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Cavalli, M., Baltzer, N., Umer, H. M., Grau, J., Lemnian, I., Pan, G., . . . Wadelius, C. (2019). Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases. Scientific Reports, 9, Article ID 2695.
Open this publication in new window or tab >>Allele specific chromatin signals, 3D interactions, and motif predictions for immune and B cell related diseases
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2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 2695Article in journal (Refereed) Published
Abstract [en]

Several Genome Wide Association Studies (GWAS) have reported variants associated to immune diseases. However, the identified variants are rarely the drivers of the associations and the molecular mechanisms behind the genetic contributions remain poorly understood. ChIP-seq data for TFs and histone modifications provide snapshots of protein-DNA interactions allowing the identification of heterozygous SNPs showing significant allele specific signals (AS-SNPs). AS-SNPs can change a TF binding site resulting in altered gene regulation and are primary candidates to explain associations observed in GWAS and expression studies. We identified 17,293 unique AS-SNPs across 7 lymphoblastoid cell lines. In this set of cell lines we interrogated 85% of common genetic variants in the population for potential regulatory effect and we identified 237 AS-SNPs associated to immune GWAS traits and 714 to gene expression in B cells. To elucidate possible regulatory mechanisms we integrated long-range 3D interactions data to identify putative target genes and motif predictions to identify TFs whose binding may be affected by AS-SNPs yielding a collection of 173 AS-SNPs associated to gene expression and 60 to B cell related traits. We present a systems strategy to find functional gene regulatory variants, the TFs that bind differentially between alleles and novel strategies to detect the regulated genes.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2019
National Category
Medical Genetics
Identifiers
urn:nbn:se:uu:diva-379258 (URN)10.1038/s41598-019-39633-0 (DOI)000459571100059 ()30804403 (PubMedID)
Funder
Swedish Research Council, 78081Swedish National Infrastructure for Computing (SNIC)EXODIAB - Excellence of Diabetes Research in SwedenSwedish Diabetes AssociationErnfors FoundationSwedish Cancer Society, 160518German Research Foundation (DFG), GR-3526/1German Research Foundation (DFG), GR-3526/2
Available from: 2019-03-15 Created: 2019-03-15 Last updated: 2019-10-07Bibliographically approved
Moghadam, B. T., Etemadikhah, M., Rajkowska, G., Stocluneier, C., Grabherr, M., Komorowski, J., . . . Lindholm Carlström, E. (2019). Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods. Journal of Psychiatric Research, 114, 41-47
Open this publication in new window or tab >>Analyzing DNA methylation patterns in subjects diagnosed with schizophrenia using machine learning methods
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2019 (English)In: Journal of Psychiatric Research, ISSN 0022-3956, E-ISSN 1879-1379, Vol. 114, p. 41-47Article in journal (Refereed) Published
Abstract [en]

Schizophrenia is a common mental disorder with high heritability. It is genetically complex and to date more than a hundred risk loci have been identified. Association of environmental factors and schizophrenia has also been reported, while epigenetic analyses have yielded ambiguous and sometimes conflicting results. Here, we analyzed fresh frozen post-mortem brain tissue from a cohort of 73 subjects diagnosed with schizophrenia and 52 control samples, using the Illumina Infinium HumanMethylation450 Bead Chip, to investigate genome-wide DNA methylation patterns in the two groups. Analysis of differential methylation was performed with the Bioconductor Minfi package and modern machine-learning and visualization techniques, which were shown previously to be successful in detecting and highlighting differentially methylated patterns in case-control studies. In this dataset, however, these methods did not uncover any significant signals discerning the patient group and healthy controls, suggesting that if there are methylation changes associated with schizophrenia, they are heterogeneous and complex with small effect.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2019
Keywords
DNA methylation, Schizophrenia, Machine learning, Classification, Clustering
National Category
Psychiatry
Identifiers
urn:nbn:se:uu:diva-390083 (URN)10.1016/j.jpsychires.2019.04.001 (DOI)000472127300006 ()31022588 (PubMedID)
Funder
Swedish Research Council FormaseSSENCE - An eScience CollaborationEU, European Research Council, 282330
Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2019-08-06Bibliographically approved
Diamanti, K., Cavalli, M., Pan, G., Pereira, M. J., Kumar, C., Skrtic, S., . . . Wadelius, C. (2019). Intra- and inter-individual metabolic profiling highlights carnitine and lysophosphatidylcholine pathways as key molecular defects in type 2 diabetes. Scientific Reports, 9, Article ID 9653.
Open this publication in new window or tab >>Intra- and inter-individual metabolic profiling highlights carnitine and lysophosphatidylcholine pathways as key molecular defects in type 2 diabetes
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2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 9653Article in journal (Refereed) Published
Abstract [en]

