Global dispersal and increasing frequency of the SARS-CoV-2 Spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of Spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large data set, well represented by both Spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the Spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant.

Background: COVID-19 can course with respiratory and extrapulmonary disease. SARS-CoV-2 RNA is detected in respiratory samples but also in blood, stool and urine. Severe COVID-19 is characterized by a dysregulated host response to this virus. We studied whether viral RNAemia or viral RNA load in plasma is associated with severe COVID-19 and also to this dysregulated response.Methods: A total of 250 patients with COVID-19 were recruited (50 outpatients, 100 hospitalized ward patients and 100 critically ill). Viral RNA detection and quantification in plasma was performed using droplet digital PCR, targeting the N1 and N2 regions of the SARS-CoV-2 nucleoprotein gene. The association between SARS-CoV-2 RNAemia and viral RNA load in plasma with severity was evaluated by multivariate logistic regression. Correlations between viral RNA load and biomarkers evidencing dysregulation of host response were evaluated by calculating the Spearman correlation coefficients.Results: The frequency of viral RNAemia was higher in the critically ill patients (78%) compared to ward patients (27%) and outpatients (2%) (p < 0.001). Critical patients had higher viral RNA loads in plasma than non-critically ill patients, with non-survivors showing the highest values. When outpatients and ward patients were compared, viral RNAemia did not show significant associations in the multivariate analysis. In contrast, when ward patients were compared with ICU patients, both viral RNAemia and viral RNA load in plasma were associated with critical illness (OR [CI 95%],p): RNAemia (3.92 [1.183–12.968], 0.025), viral RNA load (N1) (1.962 [1.244–3.096], 0.004); viral RNA load (N2) (2.229 [1.382–3.595], 0.001). Viral RNA load in plasma correlated with higher levels of chemokines (CXCL10, CCL2), biomarkers indicative of a systemic inflammatory response (IL-6, CRP, ferritin), activation of NK cells (IL-15), endothelial dysfunction (VCAM-1, angiopoietin-2, ICAM-1), coagulation activation (D-Dimer and INR), tissue damage (LDH, GPT), neutrophil response (neutrophils counts, myeloperoxidase, GM-CSF) and immunodepression (PD-L1, IL-10, lymphopenia and monocytopenia).Conclusions: SARS-CoV-2 RNAemia and viral RNA load in plasma are associated with critical illness in COVID-19. Viral RNA load in plasma correlates with key signatures of dysregulated host responses, suggesting a major role of uncontrolled viral replication in the pathogenesis of this disease.

Antibiotics have natural functions, mostly involving cell-to-cell signaling networks. The anthropogenic production of antibiotics, and its release in the microbiosphere results in a disturbance of these networks, antibiotic resistance tending to preserve its integrity. The cost of such adaptation is the emergence and dissemination of antibiotic resistance genes, and of all genetic and cellular vehicles in which these genes are located. Selection of the combinations of the different evolutionary units (genes, integrons, transposons, plasmids, cells, communities and microbiomes, hosts) is highly asymmetrical. Each unit of selection is a self-interested entity, exploiting the higher hierarchical unit for its own benefit, but in doing so the higher hierarchical unit might acquire critical traits for its spread because of the exploitation of the lower hierarchical unit. This interactive trade-off shapes the population biology of antibiotic resistance, a composed-complex array of the independent “population biologies.” Antibiotics modify the abundance and the interactive field of each of these units. Antibiotics increase the number and evolvability of “clinical” antibiotic resistance genes, but probably also many other genes with different primary functions but with a resistance phenotype present in the environmental resistome. Antibiotics influence the abundance, modularity, and spread of integrons, transposons, and plasmids, mostly acting on structures present before the antibiotic era. Antibiotics enrich particular bacterial lineages and clones and contribute to local clonalization processes. Antibiotics amplify particular genetic exchange communities sharing antibiotic resistance genes and platforms within microbiomes. In particular human or animal hosts, the microbiomic composition might facilitate the interactions between evolutionary units involved in antibiotic resistance. The understanding of antibiotic resistance implies expanding our knowledge on multi-level population biology of bacteria.

