scholarly journals Curating and Comparing 114 Strain-Specific Genome-Scale Metabolic Models of Staphylococcus aureus

2021 ◽  
Author(s):  
Alina Renz ◽  
Andreas Dräger
Keyword(s):  
Staphylococcus Aureus ◽  
Research Question ◽  
High Morbidity ◽  
Drug Resistant ◽  
Predictive Values ◽  
Model Driven ◽  
Severe Infections ◽  
Novel Targets ◽  
Metabolic Models ◽  
Genome Scale

Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes.Furthermore, all models were quality-controlled using Mᴇᴍᴏᴛᴇ, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.

scholarly journals Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Alina Renz ◽  
Andreas Dräger
Keyword(s):  
Staphylococcus Aureus ◽  
Research Question ◽  
High Morbidity ◽  
Drug Resistant ◽  
Predictive Values ◽  
Model Driven ◽  
Severe Infections ◽  
Novel Targets ◽  
Metabolic Models ◽  
Genome Scale

AbstractStaphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes. Furthermore, all models were quality-controlled using MEMOTE, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.

scholarly journals Model-driven analysis of mutant fitness experiments improves genome-scale metabolic models of Zymomonas mobilis ZM4

2020 ◽  
Vol 16 (8) ◽  
pp. e1008137
Author(s):  
Wai Kit Ong ◽  
Dylan K. Courtney ◽  
Shu Pan ◽  
Ramon Bonela Andrade ◽  
Patricia J. Kiley ◽  
...  
Keyword(s):  
Zymomonas Mobilis ◽  
Model Driven ◽  
Metabolic Models ◽  
Genome Scale

scholarly journals Antimicrobial resistance among pathogens that infect the bloodstream: a multicenter surveillance report for 1998–2017

2020 ◽  
Author(s):  
lei tian ◽  
zhen zhang ◽  
ziyong sun
Keyword(s):  
Staphylococcus Aureus ◽  
Antimicrobial Resistance ◽  
Detection Rate ◽  
Bacterial Resistance ◽  
Salmonella Typhi ◽  
High Morbidity ◽  
Drug Resistant ◽  
E Coli ◽  
Extensively Drug Resistant ◽  
Resistance Rates

Abstract Background Bloodstream infections (BSIs) are a common consequence of infectious diseases and cause high morbidity and mortality. Appropriate antibiotic use is critical for patients’ treatment and prognosis. Long-term monitoring and analyzing of bacterial resistance are important for understanding the changes in bacterial resistance and infection control. Here, we report a retrospective study on antimicrobial resistance in BSI-associated pathogens.Methods Data from the Hubei Province Antimicrobial Resistance Surveillance System (HBARSS) from 1998–2017 were retrospectively analyzed using WHONET 5.6 software.Results Data from HBARSS (1998–2017) revealed that 40,518 Gram-positive bacteria and 26,568 Gram-negative bacteria caused BSIs, the most common of which were Staphylococcus aureus and Escherichia coli. Salmonella typhi was a predominant BSI-associated pathogen in 1998–2003. Drug susceptibility data showed that the resistance rates of E. coli and Klebsiella pneumoniae to cefotaxime were significantly higher than those to ceftazidime. Carbapenem-resistant (CR) E. coli and K. pneumoniae have also emerged. In 2013–2017, K. pneumoniae showed resistance levels reaching 15.8% and 17.5% to imipenem and meropenem, respectively, and Acinetobacter baumannii showed high resistance rates ranging from 60–80% to common antibiotics. The detection rate of Salmonella typhi resistance to third-generation cephalosporins and fluoroquinolones was less than 5%. Control of methicillin-resistant Staphylococcus aureus (MRSA) remains a major challenge, and in 2009–2017, the MRSA detection rate was 40–50%. The number of extensively drug-resistant A. baumannii and P. aeruginosa has been increasing since 2008. From 1998 to 2017, the total detection rates of extensively drug-resistant A. baumannii and P. aeruginosa were 34.38% (493/1434) and 7.45% (140/1879), respectively.Conclusions Prevalence of CR K. pneumoniae has increased significantly in recent years. Resistance rates of A. baumannii to common antimicrobial agents have increased exponentially, reaching high levels. MRSA remains a challenge to control.

scholarly journals Clinically applicable rapid susceptibility testing of multi-drug resistant Staphylococcus aureus by mass spectrometry and extreme gradient boosting machine

2021 ◽  
Author(s):  
Zhuo Wang ◽  
Hsin-Yao Wang ◽  
Yuxuan Pang ◽  
Chia-Ru Chung ◽  
Jorng-Tzong Horng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Staphylococcus Aureus ◽  
Characteristic Curve ◽  
Gradient Boosting ◽  
Learning Technology ◽  
Drug Resistant ◽  
Antibiotic Susceptibility Test ◽  
Extreme Gradient Boosting ◽  
Severe Infections

