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  • 1.
    Aguet, Francois
    et al.
    Illumina Inc, Illumina Artificial Intelligence Lab, San Diego, CA 92122 USA..
    Alasoo, Kaur
    Univ Tartu, Inst Comp Sci, Tartu, Estonia..
    Li, Yang, I
    Univ Chicago, Dept Med, Sect Genet Med, 5841 S Maryland Ave, Chicago, IL 60637 USA..
    Battle, Alexis
    Johns Hopkins Univ, Dept Biomed Engn, Baltimore, MD USA.;Johns Hopkins Univ, Malone Ctr Engn Healthcare, Baltimore, MD USA.;Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA.;Johns Hopkins Univ, Dept Genet Med, Baltimore, MD USA..
    Im, Hae Kyung
    Univ Chicago, Dept Med, Sect Genet Med, 5841 S Maryland Ave, Chicago, IL 60637 USA..
    Montgomery, Stephen B.
    Stanford Univ, Dept Pathol, Stanford, CA 94305 USA.;Stanford Univ, Dept Genet, Stanford, CA 94305 USA..
    Lappalainen, Tuuli
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH Royal Inst Technol, Dept Gene Technol, Sci Life Lab, Stockholm, Sweden.;.
    Molecular quantitative trait loci2023In: NATURE REVIEWS METHODS PRIMERS, ISSN 2662-8449, Vol. 3, no 1, article id 4Article in journal (Refereed)
    Abstract [en]

    Understanding functional effects of genetic variants is one of the key challenges in human genetics, as much of disease-associated variation is located in non-coding regions with typically unknown putative gene regulatory effects. One of the most important approaches in this field has been molecular quantitative trait locus (molQTL) mapping, where genetic variation is associated with molecular traits that can be measured at scale, such as gene expression, splicing and chromatin accessibility. The maturity of the field and large-scale studies have produced a rich set of established methods for molQTL analysis, with novel technologies opening up new areas of discovery. In this Primer, we discuss the study design, input data and statistical methods for molQTL mapping and outline the properties of the resulting data as well as popular downstream applications. We review both the limitations and caveats of molQTL mapping as well as future potential approaches to tackle them. With technological development now providing many complementary methods for functional characterization of genetic variants, we anticipate that molQTLs will remain an important part of this toolkit as the only existing approach that can measure human variation in its native genomic, cellular and tissue context.

  • 2.
    Ahmadian, Afshin
    et al.
    KTH, Superseded Departments (pre-2005), Biotechnology.
    Russom, Aman
    KTH, Superseded Departments (pre-2005), Biotechnology.
    Andersson, Helene
    KTH, Superseded Departments (pre-2005), Biotechnology.
    Uhlén, Mathias
    KTH, Superseded Departments (pre-2005), Biotechnology.
    Stemme, Göran
    KTH, Superseded Departments (pre-2005), Biotechnology.
    Nilsson, Peter
    KTH, Superseded Departments (pre-2005), Biotechnology.
    SNP analysis by allele-specific extension in a micromachined filter chamber2002In: BioTechniques, ISSN 0736-6205, E-ISSN 1940-9818, Vol. 32, no 4, p. 748-754Article in journal (Refereed)
  • 3. Ameur, Adam
    et al.
    Dahlberg, Johan
    Olason, Pall
    Vezzi, Francesco
    Karlsson, Robert
    Martin, Marcel
    Viklund, Johan
    Kahari, Andreas Kusalananda
    Lundin, Par
    Che, Huiwen
    Thutkawkorapin, Jessada
    Eisfeldt, Jesper
    Lampa, Samuel
    Dahlberg, Mats
    Hagberg, Jonas
    Jareborg, Niclas
    Liljedahl, Ulrika
    Jonasson, Inger
    Johansson, Asa
    Feuk, Lars
    Lundeberg, Joakim
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Syvanen, Ann-Christine
    Lundin, Sverker
    Nilsson, Daniel
    Nystedt, Bjorn
    Magnusson, Patrik K. E.
    Gyllensten, Ulf
    SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population2017In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438, Vol. 25, no 11, p. 1253-1260Article in journal (Refereed)
    Abstract [en]

    Here we describe the SweGen data set, a comprehensive map of genetic variation in the Swedish population. These data represent a basic resource for clinical genetics laboratories as well as for sequencing-based association studies by providing information on genetic variant frequencies in a cohort that is well matched to national patient cohorts. To select samples for this study, we first examined the genetic structure of the Swedish population using high-density SNP-array data from a nation-wide cohort of over 10 000 Swedish-born individuals included in the Swedish Twin Registry. A total of 1000 individuals, reflecting a cross-section of the population and capturing the main genetic structure, were selected for whole-genome sequencing. Analysis pipelines were developed for automated alignment, variant calling and quality control of the sequencing data. This resulted in a genome-wide collection of aggregated variant frequencies in the Swedish population that we have made available to the scientific community through the website https://swefreq.nbis.se. A total of 29.2 million single-nucleotide variants and 3.8 million indels were detected in the 1000 samples, with 9.9 million of these variants not present in current databases. Each sample contributed with an average of 7199 individual-specific variants. In addition, an average of 8645 larger structural variants (SVs) were detected per individual, and we demonstrate that the population frequencies of these SVs can be used for efficient filtering analyses. Finally, our results show that the genetic diversity within Sweden is substantial compared with the diversity among continental European populations, underscoring the relevance of establishing a local reference data set.

  • 4.
    Bieder, Andrea
    et al.
    Karolinska Inst, Dept Biosci & Nutr, Halsovagen 7, S-14183 Huddinge, Sweden..
    Einarsdottir, Elisabet
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska Inst, Dept Biosci & Nutr, Hälsovagen 7, S-14183 Huddinge, Sweden.;Univ Helsinki, Stem Cells & Metab Res Program STEMM, Helsinki, Finland.;Folkhälsan Inst Genet, Helsinki, Finland..
    Matsson, Hans
    Karolinska Inst, Dept Womens & Childrens Hlth, Solna, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden..
    Nilsson, Harriet
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Structural Biotechnology. Karolinska Inst, Dept Biosci & Nutr, Hälsovagen 7, S-14183 Huddinge, Sweden..
    Eisfeldt, Jesper
    Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, Sci Pk, Solna, Sweden..
    Dragomir, Anca
    Uppsala Univ, Dept Immunol Genet & Pathol, Uppsala, Sweden.;Uppsala Univ Hosp, Dept Pathol, Uppsala, Sweden..
    Paucar, Martin
    Karolinska Inst, Dept Clin Neurosci, Stockholm, Sweden..
    Granberg, Tobias
    Karolinska Inst, Dept Clin Neurosci, Stockholm, Sweden.;Karolinska Univ Hosp, Dept Radiol, Stockholm, Sweden..
    Li, Tie-Qiang
    Karolinska Inst, Dept Clin Sci Intervent & Technol, Stockholm, Sweden..
    Lindstrand, Anna
    Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden..
    Kere, Juha
    Karolinska Inst, Dept Biosci & Nutr, Halsovagen 7, S-14183 Huddinge, Sweden.;Univ Helsinki, Stem Cells & Metab Res Program STEMM, Helsinki, Finland.;Guys Hosp, Kings Coll London, Sch Basic & Med Biosci, London, England..
    Tapia-Paez, Isabel
    Karolinska Inst, Dept Med, Solnavagen 30, S-17176 Solna, Sweden..
    Rare variants in dynein heavy chain genes in two individuals with situs inversus and developmental dyslexia: a case report2020In: BMC Medical Genetics, E-ISSN 1471-2350, Vol. 21, no 1, article id 87Article in journal (Refereed)
    Abstract [en]

    Background Developmental dyslexia (DD) is a neurodevelopmental learning disorder with high heritability. A number of candidate susceptibility genes have been identified, some of which are linked to the function of the cilium, an organelle regulating left-right asymmetry development in the embryo. Furthermore, it has been suggested that disrupted left-right asymmetry of the brain may play a role in neurodevelopmental disorders such as DD. However, it is unknown whether there is a common genetic cause to DD and laterality defects or ciliopathies. Case presentation Here, we studied two individuals with co-occurring situs inversus (SI) and DD using whole genome sequencing to identify genetic variants of importance for DD and SI. Individual 1 had primary ciliary dyskinesia (PCD), a rare, autosomal recessive disorder with oto-sino-pulmonary phenotype and SI. We identified two rare nonsynonymous variants in the dynein axonemal heavy chain 5 gene (DNAH5): a previously reported variant c.7502G > C; p.(R2501P), and a novel variant c.12043 T > G; p.(Y4015D). Both variants are predicted to be damaging. Ultrastructural analysis of the cilia revealed a lack of outer dynein arms and normal inner dynein arms. MRI of the brain revealed no significant abnormalities. Individual 2 had non-syndromic SI and DD. In individual 2, one rare variant (c.9110A > G;p.(H3037R)) in the dynein axonemal heavy chain 11 gene (DNAH11), coding for another component of the outer dynein arm, was identified. Conclusions We identified the likely genetic cause of SI and PCD in one individual, and a possibly significant heterozygosity in the other, both involving dynein genes. Given the present evidence, it is unclear if the identified variants also predispose to DD and further studies into the association between laterality, ciliopathies and DD are needed.

  • 5.
    Björn, Niclas
    et al.
    Linköping University, Department of Medical and Health Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences..
    Pradhananga, Sailendra
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sigurgeirsson, Benjamin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundeberg, Joakim
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Gréen, Henrik
    Linköping University, Department of Medical and Health Sciences, Division of Drug Research. Linköping University, Faculty of Medicine and Health Sciences..
    Sahlén, Pelin
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Comparison of Variant Calls from Whole Genome and Whole Exome Sequencing Data Using Matched Samples2018In: Journal of Next Generation Sequencing & Applications, ISSN 2469-9853, Vol. 5, no 1, p. 1-8Article in journal (Refereed)
    Abstract [en]

    Whole exome sequencing (WES) has been extensively used in genomic research. As sequencing costs decline it is being replaced by whole genome sequencing (WGS) in large-scale genomic studies, but more comparative information on WES and WGS datasets would be valuable. Thus, we have extensively compared variant calls obtained from WGS and WES of matched germline DNA samples from 96 lung cancer patients. WGS provided more homogeneous coverage with higher genotyping quality, and identified more variants, than WES, regardless of exome coverage depth. It also called more reference variants, reflecting its power to call rare variants, and more heterozygous variants that met applied quality criteria, indicating that WGS is less prone to allelic drop outs. However, increasing WES coverage reduced the discrepancy between the WES and WGS results. We believe that as sequencing costs further decline WGS will become the method of choice even for research confined to the exome.

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  • 6.
    Bocher, Ozvan
    et al.
    Univ Brest, INSERM, EFS, UMR 1078,GGB, Brest, France.;Helmholtz Zentrum Munchen, Inst Translat Genom, Munich, Germany..
    Ludwig, Thomas E.
    Univ Brest, INSERM, EFS, UMR 1078,GGB, Brest, France.;CHU Brest, Brest, France..
    Oglobinsky, Marie-Sophie
    Univ Brest, INSERM, EFS, UMR 1078,GGB, Brest, France..
    Marenne, Gaeelle
    Univ Brest, INSERM, EFS, UMR 1078,GGB, Brest, France..
    Deleuze, Jean-Francois
    Univ Paris Saclay, Ctr Natl Rech Genom Humaine CNRGH, Inst Biol Francois Jacob, CEA, Evry, France..
    Suryakant, Suryakant
    Univ Bordeaux, INSERM, Bordeaux Populat Hlth Res Ctr, Team ELEANOR,UMR 1219, Bordeaux, France..
    Odeberg, Jacob
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. Arctic Univ Tromso, Dept Clin Med, Fac Hlth Sci, Tromso, Norway..
    Morange, Pierre-Emmanuel
    Aix Marseille Univ, INSERM, INRAE, C2VN, Marseille, France..
    Tregoueet, David-Alexandre
    Univ Bordeaux, INSERM, Bordeaux Populat Hlth Res Ctr, Team ELEANOR,UMR 1219, Bordeaux, France..
    Perdry, Herve
    Univ Paris Saclay, Univ Paris Sud, UFR Med, CESP Inserm,U1018, Villejuif, France..
    Genin, Emmanuelle
    Univ Brest, INSERM, EFS, UMR 1078,GGB, Brest, France.;CHU Brest, Brest, France..
    Testing for association with rare variants in the coding and non-coding genome: RAVA-FIRST, a new approach based on CADD deleteriousness score2022In: PLOS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 18, no 9, p. e1009923-, article id e1009923Article in journal (Refereed)
    Abstract [en]

    Rare variant association tests (RVAT) have been developed to study the contribution of rare variants widely accessible through high-throughput sequencing technologies. RVAT require to aggregate rare variants in testing units and to filter variants to retain only the most likely causal ones. In the exome, genes are natural testing units and variants are usually filtered based on their functional consequences. However, when dealing with whole-genome sequence (WGS) data, both steps are challenging. No natural biological unit is available for aggregating rare variants. Sliding windows procedures have been proposed to circumvent this difficulty, however they are blind to biological information and result in a large number of tests. We propose a new strategy to perform RVAT on WGS data: "RAVA-FIRST" (RAre Variant Association using Functionally-InfoRmed STeps) comprising three steps. (1) New testing units are defined genome-wide based on functionally-adjusted Combined Annotation Dependent Depletion (CADD) scores of variants observed in the gnomAD populations, which are referred to as "CADD regions". (2) A region-dependent filtering of rare variants is applied in each CADD region. (3) A functionally-informed burden test is performed with sub-scores computed for each genomic category within each CADD region. Both on simulations and real data, RAVA-FIRST was found to outperform other WGS-based RVAT. Applied to a WGS dataset of venous thromboembolism patients, we identified an intergenic region on chromosome 18 enriched for rare variants in early-onset patients. This region that was missed by standard sliding windows procedures is included in a TAD region that contains a strong candidate gene. RAVA-FIRST enables new investigations of rare non-coding variants in complex diseases, facilitated by its implementation in the R package Ravages.

