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23 pages, 3305 KiB  
Review
Phytochemistry, Mechanisms, and Preclinical Studies of Echinacea Extracts in Modulating Immune Responses to Bacterial and Viral Infections: A Comprehensive Review
by Fatemeh Ahmadi
Antibiotics 2024, 13(10), 947; https://doi.org/10.3390/antibiotics13100947 - 9 Oct 2024
Viewed by 623
Abstract
Background: Echinacea species, particularly Echinacea purpurea, Echinacea angustifolia, and Echinacea pallida, are renowned for their immunomodulatory, antibacterial, and antiviral properties. Objectives: This review explores the mechanisms by which echinacea herbal extracts modulate immune responses, focusing on their effects on both [...] Read more.
Background: Echinacea species, particularly Echinacea purpurea, Echinacea angustifolia, and Echinacea pallida, are renowned for their immunomodulatory, antibacterial, and antiviral properties. Objectives: This review explores the mechanisms by which echinacea herbal extracts modulate immune responses, focusing on their effects on both innate and adaptive immunity in bacterial and viral infections. Results: Key bioactive compounds, such as alkamides, caffeic acid derivatives, flavonoids, and polysaccharides, contribute to these effects. These compounds enhance immune cell activity, including macrophages and natural killer cells, stimulating cytokine production and phagocytosis. The antibacterial activity of echinacea against respiratory pathogens (Streptococcus pneumoniae, Haemophilus influenzae, Legionella pneumophila) and skin pathogens (Staphylococcus aureus, Propionibacterium acnes) is reviewed, as well as its antiviral efficacy against viruses like herpes simplex, influenza, and rhinovirus. Echinacea’s potential as a complementary treatment alongside conventional antibiotics and antivirals is discussed, particularly in the context of antibiotic resistance and emerging viral threats. Conclusions: Challenges associated with variability in phytochemical content and the need for standardized extraction processes are also addressed. This review provides a comprehensive overview of echinacea’s therapeutic potential and outlines future directions for research, including clinical trials and dosage optimization. Full article
(This article belongs to the Special Issue Antimicrobial Activity of Natural Products and Plants Extracts)
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<p>Botanical illustration of <span class="html-italic">E. purpurea</span>, depicting the aerial and root structures (Figure drawn by F. Ahmadi).</p>
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<p>Structure of the main echinacea alkamides (Figure drawn by F. Ahmadi).</p>
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<p>Flavonoids found in echinacea species (Figure drawn by F. Ahmadi).</p>
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<p>A possible biosynthesis pathway for chicoric acid and caffeic acid is derived via the phenylpropanoid pathway in echinacea species. PAL: phenylalanine ammonia-lyase, C4H: cinnamate-4-hydroxylase, C3H: coumarate 3-hydroxylase, 4CL: 4-coumarate-CoA ligase, HCT: shikimate o-hydroxycinnamoyl transferase (Figure drawn by F. Ahmadi).</p>
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<p>Antiviral activity of echinacea extract against SARS-CoV-2 virus (Figure drawn by F. Ahmadi).</p>
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<p>A schematic representation of the main molecular pathways linked to inflammatory and immunomodulatory activities modulated by echinacea. The solid red line indicates the pathway’s activation, whereas the truncated red line indicates inhibition of the pathway. TLR-4: Toll-like Receptor-4; MyD88: Myeloid Differentiation Primary Response 88; NF-KB: Nuclear Factor kappa B; MAPK: Mitogen-Activated Protein Kinase; COX-2: cyclooxygenase-2; iNOS: inducible Nitric Oxide Synthase; HO-1: Heme Oxygenase-1; IL: Interleukin; TNF: Tumor Necrosis Factor (Figure drawn by F. Ahmadi).</p>
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19 pages, 817 KiB  
Review
The Adequacy of Current Legionnaires’ Disease Diagnostic Practices in Capturing the Epidemiology of Clinically Relevant Legionella: A Scoping Review
by Ryan Ha, Ashley Heilmann, Sylvain A. Lother, Christine Turenne, David Alexander, Yoav Keynan and Zulma Vanessa Rueda
Pathogens 2024, 13(10), 857; https://doi.org/10.3390/pathogens13100857 - 1 Oct 2024
Viewed by 534
Abstract
Legionella is an underdiagnosed and underreported etiology of pneumonia. Legionella pneumophila serogroup 1 (LpSG1) is thought to be the most common pathogenic subgroup. This assumption is based on the frequent use of a urinary antigen test (UAT), only capable of diagnosing LpSG1. We [...] Read more.
