scholarly journals Microbiotic particles in water and soil, water-soil microbiota coalescences, and antimicrobial resistance

2021 ◽  
Author(s):  
Fernando Baquero ◽  
Teresa M. Coque ◽  
Natalia Guerra-Pinto ◽  
Juan-Carlos Galán ◽  
David Jiménez-Lalana ◽  
...  

Bacterial organisms like surfaces. Water and soil contain a multiplicity of particulated material where bacterial populations and communities might attach. Microbiotic particles refers to any type of small particles (less than 2 mm) where bacteria (and other microbes) might attach, resulting in medium- long-term colonization. In this work, the interactions of bacterial organisms with microbiotic particles of the soil and water are reviewed. These particles include bacteria-bacteria aggregates, and aggregates with particles of fungi (particularly in the rhizosphere), protozoa, phytoplankton, zooplankton, biodetritus resulting from animal and vegetal decomposition, humus, mineral particles (clay, carbonates, silicates), and anthropogenic particles (including wastewater particles or microplastics). At they turn, these particles might interact and coalesce (as in the marine snow). Natural phenomena (from river flows to tides, tsunamis, currents, or heavy winds) and anthropogenic activity (such as agriculture, waste-water management, mining, soil-mass movement) favors interaction and merging between all these soil and water particles, and consequently coalescence of their bacterial-associated populations and communities, resulting in an enhancement of mixed-recombinant communities capable of genetic exchange, including antimicrobial resistance genes, particularly in antimicrobial-polluted environments. Particles also favor compartmentalization of bacterial populations favoring diversification and acquisition of mutational resistance by random drift. In general, microbial evolution is accelerated by the aggregation of microbiotic particles. We propose that the world spread of antimicrobial resistance might relate with the environmental dynamics of microbiotic particles, and discuss possible methods to reduce this problem influencing One Health and Planetary Health.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Irene Bueno ◽  
Amanda Beaudoin ◽  
William A. Arnold ◽  
Taegyu Kim ◽  
Lara E. Frankson ◽  
...  

AbstractThe environment plays a key role in the spread and persistence of antimicrobial resistance (AMR). Antimicrobials and antimicrobial resistance genes (ARG) are released into the environment from sources such as wastewater treatment plants, and animal farms. This study describes an approach guided by spatial mapping to quantify and predict antimicrobials and ARG in Minnesota’s waterbodies in water and sediment at two spatial scales: macro, throughout the state, and micro, in specific waterbodies. At the macroscale, the highest concentrations across all antimicrobial classes were found near populated areas. Kernel interpolation provided an approximation of antimicrobial concentrations and ARG abundance at unsampled locations. However, there was high uncertainty in these predictions, due in part to low study power and large distances between sites. At the microscale, wastewater treatment plants had an effect on ARG abundance (sul1 and sul2 in water; blaSHV, intl1, mexB, and sul2 in sediment), but not on antimicrobial concentrations. Results from sediment reflected a long-term history, while water reflected a more transient record of antimicrobials and ARG. This study highlights the value of using spatial analyses, different spatial scales, and sampling matrices, to design an environmental monitoring approach to advance our understanding of AMR persistence and dissemination.


Author(s):  
João Pedro Rueda Furlan ◽  
Lucas David Rodrigues Dos Santos ◽  
Micaela Santana Ramos ◽  
Inara Fernanda Lage Gallo ◽  
Jéssica Aparecida Silva Moretto ◽  
...  

2020 ◽  
Vol 85 (5) ◽  
pp. 601-605
Author(s):  
Freddy Francis ◽  
Ethan K. Gough ◽  
Thaddeus J. Edens ◽  
Chipo Berejena ◽  
Mutsawashe Bwakura-Dangarembizi ◽  
...  