  • 7.
    Bonet, Jose
    et al.
    Barcelona Inst Sci & Technol, Inst Res Biomed IRB Barcelona, Barcelona 08028, Spain.;Univ Pompeu Fabra, Res Program Biomed Informat, Barcelona 08002, Catalonia, Spain..
    Chen, Mandi
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Dabad, Marc
    Barcelona Inst Sci & Technol BIST, Ctr Genom Regulat CRG, CNAG CRG, Barcelona, Spain.;Univ Pompeu Fabra UPF, Barcelona, Spain..
    Heath, Simon
    Barcelona Inst Sci & Technol BIST, Ctr Genom Regulat CRG, CNAG CRG, Barcelona, Spain.;Univ Pompeu Fabra UPF, Barcelona, Spain..
    Gonzalez-Perez, Abel
    Barcelona Inst Sci & Technol, Inst Res Biomed IRB Barcelona, Barcelona 08028, Spain.;Univ Pompeu Fabra, Res Program Biomed Informat, Barcelona 08002, Catalonia, Spain..
    Lopez-Bigas, Nuria
    Barcelona Inst Sci & Technol, Inst Res Biomed IRB Barcelona, Barcelona 08028, Spain.;Univ Pompeu Fabra, Res Program Biomed Informat, Barcelona 08002, Catalonia, Spain.;Inst Catalana Recerca & Estudis Avancats ICREA, Barcelona, Spain..
    Lagergren, Jens
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data2022In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 38, no 5, p. 1235-1243Article in journal (Refereed)
    Abstract [en]

    Motivation: DNA methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to exploit Nanopore data for methylation detection. However, current approaches rely on a human-defined threshold to detect the methylation status of a genomic position and are not optimized to detect sites methylated at low frequency. Furthermore, most methods use either the Nanopore signals or the basecalling errors as the model input and do not take advantage of their combination. Results: Here, we present DeepMP, a convolutional neural network-based model that takes information from Nanopore signals and basecalling errors to detect whether a given motif in a read is methylated or not. Besides, DeepMP introduces a threshold-free position modification calling model sensitive to sites methylated at low frequency across cells. We comprehensively benchmarked DeepMP against state-of-the-art methods on Escherichia coli, human and pUC19 datasets. DeepMP outperforms current approaches at read-based and position-based methylation detection across sites methylated at different frequencies in the three datasets. Availability and implementation: DeepMP is implemented and freely available under MIT license at https://github.

  • 8.
    Brown, Andrew A.
    et al.
    Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, DD1 9SY, United Kingdom.
    Hong, Mun-Gwan
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Affinity Proteomics.
    Dale, Matilda
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Affinity Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Schwenk, Jochen M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Affinity Proteomics. Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, SE-171 21, Sweden.
    Viñuela, Ana
    Biosciences Institute, Faculty of Medical Sciences, University of Newcastle, Newcastle upon Tyne, NE1 4EP, United Kingdom.
    et al.,
    Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits2023In: Nature Communications, E-ISSN 2041-1723, Vol. 14, no 1, article id 5062Article in journal (Refereed)
    Abstract [en]

    We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

  • 9.
    Brown, Brielin C.
    et al.
    New York Genome Center, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA.
    Wang, Collin
    New York Genome Center, New York, NY, USA; Department of Computer Science, Columbia University, New York, NY, USA.
    Kasela, Silva
    New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA.
    Aguet, François
    Illumina Incorporated, San Francisco, CA, USA; The Broad Institute of MIT and Harvard, Boston, MA, USA.
    Nachun, Daniel C.
    Department of Pathology, Stanford University, Stanford, CA, USA.
    Taylor, Kent D.
    Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
    Tracy, Russell P.
    Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
    Durda, Peter
    Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA.
    Liu, Yongmei
    Department of Medicine, Duke University Medical Center, Durham, NC, USA.
    Johnson, W. Craig
    Department of Biostatistics, University of Washington, Seattle, WA, USA.
    Van Den Berg, David
    Department of Clinical Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
    Gupta, Namrata
    The Broad Institute of MIT and Harvard, Boston, MA, USA.
    Gabriel, Stacy
    The Broad Institute of MIT and Harvard, Boston, MA, USA.
    Smith, Joshua D.
    Northwest Genomics Center, University of Washington, Seattle, WA, USA.
    Gerzsten, Robert
    Beth Israel Deaconess Medical Center, Division of Cardiovascular Medicine, Boston, MA, USA.
    Clish, Clary
    The Broad Institute of MIT and Harvard, Boston, MA, USA.
    Wong, Quenna
    Department of Biostatistics, University of Washington, Seattle, WA, USA.
    Papanicolau, George
    Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, Bethesda, MD, USA.
    Blackwell, Thomas W.
    Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
    Rotter, Jerome I.
    Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
    Rich, Stephen S.
    Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
    Barr, R. Graham
    Mailman School of Public Health, Columbia University, New York, NY, USA.
    Ardlie, Kristin G.
    The Broad Institute of MIT and Harvard, Boston, MA, USA.
    Knowles, David A.
    New York Genome Center, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA; Department of Computer Science, Columbia University, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA.
    Lappalainen, Tuuli
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA.
    Multiset correlation and factor analysis enables exploration of multi-omics data2023In: Cell Genomics, E-ISSN 2666-979X, Vol. 3, no 8, p. 100359-, article id 100359Article in journal (Refereed)
    Abstract [en]

    Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

  • 10.
    Cavalli, M.
    et al.
    Uppsala Univ, Uppsala, Sweden..
    Baltzer, N.
    Uppsala Univ, Uppsala, Sweden..
    Umer, H. M.
    Uppsala Univ, Uppsala, Sweden..
    Grau, J.
    Martin Luther Univ HalleWittenberg, Halle, Germany..
    Lemnian, I.
    Martin Luther Univ HalleWittenberg, Halle, Germany..
    Pan, G.
    Uppsala Univ, Uppsala, Sweden..
    Wallerman, O.
    Uppsala Univ, Uppsala, Sweden..
    Spalinskas, Rapolas
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Sahlén, Pelin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Grosse, I.
    Martin Luther Univ HalleWittenberg, Halle, Sweden..
    Komorowski, J.
    Uppsala Univ, Uppsala, Sweden..
    Wadelius, C.
    Uppsala Univ, Uppsala, Sweden..
    Allele specific chromatin signals, 3D interactions, and refined motif predictions for immune and B cell related diseases2019In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438, Vol. 27, p. 611-611Article in journal (Other academic)
  • 11. Cavalli, M.
    et al.
    Diamanti, K.
    Pan, G.
    Spalinskas, Rapolas
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Kumar, C.
    Deshmukh, A. S.
    Mann, M.
    Sahlén, Pelin
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Komorowski, J.
    Wadelius, C.
    A Multi-Omics Approach to Liver Diseases: Integration of Single Nuclei Transcriptomics with Proteomics and HiCap Bulk Data in Human Liver2020In: Omics, ISSN 1536-2310, E-ISSN 1557-8100, Vol. 24, no 4, p. 180-194Article in journal (Refereed)
    Abstract [en]

    The liver is the largest solid organ and a primary metabolic hub. In recent years, intact cell nuclei were used to perform single-nuclei RNA-seq (snRNA-seq) for tissues difficult to dissociate and for flash-frozen archived tissue samples to discover unknown and rare cell subpopulations. In this study, we performed snRNA-seq of a liver sample to identify subpopulations of cells based on nuclear transcriptomics. In 4282 single nuclei, we detected, on average, 1377 active genes and we identified seven major cell types. We integrated data from 94,286 distal interactions (p < 0.05) for 7682 promoters from a targeted chromosome conformation capture technique (HiCap) and mass spectrometry proteomics for the same liver sample. We observed a reasonable correlation between proteomics and in silico bulk snRNA-seq (r = 0.47) using tissue-independent gene-specific protein abundancy estimation factors. We specifically looked at genes of medical importance. The DPYD gene is involved in the pharmacogenetics of fluoropyrimidine toxicity and some of its variants are analyzed for clinical purposes. We identified a new putative polymorphic regulatory element, which may contribute to variation in toxicity. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and we investigated all known risk genes. We identified a complex regulatory landscape for the SLC2A2 gene with 16 candidate enhancers. Three of them harbor somatic motif breaking and other mutations in HCC in the Pan Cancer Analysis of Whole Genomes dataset and are candidates to contribute to malignancy. Our results highlight the potential of a multi-omics approach in the study of human diseases.

  • 12.
    Chen, Ao
    et al.
    BGI-Shenzhen, Shenzhen 518103, China; Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark; BGI Research-Southwest, BGI, Chongqing 401329, China; JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing 401329, China.
    Mulder, Jan
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science. Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.
    Li, Chengyu
    Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
    et al.,
    Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex2023In: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 186, no 17, p. 24-3726Article in journal (Refereed)
    Abstract [en]

    Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.

  • 13. Choi, M. J.
    et al.
    Jung, S. -B
    Lee, S. E.
    Kang, S. G.
    Lee, J. H.
    Ryu, M. J.
    Chung, H. K.
    Chang, J. Y.
    Kim, Y. K.
    Hong, H. J.
    Kim, H.
    Kim, H. J.
    Lee, C. -H
    Mardinoglu, Adil
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology. Kings Coll London, Fac Dent Oral & Craniofacial Sci, Ctr Host Microbiome Interact, London, England.
    Yi, H. -S
    Shong, M.
    An adipocyte-specific defect in oxidative phosphorylation increases systemic energy expenditure and protects against diet-induced obesity in mouse models2020In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428Article in journal (Refereed)
    Abstract [en]

    Aims/hypothesis: Mitochondrial oxidative phosphorylation (OxPhos) is essential for energy production and survival. However, the tissue-specific and systemic metabolic effects of OxPhos function in adipocytes remain incompletely understood. Methods: We used adipocyte-specific Crif1 (also known as Gadd45gip1) knockout (AdKO) mice with decreased adipocyte OxPhos function. AdKO mice fed a normal chow or high-fat diet were evaluated for glucose homeostasis, weight gain and energy expenditure (EE). RNA sequencing of adipose tissues was used to identify the key mitokines affected in AdKO mice, which included fibroblast growth factor 21 (FGF21) and growth differentiation factor 15 (GDF15). For in vitro analysis, doxycycline was used to pharmacologically decrease OxPhos in 3T3L1 adipocytes. To identify the effects of GDF15 and FGF21 on the metabolic phenotype of AdKO mice, we generated AdKO mice with global Gdf15 knockout (AdGKO) or global Fgf21 knockout (AdFKO). Results: Under high-fat diet conditions, AdKO mice were resistant to weight gain and exhibited higher EE and improved glucose tolerance. In vitro pharmacological and in vivo genetic inhibition of OxPhos in adipocytes significantly upregulated mitochondrial unfolded protein response-related genes and secretion of mitokines such as GDF15 and FGF21. We evaluated the metabolic phenotypes of AdGKO and AdFKO mice, revealing that GDF15 and FGF21 differentially regulated energy homeostasis in AdKO mice. Both mitokines had beneficial effects on obesity and insulin resistance in the context of decreased adipocyte OxPhos, but only GDF15 regulated EE in AdKO mice. Conclusions/interpretation: The present study demonstrated that the adipose tissue adaptive mitochondrial stress response affected systemic energy homeostasis via cell-autonomous and non-cell-autonomous pathways. We identified novel roles for adipose OxPhos and adipo-mitokines in the regulation of systemic glucose homeostasis and EE, which facilitated adaptation of an organism to local mitochondrial stress.

  • 14.
    Dawed, A. Y.
    et al.
    Univ Dundee, Mol & Clin Med, Dundee, Scotland..
    Mari, A.
    CNR, Inst Neurosci, Padua, Italy..
    McDonald, T. J.
    Royal Devon & Exeter Hosp, NIHR Exeter Clin Res Facil, Exeter, Devon, England..
    Hong, Mun-Gwan
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO).
    Sharma, S.
    Helmholtz Zentrum Muenchen, Inst Epidemiol 2, Res Unit Mol Epidemiol, Munich, Germany..
    Robertson, N. R.
    Univ Oxford, Wellcome Trust Ctr Human Genet, Oxford, England..
    Mahajan, A.
    Univ Oxford, Wellcome Trust Ctr Human Genet, Oxford, England..
    Walker, M.
    Newcastle Univ, Inst Cellular Med, Newcastle Upon Tyne, Tyne & Wear, England..
    Gough, S.
    NIHR Oxford Biomed Res Ctr, Oxford Ctr Diabet Endocrinol & Metab, Oxford, England..
    Zhou, K.
    Univ Dundee, Mol & Clin Med, Dundee, Scotland..
    Forgie, I
    Univ Dundee, Mol & Clin Med, Dundee, Scotland..
    Ruetten, H.
    Sanofi Aventis Deutschland GmbH, TMED, Frankfurt, Germany..
    Jones, A. G.
    Royal Devon & Exeter Hosp, NIHR Exeter Clin Res Facil, Exeter, Devon, England..
    Pearson, E. R.
    Univ Dundee, Mol & Clin Med, Dundee, Scotland..
    Glp-1 receptor variants markedly differentiate glycaemic response to glp-1 receptor agonists: A direct study2018In: Basic & Clinical Pharmacology & Toxicology, ISSN 1742-7835, E-ISSN 1742-7843, Vol. 123, p. 13-14Article in journal (Other academic)
  • 15.
    de Thonel, Aurelie
    et al.
    Univ Paris, CNRS, Epigenet & Cell Fate, F-75013 Paris, France..
    Vihervaara, Anniina
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. Abo Akad Univ, Fac Sci & Engn, Cell Biol, Turku, Finland.;Univ Turku, Turku Biosci Ctr, Turku, Finland..
    Mezger, Valerie
    Univ Paris, CNRS, Epigenet & Cell Fate, F-75013 Paris, France..
    et al.,
    CBP-HSF2 structural and functional interplay in Rubinstein-Taybi neurodevelopmental disorder2022In: Nature Communications, E-ISSN 2041-1723, Vol. 13, no 1, article id 7002Article in journal (Refereed)
    Abstract [en]

    Rubinstein-Taybi syndrome (RSTS) is a neurodevelopmental disorder with unclear underlying mechanisms. Here, the authors unravel the contribution of a stress-responsive pathway to RSTS where impaired HSF2 acetylation, due to RSTS-associated CBP/EP300 mutations, alters the expression of neurodevelopmental players, in keeping with hallmarks of cell-cell adhesion defects. Patients carrying autosomal dominant mutations in the histone/lysine acetyl transferases CBP or EP300 develop a neurodevelopmental disorder: Rubinstein-Taybi syndrome (RSTS). The biological pathways underlying these neurodevelopmental defects remain elusive. Here, we unravel the contribution of a stress-responsive pathway to RSTS. We characterize the structural and functional interaction between CBP/EP300 and heat-shock factor 2 (HSF2), a tuner of brain cortical development and major player in prenatal stress responses in the neocortex: CBP/EP300 acetylates HSF2, leading to the stabilization of the HSF2 protein. Consequently, RSTS patient-derived primary cells show decreased levels of HSF2 and HSF2-dependent alteration in their repertoire of molecular chaperones and stress response. Moreover, we unravel a CBP/EP300-HSF2-N-cadherin cascade that is also active in neurodevelopmental contexts, and show that its deregulation disturbs neuroepithelial integrity in 2D and 3D organoid models of cerebral development, generated from RSTS patient-derived iPSC cells, providing a molecular reading key for this complex pathology.