Legionella is an underdiagnosed and underreported etiology of pneumonia. Legionella pneumophila serogroup 1 (LpSG1) is thought to be the most common pathogenic subgroup. This assumption is based on the frequent use of a urinary antigen test (UAT), only capable of diagnosing LpSG1. We aimed to explore the frequency of Legionella infections in individuals diagnosed with pneumonia and the performance of diagnostic methods for detecting Legionella infections. We conducted a scoping review to answer the following questions: (1) “Does nucleic acid testing (NAT) increase the detection of non-pneumophila serogroup 1 Legionella compared to non-NAT?”; and (2) “Does being immunocompromised increase the frequency of pneumonia caused by non-pneumophila serogroup 1 Legionella compared to non-immunocompromised individuals with Legionnaires’ disease (LD)?”. Articles reporting various diagnostic methods (both NAT and non-NAT) for pneumonia were extracted from several databases. Of the 3449 articles obtained, 31 were included in our review. The most common species were found to be L. pneumophila, L. longbeachae, and unidentified Legionella species appearing in 1.4%, 0.9%, and 0.6% of pneumonia cases. Nearly 50% of cases were caused by unspecified species or serogroups not detected by the standard UAT. NAT-based techniques were more likely to detect Legionella than non-NAT-based techniques. The identification and detection of Legionella and serogroups other than serogroup 1 is hampered by a lack of application of broader pan-Legionella or pan-serogroup diagnostics. Full article
(This article belongs to the Special Issue Legionella and Waterborne Disease)
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<p>PRISMA flowchart depicting the screening process generated by Covidence [<a href="#B34-pathogens-13-00857" class="html-bibr">34</a>].</p>
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17 pages, 2402 KiB  
Article
Occurrence of Uncultured Legionella spp. in Treated Wastewater Effluent and Its Impact on Human Health (SCA.Re.S Project)
by Osvalda De Giglio, Giusy Diella, Francesco Bagordo, Antonella Francesca Savino, Angelantonio Calabrese, Mariavirginia Campanale, Francesco Triggiano, Francesca Apollonio, Valentina Spagnuolo, Marco Lopuzzo, Tiziana Grassi, Maria Clementina Caputo, Silvia Brigida, Federica Valeriani, Vincenzo Romano Spica and Maria Teresa Montagna
Pathogens 2024, 13(9), 786; https://doi.org/10.3390/pathogens13090786 - 12 Sep 2024
Viewed by 791
Abstract
Wastewater treatment plants (WWTPs) provide optimal conditions for the environmental spread of Legionella. As part of the Evaluation of Sanitary Risk Related to the Discharge of Wastewater to the Ground (SCA.Re.S) project, this study was conducted to evaluate the presence of Legionella [...] Read more.
Wastewater treatment plants (WWTPs) provide optimal conditions for the environmental spread of Legionella. As part of the Evaluation of Sanitary Risk Related to the Discharge of Wastewater to the Ground (SCA.Re.S) project, this study was conducted to evaluate the presence of Legionella in WWTP effluent and in groundwater samples collected from two wells located downstream from the plant. The samples were analyzed to determine the concentrations of Legionella spp using the standard culture-based method and molecular techniques, followed by genomic sequencing analysis. Legionella was detected only with the molecular methods (except in one sample of effluent positive for L. pneumophila serogroup 6), which showed viable Legionella pneumophila and L. non-pneumophila through the use of free DNA removal solution in both the effluent and groundwater, with concentrations that progressively decreased downstream from the plant. Viable L. pneumophila appeared to be slightly more concentrated in warm months. However, no significant differences (p ≥ 0.05) in concentrations between cold and warm months were observed. A genotypic analysis characterized the species present in the samples and found that uncultured Legionella spp, as yet undefined, constituted the prevalent species in all the samples (range 77.15–83.17%). WWTPs play an important role in the hygienic and sanitary quality of groundwater for different uses. The application of Legionella control systems during the purification of effluents is warranted to prevent possible outbreaks of legionellosis. Full article
(This article belongs to the Special Issue Legionella and Waterborne Disease)
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<p>The study area, including the wastewater treatment plant and monitoring wells, on the Salento peninsula, Apulia, Italy.</p>
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<p>Schematic representation of the wastewater discharge path from the wastewater treatment plant into draining trenches and then to downgradient monitoring wells W1 (400 m) and W2 (1000 m).</p>
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<p>Rate (%) of positive samples and viable cell concentrations of <span class="html-italic">Legionella</span> spp. and <span class="html-italic">L. pneumophila</span>, collected from the effluent of WWTP, W1 (well 1), and W2 (well 2).</p>
