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Transcription describes the process of converting the information contained in DNA into RNA. Although, tremendous progress has been made in recent decades to uncover this complex mechanism, it is still not fully understood. Given the advances and reduction in cost of high-throughput sequencing experiments, more and more data have been generated to help elucidating this complex process. Importantly, these sequencing experiments produce massive amounts of data that are incomprehensible in their raw form for humans. Further, sequencing techniques are not always 100% accurate and are subject to a certain degree of variability and, in special cases, they might introduce technical artifacts. Thus, computational and statistical methods are indispensable to uncover the information buried in these datasets.
In this thesis, I worked with multiple high throughput datasets from herpes simplex virus 1 (HSV-1) and human cytomegalovirus (HCMV) infections. During the last decade, it has became clear that a gene might not have a single, but multiple sites at which transcription initiates. These multiple transcription start sites (TiSS) demonstrated to have regulatory effects on the gene itself depending on which TiSS is used. Specialized experimental approaches were developed to help identify TiSS (TiSS-profiling). In order to facilitate the identification of all potential TiSS that are used for cell type- and condition-specific transcription, I developed the tool iTiSS. By using a new general enrichment-based approach to predict TiSS, iTiSS proved to be applicable in integrated studies and made it less prone to false positives compared to other TiSS-calling tools. Another improvement in recent years was made in metabolic labeling experiments such as SLAM-seq. Here, they removed the time consuming and laborious step of physically separating new from old RNA in the samples. This was achieved by inducing specific nucleotide conversions in newly synthesized RNA that are later visible in the data. Consequently, the separation of new and old RNA is now done computationally and, hence, tools are needed that accurately quantify these fold-changes. My second tool that I developed, called GRAND-SLAM proved to be capable to accomplish this task and outperform competing programs. As both of my tools, iTiSS and GRAND-SLAM are not specifically tailored to my own goals, but could also facilitate the research of other groups in this field, I made them publicly available on GitHub.
I applied my tools to datasets generated in our lab as well as to publicly available data sets from HSV-1 and HCMV, respectively. For HSV-1, I was able to predict and validate TiSS with nucleotide precision using iTiSS. This has lead to the most comprehensive annotation for HSV-1 to date, which now serves as the fundamental basis of any future transcriptomic research on HSV-1. By combining both my tools, I was further able to uncover parts of the highly complex gene kinetics in HCMV and to resolve the limitations caused by the densely packed genome of HCMV.
With the ever-increasing advances in sequencing techniques and their decrease in cost, the amounts of data produced will continue to rise massively in the future. Additionally, more and more specialized omics approaches are appearing, calling for new tools to leverage their full information potential. Consequently, it has become apparent that specialized computational tools such as iTiSS and GRAND-SLAM are needed and will become an essential and indispensable part of the analysis.
Herpes Simplex Virus type 1 (HSV-1) is an ubiquitous neurotropic human pathogen that infects a large majority of the world’s population. It is the causative agent of the common cold sore but also responsible for life-threatening infections (e.g., encephalitis), particularly in immunocompromised individuals and neonates. Like other herpesviruses, HSV-1 takes over the cellular RNA machinery to facilitate productive infection while efficiently shutting down host gene expression by targeting multiple steps of RNA metabolism. The two viral proteins, vhs and ICP27, play a crucial role in this process. Delivered by the tegument of the incoming virus, the virion host shut-off (vhs) endonuclease rapidly starts cleaving both cellular and viral mRNAs. With the onset of viral gene expression, the HSV-1 immediate-early protein ICP27 promotes the expression of viral early and late genes through various mechanisms, including mRNA processing, export, and translation.
Prior research by the Dölken lab demonstrated that lytic HSV-1 infection results in the disruption of transcription termination (DoTT) of most cellular genes by the viral ICP27 protein. This significantly contributes to HSV-1 induced host shut-off. DoTT results in transcription for tens of thousands of nucleotides beyond poly(A) sites and into downstream genes. Interestingly, this was found to be accompanied by a dramatic increase in chromatin accessibility downstream of the affected poly(A) sites. This is consistent with the formation of extensive downstream open chromatin regions (dOCR) and indicative of impaired histone repositioning in the wake of RNA polymerase II (Pol II) downstream of the affected poly(A) sites.
In my PhD thesis, I demonstrate that dOCR formation is dependent on the viral ICP22 protein when poly(A) read-through transcription is triggered by the ectopic expression of ICP27 or salt stress. I show that dOCR formation occurs when a high level of transcriptional activity arises downstream of genes due to the HSV-1-induced DoTT. To investigate whether histone composition is affected downstream of genes, I established the ChIPmentation approach to study associated changes and the influence of DoTT and dOCR formation on major histone modification marks. In HSV-1 WT infection, dOCR formation was reflected in alterations of canonical H1 histone downstream of affected genes, which was absent in ICP22 infection. To elucidate the underlying molecular mechanism, two major histone chaperones SPT6 and FACT (SPT16 and SSRP1), which govern histone repositioning and may thus play a role in H1 homeostasis, were extensively studied. Both histone chaperones have been recently shown to be recruited to the viral genome by interactions with ICP22 protein. To investigate whether the depletion of SSRP1 or SPT6 would complement the loss of ICP22 to induce dOCR, T-HF cells with doxycycline-inducible knock-down of either of the two factors were generated. ATAC-seq analysis revealed that the interaction between the two histone chaperones and ICP22 is not involved in HSV-1-induced dOCR formation, suggesting the involvement of other proteins. In summary, this work sheds new light on a fundamental molecular mechanism of the cellular transcriptional machinery that is manipulated by the concerted actions of the two HSV-1 immediate-early proteins ICP22 and ICP27.
