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In the past 3 years, a large number of emendations of circumscriptions of species, subspecies and higher taxa were published outside the International Journal of Systematic and Evolutionary Microbiology (IJSEM) that only marginally modify the earlier circumscription and may not meet the requirements of Rule 35 of the International Code of Nomenclature of Prokaryotes. Thus far, these emendations were included in the Lists of Changes in Taxonomic Opinion in the IJSEM. The list editors propose to list in the future only meaningful emendations that in their opinion significantly modify the diagnostic characters or the circumscription of taxa.Infection of chicken coronavirus infectious bronchitis virus (IBV) is initiated by binding of the viral heavily N-glycosylated attachment protein spike to the alpha-2,3-linked sialic acid receptor Neu5Ac. Previously, we have shown that N-glycosylation of recombinantly expressed receptor binding domain (RBD) of the spike of IBV-M41 is of critical importance for binding to chicken trachea tissue. Here we investigated the role of N-glycosylation of the RBD on receptor specificity and virus replication in the context of the virus particle. Using our reverse genetics system we were able to generate recombinant IBVs for nine-out-of-ten individual N-glycosylation mutants. In vitro growth kinetics of these viruses were comparable to the virus containing the wild-type M41-S1. Furthermore, Neu5Ac binding by the recombinant viruses containing single N-glycosylation site knock-out mutations matched the Neu5Ac binding observed with the recombinant RBDs. Five N-glycosylation mutants lost the ability to bind Neu5Ac and gained binding to a different, yet unknown, sialylated glycan receptor on host cells. These results demonstrate that N-glycosylation of IBV is a determinant for receptor specificity.Introduction. The human gut microbiota is currently seen as an important factor that can promote autism spectrum disorder (ASD) development in children.Aim. This study aimed to detect differences in the taxonomic composition and content of bacterial genes encoding key enzymes involved in the metabolism of neuroactive biomarker compounds in the metagenomes of gut microbiota of children with ASD and neurotypical children.Methodology. A whole metagenome sequencing approach was used to obtain metagenomic data on faecal specimens of 36 children with ASD and 21 healthy neurotypical children of 3-5 years old. Taxonomic analysis was conducted using MetaPhlAn2. The developed bioinformatics algorithm and created catalogue of the orthologues were applied to identify bacterial genes of neuroactive compounds in the metagenomes. For the identification of metagenomic signatures of children with ASD, Wilcoxon's test and adjustment for multiple comparisons were used.Results. Statistically significant differences with decreases in average abundance in the microbiota of ASD children were found for the genera Barnesiella and Parabacteroides and species Alistipes putredinis, B. caccae, Bacteroides intestinihominis, Eubacterium rectale, Parabacteroides distasonis and Ruminococcus lactaris. Average relative abundances of the detected genes and neurometabolic signature approach did not reveal many significant differences in the metagenomes of the groups that were compared. We noted decreases in the abundance of genes linked to production of GABA, melatonine and butyric acid in the ASD metagenomes.Conclusion. For the first time, the neurometabolic signature of the gut microbiota of young children with ASD is presented. The data can help to provide a comparative assessment of the transcriptional and metabolomic activity of the identified genes.There is increasing recognition that microbiomes are important for host health and ecology, and understanding host microbiomes is important for planning appropriate conservation strategies. However, microbiome data are lacking for many taxa, including turtles. To further our understanding of the interactions between aquatic microbiomes and their hosts, we used next generation sequencing technology to examine the microbiomes of the Krefft's river turtle (Emydura macquarii krefftii). We examined the microbiomes of the buccal (oral) cavity, skin on the head, parts of the shell with macroalgae and parts of the shell without macroalgae. Bacteria in the phyla Proteobacteria and Bacteroidetes were the most common in most samples (particularly buccal samples), but Cyanobacteria, Deinococcus-thermus and Chloroflexi were also common (particularly in external microbiomes). We found significant differences in community composition among each body area, as well as significant differences among individuals. The buccal cavity had lower bacterial richness and evenness than any of the external microbiomes, and it had many amplicon sequence variants (ASVs) with a low relative abundance compared to other body areas. Nevertheless, the buccal cavity also had the most unique ASVs. Parts of the shell with and without algae also had different microbiomes, with particularly obvious differences in the relative abundances of the families Methylomonaceae, Saprospiraceae and Nostocaceae. This study provides novel, baseline information about the external microbiomes of turtles and is a first step in understanding their ecological roles.The last few decades have led to an explosion in our understanding of the major roles that small regulatory RNAs (sRNAs) play in regulatory circuits and the responses to stress in many bacterial species. Much of the foundational work was carried out with Escherichia coli and Salmonella enterica serovar Typhimurium. The studies of these organisms provided an overview of how the sRNAs function and their impact on bacterial physiology, serving as a blueprint for sRNA biology in many other prokaryotes. They also led to the development of new technologies. Taurine manufacturer In this chapter, we first summarize how these sRNAs were identified, defining them in the process. We discuss how they are regulated and how they act and provide selected examples of their roles in regulatory circuits and the consequences of this regulation. Throughout, we summarize the methodologies that were developed to identify and study the regulatory RNAs, most of which are applicable to other bacteria. Newly updated databases of the known sRNAs in E. coli K-12 and S.