  • 16.
    Dezfouli, Mahya
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Affinity Proteomics. Karolinska Univ Hosp Huddinge, Dept Lab Med, Div Clin Immunol & Transfus Med, Stockholm, Sweden..
    Bergström, Sofia
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Affinity Proteomics.
    Skattum, Lillemor
    Lund Univ, Sect Microbiol Immunol & Glycobiol, Dept Lab Med, Lund, Sweden.;Reg Skane, Clin Immunol & Transfus Med, Lund, Sweden..
    Abolhassani, Hassan
    Karolinska Univ Hosp Huddinge, Dept Lab Med, Div Clin Immunol & Transfus Med, Stockholm, Sweden.;Univ Tehran Med Sci, Res Ctr Immunodeficiencies, Pediat Ctr Excellence, Childrens Med Ctr, Tehran, Iran..
    Neiman, Maja
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Affinity Proteomics.
    Torabi-Rahvar, Monireh
    Univ Tehran Med Sci, Sch Med, Dept Immunol, Tehran, Iran..
    Franco Jarava, Clara
    Univ Autonoma Barcelona, Hosp Univ Vall dHebron, Vall dHebron Res Inst, Immunol Dept, Barcelona, Spain..
    Martin-Nalda, Andrea
    Univ Autonoma Barcelona, Hosp Univ Vall dHebron, Vall dHebron Res Inst, Pediat Infect Dis & Immunodeficiencies Unit, Barcelona, Spain..
    Ferrer Balaguer, Juana M.
    Hosp Univ Son Espases, Immunol, Inst Invest Sanitaria Illes Balears, Palma De Mallorca, Spain..
    Slade, Charlotte A.
    Royal Melbourne Hosp, Melbourne, Vic, Australia.;Walter & Eliza Hall Inst Med Res, Melbourne, Vic, Australia..
    Roos, Anja
    St Antonius Hosp, Dept Microbiol & Immunol, Nieuwegein, Netherlands..
    Fernandez Pereira, Luis M.
    Hosp San Pedro Alcantara, Dept Immunol, Caceres, Spain..
    Lopez-Trascasa, Margarita
    Univ Autonoma Madrid, Hosp La Paz Inst Hlth Res IdiPAZ, Dept Med, Madrid, Spain.;Complement Res Grp, Madrid, Spain..
    Gonzalez-Granado, Luis, I
    Univ Hosp 12 Octubre, Res Inst Hosp 12 Octubre 1 12, Dept Pediat, Primary Immunodeficiencies Unit, Madrid, Spain..
    Allende-Martinez, Luis M.
    Univ Hosp 12 Octubre, Res Inst Hosp 12 Octubre 1 12, Immunol Dept, Madrid, Spain..
    Mizuno, Yumi
    Kyushu Univ, Fukuoka Childrens Hosp, Fukuoka, Japan..
    Yoshida, Yusuke
    Natl Def Med Coll, Dept Pediat, Saitama, Japan..
    Friman, Vanda
    Univ Gothenburg, Sahlgrenska Acad, Inst Biomed, Dept Infect Dis, Gothenburg, Sweden..
    Lundgren, Asa
    Cent Hosp Kristianstad, Dept Infect Dis, Kristianstad, Sweden..
    Aghamohammadi, Asghar
    Univ Tehran Med Sci, Res Ctr Immunodeficiencies, Pediat Ctr Excellence, Childrens Med Ctr, Tehran, Iran..
    Rezaei, Nima
    Univ Tehran Med Sci, Res Ctr Immunodeficiencies, Pediat Ctr Excellence, Childrens Med Ctr, Tehran, Iran..
    Hernandez-Gonzalez, Manuel
    Univ Autonoma Barcelona, Hosp Univ Vall dHebron, Vall dHebron Res Inst, Immunol Dept, Barcelona, Spain..
    von Dobeln, Ulrika
    Karolinska Univ Hosp Solna, Karolinska Inst, Dept Lab Med, Div Metab Dis, Stockholm, Sweden..
    Truedsson, Lennart
    Lund Univ, Sect Microbiol Immunol & Glycobiol, Dept Lab Med, Lund, Sweden..
    Hara, Toshiro
    Kyushu Univ, Fukuoka Childrens Hosp, Fukuoka, Japan..
    Nonoyama, Shigeaki
    Natl Def Med Coll, Dept Pediat, Saitama, Japan..
    Schwenk, Jochen M.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Affinity Proteomics.
    Nilsson, Peter
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Affinity Proteomics.
    Hammarstrom, Lennart
    Karolinska Univ Hosp Huddinge, Dept Lab Med, Div Clin Immunol & Transfus Med, Stockholm, Sweden..
    Newborn Screening for Presymptomatic Diagnosis of Complement and Phagocyte Deficiencies2020In: Frontiers in Immunology, E-ISSN 1664-3224, Vol. 11, article id 455Article in journal (Refereed)
    Abstract [en]

    The clinical outcomes of primary immunodeficiencies (PIDs) are greatly improved by accurate diagnosis early in life. However, it is not common to consider PIDs before the manifestation of severe clinical symptoms. Including PIDs in the nation-wide newborn screening programs will potentially improve survival and provide better disease management and preventive care in PID patients. This calls for the detection of disease biomarkers in blood and the use of dried blood spot samples, which is a part of routine newborn screening programs worldwide. Here, we developed a newborn screening method based on multiplex protein profiling for parallel diagnosis of 22 innate immunodeficiencies affecting the complement system and respiratory burst function in phagocytosis. The proposed method uses a small fraction of eluted blood from dried blood spots and is applicable for population-scale performance. The diagnosis method is validated through a retrospective screening of immunodeficient patient samples. This diagnostic approach can pave the way for an earlier, more comprehensive and accurate diagnosis of complement and phagocytic disorders, which ultimately lead to a healthy and active life for the PID patients.

  • 17.
    Dou, Diana R.
    et al.
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
    Zhao, Yanding
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
    Belk, Julia A.
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
    Zhao, Yang
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
    Casey, Kerriann M.
    Department of Comparative Medicine, Stanford University, Stanford, CA, USA.
    Chen, Derek C.
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
    Li, Rui
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
    Yu, Bingfei
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
    Srinivasan, Suhas
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
    Abe, Brian T.
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
    Kraft, Katerina
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
    Hellström, Cecilia
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Affinity Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sjöberg, Ronald
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Affinity Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Chang, Sarah
    Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA, USA.
    Feng, Allan
    Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA, USA.
    Goldman, Daniel W.
    Department of Medicine, Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
    Shah, Ami A.
    Department of Medicine, Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
    Petri, Michelle
    Department of Medicine, Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
    Chung, Lorinda S.
    Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA, USA.
    Fiorentino, David F.
    Department of Dermatology, Stanford University School of Medicine, Redwood City, CA, USA.
    Käller Lundberg, Emma
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Cellular and Clinical Proteomics. KTH, Centres, Science for Life Laboratory, SciLifeLab. Departments of Bioengineering and Pathology, Stanford University, Stanford, CA, USA.
    Wutz, Anton
    Department of Biology, Institute of Molecular Health Sciences, Swiss Federal Institute of Technology, ETH Honggerberg, Zurich, Switzerland.
    Utz, Paul J.
    Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, CA, USA; Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA.
    Chang, Howard Y.
    Center for Personal Dynamic Regulomes, Program in Epithelial Biology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
    Xist ribonucleoproteins promote female sex-biased autoimmunity2024In: Cell, ISSN 0092-8674, E-ISSN 1097-4172, Vol. 187, no 3, p. 16-733Article in journal (Refereed)
    Abstract [en]

    Autoimmune diseases disproportionately affect females more than males. The XX sex chromosome complement is strongly associated with susceptibility to autoimmunity. Xist long non-coding RNA (lncRNA) is expressed only in females to randomly inactivate one of the two X chromosomes to achieve gene dosage compensation. Here, we show that the Xist ribonucleoprotein (RNP) complex comprising numerous autoantigenic components is an important driver of sex-biased autoimmunity. Inducible transgenic expression of a non-silencing form of Xist in male mice introduced Xist RNP complexes and sufficed to produce autoantibodies. Male SJL/J mice expressing transgenic Xist developed more severe multi-organ pathology in a pristane-induced lupus model than wild-type males. Xist expression in males reprogrammed T and B cell populations and chromatin states to more resemble wild-type females. Human patients with autoimmune diseases displayed significant autoantibodies to multiple components of XIST RNP. Thus, a sex-specific lncRNA scaffolds ubiquitous RNP components to drive sex-biased immunity.

  • 18. Einson, Jonah
    et al.
    Lappalainen, Tuuli
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    et al.,
    Genetic control of mRNA splicing as a potential mechanism for incomplete penetrance of rare coding variants2023In: Genetics, ISSN 0016-6731, E-ISSN 1943-2631, Vol. 224, no 4Article in journal (Refereed)
    Abstract [en]

    Exonic variants present some of the strongest links between genotype and phenotype. However, these variants can have significant inter-individual pathogenicity differences, known as variable penetrance. In this study, we propose a model where genetically controlled mRNA splicing modulates the pathogenicity of exonic variants. By first cataloging exonic inclusion from RNA-sequencing data in GTEx V8, we find that pathogenic alleles are depleted on highly included exons. Using a large-scale phased whole genome sequencing data from the TOPMed consortium, we observe that this effect may be driven by common splice-regulatory genetic variants, and that natural selection acts on haplotype configurations that reduce the transcript inclusion of putatively pathogenic variants, especially when limiting to haploinsufficient genes. Finally, we test if this effect may be relevant for autism risk using families from the Simons Simplex Collection, but find that splicing of pathogenic alleles has a penetrance reducing effect here as well. Overall, our results indicate that common splice-regulatory variants may play a role in reducing the damaging effects of rare exonic variants.

  • 19.
    Einson, Jonah
    et al.
    Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, United States of America; New York Genome Center, New York, NY, United States of America.
    Minaeva, Mariia
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Rafi, Faiza
    New York Genome Center, New York, NY, United States of America; Department of Biotechnology, The City College of New York, New York, NY, United States of America.
    Lappalainen, Tuuli
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. New York Genome Center, New York, NY, United States of America; Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, United States of America.
    The impact of genetically controlled splicing on exon inclusion and protein structure2024In: PLOS ONE, E-ISSN 1932-6203, Vol. 19, no 3 March, article id e0291960Article in journal (Refereed)
    Abstract [en]

    Common variants affecting mRNA splicing are typically identified though splicing quantitative trait locus (sQTL) mapping and have been shown to be enriched for GWAS signals by a similar degree to eQTLs. However, the specific splicing changes induced by these variants have been difficult to characterize, making it more complicated to analyze the effect size and direction of sQTLs, and to determine downstream splicing effects on protein structure. In this study, we catalogue sQTLs using exon percent spliced in (PSI) scores as a quantitative phenotype. PSI is an interpretable metric for identifying exon skipping events and has some advantages over other methods for quantifying splicing from short read RNA sequencing. In our set of sQTL variants, we find evidence of selective effects based on splicing effect size and effect direction, as well as exon symmetry. Additionally, we utilize AlphaFold2 to predict changes in protein structure associated with sQTLs overlapping GWAS traits, highlighting a potential new use-case for this technology for interpreting genetic effects on traits and disorders.

  • 20. Eisfeldt, J.
    et al.
    Pettersson, M.
    Vezzi, F.
    Wincent, J.
    Käller, Max
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Gruselius, J.
    Nilsson, D.
    Syk Lundberg, E.
    Carvalho, C. M. B.
    Lindstrand, A.
    Comprehensive structural variation genome map of individuals carrying complex chromosomal rearrangements2019In: PLOS Genetics, ISSN 1553-7390, E-ISSN 1553-7404, Vol. 15, no 2Article in journal (Refereed)
    Abstract [en]

    Complex chromosomal rearrangements (CCRs) are rearrangements involving more than two chromosomes or more than two breakpoints. Whole genome sequencing (WGS) allows for outstanding high resolution characterization on the nucleotide level in unique sequences of such rearrangements, but problems remain for mapping breakpoints in repetitive regions of the genome, which are known to be prone to rearrangements. Hence, multiple complementary WGS experiments are sometimes needed to solve the structures of CCRs. We have studied three individuals with CCRs: Case 1 and Case 2 presented with de novo karyotypically balanced, complex interchromosomal rearrangements (46,XX,t(2;8;15)(q35;q24.1;q22) and 46,XY,t(1;10;5)(q32;p12;q31)), and Case 3 presented with a de novo, extremely complex intrachromosomal rearrangement on chromosome 1. Molecular cytogenetic investigation revealed cryptic deletions in the breakpoints of chromosome 2 and 8 in Case 1, and on chromosome 10 in Case 2, explaining their clinical symptoms. In Case 3, 26 breakpoints were identified using WGS, disrupting five known disease genes. All rearrangements were subsequently analyzed using optical maps, linked-read WGS, and short-read WGS. In conclusion, we present a case series of three unique de novo CCRs where we by combining the results from the different technologies fully solved the structure of each rearrangement. The power in combining short-read WGS with long-molecule sequencing or optical mapping in these unique de novo CCRs in a clinical setting is demonstrated.