Type 2 diabetes (T2D) mellitus is a complex metabolic disease commonly caused by insulin resistance in several tissues. We performed a matched two-dimensional metabolic screening in tissue samples from 43 multi-organ donors. The intra-individual analysis was assessed across five key metabolic tissues (serum, visceral adipose tissue, liver, pancreatic islets and skeletal muscle), and the inter-individual across three different groups reflecting T2D progression. We identified 92 metabolites differing significantly between non-diabetes and T2D subjects. In diabetes cases, carnitines were significantly higher in liver, while lysophosphatidylcholines were significantly lower in muscle and serum. We tracked the primary tissue of origin for multiple metabolites whose alterations were reflected in serum. An investigation of three major stages spanning from controls, to pre-diabetes and to overt T2D indicated that a subset of lysophosphatidylcholines was significantly lower in the muscle of pre-diabetes subjects. Moreover, glycodeoxycholic acid was significantly higher in liver of pre-diabetes subjects while additional increase in T2D was insignificant. We confirmed many previously reported findings and substantially expanded on them with altered markers for early and overt T2D. Overall, the analysis of this unique dataset can increase the understanding of the metabolic interplay between organs in the development of T2D.

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP, 2019
National Category
Endocrinology and Diabetes
Identifiers
urn:nbn:se:uu:diva-391017 (URN)10.1038/s41598-019-45906-5 (DOI)000474222900010 ()31273253 (PubMedID)
Funder
AstraZenecaSwedish Research Council FormaseSSENCE - An eScience CollaborationSwedish Diabetes AssociationErnfors Foundation
Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-09-22Bibliographically approved
Cavalli, M., Baltzer, N., Pan, G., Walls, J. R., Garbulowska, K. S., Kumar, C., . . . Wadelius, C. (2019). Studies of liver tissue identify functional gene regulatory elements associated to gene expression, type 2 diabetes, and other metabolic diseases. HUMAN GENOMICS, 13, Article ID 20.
Open this publication in new window or tab >>Studies of liver tissue identify functional gene regulatory elements associated to gene expression, type 2 diabetes, and other metabolic diseases
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2019 (English)In: HUMAN GENOMICS, ISSN 1473-9542, Vol. 13, article id 20Article in journal (Refereed) Published
Abstract [en]

Background:

Genome-wide association studies (GWAS) of diseases and traits have found associations to gene regions but not the functional SNP or the gene mediating the effect. Difference in gene regulatory signals can be detected using chromatin immunoprecipitation and next-gen sequencing (ChIP-seq) of transcription factors or histone modifications by aligning reads to known polymorphisms in individual genomes. The aim was to identify such regulatory elements in the human liver to understand the genetics behind type 2 diabetes and metabolic diseases.

Methods:

The genome of liver tissue was sequenced using 10X Genomics technology to call polymorphic positions. Using ChIP-seq for two histone modifications, H3K4me3 and H3K27ac, and the transcription factor CTCF, and our established bioinformatics pipeline, we detected sites with significant difference in signal between the alleles.

Results:

We detected 2329 allele-specific SNPs (AS-SNPs) including 25 associated to GWAS SNPs linked to liver biology, e.g., 4 AS-SNPs at two type 2 diabetes loci. Two hundred ninety-two AS-SNPs were associated to liver gene expression in GTEx, and 134 AS-SNPs were located on 166 candidate functional motifs and most of them in EGR1-binding sites.

Conclusions:

This study provides a valuable collection of candidate liver regulatory elements for further experimental validation.

