The general stress response of Staphylococcus aureus promotes tolerance of antibiotics and survival in whole human blood.

Staphylococcus aureus is a frequent cause of invasive human infections such as bacteraemia and infective endocarditis. These infections frequently relapse or become chronic, suggesting that the pathogen has mechanisms to tolerate the twin threats of therapeutic antibiotics and host immunity. The general stress response of S. aureus is regulated by the alternative sigma factor B (σB) and provides protection from multiple stresses including oxidative, acidic and heat. σB also contributes to virulence, intracellular persistence and chronic infection. However, the protective effect of σB on bacterial survival during exposure to antibiotics or host immune defences is poorly characterized. We found that σB promotes the survival of S. aureus exposed to the antibiotics gentamicin, ciprofloxacin, vancomycin and daptomycin, but not oxacillin or clindamycin. We also found that σB promoted staphylococcal survival in whole human blood, most likely via its contribution to oxidative stress resistance. Therefore, we conclude that the general stress response of S. aureus may contribute to the development of chronic infection by conferring tolerance to both antibiotics and host immune defences.

An inventory of supranational antimicrobial resistance surveillance networks involving low- and middle-income countries since 2000.

Low- and middle-income countries (LMICs) shoulder the bulk of the global burden of infectious diseases and drug resistance. We searched for supranational networks performing antimicrobial resistance (AMR) surveillance in LMICs and assessed their organization, methodology, impacts and challenges. Since 2000, 72 supranational networks for AMR surveillance in bacteria, fungi, HIV, TB and malaria have been created that have involved LMICs, of which 34 are ongoing. The median (range) duration of the networks was 6 years (1-70) and the number of LMICs included was 8 (1-67). Networks were categorized as WHO/governmental (n = 26), academic (n = 24) or pharma initiated (n = 22). Funding sources varied, with 30 networks receiving public or WHO funding, 25 corporate, 13 trust or foundation, and 4 funded from more than one source. The leading global programmes for drug resistance surveillance in TB, malaria and HIV gather data in LMICs through periodic active surveillance efforts or combined active and passive approaches. The biggest challenges faced by these networks has been achieving high coverage across LMICs and complying with the recommended frequency of reporting. Obtaining high quality, representative surveillance data in LMICs is challenging. Antibiotic resistance surveillance requires a level of laboratory infrastructure and training that is not widely available in LMICs. The nascent Global Antimicrobial Resistance Surveillance System (GLASS) aims to build up passive surveillance in all member states. Past experience suggests complementary active approaches may be needed in many LMICs if representative, clinically relevant, meaningful data are to be obtained. Maintaining an up-to-date registry of networks would promote a more coordinated approach to surveillance.

International cooperation to improve access to and sustain effectiveness of antimicrobials.

Securing access to effective antimicrobials is one of the greatest challenges today. Until now, efforts to address this issue have been isolated and uncoordinated, with little focus on sustainable and international solutions. Global collective action is necessary to improve access to life-saving antimicrobials, conserving them, and ensuring continued innovation. Access, conservation, and innovation are beneficial when achieved independently, but much more effective and sustainable if implemented in concert within and across countries. WHO alone will not be able to drive these actions. It will require a multisector response (including the health, agriculture, and veterinary sectors), global coordination, and financing mechanisms with sufficient mandates, authority, resources, and power. Fortunately, securing access to effective antimicrobials has finally gained a place on the global political agenda, and we call on policy makers to develop, endorse, and finance new global institutional arrangements that can ensure robust implementation and bold collective action.

Listeria monocytogenes infections: presentation, diagnosis and treatment.

Listeriosis is an infective complication that primarily affects pregnant women, patients at extremes of age or those with weakened immune systems. Ingestion of food contaminated with Listeria monocytogenes is the most common source of infection, causing self-limiting illness in immunocompetent hosts but associated with invasive infection and high mortality in high-risk patient groups. Milder illness presents as gastroenteritis with fever, diarrhoea, nausea and vomiting common in the 7 days post exposure. Invasive infection, characterised by bacteraemia and encephalitis, can develop in high-risk patients. Fetal loss is a major complication of listeriosis during pregnancy. Penicillin-based therapy (high dose penicillin or amoxicillin) in combination with gentamicin is advised for invasive infection; co-trimoxazole may be considered for patients intolerant to penicillin. Vulnerable individuals, notably pregnant women, should be counseled on appropriate preventative strategies including avoiding foods commonly contaminated with L. monocytogenes, such as soft ripened cheeses, pate, cooked chilled meats, unpasteurised milk, and ready to eat poultry unless thoroughly cooked.

Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19.

BACKGROUND: Bacterial infection has been challenging to diagnose in patients with COVID-19. We developed and evaluated supervised machine learning algorithms to support the diagnosis of secondary bacterial infection in hospitalized patients during the COVID-19 pandemic. METHODS: Inpatient data at three London hospitals for the first COVD-19 wave in March and April 2020 were extracted. Demographic, blood test and microbiology data for individuals with and without SARS-CoV-2-positive PCR were obtained. A Gaussian Naive Bayes, Support Vector Machine (SVM) and Artificial Neural Network were trained and compared using the area under the receiver operating characteristic curve (AUCROC). The best performing algorithm (SVM with 21 blood test variables) was prospectively piloted in July 2020. AUCROC was calculated for the prediction of a positive microbiological sample within 48 h of admission. RESULTS: A total of 15 599 daily blood profiles for 1186 individual patients were identified to train the algorithms; 771/1186 (65%) individuals were SARS-CoV-2 PCR positive. Clinically significant microbiology results were present for 166/1186 (14%) patients during admission. An SVM algorithm trained with 21 routine blood test variables and over 8000 individual profiles had the best performance. AUCROC was 0.913, sensitivity 0.801 and specificity 0.890. Prospective testing on 54 patients on admission (28/54, 52% SARS-CoV-2 PCR positive) demonstrated an AUCROC of 0.960 (95% CI: 0.90-1.00). CONCLUSIONS: An SVM using 21 routine blood test variables had excellent performance at inferring the likelihood of positive microbiology. Further prospective evaluation of the algorithms ability to support decision making for the diagnosis of bacterial infection in COVID-19 cohorts is underway.

MALDI-TOF mass spectrometry on intact bacteria combined with a refined analysis framework allows accurate classification of MSSA and MRSA.

Fast and reliable detection coupled with accurate data-processing and analysis of antibiotic-resistant bacteria is essential in clinical settings. In this study, we use MALDI-TOF on intact cells combined with a refined analysis framework to demonstrate discrimination between methicillin-susceptible (MSSA) and methicillin-resistant (MRSA) Staphylococcus aureus. By combining supervised and unsupervised machine learning methods, we firstly show that the mass spectroscopy data contains strong signal for the clustering of MSSA and MRSA. Then we concentrate on applying supervised learning to extract and verify the important features. A new workflow is proposed that allows for extracting a fixed set of reference peaks so that any new data can be aligned to it and hence consistent feature matrices can be obtained. Also note that by doing so we are able to examine the robustness of the important features that have been found. We also show that appropriate size of the benchmark data, appropriate alignment of the testing data and use of an optimal set of features via feature selection results in prediction accuracy over 90%. In summary, as proof-of-principle, our integrated experimental and bioinformatics study suggests a novel intact cell MALDI-TOF to be of great promise for fast and reliable detection of MRSA strains.