  • 21.
    Falzarano, Maria Sofia
    et al.
    Univ Ferrara, UOL Unita Operat Logist Med Genet, Ferrara, Italy..
    Rossi, Rachele
    Univ Ferrara, UOL Unita Operat Logist Med Genet, Ferrara, Italy.;UCL Great Ormond St Inst Child Hlth, Dubowitz Neuromuscular Ctr, London, England..
    Grilli, Andrea
    Univ Modena & Reggio Emilia, Dept Life Sci, Modena, Italy..
    Fang, Mingyan
    Beijing Genom Inst BGI Shenzhen, Shenzhen, Peoples R China..
    Osman, Hana
    Univ Ferrara, UOL Unita Operat Logist Med Genet, Ferrara, Italy.;Univ Khartoum, Fac Med Lab Sci, Dept Med Microbiol, Khartoum, Sudan..
    Sabatelli, Patrizia
    CNR Inst Mol Genet Luigi Luca Cavalli Sforza, Unit Bologna, Bologna, Italy.;Ist Ortoped Rizzoli, Ist Ricovero & Cura Carattere Sci IRCCS, Bologna, Italy..
    Antoniel, Manuela
    CNR Inst Mol Genet Luigi Luca Cavalli Sforza, Unit Bologna, Bologna, Italy.;Ist Ortoped Rizzoli, Ist Ricovero & Cura Carattere Sci IRCCS, Bologna, Italy..
    Lu, Zhiyuan
    Beijing Genom Inst BGI Shenzhen, Shenzhen, Peoples R China..
    Li, Wenyan
    Beijing Genom Inst BGI Shenzhen, Shenzhen, Peoples R China..
    Selvatici, Rita
    Univ Ferrara, UOL Unita Operat Logist Med Genet, Ferrara, Italy..
    Al-Khalili Szigyarto, Cristina
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Protein Engineering. KTH Royal Inst Technol, Dept Prote, Stockholm, Sweden..
    Gualandi, Francesca
    Univ Ferrara, UOL Unita Operat Logist Med Genet, Ferrara, Italy..
    Bicciato, Silvio
    Univ Modena & Reggio Emilia, Dept Life Sci, Modena, Italy..
    Torelli, Silvia
    UCL Great Ormond St Inst Child Hlth, Dubowitz Neuromuscular Ctr, London, England.;UCL, Natl Inst Hlth Res, Great Ormond St Inst Child Hlth, Biomed Res Ctr, London, England..
    Ferlini, Alessandra
    Univ Ferrara, UOL Unita Operat Logist Med Genet, Ferrara, Italy.;UCL Great Ormond St Inst Child Hlth, Dubowitz Neuromuscular Ctr, London, England..
    Urine-Derived Stem Cells Express 571 Neuromuscular Disorders Causing Genes, Making Them a Potential in vitro Model for Rare Genetic Diseases2021In: Frontiers in Physiology, E-ISSN 1664-042X, Vol. 12, article id 716471Article in journal (Refereed)
    Abstract [en]

    Background: Neuromuscular disorders (NMDs) are a heterogeneous group of genetic diseases, caused by mutations in genes involved in spinal cord, peripheral nerve, neuromuscular junction, and muscle functions. To advance the knowledge of the pathological mechanisms underlying NMDs and to eventually identify new potential drugs paving the way for personalized medicine, limitations regarding the availability of neuromuscular disease-related biological samples, rarely accessible from patients, are a major challenge.</p> & nbsp;</p> Aim: We characterized urinary stem cells (USCs) by in-depth transcriptome and protein profiling to evaluate whether this easily accessible source of patient-derived cells is suitable to study neuromuscular genetic diseases, focusing especially on those currently involved in clinical trials.</p> & nbsp;</p> Methods: The global transcriptomics of either native or MyoD transformed USCs obtained from control individuals was performed by RNA-seq. The expression of 610 genes belonging to 16 groups of disorders () whose mutations cause neuromuscular diseases, was investigated on the RNA-seq output. In addition, protein expression of 11 genes related to NMDs including COL6A, EMD, LMNA, SMN, UBA1, DYNC1H1, SOD1, C9orf72, DYSF, DAG1, and HTT was analyzed in native USCs by immunofluorescence and/or Western blot (WB).</p> & nbsp;</p> Results: RNA-seq profile of control USCs shows that 571 out of 610 genes known to be involved in NMDs, are expressed in USCs. Interestingly, the expression levels of the majority of NMD genes remain unmodified following USCs MyoD transformation. Most genes involved in the pathogenesis of all 16 groups of NMDs are well represented except for channelopathies and malignant hyperthermia related genes. All tested proteins showed high expression values, suggesting consistency between transcription and protein representation in USCs.</p> & nbsp;</p> Conclusion: Our data suggest that USCs are human cells, obtainable by non-invasive means, which might be used as a patient-specific cell model to study neuromuscular disease-causing genes and that they can be likely adopted for a variety of in vitro functional studies such as mutation characterization, pathway identification, and drug screening.</p>

  • 22.
    Fasterius, Erik
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Al-Khalili Szigyarto, Cristina
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Analysis of public RNA-sequencing data reveals biological consequences of genetic heterogeneity in cell line populations2018In: Scientific Reports, E-ISSN 2045-2322, Vol. 8, article id 11226Article in journal (Refereed)
    Abstract [en]

    Meta-analysis of datasets available in public repositories are used to gather and summarise experiments performed across laboratories, as well as to explore consistency of scientific findings. As data quality and biological equivalency across samples may obscure such analyses and consequently their conclusions, we investigated the comparability of 85 public RNA-seq cell line datasets. Thousands of pairwise comparisons of single nucleotide variants in 139 samples revealed variable genetic heterogeneity of the eight cell line populations analysed as well as variable data quality. The H9 and HCT116 cell lines were found to be remarkably stable across laboratories (with median concordances of 99.2% and 98.5%, respectively), in contrast to the highly variable HeLa cells (89.3%). We show that the genetic heterogeneity encountered greatly affects gene expression between same-cell comparisons, highlighting the importance of interrogating the biological equivalency of samples when comparing experimental datasets. Both the number of differentially expressed genes and the expression levels negatively correlate with the genetic heterogeneity. Finally, we demonstrate how comparing genetically heterogeneous datasets affect gene expression analyses and that high dissimilarity between same-cell datasets alters the expression of more than 300 cancer-related genes, which are often the focus of studies using cell lines.

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  • 23.
    Fasterius, Erik
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Al-Khalili Szigyarto, Cristina
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    SeqCAT: A bioconductor R-package for variant analysis of high throughput sequencing data [version 1; peer review: 1 approved with reservations, 1 not approved]2018In: F1000 Research, E-ISSN 2046-1402, Vol. 7, article id 1466Article in journal (Refereed)
    Abstract [en]

    High throughput sequencing technologies are flourishing in the biological sciences, enabling unprecedented insights into e.g. genetic variation, but require extensive bioinformatic expertise for the analysis. There is thus a need for simple yet effective software that can analyse both existing and novel data, providing interpretable biological results with little bioinformatic prowess. We present seqCAT, a Bioconductor toolkit for analysing genetic variation in high throughput sequencing data. It is a highly accessible, easy-to-use and well-documented R-package that enables a wide range of researchers to analyse their own and publicly available data, providing biologically relevant conclusions and publication-ready figures. SeqCAT can provide information regarding genetic similarities between an arbitrary number of samples, validate specific variants as well as define functionally similar variant groups for further downstream analyses. Its ease of use, installation, complete data-to-conclusions functionality and the inherent flexibility of the R programming language make seqCAT a powerful tool for variant analyses compared to already existing solutions. A publicly available dataset of liver cancer-derived organoids is analysed herein using the seqCAT package, demonstrating that the organoids are genetically stable. A previously known liver cancer-related mutation is additionally shown to be present in a sample though it was not listed in the original publication. Differences between DNA- and RNA-based variant calls in this dataset are also analysed revealing a high median concordance of 97.5%. 

  • 24.
    Fasterius, Erik
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science.
    Al-Khalili Szigyarto, Cristina
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Systems Biology.
    Single-cell RNA-seq variant analysis for exploration of genetic heterogeneity in cancer2019In: Scientific Reports, E-ISSN 2045-2322, Vol. 9, article id 9524Article in journal (Refereed)
    Abstract [en]

    Inter-and intra-tumour heterogeneity is caused by genetic and non-genetic factors, leading to severe clinical implications. High-throughput sequencing technologies provide unprecedented tools to analyse DNA and RNA in single cells and explore both genetic heterogeneity and phenotypic variation between cells in tissues and tumours. Simultaneous analysis of both DNA and RNA in the same cell is, however, still in its infancy. We have thus developed a method to extract and analyse information regarding genetic heterogeneity that affects cellular biology from single-cell RNA-seq data. The method enables both comparisons and clustering of cells based on genetic variation in single nucleotide variants, revealing cellular subpopulations corroborated by gene expression-based methods. Furthermore, the results show that lymph node metastases have lower levels of genetic heterogeneity compared to their original tumours with respect to variants affecting protein function. The analysis also revealed three previously unknown variants common across cancer cells in glioblastoma patients. These results demonstrate the power and versatility of scRNA-seq variant analysis and highlight it as a useful complement to already existing methods, enabling simultaneous investigations of both gene expression and genetic variation.

  • 25.
    Figiel, S.
    et al.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Yin, W.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Doultsinos, D.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Erickson, A.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Poulose, N.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Singh, R.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Magnussen, A.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    He, Mengxiao
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundeberg, Joakim
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Verrill, C.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Colling, R.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Gill, P. S.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Bryant, R. J.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Hamdy, F. C.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Woodcock, D. J.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Mills, I. G.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Cussenot, O.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Lamb, A. D.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Spatial transcriptomic analysis of virtual prostate biopsy reveals confounding effect of heterogeneity on genomic signature scoring2023In: European Urology, ISSN 0302-2838, E-ISSN 1873-7560, Vol. 83Article in journal (Other academic)
  • 26.
    Flynn, Elise D.
    et al.
    New York Genome Ctr, New York, NY 10003 USA.;Columbia Univ, Dept Syst Biol, New York, NY 10032 USA..
    Lappalainen, Tuuli
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. New York Genome Ctr, New York, NY 10003 USA.;Columbia Univ, Dept Syst Biol, New York, NY 10032 USA.
    Functional Characterization of Genetic Variant Effects on Expression2022In: Annual Review of Biomedical Data Science, ISSN 2574-3414, Vol. 5, p. 119-139Article in journal (Refereed)
    Abstract [en]

    Thousands of common genetic variants in the human population have been associated with disease risk and phenotypic variation by genome-wide association studies (GWAS). However, the majority of GWAS variants fall into noncoding regions of the genome, complicating our understanding of their regulatory functions, and few molecular mechanisms of GWAS variant effects have been clearly elucidated. Here, we set out to review genetic variant effects, focusing on expression quantitative trait loci (eQTLs), including their utility in interpreting GWAS variant mechanisms. We discuss the interrelated challenges and opportunities for eQTL analysis, covering determining causal variants, elucidating molecular mechanisms of action, and understanding context variability. Addressing these questions can enable better functional characterization of disease-associated loci and provide insights into fundamental biological questions of the noncoding genetic regulatory code and its control of gene expression.

  • 27.
    Habibi, Mahnaz
    et al.
    Islamic Azad Univ, Dept Math, Qazvin Branch, Qazvin, Iran..
    Taheri, Golnaz
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    A new machine learning method for cancer mutation analysis2022In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 18, no 10, article id e1010332Article in journal (Refereed)
    Abstract [en]

    It is complicated to identify cancer-causing mutations. The recurrence of a mutation in patients remains one of the most reliable features of mutation driver status. However, some mutations are more likely to happen than others for various reasons. Different sequencing analysis has revealed that cancer driver genes operate across complex pathways and networks, with mutations often arising in a mutually exclusive pattern. Genes with low-frequency mutations are understudied as cancer-related genes, especially in the context of networks. Here we propose a machine learning method to study the functionality of mutually exclusive genes in the networks derived from mutation associations, gene-gene interactions, and graph clustering. These networks have indicated critical biological components in the essential pathways, especially those mutated at low frequency. Studying the network and not just the impact of a single gene significantly increases the statistical power of clinical analysis. The proposed method identified important driver genes with different frequencies. We studied the function and the associated pathways in which the candidate driver genes participate. By introducing lower-frequency genes, we recognized less studied cancer-related pathways. We also proposed a novel clustering method to specify driver modules. We evaluated each driver module with different criteria, including the terms of biological processes and the number of simultaneous mutations in each cancer. Materials and implementations are available at: https://github.com/MahnazHabibi/MutationAnalysis. Author summary It can be challenging to find mutations that cause cancer. One of the most trustworthy characteristics for identifying cancer-causing mutations is the recurrence of a mutation in patients. However, some uncommon and low-frequency mutations should also be explored as cancer-related mutations, particularly in the setting of networks. In this study, we suggested a unique approach to discover prospective driver genes and investigate the functionality of mutually exclusive genes in networks formed from mutation connections and gene-gene interactions. These networks have identified critical biological elements in the vital pathways, notably in those that experience infrequent mutations. In the first step, we established six enlightening topological features for each gene acting as a network node. For each gene, we computed the score for our predefined features. Then, we suggested the high-scoring genes with significant connections to cancer as potential targets for further research. In the second step, we constructed a network based on the relationships between the high-score genes to find the cancer-related modules. We used what we had learned in the first step about how the high-score potential driver genes interact physically, biologically, and in terms of how they work to build this network.

  • 28. Hammarsjö, A.
    et al.
    Pettersson, M.
    Chitayat, D.
    Handa, A.
    Anderlid, B. -M
    Bartocci, M.
    Basel, D.
    Batkovskyte, D.
    Beleza-Meireles, A.
    Conner, P.
    Eisfeldt, J.
    Girisha, K. M.
    Chung, B. H. -Y
    Horemuzova, E.
    Hyodo, H.
    Korņejeva, L.
    Lagerstedt-Robinson, K.
    Lin, A. E.
    Magnusson, M.
    Moosa, S.
    Nayak, S. S.
    Nilsson, D.
    Ohashi, H.
    Ohashi-Fukuda, N.
    Stranneheim, H.
    Taylan, F.
    Traberg, R.
    Voss, U.
    Wirta, Valtteri
    KTH, School of Biotechnology (BIO), Centres, KTH Genome Center. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nordgren, A.
    Nishimura, G.
    Lindstrand, A.
    Grigelioniene, G.
    High diagnostic yield in skeletal ciliopathies using massively parallel genome sequencing, structural variant screening and RNA analyses2021In: Journal of Human Genetics, ISSN 1434-5161, E-ISSN 1435-232X, Vol. 66, no 10, p. 995-1008Article in journal (Refereed)
    Abstract [en]

    Skeletal ciliopathies are a heterogenous group of disorders with overlapping clinical and radiographic features including bone dysplasia and internal abnormalities. To date, pathogenic variants in at least 30 genes, coding for different structural cilia proteins, are reported to cause skeletal ciliopathies. Here, we summarize genetic and phenotypic features of 34 affected individuals from 29 families with skeletal ciliopathies. Molecular diagnostic testing was performed using massively parallel sequencing (MPS) in combination with copy number variant (CNV) analyses and in silico filtering for variants in known skeletal ciliopathy genes. We identified biallelic disease-causing variants in seven genes: DYNC2H1, KIAA0753, WDR19, C2CD3, TTC21B, EVC, and EVC2. Four variants located in non-canonical splice sites of DYNC2H1, EVC, and KIAA0753 led to aberrant splicing that was shown by sequencing of cDNA. Furthermore, CNV analyses showed an intragenic deletion of DYNC2H1 in one individual and a 6.7 Mb de novo deletion on chromosome 1q24q25 in another. In five unsolved cases, MPS was performed in family setting. In one proband we identified a de novo variant in PRKACA and in another we found a homozygous intragenic deletion of IFT74, removing the first coding exon and leading to expression of a shorter message predicted to result in loss of 40 amino acids at the N-terminus. These findings establish IFT74 as a new skeletal ciliopathy gene. In conclusion, combined single nucleotide variant, CNV and cDNA analyses lead to a high yield of genetic diagnoses (90%) in a cohort of patients with skeletal ciliopathies.