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<p>Average concentrations ± SE of viable <span class="html-italic">Legionella</span> spp. and <span class="html-italic">L. pneumophila</span> in WWTP effluent W1 (well 1) and W2 (well 2) recorded in cold months (November 2022–April 2023) and warm months (May–October 2022).</p>
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<p><span class="html-italic">Legionella</span> spp.’s composition in water samples assessed by sequencing 16S rRNA gene V3–V4 domain amplicon with Illumina MiSeq (similarity &gt; 97%).</p>
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<p>(<b>a</b>) NCBI BLASTN phylogenetic tree showing the similarities (&gt;97%) between the uncultured Legionella sp. clone BFI-A19-1 (in yellow) and known Legionella species (in green), determined with 16S rDNA sequencing. The scale bar corresponds to 0.02 substitutions per nucleotide position. (<b>b</b>) NCBI BLASTN phylogenetic tree showing the similarities (&gt;97%) between uncultured Legionella sp. clone Tag 4-1 (in yellow) and known <span class="html-italic">Legionella</span> species (in green), determined with 16S rRNA sequencing. The scale bar corresponds to 0.02 substitutions per nucleotide position.</p>
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<p>(<b>a</b>) NCBI BLASTN phylogenetic tree showing the similarities (&gt;97%) between the uncultured Legionella sp. clone BFI-A19-1 (in yellow) and known Legionella species (in green), determined with 16S rDNA sequencing. The scale bar corresponds to 0.02 substitutions per nucleotide position. (<b>b</b>) NCBI BLASTN phylogenetic tree showing the similarities (&gt;97%) between uncultured Legionella sp. clone Tag 4-1 (in yellow) and known <span class="html-italic">Legionella</span> species (in green), determined with 16S rRNA sequencing. The scale bar corresponds to 0.02 substitutions per nucleotide position.</p>
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16 pages, 2878 KiB  
Article
The Contribution of Legionella anisa to Legionella Contamination of Water in the Built Environment
by Brian Crook, Charlotte Young, Ceri Rideout and Duncan Smith
Int. J. Environ. Res. Public Health 2024, 21(8), 1101; https://doi.org/10.3390/ijerph21081101 - 20 Aug 2024
Viewed by 1363
Abstract
Legionella bacteria can proliferate in poorly maintained water systems, posing risks to users. All Legionella species are potentially pathogenic, but Legionella pneumophila (L. pneumophila) is usually the primary focus of testing. However, Legionella anisa (L. anisa) also colonizes water [...] Read more.
Legionella bacteria can proliferate in poorly maintained water systems, posing risks to users. All Legionella species are potentially pathogenic, but Legionella pneumophila (L. pneumophila) is usually the primary focus of testing. However, Legionella anisa (L. anisa) also colonizes water distribution systems, is frequently found with L. pneumophila, and could be a good indicator for increased risk of nosocomial infection. Anonymized data from three commercial Legionella testing laboratories afforded an analysis of 565,750 water samples. The data covered July 2019 to August 2021, including the COVID-19 pandemic. The results confirmed that L. anisa commonly colonizes water distribution systems, being the most frequently identified non-L. pneumophila species. The proportions of L. anisa and L. pneumophila generally remained similar, but increases in L. pneumophila during COVID-19 lockdown suggest static water supplies might favor its growth. Disinfection of hospital water systems was effective, but re-colonization did occur, appearing to favor L. pneumophila; however, L. anisa colony numbers also increased as a proportion of the total. While L. pneumophila remains the main species of concern as a risk to human health, L. anisa’s role should not be underestimated, either as a potential infection risk or as an indicator of the need to intervene to control Legionella’s colonization of water supplies. Full article
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<p>Data Set 1—Total number of <span class="html-italic">Legionella</span>-positive samples recorded, categorized by date and species.</p>
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<p>Data Set 1—Proportion of <span class="html-italic">Legionella</span>-positive samples recorded, categorized by date and species.</p>
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<p>Data Set 2—Total number of <span class="html-italic">Legionella</span>-positive samples recorded, categorized by species, for pre- and post-cleaning.</p>
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<p>Data Set 2—Total number of <span class="html-italic">Legionella</span>-positive samples recorded, categorized by date, for pre- and post-cleaning.</p>
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<p>Data Set 2—Number of <span class="html-italic">Legionella</span>-positive samples recorded before and after the COVID-19 outbreak, categorized by species.</p>
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<p>Data Set 2—Changes in species isolated pre- and post-cleaning in shower hoses (numbers = percentage of samples positive for <span class="html-italic">L. pneumophila</span> or <span class="html-italic">L. anisa</span>, or none detected (other <span class="html-italic">Legionella</span> species not relevant to this analysis were excluded).</p>
