Advancing Precision Vaccinology by Molecular and Genomic Surveillance of Severe Acute Respiratory Syndrome Coronavirus 2 in Germany, 2021.

BACKGROUND: Comprehensive pathogen genomic surveillance represents a powerful tool to complement and advance precision vaccinology. The emergence of the Alpha variant in December 2020 and the resulting efforts to track the spread of this and other severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern led to an expansion of genomic sequencing activities in Germany. METHODS: At Robert Koch Institute (RKI), the German National Institute of Public Health, we established the Integrated Molecular Surveillance for SARS-CoV-2 (IMS-SC2) network to perform SARS-CoV-2 genomic surveillance at the national scale, SARS-CoV-2-positive samples from laboratories distributed across Germany regularly undergo whole-genome sequencing at RKI. RESULTS: We report analyses of 3623 SARS-CoV-2 genomes collected between December 2020 and December 2021, of which 3282 were randomly sampled. All variants of concern were identified in the sequenced sample set, at ratios equivalent to those in the 100-fold larger German GISAID sequence dataset from the same time period. Phylogenetic analysis confirmed variant assignments. Multiple mutations of concern emerged during the observation period. To model vaccine effectiveness in vitro, we employed authentic-virus neutralization assays, confirming that both the Beta and Zeta variants are capable of immune evasion. The IMS-SC2 sequence dataset facilitated an estimate of the SARS-CoV-2 incidence based on genetic evolution rates. Together with modeled vaccine efficacies, Delta-specific incidence estimation indicated that the German vaccination campaign contributed substantially to a deceleration of the nascent German Delta wave. CONCLUSIONS: SARS-CoV-2 molecular and genomic surveillance may inform public health policies including vaccination strategies and enable a proactive approach to controlling coronavirus disease 2019 spread as the virus evolves.

A bioinformatic pipeline for simulating viral integration data.

Viral integration is a complex biological process, and it is useful to have a reference integration dataset with known properties to compare experimental data against, or for comparing with the results from computational tools that detect integration. To generate these data, we developed a pipeline for simulating integrations of a viral or vector genome into a host genome. Our method reproduces more complex characteristics of vector and viral integration, including integration of sub-genomic fragments, structural variation of the integrated genomes, and deletions from the host genome at the integration site. Our method [1] takes the form of a snakemake [2] pipeline, consisting of a Python [3] script using the Biopython [4] module that simulates integrations of a viral reference into a host reference. This produces a reference containing integrations, from which sequencing reads are simulated using ART [5]. The IDs of the reads crossing integration junctions are then annotated using another python script to produce the final output, consisting of the simulated reads and a table of the locations of those integrations and the reads crossing each integration junction. To illustrate our method, we provide simulated reads, integration locations, as well as the code required to simulate integrations using any virus and host reference. This simulation method was used to investigate the performance of viral integration tools in our research [6].

Investigation of a Limited but Explosive COVID-19 Outbreak in a German Secondary School.

The role of schools as a source of infection and driver in the coronavirus-pandemic has been controversial and is still not completely clarified. To prevent harm and disadvantages for children and adolescents, but also adults, detailed data on school outbreaks is needed, especially when talking about open schools employing evidence-based safety concepts. Here, we investigated the first significant COVID-19 school outbreak in Hamburg, Germany, after the re-opening of schools in 2020. Using clinical, laboratory, and contact data and spatial measures for epidemiological and environmental studies combined with whole-genome sequencing (WGS) analysis, we examined the causes and the course of the secondary school outbreak. The potential index case was identified by epidemiological tracking and the lessons in classrooms with presumably high virus spreading rates and further infection chains in the setting. Sequence analysis of samples detected one sample of a different virus lineage and 25 virus genomes with almost identical sequences, of which 21 showed 100% similarity. Most infections occurred in connection with two lesson units of the primary case. Likely, 31 students (12-14 years old), two staff members, and three family members were infected in the school or the typical household. Sequence analysis revealed an outbreak cluster with a single source that was epidemiologically identified as a member of the educational staff. In lesson units, two superspreading events of varying degrees with airborne transmission took place. These were influenced by several parameters including the exposure times, the use of respiratory masks while speaking and spatial or structural conditions at that time.

