve as promising disease biomarkers due to the possibility to detect changes in abundance at early stages of a disease. Putative biomarkers can be identified by modelling different levels of gene regulation from high-throughput measurements of gene product abundance. Extracting information of disease relevance from high-throughput data is a complex problem which requires the use of efficient and targeted computational algorithms. The aim of this thesis was to develop and refine methods for identifying key biomarkers involved in the development and progression of complex diseases, with the main focus on MS. In Paper I, we used a machine learning approach to identify a combination of protein biomarkers, present in the cerebrospinal fluid, which could predict the disease trajectory of persons in the early stages of MS. The abundance of proteins is a result of an intricate network of multiple regulatory factors controlling the expression of genes. A large part of the expression of genes is controlled by a few key regulators, which are believed to be crucial for the development of diseases. In addition, disease-associated genes are believed to colocalise in these networks forming so called disease modules. In Paper II, we developed a method, named ComHub, for extracting the key regulators of gene expression. In Paper III, we combined ComHub with the tool MODifieR, for disease module predictions, in a network analysis pipeline for identifying a limited set of disease-associated genes. Using this network analysis pipeline we identified a set of MS-associated genes, as well as a promising key regulator of MS. The work performed in this doctoral thesis covers development of new and refined methods for modelling complex diseases, while simultaneously utilising these methods to identify disease biomarkers important for the development and progression of MS. The identified biomarkers can be used for understanding the pathology of MS, as candidate drug targets, and as promising biomarkers to aid clinicians in tailoring treatment strategies to individual persons.
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| 38. |
- Åkesson, Julia, et al.
(författare)
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Proteomics reveal biomarkers for diagnosis, disease activity and long-term disability outcomes in multiple sclerosis
- 2023
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- 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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