Biology subjects

Dietrich, S.

Publications and source records attributed to Dietrich, S..

28 records · Page 2Linked to original sources

bioRxiv2021

Mapping drug-microenvironment-genetic interplay in CLL reveals trisomy 12 as a modulator of microenvironmental signals

The tumour microenvironment and genetic alterations collectively influence drug efficacy in cancer, but current evidence is limited to small scale studies and systematic analyses are lacking. We chose Chronic Lymphocytic Leukaemia (CLL), the most common leukaemia in adults, as a model disease to study this complex interplay systematically. We performed a combinatorial assay using 12 drugs individually co-applied with each of 17 microenvironmental stimuli in 192 primary CLL samples, generating a comprehensive map of drug-microenvironment interactions in CLL. This data was combined with whole-exome sequencing, DNA-methylation, RNA-sequencing and copy number variant annotation. Our assay identified four distinct CLL subgroups that differed in their responses to the panel of microenvironmental stimuli. These subgroups were characterized by distinct clinical outcomes independently of known prognostic markers. We investigated the effect of CLL- specific recurrent genetic alterations on microenvironmental responses and identified trisomy 12 as an amplifier of multiple microenvironmental stimuli. We further quantified the impact of microenvironmental stimuli on drug response, confirmed known interactions such as Interleukin (IL) 4 mediated resistance to B cell receptor (BCR) inhibitors, and identified new interactions such as Interferon-{gamma} induced resistance to BCR inhibitors. Finally, we identified interactions which were limited to genetic subgroups. Resistance to chemotherapeutics, such as Fludarabine, induced by Toll-Like Receptor (TLR) agonists could be observed in IGHV unmutated patient samples and IGHV mutated samples with trisomy 12. In-vivo relevance was investigated in CLL-infiltrated lymph nodes, which showed increased IL4 and TLR signalling activity compared to healthy samples (p<0.001). High IL4 activity in lymph nodes correlated with faster disease progression (p=0.038). We provide a publicly available resource (www.dietrichlab.de/CLL_Microenvironment/) which uncovers tumour cell extrinsic influences on drug response and disease progression in CLL, and how these interactions are modulated by cell intrinsic molecular features.

cancer biology↗

bioRxiv2021

A synergistic interaction between HDAC- and PARP inhibitors in childhood tumors with chromothripsis

Chromothripsis is a form of genomic instability characterized by the occurrence of tens to hundreds of clustered DNA double-strand breaks in a one-off catastrophic event. Rearrangements associated with chromothripsis are detectable in numerous tumor entities and linked with poor prognosis in some of these, such as Sonic Hedgehog medulloblastoma, neuroblastoma and osteosarcoma. Hence, there is a need for therapeutic strategies eliminating tumor cells with chromothripsis. Defects in DNA double-strand break repair, and in particular homologous recombination repair, have been linked with chromothripsis. Targeting DNA repair deficiencies by synthetic lethality approaches, we performed a synergy screen using drug libraries (n = 375 compounds, 15 models) combined with either a PARP inhibitor or cisplatin. This revealed a synergistic interaction between the HDAC inhibitor romidepsin and PARP inhibition. Functional assays, transcriptome analyses, and in vivo validation in patient-derived xenograft mouse models confirmed the efficacy of the combinatorial treatment.

cancer biology↗

bioRxiv2021

Subgroup-specific gene expression profiles and mixed epistasis in chronic lymphocytic leukemia

Despite the extensive catalogue of recurrent mutations in chronic lymphocytic leukaemia (CLL), the diverse molecular driving events and the resulting range of disease phenotypes remain incompletely understood. To study the molecular heterogeneity of CLL, we performed RNA-sequencing on 184 CLL patient samples. Unsupervised analysis revealed two major independent axes of gene expression variation: the first one aligned with the mutational status of the immunoglobulin heavy variable (IGHV) genes, and concomitantly, with the three-group stratification of CLL by global DNA methylation pattern, and affected biological functions including B- and T-cell receptor signaling. The second one aligned with trisomy 12 status and affected chemokine signaling. Furthermore, we searched for differentially expressed genes associated with gene mutations and copy-number aberrations and detected strong signatures for TP53, BRAF and SF3B1, as well as for del(11)(q22.3), del(17)(p13) and del(13)(q14) beyond the dosage effect. We discovered strong non-additive effects (i.e., genetic interactions, or epistasis) of IGHV mutation status and trisomy 12 on multiple phenotypes, including the expression of 893 genes. Multiple types of epistasis were observed, including synergy, buffering, suppression and inversion. Our study reveals previously underappreciated gene expression signatures for (epi)genomic variants in CLL and the presence of epistasis between them. The findings will serve as a reference for a functional resolution of CLL molecular heterogeneity.