Isling: A Tool for Detecting Integration of Wild-Type Viruses and Clinical Vectors.

Detecting viral and vector integration events is a key step when investigating interactions between viral and host genomes. This is relevant in several fields, including virology, cancer research and gene therapy. For example, investigating integrations of wild-type viruses such as human papillomavirus and hepatitis B virus has proven to be crucial for understanding the role of these integrations in cancer. Furthermore, identifying the extent of vector integration is vital for determining the potential for genotoxicity in gene therapies. To address these questions, we developed isling, the first tool specifically designed for identifying viral integrations in both wild-type and vector from next-generation sequencing data. Isling addresses complexities in integration behaviour including integration of fragmented genomes and integration junctions with ambiguous locations in a host or vector genome, and can also flag possible vector recombinations. We show that isling is up to 1.6-fold faster and up to 170% more accurate than other viral integration tools, and performs well on both simulated and real datasets. Isling is therefore an efficient and application-agnostic tool that will enable a broad range of investigations into viral and vector integration. These include comparisons between integrations of wild-type viruses and gene therapy vectors, as well as assessing the genotoxicity of vectors and understanding the role of viruses in cancer.

Perspective on Proteomics for Virus Detection in Clinical Samples.

One of the most widely used methods to detect an acute viral infection in clinical specimens is diagnostic real-time polymerase chain reaction. However, because of the COVID-19 pandemic, mass-spectrometry-based proteomics is currently being discussed as a potential diagnostic method for viral infections. Because proteomics is not yet applied in routine virus diagnostics, here we discuss its potential to detect viral infections. Apart from theoretical considerations, the current status and technical limitations are considered. Finally, the challenges that have to be overcome to establish proteomics in routine virus diagnostics are highlighted.

Stable Isotope-Triggered Offset Fragmentation Allows Massively Multiplexed Target Profiling on Quadrupole-Orbitrap Mass Spectrometers.

Parallel-reaction monitoring (PRM) using high resolution, accurate mass (HR/AM) analysis on quadrupole-Orbitrap mass spectrometers, like the Q Exactive, is one of the most promising approaches for targeted protein analysis. However, PRM has a limited multiplexing capacity, which depends heavily on the reproducibility of peptide retention times. To overcome these limitations, we aimed to establish an easily applicable data acquisition mode that allows retention-time-independent massive multiplexing on Q Exactive mass spectrometers. The presented method is based on data-dependent acquisition and is called pseudo-PRM. In principle, high-intensity stable isotope-labeled peptides are used to trigger the repeated fragmentation of the corresponding light peptides. In this way, pseudo-PRM data can be analyzed like normal PRM data. We tested pseudo-PRM for the target detection from yeast, human cells, and serum, showing good reproducibility and sensitivities comparable to normal PRM. We demonstrated further that pseudo-PRM can be used for accurate and precise quantification of target peptides, using both precursor and fragment ion areas. Moreover, we showed multiplexing of more than 1000 targets in a single run. Finally, we applied pseudo-PRM to quantify vaccinia virus proteins during infection, verifying that pseudo-PRM presents an alternative method for multiplexed target profiling on Q Exactive mass spectrometers.

Whole genome sequencing reveals extended natural transformation in Campylobacter impacting diagnostics and the pathogens adaptive potential.

