A Narrative Review of COVID-19 Vaccines

The COVID-19 pandemic has shaken the world since early 2020 and its health, social, economic, and societal negative impacts at the global scale have been catastrophic. Since the early days of the pandemic, development of safe and effective vaccines was judged to be the best possible tool to minimize the effects of this pandemic. Drastic public health measures were put into place to stop the spread of the virus, with the hope that vaccines would be available soon. Thanks to the extraordinary commitments of many organizations and individuals from around the globe and the collaborative effort of many international scientists, vaccines against COVID-19 received regulatory approval for emergency human use in many jurisdictions in less than a year after the identification of the viral sequence. Several of these vaccines have been in use for some time; however, the pandemic is still ongoing and likely to persist for the foreseeable future. This is due to many reasons including reduced compliance with public health restrictions, limited vaccine manufacturing/distribution capacity, high rates of vaccine hesitancy, and the emergence of new variants with the capacity to spread more easily and to evade current vaccines. Here we discuss the discovery and availability of COVID-19 vaccines and evolving issues around mass vaccination programs.

TreeKnit: Inferring Ancestral Reassortment Graphs of influenza viruses

When two influenza viruses co-infect the same cell, they can exchange genome segments in a process known as reassortment. Reassortment is an important source of genetic diversity and is known to have been involved in the emergence of most pandemic influenza strains. However, because of the difficulty in identifying reassortments events from viral sequence data, little is known about its role in the evolution of the seasonal influenza viruses. Here we introduce TreeKnit, a method that infers ancestral reassortment graphs (ARG) from two segment trees. It is based on topological differences between trees, and proceeds in a greedy fashion by finding regions that are compatible in the two trees. Using simulated genealogies with reassortments, we show that TreeKnit performs well in a wide range of settings and that it is as accurate as a more principled bayesian method, while being orders of magnitude faster. Finally, we show that it is possible to use the inferred ARG to better resolve segment trees and to construct more informative visualizations of reassortments.

Rapid screening and identification of viral pathogens in metagenomic data

Background Virus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to identify viral pathogens in host-associated and environmental samples without prior knowledge. Despite the availability of software, data analysis still requires human operations. A mature pipeline is urgently needed when thousands of viral pathogen and viral genome reconstruction samples need to be rapidly identified. Results In this paper, we present a rapid and accurate workflow to screen metagenomics sequencing data for viral pathogens and other compositions, as well as enable a reference-based assembler to reconstruct viral genomes. Moreover, we tested our workflow on several metagenomics datasets, including a SARS-CoV-2 patient sample with NGS data, pangolins tissues with NGS data, Middle East Respiratory Syndrome (MERS)-infected cells with NGS data, etc. Our workflow demonstrated high accuracy and efficiency when identifying target viruses from large scale NGS metagenomics data. Our workflow was flexible when working with a broad range of NGS datasets from small (kb) to large (100 Gb). This took from a few minutes to a few hours to complete each task. At the same time, our workflow automatically generates reports that incorporate visualized feedback (e.g., metagenomics data quality statistics, host and viral sequence compositions, details about each of the identified viral pathogens and their coverages, and reassembled viral pathogen sequences based on their closest references). Conclusions Overall, our system enabled the rapid screening and identification of viral pathogens from metagenomics data, providing an important piece to support viral pathogen research during a pandemic. The visualized report contains information from raw sequence quality to a reconstructed viral sequence, which allows non-professional people to screen their samples for viruses by themselves (Additional file 1).

