Capturing intrahost recombination of SARS-CoV-2 during superinfection with Alpha and Epsilon variants in New York City (preprint)

Recombination is an evolutionary process by which many pathogens generate diversity and acquire novel functions. Although a common occurrence during coronavirus replication, recombination can only be detected when two genetically distinct viruses contemporaneously infect the same host. Here, we identify an instance of SARS-CoV-2 superinfection, whereby an individual was simultaneously infected with two distinct viral variants: Alpha (B.1.1.7) and Epsilon (B.1.429). This superinfection was first noted when an Alpha genome sequence failed to exhibit the classic S gene target failure behavior used to track this variant. Full genome sequencing from four independent extracts revealed that Alpha variant alleles comprised between 70-80% of the genomes, whereas the Epsilon variant alleles comprised between 20-30% of the sample. Further investigation revealed the presence of numerous recombinant haplotypes spanning the genome, specifically in the spike, nucleocapsid, and ORF 8 coding regions. These findings support the potential for recombination to reshape SARS-CoV-2 genetic diversity.

RASCL: Rapid Assessment Of SARS-CoV-2 Clades Through Molecular Sequence Analysis (preprint)

An important component of efforts to manage the ongoing COVID19 pandemic is the Rapid Assessment of how natural selection contributes to the emergence and proliferation of potentially dangerous SARS-CoV-2 lineages and CLades (RASCL). The RASCL pipeline enables continuous comparative phylogenetics-based selection analyses of rapidly growing clade-focused genome surveillance datasets, such as those produced following the initial detection of potentially dangerous variants. From such datasets RASCL automatically generates down-sampled codon alignments of individual genes/ORFs containing contextualizing background reference sequences, analyzes these with a battery of selection tests, and outputs results as both machine readable JSON files, and interactive notebook-based visualizations.

Selection analysis identifies unusual clustered mutational changes in Omicron lineage BA.1 that likely impact Spike function (preprint)

Among the 30 non-synonymous nucleotide substitutions in the Omicron S-gene are 13 that have only rarely been seen in other SARS-CoV-2 sequences. These mutations cluster within three functionally important regions of the S-gene at sites that will likely impact (i) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (ii) interactions of Spike with ACE2 receptors, and (iii) the priming of Spike for membrane fusion. We show here that, based on both the rarity of these 13 mutations in intrapatient sequencing reads and patterns of selection at the codon sites where the mutations occur in SARS-CoV-2 and related sarbecoviruses, prior to the emergence of Omicron the mutations would have been predicted to decrease the fitness of any virus within which they occurred. We further propose that the mutations in each of the three clusters therefore cooperatively interact to both mitigate their individual fitness costs, and, in combination with other mutations, adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron over all previously known SARS-CoV-2 lineages, it is crucial to determine both how such complex and highly adaptive mutation constellations were assembled within the Omicron S-gene, and why, despite unprecedented global genomic surveillance efforts, the early stages of this assembly process went completely undetected.

Stepwise evolution and exceptional conservation of ORF1a/b overlap in coronaviruses (preprint)

The programmed frameshift element (PFE) rerouting translation from ORF1a to ORF1b is essential for propagation of coronaviruses. A combination of genomic features that make up PFE--the overlap between the two reading frames, a slippery sequence, as well as an ensemble of complex secondary structure elements--puts severe constraints on this region as most possible nucleotide substitution may disrupt one or more of these elements. The vast amount of SARS-CoV-2 sequencing data generated within the past year provides an opportunity to assess evolutionary dynamics of PFE in great detail. Here we performed a comparative analysis of all available coronaviral genomic data available to date. We show that the overlap between ORF1a and b evolved as a set of discrete 7, 16, 22, 25, and 31 nucleotide stretches with a well defined phylogenetic specificity. We further examined sequencing data from over 350,000 complete genomes and 55,000 raw read datasets to demonstrate exceptional conservation of the PFE region.

Freely accessible ready to use global infrastructure for SARS-CoV-2 monitoring (preprint)

The COVID-19 pandemic is the first global health crisis to occur in the age of big genomic data. Although data generation capacity is well established and sufficiently standardized, analytical capacity is not. To establish analytical capacity it is necessary to pull together global computational resources and deliver the best open source tools and analysis workflows within a ready to use, universally accessible resource. Such a resource should not be controlled by a single research group, institution, or country. Instead it should be maintained by a community of users and developers who ensure that the system remains operational and populated with current tools. A community is also essential for facilitating the types of discourse needed to establish best analytical practices. Bringing together public computational research infrastructure from the USA, Europe, and Australia, we developed a distributed data analysis platform that accomplishes these goals. It is immediately accessible to anyone in the world and is designed for the analysis of rapidly growing collections of deep sequencing datasets. We demonstrate its utility by detecting allelic variants in high-quality existing SARS-CoV-2 sequencing datasets and by continuous reanalysis of COG-UK data. All workflows, data, and documentation is available at https://covid19.galaxyproject.org.

Fast and accurate genome-wide predictions and structural modeling of protein-protein interactions using Galaxy (preprint)

Protein-protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein-protein interactions and produce high-quality multimeric structural models. Application of our method to the Human and Yeast genomes yield protein-protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2’s non-structural protein 3 (Nsp3). We also produced models of SARS-CoV2’s spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor (MOG) and dipeptidyl peptidase-4 (DPP4). The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.

Fostering Accessible Online Education Using Galaxy as an e-learning Platform (preprint)

The COVID-19 pandemic is shifting the teaching paradigms to an online setting all over the world. The Galaxy framework caters to computational biologists a set of features to facilitate the online learning process and make it accessible to everyone. Besides the high-quality training materials, Galaxy provides easy access to data and the possibility to share the progress and achievements, both student to student and student to instructor. By combining the different features offered by the Galaxy framework and by choosing the adequate communication channels, effective training activities can be designed inclusively, regardless of the students' environments.

The landscape of SARS-CoV-2 RNA modifications (preprint)

In 2019 the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the first documented cases of severe lung disease COVID-19. Since then, SARS-CoV-2 has been spreading around the globe resulting in a severe pandemic with over 500.000 fatalities and large economical and social disruptions in human societies. Gaining knowledge on how SARS-Cov-2 interacts with its host cells and causes COVID-19 is crucial for the intervention of novel therapeutic strategies. SARS-CoV-2, like other coronaviruses, is a positive-strand RNA virus. The viral RNA is modified by RNA-modifying enzymes provided by the host cell. Direct RNA sequencing (DRS) using nanopores enables unbiased sensing of canonical and modified RNA bases of the viral transcripts. In this work, we used DRS to precisely annotate the open reading frames and the landscape of SARS-CoV-2 RNA modifications. We provide the first DRS data of SARS-CoV-2 in infected human lung epithelial cells. From sequencing three isolates, we derive a robust identification of SARS-CoV-2 modification sites within a physiologically relevant host cell type. A comparison of our data with the DRS data from a previous SARS-CoV-2 isolate, both raised in monkey renal cells, reveals consistent RNA modifications across the viral genome. Conservation of the RNA modification pattern during progression of the current pandemic suggests that this pattern is likely essential for the life cycle of SARS-CoV-2 and represents a possible target for drug interventions.