Multi drug resistant Staphylococcus aureus is one of the major causes of severe infections. Due to the delays of conventional antibiotic susceptibility test (AST), most cases were prescribed by experience with a lower recovery rate. Linking a 7 year study of over 20,000 Staphylococcus aureus infected patients, we incorporated mass spectrometry and machine learning technology to predict the susceptibilities of patients for 4 different antibiotics that can enable early antibiotic decisions. The predictive models were externally validated in an independent patient cohort, resulting in an area under the receiver operating characteristic curve of 0.94 , 0.90, 50 0.86, 0.91 and an area under the precision recall curve of 0.93, 0.87, 0.87, 0.81 for oxacillin (OXA), clindamycin (CLI), erythromycin (ERY) and trimethoprim sulfamethoxazole (SXT), respectively. Moreover, our pipeline provides AST 24-36 h faster than standard workflows, reduction of inappropriate antibiotic usage with preclinical prediction, and demonstrates the potential of combining mass spectrometry with machine learning (ML) to assist early and accurate prescription. Therapies to individual patients could be tailored in the process of precision medicine.

scholarly journals Synthesis, ADMET Properties, and In Vitro Antimicrobial and Antibiofilm Activity of 5-Nitro-2-thiophenecarbaldehyde N-((E)-(5-Nitrothienyl)methylidene)hydrazone (KTU-286) against Staphylococcus aureus with Defined Resistance Mechanisms

Antibiotics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 612
Author(s):  
Povilas Kavaliauskas ◽  
Birute Grybaite ◽  
Vytautas Mickevicius ◽  
Ruta Petraitiene ◽  
Ramune Grigaleviciute ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Antimicrobial Activity ◽  
In Silico ◽  
Resistance Mechanisms ◽  
Skin Permeability ◽  
Antibiofilm Activity ◽  
High Morbidity ◽  
Drug Resistant ◽  
In Silico Modeling

The emergence of drug-resistant Staphylococcus aureus is responsible for high morbidity and mortality worldwide. New therapeutic options are needed to fight the increasing antimicrobial resistance among S. aureus in the clinical setting. We, therefore, characterized the in silico absorption, distribution, metabolism, elimination, and toxicity (ADMET) and in vitro antimicrobial activity of 5-nitro-2-thiophenecarbaldehyde N-((E)-(5-nitrothienyl)methylidene)hydrazone (KTU-286) against drug-resistant S. aureus strains with genetically defined resistance mechanisms. The antimicrobial activity of KTU-286 was determined by CLSI recommendations. The ADMET properties were estimated by using in silico modeling. The activity on biofilm integrity was examined by crystal violet assay. KTU-286 demonstrated low estimated toxicity and low skin permeability. The highest antimicrobial activity was observed among pan-susceptible (Pan-S) S. aureus (minimal inhibitory concentration (MIC) 0.5–2.0 µg/mL, IC50 = 0.460 µg/mL), followed by vancomycin resistant S. aureus (VRSA) (MIC 4.0 µg/mL, IC50 = 1.697 µg/mL) and methicillin-resistant S. aureus (MRSA) (MIC 1.0–16.0 µg/mL, IC50 = 2.282 µg/mL). KTU-286 resulted in significant (p < 0.05) loss of S. aureus biofilm integrity in vitro. Further studies are needed for a better understanding of safety, synergistic relationship, and therapeutic potency of KTU-286.

Learning from metabolic networks: current trends and future directions for precision medicine.

2020 ◽  
Vol 28 ◽  
Author(s):  
Ilaria Granata ◽  
Mario Manzo ◽  
Ari Kusumastuti ◽  
Mario R Guarracino
Keyword(s):  
Systems Biology ◽  
Precision Medicine ◽  
Metabolic Networks ◽  
Information Modeling ◽  
Model Systems ◽  
Specific Information ◽  
Important Indicator ◽  
Predictive Values ◽  
Metabolic Systems ◽  
Metabolic Models

Purpose: Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype-phenotype associations. In metabolic systems biology, the genome-scale metabolic models are the primary scaffolds to integrate multi-omics data as well as cell-, tissue-, condition-specific information. Modeling the metabolism has both investigative and predictive values. Several methods have been proposed to model systems, which involve steady-state or kinetic approaches, and to extract knowledge through machine and deep learning. Method: This review collects, analyzes, and compares the suitable data and computational approaches for the exploration of metabolic networks as tools for the development of precision medicine. To this extent, we organized it into three main sections: "Data and Databases", "Methods and Tools", and "Metabolic Networks for medicine". In the first one, we have collected the most used data and relative databases to build and annotate metabolic models. In the second section, we have reported the state-of-the-art methods and relative tools to reconstruct, simulate, and interpret metabolic systems. Finally, we have reported the most recent and innovative studies which exploited metabolic networks for the study of several pathological conditions, not only those directly related to the metabolism. Conclusion: We think that this review can be a guide to researchers of different disciplines, from computer science to biology and medicine, in exploring the power, challenges and future promises of the metabolism as predictor and target of the so-called P4 medicine (predictive, preventive, personalized and participatory).