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<p>Data Set 2—Changes in species isolated pre- and post-cleaning in TMVs (numbers = percentage of samples positive for <span class="html-italic">L. pneumophila</span> or <span class="html-italic">L. anisa</span>, or none detected (other <span class="html-italic">Legionella</span> species not relevant to this analysis were excluded).</p>
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<p>Data Set 2—Number of <span class="html-italic">Legionella</span>-positive samples recorded before and after the COVID-19 outbreak, categorized by species.</p>
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<p>Data Set 3—Total number of <span class="html-italic">Legionella</span>-positive samples recorded, categorized by species.</p>
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11 pages, 1217 KiB  
Article
Diversity of Free-Living Amoebae in New Zealand Groundwater and Their Ability to Feed on Legionella pneumophila
by Sujani Ariyadasa, Sophie van Hamelsveld, William Taylor, Susan Lin, Panan Sitthirit, Liping Pang, Craig Billington and Louise Weaver
Pathogens 2024, 13(8), 665; https://doi.org/10.3390/pathogens13080665 - 7 Aug 2024
Viewed by 740
Abstract
Free-living amoebae (FLA) are common in both natural and engineered freshwater ecosystems. They play important roles in biofilm control and contaminant removal through the predation of bacteria and other taxa. Bacterial predation by FLA is also thought to contribute to pathogen dispersal and [...] Read more.
Free-living amoebae (FLA) are common in both natural and engineered freshwater ecosystems. They play important roles in biofilm control and contaminant removal through the predation of bacteria and other taxa. Bacterial predation by FLA is also thought to contribute to pathogen dispersal and infectious disease transmission in freshwater environments via the egestion of viable bacteria. Despite their importance in shaping freshwater microbial communities, the diversity and function of FLA in many freshwater ecosystems are poorly understood. In this study, we isolated and characterized FLA from two groundwater sites in Canterbury, New Zealand using microbiological, microscopic, and molecular techniques. Different methods for groundwater FLA isolation and enrichment were trialed and optimized. The ability of these isolated FLA to predate on human pathogen Legionella pneumophila was assessed. FLA were identified by 18S metagenomic amplicon sequencing. Our study showed that Acanthamoeba spp. (including A. polyphaga) and Vermamoeba veriformis were the main FLA species present in both groundwater sites examined. While most of the isolated FLA co-existed with L. pneumophila, the FLA populations in the L. pneumophila co-culture experiments predominantly consisted of A. polyphaga, Acanthamoeba spp., Naegleria spp., V. vermiformis, Paravahlkampfia spp., and Echinamoeba spp. These observations suggest that FLA may have the potential to act as reservoirs for L. pneumophila in Canterbury, New Zealand groundwater systems and could be introduced into the local drinking water infrastructure, where they may promote the survival, multiplication, and dissemination of Legionella. This research addresses an important gap in our understanding of FLA-mediated pathogen dispersal in freshwater ecosystems. Full article
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<p>Inverted light microscopy images of FLA isolated from wet agar block transfer ((<b>A</b>), 40× magnification) and newly developed starvation isolation method ((<b>B</b>), 20× magnification). Red arrows point to FLA. Incorporating starvation to agar block transfer method reduced ciliate carryover and promoted FLA propagation.</p>
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<p>Relative abundances of FLA isolated from groundwater sites (site A, left; site B, right) in <span class="html-italic">E. coli</span> and <span class="html-italic">L. pneumophila</span> feeding experiments at genus level (<b>A</b>) and species level (<b>B</b>). Two samples were analyzed, except in the case of the ‘Site B, <span class="html-italic">E coli</span>’ samples, where five were used for the analysis.</p>
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24 pages, 2321 KiB  
Article
Legionnaires’ Disease Surveillance and Public Health Policies in Italy: A Mathematical Model for Assessing Prevention Strategies
by Vincenzo Romano Spica, Paola Borella, Agnese Bruno, Cristian Carboni, Martin Exner, Philippe Hartemann, Gianluca Gianfranceschi, Pasqualina Laganà, Antonella Mansi, Maria Teresa Montagna, Osvalda De Giglio, Serena Platania, Caterina Rizzo, Alberto Spotti, Francesca Ubaldi, Matteo Vitali, Paul van der Wielen and Federica Valeriani
Water 2024, 16(15), 2167; https://doi.org/10.3390/w16152167 - 31 Jul 2024
Viewed by 1276
Abstract
Legionella is the pathogen that causes Legionnaires’ disease, an increasingly prevalent and sometimes fatal disease worldwide. In 2021, 97% of cases in Europe were caused by Legionella pneumophila. We present a mathematical model that can be used by public health officials to [...] Read more.
