The 2023 monkeypox (mpox) epidemic was caused by a subclade IIb descendant of a monkeypox virus (MPXV) lineage traced back to Nigeria in 1971. Person-to-person transmission appears higher than for clade I or subclade IIa MPXV, possibly caused by genomic changes in subclade IIb MPXV. Key genomic changes could occur in the genome’s low-complexity regions (LCRs), which are challenging to sequence and are often dismissed as uninformative. Here, using a combination of highly sensitive techniques, we determine a high-quality MPXV genome sequence of a representative of the current epidemic with LCRs resolved at unprecedented accuracy. This reveals significant variation in short tandem repeats within LCRs. We demonstrate that LCR entropy in the MPXV genome is significantly higher than that of single-nucleotide polymorphisms (SNPs) and that LCRs are not randomly distributed. In silico analyses indicate that expression, translation, stability, or function of MPXV orthologous poxvirus genes (OPGs), including OPG153, OPG204, and OPG208, could be affected in a manner consistent with the established “genomic accordion” evolutionary strategies of orthopoxviruses. We posit that genomic studies focusing on phenotypic MPXV differences should consider LCR variability.

Dengue is the most prevalent mosquito-borne viral disease in humans, and cases are continuing to rise globally. In particular, islands in the Caribbean have experienced more frequent outbreaks, and all four dengue virus (DENV) serotypes have been reported in the region, leading to hyperendemicity and increased rates of severe disease. However, there is significant variability regarding virus surveillance and reporting between islands, making it difficult to obtain an accurate understanding of the epidemiological patterns in the Caribbean. To investigate this, we used travel surveillance and genomic epidemiology to reconstruct outbreak dynamics, DENV serotype turnover, and patterns of spread within the region from 2009-2022. We uncovered two recent DENV-3 introductions from Asia, one of which resulted in a large outbreak in Cuba, which was previously under-reported. We also show that while outbreaks can be synchronized between islands, they are often caused by different serotypes. Our study highlights the importance of surveillance of infected travelers to provide a snapshot of local introductions and transmission in areas with limited local surveillance and suggests that the recent DENV-3 introductions may pose a major public health threat in the region.

The characterization of population structures plays a main role for understanding outbreaks and the dynamics of bacterial spreading. In Escherichia coli, the widely used combination of multiplex-PCR scheme together with goeBURST has some limitations. The purpose of this study is to show that the combination of different phylogenetic approaches based on concatenated sequences of MLST genes results in a more precise assignment of E. coli phylogenetic groups, complete understanding of population structure and reconstruction of ancestral clones. A collection of 80 Escherichia coli strains of different origins was analyzed following the Clermont and Doumith's multiplex-PCR schemes. Doumith's multiplex-PCR showed only 1.7% of misassignment, whereas Clermont's-2000 protocol reached 14.0%, although the discrepancies reached 30% and 38.7% respectively when recombinant C, F and E phylogroups were considered. Therefore, correct phylogroup attribution is highly variable and depends on the clonal composition of the sample. As far as population structure of these E. coli strains, including 48 E. coli genomes from GenBank, goeBURST provides a quite dispersed population structure; whereas NeighborNet approach reveals a complex population structure. MLST-based eBURST can infer different founder genotypes, for instance ST23/ST88 could be detected as the founder genotypes for STC23; however, phylogenetic reconstructions might suggest ST410 as the ancestor clone and several evolutionary trajectories with different founders. To improve our routine understanding of E. coli molecular epidemiology, we propose a strategy based on three successive steps; first, to discriminate three main groups A/B1/C, D/F/E and B2 following Doumith's protocol; second, visualization of population structure based on MLST genes according to goeBURST, using NeighborNet to establish more complex relationships among STs; and third, to perform, a cost-free characterization of evolutionary trajectories in variants emerging along the clonal expansion using parsimony methods of phylogenetic analysis.

Background: Phylogenetic analyses of the bacterial genomes based on the simple classification in core- genes and accessory genes pools could offer an incomplete view of the evolutionary processes, of which some are still unresolved. A combined strategy based on stratified phylogeny and ancient molecular polymorphisms is proposed to infer detailed evolutionary reconstructions by using a large number of whole genomes. This strategy, based on the highest number of genomes available in public databases, was evaluated for improving knowledge of the ancient diversification of E. coli. This staggered evolutionary scenario was also used to investigate whether the diversification of the ancient E. coli lineages could be associated with particular lifestyles and adaptive strategies. Results: Phylogenetic reconstructions, exploiting 6220 available genomes in Genbank, established the E. coli core genome in 1023 genes, representing about 20% of the complete genome. The combined strategy using stratified phylogeny plus molecular polymorphisms inferred three ancient lineages (D, EB1A and FGB2). Lineage D was the closest to E. coli root. A staggered diversification could also be proposed in EB1A and FGB2 lineages and the phylogroups into these lineages. Several molecular markers suggest that each lineage had different adaptive trajectories. The analysis of gained and lost genes in the main lineages showed that functions of carbohydrates utilization (uptake of and metabolism) were gained principally in EB1A lineage, whereas loss of environmental-adaptive functions in FGB2 lineage were observed, but this lineage showed higher accumulated mutations and ancient recombination events. The population structure of E. coli was re-evaluated including up to 7561 new sequenced genomes, showing a more complex population structure of E. coli, as a new phylogroup, phylogroup I, was proposed. Conclusions: A staggered reconstruction of E. coli phylogeny is proposed, indicating evolution from three ancestral lineages to reach all main known phylogroups. New phylogroups were confirmed, suggesting an increasingly complex population structure of E. coli. However these new phylogroups represent < 1% of the global E. coli population. A few key evolutionary forces have driven the diversification of the two main E. coli lineages, metabolic flexibility in one of them and colonization-virulence in the other.

Background: Rapid diagnostic tests (RDTs) are being widely used to manage COVID-19 pandemic. However, many results remain unreported or unconfirmed, altering a correct epidemiological surveillance. Objective: Our aim was to evaluate an artificial intelligence–based smartphone app, connected to a cloud web platform, to automatically and objectively read RDT results and assess its impact on COVID-19 pandemic management. Methods: Overall, 252 human sera were used to inoculate a total of 1165 RDTs for training and validation purposes. We then conducted two field studies to assess the performance on real-world scenarios by testing 172 antibody RDTs at two nursing homes and 96 antigen RDTs at one hospital emergency department. Results: Field studies demonstrated high levels of sensitivity (100%) and specificity (94.4%, CI 92.8%-96.1%) for reading IgG band of COVID-19 antibody RDTs compared to visual readings from health workers. Sensitivity of detecting IgM test bands was 100%, and specificity was 95.8% (CI 94.3%-97.3%). All COVID-19 antigen RDTs were correctly read by the app. Conclusions: The proposed reading system is automatic, reducing variability and uncertainty associated with RDTs interpretation and can be used to read different RDT brands. The web platform serves as a real-time epidemiological tracking tool and facilitates reporting of positive RDTs to relevant health authorities.