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The Coronaviridae is a family of positive-strand RNA viruses that includes SARS-CoV-2, the etiologic agent of the COVID-19 pandemic. Bearing the largest single-stranded RNA genomes in nature, coronaviruses are critically dependent on long-distance RNA-RNA interactions to regulate the viral transcription and replication pathways. Here we experimentally mapped the in vivo RNA-RNA interactome of the full-length SARS-CoV-2 genome and subgenomic mRNAs. We uncovered a network of RNA-RNA interactions spanning tens of thousands of nucleotides. These interactions reveal that the viral genome and subgenomes adopt alternative topologies inside cells and engage in different interactions with host RNAs. Notably, we discovered a long-range RNA-RNA interaction, the FSE-arch, that encircles the programmed ribosomal frameshifting element. The FSE-arch is conserved in the related MERS-CoV and is under purifying selection. Our findings illuminate RNA structure-based mechanisms governing replication, discontinuous transcription, and translation of coronaviruses and will aid future efforts to develop antiviral strategies., Graphical Abstract, Highlights • Comprehensive RNA-RNA networks of the SARS-CoV-2 genome and subgenomes inside cells • Long-range structures spanning thousands of bases resulting in dynamic topologies • Multiple site-specific interactions between host and virus RNAs • An arch around the ribosomal frameshifting element is under purifying selection, Coronaviruses use RNA structure to regulate their function. Ziv et al. identified maps of RNA-RNA interactions along the SARS-CoV-2 genome and subgenomes inside cells, revealing long-range base-pairing between distal elements, alternative co-existing RNA topologies, and interactions between virus and host RNA and providing insights into the coronavirus modes of action.

In my thesis, I studied eco-evolutionary dynamics with the focus to advance this relatively novel research field. In general, I aim to develop a detailed mechanistic understanding of eco-evolutionary dynamics in host-virus systems and investigate the effects and important consequences of such dynamics in simple and increasingly complex food webs. The first chapter of this thesis serves as a general introduction into eco-evolutionary dynamics. Here, I review most recent findings concerning the field of eco-evolutionary dynamics and propose several further research directions by identifying important gaps in our knowledge, which then served as the most important driver for my thesis work. The second chapter addresses several missing links identified in chapter one; i) study eco-evolutionary dynamics with different types of biotic species interactions, ii) use systems with more than one evolving species, iii) use systems with more than one evolving trait, iv) combine empirical work with modeling. I test in great detail for eco-evolutionary dynamics in a host-virus system and combine this empirical work with mathematical modeling. In the third chapter, I extend the relatively simple host-virus community of chapter two with an additional player. As eco-evolutionary dynamics are not well understood in more complex systems, this approach enabled testing for increasing complexity in a controlled experimental design by comparing more complex systems with the relatively simple two species host-virus system. In the last chapter I take a different approach. As the results from the second chapter show a tight link between ecology and evolution, I investigate here the result of these eco-evolutionary dynamics on parallel and divergent evolution between replicate host populations that coevolved with a virus. In meiner Doktorarbeit untersuchte ich ökologisch - evolutionäre Dynamiken mit dem Fokus dieses relativ neue Forschungsgebiet voranzutreiben. Im Allgemeinen versuche ich ein detailliertes Verständnis dieser dynamischen Abläufe in einem Wirt-Virus-System zu entwickeln, und deren Auswirkungen und wichtigen Folgen in einfachen und immer komplexer werdenden Nahrungsnetzen zu untersuchen. Das erste Kapitel dieser Arbeit dient als allgemeine Einführung in ökologisch - evolutionäre Dynamiken. Das zweite Kapitel befasst sich mit einigen der Lüken, die ich in Kapitel I dargestellt habe: ökologisch - evolutionären Dynamiken mit verschiedenen Typen biotischer Interaktionen, Systeme in denen mehr als eine Art und mehrere Merkmale evolvieren und die Kombination empirischer Arbeit mit Modellierung. Im Detail, teste ich ökologisch - evolutionäre Dynamiken in einem Wirt-Virus-System und kombiniere diese empirische Arbeit mit mathematischen Modellierung. Im dritten Kapitel erweitere ich das relativ einfache Wirt-Virus-System von Kapitel II mit einem zusätzlichen Organismus. Das Verständnis von ökologisch - evolutionäre Dynamiken in komplexeren Systemen ist noch nicht hinreichend untersucht, durch den hier beschriebenen Ansatz ist es mir möglich ansteigende Komplexität in einem kontrollierten experimentellen System zu testen, indem komplexere Systeme mit dem einfachen Wits-Virus-System verglichen werden. Im letzten Kapitel verfolge ich einen anderen Ansatz. Die Ergebnisse aus dem vorangegangenen Kapitel zeigen einen engen Zusammenhang zwischen Ökologie und Evolution, in diesem Kapitel untersuche ich vergleichend die aus den ökologisch - evolutionäre Dynamiken resultierende parallele und divergierende Selektion zwischen replizierten Wirts-Populationen die mit einem Virus koevolvieren.