scholarly journals New Antimicrobials Based on the Adarotene Scaffold with Activity against Multi-Drug Resistant Staphylococcus aureus and Vancomycin-Resistant Enterococcus

Antibiotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 126
Author(s):  
Salvatore Princiotto ◽  
Stefania Mazzini ◽  
Loana Musso ◽  
Fabio Arena ◽  
Sabrina Dallavalle ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Antibacterial Activity ◽  
Anticancer Activity ◽  
Bactericidal Effect ◽  
Biological Assessment ◽  
Drug Resistant ◽  
Vancomycin Resistant Enterococcus ◽  
Vancomycin Resistant ◽  
Global Increase ◽  
Common Infections

The global increase in infections by multi-drug resistant (MDR) pathogens is severely impacting our ability to successfully treat common infections. Herein, we report the antibacterial activity against S. aureus and E. faecalis (including some MDR strains) of a panel of adarotene-related synthetic retinoids. In many cases, these compounds showed, together with favorable MICs, a detectable bactericidal effect. We found that the pattern of substitution on adarotene could be modulated to obtain selectivity for antibacterial over the known anticancer activity of these compounds. NMR experiments allowed us to define the interaction between adarotene and a model of microorganism membrane. Biological assessment confirmed that the scaffold of adarotene is promising for further developments of non-toxic antimicrobials active on MDR strains.

scholarly journals Hospital Microbiologic Culture Results to Predict the Use of Anti–methicillin-Resistant Staphylococcus aureus (MRSA)

2020 ◽  
Vol 41 (S1) ◽  
pp. s40-s40
Author(s):  
Hsiu Wu ◽  
Tyler Kratzer ◽  
Liang Zhou ◽  
Minn Soe ◽  
Jonathan Edwards ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Risk Adjustment ◽  
Negative Binomial ◽  
Average Length ◽  
Negative Binomial Regression ◽  
Drug Resistant ◽  
Gram Positive ◽  
Average Length Of Stay ◽  
Control And Prevention ◽  
Hospital Characteristics

Background: To provide a standardized, risk-adjusted method for summarizing antimicrobial use (AU), the Centers for Disease Control and Prevention developed the standardized antimicrobial administration ratio, an observed-to-predicted use ratio in which predicted use is estimated from a statistical model accounting for patient locations and hospital characteristics. The infection burden, which could drive AU, was not available for assessment. To inform AU risk adjustment, we evaluated the relationship between the burden of drug-resistant gram-positive infections and the use of anti-MRSA agents. Methods: We analyzed data from acute-care hospitals that reported ≥10 months of hospital-wide AU and microbiologic data to the National Healthcare Safety Network (NHSN) from January 2018 through June 2019. Hospital infection burden was estimated using the prevalence of deduplicated positive cultures per 1,000 admissions. Eligible cultures included blood and lower respiratory specimens that yielded oxacillin/cefoxitin–resistant Staphylococcus aureus (SA) and ampicillin-nonsusceptible enterococci, and cerebrospinal fluid that yielded SA. The anti-MRSA use rate is the total antimicrobial days of ceftaroline, dalbavancin, daptomycin, linezolid, oritavancin, quinupristin/dalfopristin, tedizolid, telavancin, and intravenous vancomycin per 1,000 days patients were present. AU rates were modeled using negative binomial regression assessing its association with infection burden and hospital characteristics. Results: Among 182 hospitals, the median (interquartile range, IQR) of anti-MRSA use rate was 86.3 (59.9–105.0), and the median (IQR) prevalence of drug-resistant gram-positive infections was 3.4 (2.1–4.8). Higher prevalence of drug-resistant gram-positive infections was associated with higher use of anti-MRSA agents after adjusting for facility type and percentage of beds in intensive care units (Table 1). Number of hospital beds, average length of stay, and medical school affiliation were nonsignificant. Conclusions: Prevalence of drug-resistant gram-positive infections was independently associated with the use of anti-MRSA agents. Infection burden should be used for risk adjustment in predicting the use of anti-MRSA agents. To make this possible, we recommend that hospitals reporting to NHSN’s AU Option also report microbiologic culture results.Funding: NoneDisclosures: None

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