A03: Detecting time-resolved and virulence-associated host responses to viral infection
Projects of the CRC 1768
A03: Detecting time-resolved and virulence-associated host responses to viral infection
Understanding the cell’s transcriptional response to virus infections is crucial for comprehending the host’s molecular defence, the pathogen’s strategies to circumvent these mechanisms, and hence the viral capability to cause severe disease (i.e. their virulence). However, currently available methods are not sufficient to accurately determine the cellular transcription response, especially for viruses that cause a global host cell shut-off. In these cases, available computational read normalisation strategies prohibit an accurate analysis of differential gene expression. Moreover, current off-the-shelf methods also cannot assess the expression of all relevant transcript classes, in particular the highly repetitive transposable elements (TEs) that have been reported to trigger innate immunity in virus-infected cells. Currently used analysis pipelines do not yet enable the assessment of individual TE copy expression.
Thus, we aim to develop tools for (i) normalisation of reads to accurately measure the host transcription shut-off imposed by viruses and (ii) mapping expression of individual TE copies. Moreover, we aim to (iii) combine these methods into a tool to systematically analyse and cluster expression time series to characterise expression trajectories and infer regulatory interactions during viral infections. We expect that the development and implementation of the proposed software will subsequently enable us to improve the transcriptome-based prediction of a pathogen’s virulence.
Project Overview
We hypothesise that the virulence of viruses in an immune-naive human host can be assessed based on the cellular infection response. Our project thereby deals with the transcriptional aspect, but the collaboration with B01 (Böcker/ Pohnert), Z02 (Barth/Cassman/Gerlach/König-Ries), and C04 (Beer/Popp) will also encompass metabolomics, proteomics, and holistic analysis from cells infected with the same viruses. Analyses of bulk transcriptomes from virus-infected cells, however, currently suffer from two main problems, namely: (i) normalisation methods can produce in silico artefacts when viruses cause transcriptional shut-off, and (ii) it is unclear how TEs, whose suppression or spectrum is a likely hallmark of pathogenic viruses, contribute to the host’s defence. This analysis requires novel computational methods that enable the investigation of the expression of individual loci. We aim to develop methods to address these issues and integrate them into a tool for transcription-based virulence prediction.
Improving in silico DGE analysis under conditions of transcriptional shut-off.
Since the SARS-CoV-2 pandemic, there has been a notable upsurge in studies examining transcriptomic host responses to viral infections. Identifying infection-associated host genes can be bioinformatically challenging, as biologically relevant changes are often swamped by non-specific deregulation. This is especially critical when virus- host interactions globally disturb the host’s transcriptional program, causing a shut-off. Equipping statistical models for DGE analysis with an alternative normalisation method is imperative. Only then is it possible to confidently chart dynamically co-regulated gene modules using, for instance, weighted gene correlation network analysis (WGCNA). Here, we seek to leverage our previous experience in method development for RNA-seq data and other data modalities and to further customise our bionorm method for virus infections.
Reliably measuring the RNA expression of individual TE copies.
Due to the high sequence similarity among TEs, the locus-specific analysis of TE expression and regulation at a genomic scale is still in its infancy. To remedy this shortcoming, we have developed and published an analysis strategy that accurately measures the locus-specific expression of TEs using standard total RNA-seq protocols. As the up regulation of TEs is part of the antiviral cell response, potential viral antagonism will also be investigated, using, e.g., RVFV, SFSV, and others from the genus phleboviruses (family Phenuiviridae, class Bunyaviricetes), for which we have years-long experience, a wide variety of molecular tools, and representative strains and species exhibiting different levels of virulence.
Methodological integration to enhance virulence prediction.
Our final aim is to develop an integrated tool that enables a comprehensive investigation of host cell transcriptional responses to viral infections using high-throughput sequencing data. The tool shall enable more accurate normalisation of read count data in the presence of global transcriptional changes, i.e., transcriptional shut-offs or hypertranscription, and facilitate the quantification of the understudied TEs. Implemented in an easy-to-use manner, it will apply to various viruses and host cells across multiple species. Ultimately, we aim to leverage the enhanced insights into transcriptomic responses to viral infections to develop transcriptome-based predictors of virulence.
- Tool to be developed: Tools to identify and characterize time-resolved host responses to viral infections in light of global transcriptional changes, accurately measure the expression of individual TEs, and generate a host transcriptome-based predictor of virulence.
Hypothesis enabled by the proposed tool: The proposed tool will allow deeper insights into the host’s transcriptional response to viral infections and enable us to test whether the virulence of viruses can be predicted by the transcriptional response of human cells.
Overarching CRC goals: Our project develops read-normalisation for quantifying virus-induced host transcriptional shut-off, TE-copy–resolved expression mapping, and time-series clustering to obtain accurate, multi-class transcriptome signatures during infection (G1). Applying these tools across viruses and host contexts will yield generalizable rules linking TE activation and innate immune signaling to virulence and culminate in a calibrated transcriptome-based virulence predictor (G3, G4).
Work Packages (WP):
- WP 1: Shut-off-corrected comparison of recurring expression profiles (Weber/Hoffmann)
- WP 2: Locus-specific expression of endogenous retroviruses in response to viral infection (Hoffmann/Weber)
- WP 3: Integrative analysis and transcriptome-based virulence prediction (Hoffmann/Weber)
Team Members
PhD A03 1
PhD Student
PhD A03 2
PhD Student
2025
Gene regulation by convergent promoters Journal Article
In: Nat Genet, vol. 57, iss. 1, no. 1, pp. 206-217, 2025.
2024
Distinct negative-sense RNA viruses induce a common set of transcripts encoding proteins forming an extensive network. Journal Article
In: J Virol, vol. 98, iss. 10, no. 10, pp. e0093524, 2024.
Nonlinear DNA methylation trajectories in aging male mice Journal Article
In: Nat Commun, vol. 15, iss. 1, no. 1, pp. 3074, 2024, ISSN: 2041-1723 (Electronic) 2041-1723 (Linking).
2022
Locus-specific expression analysis of transposable elements. Journal Article
In: Brief Bioinform, vol. 23, iss. 1, 2022, ISSN: 1477-4054.
2020
Elongin C contributes to RNA polymerase II degradation by the interferon antagonist NSs of La Crosse Orthobunyavirus Journal Article
In: J Virol, vol. 94, iss. 7, 2020.
2019
Virus- and interferon alpha-induced transcriptomes of cells from the microbat emphMyotis daubentonii Journal Article
In: iScience, vol. 19, pp. 647-661, 2019, ISSN: 2589-0042.
2018
In: J Virol, vol. 92, iss. 23, 2018.
2014
A multi-split mapping algorithm for circular RNA, splicing, trans-splicing and fusion detection. Journal Article
In: Genome Biol, vol. 15, iss. 2, pp. R34, 2014, ISSN: 1474-760X.
Virulence factor NSs of Rift Valley fever virus recruits the F-box protein FBXO3 to degrade subunit p62 of general transcription factor TFIIH Journal Article
In: J Virol, vol. 88, no. 6, pp. 3464-3473, 2014.
2009
Fast mapping of short sequences with mismatches, insertions and deletions using index structures. Journal Article
In: PLoS Comput Biol, vol. 5, iss. 9, pp. e1000502, 2009, ISSN: 1553-7358.