Novel Organism Verification and Analysis (NOVA) study: identification of 35 clinical isolates representing potentially novel bacterial taxa using a pipeline based on whole genome sequencing.

BACKGROUND: Reliable species identification of cultured isolates is essential in clinical bacteriology. We established a new study algorithm named NOVA - Novel Organism Verification and Analysis to systematically analyze bacterial isolates that cannot be characterized by conventional identification procedures MALDI-TOF MS and partial 16 S rRNA gene sequencing using Whole Genome Sequencing (WGS). RESULTS: We identified a total of 35 bacterial strains that represent potentially novel species. Corynebacterium sp. (n = 6) and Schaalia sp. (n = 5) were the predominant genera. Two strains each were identified within the genera Anaerococcus, Clostridium, Desulfovibrio, and Peptoniphilus, and one new species was detected within Citrobacter, Dermabacter, Helcococcus, Lancefieldella, Neisseria, Ochrobactrum (Brucella), Paenibacillus, Pantoea, Porphyromonas, Pseudoclavibacter, Pseudomonas, Psychrobacter, Pusillimonas, Rothia, Sneathia, and Tessaracoccus. Twenty-seven of 35 strains were isolated from deep tissue specimens or blood cultures. Seven out of 35 isolated strains identified were clinically relevant. In addition, 26 bacterial strains that could only be identified at the species level using WGS analysis, were mainly organisms that have been identified/classified very recently. CONCLUSION: Our new algorithm proved to be a powerful tool for detection and identification of novel bacterial organisms. Publicly available clinical and genomic data may help to better understand their clinical and ecological role. Our identification of 35 novel strains, 7 of which appear to be clinically relevant, shows the wide range of undescribed pathogens yet to define.

Multilab EcoFAB study shows highly reproducible physiology and depletion of soil metabolites by a model grass.

There is a dynamic reciprocity between plants and their environment: soil physiochemical properties influence plant morphology and metabolism, and root morphology and exudates shape the environment surrounding roots. Here, we investigate the reproducibility of plant trait changes in response to three growth environments. We utilized fabricated ecosystem (EcoFAB) devices to grow the model grass Brachypodium distachyon in three distinct media across four laboratories: phosphate-sufficient and -deficient mineral media allowed assessment of the effects of phosphate starvation, and a complex, sterile soil extract represented a more natural environment with yet uncharacterized effects on plant growth and metabolism. Tissue weight and phosphate content, total root length, and root tissue and exudate metabolic profiles were consistent across laboratories and distinct between experimental treatments. Plants grown in soil extract were morphologically and metabolically distinct, with root hairs four times longer than with other growth conditions. Further, plants depleted half of the metabolites investigated from the soil extract. To interact with their environment, plants not only adapt morphology and release complex metabolite mixtures, but also selectively deplete a range of soil-derived metabolites. The EcoFABs utilized here generated high interlaboratory reproducibility, demonstrating their value in standardized investigations of plant traits.

Outbreak with OXA-23-producing Acinetobacter baumannii in a COVID-19 ICU cohort: unraveling routes of transmission.

An outbreak of OXA-23-producing carbapenem-resistant Acinetobacter baumannii amongst ICU-patients with COVID-19 likely occurred by transmission through inanimate surfaces, potentially facilitated by a contaminated positioning pillow shared between patients. Subsequent rapid spread may have been caused by exposure to respiratory secretions contaminating healthcare worker's gloves and gowns during prone positioning.

How much should we sequence? An analysis of the Swiss SARS-CoV-2 surveillance effort.

During the SARS-CoV-2 pandemic, many countries directed substantial resources toward genomic surveillance to detect and track viral variants. There is a debate over how much sequencing effort is necessary in national surveillance programs for SARS-CoV-2 and future pandemic threats. We aimed to investigate the effect of reduced sequencing on surveillance outcomes in a large genomic data set from Switzerland, comprising more than 143k sequences. We employed a uniform downsampling strategy using 100 iterations each to investigate the effects of fewer available sequences on the surveillance outcomes: (i) first detection of variants of concern (VOCs), (ii) speed of introduction of VOCs, (iii) diversity of lineages, (iv) first cluster detection of VOCs, (v) density of active clusters, and (vi) geographic spread of clusters. The impact of downsampling on VOC detection is disparate for the three VOC lineages, but many outcomes including introduction and cluster detection could be recapitulated even with only 35% of the original sequencing effort. The effect on the observed speed of introduction and first detection of clusters was more sensitive to reduced sequencing effort for some VOCs, in particular Omicron and Delta, respectively. A genomic surveillance program needs a balance between societal benefits and costs. While the overall national dynamics of the pandemic could be recapitulated by a reduced sequencing effort, the effect is strongly lineage-dependent-something that is unknown at the time of sequencing-and comes at the cost of accuracy, in particular for tracking the emergence of potential VOCs.IMPORTANCESwitzerland had one of the most comprehensive genomic surveillance systems during the COVID-19 pandemic. Such programs need to strike a balance between societal benefits and program costs. Our study aims to answer the question: How would surveillance outcomes have changed had we sequenced less? We find that some outcomes but also certain viral lineages are more affected than others by sequencing less. However, sequencing to around a third of the original effort still captured many important outcomes for the variants of concern such as their first detection but affected more strongly other measures like the detection of first transmission clusters for some lineages. Our work highlights the importance of setting predefined targets for a national genomic surveillance program based on which sequencing effort should be determined. Additionally, the use of a centralized surveillance platform facilitates aggregating data on a national level for rapid public health responses as well as post-analyses.