scholarly journals Climatic Alterations Influence Bacterial Growth, Biofilm Production and Antimicrobial Resistance Profiles in Aeromonas spp.

Antibiotics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1008
Author(s):  
Miguel L. Grilo ◽  
Ana Pereira ◽  
Carla Sousa-Santos ◽  
Joana I. Robalo ◽  
Manuela Oliveira
Keyword(s):  
Antimicrobial Resistance ◽  
Bacterial Growth ◽  
Bacterial Species ◽  
Environmental Parameters ◽  
Freshwater Ecosystems ◽  
Aeromonas Species ◽  
Phenotypic Resistance ◽  
Biofilm Production ◽  
Animal Populations ◽  
Environmental Variations

Climate change is expected to create environmental disruptions that will impact a wide array of biota. Projections for freshwater ecosystems include severe alterations with gradients across geographical areas. Life traits in bacteria are modulated by environmental parameters, but there is still uncertainty regarding bacterial responses to changes caused by climatic alterations. In this study, we used a river water microcosm model to evaluate how Aeromonas spp., an important pathogenic and zoonotic genus ubiquitary in aquatic ecosystems, responds to environmental variations of temperature and pH as expected by future projections. Namely, we evaluated bacterial growth, biofilm production and antimicrobial resistance profiles of Aeromonas species in pure and mixed cultures. Biofilm production was significantly influenced by temperature and culture, while temperature and pH affected bacterial growth. Reversion of antimicrobial susceptibility status occurred in the majority of strains and tested antimicrobial compounds, with several combinations of temperature and pH contributing to this effect. Current results highlight the consequences that bacterial genus such as Aeromonas will experience with climatic alterations, specifically how their proliferation and virulence and phenotypic resistance expression will be modulated. Such information is fundamental to predict and prevent future outbreaks and deleterious effects that these bacterial species might have in human and animal populations.

scholarly journals Virulence and Antimicrobial Resistance in Canine Staphylococcus spp. Isolates

2021 ◽  
Vol 9 (3) ◽  
pp. 515
Author(s):  
Fabrizio Bertelloni ◽  
Giulia Cagnoli ◽  
Valentina Virginia Ebani
Keyword(s):  
Drug Resistance ◽  
Antimicrobial Resistance ◽  
Virulence Genes ◽  
Methicillin Resistance ◽  
Bacterial Species ◽  
Multi Drug Resistance ◽  
Species Determination ◽  
Opportunistic Bacteria ◽  
Biofilm Production ◽  
Staphylococcus Spp

Dogs are reservoirs of different Staphylococcus species, but at the same time, they could develop several clinical forms caused by these bacteria. The aim of the present investigation was to characterize 50 clinical Staphylococcus isolates cultured from sick dogs. Bacterial species determination, hemolysins, protease, lipase, gelatinase, slime, and biofilm production, presence of virulence genes (lukS/F-PV, eta, etb, tsst, icaA, and icaD), methicillin resistance, and antimicrobial resistance were investigated. Most isolates (52%) were Staphylococcus pseudointermedius, but 20% and 8% belonged to Staphylococcusxylosus and Staphylococcus chromogenes, respectively. Gelatinase, biofilm, and slime production were very common characters among the investigated strains with 80%, 86%, and 76% positive isolates, respectively. Virulence genes were detected in a very small number of the tested strains. A percentage of 14% of isolates were mecA-positive and phenotypically-resistant to methicillin. Multi-drug resistance was detected in 76% of tested staphylococci; in particular, high levels of resistance were detected for ampicillin, amoxicillin, clindamycin, and erythromycin. In conclusion, although staphylococci are considered to be opportunistic bacteria, the obtained data showed that dogs may be infected by Staphylococcus strains with important virulence characteristics and a high antimicrobial resistance.

