Metabolic profiling for detection of Staphylococcus aureus infection and antibiotic resistance

Due to slow diagnostics, physicians must optimize antibiotic therapies based on clinical evaluation of patients without specific information on causative bacteria. We have investigated metabolomic analysis of blood for the detection of acute bacterial infection and early differentiation between ineffective and effective antibiotic treatment. A vital and timely therapeutic difficulty was thereby addressed: the ability to rapidly detect treatment failures because of antibiotic-resistant bacteria. Methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) were used in vitro and for infecting mice, while natural MSSA infection was studied in humans. Samples of bacterial growth media, the blood of infected mice and of humans were analyzed with combined Gas Chromatography/Mass Spectrometry. Multivariate data analysis was used to reveal the metabolic profiles of infection and the responses to different antibiotic treatments. In vitro experiments resulted in the detection of 256 putative metabolites and mice infection experiments resulted in the detection of 474 putative metabolites. Importantly, ineffective and effective antibiotic treatments were differentiated already two hours after treatment start in both experimental systems. That is, the ineffective treatment of MRSA using cloxacillin and untreated controls produced one metabolic profile while all effective treatment combinations using cloxacillin or vancomycin for MSSA or MRSA produced another profile. For further evaluation of the concept, blood samples of humans admitted to intensive care with severe sepsis were analyzed. One hundred thirty-three putative metabolites differentiated severe MSSA sepsis (n = 6) from severe Escherichia coli sepsis (n = 10) and identified treatment responses over time. Combined analysis of human, in vitro, and mice samples identified 25 metabolites indicative of effective treatment of S. aureus sepsis. Taken together, this study provides a proof of concept of the utility of analyzing metabolite patterns in blood for early differentiation between ineffective and effective antibiotic treatment in acute S. aureus infections.