Keywords
ChIP-seq, T2D, Regulatory SNPs
National Category
Medical Genetics Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:uu:diva-383513 (URN)10.1186/s40246-019-0204-8 (DOI)000466335200001 ()31036066 (PubMedID)
Available from: 2019-05-16 Created: 2019-05-16 Last updated: 2019-10-07Bibliographically approved
Grabherr, M., Kaminska, B. & Komorowski, J. (2018). Special Issue Introduction: The Wonders and Mysteries Next Generation Sequencing Technologies Help Reveal. Genes, 9(10), Article ID 505.
Open this publication in new window or tab >>Special Issue Introduction: The Wonders and Mysteries Next Generation Sequencing Technologies Help Reveal
2018 (English)In: Genes, ISSN 2073-4425, E-ISSN 2073-4425, Vol. 9, no 10, article id 505Article in journal, Editorial material (Other academic) Published
Place, publisher, year, edition, pages
MDPI, 2018
National Category
Cell and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-370044 (URN)10.3390/genes9100505 (DOI)000448656700043 ()30340386 (PubMedID)
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved
Dabrowski, M. J., Draminski, M., Diamanti, K., Stepniak, K., Mozolewska, M. A., Teisseyre, P., . . . Wojtas, B. (2018). Unveiling new interdependencies between significant DNA methylation sites, gene expression profiles and glioma patients survival. Scientific Reports, 8, Article ID 4390.
Open this publication in new window or tab >>Unveiling new interdependencies between significant DNA methylation sites, gene expression profiles and glioma patients survival
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2018 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 4390Article in journal (Refereed) Published
Abstract [en]

In order to find clinically useful prognostic markers for glioma patients' survival, we employed Monte Carlo Feature Selection and Interdependencies Discovery (MCFS-ID) algorithm on DNA methylation (HumanMethylation450 platform) and RNA-seq datasets from The Cancer Genome Atlas (TCGA) for 88 patients observed until death. The input features were ranked according to their importance in predicting patients' longer (400+ days) or shorter (<= 400 days) survival without prior classification of the patients. Interestingly, out of the 65 most important features found, 63 are methylation sites, and only two mRNAs. Moreover, 61 out of the 63 methylation sites are among those detected by the 450 k array technology, while being absent in the HumanMethylation27. The most important methylation feature (cg15072976) overlaps with the RE1 Silencing Transcription Factor (REST) binding site, and was confirmed to intersect with the REST binding motif in human U87 glioma cells. Six additional methylation sites from the top 63 overlap with REST sites. We found that the methylation status of the cg15072976 site affects transcription factor binding in U87 cells in gel shift assay. The cg15072976 methylation status discriminates <= 400 and 400+ patients in an independent dataset from TCGA and shows positive association with survival time as evidenced by Kaplan-Meier plots.

National Category
Cancer and Oncology Cell and Molecular Biology
Identifiers
urn:nbn:se:uu:diva-350614 (URN)10.1038/s41598-018-22829-1 (DOI)000427237200001 ()29535343 (PubMedID)
Funder
AstraZeneca
Available from: 2018-05-23 Created: 2018-05-23 Last updated: 2018-05-23Bibliographically approved
Torabi Moghadam, B., Zamani, N., Komorowski, J. & Grabherr, M. (2017). PiiL: visualization of DNA methylation and gene expression data in gene pathways. BMC Genomics, 18, Article ID 571.
Open this publication in new window or tab >>PiiL: visualization of DNA methylation and gene expression data in gene pathways
2017 (English)In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 18, article id 571Article in journal (Refereed) Published
Abstract [en]

DNA methylation is a major mechanism involved in the epigenetic state of a cell. It has been observed that the methylation status of certain CpG sites close to or within a gene can directly affect its expression, either by silencing or, in some cases, up-regulating transcription. However, a vertebrate genome contains millions of CpG sites, all of which are potential targets for methylation modification, and the specific effects of most sites has not been characterized to date. To study the complex interplay between methylation status, cellular programs, and the resulting phenotypes, we present PiiL, an interactive gene expression pathway browser, facilitating the analysis through an integrated view of methylation and expression on multiple levels.