A quantitative approach to analyse linkages between antimicrobial resistance properties inSalmonellaTyphimurium isolates

2011 ◽  
Vol 140 (1) ◽  
pp. 157-167 ◽  
Author(s):  
I. RUDDAT ◽  
S. SCHWARZ ◽  
E. TIETZE ◽  
D. ZIEHM ◽  
L. KREIENBROCK
Keyword(s):  
Antimicrobial Resistance ◽  
Antimicrobial Agent ◽  
Antimicrobial Agents ◽  
Population Sample ◽  
Rank Correlation ◽  
Resistance Index ◽  
Lower Saxony ◽  
Phenotypic Resistance ◽  
Spearman’S Rank Correlation ◽  
Animal Populations

SUMMARYThis study used statistical methods to investigate linkages in phenotypic resistance profiles in a population sample of 321SalmonellaTyphimurium isolates from sporadic salmonellosis cases in Lower Saxony, Germany, collected during 2008–2010. A resistance index was applied to calculate the conditional probability of resistance to one antimicrobial agent given the resistance to one or more other antimicrobial agent(s). A susceptibility index was defined analogously. A contingency plot, which visualizes the association between resistances to two antimicrobial agents, facilitated the interpretation. Linkages between minimum inhibitory concentrations (MIC) were analysed using Spearman's rank correlation coefficient and jittered scatter plots. Applying these methods provided a compact description of multi-resistance and linkages between resistance properties in large datasets. Moreover, this approach will improve monitoring of antimicrobial resistance dynamics of bacteria in human or animal populations by identifying linked resistance to antimicrobial agents (cross- or co-resistance) with a non-molecular method.

Clin-mNGS: Automated Pipeline for Pathogen Detection from Clinical Metagenomic Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Akshatha Prasanna ◽  
Vidya Niranjan
Keyword(s):  
Antimicrobial Resistance ◽  
High Performance ◽  
Pathogen Detection ◽  
Bacterial Species ◽  
Workflow Management ◽  
Metagenomic Data ◽  
Antimicrobial Resistance Genes ◽  
Culture Independent ◽  
Automated Pipeline ◽  
User Friendly

Background: Since bacteria are the earliest known organisms, there has been significant interest in their variety and biology, most certainly concerning human health. Recent advances in Metagenomics sequencing (mNGS), a culture-independent sequencing technology have facilitated an accelerated development in clinical microbiology and our understanding of pathogens. Objective: For the implementation of mNGS in routine clinical practice to become feasible, a practical and scalable strategy for the study of mNGS data is essential. This study presents a robust automated pipeline to analyze clinical metagenomic data for pathogen identification and classification. Method: The proposed Clin-mNGS pipeline is an integrated, open-source, scalable, reproducible, and user-friendly framework scripted using the Snakemake workflow management software. The implementation avoids the hassle of manual installation and configuration of the multiple command-line tools and dependencies. The approach directly screens pathogens from clinical raw reads and generates consolidated reports for each sample. Results: The pipeline is demonstrated using publicly available data and is tested on a desktop Linux system and a High-performance cluster. The study compares variability in results from different tools and versions. The versions of the tools are made user modifiable. The pipeline results in quality check, filtered reads, host subtraction, assembled contigs, assembly metrics, relative abundances of bacterial species, antimicrobial resistance genes, plasmid finding, and virulence factors identification. The results obtained from the pipeline are evaluated based on sensitivity and positive predictive value. Conclusion: Clin-mNGS is an automated Snakemake pipeline validated for the analysis of microbial clinical metagenomics reads to perform taxonomic classification and antimicrobial resistance prediction.

scholarly journals Patient-Specific Predictive Antibiogram in Decision Support for Empiric Antibiotic Treatment

2020 ◽  
Vol 41 (S1) ◽  
pp. s521-s522
Author(s):  
Debarka Sengupta ◽  
Vaibhav Singh ◽  
Seema Singh ◽  
Dinesh Tewari ◽  
Mudit Kapoor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Antimicrobial Resistance ◽  
Model Building ◽  
Medical Center ◽  
Bacterial Species ◽  
Model Performance ◽  
The United States ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Comparative Performance