  • 29.
    Hannula-Jouppi, Katariina
    et al.
    Univ Helsinki, ERN Skin, Skin & Allergy Hosp, Dept Dermatol, Helsinki, Finland.;Helsinki Univ Hosp, Helsinki, Finland.;Folkbalsan Res Ctr, Helsinki, Finland.;Univ Helsinki, Stem Cells & Metab Res Program, Fac Med, Helsinki, Finland.;Folkhalsan Inst Genet, Helsinki, Finland.;Univ Helsinki, Res Programs Unit, Mol Neurol, Helsinki, Finland..
    Harjama, Liisa
    Univ Helsinki, ERN Skin, Skin & Allergy Hosp, Dept Dermatol, Helsinki, Finland.;Helsinki Univ Hosp, Helsinki, Finland..
    Einarsdottir, Elisabet
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Folkbalsan Res Ctr, Helsinki, Finland.;Univ Helsinki, Stem Cells & Metab Res Program, Fac Med, Helsinki, Finland.;Folkhalsan Inst Genet, Helsinki, Finland.;Univ Helsinki, Res Programs Unit, Mol Neurol, Helsinki, Finland.;Karolinska Inst, Dept Biosci & Nutr, Huddinge, Sweden.
    Elomaa, Outi
    Folkbalsan Res Ctr, Helsinki, Finland.;Univ Helsinki, Stem Cells & Metab Res Program, Fac Med, Helsinki, Finland.;Folkhalsan Inst Genet, Helsinki, Finland.;Univ Helsinki, Res Programs Unit, Mol Neurol, Helsinki, Finland..
    Kettunen, Kaisa
    Helsinki Univ Hosp, Helsinki, Finland.;Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland.;Univ Helsinki, Lab Genet, Helsinki, Finland..
    Saarela, Janna
    Univ Helsinki, Inst Mol Med Finland FIMM, Helsinki Inst Life Sci HiLIFE, Helsinki, Finland.;Univ Oslo, Ctr Mol Med Norway NCMM, Oslo, Norway..
    Soronen, Minna
    Univ Oulu, PEDEGO Res Unit, Oulu, Finland.;Oulu Univ Hosp, Dept Dermatol, Oulu, Finland.;Oulu Univ Hosp, Med Res Ctr Oulu, Oulu, Finland..
    Bouchard, Laura
    Univ Helsinki, ERN Skin, Skin & Allergy Hosp, Dept Dermatol, Helsinki, Finland.;Helsinki Univ Hosp, Helsinki, Finland..
    Lappalainen, Katriina
    Univ Helsinki, ERN Skin, Skin & Allergy Hosp, Dept Dermatol, Helsinki, Finland.;Helsinki Univ Hosp, Helsinki, Finland..
    Heikkila, Hannele
    Univ Helsinki, ERN Skin, Skin & Allergy Hosp, Dept Dermatol, Helsinki, Finland.;Helsinki Univ Hosp, Helsinki, Finland..
    Kivirikko, Sirpa
    Helsinki Univ Hosp, Helsinki, Finland.;Univ Helsinki, Dept Clin Genet, Helsinki, Finland.;Univ Helsinki, Dept Med & Clin Genet, Helsinki, Finland..
    Seppanen, Mikko R. J.
    Univ Helsinki, Rare Dis Ctr, New Childrens Hosp, Helsinki, Finland..
    Kere, Juha
    Folkbalsan Res Ctr, Helsinki, Finland.;Univ Helsinki, Stem Cells & Metab Res Program, Fac Med, Helsinki, Finland.;Folkhalsan Inst Genet, Helsinki, Finland.;Univ Helsinki, Res Programs Unit, Mol Neurol, Helsinki, Finland.;Karolinska Inst, Dept Biosci & Nutr, Huddinge, Sweden.;Kings Coll London, Sch Basic & Med Biosci, London, England..
    Ranki, Annamari
    Univ Helsinki, ERN Skin, Skin & Allergy Hosp, Dept Dermatol, Helsinki, Finland.;Helsinki Univ Hosp, Helsinki, Finland..
    Nagashima-type palmoplantar keratosis in Finland caused by a SERPINB7 founder mutation2020In: The Journal of American Academy of Dermatology, ISSN 0190-9622, E-ISSN 1097-6787, Vol. 83, no 2, p. 643-645Article in journal (Refereed)
  • 30. Harjama, L.
    et al.
    Karvonen, V.
    Kettunen, K.
    Elomaa, O.
    Einarsdottir, Elisabet
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. Univ Helsinki, Metab Res Program, Helsinki, Finland ; Karolinska Inst, Dept Biosci & Nutr, Huddinge, Sweden.
    Heikkilä, H.
    Kivirikko, S.
    Ellonen, P.
    Saarela, J.
    Ranki, A.
    Kere, J.
    Hannula-Jouppi, K.
    Hereditary palmoplantar keratoderma – phenotypes and mutations in 64 patients2021In: Journal of the European Academy of Dermatology and Venereology, ISSN 0926-9959, E-ISSN 1468-3083, Vol. 35, no 9, p. 1874-1880Article in journal (Refereed)
    Abstract [en]

    Background: Hereditary palmoplantar keratodermas (PPK) represent a heterogeneous group of rare skin disorders with epidermal hyperkeratosis of the palms and soles, with occasional additional manifestations in other tissues. Mutations in at least 69 genes have been implicated in PPK, but further novel candidate genes and mutations are still to be found. Objectives: To identify mutations underlying PPK in a cohort of 64 patients. Methods: DNA of 48 patients was analysed on a custom-designed in-house panel for 35 PPK genes, and 16 patients were investigated by a diagnostic genetic laboratory either by whole-exome sequencing, gene panels or targeted single-gene sequencing. Results: Of the 64 PPK patients, 32 had diffuse (50%), 19 focal (30%) and 13 punctate (20%) PPK. None had striate PPK. Pathogenic mutations in altogether five genes were identified in 31 of 64 (48%) patients, the majority (22/31) with diffuse PPK. Of them, 11 had a mutation in AQP5, five in SERPINB7, four in KRT9 and two in SLURP1. AAGAB mutations were found in nine punctate PPK patients. New mutations were identified in KRT9 and AAGAB. No pathogenic mutations were detected in focal PPK. Variants of uncertain significance (VUS) in PPK-associated and other genes were observed in 21 patients that might explain their PPK. No suggestive pathogenic variants were found for 12 patients. Conclusions: Diffuse PPK was the most common (50%) and striate PPK was not observed. We identified pathogenic mutations in 48% of our PPK patients, mainly in five genes: AQP5, AAGAB, KRT9, SERPINB7 and SLURP1. 

  • 31. Hirsch, S. D.
    et al.
    Elling, C. L.
    Bootpetch, T. C.
    Scholes, M. A.
    Hafrén, L.
    Streubel, S. -O
    Pine, H. S.
    Wine, T. M.
    Szeremeta, W.
    Prager, J. D.
    Einarsdottir, Elisabet
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Folkhälsan Institute of Genetics and Molecular Neurology Research Program, University of Helsinki, PO Box 63, Biomedicum 1, 3rd floor, Haartmaninkatu 8, 00014, Helsinki, Finland; Department of Biosciences and Nutrition, Karolinska Institute, 141 86, Huddinge, Stockholm, Sweden..
    Yousaf, A.
    Baschal, E. E.
    Rehman, S.
    Bamshad, M. J.
    Nickerson, D. A.
    Riazuddin, S.
    Leal, S. M.
    Ahmed, Z. M.
    Yoon, P. J.
    Kere, J.
    Chan, K. H.
    Mattila, P. S.
    Friedman, N. R.
    Chonmaitree, T.
    Frank, D. N.
    Ryan, A. F.
    Santos-Cortez, R. L. P.
    The role of CDHR3 in susceptibility to otitis media2021In: Journal of Molecular Medicine, ISSN 0946-2716, E-ISSN 1432-1440, Vol. 99, no 11, p. 1571-1583Article in journal (Refereed)
    Abstract [en]

    Abstract: Otitis media (OM) is common in young children and can cause hearing loss and speech, language, and developmental delays. OM has high heritability; however, little is known about OM-related molecular and genetic processes. CDHR3 was previously identified as a locus for OM susceptibility, but to date, studies have focused on how the CDHR3 p.Cys529Tyr variant increases epithelial binding of rhinovirus-C and risk for lung or sinus pathology. In order to further delineate a role for CDHR3 in OM, we performed the following: exome sequencing using DNA samples from OM-affected individuals from 257 multi-ethnic families; Sanger sequencing, logistic regression and transmission disequilibrium tests for 407 US trios or probands with OM; 16S rRNA sequencing and analysis for middle ear and nasopharyngeal samples; and single-cell RNA sequencing and differential expression analyses for mouse middle ear. From exome sequence data, we identified a novel pathogenic CDHR3 splice variant that co-segregates with OM in US and Finnish families. Additionally, a frameshift and six missense rare or low-frequency variants were identified in Finnish probands. In US probands, the CDHR3 p.Cys529Tyr variant was associated with the absence of middle ear fluid at surgery and also with increased relative abundance of Lysobacter in the nasopharynx and Streptomyces in the middle ear. Consistent with published data on airway epithelial cells and our RNA-sequence data from human middle ear tissues, Cdhr3 expression is restricted to ciliated epithelial cells of the middle ear and is downregulated after acute OM. Overall, these findings suggest a critical role for CDHR3 in OM susceptibility. Key messages: • Novel rare or low-frequency CDHR3 variants putatively confer risk for otitis media. • Pathogenic variant CDHR3 c.1653 + 3G > A was found in nine families with otitis media. • CDHR3 p.Cys529Tyr was associated with lack of effusion and bacterial otopathogens. • Cdhr3 expression was limited to ciliated epithelial cells in mouse middle ear. • Cdhr3 was downregulated 3 h after infection of mouse middle ear.

  • 32. Huang, Cheng-cai
    et al.
    Yan, Shi-hai
    Chen, Dan
    Chen, Bi-cheng
    Zhao, Ning-wei
    KTH, School of Biotechnology (BIO).
    Application of On-Line nanoLC-IT-TOF in the Identification of Serum beta-Catenin Complex in Mice Scald Model2012In: PLOS ONE, E-ISSN 1932-6203, Vol. 7, no 10, p. e46530-Article in journal (Refereed)
    Abstract [en]

    Severe burn shock remains an unresolved clinical problem with an urgent need to explore novel therapeutic treatments. Intracellular beta-catenin, through interaction with other proteins, has been reported to be able to regulate the size of cutaneous wounds. Higher expression of beta-catenin is associated with larger sized wounds. However, the identification of serum beta-catenin complex is difficult and has been rarely reported. The exploitation of more binding partners can contribute to uncovering the exact mechanisms behind serum beta-catenin mediated biological effects. Here, we describe a method that consists of immunoprecipitation, SDS-PAGE, in-gel digestion, and nanoLC coupled to LCMS-IT-TOF for the investigation of serum beta-catenin complex in mice scald model. Among selected gel bands obtained from the protein gels, a total of 31 peptides were identified and sequenced with high statistical significance (p<0.01). Three proteins (alpha-2-marcoglobulin, serine protease inhibitor A3K, and serine protease inhibitor A1A) were identified and validated with high reliability and high reproducibility. It was inferred that these proteins might interact with serum beta-catenin, which could affect the wound healing resulting from burn shock. Our study demonstrated that the on-line coupling of nano-LC with a LCMS-IT-TOF mass spectrometer was capable of sensitive and automated characterization of the serum beta-catenin complex in mice scald model.

  • 33. Hudson, Thomas J.
    et al.
    Anderson, Warwick
    Aretz, Axel
    Barker, Anna D.
    Bell, Cindy
    Bernabe, Rosa R.
    Bhan, M. K.
    Calvo, Fabien
    Eerola, Iiro
    Gerhard, Daniela S.
    Guttmacher, Alan
    Guyer, Mark
    Hemsley, Fiona M.
    Jennings, Jennifer L.
    Kerr, David
    Klatt, Peter
    Kolar, Patrik
    Kusuda, Jun
    Lane, David P.
    Laplace, Frank
    Lu, Youyong
    Nettekoven, Gerd
    Ozenberger, Brad
    Peterson, Jane
    Rao, T. S.
    Remacle, Jacques
    Schafer, Alan J.
    Shibata, Tatsuhiro
    Stratton, Michael R.
    Vockley, Joseph G.
    Watanabe, Koichi
    Yang, Huanming
    Yuen, Matthew M. F.
    Knoppers, M.
    Bobrow, Martin
    Cambon-Thomsen, Anne
    Dressler, Lynn G.
    Dyke, Stephanie O. M.
    Joly, Yann
    Kato, Kazuto
    Kennedy, Karen L.
    Nicolas, Pilar
    Parker, Michael J.
    Rial-Sebbag, Emmanuelle
    Romeo-Casabona, Carlos M.
    Shaw, Kenna M.
    Wallace, Susan
    Wiesner, Georgia L.
    Zeps, Nikolajs
    Lichter, Peter
    Biankin, Andrew V.
    Chabannon, Christian
    Chin, Lynda
    Clement, Bruno
    de Alava, Enrique
    Degos, Francoise
    Ferguson, Martin L.
    Geary, Peter
    Hayes, D. Neil
    Johns, Amber L.
    Nakagawa, Hidewaki
    Penny, Robert
    Piris, Miguel A.
    Sarin, Rajiv
    Scarpa, Aldo
    van de Vijver, Marc
    Futreal, P. Andrew
    Aburatani, Hiroyuki
    Bayes, Monica
    Bowtell, David D. L.
    Campbell, Peter J.
    Estivill, Xavier
    Grimmond, Sean M.
    Gut, Ivo
    Hirst, Martin
    Lopez-Otin, Carlos
    Majumder, Partha
    Marra, Marco
    Ning, Zemin
    Puente, Xose S.
    Ruan, Yijun
    Stunnenberg, Hendrik G.
    Swerdlow, Harold
    Velculescu, Victor E.
    Wilson, Richard K.
    Xue, Hong H.
    Yang, Liu
    Spellman, Paul T.
    Bader, Gary D.
    Boutros, Paul C.
    Flicek, Paul
    Getz, Gad
    Guigo, Roderic
    Guo, Guangwu
    Haussler, David
    Heath, Simon
    Hubbard, Tim J.
    Jiang, Tao
    Jones, Steven M.
    Li, Qibin
    Lopez-Bigas, Nuria
    Luo, Ruibang
    Pearson, John V.
    Quesada, Victor
    Raphael, Benjamin J.
    Sander, Chris
    Speed, Terence P.
    Stuart, Joshua M.
    Teague, Jon W.
    Totoki, Yasushi
    Tsunoda, Tatsuhiko
    Valencia, Alfonso
    Wheeler, David A.
    Wu, Honglong
    Zhao, Shancen
    Zhou, Guangyu
    Stein, Lincoln D.
    Lathrop, Mark
    Ouellette, B. F. Francis
    Thomas, Gilles
    Yoshida, Teruhiko
    Axton, Myles
    Gunter, Chris
    McPherson, John D.
    Miller, Linda J.
    Kasprzyk, Arek
    Zhang, Junjun
    Haider, Syed A.
    Wang, Jianxin
    Yung, Christina K.
    Cros, Anthony
    Liang, Yong
    Gnaneshan, Saravanamuttu
    Guberman, Jonathan
    Hsu, Jack
    Chalmers, Don R. C.
    Hasel, Karl W.
    Kaan, Terry S. H.
    Knoppers, Bartha M.
    Lowrance, William W.
    Masui, Tohru
    Rodriguez, Laura Lyman
    Vergely, Catherine
    Cloonan, Nicole
    Defazio, Anna
    Eshleman, James R.
    Etemadmoghadam, Dariush
    Gardiner, Brooke B.
    Kench, James G.
    Sutherland, Robert L.
    Tempero, Margaret A.
    Waddell, Nicola J.
    Wilson, Peter J.
    Gallinger, Steve
    Tsao, Ming-Sound
    Shaw, Patricia A.
    Petersen, Gloria M.
    Mukhopadhyay, Debabrata
    DePinho, Ronald A.
    Thayer, Sarah
    Muthuswamy, Lakshmi
    Shazand, Kamran
    Beck, Timothy
    Sam, Michelle
    Timms, Lee
    Ballin, Vanessa
    Ji, Jiafu
    Zhang, Xiuqing
    Chen, Feng
    Hu, Xueda
    Yang, Qi
    Tian, Geng
    Zhang, Lianhai
    Xing, Xiaofang
    Li, Xianghong
    Zhu, Zhenggang
    Yu, Yingyan
    Yu, Jun
    Tost, Joerg
    Brennan, Paul
    Holcatova, Ivana
    Zaridze, David
    Brazma, Alvis
    Egevad, Lars
    Prokhortchouk, Egor
    Banks, Rosamonde Elizabeth
    Uhlén, Mathias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics.
    Viksna, Juris
    Pontén, Fredrik
    Skryabin, Konstantin
    Birney, Ewan
    Borg, Ake
    Borresen-Dale, Anne-Lise
    Caldas, Carlos
    Foekens, John A.
    Martin, Sancha
    Reis-Filho, Jorge S.
    Richardson, Andrea L.
    Sotiriou, Christos
    van't Veer, Laura
    Birnbaum, Daniel
    Blanche, Helene
    Boucher, Pascal
    Boyault, Sandrine
    Masson-Jacquemier, Jocelyne D.
    Pauporte, Iris
    Pivot, Xavier
    Vincent-Salomon, Anne
    Tabone, Eric
    Theillet, Charles
    Treilleux, Isabelle
    Bioulac-Sage, Paulette
    Decaens, Thomas
    Franco, Dominique
    Gut, Marta
    Samuel, Didier
    Zucman-Rossi, Jessica
    Eils, Roland
    Brors, Benedikt
    Korbel, Jan O.
    Korshunov, Andrey
    Landgraf, Pablo
    Lehrach, Hans
    Pfister, Stefan
    Radlwimmer, Bernhard
    Reifenberger, Guido
    Taylor, Michael D.
    von Kalle, Christof
    Majumder, Partha P.
    Pederzoli, Paolo
    Lawlor, Rita T.
    Delledonne, Massimo
    Bardelli, Alberto
    Gress, Thomas
    Klimstra, David
    Zamboni, Giuseppe
    Nakamura, Yusuke
    Miyano, Satoru
    Fujimoto, Akihiro
    Campo, Elias
    de Sanjose, Silvia
    Montserrat, Emili
    Gonzalez-Diaz, Marcos
    Jares, Pedro
    Himmelbauer, Heinz
    Bea, Silvia
    Aparicio, Samuel
    Easton, Douglas F.
    Collins, Francis S.
    Compton, Carolyn C.
    Lander, Eric S.
    Burke, Wylie
    Green, Anthony R.
    Hamilton, Stanley R.
    Kallioniemi, Olli P.
    Ley, Timothy J.
    Liu, Edison T.
    Wainwright, Brandon J.
    International network of cancer genome projects2010In: Nature, ISSN 0028-0836, E-ISSN 1476-4687, Vol. 464, no 7291, p. 993-998Article in journal (Refereed)
    Abstract [en]