Legionella is the pathogen that causes Legionnaires’ disease, an increasingly prevalent and sometimes fatal disease worldwide. In 2021, 97% of cases in Europe were caused by Legionella pneumophila. We present a mathematical model that can be used by public health officials to assess the effectiveness and efficiency of different Legionella monitoring and control strategies to inform government requirements to prevent community-acquired Legionnaires’ disease in non-hospital buildings. This simulation model was built using comprehensive data from multiple scientific and field-based sources. It is a tool for estimating the relative economic and human costs of monitoring and control efforts targeting either L. pneumophila or Legionella species and was designed to analyze the potential application of each approach to specific building classes across Italy. The model results consistently showed that targeting L. pneumophila is not only sufficient but preferable in optimizing total cost (direct and economic) for similar human health benefits, even when stress-tested with extreme inputs. This cost–benefit analytical tool allows the user to run different real-life scenarios with a broad range of epidemiological and prevalence assumptions across different geographies in Italy. With appropriate modifications, this tool can be localized and applied to other countries, states, or provinces. Full article
(This article belongs to the Special Issue Legionella: A Key Organism in Water Management)
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Graphical abstract

Graphical abstract
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<p>Analytical framework for estimating the financial and economic impact of different <span class="html-italic">Legionella</span> control strategies.</p>
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<p>Model output for health outcomes of each <span class="html-italic">Legionella</span> control approach.</p>
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<p>Model output estimating the economic and total cost of each Legionella control approach.</p>
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<p>Projected total 10-year cost of each control approach using base case assumptions.</p>
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<p>The cost of each control strategy with individual input options varied. (<b>A</b>) Varying “the % of buildings contaminated at action level” while holding the three other base case assumptions constant. (<b>B</b>) Varying “% of contaminated buildings with <span class="html-italic">L. pneumophila</span> detection” while holding the three other base case assumptions constant. (<b>C</b>) Varying “the % of total Legionnaires’ disease cases est. caused by <span class="html-italic">L. pneumophila</span> (all serogroups)” while holding the three other base case assumptions constant. (<b>D</b>) Varied rate of hospitalization (%) while holding the three other base case assumptions constant and (<b>E</b>) varied rate of compliance with remediation when the action limit is exceeded while holding the other base case assumptions constant.</p>
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<p>The cost of each control strategy with individual input options varied. (<b>A</b>) Varying “the % of buildings contaminated at action level” while holding the three other base case assumptions constant. (<b>B</b>) Varying “% of contaminated buildings with <span class="html-italic">L. pneumophila</span> detection” while holding the three other base case assumptions constant. (<b>C</b>) Varying “the % of total Legionnaires’ disease cases est. caused by <span class="html-italic">L. pneumophila</span> (all serogroups)” while holding the three other base case assumptions constant. (<b>D</b>) Varied rate of hospitalization (%) while holding the three other base case assumptions constant and (<b>E</b>) varied rate of compliance with remediation when the action limit is exceeded while holding the other base case assumptions constant.</p>
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14 pages, 1134 KiB  
Article
Development of Polymerase Chain Reaction–High-Resolution Melt Assay for Waterborne Pathogens Legionella pneumophila, Vibrio parahaemolyticus, and Camplobacter jejuni
by Shannon M. Carr and Kelly M. Elkins
Microorganisms 2024, 12(7), 1366; https://doi.org/10.3390/microorganisms12071366 - 3 Jul 2024
Viewed by 862
Abstract
Legionella pneumophila is the waterborne pathogen primarily responsible for causing both Pontiac Fever and Legionnaire’s Disease in humans. L. pneumophila is transmitted via aerosolized water droplets. The purpose of this study was to design and test primers to allow for rapid polymerase chain [...] Read more.
