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.

Comparative assessment of differential network analysis methods.

Differential network analysis (DiNA) denotes a recent class of network-based Bioinformatics algorithms which focus on the differences in network topologies between two states of a cell, such as healthy and disease, to identify key players in the discriminating biological processes. In contrast to conventional differential analysis, DiNA identifies changes in the interplay between molecules, rather than changes in single molecules. This ability is especially important in cases where effectors are changed, e.g. mutated, but their expression is not. A number of different DiNA approaches have been proposed, yet a comparative assessment of their performance in different settings is still lacking. In this paper, we evaluate 10 different DiNA algorithms regarding their ability to recover genetic key players from transcriptome data. We construct high-quality regulatory networks and enrich them with co-expression data from four different types of cancer. Next, we assess the results of applying DiNA algorithms on these data sets using a gold standard list (GSL). We find that local DiNA algorithms are generally superior to global algorithms, and that all DiNA algorithms outperform conventional differential expression analysis. We also assess the ability of DiNA methods to exploit additional knowledge in the underlying cellular networks. To this end, we enrich the cancer-type specific networks with known regulatory miRNAs and compare the algorithms performance in networks with and without miRNA. We find that including miRNAs consistently and considerably improves the performance of almost all tested algorithms. Our results underline the advantages of comprehensive cell models for the analysis of -omics data.

Molecular identification of the source of an uncommon tularaemia outbreak, Germany, autumn 2016.

BackgroundIn 2016, an uncommon outbreak of oropharyngeal tularaemia involving six human cases occurred in Germany, caused by drinking contaminated fresh must after a grape harvest.AimWe describe the details of laboratory investigations leading to identification of the outbreak strain, its characterisation by next generation sequencing (NGS) and the finding of the possible source of contamination.MethodsWe incubated wine samples in different media and on agar plates. NGS was performed on DNA isolated from young wine, sweet reserve and an outbreak case's lymph node. A draft genome of the outbreak strain was generated. Vertebrate-specific PCRs using primers targeting the mitochondrial cytochrome b gene and product analyses by blast search were used to identify the putative source of must contamination.ResultsNo bacterial isolate could be obtained. Analysis of the draft genome sequence obtained from the sweet reserve attributed this sequence to Francisella tularensis subsp. holarctica, belonging to the B.12/B.34 phylogenetic clade (erythromycin-resistant biovar II). In addition, the DNA sequence obtained from the case's isolate supported our hypothesis that infection was caused by drinking contaminated must. The vertebrate-specific cytochrome b sequence derived from the young wine and the sweet reserve could be assigned to Apodemus sylvaticus (wood mouse), suggesting that a wood mouse infected with F. tularensis may have contaminated the must.ConclusionThe discovered source of infection and the transmission scenario of F. tularensis in this outbreak have not been observed previously and suggest the need for additional hygienic precautionary measures when processing and consuming freshly pressed must.

Commonly altered genomic regions in acute myeloid leukemia are enriched for somatic mutations involved in chromatin remodeling and splicing.

Acute myeloid leukemia (AML) is characterized by molecular heterogeneity. As commonly altered genomic regions point to candidate genes involved in leukemogenesis, we used microarray-based comparative genomic hybridization and single nucleotide polymorphism profiling data of 391 AML cases to further narrow down genomic regions of interest. Targeted resequencing of 1000 genes located in the critical regions was performed in a representative cohort of 50 AML samples comprising all major cytogenetic subgroups. We identified 120 missense/nonsense mutations as well as 60 insertions/deletions affecting 73 different genes (∼ 3.6 tumor-specific aberrations/AML). While most of the newly identified alterations were nonrecurrent, we observed an enrichment of mutations affecting genes involved in epigenetic regulation including known candidates like TET2, TET1, DNMT3A, and DNMT1, as well as mutations in the histone methyltransferases NSD1, EZH2, and MLL3. Furthermore, we found mutations in the splicing factor SFPQ and in the nonclassic regulators of mRNA processing CTCF and RAD21. These splicing-related mutations affected 10% of AML patients in a mutually exclusive manner. In conclusion, we could identify a large number of alterations in genes involved in aberrant splicing and epigenetic regulation in genomic regions commonly altered in AML, highlighting their important role in the molecular pathogenesis of AML.

A Next-Generation Sequencing Approach Uncovers Viral Transcripts Incorporated in Poxvirus Virions.

Transcripts are known to be incorporated in particles of DNA viruses belonging to the families of Herpesviridae and Mimiviridae, but the presence of transcripts in other DNA viruses, such as poxviruses, has not been analyzed yet. Therefore, we first established a next-generation-sequencing (NGS)-based protocol, enabling the unbiased identification of transcripts in virus particles. Subsequently, we applied our protocol to analyze RNA in an emerging zoonotic member of the Poxviridae family, namely Cowpox virus. Our results revealed the incorporation of 19 viral transcripts, while host identifications were restricted to ribosomal and mitochondrial RNA. Most viral transcripts had an unknown and immunomodulatory function, suggesting that transcript incorporation may be beneficial for poxvirus immune evasion. Notably, the most abundant transcript originated from the D5L/I1R gene that encodes a viral inhibitor of the host cytoplasmic DNA sensing machinery.