    The International Cancer Genome Consortium (ICGC) was launched to coordinate large-scale cancer genome studies in tumours from 50 different cancer types and/or subtypes that are of clinical and societal importance across the globe. Systematic studies of more than 25,000 cancer genomes at the genomic, epigenomic and transcriptomic levels will reveal the repertoire of oncogenic mutations, uncover traces of the mutagenic influences, define clinically relevant subtypes for prognosis and therapeutic management, and enable the development of new cancer therapies.

  • 34.
    Hård, Joanna
    et al.
    Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden; Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland; ETH AI Center, ETH Zurich, Zurich, Switzerland.
    Mold, Jeff E.
    Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
    Eisfeldt, Jesper
    Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden.
    Tellgren-Roth, Christian
    Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
    Häggqvist, Susana
    Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
    Bunikis, Ignas
    Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
    Contreras-Lopez, Orlando
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Chin, Chen Shan
    GeneDX LLC, Stamford, CT, 06902, USA.
    Nordlund, Jessica
    Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
    Rubin, Carl Johan
    Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
    Feuk, Lars
    Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
    Michaëlsson, Jakob
    Center for Infectious Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
    Ameur, Adam
    Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
    Long-read whole-genome analysis of human single cells2023In: Nature Communications, E-ISSN 2041-1723, Vol. 14, no 1, article id 5164Article in journal (Refereed)
    Abstract [en]

    Long-read sequencing has dramatically increased our understanding of human genome variation. Here, we demonstrate that long-read technology can give new insights into the genomic architecture of individual cells. Clonally expanded CD8+ T-cells from a human donor were subjected to droplet-based multiple displacement amplification (dMDA) to generate long molecules with reduced bias. PacBio sequencing generated up to 40% genome coverage per single-cell, enabling detection of single nucleotide variants (SNVs), structural variants (SVs), and tandem repeats, also in regions inaccessible by short reads. 28 somatic SNVs were detected, including one case of mitochondrial heteroplasmy. 5473 high-confidence SVs/cell were discovered, a sixteen-fold increase compared to Illumina-based results from clonally related cells. Single-cell de novo assembly generated a genome size of up to 598 Mb and 1762 (12.8%) complete gene models. In summary, our work shows the promise of long-read sequencing toward characterization of the full spectrum of genetic variation in single cells.

  • 35.
    Kasela, Silva
    et al.
    New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA.
    Aguet, François
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
    Kim-Hellmuth, Sarah
    New York Genome Center, New York, NY, USA; Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital LMU Munich, Munich, Germany; Computational Health Center, Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany.
    Brown, Brielin C.
    New York Genome Center, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA.
    Nachun, Daniel C.
    Department of Pathology, Stanford University, Stanford, CA, USA.
    Tracy, Russell P.
    Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA.
    Durda, Peter
    Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA.
    Liu, Yongmei
    Department of Medicine, Duke University, Durham, NC, USA.
    Taylor, Kent D.
    The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
    Johnson, W. Craig
    Department of Biostatistics, University of Washington, Seattle, WA, USA.
    Van Den Berg, David
    Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
    Gabriel, Stacey
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
    Gupta, Namrata
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
    Smith, Joshua D.
    Northwest Genomics Center, University of Washington, Seattle, WA, USA.
    Blackwell, Thomas W.
    Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
    Rotter, Jerome I.
    The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA.
    Ardlie, Kristin G.
    Broad Institute of MIT and Harvard, Cambridge, MA, USA.
    Manichaikul, Ani
    Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
    Rich, Stephen S.
    Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
    Barr, R. Graham
    Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY, USA.
    Lappalainen, Tuuli
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA.
    Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects2024In: American Journal of Human Genetics, ISSN 0002-9297, E-ISSN 1537-6605, Vol. 111, no 1, p. 133-149Article in journal (Refereed)
    Abstract [en]

    Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.

  • 36.
    Kliuchnikov, Evgenii
    et al.
    Univ Massachusetts, Dept Chem, Lowell, MA 01854 USA..
    Zhmurov, Artem
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Marx, Kenneth A.
    Univ Massachusetts, Dept Chem, Lowell, MA 01854 USA..
    Mogilner, Alex
    NYU, Courant Inst Math Sci, 251 Mercer St, New York, NY 10003 USA.;NYU, Dept Biol, 251 Mercer St, New York, NY 10003 USA..
    Barsegov, Valeri
    Univ Massachusetts, Dept Chem, Lowell, MA 01854 USA..
    CellDynaMo-stochastic reaction-diffusion-dynamics model: Application to search-and-capture process of mitotic spindle assembly2022In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 18, no 6, article id e1010165Article in journal (Refereed)
    Abstract [en]

    We introduce a Stochastic Reaction-Diffusion-Dynamics Model (SRDDM) for simulations of cellular mechanochemical processes with high spatial and temporal resolution. The SRDDM is mapped into the CellDynaMo package, which couples the spatially inhomogeneous reaction-diffusion master equation to account for biochemical reactions and molecular transport within the Langevin Dynamics (LD) framework to describe dynamic mechanical processes. This computational infrastructure allows the simulation of hours of molecular machine dynamics in reasonable wall-clock time. We apply SRDDM to test performance of the Search-and-Capture of mitotic spindle assembly by simulating, in three spatial dimensions, dynamic instability of elastic microtubules anchored in two centrosomes, movement and deformations of geometrically realistic centromeres with flexible kinetochores and chromosome arms. Furthermore, the SRDDM describes the mechanics and kinetics of Ndc80 linkers mediating transient attachments of microtubules to the chromosomal kinetochores. The rates of these attachments and detachments depend upon phosphorylation states of the Ndc80 linkers, which are regulated in the model by explicitly accounting for the reactions of Aurora A and B kinase enzymes undergoing restricted diffusion. We find that there is an optimal rate of microtubule-kinetochore detachments which maximizes the accuracy of the chromosome connections, that adding chromosome arms to kinetochores improve the accuracy by slowing down chromosome movements, that Aurora A and kinetochore deformations have a small positive effect on the attachment accuracy, and that thermal fluctuations of the microtubules increase the rates of kinetochore capture and also improve the accuracy of spindle assembly. Author summary The CellDynaMo package models, in 3D, any cellular subsystem where sufficient detail of the macromolecular players and the kinetics of relevant reactions are available. The package is based on the Stochastic Reaction-Diffusion-Dynamics model that combines the stochastic description of chemical kinetics, Brownian diffusion-based description of molecular transport, and Langevin dynamics-based representation of mechanical processes most pertinent to the system. We apply the model to test the Search-and-Capture mechanism of mitotic spindle assembly. We find that there is an optimal rate of microtubule-kinetochore detachments which maximizes the accuracy of chromosome connections, that chromosome arms improve the attachment accuracy by slowing down chromosome movements, that Aurora A kinase and kinetochore deformations have small positive effects on the accuracy, and that thermal fluctuations of the microtubules increase the rates of kinetochore capture and also improve the accuracy.

  • 37.
    Lappalainen, Tuuli
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. New York Genome Center, New York, NY, United States.
    MacArthur, D. G.
    From variant to function in human disease genetics2021In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 373, no 6562, p. 1464-1468Article, review/survey (Refereed)
    Abstract [en]

    Over the next decade, the primary challenge in human genetics will be to understand the biological mechanisms by which genetic variants influence phenotypes, including disease risk. Although the scale of this challenge is daunting, better methods for functional variant interpretation will have transformative consequences for disease diagnosis, risk prediction, and the development of new therapies. An array of new methods for characterizing variant impact at scale, using patient tissue samples as well as in vitro models, are already being applied to dissect variant mechanisms across a range of human cell types and environments. These approaches are also increasingly being deployed in clinical settings. We discuss the rationale, approaches, applications, and future outlook for characterizing the molecular and cellular effects of genetic variants.

  • 38. Larsson, Anna H.
    et al.
    Lehn, Sophie
    Wangefjord, Sakarias
    Karnevi, Emelie
    Kuteeva, Eugenia
    Sundström, Magnus
    Nodin, Björn
    Uhlén, Mathias
    KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Eberhard, Jakob
    Birgisson, Helgi
    Jirström, Karin
    Significant association and synergistic adverse prognostic effect of podocalyxin-like protein and epidermal growth factor receptor expression in colorectal cancer2016In: Journal of Translational Medicine, ISSN 1479-5876, E-ISSN 1479-5876, Vol. 14, article id 128Article in journal (Refereed)
    Abstract [en]

    Background: Podocalyxin-like 1 (PODXL) is an anti-adhesive transmembrane protein that has been demonstrated to be an independent factor of poor prognosis in colorectal cancer (CRC). The gene encoding PODXL is located to chromosome 7, which also harbours the gene for the epidermal growth factor receptor (EGFR). The aim of this study was to examine the associations between PODXL and EGFR expression in CRC in vitro and in vivo. Methods: EGFR expression was analysed in tumours from three independent patient cohorts; cohort 1 (n = 533), cohort 2 (n = 259) and cohort 3 (n = 310), previously analysed for immunohistochemical PODXL expression and KRAS and BRAF mutations (cohort 1 and 3). Levels of EGFR and PODXL were determined by western blot in six different CRC cell lines. Results: High expression of PODXL was significantly associated with high EGFR expression (p < 0.001) in all three cohorts, and with BRAF mutation (p < 0.001) in cohort 1 and 3. High EGFR expression correlated with BRAF mutation (p < 0.001) in cohort 1. High EGFR expression was associated with adverse clinicopathological factors and independently predicted a reduced 5-year overall survival (OS) in cohort 1 (HR 1.77; 95 % CI 1.27-2.46), cohort 2 (HR 1.58; 95 % CI 1.05-2.38) and cohort 3 (HR 1.83; 95 % CI 1.19-2.81). The highest risk of death within 5 years was observed in patients with tumours displaying high expression of both EGFR and PODXL in cohort 1 and 3 (HR 1.97; 95 % CI 1.18-3.28 and HR 3.56; 95 % CI 1.75-7.22, respectively). Western blot analysis showed a uniform expression of PODXL and EGFR in all six examined CRC cell lines. Conclusions: The results from this study demonstrate that high expression of EGFR is an independent factor of poor prognosis in CRC. Moreover, strong links have been uncovered between expression of the recently proposed biomarker candidate PODXL with EGFR expression in CRC in vivo and in vitro, and with BRAF mutation in vivo. High expression of both PODXL and EGFR may also have a synergistic adverse effect on survival. These findings suggest a potential functional link in CRC between PODXL, EGFR and BRAF, all originating from chromosome 7, which may be highly relevant in the clinical setting and therefore merit future in-depth study.