genomics↗

bioRxiv2020

Phagocytosis by stroma confounds co-culture studies

Signals provided by the microenvironment can modify and circumvent pathway activities that are therapeutically targeted by drugs. Bone marrow stromal cell co-culture models are frequently used to study the influence of the bone marrow niche on ex-vivo drug response. Here we show that mesenchymal stromal cells from selected donors and NKTert, a stromal cell line which is commonly used for co-culture studies with primary leukemia cells, extensively phagocytose apoptotic cells. This could lead to misinterpretation of the results, especially if the viability readout of the target cells in such co-culture models is based on the relative proportions of dead and alive cells. Future co-culture studies which aim to investigate the impact of bone marrow stromal cells on drug response should take into account that stromal cells have the capacity to phagocytose apoptotic cells.

cancer biology↗

bioRxiv2020

EOMES and IL-10 regulate anti-tumor activity of PD-1+ CD4+ T-cells in B-cell Non-Hodgkin lymphoma

The transcription factor Eomesodermin (EOMES) promotes IL-10 production of CD4+ T-cells, which has been linked to immunosuppressive and cytotoxic activities. We detected EOMES-expressing CD4+ T-cells in lymph node samples of patients with chronic lymphocytic leukemia (CLL) or diffuse large B-cell lymphoma. This was in line with an observed expansion of EOMES-positive CD4+ T-cells in leukemic E{micro}-TCL1 mice, a well-established model of CLL, and upon adoptive transfer of TCL1 leukemia in mice. Transcriptome and flow cytometry analyses revealed that EOMES does not only drive the transcription of IL-10, but rather controls a unique differentiation program in CD4+ T-cells. Moreover, EOMES was necessary for the accumulation of a specific CD4+ T-cell subset that expresses IFN{gamma} and IL-10, as well as inhibitory receptors, like PD-1 and LAG3. T-cell transfer studies in leukopenic Rag2-/- mice showed that EOMES-deficient CD4+ T-cells were inferior in controlling TCL1 leukemia development compared to wildtype T-cells, even though expansion of Eomes-/- CD4+ T-cells was observed. We further showed that control of TCL1 leukemia was driven by IL-10 receptor-mediated signals, as Il10rb-deficient CD4+ T-cells showed impaired anti-leukemia activity. Altogether, our data suggest that IL-10 producing PD-1+ CD4+ T-cells contribute to CLL control in an EOMES- and IL-10R-dependent manner.

cancer biology↗

bioRxiv2019

Dissecting intratumor heterogeneity of nodal B cell lymphomas on the transcriptional, genetic, and drug response level

Tumor heterogeneity encompasses both the malignant cells and their microenvironment. While heterogeneity between individual patients is well-known to affect the efficacy of anti-cancer drugs, most personalized treatment approaches do not account for intratumor heterogeneity. We addressed this issue by studying the heterogeneity of lymph node-derived B cell non-Hodgkin lymphoma (B-NHL) by single cell RNA-sequencing (scRNA-seq) and transcriptome-informed flow cytometry. We identified transcriptionally distinct malignant subclones and compared their drug response and genomic profiles. Malignant subclones of the same patient responded strikingly different to anti-cancer drugs ex vivo, which recapitulated subclone-specific drug sensitivity during in vivo treatment. Tumor infiltrating T cells represented the majority of non-malignant cells, whose gene expression signatures were similar across all donors, whereas the frequencies of T cell subsets varied significantly between the donors. Our data provide new insights into the heterogeneity of B-NHL and highlight the relevance of intratumor heterogeneity for personalized cancer therapies.

cancer biology↗

bioRxiv2019

Cardiac competence of the head mesoderm fades concomitant with a shift towards the head skeletal muscle programme

The vertebrate head mesoderm provides the heart, the great vessels, some smooth and most head skeletal muscle, in addition to parts of the skull. It has been speculated that the ability to generate cardiac and smooth muscle is the evolutionary ground-state of the tissue. However, whether indeed the entire head mesoderm has generic cardiac competence, how long this may last, and what happens as cardiac competence fades, is not clear. Bone morphogenetic proteins (Bmps) are known to promote cardiogenesis. Using 41 different marker genes in the chicken embryo, we show that the paraxial head mesoderm that normally does not engage in cardiogenesis has the ability to respond to Bmp for a long time. However, Bmp signals are interpreted differently at different time points. Up to early head fold stages, the paraxial head mesoderm is able to read Bmps as signal to engage in the cardiac programme; the ability to upregulate smooth muscle markers is retained slightly longer. Notably, as cardiac competence fades, Bmp promotes the head skeletal muscle programme instead. The switch from cardiac to skeletal muscle competence is Wnt-independent as Wnt caudalises the head mesoderm and also suppresses Msc-inducing Bmp provided by the prechordal plate, thus suppressing both the cardiac and the head skeletal muscle programmes. Our study for the first time suggests a specific transition state in the embryo when cardiac competence is replaced by skeletal muscle competence. It sets the stage to unravel the cardiac-skeletal muscle antagonism that is known to partially collapse in heart failure. Summary statementThe head mesoderm has generic cardiac competence until early head fold stages. Thereafter, cardiac competence fades in the paraxial region, and Bmp promotes head skeletal muscle programmes instead of cardiac programmes.