PiiL allows for specific hypothesis testing by quickly assessing pathways or gene networks, where the data is projected onto pathways that can be downloaded directly from the online KEGG database. PiiL provides a comprehensive set of analysis features, allowing for quickly searching for specific patterns, as well as to examine individual CpG sites and their impact on expression of the host gene and other genes in regulatory networks. To exemplify the power of this approach, we analyzed two types of brain tumors, Glioblastoma multiform and lower grade gliomas.

At a glance, we could confirm earlier findings that the predominant methylation and expression patterns separate perfectly by mutations in the IDH genes, rather than by histology. We could also infer the IDH mutation status for samples for which the genotype was not known. By applying different filtering methods, we show that a subset of CpG sites exhibits consistent methylation patterns, and that the status of sites affect the expression of key regulator genes, as well as other genes located downstream in the same pathways.

PiiL is implemented in Java with focus on a user-friendly graphical interface. The source code is available under the GPL license from https://github.com/behroozt/PiiL.git.

Keywords
DNA methylation, gene expression, KEGG pathways, visualization
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-320675 (URN)10.1186/s12864-017-3950-9 (DOI)000406759000002 ()
Funder
Swedish Research Council FormaseSSENCE - An eScience Collaboration
Available from: 2017-04-23 Created: 2017-04-23 Last updated: 2018-01-13Bibliographically approved
Baltzer, N., Sundström, K., Nygård, J. F., Dillner, J. & Komorowski, J. (2017). Risk stratification in cervical cancer screening by complete screening history: Applying bioinformatics to a general screening population. International Journal of Cancer, 141(1), 200-209
Open this publication in new window or tab >>Risk stratification in cervical cancer screening by complete screening history: Applying bioinformatics to a general screening population
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2017 (English)In: International Journal of Cancer, ISSN 0020-7136, E-ISSN 1097-0215, Vol. 141, no 1, p. 200-209Article in journal (Refereed) Published
Abstract [en]

Women screened for cervical cancer in Sweden are currently treated under a one-size-fits-all programme, which has been successful in reducing the incidence of cervical cancer but does not use all of the participants' available medical information. This study aimed to use women's complete cervical screening histories to identify diagnostic patterns that may indicate an increased risk of developing cervical cancer. A nationwide case-control study was performed where cervical cancer screening data from 125,476 women with a maximum follow-up of 10 years were evaluated for patterns of SNOMED diagnoses. The cancer development risk was estimated for a number of different screening history patterns and expressed as Odds Ratios (OR), with a history of 4 benign cervical tests as reference, using logistic regression. The overall performance of the model was moderate (64% accuracy, 71% area under curve) with 61-62% of the study population showing no specific patterns associated with risk. However, predictions for high-risk groups as defined by screening history patterns were highly discriminatory with ORs ranging from 8 to 36. The model for computing risk performed consistently across different screening history lengths, and several patterns predicted cancer outcomes. The results show the presence of risk-increasing and risk-decreasing factors in the screening history. Thus it is feasible to identify subgroups based on their complete screening histories. Several high-risk subgroups identified might benefit from an increased screening density. Some low-risk subgroups identified could likely have a moderately reduced screening density without additional risk.

Keywords
bioinformatics, cervical cancer, screening, personalized medicine, machine learning
National Category
Cancer and Oncology Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-323754 (URN)10.1002/ijc.30725 (DOI)000400766500021 ()28383102 (PubMedID)
Available from: 2017-06-12 Created: 2017-06-12 Last updated: 2019-10-07Bibliographically approved
Torabi Moghadam, B., Dabrowski, M., Kaminska, B., Grabherr, M. G. & Komorowski, J. (2016). Combinatorial identification of DNA methylation patterns over age in the human brain. BMC Bioinformatics, 17, Article ID 393.
Open this publication in new window or tab >>Combinatorial identification of DNA methylation patterns over age in the human brain
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2016 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 17, article id 393Article in journal (Refereed) Published
Abstract [en]