  • 39. Leandro-Garcia, Luis J.
    et al.
    Inglada-Perez, Lucia
    Pita, Guillermo
    Hjerpe, Elisabet
    Leskelae, Susanna
    Jara, Carlos
    Mielgo, Xabier
    Gonzalez-Neira, Anna
    Robledo, Mercedes
    Avall-Lundqvist, Elisabeth
    Gréen, Henrik
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Rodriguez-Antona, Cristina
    Genome-wide association study identifies ephrin type A receptors implicated in paclitaxel induced peripheral sensory neuropathy2013In: Journal of Medical Genetics, ISSN 0022-2593, E-ISSN 1468-6244, Vol. 50, no 9, p. 599-605Article in journal (Refereed)
    Abstract [en]

    Background Peripheral neuropathy is the dose limiting toxicity of paclitaxel, a chemotherapeutic drug widely used to treat solid tumours. This toxicity exhibits great inter-individual variability of unknown origin. The present study aimed to identify genetic variants associated with paclitaxel induced neuropathy via a whole genome approach. Methods A genome-wide association study (GWAS) was performed in 144 white European patients uniformly treated with paclitaxel/carboplatin and for whom detailed data on neuropathy was available. Per allele single nucleotide polymorphism (SNP) associations were assessed by Cox regression, modelling the cumulative dose of paclitaxel up to the development of grade 2 sensory neuropathy. Results The strongest evidence of association was observed for the ephrin type A receptor 4 (EPHA4) locus (rs17348202, p=1.0x10(-6)), and EPHA6 and EPHA5 were among the top 25 and 50 hits (rs301927, p=3.4x10(-5) and rs1159057, p=6.8x10(-5)), respectively. A meta-analysis of EPHA5-rs7349683, the top marker for paclitaxel induced neuropathy in a previous GWAS (r(2)=0.79 with rs1159057), gave a hazard ratio (HR) estimate of 1.68 (p=1.4x10(-9)). Meta-analysis of the second hit of this GWAS, XKR4-rs4737264, gave a HR of 1.71 (p=3.1x10(-8)). Imputed SNPs at LIMK2 locus were also strongly associated with this toxicity (HR=2.78, p=2.0x10(-7)). Conclusions This study provides independent support of EPHA5-rs7349683 and XKR4-rs4737264 as the first markers of risk of paclitaxel induced neuropathy. In addition, it suggests that other EPHA genes also involved in axonal guidance and repair following neural injury, as well as LIMK2 locus, may play an important role in the development of this toxicity. The identified SNPs could form the basis for individualised paclitaxel chemotherapy.

  • 40.
    Li, Xiaofei
    et al.
    Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Neurogeriatr, Stockholm, Sweden..
    Andrusivova, Zaneta
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Czarnewski, Paulo
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Natl Bioinformat Infrastruct Sweden, Stockholm, Sweden..
    Langseth, Christoffer Mattsson
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Andersson, Alma
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Genentech Inc, Dept Artificial Intelligence & Machine Learning, Res & Early Dev, South San Francisco, CA USA..
    Liu, Yang
    Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Neurogeriatr, Stockholm, Sweden.;Capital Med Univ, Beijing Tiantan Hosp, China Natl Clin Res Ctr Neurol Dis, Beijing, Peoples R China..
    Gyllborg, Daniel
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Braun, Emelie
    Karolinska Inst, Dept Med Biochem & Biophys, Div Mol Neurobiol, Stockholm, Sweden..
    Larsson, Ludvig
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Hu, Lijuan
    Karolinska Inst, Dept Med Biochem & Biophys, Div Mol Neurobiol, Stockholm, Sweden..
    Alekseenko, Zhanna
    Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Neurogeriatr, Stockholm, Sweden.;Karolinska Inst, Dept Cell & Mol Biol, Stockholm, Sweden..
    Lee, Hower
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Avenel, Christophe
    Uppsala Univ, Dept Informat Technol, Uppsala, Sweden.;SciLifeLab, BioImage Informat Facil, Sci Life Lab, Stockholm, Sweden..
    Kallner, Helena Kopp
    Karolinska Inst, Danderyd Hosp, Dept Clin Sci, Stockholm, Sweden.;Danderyd Hosp, Dept Obstet & Gynecol, Danderyd, Sweden..
    Akesson, Elisabet
    Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Neurogeriatr, Stockholm, Sweden.;Stockholms Sjukhem, R&D Unit, Stockholm, Sweden..
    Adameyko, Igor
    Karolinska Inst, Dept Physiol & Pharmacol, Stockholm, Sweden.;Med Univ Vienna, Ctr Brain Res, Dept Neuroimmunol, Vienna, Austria..
    Nilsson, Mats
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Stockholm, Sweden..
    Linnarsson, Sten
    Karolinska Inst, Dept Med Biochem & Biophys, Div Mol Neurobiol, Stockholm, Sweden..
    Lundeberg, Joakim
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Sundstrom, Erik
    Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Neurogeriatr, Stockholm, Sweden..
    Profiling spatiotemporal gene expression of the developing human spinal cord and implications for ependymoma origin2023In: Nature Neuroscience, ISSN 1097-6256, E-ISSN 1546-1726, Vol. 26, no 5, p. 891-901Article in journal (Refereed)
    Abstract [en]

    The authors created a comprehensive developmental cell atlas for spatiotemporal gene expression of the human spinal cord, revealed species-specific regulation during development and used the atlas to infer novel markers for pediatric ependymomas. The spatiotemporal regulation of cell fate specification in the human developing spinal cord remains largely unknown. In this study, by performing integrated analysis of single-cell and spatial multi-omics data, we used 16 prenatal human samples to create a comprehensive developmental cell atlas of the spinal cord during post-conceptional weeks 5-12. This revealed how the cell fate commitment of neural progenitor cells and their spatial positioning are spatiotemporally regulated by specific gene sets. We identified unique events in human spinal cord development relative to rodents, including earlier quiescence of active neural stem cells, differential regulation of cell differentiation and distinct spatiotemporal genetic regulation of cell fate choices. In addition, by integrating our atlas with pediatric ependymomas data, we identified specific molecular signatures and lineage-specific genes of cancer stem cells during progression. Thus, we delineate spatiotemporal genetic regulation of human spinal cord development and leverage these data to gain disease insight.

  • 41.
    Li, Xiaoting
    et al.
    Department of Biological Sciences, Columbia University, New York, NY 10027, USA.
    Lappalainen, Tuuli
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. New York Genome Center, New York, NY 10013, USA; Department of Systems Biology, Columbia University, New York, NY 10032, USA.
    Bussemaker, Harmen J.
    Department of Biological Sciences, Columbia University, New York, NY 10027, USA; Department of Systems Biology, Columbia University, New York, NY 10032, USA.
    Identifying genetic regulatory variants that affect transcription factor activity2023In: Cell Genomics, E-ISSN 2666-979X, Vol. 3, no 9, article id 100382Article in journal (Refereed)
    Abstract [en]

    Genetic variants affecting gene expression levels in humans have been mapped in the Genotype-Tissue Expression (GTEx) project. Trans-acting variants impacting many genes simultaneously through a shared transcription factor (TF) are of particular interest. Here, we developed a generalized linear model (GLM) to estimate protein-level TF activity levels in an individual-specific manner from GTEx RNA sequencing (RNA-seq) profiles. It uses observed differential gene expression after TF perturbation as a predictor and, by analyzing differential expression within pairs of neighboring genes, controls for the confounding effect of variation in chromatin state along the genome. We inferred genotype-specific activities for 55 TFs across 49 tissues. Subsequently performing genome-wide association analysis on this virtual trait revealed TF activity quantitative trait loci (aQTLs) that, as a set, are enriched for functional features. Altogether, the set of tools we introduce here highlights the potential of genetic association studies for cellular endophenotypes based on a network-based multi-omics approach. The transparent peer review record is available.

  • 42.
    Lindstrand, Anna
    et al.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Eisfeldt, Jesper
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, Stockholm, Sweden..
    Pettersson, Maria
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Carvalho, Claudia M. B.
    Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA..
    Kvarnung, Malin
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Grigelioniene, Giedre
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Anderlid, Britt-Marie
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Bjerin, Olof
    Karolinska Inst, Dept Womens & Childrens Hlth, Stockholm, Sweden..
    Gustavsson, Peter
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Hammarsjö, Anna
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Georgii-Hemming, Patrik
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden..
    Iwarsson, Erik
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Johansson-Soller, Maria
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Lagerstedt-Robinson, Kristina
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Lieden, Agne
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Magnusson, Mans
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Martin, Marcel
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Natl Bioinformat Infrastruct Sweden, Stockholm, Sweden..
    Malmgren, Helena
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Nordenskjöld, Magnus
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Norling, Ameli
    Karolinska Inst, Dept Womens & Childrens Hlth, Stockholm, Sweden..
    Sahlin, Ellika
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Stranneheim, Henrik
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Tham, Emma
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Wincent, Josephine
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Ygberg, Sofia
    Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Wedell, Anna
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Wirta, Valtteri
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Nordgren, Ann
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Lundin, Johanna
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Baylor Coll Med, Dept Mol & Human Genet, Houston, TX 77030 USA..
    Nilsson, Daniel
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Sci Life Lab, Stockholm, Sweden..
    From cytogenetics to cytogenomics: whole-genome sequencing as a first-line test comprehensively captures the diverse spectrum of disease-causing genetic variation underlying intellectual disability2019In: Genome Medicine, ISSN 1756-994X, E-ISSN 1756-994X, Vol. 11, no 1, article id 68Article in journal (Refereed)
    Abstract [en]

    BackgroundSince different types of genetic variants, from single nucleotide variants (SNVs) to large chromosomal rearrangements, underlie intellectual disability, we evaluated the use of whole-genome sequencing (WGS) rather than chromosomal microarray analysis (CMA) as a first-line genetic diagnostic test.MethodsWe analyzed three cohorts with short-read WGS: (i) a retrospective cohort with validated copy number variants (CNVs) (cohort 1, n=68), (ii) individuals referred for monogenic multi-gene panels (cohort 2, n=156), and (iii) 100 prospective, consecutive cases referred to our center for CMA (cohort 3). Bioinformatic tools developed include FindSV, SVDB, Rhocall, Rhoviz, and vcf2cytosure.ResultsFirst, we validated our structural variant (SV)-calling pipeline on cohort 1, consisting of three trisomies and 79 deletions and duplications with a median size of 850kb (min 500bp, max 155Mb). All variants were detected. Second, we utilized the same pipeline in cohort 2 and analyzed with monogenic WGS panels, increasing the diagnostic yield to 8%. Next, cohort 3 was analyzed by both CMA and WGS. The WGS data was processed for large (>10kb) SVs genome-wide and for exonic SVs and SNVs in a panel of 887 genes linked to intellectual disability as well as genes matched to patient-specific Human Phenotype Ontology (HPO) phenotypes. This yielded a total of 25 pathogenic variants (SNVs or SVs), of which 12 were detected by CMA as well. We also applied short tandem repeat (STR) expansion detection and discovered one pathologic expansion in ATXN7. Finally, a case of Prader-Willi syndrome with uniparental disomy (UPD) was validated in the WGS data.Important positional information was obtained in all cohorts. Remarkably, 7% of the analyzed cases harbored complex structural variants, as exemplified by a ring chromosome and two duplications found to be an insertional translocation and part of a cryptic unbalanced translocation, respectively.ConclusionThe overall diagnostic rate of 27% was more than doubled compared to clinical microarray (12%). Using WGS, we detected a wide range of SVs with high accuracy. Since the WGS data also allowed for analysis of SNVs, UPD, and STRs, it represents a powerful comprehensive genetic test in a clinical diagnostic laboratory setting.

  • 43.
    Llinàs-Arias, Pere
    et al.
    Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Health Research Institute of the Balearic Islands (IdISBa), 07120, Palma, Spain.
    Ensenyat-Méndez, Miquel
    Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Health Research Institute of the Balearic Islands (IdISBa), 07120, Palma, Spain.
    Orozco, Javier I.J.
    Saint John’s Cancer Institute, Providence Saint John’s Health Center, Santa Monica, CA, USA.
    Íñiguez-Muñoz, Sandra
    Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Health Research Institute of the Balearic Islands (IdISBa), 07120, Palma, Spain.
    Valdez, Betsy
    Saint John’s Cancer Institute, Providence Saint John’s Health Center, Santa Monica, CA, USA.
    Wang, Chuan
    Department of Biosciences and Nutrition, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden.
    Mezger, Anja
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Choi, Eun Kyoung
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Tran, Yan Zhou
    Department of Biosciences and Nutrition, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden.
    Yao, Liqun
    Department of Biosciences and Nutrition, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden.
    Bonath, Franziska
    Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.
    Olsen, Remi André
    Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden.
    Ormestad, Mattias
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Esteller, Manel
    Josep Carreras Leukaemia Research Institute, Badalona, Barcelona, Catalonia, Spain, Catalonia; Centro de Investigación Biomédica en Red Cancer (CIBERONC), 28029, Madrid, Spain; Institució Catalana de Recerca I Estudis Avançats (ICREA), Barcelona, Catalonia, Spain, Catalonia; Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), Barcelona, Catalonia, Spain, Catalonia.
    Lupien, Mathieu
    Princess Margaret Cancer Centre, Toronto, Toronto, ON, M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, M5G 1L7, Canada; Ontario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada.
    Marzese, Diego M.
    Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Health Research Institute of the Balearic Islands (IdISBa), 07120, Palma, Spain.
    3-D chromatin conformation, accessibility, and gene expression profiling of triple-negative breast cancer2023In: BMC Genomic Data, ISSN 2730-6844, Vol. 24, no 1, article id 61Article in journal (Refereed)
    Abstract [en]

    Objectives: Triple-negative breast cancer (TNBC) is a highly aggressive breast cancer subtype with limited treatment options. Unlike other breast cancer subtypes, the scarcity of specific therapies and greater frequencies of distant metastases contribute to its aggressiveness. We aimed to find epigenetic changes that aid in the understanding of the dissemination process of these cancers. Data description: Using CRISPR/Cas9, our experimental approach led us to identify and disrupt an insulator element, IE8, whose activity seemed relevant for cell invasion. The experiments were performed in two well-established TNBC cellular models, the MDA-MB-231 and the MDA-MB-436. To gain insights into the underlying molecular mechanisms of TNBC invasion ability, we generated and characterized high-resolution chromatin interaction (Hi-C) and chromatin accessibility (ATAC-seq) maps in both cell models and complemented these datasets with gene expression profiling (RNA-seq) in MDA-MB-231, the cell line that showed more significant changes in chromatin accessibility. Altogether, our data provide a comprehensive resource for understanding the spatial organization of the genome in TNBC cells, which may contribute to accelerating the discovery of TNBC-specific alterations triggering advances for this devastating disease.

