Molecular basis of gene regulation of diseases - Carsten Daub
We focus on understanding the molecular basis of gene regulation of diseases, specifically related to inflammation. Our work includes genome-wide gene expression analysis from human patient samples employing various RNA-Sequencing. Bioinformatics analysis identifies elements responsible for the observed expression differences in the diseased patients and the associated clinical phenotypes.
Examples include transcription factors (TFs), closely related but distinct alternative promoters resulting in the same protein but employing different sets of regulatory TFs, expression of anti-sense RNA to modulate the sense-RNA and the regulatory role of enhancers, expressed repeat elements and miRNAs. We are also involved in several genome annotation consortia.
Development of sequencing technologies and sequencing library methods for genome, metagenome, transcriptome and epigenome data is moving at a breathtaking pace. We are working with the development of corresponding bioinformatics data analysis technologies for these genomics data. For example, our group identified gene enhancers in transcriptome data and assigned gene regulatory roles to these enhancers in diseases and development.
Understanding the molecular basis of perturbed gene regulation in diseases is one aspect of our research. We are using genome-wide analysis technologies based on high-throughput sequencing extensively. Developing the necessary bioinformatics tools together with best-practice analysis methods constitutes an important aspect.
For the last ten years, we have been working on obesity-related type 2 diabetes and on asthma. Close collaboration with clinical research groups has been of key importance. We usually join the projects during the design phase where we contribute to the experimental design in terms of cohort stratification, statistical power, selection of tissue types, annotation of samples and data as well as selecting the high-throughput analysis technologies used. Addressing the specific biomedical questions of the projects by analysing the sequencing data together with the clinical data constitutes one of the main aspects of our work. Very close interaction with the clinicians is of outmost importance in connecting the findings of the data analysis to the biology underlying the disease.
We contributed to genome annotation projects for the zebrafish as part of the DANIO-CODE consortium and for the dog as part of the DoGA consortium. We first developed a sample and data annotation framework since both consortia are consolidating data from various sources. Main goals include improved genome annotation and adding gene enhancers. We work with Spatial Transcriptomics (ST) data, where we used the ST data in a gene independent way and employed machine learning methods to identify breast cancer signatures.
Previous group members
Johanna Labate, Erasmus summer student, 2015
Olga Hrydziuszko, Postdoc (2012-2014)
Nancy Yu, Postdoc (2012-2014)
Amitha Raman, Laboratory technician (2012-2014)
Wenjing Kang, Masters student (Feb-June 2014)
Jacqueline Nowak, Masters student (April-August 2014)
Kubra Altinel, Erasmus summer student, 2014
Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.
Yoosuf N, Navarro JF, Salmén F, Ståhl PL, Daub CO
Breast Cancer Res. 2020 01;22(1):6
Characterization of the human RFX transcription factor family by regulatory and target gene analysis.
Sugiaman-Trapman D, Vitezic M, Jouhilahti EM, Mathelier A, Lauter G, Misra S, et al
BMC Genomics 2018 03;19(1):181
Analysis of the human monocyte-derived macrophage transcriptome and response to lipopolysaccharide provides new insights into genetic aetiology of inflammatory bowel disease.
Baillie JK, Arner E, Daub C, De Hoon M, Itoh M, Kawaji H, et al
PLoS Genet. 2017 03;13(3):e1006641
The Adipose Transcriptional Response to Insulin Is Determined by Obesity, Not Insulin Sensitivity.
Rydén M, Hrydziuszko O, Mileti E, Raman A, Bornholdt J, Boyd M, et al
Cell Rep 2016 08;16(9):2317-26
Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells.
Arner E, Daub CO, Vitting-Seerup K, Andersson R, Lilje B, Drabløs F, et al
Science 2015 Feb;347(6225):1010-4
Complementing tissue characterization by integrating transcriptome profiling from the Human Protein Atlas and from the FANTOM5 consortium.
Yu NY, Hallström BM, Fagerberg L, Ponten F, Kawaji H, Carninci P, et al
Nucleic Acids Res. 2015 Aug;43(14):6787-98
Transcriptome analysis of controlled and therapy-resistant childhood asthma reveals distinct gene expression profiles.
Persson H, Kwon AT, Ramilowski JA, Silberberg G, Söderhäll C, Orsmark-Pietras C, et al
J. Allergy Clin. Immunol. 2015 Sep;136(3):638-48
Adipose tissue microRNAs as regulators of CCL2 production in human obesity.
Arner E, Mejhert N, Kulyté A, Balwierz PJ, Pachkov M, Cormont M, et al
Diabetes 2012 Aug;61(8):1986-93
The platform is used by the @daniocode (screenshots take from there) and the @DogGenome consortia. For more details see our newly published @GigaScience paper https://t.co/8Xd5muI4EZ— Daub Lab (@daublab) March 17, 2020
Thanks to everybody involved, especially @ZFINmod, for their input and work to accomplish this!