RESUMO
The problematic microbial resistance to antibiotics has led to an increasing interest in bacterial persistence and its impact on infection. Nonetheless, these two mechanisms are often assessed in independent studies, and there is a lack of knowledge about their relation or possible interactions, both at cellular and population levels. This work shows evidence that the insertion of the resistance gene Chloramphenicol Acetyl Transferase (cat) together with its cognate antibiotic chloramphenicol (CAM), is capable to modulate Salmonella Typhimurium persistence to several antibiotics and decrease its survival. This effect is independent of the antibiotics' mechanisms of action or the locus of cat. RelA [p(ppGpp) syntetase] has been shown to be involved in persistence. It was recently proposed that RelA [(p)ppGpp synthetase], binds to uncharged tRNAs, forming RelA.tRNA complexes. These complexes bind to vacant A-sites in the ribosome, and this mechanism is essential for the activation of RelA. In this study, we propose that the antibiotic chloramphenicol blocks the A-site of the ribosome, hindering the binding of RelA.tRNA complexes to the ribosome thus preventing the activation of RelA and (p)ppGpp synthesis, with a consequent decrease in the level of persistence of the population. Our discovery that the concomitant use of chloramphenicol and other antibiotics in chloramphenicol resistant bacteria can decrease the persister levels can be the basis of novel therapeutics aiming to decrease the persisters and recalcitrant infections.
RESUMO
Species occurrence records provide the basis for many biodiversity studies. They derive from georeferenced specimens deposited in natural history collections and visual observations, such as those obtained through various mobile applications. Given the rapid increase in availability of such data, the control of quality and accuracy constitutes a particular concern. Automatic filtering is a scalable and reproducible means to identify potentially problematic records and tailor datasets from public databases such as the Global Biodiversity Information Facility (GBIF; http://www.gbif.org), for biodiversity analyses. However, it is unclear how much data may be lost by filtering, whether the same filters should be applied across all taxonomic groups, and what the effect of filtering is on common downstream analyses. Here, we evaluate the effect of 13 recently proposed filters on the inference of species richness patterns and automated conservation assessments for 18 Neotropical taxa, including terrestrial and marine animals, fungi, and plants downloaded from GBIF. We find that a total of 44.3% of the records are potentially problematic, with large variation across taxonomic groups (25-90%). A small fraction of records was identified as erroneous in the strict sense (4.2%), and a much larger proportion as unfit for most downstream analyses (41.7%). Filters of duplicated information, collection year, and basis of record, as well as coordinates in urban areas, or for terrestrial taxa in the sea or marine taxa on land, have the greatest effect. Automated filtering can help in identifying problematic records, but requires customization of which tests and thresholds should be applied to the taxonomic group and geographic area under focus. Our results stress the importance of thorough recording and exploration of the meta-data associated with species records for biodiversity research.
