Molecular evolution of multiresistance in nontyphoid Salmonella spp. was investigated with 155 isolates obtained in Argentina from 1984 to 1998. In 74 isolates obtained from 1984 to 1988 resistance was associated with the presence of Tn3, Tn9, class I (In0) and II (Tn7) integrons, and the aac(3)-IIa gene. Extended-spectrum cephalosporin (ESC) resistance in Salmonella spp. emerged in 1989, and 81 isolates resistant to at least one ESC and one aminoglycoside were collected thereafter. Among these, two patterns of antimicrobial resistance mechanisms were found: from 1989 to 1992, resistance was related to the spreading of Tn1331 and bla CTX-M-2 , in addition to the persistence of In0 and Tn7. From 1993 to 1998, several integrons were added to the first pattern and three integron groups (IG), namely, IG1 (38% of the isolates), IG2 (51%), and IG3 (11%), were identified. At least two ␤-lactamase genes were detected in 65% of the isolates (after 1989) by PCR analysis. Furthermore, five ␤-lactamase genes, bla CTX-M-2 , bla OXA-9 , bla OXA-2 , bla TEM-1 , and bla PER-2 , were found in two isolates. The bla CTX-M-2 gene was found in several complex sulI-type integrons with different rearrays within the variable region of class I integrons, suggesting evolution of these integrons in nontyphoid Salmonella. In conclusion, progressive acquisition and accumulation of plasmid-mediated resistance determinants occurred from 1984 to 1998 in nontyphoid Salmonella isolates of the most prevalent serovars from Argentina. It is suggested that antimicrobial resistance mechanisms in these bacteria may have been the consequence of plasmid exchange between Salmonella enterica serovar Typhimurium and Escherichia coli or Shigella flexneri and/or spreading of mobile elements from the nosocomial environment.

In order to control and eradicate epidemic cholera, we need to understand how epidemics begin, how they spread, and how they decline and eventually end. This requires extensive sampling of epidemic disease over time, alongside the background of endemic disease that may exist concurrently with the epidemic. The unique circumstances surrounding the Argentinian cholera epidemic of 1992–1998 presented an opportunity to do this. Here, we use 490 Argentinian V. cholerae genome sequences to characterise the variation within, and between, epidemic and endemic V. cholerae. We show that, during the 1992–1998 cholera epidemic, the invariant epidemic clone co-existed alongside highly diverse members of the Vibrio cholerae species in Argentina, and we contrast the clonality of epidemic V. cholerae with the background diversity of local endemic bacteria. Our findings refine and add nuance to our genomic definitions of epidemic and endemic cholera, and are of direct relevance to controlling current and future cholera epidemics.

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BackgroundTo implement effective control measures, timely outbreak detection is essential. Shigella is the most common cause of bacterial diarrhea in Argentina. Highly resistant clones of Shigella have emerged, and outbreaks have been recognized in closed settings and in whole communities. We hereby report our experience with an evolving, integrated, laboratory-based, near real-time surveillance system operating in six contiguous provinces of Argentina during April 2009 to March 2012.MethodologyTo detect localized shigellosis outbreaks timely, we used the prospective space-time permutation scan statistic algorithm of SaTScan, embedded in WHONET software. Twenty three laboratories sent updated Shigella data on a weekly basis to the National Reference Laboratory. Cluster detection analysis was performed at several taxonomic levels: for all Shigella spp., for serotypes within species and for antimicrobial resistance phenotypes within species. Shigella isolates associated with statistically significant signals (clusters in time/space with recurrence interval ≥365 days) were subtyped by pulsed field gel electrophoresis (PFGE) using PulseNet protocols.Principal FindingsIn three years of active surveillance, our system detected 32 statistically significant events, 26 of them identified before hospital staff was aware of any unexpected increase in the number of Shigella isolates. Twenty-six signals were investigated by PFGE, which confirmed a close relationship among the isolates for 22 events (84.6%). Seven events were investigated epidemiologically, which revealed links among the patients. Seventeen events were found at the resistance profile level. The system detected events of public health importance: infrequent resistance profiles, long-lasting and/or re-emergent clusters and events important for their duration or size, which were reported to local public health authorities.Conclusions/SignificanceThe WHONET-SaTScan system may serve as a model for surveillance and can be applied to other pathogens, implemented by other networks, and scaled up to national and international levels for early detection and control of outbreaks.