RESULTS: To investigate the genomic properties and taxonomic status of these strains, we employed both 16S rRNA Sanger sequencing and whole-genome sequencing using the Illumina HiSeq X Ten platform with PE151 (paired-end) sequencing. Our analyses revealed that the draft genome of Actinomyces acetigenes ATCC 49340 T was 3.27 Mbp with a 68.0% GC content, and Actinomyces stomatis ATCC 51655 T has a genome size of 3.08 Mbp with a 68.1% GC content. Multi-locus (atpA, rpoB, pgi, metG, gltA, gyrA, and core genome SNPs) sequence analysis supported the phylogenetic placement of strains ATCC 51655 T and ATCC 49340 T as independent lineages. Digital DNA-DNA hybridization (dDDH), average nucleotide identity (ANI), and average amino acid identity (AAI) analyses indicated that both strains represented novel Actinomyces species, with values below the threshold for species demarcation (70% dDDH, 95% ANI and AAI). Pangenome analysis identified 5,731 gene clusters with strains ATCC 49340 T and ATCC 51655 T possessing 1,515 and 1,518 unique gene clusters, respectively. Additionally, genomic islands (GIs) prediction uncovered 24 putative GIs in strain ATCC 49340 T and 16 in strain ATCC 51655 T, contributing to their genetic diversity and potential adaptive capabilities. Pathogenicity analysis highlighted the potential human pathogenicity risk associated with both strains, with several virulence-associated factors identified. CRISPR-Cas analysis exposed the presence of CRISPR and Cas genes in both strains, indicating these strains might evolve a robust defense mechanism against them.
CONCLUSION: This study supports the classification of strains ATCC 49340 T and ATCC 51655 T as novel species within the Actinomyces, in which the name Actinomyces acetigenes sp. nov. (type strain ATCC 49340 T = VPI D163E-3 T = CCUG 34286 T = CCUG 35339 T) and Actinomyces stomatis sp. nov. (type strain ATCC 51655 T = PK606T = CCUG 33930 T) are proposed.
METHODS: Pkdhps were amplified and sequenced from 28 P. knowlesi samples collected in 2008 and 2020 from nine provinces across Thailand. Combining pkdhfr sequencing data from previous work with pkdhps data to analyze polymorphisms of pkdhfr and pkdhps haplotype. Protein modeling and molecular docking were constructed using two inhibitors, sulfadoxine and sulfamethoxazole, and further details were obtained through analyses of protein-ligand interactions by using the Genetic Optimisation for Ligand Docking program. A phylogenetic tree cluster analysis was reconstructed to compare the P. knowlesi Malaysia isolates.
RESULTS: Five nonsynonymous mutations in the pkdhps were detected outside the equivalence of the binding pocket sites to sulfadoxine and sulfamethoxazole, which are at N391S, E421G, I425R, A449S, and N517S. Based on the modeling and molecular docking analyses, the N391S and N517S mutations located close to the enzyme-binding pocket demonstrated a different docking score and protein-ligand interaction in loop 2 of the enzyme. These findings indicated that it was less likely to induce drug resistance. Of the four haplotypes of pkdhfr-pkdhps, the most common one is the R34L pkdhfr mutation and the pkdhps quadruple mutation (GRSS) at E421G, I425R, A449S, and N517S, which were observed in P. knowlesi in southern Thailand (53.57%). Based on the results of neighbor-joining analysis for pkdhfr and pkdhps, the samples isolated from eastern Thailand displayed a close relationship with Cambodia isolates, while southern Thailand isolates showed a long branch separated from the Malaysian isolates.
CONCLUSIONS: A new PCR protocol amplification and evaluation of dihydropteroate synthase mutations in Knowlesi (pkdhps) has been developed. The most prevalent pkdhfr-pkdhps haplotypes (53.57%) in southern Thailand are R34L pkdhfr mutation and pkdhps quadruple mutation. Further investigation requires additional phenotypic data from clinical isolates, transgenic lines expressing mutant alleles, or recombinant proteins.
METHODS: We constructed phylogenetic trees based on ZIKV coding sequences (CDS) and determined the geographical distribution of the representative viruses by genetic relationship and timeline. We determined genetic recombination among ZIKV and between ZIKV and other flaviviruses using similarity plot and bootscan analyzes, together with the phylogeny encompassing the CDS and eight subgenomic regions.
RESULTS: The phylogenetic trees comprising 717 CDS showed two distinct African and Asian lineages. ZIKV in the African lineage formed two sublineages, and ZIKV in the Asian lineage diversified into the Asian and American sublineages. The 1966 Malaysian isolate was designated the prototype of the Asian sublineage and formed a node of only one member, while the newer viruses formed a distinct node. We detected no genetic recombination in the Thailand ZIKV.
CONCLUSION: Five Thailand isolates discovered in 2006 were the second oldest ZIKV after the Malaysian prototype. Our result suggested two independent routes of ZIKV spread from Southeast Asia to Micronesia in 2007 and French Polynesia in 2013 before further spreading to South American countries.
OBJECTIVES: Our study focuses on determining the presence of bat CoVs in dusky fruit bat (Penthetor lucasi).
METHODS: Guano samples belonging to P. lucasi were collected from Wind Cave Nature Reserve. The samples were screened for the presence of CoVs using validated hemi-nested consensus RNA-dependent RNA polymerase consensus primers.
RESULTS: The bat CoV positivity rate was 38.5% (n = 15/39), with the viruses belonging to two subgenera: Alphacoronavirus (α-CoV) and Betacoronavirus (β-CoV). Phylogenetic analysis revealed that CoVs from 14 samples of P. lucasi belong to the genus α-CoV and may represent previously described genetic lineages in insectivorous bats in Wind Cave. However, only one sample of P. lucasi was detected with β-CoV which is closely related to subgenus Nobecovirus, which is commonly seen in frugivorous bats.
CONCLUSIONS: This study provides the first available data on CoVs circulating in P. lucasi.