Human sewage from Florianopolis (Santa Catarina, Brazil) was analyzed for severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) from October 2019 until March 2020. Twenty five ml of sewage samples were clarified and viruses concentrated using a glycine buffer method coupled with polyethylene glycol precipitation, and viral RNA extracted using a commercial kit. SARS-CoV-2 RNA was detected by RT-qPCR using oligonucleotides targeting N1, S and two RdRp regions. The results of all positive samples were further confirmed by a different RT-qPCR system in an independent laboratory. S and RdRp amplicons were sequenced to confirm identity with SARS-CoV-2. Genome sequencing was performed using two strategies; a sequence-independent single-primer amplification (SISPA) approach, and by direct metagenomics using Illumina's NGS. SARS-CoV-2 RNA was detected on 27th November 2019 (5.49 ± 0.02 log 10 SARS-CoV-2 genome copies (GC) L −1 ), detection being confirmed by an independent laboratory and genome sequencing analysis. The samples in the subsequent three events were positive by all RT-qPCR assays; these positive results were also confirmed by an independent laboratory. The average load was 5.83 ± 0.12 log 10 SARS-CoV-2 GC L −1 , ranging from 5.49 ± 0.02 log 10 GC L −1 (27th November 2019) to 6.68 ± 0.02 log 10 GC L −1 (4th March 2020). Our findings demonstrate that SARS-CoV-2 was likely circulating undetected in the community in Brazil since November 2019, earlier than the first reported case in the Americas (21st January 2020).

High-throughput sequencing of 16S rRNA amplicon has been extensively employed to perform microbiome characterization worldwide. As a culture-independent methodology, it has allowed high-level profiling of sample bacterial composition directly from samples. However, most studies are limited to information regarding relative bacterial abundances (sample proportions), ignoring scenarios in which sample microbe biomass can vary widely. Here, we use an equivolumetric protocol for 16S rRNA amplicon library preparation capable of generating Illumina sequencing data responsive to input DNA, recovering proportionality between observed read counts and absolute bacterial abundances within each sample. Under specified conditions, we show that the estimation of colony-forming units (CFU), the most common unit of bacterial abundance in classical microbiology, is challenged mostly by resolution and taxon-to-taxon variation. We propose Bayesian cumulative probability models to address such issues. Our results indicate that predictive errors vary consistently below one order of magnitude for total microbial load and abundance of observed bacteria. We also demonstrate our approach has the potential to generalize to previously unseen bacteria, but predictive performance is hampered by specific taxa of uncommon profile. Finally, it remains clear that high-throughput sequencing data are not inherently restricted to sample proportions only, and such technologies bear the potential to meet the working scales of traditional microbiology.

Hospital-built environment colonization by healthcare-associated infections-related bacteria (HAIrB) and the interaction with their occupants have been studied to support more effective tools for HAI control. To investigate HAIrB dynamics and antimicrobial resistance (AMR) profile we carried out a 6-month surveillance program in a developing country public hospital, targeting patients, hospital environment, and healthcare workers, using culture-dependent and culture-independent 16S rRNA gene sequencing methods. The bacterial abundance in both approaches shows that the HAIrB group has important representativeness, with the taxa Enterobacteriaceae, Pseudomonas, Staphylococcus, E. coli, and A. baumannii widely dispersed and abundant over the time at the five different hospital units included in the survey. We observed a high abundance of HAIrB in the patient rectum, hands, and nasal sites. In the healthcare workers, the HAIrB distribution was similar for the hands, protective clothing, and mobile phones. In the hospital environment, the healthcare workers resting areas, bathrooms, and bed equipment presented a wide distribution of HAIrB and AMR, being classified as contamination hotspots. AMR is highest in patients, followed by the environment and healthcare workers. The most frequently detected beta-lactamases genes were, blaSHV–like, blaOXA–23–like, blaOXA–51–like, blaKPC–like, blaCTX–M–1, blaCTX–M–8, and blaCTX–M–9 groups. Our results demonstrate that there is a wide spread of antimicrobial resistance due to HAIrB in the hospital environment, circulating among patients and healthcare workers. The contamination hotspots identified proved to be constant over time. In the fight for patient safety, these findings can reorient practices and help to set up new guidelines for HAI control.

No abstract

The western mesoregion of the state of Santa Catarina (SC), Southern Brazil, was heavily affected as a whole by the COVID-19 pandemic in early 2021. This study aimed to evaluate the dynamics of the SARS-CoV-2 virus spreading patterns in the SC state from March 2020 to April 2021 using genomic surveillance. During this period, there were 23 distinct variants, including Beta and Gamma, among which the Gamma and related lineages were predominant in the second pandemic wave within SC. A regionalization of P.1-like-II in the Western SC region was observed, concomitant to the increase in cases, mortality, and the case fatality rate (CFR) index. This is the first evidence of the regionalization of the SARS-CoV-2 transmission in SC and it highlights the importance of tracking the variants, dispersion, and impact of SARS-CoV-2 on the public health systems.

Background Next-generation sequencing (NGS) has been extensively employed to perform microbiome characterization worldwide. As a culture-independent methodology, it has allowed high-level profiling of sample microbial composition. However, most studies are limited to sample information regarding relative bacterial abundances (sample proportions), ignoring scenarios in which sample microbe biomass can vary widely. Here, we develop an equivolumetric protocol for amplicon library preparation capable of generating NGS data responsive to input DNA, recovering proportionality between observed read counts and absolute bacterial abundances within each sample. Within a determined range, we show that the estimation of sample colony-forming units (CFU), the most common unit of bacterial abundance in classical microbiology, is challenged mostly by resolution and taxon-to-taxon variation. We propose Bayesian cumulative probability models to address such issues.Results Observed read counts were consistently proportional to input DNA, total microbial load, and bacterium-specific sample abundances, although a saturation tendency was observed as abundances increased. Using Bayesian cumulative probability models, predictive errors in sample CFU estimation varied constantly below one order of magnitude - as measured by the mean absolute log10-ratio (MALR). For total microbial load, observed MALR was no greater than 0.2 during both cross-validation and validation on a test dataset. For observed bacteria, estimation of taxon-specific CFU showed MALR values of at most 0.5. We also performed leave-one-group-out cross-validation to assess predictive performance for previously unseen bacteria. While most bacteria showed MALR no greater than 1, such a threshold was exceeded only by Bacillus cereus.Conclusions Being able to estimate sample CFU in a high-throughput fashion has a wide range of applications, from the study of built environments to public health surveillance. This study shows that equivolumetric protocols along with cumulative probability models allow sample CFU estimation from microbiome datasets. Further, our approach has the potential to generalize to previously unmodeled bacteria, an important feature in high-throughput settings. Lastly, it remains clear that NGS data are not inherently restricted to sample proportions only, and microbiome science can finally meet the working scales of classical microbiology.