Retrospective screening of routine respiratory samples revealed undetected community transmission and missed intervention opportunities for SARS-CoV-2 in the United Kingdom.

In the early phases of the SARS coronavirus type 2 (SARS-CoV-2) pandemic, testing focused on individuals fitting a strict case definition involving a limited set of symptoms together with an identified epidemiological risk, such as contact with an infected individual or travel to a high-risk area. To assess whether this impaired our ability to detect and control early introductions of the virus into the UK, we PCR-tested archival specimens collected on admission to a large UK teaching hospital who retrospectively were identified as having a clinical presentation compatible with COVID-19. In addition, we screened available archival specimens submitted for respiratory virus diagnosis, and dating back to early January 2020, for the presence of SARS-CoV-2 RNA. Our data provides evidence for widespread community circulation of SARS-CoV-2 in early February 2020 and into March that was undetected at the time due to restrictive case definitions informing testing policy. Genome sequence data showed that many of these early cases were infected with a distinct lineage of the virus. Sequences obtained from the first officially recorded case in Nottinghamshire - a traveller returning from Daegu, South Korea - also clustered with these early UK sequences suggesting acquisition of the virus occurred in the UK and not Daegu. Analysis of a larger sample of sequences obtained in the Nottinghamshire area revealed multiple viral introductions, mainly in late February and through March. These data highlight the importance of timely and extensive community testing to prevent future widespread transmission of the virus.

Clostridium beijerinckii strain degeneration is driven by the loss of Spo0A activity.

Solventogenic clostridia represent a diverse group of anaerobic, spore-forming bacteria capable of producing acetone, butanol and ethanol through their unique biphasic metabolism. An intrinsic problem with these organisms however is their tendency to degenerate when repeatedly subcultured or when grown continuously. This phenomenon sees cells lose their ability to produce solvents and spores, posing a significant problem for industrial applications. To investigate the mechanistic and evolutionary basis of degeneration we combined comparative genomics, ultra-deep sequencing, and concepts of sociomicrobiology using Clostridium beijerinckii NCIMB 8052 as our model organism. These approaches revealed spo0A, the master regulator gene involved in spore and solvent formation, to be key to the degeneration process in this strain. Comparative genomics of 71 degenerate variants revealed four distinct hotspot regions that contained considerably more mutations than the rest of the genome. These included spo0A as well as genes suspected to regulate its expression and activity. Ultra-deep sequencing of populations during the subculturing process showed transient increases in mutations we believe linked to the spo0A network, however, these were ultimately dominated by mutations in the master regulator itself. Through frequency-dependent fitness assays, we found that spo0A mutants gained a fitness advantage, relative to the wild type, presumably allowing for propagation throughout the culture. Combined, our data provides new insights into the phenomenon of clostridial strain degeneration and the C. beijerinckii NCIMB 8052 solvent and spore regulation network.

Readfish enables targeted nanopore sequencing of gigabase-sized genomes.

Nanopore sequencers can be used to selectively sequence certain DNA molecules in a pool by reversing the voltage across individual nanopores to reject specific sequences, enabling enrichment and depletion to address biological questions. Previously, we achieved this using dynamic time warping to map the signal to a reference genome, but the method required substantial computational resources and did not scale to gigabase-sized references. Here we overcome this limitation by using graphical processing unit (GPU) base-calling. We show enrichment of specific chromosomes from the human genome and of low-abundance organisms in mixed populations without a priori knowledge of sample composition. Finally, we enrich targeted panels comprising 25,600 exons from 10,000 human genes and 717 genes implicated in cancer, identifying PML-RARA fusions in the NB4 cell line in <15 h sequencing. These methods can be used to efficiently screen any target panel of genes without specialized sample preparation using any computer and a suitable GPU. Our toolkit, readfish, is available at https://www.github.com/looselab/readfish .

Identifying the immune interactions underlying HLA class I disease associations.

Variation in the risk and severity of many autoimmune diseases, malignancies and infections is strongly associated with polymorphisms at the HLA class I loci. These genetic associations provide a powerful opportunity for understanding the etiology of human disease. HLA class I associations are often interpreted in the light of 'protective' or 'detrimental' CD8+ T cell responses which are restricted by the host HLA class I allotype. However, given the diverse receptors which are bound by HLA class I molecules, alternative interpretations are possible. As well as binding T cell receptors on CD8+ T cells, HLA class I molecules are important ligands for inhibitory and activating killer immunoglobulin-like receptors (KIRs) which are found on natural killer cells and some T cells; for the CD94:NKG2 family of receptors also expressed mainly by NK cells and for leukocyte immunoglobulin-like receptors (LILRs) on myeloid cells. The aim of this study is to develop an immunogenetic approach for identifying and quantifying the relative contribution of different receptor-ligand interactions to a given HLA class I disease association and then to use this approach to investigate the immune interactions underlying HLA class I disease associations in three viral infections: Human T cell Leukemia Virus type 1, Human Immunodeficiency Virus type 1 and Hepatitis C Virus as well as in the inflammatory condition Crohn's disease.

When considering someone's risk of disease, every person is different but some similarities can be found when looking across populations. Some people are more likely to develop a certain disease, while others are protected in some way. Part of this variation is explained by the individual's genes, while their lifestyle and environment are other factors. Numerous studies have looked for associations between different versions of genes, known as gene variants, and the occurrence of disease to identify who is at risk. There is one cluster of genes called the HLA genes that is a well-known hotspot for disease associations. The HLA cluster is named for the group of proteins it encodes, called the human leukocyte antigen (HLA) complex. These cell-surface proteins regulate the immune system in humans. These proteins are present on the surface of cells, and they help the immune system distinguish foreign invaders such as viruses and bacteria from the body's own cells. Variants in the HLA genes are associated with more than 100 diseases, including infectious diseases like HIV, autoimmune conditions such as multiple sclerosis, and some cancers. However, while identifying which genetic variants are associated with an increased or decreased risk of disease is relatively simple, understanding why those genetic variants are associated with a particular disease is much harder. Debebe et al. have developed a new method to find out why certain gene variants in the HLA cluster are associated with disease in humans. They used this method to investigate known genetic variants associated with three viral infections: HIV, hepatitis C, and human leukemia virus ­ and one inflammatory disease: Crohn's disease. Critically, Debebe et al. looked at the interactions between different immune cells and the cell-surface proteins encoded by the HLA gene variants in different cases of these diseases. In doing so, the analysis was able to identify which cells of the immune system were responsible for the associations between gene variants and diseases. In principle, this method could be applied to study any disease in any species. It could also be used in classic gene association studies to test for false positive results and "passenger" mutations, two common problems that beset sound interpretations from these studies.