Background: The rising trend of antibiotic resistance imposes a heavy burden on healthcare both clinically and economically (US$55 billion), with 23,000 estimated annual deaths in the United States as well as increased length of stay and morbidity. Machine-learning–based methods have, of late, been used for leveraging patient’s clinical history and demographic information to predict antimicrobial resistance. We developed a machine-learning model ensemble that maximizes the accuracy of such a drug-sensitivity versus resistivity classification system compared to the existing best-practice methods. Methods: We first performed a comprehensive analysis of the association between infecting bacterial species and patient factors, including patient demographics, comorbidities, and certain healthcare-specific features. We leveraged the predictable nature of these complex associations to infer patient-specific antibiotic sensitivities. Various base-learners, including k-NN (k-nearest neighbors) and gradient boosting machine (GBM), were used to train an ensemble model for confident prediction of antimicrobial susceptibilities. Base learner selection and model performance evaluation was performed carefully using a variety of standard metrics, namely accuracy, precision, recall, F1 score, and Cohen κ. Results: For validating the performance on MIMIC-III database harboring deidentified clinical data of 53,423 distinct patient admissions between 2001 and 2012, in the intensive care units (ICUs) of the Beth Israel Deaconess Medical Center in Boston, Massachusetts. From ~11,000 positive cultures, we used 4 major specimen types namely urine, sputum, blood, and pus swab for evaluation of the model performance. Figure 1 shows the receiver operating characteristic (ROC) curves obtained for bloodstream infection cases upon model building and prediction on 70:30 split of the data. We received area under the curve (AUC) values of 0.88, 0.92, 0.92, and 0.94 for urine, sputum, blood, and pus swab samples, respectively. Figure 2 shows the comparative performance of our proposed method as well as some off-the-shelf classification algorithms. Conclusions: Highly accurate, patient-specific predictive antibiogram (PSPA) data can aid clinicians significantly in antibiotic recommendation in ICU, thereby accelerating patient recovery and curbing antimicrobial resistance.Funding: This study was supported by Circle of Life Healthcare Pvt. Ltd.Disclosures: None

scholarly journals Diversity and Antimicrobial Resistance in the Streptococcus bovis/Streptococcus equinus Complex (SBSEC) Isolated from Korean Domestic Ruminants

2021 ◽  
Vol 9 (1) ◽  
pp. 98
Author(s):  
Seon Young Park ◽  
Mingyung Lee ◽  
Se Ra Lim ◽  
Hyemin Kwon ◽  
Ye Seul Lee ◽  
...  
Keyword(s):  
Antimicrobial Resistance ◽  
Phylogenetic Analyses ◽  
Tetracycline Resistance ◽  
Current Status ◽  
Streptococcus Bovis ◽  
Limited Information ◽  
Phenotypic Resistance ◽  
Domestic Ruminants ◽  
Antimicrobial Resistance Genes ◽  
Acid Producing Bacteria

S. bovis/S. equinus complex (SBSEC) includes lactic acid-producing bacteria considered as the causative agent associated with acute rumen lactic acidosis in intensive ruminants. Considering the limited information on the detailed characteristics and diversity of SBSEC in Korea and the emergence of antimicrobial resistance (AMR), we investigated the diversity of SBSEC from domestic ruminants and verified the presence of antimicrobial resistance genes (ARGs) against several antimicrobials with their phenotypic resistance. Among 51 SBSEC isolates collected, two SBSEC members (S. equinus and S. lutetiensis) were identified; sodA-based phylogenetic analyses and comparisons of overall genome relatedness revealed potential plasticity and diversity. The AMR rates of these SBSEC against erythromycin, clindamycin, and tetracycline were relatively lower than those of other SBSEC isolates of a clinical origin. An investigation of the ARGs against those antimicrobials indicated that tetracycline resistance of SBSECs generally correlated with the presence of tet(M)-possessing Tn916-like transposon. However, no correlation between the presence of ARGs and phenotypic resistance to erythromycin and clindamycin was observed. Although a limited number of animals and their SBSEC isolates were examined, this study provides insights into the potential intraspecies biodiversity of ruminant-origin SBSEC and the current status on antimicrobial resistance of the bacteria in the Korean livestock industry.

Antimicrobial resistance in zoonotic bacteria: lessons learned from host-specific pathogens

2008 ◽  
Vol 9 (2) ◽  
pp. 177-186 ◽  
Author(s):  
Trudy M. Wassenaar ◽  
Peter Silley
Keyword(s):  
Antimicrobial Resistance ◽  
Bacterial Species ◽  
Lessons Learned ◽  
Animal Host ◽  
Relative Contribution ◽  
Single Animal ◽  
Resistance Patterns ◽  
Animal Hosts ◽  
Zoonotic Bacteria ◽  
Selection Of