developmental biology↗

bioRxiv2019

Efficient depletion of ribosomal RNA for RNA sequencing in planarians

BackgroundThe astounding regenerative abilities of planarian flatworms prompt a steadily growing interest in examining their molecular foundation. Planarian regeneration was found to require hundreds of genes and is hence a complex process. Thus, RNA interference followed by transcriptome-wide gene expression analysis by RNA-seq is a popular technique to study the impact of any particular planarian gene on regeneration. Typically, the removal of ribosomal RNA (rRNA) is the first step of all RNA-Seq library preparation protocols. To date, rRNA removal in planarians was primarily achieved by the enrichment of polyadenylated (poly(A)) transcripts. However, to better reflect transcriptome dynamics and to cover also non-poly(A) transcripts, a procedure for the targeted removal of rRNA in planarians is needed.\n\nResultsIn this study, we describe a workflow for the efficient depletion of rRNA in the planarian model species S. mediterranea. Our protocol is based on subtractive hybridization using organism-specific probes. Importantly, the designed probes also deplete rRNA of other freshwater triclad families, a fact that considerably broadens the applicability of our protocol. We tested our approach on total RNA isolated stem cells (termed neoblasts) of S. mediterranea and compared ribodepleted libraries with publicly available poly(A)-enriched ones. Overall, mRNA levels after ribodepletion were consisted with poly(A) libraries. However, ribodepleted libraries revealed higher transcript levels for transposable elements and histone mRNAs that remained underrepresented in poly(A) libraries. As neoblasts experience high transposon activity this suggests that ribodepleted libraries better reflect the transcriptional dynamics of planarian stem cells. Furthermore, the presented ribodepletion procedure was successfully expanded to the removal of ribosomal RNA from the gram-negative bacterium Salmonella typhimurium.\n\nConclusionsThe ribodepletion protocol presented here ensures the efficient rRNA removal from low input total planarian RNA, which can be further processed for RNA-Seq applications. Resulting libraries contain less than 2% rRNA. Moreover, for a cost-effective and efficient removal of rRNA prior to sequencing applications our procedure might be adapted to any prokaryotic or eukaryotic species of choice.

molecular biology↗

bioRxiv2019

ClassiPhages 2.0: Sequence-based classification of phages using Artificial Neural Networks

Background/ MotivationIn the era of affordable next generation sequencing technologies we are facing an exploding amount of new phage genome sequences. This requests high throughput phage classification tools that meet the standards of the International Committee on Taxonomy of Viruses (ICTV). However, an accurate prediction of phage taxonomic classification derived from phage sequences still poses a challenge due to the lack of performant taxonomic markers. Since machine learning methods have proved to be efficient for the classification of biological data we investigated how artificial neural networks perform on the task of phage taxonomy. ResultsIn this work, 5,920 constructed and refined profile Hidden Markov Models (HMMs), derived from 8,721 phage sequences classified into 12 well known phage families, were used to scan phage proteome datasets. The resulting Phage Family-proteome to Phage-derived-HMMs scoring matrix was used to develop and train an Artificial Neural Network (ANN) to find patterns for phage classification into one of the phage families. Results show that using the 100 fold cross-validation test, the proposed method achieved an overall accuracy of 84.18 %. The ANN was tested on a set of unclassified phages and resulted in a taxonomic prediction. The ANN prediction was benchmarked against the prediction resulting of multi-HMM hits, and showed that the ANN performance is dependent on the quality of the input matrix. ConclusionsWe believe that, as long as some phage families on public databases are underrepresented, multi-HMM hits can be used as a classification method to populate those phage families, which in turn will improve the performance and accuracy of the ANN. We believe that the proposed method is an effective and promising method for phage classification. The good performance of the ANN and HMM based predictor indicates the efficiency of the method for phage classification, where we foresee its improvement with an increasing number of sequenced viral genomes.

bioinformatics↗

bioRxiv2017

Multi-Omics factor analysis disentangles heterogeneity in blood cancer

Multi-omic studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous datasets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omic datasets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation, and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex-vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multiomics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.

bioinformatics↗