The Critical Contribution of Pseudouridine to mRNA COVID-19 Vaccines

The current COVID-19 pandemic is a massive source of global disruption, having led so far to two hundred and fifty million COVID-19 cases and almost five million deaths worldwide. It was recognized in the beginning that only an effective vaccine could lead to a way out of the pandemic, and therefore the race for the COVID-19 vaccine started immediately, boosted by the availability of the viral sequence data. Two novel vaccine platforms, based on mRNA technology, were developed in 2020 by Pfizer-BioNTech and Moderna Therapeutics (comirnaty® and spikevax®, respectively), and were the first ones presenting efficacies higher than 90%. Both consisted of N1-methyl-pseudouridine-modified mRNA encoding the SARS-COVID-19 Spike protein and were delivered with a lipid nanoparticle (LNP) formulation. Because the delivery problem of ribonucleic acids had been known for decades, the success of LNPs was quickly hailed by many as the unsung hero of COVID-19 mRNA vaccines. However, the clinical trial efficacy results of the Curevac mRNA vaccine (CVnCoV) suggested that the delivery system was not the only key to the success. CVnCoV consisted of an unmodified mRNA (encoding the same spike protein as Moderna and Pfizer-BioNTech’s mRNA vaccines) and was formulated with the same LNP as Pfizer-BioNTech’s vaccine (Acuitas ALC-0315). However, its efficacy was only 48%. This striking difference in efficacy could be attributed to the presence of a critical RNA modification (N1-methyl-pseudouridine) in the Pfizer-BioNTech and Moderna’s mRNA vaccines (but not in CVnCoV). Here we highlight the features of N1-methyl-pseudouridine and its contributions to mRNA vaccines.

Complete genome sequence of GII.9 norovirus

Norovirus is recognized as one of the leading causes of acute gastroenteritis outbreaks. Genotype GII.9 was first detected in Norfolk, VA, USA, in 1997. However, the complete genome sequence of this genotype has not yet been determined. In this study, a complete genome sequence of GII.9[P7] norovirus, SCD1878_GII.9[P7], from a patient was determined using high-throughput sequencing and rapid amplification of cDNA ends (RACE) technology. The complete genome sequence of SCD1878_GII.9[P7] is 7544 nucleotides (nt) in length with a 3’ poly(A) tail and contains three open reading frames. Sequence comparisons indicated that SCD1878_GII.9[P7] shares 92.1%-92.3% nucleotide sequence identity with GII.P7 (AB258331 and AB039777) and 96.7%-97.4% identity with GII.9 (AY038599 and DQ379715). The results suggested that SCD1878_GII.9[P7] is a member of P genotype GII.P7 and G genotype GII.9. This viral sequence fills a gap at the whole-genome level for the GII.9 genotype.

Anti-COVID-19 Vaccination in Patients with Autoimmune-Autoinflammatory Disorders and Primary/Secondary Immunodeficiencies: The Position of the Task Force on Behalf of the Italian Immunological Societies

The Coronavirus disease 2019 (COVID-19) pandemic has represented an unprecedented challenge for humankind from health, economic, and social viewpoints. In February 2020, Italy was the first western country to be deeply hit by the pandemic and suffered the highest case/fatality rate among western countries. Brand new anti-COVID-19 vaccines have been developed and made available in <1-year from the viral sequence publication. Patients with compromised immune systems, such as autoimmune-autoinflammatory disorders (AIAIDs), primary (PIDs) and secondary (SIDs) immunodeficiencies, have received careful attention for a long time regarding their capacity to safely respond to traditional vaccines. The Italian Immunological Societies, therefore, have promptly faced the issues of safety, immunogenicity, and efficacy/effectiveness of the innovative COVID-19 vaccines, as well as priority to vaccine access, in patients with AIADs, PIDs, and SIDs, by organizing an ad-hoc Task Force. Patients with AIADs, PIDs, and SIDs: (1) Do not present contraindications to COVID-19 vaccines if a mRNA vaccine is used and administered in a stabilized disease phase without active infection. (2) Should usually not discontinue immunosuppressive therapy, which may be modulated depending on the patient’s clinical condition. (3) When eligible, should have a priority access to vaccination. In fact, immunizing these patients may have relevant social/health consequences, since these patients, if infected, may develop chronic infection, which prolongs viral spread and facilitates the emergence of viral variants.

An Alignment-Independent Approach for the Study of Viral Sequence Diversity at Any Given Rank of Taxonomy Lineage