AbstractThe relative contribution of veterinary and human clinical treatments to the selection of antimicrobial resistance in zoonotic pathogens remains controversial. In this review, we consider bacterial pathogens that differ in host specificity and address their resistance profiles: pathogens that only occur in the human host, pathogens that are specific to particular food-producing animals and pathogens that occur in both host types. Compared with those pathogens restricted to a single animal host, pathogens found in both human and animal hosts appear to have higher incidences of resistance. However, the most urgent and severe resistance problems occur with pathogens exclusively infecting humans. Differences exist in the available genetic repertoire of a bacterial species and these are reflected in the observed resistance patterns; it is important to note that different bacterial species do not automatically result in similarly resistant populations when they undergo comparable selection in different host species. Thus, within a bacterial species, prevalence of resistance can differ between populations isolated from different hosts. For some species, fluctuations in dominant subpopulations, for instance particular serotypes, can be the most important factor determining resistance. The frequently expressed opinion that veterinary use of antimicrobials is at the heart of many resistance problems may be an oversimplification of the complex forces at play.

scholarly journals Multiple Antimicrobial Resistance in Methicillin-Resistant Staphylococcus sciuri Group Isolates from Wild Ungulates in Spain

Antibiotics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 920
Author(s):  
Joaquín Rey Pérez ◽  
Laura Zálama Rosa ◽  
Alfredo García Sánchez ◽  
Javier Hermoso de Mendoza Salcedo ◽  
Juan Manuel Alonso Rodríguez ◽  
...  
Keyword(s):  
Cervus Elaphus ◽  
Methicillin Resistance ◽  
Bacterial Species ◽  
Sccmec Type ◽  
Meca Gene ◽  
Domestic Animal ◽  
Wild Ungulates ◽  
Methicillin Resistant ◽  
Phenotypic Resistance ◽  
Columbia Blood Agar

The aim of this study was to investigate the presence of methicillin-resistant Staphylococcus (MRS) strains in non-managed wild ungulates present in a typical Mediterranean forest in Spain. For this purpose, nasal swabs were obtained from 139 animals: 90 wild boar (Sus scrofa), 42 red deer (Cervus elaphus) and 7 fallow deer (Dama dama), which were subsequently pre-enriched in BHI+ NaCl (6.5%) (24 h/37 °C), and then seeded in Columbia blood agar (24 h/37 °C)). The presence of the mecA gene was investigated by PCR, first from the confluent and then from individual colonies. A total of 10 mecA+ colonies were obtained of which only seven showed phenotypic resistance to oxacillin/cefoxitin (methicillin resistance). All MRS strains belonged to the Staphylococcus sciuri group. Methicillin-resistant Staphylococcus aureus (MRSA) was not detected. In addition, a significant number of MRS strains showed resistance to other antimicrobials, mainly β-lactam (7/7), gentamicin (7/7), fusidic acid (6/7) and quinupristin–dalfopristin (6/7), showing an irregular correlation with their coding genes. The genetic profiles grouped the seven strains obtained according to the bacterial species but not in relation to the animal source or the geographical place of origin. The presence of SCCmec type III, common to animals and humans, has been detected in three of the strains obtained. In conclusion, the study reveals that the wild ungulates investigated play a role as potential reservoirs of multi-resistant strains of MRS. Such strains, due to their characteristics, can be easily transferred to other wild or domestic animal species and ultimately to humans through their products.

scholarly journals Genomic Analysis of Antimicrobial Resistance Genotype-to-Phenotype Agreement in Helicobacter pylori

2020 ◽  
Vol 9 (1) ◽  
pp. 2
Author(s):  
Tal Domanovich-Asor ◽  
Yair Motro ◽  
Boris Khalfin ◽  
Hillary A. Craddock ◽  
Avi Peretz ◽  
...  
Keyword(s):  
Helicobacter Pylori ◽  
Antimicrobial Resistance ◽  
Point Mutations ◽  
Genomic Analysis ◽  
23S Rrna ◽  
Phenotype Correlation ◽  
Bioinformatics Pipeline ◽  
Phenotypic Resistance ◽  
Commercial Kits ◽  
H Pylori