Background When biomolecules physically interact, natural selection operates on them jointly. Contacting positions in protein and RNA structures exhibit correlated patterns of sequence evolution due to constraints imposed by the interaction, and molecular arms races can develop between interacting proteins in pathogens and their hosts. To evaluate how well methods developed to detect coevolving residues within proteins can be adapted for cross-species, inter-protein analysis, we used statistical criteria to quantify the performance of these methods in detecting inter-protein residues within 8 angstroms of each other in the co-crystal structures of 33 bacterial protein interactions. We also evaluated their performance for detecting known residues at the interface of a host-virus protein complex with a partially solved structure. Results Our quantitative benchmarking showed that all coevolutionary methods clearly benefit from alignments with many sequences. Methods that aim to detect direct correlations generally outperform other approaches. However, faster mutual information based methods are occasionally competitive in small alignments and with relaxed false positive rates. Two commonly used null distributions are anti-conservative and have high false positive rates in some scenarios, although the empirical distribution of scores performs reasonably well with deep alignments. Conclusions We conclude that coevolutionary analysis of cross-species protein interactions holds great promise but requires sequencing many more species pairs. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0677-y) contains supplementary material, which is available to authorized users.

Viral infection depends on a complex interplay between host and viral factors. Here, the authors link host susceptibility to viral infection to a network encompassing sulfur metabolism, tRNA modification, competitive binding, and programmed ribosomal frameshifting., The iron-sulfur cluster biosynthesis pathway in Escherichia coli exerts a protective effect during lambda phage infection, while a tRNA thiolation pathway enhances viral infection.tRNALys uridine 34 modification inhibits programmed ribosomal frameshifting to influence the ratio of lambda phage proteins gpG and gpGT.The role of the iron-sulfur cluster biosynthesis pathway in infection is indirect, via competitive binding of the shared sulfur donor IscS.Based on the universality of many key components of this network, in both the host and the virus, these findings may have broad relevance to understanding other infections, including viral infection of humans., Viral infection depends on a complex interplay between host and viral factors. Here, we link host susceptibility to viral infection to a network encompassing sulfur metabolism, tRNA modification, competitive binding, and programmed ribosomal frameshifting (PRF). We first demonstrate that the iron-sulfur cluster biosynthesis pathway in Escherichia coli exerts a protective effect during lambda phage infection, while a tRNA thiolation pathway enhances viral infection. We show that tRNALys uridine 34 modification inhibits PRF to influence the ratio of lambda phage proteins gpG and gpGT. Computational modeling and experiments suggest that the role of the iron-sulfur cluster biosynthesis pathway in infection is indirect, via competitive binding of the shared sulfur donor IscS. Based on the universality of many key components of this network, in both the host and the virus, we anticipate that these findings may have broad relevance to understanding other infections, including viral infection of humans.

The Coronaviridae is a family of positive-strand RNA viruses that includes SARS-CoV-2, the etiologic agent of the COVID-19 pandemic. Bearing the largest single-stranded RNA genomes in nature, coronaviruses are critically dependent on long-distance RNA-RNA interactions to regulate the viral transcription and replication pathways. Here we experimentally mapped the in vivo RNA-RNA interactome of the full-length SARS-CoV-2 genome and subgenomic mRNAs. We uncovered a network of RNA-RNA interactions spanning tens of thousands of nucleotides. These interactions reveal that the viral genome and subgenomes adopt alternative topologies inside cells and engage in different interactions with host RNAs. Notably, we discovered a long-range RNA-RNA interaction, the FSE-arch, that encircles the programmed ribosomal frameshifting element. The FSE-arch is conserved in the related MERS-CoV and is under purifying selection. Our findings illuminate RNA structure-based mechanisms governing replication, discontinuous transcription, and translation of coronaviruses and will aid future efforts to develop antiviral strategies.