The study of viral diversity is imperative in understanding sequence change and its implications for intervention strategies. The widely used alignment-dependent approaches to study viral diversity are limited in their utility as sequence dissimilarity increases, particularly when expanded to the genus or higher ranks of viral species lineage. Herein, we present an alignment-independent algorithm, implemented as a tool, UNIQmin, to determine the effective viral sequence diversity at any rank of the viral taxonomy lineage. This is done by performing an exhaustive search to generate the minimal set of sequences for a given viral non-redundant sequence dataset. The minimal set is comprised of the smallest possible number of unique sequences required to capture the diversity inherent in the complete set of overlapping k-mers encoded by all the unique sequences in the given dataset. Such dataset compression is possible through the removal of unique sequences, whose entire repertoire of overlapping k-mers can be represented by other sequences, thus rendering them redundant to the collective pool of sequence diversity. A significant reduction, namely ~44%, ~45%, and ~53%, was observed for all reported unique sequences of species Dengue virus, genus Flavivirus, and family Flaviviridae, respectively, while still capturing the entire repertoire of nonamer (9-mer) viral peptidome diversity present in the initial input dataset. The algorithm is scalable for big data as it was applied to ~2.2 million non-redundant sequences of all reported viruses. UNIQmin is open source and publicly available on GitHub. The concept of a minimal set is generic and, thus, potentially applicable to other pathogenic microorganisms of non-viral origin, such as bacteria.

The infinite alleles model revisited: a Gibbs sampling approach

The SARS-CoV-2 outbreak started in late 2019 in the Hubei province in China and the first viral sequence was made available to the scientific community on early January 2020. From there, viral genomes from all over the world have followed at an outstanding rate, reaching already more than 10^5 on early May 2020, and more than 10^6 by early March 2021. Phylodynamics methods have been designed in recent years to process such datasets and infer population dynamics and sampling intensities in the past. However, the unprecedented scale of the SARS-CoV-2 dataset now calls for new methodological developments, relying e.g. on simplifying assumptions of the mutation process. In this article, I build on the infinite alleles model stemming from the field of population genetics to develop a new Bayesian statistical method allowing the joint reconstruction of the outbreak's effective population sizes and sampling intensities through time. This relies on prior conjugacy properties that prove useful both to develop a Gibbs sampler and to gain intuition on the way different parameters of the model are linked and inferred. I finally illustrate the use of this method on SARS-CoV-2 genomes sequenced during the first wave of the outbreak in four distinct European countries, thus offering a new perspective on the evolution of the sampling intensity through time in these countries from genetic data only.

Illuminating the Virosphere Through Global Metagenomics

Viruses are the most abundant biological entity on Earth, infect cellular organisms from all domains of life, and are central players in the global biosphere. Over the last century, the discovery and characterization of viruses have progressed steadily alongside much of modern biology. In terms of outright numbers of novel viruses discovered, however, the last few years have been by far the most transformative for the field. Advances in methods for identifying viral sequences in genomic and metagenomic datasets, coupled to the exponential growth of environmental sequencing, have greatly expanded the catalog of known viruses and fueled the tremendous growth of viral sequence databases. Development and implementation of new standards, along with careful study of the newly discovered viruses, have transformed and will continue to transform our understanding of microbial evolution, ecology, and biogeochemical cycles, leading to new biotechnological innovations across many diverse fields, including environmental, agricultural, and biomedical sciences.

Proteo-Trancriptomic Analyses Reveal a Large Expansion of Metalloprotease-Like Proteins in Atypical Venom Vesicles of the Wasp Meteorus pulchricornis (Braconidae)

Meteorus pulchricornis (Ichneumonoidea, Braconidae) is an endoparasitoid wasp of lepidopteran caterpillars. Its parasitic success relies on vesicles (named M. pulchricornis Virus-Like Particles or MpVLPs) that are synthesized in the venom gland and injected into the parasitoid host along with the venom during oviposition. In order to define the content and understand the biogenesis of these atypical vesicles, we performed a transcriptome analysis of the venom gland and a proteomic analysis of the venom and purified MpVLPs. About half of the MpVLPs and soluble venom proteins identified were unknown and no similarity with any known viral sequence was found. However, MpVLPs contained a large number of proteins labelled as metalloproteinases while the most abundant protein family in the soluble venom was that of proteins containing the Domain of Unknown Function DUF-4803. The high number of these proteins identified suggests that a large expansion of these two protein families occurred in M. pulchricornis. Therefore, although the exact mechanism of MpVLPs formation remains to be elucidated, these vesicles appear to be “metalloproteinase bombs” that may have several physiological roles in the host including modifying the functions of its immune cells. The role of DUF4803 proteins, also present in the venom of other braconids, remains to be clarified.