Gene expression in eukaryotic cells is regulated by the combinatorial action of numerous gene-regulatory factors, among which microRNAs (miRNAs) play a fundamental role at the post-transcriptional level. miRNAs are single-stranded, small non-coding RNA molecules that emerge in a cascade-like fashion via the generation of primary and precursor miRNAs. Mature miRNAs become functional when incorporated into the RNA induced silencing complex (RISC). miRNAs guide RISCs to target mRNAs in a sequence-specific fashion. To this end, base-pairs are usually formed between the miRNA seed region, spanning nucleotide positions 2 to 8 (from the 5' end) and the 3'UTR of the target mRNA. Once miRNA-mRNA interaction is established, RISC represses translation and occasionally induces direct or indirect target mRNA degradation. Interestingly, miRNAs are expressed not only in every multicellular organism but are also encoded by several viruses, predominately by herpesviruses. By controlling both, cellular as well as viral mRNA transcripts, virus-encoded miRNAs confer many beneficial effects on viral growth and persistence. Murine cytomegalovirus (MCMV) is a ß-herpesvirus and so far, 29 mature MCMV-encoded miRNAs have been identified during lytic infection. Computational analysis of previously conducted photoactivated ribonucleotide-enhanced individual nucleotide resolution crosslinking immunoprecipitation (PAR-iCLIP) experiments identified a read cluster within the 3' untranslated region (3'UTR) of the immediate early 3 (IE3) transcript in MCMV. Based on miRNA target predictions, two highly abundant MCMV miRNAs, namely miR-m01-2-3p and miR-M23-2-3p were found to potentially bind to two closely positioned target sites within the IE3 PAR-iCLIP peak. To confirm this hypothesis, we performed luciferase assays and showed that activity values of a luciferase fused with the 3'UTR of IE3 were downregulated in the presence of miR-m01- 2 and miR-M23-2. In a second step, we investigated the effect of pre-expression of miR-m01-2 and miR-M23-2 on the induction of virus replication. After optimizing the transfection procedure by comparing different reagents and conditions, plaque formation was monitored. We could demonstrate that the replication cycle of the wild-type but not of our MCMV mutant that harbored point mutations in both miRNA binding sites within the IE3-3'UTR, was significantly delayed in the presence of miR-m01-2 and miR-M23-2. This confirmed that miR-m01-2 and miR-M23-2 functionally target the major transcription factor IE3 which acts as an indispensable regulator of viral gene expression during MCMV lytic infection. Repression of the major immediate early genes by viral miRNAs is a conserved feature of cytomegaloviruses. The functional role of this type of regulation can now be studied in the MCMV mouse model.
Interactions between host and pathogen determine the development, progression and outcomes
of disease. Medicine benefits from better descriptions of these interactions through increased
precision of prevention, diagnosis and treatment of diseases. Single-cell genomics is a
disruptive technology revolutionizing science by increasing the resolution with which we study
diseases. Cell type specific changes in abundance or gene expression are now routinely investigated
in diseases. Meanwhile, detecting cellular phenotypes across diseases can connect
scientific fields and fuel discovery. Insights acquired through systematic analysis of high resolution
data will soon be translated into clinical practice and improve decision making. Therefore,
the continued use of single-cell technologies and their application towards clinical samples will
improve molecular interpretation, patient stratification, and the prediction of outcomes.
In the past years, I was fortunate to participate in interdisciplinary research groups bridging
biology, clinical research and data science. I was able to contribute to diverse projects through
computational analysis and biological interpretation of sequencing data. Together, we were
able to discover cellular phenotypes that influence disease progression and outcomes as well
as the response to treatment. Here, I will present four studies that I have conducted in my PhD.
First, we performed a case study of relapse from cell-based immunotherapy in Multiple Myeloma.
We identified genomic deletion of the epitope as mechanism of immune escape and implicate
heterozygosity or monosomy of the genomic locus at baseline as a potential risk factor. Second,
we investigated the pathomechanisms of severe COVID-19 at the earliest stage of the COVID-
19 pandemic in Germany in March 2020. We discovered that profibrotic macrophages and
lung fibrosis can be caused by SARS-CoV-2 infection. Third, we used a mouse model of chronic
infection with Staphylococcus aureus that causes Osteomyelitis similar to the human disease.
We were able to identify dysregulated immunometabolism associated with the generation of
myeloid-derived suppressor cells (MDSC). Fourth, we investigated Salmonella infection of the
human small intestine in an in vitro model and describe features of pathogen invasion and host
response.
Overall, I have been able to successfully employ single-cell sequencing to discover important
aspects of diseases ranging from development to treatment and outcome. I analyzed samples
from the clinics, human donors, mouse models and organoid models to investigate different
aspects of diseases and managed to integrate data across sample types, technologies and
diseases. Based on successful studies, we increased our efforts to combine data from multiple
sources to build comprehensive references for the integration of large collections of clinical
samples. Our findings exemplify how single-cell sequencing can improve clinical research and
highlights the potential of mechanistic discoveries to drive precision medicine.