2014 ◽  
Vol 63 (11) ◽  
pp. 1531-1541 ◽  
Author(s):  
Chih-Ming Chen ◽  
Se-Chin Ke ◽  
Chia-Ru Li ◽  
Chien-Shun Chiou ◽  
Chao-Chin Chang

From 2007 to 2009, we collected a total of 83 bacteraemic isolates of Escherichia coli with reduced susceptibility or resistance to third-generation cephalosporins (TGCs). Isolates were genotyped by PFGE and multilocus sequence typing (MLST). The PFGE patterns revealed two highly correlated clusters (cluster E: nine isolates; cluster G: 22 isolates) associated with this prolonged clonal spreading. Compared with cluster E isolates, cluster G isolates were significantly more likely to harbour aac(6')-Ib-cr (P<0.05), and most of these isolates were isolated during a later year than cluster E isolates (P<0.05). By MLST analysis, 94 % of cluster E and G isolates (29/31) were ST68. Although no time or space clustering could be identified by the conventional hospital-acquired infection monitoring system, E. coli cases caused by cluster E and G isolates were significantly associated with having stayed in our hospital’s respiratory care ward (P<0.05). Isolates obtained from patients who had stayed in the respiratory care ward had a significantly higher rate of aac(6')-Ib-cr and bla CTX-M-14 positivity, and were more likely to belong to ST68/S68-like (all P<0.05). To our knowledge, this is the first report of prolonged clonal spreading caused by E. coli ST68 associated with a stay in a long-term care facility. Using epidemiological investigations and PFGE and MLST analyses, we have identified long-term clonal spreading caused by E. coli ST68, with extra antimicrobial-resistance genes possibly acquired during the prolonged spreading period.


2017 ◽  
Vol 24 (4) ◽  
pp. 746-753
Author(s):  
Ariel Eurides Stella ◽  
Angélica Franco de Oliveira ◽  
Cecília Nunes Moreira ◽  
Raphaella Barbosa Meirelles Bartoli ◽  
Vera Lúcia Dias da Silva

Antimicrobial resistance is currently one of authorities’ major concerns in healthcare, mainlydue to the danger that may arise from multiresistant strains in situations of contamination andinfection of patients in hospital settings. The origin of this resistance is linked to the dynamicsof natural bacteria populations in soil and water, but also to the excessive and inappropriate useof antimicrobials in clinical treatment and as growth promoters in herds. In this study,antimicrobial resistance profiles were analyzed in potentially pathogenic populations ofEscherichia coli in the gastrointestinal tract of poultry, cattle and sheep. This bacterial specie,although harboring pathogenic pathotypes, is part of the normal microflora of these animals’intestinal tracts. The lowest antimicrobial resistance rates were observed in sheep isolates.Resistance highest rates of were observed among bacterial populations derived from thepoultry. In bacterial population from cattle feces, resistance to ampicillin, cephalothin anderythromycin was observed. Resistance to cephalothin was noted to be widespread amonganalyzed populations. Furthermore, the conscious use of growth promoters, and supported on aproper diagnosis in clinical cases it is essential to inhibit the emergence of multidrug-resistantstrains.


2017 ◽  
Vol 68 (11) ◽  
pp. 2546-2550
Author(s):  
Monica Licker ◽  
Andrei Anghel ◽  
Roxana Moldovan ◽  
Elena Hogea ◽  
Delia Muntean ◽  
...  

Antimicrobial resistance (AMR) represents a real burden for the modern medicine. One of the most frecvently isolated hospital acquired (HA) pathogens wordlwide, is Methicillin resistant Staphylococcus aureus (MRSA). Recently not only HA, but also community-acquired MRSA (CA-MRSA) infections have been reported. A prospective study was performed between February 2009 and October 2010, with the aim to investigate bacterial resistance of CA-MRSA and HA-MRSA. DNA microarray technology has been used for the detection of 4 AMR genes for the studied MRSA strains. A number of 218 HA- S.aureus strains have been isolated, from which 89 (40. 82%) were MRSA. In the community, 1.553 S.aureus strains were isolated, out of which, 356 (22. 92%) were MRSA. From these, a number of 17 HA and 12 CA �MRSA strains have been analyzed by DNA microarray technology. From 100% phenotypically described HA- MRSA, we identified mecA gene in 10 strains (58. 83%). Other 6 strains (35. 29%) have been erm(A) positive and 4 (23. 53%) - tet(O) positive. 83. 33% (10 strains) from the CA strains had mecA gene, only one (8. 33%) was erm(A) positive and 4 (33. 33%) were erm(C) positive. DNA microarray is a method allowing the concomitant scan of multiple genes and can be done within a few hours. That type of rapid and reliable methods for antimicrobial sensitivity tests are important to start an appropriate therapy.


2020 ◽  
Vol 15 ◽  
Author(s):  
Akshatha Prasanna ◽  
Vidya Niranjan

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.


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