  • 44.
    Magnusson, Måns
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Eisfeldt, Jesper
    Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden.;Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.;Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden..
    Nilsson, Daniel
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Rosenbaum, Adam
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Wirta, Valtteri
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. Karolinska Inst, Dept Microbiol Tumor & Cell Biol, Sci Life Lab, Stockholm, Sweden..
    Lindstrand, Anna
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Wedell, Anna
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Stranneheim, Henrik
    Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Loqusdb: added value of an observations database of local genomic variation2020In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 21, no 1, article id 273Article in journal (Refereed)
    Abstract [en]

    Background Exome and genome sequencing is becoming the method of choice for rare disease diagnostics. One of the key challenges remaining is distinguishing the disease causing variants from the benign background variation. After analysis and annotation of the sequencing data there are typically thousands of candidate variants requiring further investigation. One of the most effective and least biased ways to reduce this number is to assess the rarity of a variant in any population. Currently, there are a number of reliable sources of information for major population frequencies when considering single nucleotide variants (SNVs) and small insertion and deletions (INDELs), with gnomAD as the most prominent public resource available. However, local variation or frequencies in sub-populations may be underrepresented in these public resources. In contrast, for structural variation (SV), the background frequency in the general population is more or less unknown mostly due to challenges in calling SVs in a consistent way. Keeping track of local variation is one way to overcome these problems and significantly reduce the number of potential disease causing variants retained for manual inspection, both for SNVs and SVs. Results Here, we present loqusdb, a tool to solve the challenge of keeping track of any type of variant observations from genome sequencing data. Loqusdb was designed to handle a large flow of samples and unlike other solutions, samples can be added continuously to the database without rebuilding it, facilitating improvements and additions. We assessed the added value of a local observations database using 98 samples annotated with information from a background of 888 unrelated individuals. Conclusions We show both how powerful SV analysis can be when filtering for population frequencies and how the number of apparently rare SNVs/INDELs can be reduced by adding local population information even after annotating the data with other large frequency databases, such as gnomAD. In conclusion, we show that a local frequency database is an attractive, and a necessary addition to the publicly available databases that facilitate the analysis of exome and genome data in a clinical setting.

  • 45.
    Marklund, Maja
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Schultz, Niklas
    Karolinska Inst, Div Translat Med & Chem Biol, Sci Life Lab, Solna, Sweden..
    Friedrich, Stefanie
    Stockholm Univ, Dept Biochem & Biophys, Sci Lab, Solna, Sweden..
    Berglund, Emelie
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Tarish, Firas
    Karolinska Inst, Div Translat Med & Chem Biol, Sci Life Lab, Solna, Sweden..
    Tanoglidi, Anna
    Evangelismos Gen Hosp, Dept Pathol, 45-47 Ipsilantou Str, Athens, Greece..
    Liu, Yao
    Karolinska Inst, Div Translat Med & Chem Biol, Sci Life Lab, Solna, Sweden..
    Bergenstråhle, Ludvig
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Erickson, Andrew
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Helleday, Thomas
    Karolinska Inst, Div Translat Med & Chem Biol, Sci Life Lab, Solna, Sweden..
    Lamb, Alastair D.
    Univ Oxford, Nuffield Dept Surg Sci, Oxford, England..
    Sonnhammer, Erik
    Stockholm Univ, Dept Biochem & Biophys, Sci Lab, Solna, Sweden..
    Lundeberg, Joakim
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones2022In: Nature Communications, E-ISSN 2041-1723, Vol. 13, no 1, article id 5475Article in journal (Refereed)
    Abstract [en]

    The molecular mechanisms underlying lethal castration-resistant prostate cancer remain poorly understood, with intratumoral heterogeneity a likely contributing factor. To examine the temporal aspects of resistance, we analyze tumor heterogeneity in needle biopsies collected before and after treatment with androgen deprivation therapy. By doing so, we are able to couple clinical responsiveness and morphological information such as Gleason score to transcriptome-wide data. Our data-driven analysis of transcriptomes identifies several distinct intratumoral cell populations, characterized by their unique gene expression profiles. Certain cell populations present before treatment exhibit gene expression profiles that match those of resistant tumor cell clusters, present after treatment. We confirm that these clusters are resistant by the localization of active androgen receptors to the nuclei in cancer cells post-treatment. Our data also demonstrates that most stromal cells adjacent to resistant clusters do not express the androgen receptor, and we identify differentially expressed genes for these cells. Altogether, this study shows the potential to increase the power in predicting resistant tumors. Spatial heterogeneity in prostate cancer can contribute to its resistance to androgen deprivation therapy (ADT). Here, the authors analyse prostate cancer samples before and after ADT using Spatial Transcriptomics, and find heterogeneous pre-treatment tumour cell populations and stromal cells that are associated with resistance.

  • 46.
    Mold, Jeff E.
    et al.
    Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
    Weissman, Martin H.
    Mathematics Department, University of California, Santa Cruz, CA, USA.
    Ratz, Michael
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
    Hagemann-Jensen, Michael
    Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
    Hård, Joanna
    Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
    Eriksson, Carl Johan
    Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
    Toosi, Hosein
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bergenstråhle, Joseph
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Ziegenhain, Christoph
    Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
    von Berlin, Leonie
    Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
    Martin, Marcel
    Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, SciLifeLab, Stockholm University, Stockholm, Sweden.
    Blom, Kim
    Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institute, Stockholm, Sweden.
    Lagergren, Jens
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lundeberg, Joakim
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology.
    Sandberg, Rickard
    Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
    Michaëlsson, Jakob
    Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institute, Stockholm, Sweden.
    Frisén, Jonas
    Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden.
    Clonally heritable gene expression imparts a layer of diversity within cell types2024In: Cell systems, E-ISSN 2405-4720, Vol. 15, no 2, p. 149-Article in journal (Refereed)
    Abstract [en]

    Cell types can be classified according to shared patterns of transcription. Non-genetic variability among individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. Using high-coverage single-cell RNA profiling, we asked whether long-term, heritable differences in gene expression can impart diversity within cells of the same type. Studying clonal human lymphocytes and mouse brain cells, we uncovered a vast diversity of heritable gene expression patterns among different clones of cells of the same type in vivo. We combined chromatin accessibility and RNA profiling on different lymphocyte clones to reveal thousands of regulatory regions exhibiting interclonal variation, which could be directly linked to interclonal variation in gene expression. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging, and disease. A record of this paper's transparent peer review process is included in the supplemental information.

  • 47.
    Morris, John A.
    et al.
    New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA.
    Caragine, Christina
    New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA.
    Daniloski, Zharko
    New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA.
    Domingo, Júlia
    New York Genome Center, New York, NY 10013, USA.
    Barry, Timothy
    Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
    Lu, Lu
    New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA.
    Davis, Kyrie
    New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA.
    Ziosi, Marcello
    New York Genome Center, New York, NY 10013, USA.
    Glinos, Dafni A.
    New York Genome Center, New York, NY 10013, USA.
    Hao, Stephanie
    Technology Innovation Lab, New York Genome Center, New York, NY 10013, USA.
    Mimitou, Eleni P.
    Technology Innovation Lab, New York Genome Center, New York, NY 10013, USA.
    Smibert, Peter
    Technology Innovation Lab, New York Genome Center, New York, NY 10013, USA.
    Roeder, Kathryn
    Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
    Katsevich, Eugene
    Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA.
    Lappalainen, Tuuli
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. New York Genome Center, New York, NY 10013, USA.
    Sanjana, Neville E.
    New York Genome Center, New York, NY 10013, USA; Department of Biology, New York University, New York, NY 10003, USA.
    Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens2023In: Science, ISSN 0036-8075, E-ISSN 1095-9203, Vol. 380, no 6646, article id eadh7699Article in journal (Refereed)
    Abstract [en]

    Most variants associated with complex traits and diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown effects. Using ancestrally diverse, biobank-scale GWAS data, massively parallel CRISPR screens, and single-cell transcriptomic and proteomic sequencing, we discovered 124 cis-target genes of 91 noncoding blood trait GWAS loci. Using precise variant insertion through base editing, we connected specific variants with gene expression changes. We also identified trans-effect networks of noncoding loci when cis target genes encoded transcription factors or microRNAs. Networks were themselves enriched for GWAS variants and demonstrated polygenic contributions to complex traits. This platform enables massively parallel characterization of the target genes and mechanisms of human noncoding variants in both cis and trans.

  • 48.
    Nilsson, D.
    et al.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, SciLifeLab, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Eisfeldt, J.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, SciLifeLab, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden..
    Lundin, J.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Womens & Childrens Hlth, Stockholm, Sweden..
    Pettersson, M.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Kvarnung, M.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Lieden, A.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Sahlin, E.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Lagerstedt, K.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Martin, M.
    Stockholm Univ, Natl Bioinformat Infrastruct Sweden, Sci Life Lab, Dept Biochem & Biophys, Solna, Sweden..
    Ygberg, S.
    Karolinska Inst, Inst Womens & Childrens Hlth, Neuropediat Unit, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Bjerin, O.
    Karolinska Inst, Inst Womens & Childrens Hlth, Neuropediat Unit, Stockholm, Sweden..
    Stranneheim, H.
    Karolinska Inst, Dept Mol Med & Surg, SciLifeLab, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Wedell, A.
    Karolinska Inst, Dept Mol Med & Surg, SciLifeLab, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Univ Hosp, Ctr Inherited Metab Dis, Stockholm, Sweden..
    Nordenskjold, M.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Soller, M. Johansson
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Nordgren, A.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    Wirta, Valtteri
    KTH, School of Biotechnology (BIO), Centres, KTH Genome Center. KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. Karolinska Inst, Dept Microbiol Tumor & Cell Biol, SciLifeLab, Stockholm, Sweden..
    Lindstrand, A.
    Karolinska Univ Hosp, Dept Clin Genet, Stockholm, Sweden.;Karolinska Inst, Ctr Mol Med, Stockholm, Sweden.;Karolinska Inst, Dept Mol Med & Surg, Stockholm, Sweden..
    From cytogenetics to cytogenomics: whole genome sequencing as a comprehensive genetic test in rare disease diagnostics2019In: European Journal of Human Genetics, ISSN 1018-4813, E-ISSN 1476-5438, Vol. 27, p. 1666-1667Article in journal (Other academic)
    Abstract [en]

    Rare genetic diseases are caused by different types of genetic variants, from single nucleotide variants (SNVs) to large chromosomal rearrangements. Recent data indicates that whole genome sequencing (WGS) may be used as a comprehensive test to identify multiple types of pathologic genetic aberrations in a single analysis.

    We present FindSV, a bioinformatic pipeline for detection of balanced (inversions and translocations) and unbalanced (deletions and duplications) structural variants (SVs). First, FindSV was tested on 106 validated deletions and duplications with a median size of 850 kb (min: 511 bp, max: 155 Mb). All variants were detected. Second, we demonstrated the clinical utility in 138 monogenic WGS panels. SV analysis yielded 11 diagnostic findings (8%). Remarkably, a complex structural rearrangement involving two clustered deletions disrupting SCN1A, SCN2A, and SCN3A was identified in a three months old girl with epileptic encephalopathy. Finally, 100 consecutive samples referred for clinical microarray were also analyzed by WGS. The WGS data was screened for large (>2 kbp) SVs genome wide, processed for visualization in our clinical routine arrayCGH workflow with the newly developed tool vcf2cytosure, and for exonic SVs and SNVs in a panel of 700 genes linked to intellectual disability. We also applied short tandem repeat (STR) expansion detection and discovered one pathologic expansion in ATXN7. The diagnostic rate (29%) was doubled compared to clinical microarray (12%).

    In conclusion, using WGS we have detected a wide range of structural variation with high accuracy, confirming it a powerful comprehensive genetic test in a clinical diagnostic laboratory setting.

  • 49.
    Nousiainen, Ruth
    et al.
    Univ Helsinki, Helsinki Univ Hosp, Childrens Hosp, Pediat Res Ctr, Helsinki, Finland..
    Eloranta, Katja
    Univ Helsinki, Helsinki Univ Hosp, Childrens Hosp, Pediat Res Ctr, Helsinki, Finland..
    Isoaho, Noora
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Cairo, Stefano
    Champ Oncol, Hackensack, NJ USA.;Ist Ric Pediat, Padua, Italy.;XenTech, Evry, France..
    Wilson, David B.
    Washington Univ, Sch Med, Dept Dev Biol, St. Louis, MO USA.;Washington Univ, St Louis Childrens Hosp, Sch Med, Dept Pediat, St Louis, MO USA..
    Heikinheimo, Markku
    Univ Helsinki, Helsinki Univ Hosp, Childrens Hosp, Pediat Res Ctr, Helsinki, Finland.;Washington Univ, St Louis Childrens Hosp, Sch Med, Dept Pediat, St Louis, MO USA.;Tampere Univ, Fac Med & Hlth Technol, Ctr Child Adolescent & Maternal Hlth Res, Tampere, Finland..
    Pihlajoki, Marjut
    Univ Helsinki, Helsinki Univ Hosp, Childrens Hosp, Pediat Res Ctr, Helsinki, Finland..
    UBE2C expression is elevated in hepatoblastoma and correlates with inferior patient survival2023In: Frontiers in Genetics, E-ISSN 1664-8021, Vol. 14, article id 1170940Article in journal (Refereed)
    Abstract [en]

    Hepatoblastoma (HB) is the most common malignant liver tumor among children. To gain insight into the pathobiology of HB, we performed RNA sequence analysis on 5 patient-derived xenograft lines (HB-243, HB-279, HB-282, HB-284, HB-295) and 1 immortalized cell line (HUH6). Using cultured hepatocytes as a control, we found 2,868 genes that were differentially expressed in all of the HB lines on mRNA level. The most upregulated genes were ODAM, TRIM71, and IGDCC3, and the most downregulated were SAA1, SAA2, and NNMT. Protein-protein interaction analysis identified ubiquitination as a key pathway dysregulated in HB. UBE2C, encoding an E2 ubiquitin ligase often overexpressed in cancer cells, was markedly upregulated in 5 of the 6 HB cell lines. Validation studies confirmed UBE2C immunostaining in 20 of 25 HB tumor specimens versus 1 of 6 normal liver samples. The silencing of UBE2C in two HB cell models resulted in decreased cell viability. RNA sequencing analysis showed alterations in cell cycle regulation after UBE2C knockdown. UBE2C expression in HB correlated with inferior patient survival. We conclude that UBE2C may hold prognostic utility in HB and that the ubiquitin pathway is a potential therapeutic target in this tumor.

  • 50.
    Orlova, A.
    et al.
    Uppsala Univ, Uppsala, Sweden..
    Rosestedt, M.
    Uppsala Univ, Uppsala, Sweden..
    Rinne, S. S.
    Uppsala Univ, Uppsala, Sweden..
    Mitran, B.
    Uppsala Univ, Uppsala, Sweden..
    Vorobyeva, A.
    Uppsala Univ, Uppsala, Sweden..
    Andersson, Ken G.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Protein Engineering.
    Löfblom, John
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Protein Science, Protein Engineering.
    Tolmachev, V.
    Uppsala Univ, Uppsala, Sweden..
    Imaging contrast of HER3 expression using monomeric affibody-based imaging probe can be improved by co-injection of affibody trimer2018In: European Journal of Nuclear Medicine and Molecular Imaging, ISSN 1619-7070, E-ISSN 1619-7089, Vol. 45, p. S673-S673Article in journal (Other academic)
12 1 - 50 of 82
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