Legionella pneumophila is the waterborne pathogen primarily responsible for causing both Pontiac Fever and Legionnaire’s Disease in humans. L. pneumophila is transmitted via aerosolized water droplets. The purpose of this study was to design and test primers to allow for rapid polymerase chain reaction (PCR) melt detection and identification of this infectious agent in cases of clinical or emergency response detection. New PCR primers were designed for this species of bacteria; the primer set was purchased from IDT and the target bacterial DNA was purchased from ATCC. The L. pneumophila primers targeted the macrophage infectivity potentiator gene (mip), which inhibits macrophage phagocytosis. The primers were tested for specificity, repeatability, and sensitivity using PCR–high-resolution melt (HRM) assays. The primer set was found to be specific to the designated bacteria and did not amplify the other twenty-one species from the panel. The L. pneumophila assay was able to be multiplexed. The duplex assay consists of primers for L. pneumophila and Vibrio parahaemolyticus, which are both waterborne pathogens. The triplex assay consists of primers for L. pneumophila, V. parahaemolyticus, and Campylobacter jejuni. The unique melting temperature for the L. pneumophila primer assay is 82.84 ± 0.19 °C, the C. jejuni assay is 78.10 ± 0.58 °C, and the V. parahaemolyticus assay is 86.74 ± 0.65 °C. Full article
(This article belongs to the Special Issue Advances in Research on Waterborne Pathogens)
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Figure 1
<p>PCR melt curves of (<b>a</b>) <span class="html-italic">L. pneumophila mip</span>, (<b>b</b>) <span class="html-italic">C. jejuni cadF</span>, and (<b>c</b>) <span class="html-italic">V. parahaemolyticus thl</span> primers as single primer assays. Melt curves of <span class="html-italic">L. pneumophila mip</span> primers as a duplex assay with <span class="html-italic">V. parahaemolyticus thl</span> primers (<b>d</b>) testing bacterial DNA individually as a duplex assay and (<b>e</b>) testing a bacterial DNA mixture as a duplex assay. Melt curves of <span class="html-italic">L. pneumophila mip</span> primers as a triplex assay with <span class="html-italic">V. parahaemolyticus thl</span> and <span class="html-italic">C. jejuni cadF</span> primers (<b>f</b>) testing bacterial DNA individually as a triplex assay and (<b>g</b>) testing a bacterial DNA mixture as a triplex assay.</p>
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<p>PCR–HRM results from repeatability testing of the primers sets for <span class="html-italic">C. jejuni</span> (green line), <span class="html-italic">L. pneumophila</span> (red line), and <span class="html-italic">V. parahaemolyticus</span> (blue line) and the triplex assay (solid black line). The negative amplification controls are shown with the gray lines (primers and master mix). The initial hold was 95 °C for 10 min followed by 40 cycles of 15 s each at 60 °C, 72 °C, and 95 °C, including the first ten cycles of touchdown from 60 °C to 55 °C in 0.5 °C increments, hold at 72 °C for 5 min, hold at 45 °C for 60 s, and melt from 65 °C to 95 °C, increasing by 0.3 °C every 3 s with gain optimization and detecting HRM.</p>
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<p>Results from 3% agarose gel with primer sets for <span class="html-italic">L. pneumophila</span> (LP), <span class="html-italic">V. parahaemolyticus</span> (VP), and <span class="html-italic">C. jejuni</span> (CJ) and target DNA each (lanes 1, 2, and 3, respectively), ThermoFisher TrackIt 100 bp DNA ladder (sizes labelled) (lane 4), Fast Ruler Ultra-Low Range DNA ladder (lane 5), triplex primer set with all three target DNAs (lane 6, species labelled), and the negative ampflification control with the triplex primers was run in lane 7.</p>
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<p>Sensitivity of <span class="html-italic">L. pneumophila mip</span> primers as a (<b>a</b>) single primer assay, (<b>b</b>) duplex assay with <span class="html-italic">V. parahaemolyticus thl</span> primers (left peak) testing a mixture of <span class="html-italic">L. pneumophila</span> and <span class="html-italic">V. parahaemolyticus</span> DNA with concentrations as shown for both, and (<b>c</b>) triplex assay with <span class="html-italic">V. parahaemolyticus thl</span> and <span class="html-italic">C. jejuni cadF</span> primers testing a mixture of <span class="html-italic">L. pneumophila</span>, <span class="html-italic">V. parahaemolyticus</span>, and <span class="html-italic">C. jejuni</span> DNA in triplicate (1 ng/µL, solid black line; 0.1 ng/µL, dashed line; 0.05 ng/µL, dotted line).</p>
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<p>Specificity of <span class="html-italic">L. pneumophila mip</span> primers tested against 21 other bacterial strains (gray lines) as a (<b>a</b>) single primer assay (solid black trace), (<b>b</b>) duplex assay with <span class="html-italic">V. parahaemolyticus thl</span> primers (dot black trace), and (<b>c</b>) triplex assay with <span class="html-italic">V. parahaemolyticus thl</span> (dash dotted black trace) and <span class="html-italic">C. jejuni cadF</span> (dot black trace) primers.</p>
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18 pages, 3409 KiB  
Article
Impact of Prophylactic Antibiotic Use in Ornamental Fish Tanks on Microbial Communities and Pathogen Selection in Carriage Water in Hong Kong Retail Shops
by Chun Au-Yeung, Kit-Ling Lam, Man-Hay Choi, Ka-Wai Chan, Yu-Sum Cheung, Yat-Lai Tsui and Wing-Yin Mo
Microorganisms 2024, 12(6), 1184; https://doi.org/10.3390/microorganisms12061184 - 12 Jun 2024
Viewed by 1021
Abstract
Antibiotics are routinely added to ornamental fish tanks for treating bacterial infection or as a prophylactic measure. However, the overuse or subtherapeutical application of antibiotics could potentially facilitate the selection of antibiotic resistance in bacteria, yet no studies have investigated antibiotic use in [...] Read more.