Vancomycin-resistant Enterococcus faecium (VREfm) have been increasingly reported since the 1980s. Despite the high number of published studies about VRE epidemiology, the dynamics and evolvability of these microorganisms are still not fully understood. A multilevel population genetic analysis of VREfm outbreak strains since 1986, representing the first comprehensive characterization of plasmid content in E. faecium, was performed to provide a detailed view of potential transmissible units. From a comprehensive MeSH search, we identified VREfm strains causing hospital outbreaks (1986–2012). In total, 53 VanA and 18 VanB isolates (27 countries, 5 continents) were analysed and 82 vancomycin-susceptible E. faecium (VSEfm) were included for comparison. Clonal relatedness was established by PFGE and MLST (goeBURST/Bayesian Analysis of Population Structure, BAPS). Characterization of van transposons (PCR mapping, RFLP, sequencing), plasmids (transfer, ClaI-RFLP, PCR typing of relaxases, replication-initiation proteins and toxin–antitoxin systems, hybridization, sequencing), bacteriocins and virulence determinants (PCR, hybridization, sequencing) was performed. VREfm were mainly associated with major human lineages ST17, ST18 and ST78. VREfm and VSEfm harboured plasmids of different families [RCR, small theta plasmids, RepA_N (pRUM/pLG1) and Inc18] able to yield mosaic elements. Tn1546-vanA was mainly located on pRUM/Axe-Txe (USA) and Inc18-pIP186 (Europe) plasmids. The VanB2 type (Tn5382/Tn1549) was predominant among VanB strains (chromosome and plasmids). Both strains and plasmids contributed to the spread and persistence of vancomycin resistance among E. faecium. Horizontal gene transfer events among genetic elements from different clonal lineages (same or different species) result in chimeras with different stability and host range, complicating the surveillance of epidemic plasmids.

We present CoronaHiT, a platform and throughput flexible method for sequencing SARS-CoV-2 genomes (≤ 96 on MinION or > 96 on Illumina NextSeq) depending on changing requirements experienced during the pandemic. CoronaHiT uses transposase-based library preparation of ARTIC PCR products. Method performance was demonstrated by sequencing 2 plates containing 95 and 59 SARS-CoV-2 genomes on nanopore and Illumina platforms and comparing to the ARTIC LoCost nanopore method. Of the 154 samples sequenced using all 3 methods, ≥ 90% genome coverage was obtained for 64.3% using ARTIC LoCost, 71.4% using CoronaHiT-ONT and 76.6% using CoronaHiT-Illumina, with almost identical clustering on a maximum likelihood tree. This protocol will aid the rapid expansion of SARS-CoV-2 genome sequencing globally.

The phylum Firmicutes is one of the most abundant groups of prokaryotes in the microbiota of humans and animals and includes genera of outstanding relevance in biomedicine, health care, and industry. Antimicrobial drug resistance is now considered a global health security challenge of the 21st century, and this heterogeneous group of microorganisms represents a significant part of this public health issue. The presence of the same resistant genes in unrelated bacterial genera indicates a complex history of genetic interactions. Plasmids have largely contributed to the spread of resistance genes among Staphylococcus , Enterococcus , and Streptococcus species, also influencing the selection and ecological variation of specific populations. However, this information is fragmented and often omits species outside these genera. To date, the antimicrobial resistance problem has been analyzed under a “single centric” perspective (“gene tracking” or “vehicle centric” in “single host-single pathogen” systems) that has greatly delayed the understanding of gene and plasmid dynamics and their role in the evolution of bacterial communities. This work analyzes the dynamics of antimicrobial resistance genes using gene exchange networks; the role of plasmids in the emergence, dissemination, and maintenance of genes encoding resistance to antimicrobials (antibiotics, heavy metals, and biocides); and their influence on the genomic diversity of the main Gram-positive opportunistic pathogens under the light of evolutionary ecology. A revision of the approaches to categorize plasmids in this group of microorganisms is given using the 1,326 fully sequenced plasmids of Gram-positive bacteria available in the GenBank database at the time the article was written.