Antimicrobial resistance (AMR) in Helicobacter pylori is increasing and can result in treatment failure and inappropriate antibiotic usage. This study used whole genome sequencing (WGS) to comprehensively analyze the H. pylori resistome and phylogeny in order to characterize Israeli H. pylori. Israeli H. pylori isolates (n = 48) underwent antimicrobial susceptibility testing (AST) against five antimicrobials and WGS analysis. Literature review identified 111 mutations reported to correlate with phenotypic resistance to these antimicrobials. Analysis was conducted via our in-house bioinformatics pipeline targeting point mutations in the relevant genes (pbp1A, 23S rRNA, gyrA, rdxA, frxA, and rpoB) in order to assess genotype-to-phenotype correlation. Resistance rates of study isolates were as follows: clarithromycin 54%, metronidazole 31%, amoxicillin 10%, rifampicin 4%, and levofloxacin 2%. Genotype-to-phenotype correlation was inconsistent; for every analyzed gene at least one phenotypically susceptible isolate was found to have a mutation previously associated with resistance. This was also observed regarding mutations commonly used in commercial kits to diagnose AMR in H. pylori cases. Furthermore, 11 novel point mutations associated with a resistant phenotype were detected. Analysis of a unique set of H. pylori isolates demonstrates that inferring resistance phenotypes from WGS in H. pylori remains challenging and should be optimized further.

scholarly journals Bacterial response to graphene oxide and reduced graphene oxide integrated in agar plates

2018 ◽  
Vol 5 (11) ◽  
pp. 181083 ◽  
Author(s):  
V. R. S. S. Mokkapati ◽  
Santosh Pandit ◽  
Jinho Kim ◽  
Anders Martensson ◽  
Martin Lovmar ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Reduced Graphene Oxide ◽  
Bacterial Growth ◽  
Bacterial Species ◽  
Bacterial Activity ◽  
Bacterial Physiology ◽  
Bacterial Response ◽  
Solid Agar ◽  
Reduced Graphene ◽  
Set Up

There are contradictory reports in the literature regarding the anti-bacterial activity of graphene, graphene oxide (GO) and reduced graphene oxide (rGO). This controversy is mostly due to variations in key parameters of the reported experiments, like: type of substrate, form of graphene, number of layers, type of solvent and most importantly, type of bacteria. Here, we present experimental data related to bacterial response to GO and rGO integrated in solid agar-based nutrient plates—a standard set-up for bacterial growth that is widely used by microbiologists. Bacillus subtilis and Pseudomonas aeruginosa strains were used for testing bacterial growth. We observed that plate-integrated rGO showed strong anti-bacterial activity against both bacterial species. By contrast, plate-integrated GO was harmless to both bacteria. These results reinforce the notion that the response of bacteria depends critically on the type of graphene material used and can vary dramatically from one bacterial strain to another, depending on bacterial physiology.

scholarly journals Impact of Different Carbon Sources on the in vitro Growth and Viability of Escherichia coli (SUBE01) and Salmonella spp. (SUBS01) Cells

2016 ◽  
pp. 39-44
Author(s):  
Ifra Tun Nur ◽  
Jannatun Tahera ◽  
Md Sakil Munna ◽  
M Majibur Rahman ◽  
Rashed Noor
Keyword(s):  
Escherichia Coli ◽  
Bacterial Growth ◽  
Culture Media ◽  
Bacterial Species ◽  
Carbon Sources ◽  
Growth Dynamics ◽  
Luria Bertani ◽  
Tween 20 ◽  
Salmonella Spp

With a previous observation of Escherichia coli growth cessation along with temperature variation within three different bacteriological culture media (nutrient agar, Luria-Bertani agar and minimal agar), current investigation further depicted on the possible growth dynamics of Escherichia coli (SUBE01) and Salmonella (SUBS01) growth and viability upon supplementation of different carbon sources (dextrose, sucrose, lactose, glycerol and tween 20) at 37°C under the aeration of 100 rpm. Viability of the tested bacterial species was assessed through the enumeration of the colony forming unit (cfu) appeared upon prescribed incubation for 12-24 hours on different agar plates consisting of the above mentioned carbon sources. Besides, to inspect the cellular phenotypic changes, morphological observations were conducted under the light microscope. Variations in bacterial growth (either growth acceleration or cessation) were further noticed through the spot tests on the agar plates. Considerable shortfalls in the culturable cells of E. coli and Salmonella spp. were noted in the minimal media separately consisting of sucrose, lactose, glycerol or tween 20 while an opposite impact of accelerated growth was noticed in the media supplied with dextrose. The data revealed a hierarchy of consequence of carbon sources as nutrient generators whereby the favourable bacterial growth and survival order of the carbon sources was estimated as dextrose > glycerol > lactose > tween 20 > sucrose.Bangladesh J Microbiol, Volume 32, Number 1-2,June-Dec 2015, pp 39-44

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