Antibiotics are routinely added to ornamental fish tanks for treating bacterial infection or as a prophylactic measure. However, the overuse or subtherapeutical application of antibiotics could potentially facilitate the selection of antibiotic resistance in bacteria, yet no studies have investigated antibiotic use in the retail ornamental fish sector and its impact on microbial communities. The present study analyzed the concentrations of twenty antibiotics in the carriage water (which also originates from fish tanks in retail shops) collected monthly from ten local ornamental fish shops over a duration of three months. The antibiotic concentrations were correlated with the sequenced microbial community composition, and the risk of resistance selection in bacteria was assessed. Results revealed that the detected concentrations of tetracyclines were the highest among samples, followed by fluoroquinolones and macrolides. The concentrations of oxytetracycline (44.3 to 2,262,064.2 ng L−1) detected across three months demonstrated a high risk for resistance selection at most of the sampled shops. Zoonotic pathogens (species of Rhodococcus, Legionella, and Citrobacter) were positively correlated with the concentrations of oxytetracycline, tetracycline, chlortetracycline, and enrofloxacin. This suggests that antibiotic use in retail shops may increase the likelihood of selecting for zoonotic pathogens. These findings shed light on the potential for ornamental fish retail shops to create a favorable environment for the selection of pathogens with antibiotics, thereby highlighting the urgent need for enhanced antibiotic stewardship within the industry. Full article
(This article belongs to the Special Issue New Insights into the Antibiotic Resistance of Aquatic Microorganisms)
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<p>Cluster analysis dendrogram of antibiotics detected in carriage water samples.</p>
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<p>Principal component analysis of antibiotics detected in carriage water samples.</p>
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<p>Relative abundance of bacterial phylum in carriage water samples.</p>
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<p>Nonmetric multidimensional scaling (NMDS) ordination plot of the microbial composition of carriage water samples. Different symbols represent different shops: fill-circle = S1; plus = S2; square = S3; fill-square = S4; triangle = S5; fill-triangle = S6; bar = S7; star = S8; circle = S9; cross = S10.</p>
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<p>Comparison of alpha diversity indices among sampling shops. Diversity in the microbial community of ornamental fish carriage water was measured using (<b>a</b>) observed species, (<b>b</b>) Chao1, (<b>c</b>) Shannon, and (<b>d</b>) Simpson index. Asterisks indicate statistically significant differences in pairwise comparisons (* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01) (<span class="html-italic">n</span> = 3).</p>
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<p>Heatmap of log10 relative abundance of microbial composition at (<b>a</b>) genus level and (<b>b</b>) species level (relative abundance &gt; 0.0005%) in carriage water samples.</p>
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<p>Redundancy analysis of microbial communities at phylum level of carriage water and antibiotic concentrations. Different symbols represent different shops: fill-circle = S1; plus = S2; square = S3; fill-square = S4; triangle = S5; fill-triangle = S6; bar = S7; star = S8; circle = S9; cross = S10.</p>
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<p>Pearson correlations between antibiotic concentrations and the abundance of pathogen genera in samples (<span class="html-italic">n</span> = 30). Black boxes indicate a significant value at <span class="html-italic">p</span> &lt; 0.05.</p>
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144 KiB  
Abstract
Thermodynamic Analysis of Interactions in Langmuir Monolayers Imitating Bacterial Membranes
by Katarzyna Pastuszak, Małgorzata Jurak and Marta Palusińska-Szysz
Proceedings 2024, 107(1), 41; https://doi.org/10.3390/proceedings2024107041 - 15 May 2024
Cited by 1 | Viewed by 96
Abstract
Bacteria belonging to the Legionella gormanii species cause respiratory diseases [...] Full article
58 pages, 5337 KiB  
Review
Opportunistic Pathogens in Drinking Water Distribution Systems—A Review
by Mark W. LeChevallier, Toby Prosser and Melita Stevens
Microorganisms 2024, 12(5), 916; https://doi.org/10.3390/microorganisms12050916 - 30 Apr 2024
Cited by 1 | Viewed by 3641
Abstract
In contrast to “frank” pathogens, like Salmonella entrocolitica, Shigella dysenteriae, and Vibrio cholerae, that always have a probability of disease, “opportunistic” pathogens are organisms that cause an infectious disease in a host with a weakened immune system and rarely in [...] Read more.