Campylobacter is the major bacterial agent of human gastroenteritis worldwide and represents a crucial global public health burden. Species differentiation of C. jejuni and C. coli and phylogenetic analysis is challenged by inter-species horizontal gene transfer. Routine real-time PCR on more than 4000 C. jejuni and C. coli field strains identified isolates with ambiguous PCR results for species differentiation, in particular, from the isolation source eggs. K-mer analysis of whole genome sequencing data indicated the presence of C. coli hybrid strains with huge amounts of C. jejuni introgression. Recombination events were distributed over the whole chromosome. MLST typing was impaired, since C. jejuni sequences were also found in six of the seven housekeeping genes. cgMLST suggested that the strains were phylogenetically unrelated. Intriguingly, the strains shared a stress response set of C. jejuni variant genes, with proposed roles in oxidative, osmotic and general stress defence, chromosome maintenance and repair, membrane transport, cell wall and capsular biosynthesis and chemotaxis. The results have practical impact on routine typing and on the understanding of the functional adaption to harsh environments, enabling successful spreading and persistence of Campylobacter.

gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms.

The study of bacterial symbioses has grown exponentially in the recent past. However, existing bioinformatic workflows of microbiome data analysis do commonly not integrate multiple meta-omics levels and are mainly geared toward human microbiomes. Microbiota are better understood when analyzed in their biological context; that is together with their host or environment. Nevertheless, this is a limitation when studying non-model organisms mainly due to the lack of well-annotated sequence references. Here, we present gNOMO, a bioinformatic pipeline that is specifically designed to process and analyze non-model organism samples of up to three meta-omics levels: metagenomics, metatranscriptomics and metaproteomics in an integrative manner. The pipeline has been developed using the workflow management framework Snakemake in order to obtain an automated and reproducible pipeline. Using experimental datasets of the German cockroach Blattella germanica, a non-model organism with very complex gut microbiome, we show the capabilities of gNOMO with regard to meta-omics data integration, expression ratio comparison, taxonomic and functional analysis as well as intuitive output visualization. In conclusion, gNOMO is a bioinformatic pipeline that can easily be configured, for integrating and analyzing multiple meta-omics data types and for producing output visualizations, specifically designed for integrating paired-end sequencing data with mass spectrometry from non-model organisms.

Erratum: gNOMO: a multi-omics pipeline for integrated host and microbiome analysis of non-model organisms.

[This corrects the article DOI: 10.1093/nargab/lqaa058.].

Purple: A Computational Workflow for Strategic Selection of Peptides for Viral Diagnostics Using MS-Based Targeted Proteomics.

Emerging virus diseases present a global threat to public health. To detect viral pathogens in time-critical scenarios, accurate and fast diagnostic assays are required. Such assays can now be established using mass spectrometry-based targeted proteomics, by which viral proteins can be rapidly detected from complex samples down to the strain-level with high sensitivity and reproducibility. Developing such targeted assays involves tedious steps of peptide candidate selection, peptide synthesis, and assay optimization. Peptide selection requires extensive preprocessing by comparing candidate peptides against a large search space of background proteins. Here we present Purple (Picking unique relevant peptides for viral experiments), a software tool for selecting target-specific peptide candidates directly from given proteome sequence data. It comes with an intuitive graphical user interface, various parameter options and a threshold-based filtering strategy for homologous sequences. Purple enables peptide candidate selection across various taxonomic levels and filtering against backgrounds of varying complexity. Its functionality is demonstrated using data from different virus species and strains. Our software enables to build taxon-specific targeted assays and paves the way to time-efficient and robust viral diagnostics using targeted proteomics.

A Potential Golden Age to Come-Current Tools, Recent Use Cases, and Future Avenues for De Novo Sequencing in Proteomics.

In shotgun proteomics, peptide and protein identification is most commonly conducted using database search engines, the method of choice when reference protein sequences are available. Despite its widespread use the database-driven approach is limited, mainly because of its static search space. In contrast, de novo sequencing derives peptide sequence information in an unbiased manner, using only the fragment ion information from the tandem mass spectra. In recent years, with the improvements in MS instrumentation, various new methods have been proposed for de novo sequencing. This review article provides an overview of existing de novo sequencing algorithms and software tools ranging from peptide sequencing to sequence-to-protein mapping. Various use cases are described for which de novo sequencing was successfully applied. Finally, limitations of current methods are highlighted and new directions are discussed for a wider acceptance of de novo sequencing in the community.