The first extensively drug-resistant (XDR) strains of Salmonella typhimurium have been identified in the Democratic Republic of Congo (DRC). A multifaceted approach will be needed to track and control the spread of XDR Salmonella, including further microbiological and genomic surveillance.
Most Salmonella infections result in symptoms associated with food poisoning. But in sub-Saharan Africa, Salmonella such as S. typhimurium can cause infections of the blood known as invasive non-typhoidal Salmonella (iNTS) infections that result in 681,316 deaths globally every year. The majority of cases are caused by a type of S. typhimurium known as ST313. Now, a global research partnership is working to understand how Salmonella ST313 continues to evolve and develop drug resistance. The results were published today in Nature Communications.
Working on blood samples from people with suspected bloodstream infections, researchers observed antibiotic resistance levels never seen before in S. typhimurium-causing bloodstream infections, including resistance to the antibiotic azithromycin—a drug normally held in reserve in case others prove ineffective. Analysis of these S. typhimurium genomes identified a new sub-group that is branching off from ST313 that exhibits extensive drug resistance (XDR).
“All antibiotic resistance genes contributing to ‘XDR’ are present on the same plasmid,” says first author Sandra Van Puyvelde, Visiting Scientist at the Wellcome Sanger Institute. “This is worrying because a plasmid is a mobile genetic element that could be transferred to other bacteria. While accumulating more antibiotic resistance, we discovered that the novel Salmonella typhimurium line is also showing further genetic and behavioral changes which suggest ongoing evolution of the bacteria towards bloodstream infections.”
The researchers also studied the way S. typhimurium is adapting to an invasive ‘lifestyle,’ moving away from the forms of Salmonella that cause gastrointestinal illness towards the types that cause dangerous invasive bloodstream infections. The team used a machine-learning algorithm designed to look for characteristic patterns in the DNA of Salmonella that indicate the potential to cause dangerous invasive infections.
“In the lab, we’ve observed changes in this new group of Salmonella typhimurium that we’ve seen in other invasive salmonella,” says coauthor Nicole Wheeler of the Wellcome Sanger Institute. “What’s interesting as a bioinformatician is that we’ve been able to pick up these changes using machine learning. The hope is that in the near future we’ll be able to deploy machine learning in a more predictive role to help control the emergence and spread of drug-resistant strains of bacteria such as S. typhimurium.”