Acute myeloid leukemia (AML) encompasses a cytogenetically heterogeneous group of myeloid malignancies characterized by clonal expansion of abnormally or poorly differentiated myeloid cells in the bone marrow, blood and other tissues. Treatment failure, relapse and mortality of AML is unacceptably high and it is reasonable to predict that cure rates will not improve unless treatment modalities alternative to conventional chemotherapy and bone marrow transplantation are developed. A hallmark of AML is a failure to properly differentiate and the retinoic acid receptor (RAR) ligand all-trans-retinoic acid (ATRA) has demonstrated remarkable efficacy in inducing differentiation in a sub-type of AML, acute promyelocytic leukemia (APL). However, ATRA based treatment has failed to replicate this success in non-APL AML.

Our research is aimed towards the identification and characterization of mechanisms underlying the maturation arrest and ATRA-resistance of leukemic cells eventually enabling efficient therapy by induction of differentiation in all types of AML. We especially focus on epigenetic factors preventing the induction of myeloid differentiation by ATRA. Currently we investigate the roles of the lysine demethylase LSD1 and the lysine acetyltransferase GCN5.

Within this course we will test the effects of compounds inhibiting epigenetic modifiers as LSD1 and GCN5 on AML cells. We will assess cell growth using cell viability assays and perform FACS analyses measuring changes in myeloid differentiation and apoptosis markers following treatments. Further, we will perform basic analyses of RNA-seq data previously obtained.

Most often, the disease or cellular condition under study has been investigated in some related
context by others having performed transcritional profiling or other sequencing based investigations
(genomics, ChIP-seq, epigenomics), Crispr/Cas9 or RNAI knockout/down screens or protein mass
spectrometry. According to the FAIR principles (Findable, Accessible, Interoperable, Reusable), such
data should be accessible, and, indeed this is often the case. Interrogating such data may be a useful
shortcut to bring up or confirm own hypotheses. However, having a bioinformatician at hand may
turn out to be the bottleneck. Hence, learning the basics for analysing such omics data can be useful
also for experimentally working biomedical scientists!
We will introduce you into the R programming language. You will learn how to write small computer
programs enabling to download such omics data and apply basic analysis and statistical methods.
Exemplarily, we will download transcriptomics data, e.g. from an infectious disease, such as from
COVID19 patients or a disease caused by another pathogen. The data will be normalized,
differentially expressed genes identified, genes set enrichment tests performed to elucidate
pathways being affected by the disease, clustering to find similarly regulated genes or samples (from
e.g. patients) and classification performed to construct a diagnosis tool.
You do not need any prior knowledge in programming and will learn how to use such an analysis
pipeline from scratch.