610 Medizin und Gesundheit
Refine
Document Type
- Preprint (3) (remove)
Language
- English (3)
Has Fulltext
- yes (3) (remove)
Is part of the Bibliography
- no (3)
Institute
- Medizin (3)
Vaccines are central to controlling the coronavirus disease 2019 (COVID-19) pandemic but the durability of protection is limited for currently approved COVID-19 vaccines. Further, the emergence of variants of concern (VoCs) that evade immune recognition has reduced vaccine effectiveness, compounding the problem. Here, we show that a single dose of a murine cytomegalovirus (MCMV)-based vaccine, which expresses the spike (S) protein of the virus circulating early in the pandemic (MCMVS), protects highly susceptible K18-hACE2 mice from clinical symptoms and death upon challenge with a lethal dose of D614G SARS-CoV-2. Moreover, MCMVS vaccination controlled two immune-evading VoCs, the Beta (B.1.135) and the Omicron (BA.1) variants in BALB/c mice, and S-specific immunity was maintained for at least 5 months after immunization, where neutralizing titers against all tested VoCs were higher at 5-months than at 1-month post-vaccination. Thus, cytomegalovirus (CMV)-based vector vaccines might allow for long-term protection against COVID-19.
Background: Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic data sets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multi-modal data sets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., ChIP-seq, ATAC-seq, or DNase-seq) and RNA-seq data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results.
Results: We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multi-modal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE data sets for cell lines K562 and MCF-7, including twelve histone modification ChIP-seq as well as ATAC-seq and DNase-seq datasets, where we observe and discuss assay-specific differences.
Conclusion: TF-Prioritizer accepts ATAC-seq, DNase-seq, or ChIP-seq and RNA-seq data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.
Background Eukaryotic gene expression is controlled by cis-regulatory elements (CREs) including promoters and enhancers which are bound by transcription factors (TFs). Differential expression of TFs and their putative binding sites on CREs cause tissue and developmental-specific transcriptional activity. Consolidating genomic data sets can offer further insights into the accessibility of CREs, TF activity, and thus gene regulation. However, the integration and analysis of multi-modal data sets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined ChIP-seq and RNA-seq data exist, they do not offer good usability, have limited support for large-scale data processing, and provide only minimal functionality for visual result interpretation.
Results We developed TF-Prioritizer, an automated java pipeline to prioritize condition-specific TFs derived from multi-modal data. TF-Prioritizer creates an interactive, feature-rich, and user-friendly web report of its results. To showcase the potential of TF-Prioritizer, we identified known active TFs (e.g., Stat5, Elf5, Nfib, Esr1), their target genes (e.g., milk proteins and cell-cycle genes), and newly classified lactating mammary gland TFs (e.g., Creb1, Arnt).
Conclusion TF-Prioritizer accepts ChIP-seq and RNA-seq data, as input and suggests TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.