No abstract

16S rRNA based analysis is the established standard for elucidating microbial community composition. While short read 16S analyses are largely confined to genus-level resolution at best since only a portion of the gene is sequenced, full-length 16S sequences have the potential to provide species-level accuracy. However, existing taxonomic identification algorithms are not optimized for the increased read length and error rate of long-read data. Here we present Emu, a novel approach that employs an expectation-maximization (EM) algorithm to generate taxonomic abundance profiles from full-length 16S rRNA reads. Results produced from one simulated data set and two mock communities prove Emu capable of accurate microbial community profiling while obtaining fewer false positives and false negatives than alternative methods. Additionally, we illustrate a real-world application of our new software by comparing clinical sample composition estimates generated by an established whole-genome shotgun sequencing workflow to those returned by full-length 16S sequences processed with Emu.

Background Tracing of SARS-CoV-2 transmission chains is still a major challenge for public health authorities, when incidental contacts are not recalled or are not perceived as potential risk contacts. Viral sequencing can address key questions about SARS-CoV-2 evolution and may support reconstruction of viral transmission networks by integration of molecular epidemiology into classical contact tracing. Methods In collaboration with local public health authorities, we set up an integrated system of genomic surveillance in an urban setting, combining a) viral surveillance sequencing, b) genetically based identification of infection clusters in the population, c) integration of public health authority contact tracing data, and d) a user-friendly dashboard application as a central data analysis platform. Results Application of the integrated system from August to December 2020 enabled a characterization of viral population structure, analysis of four outbreaks at a maximum care hospital, and genetically based identification of five putative population infection clusters, all of which were confirmed by contact tracing. The system contributed to the development of improved hospital infection control and prevention measures and enabled the identification of previously unrecognized transmission chains, involving a martial arts gym and establishing a link between the hospital to the local population. Conclusions Integrated systems of genomic surveillance could contribute to the monitoring and, potentially, improved management of SARS-CoV-2 transmission in the population.

Viral genome sequencing can address key questions about SARS-CoV-2 evolution and viral transmission. Here, we present an integrated system of genomic surveillance in the German city of Düsseldorf, combining a) viral surveillance sequencing, b) genetically based identification of infection clusters in the population, c) analysis of hospital outbreaks, d) integration of public health authority contact tracing data, and e) a user-friendly dashboard application as a central data analysis platform. The generated surveillance sequencing data (n = 320 SARS-CoV-2 genomes) showed that the development of the local viral population structure from August to December 2020 was consistent with European trends, with the notable absence of SARS-CoV-2 variants 20I/501Y.V1/B.1.1.7 and B.1.351 until the end of the local sampling period. Against a background of local surveillance and other publicly available SARS-CoV-2 data, four putative SARS-CoV-2 outbreaks at Düsseldorf University Hospital between October and December 2020 (n = 44 viral genomes) were investigated and confirmed as clonal, contributing to the development of improved infection control and prevention measures. An analysis of the generated surveillance sequencing data with respect to infection clusters in the population based on a greedy clustering algorithm identified five candidate clusters, all of which were subsequently confirmed by the integration of public health authority contact tracing data and shown to be represent transmission settings of particular relevance (schools, care homes). A joint analysis of outbreak and surveillance data identified a potential transmission of an outbreak strain from the local population into the hospital and back; and an in-depth analysis of one population infection cluster combining genetic with contact tracing data enabled the identification of a previously unrecognized population transmission chain involving a martial arts gym. Based on these results and a real-time sequencing experiment in which we demonstrated the feasibility of achieving sample-to-turnaround times of <30 hours with the Oxford Nanopore technology, we discuss the potential benefits of routine ultra-fast sequencing of all detected infections for contact tracing, infection cluster detection, and, ultimately, improved management of the SARS-CoV-2 pandemic.

Alzheimer`s disease (AD) is the most prevalent cause of dementia. It is often assumed that AD is caused by an aggregation of extracellular beta-amyloid and intracellular tau-protein, supported by a recent study showing reduced brain amyloid levels and reduced cognitive decline under treatment with a beta-amyloid-binding antibody. Confirmation of the importance of amyloid as a therapeutic target notwithstanding, the underlying causes of beta-amyloid aggregation in the human brain, however, remain to be elucidated. Multiple lines of evidence point towards an important role of infectious agents and/or inflammatory conditions in the etiology of AD. Various microorganisms have been detected in the cerebrospinal fluid and brains of AD-patients and have thus been hypothesized to be linked to the development of AD, including Porphyromonas gingivalis (PG) and Spirochaetes. Intriguingly, these microorganisms are also found in the oral cavity under normal physiological conditions, which is often affected by multiple pathologies like caries or tooth loss in AD patients. Oral cavity pathologies are mostly accompanied by a compositional shift in the community of oral microbiota, mainly affecting commensal microorganisms and referred to as ‘dysbiosis’. Oral dysbiosis seems to be at least partly mediated by key pathogens such as PG, and it is associated with a pro-inflammatory state that promotes the destruction of connective tissue in the mouth, possibly enabling the translocation of pathogenic microbiota from the oral cavity to the nervous system. It has therefore been hypothesized that dysbiosis of the oral microbiome may contribute to the development of AD. In this review, we discuss the infectious hypothesis of AD in the light of the oral microbiome and microbiome-host interactions, which may contribute to or even cause the development of AD. We discuss technical challenges relating to the detection of microorganisms in relevant body fluids and approaches for avoiding false-positives, and introduce the antibacterial protein lactoferrin as a potential link between the dysbiotic microbiome and the host inflammatory reaction.