Background: DNA methylation plays a key role in developmental processes, which is reflected in changing methylation patterns at specific CpG sites over the lifetime of an individual. The underlying mechanisms are complex and possibly affect multiple genes or entire pathways. Results: We applied a multivariate approach to identify combinations of CpG sites that undergo modifications when transitioning between developmental stages. Monte Carlo feature selection produced a list of ranked and statistically significant CpG sites, while rule-based models allowed for identifying particular methylation changes in these sites. Our rule-based classifier reports combinations of CpG sites, together with changes in their methylation status in the form of easy-to-read IF-THEN rules, which allows for identification of the genes associated with the underlying sites. Conclusion: We utilized machine learning and statistical methods to discretize decision class (age) values to get a general pattern of methylation changes over the lifespan. The CpG sites present in the significant rules were annotated to genes involved in brain formation, general development, as well as genes linked to cancer and Alzheimer's disease.

Keywords
DNA methylation, Aging, Rule-based classification, Feature selection
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
urn:nbn:se:uu:diva-305330 (URN)10.1186/s12859-016-1259-3 (DOI)000383750700001 ()
Funder
Swedish Research Council FormaseSSENCE - An eScience Collaboration
Available from: 2016-10-14 Created: 2016-10-14 Last updated: 2017-11-29Bibliographically approved
Dramiński, M., Da̧browski, M. J., Diamanti, K., Koronacki, J. & Komorowski, J. (2016). Discovering Networks of Interdependent Features in High-Dimensional Problems. In: Japkowicz, Nathalie; Stefanowski, Jerzy (Ed.), Big Data Analysis: New Algorithms for a New Society (pp. 285-304). Cham: Springer
Open this publication in new window or tab >>Discovering Networks of Interdependent Features in High-Dimensional Problems
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2016 (English)In: Big Data Analysis: New Algorithms for a New Society / [ed] Japkowicz, Nathalie; Stefanowski, Jerzy, Cham: Springer, 2016, p. 285-304Chapter in book (Refereed)
Abstract [en]

The availability of very large data sets in Life Sciences provided earlier by the technological breakthroughs such as microarrays and more recently by various forms of sequencing has created both challenges in analyzing these data as well as new opportunities. A promising, yet underdeveloped approach to Big Data, not limited to Life Sciences, is the use of feature selection and classification to discover interdependent features. Traditionally, classifiers have been developed for the best quality of supervised classification. In our experience, more often than not, rather than obtaining the best possible supervised classifier, the Life Scientist needs to know which features contribute best to classifying observations (objects, samples) into distinct classes and what the interdependencies between the features that describe the observation. Our underlying hypothesis is that the interdependent features and rule networks do not only reflect some syntactical properties of the data and classifiers but also may convey meaningful clues about true interactions in the modeled biological system. In this chapter we develop further our method of Monte Carlo Feature Selection and Interdependency Discovery (MCFS and MCFS-ID, respectively), which are particularly well suited for high-dimensional problems, i.e., those where each observation is described by very many features, often many more features than the number of observations. Such problems are abundant in Life Science applications. Specifically, we define Inter-Dependency Graphs (termed, somewhat confusingly, ID Graphs) that are directed graphs of interactions between features extracted by aggregation of information from the classification trees constructed by the MCFS algorithm. We then proceed with modeling interactions on a finer level with rule networks. We discuss some of the properties of the ID graphs and make a first attempt at validating our hypothesis on a large gene expression data set for CD4+ T-cells. The MCFS-ID and ROSETTA including the Ciruvis approach offer a new methodology for analyzing Big Data from feature selection, through identification of feature interdependencies, to classification with rules according to decision classes, to construction of rule networks. Our preliminary results confirm that MCFS-ID is applicable to the identification of interacting features that are functionally relevant while rule networks offer a complementary picture with finer resolution of the interdependencies on the level of feature-value pairs.

Place, publisher, year, edition, pages
Cham: Springer, 2016
Series
Studies in Big Data, ISSN 2197-6503 ; 16
National Category
Bioinformatics and Systems Biology
Research subject
Bioinformatics
Identifiers
urn:nbn:se:uu:diva-325593 (URN)10.1007/978-3-319-26989-4_12 (DOI)978-3-319-26989-4 (ISBN)
Available from: 2017-06-26 Created: 2017-06-26 Last updated: 2017-06-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0766-8789

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