In contrast to “frank” pathogens, like Salmonella entrocolitica, Shigella dysenteriae, and Vibrio cholerae, that always have a probability of disease, “opportunistic” pathogens are organisms that cause an infectious disease in a host with a weakened immune system and rarely in a healthy host. Historically, drinking water treatment has focused on control of frank pathogens, particularly those from human or animal sources (like Giardia lamblia, Cryptosporidium parvum, or Hepatitis A virus), but in recent years outbreaks from drinking water have increasingly been due to opportunistic pathogens. Characteristics of opportunistic pathogens that make them problematic for water treatment include: (1) they are normally present in aquatic environments, (2) they grow in biofilms that protect the bacteria from disinfectants, and (3) under appropriate conditions in drinking water systems (e.g., warm water, stagnation, low disinfectant levels, etc.), these bacteria can amplify to levels that can pose a public health risk. The three most common opportunistic pathogens in drinking water systems are Legionella pneumophila, Mycobacterium avium, and Pseudomonas aeruginosa. This report focuses on these organisms to provide information on their public health risk, occurrence in drinking water systems, susceptibility to various disinfectants, and other operational practices (like flushing and cleaning of pipes and storage tanks). In addition, information is provided on a group of nine other opportunistic pathogens that are less commonly found in drinking water systems, including Aeromonas hydrophila, Klebsiella pneumoniae, Serratia marcescens, Burkholderia pseudomallei, Acinetobacter baumannii, Stenotrophomonas maltophilia, Arcobacter butzleri, and several free-living amoebae including Naegleria fowleri and species of Acanthamoeba. The public health risk for these microbes in drinking water is still unclear, but in most cases, efforts to manage Legionella, mycobacteria, and Pseudomonas risks will also be effective for these other opportunistic pathogens. The approach to managing opportunistic pathogens in drinking water supplies focuses on controlling the growth of these organisms. Many of these microbes are normal inhabitants in biofilms in water, so the attention is less on eliminating these organisms from entering the system and more on managing their occurrence and concentrations in the pipe network. With anticipated warming trends associated with climate change, the factors that drive the growth of opportunistic pathogens in drinking water systems will likely increase. It is important, therefore, to evaluate treatment barriers and management activities for control of opportunistic pathogen risks. Controls for primary treatment, particularly for turbidity management and disinfection, should be reviewed to ensure adequacy for opportunistic pathogen control. However, the major focus for the utility’s opportunistic pathogen risk reduction plan is the management of biological activity and biofilms in the distribution system. Factors that influence the growth of microbes (primarily in biofilms) in the distribution system include, temperature, disinfectant type and concentration, nutrient levels (measured as AOC or BDOC), stagnation, flushing of pipes and cleaning of storage tank sediments, and corrosion control. Pressure management and distribution system integrity are also important to the microbial quality of water but are related more to the intrusion of contaminants into the distribution system rather than directly related to microbial growth. Summarizing the identified risk from drinking water, the availability and quality of disinfection data for treatment, and guidelines or standards for control showed that adequate information is best available for management of L. pneumophila. For L. pneumophila, the risk for this organism has been clearly established from drinking water, cases have increased worldwide, and it is one of the most identified causes of drinking water outbreaks. Water management best practices (e.g., maintenance of a disinfectant residual throughout the distribution system, flushing and cleaning of sediments in pipelines and storage tanks, among others) have been shown to be effective for control of L. pneumophila in water supplies. In addition, there are well documented management guidelines available for the control of the organism in drinking water distribution systems. By comparison, management of risks for Mycobacteria from water are less clear than for L. pneumophila. Treatment of M. avium is difficult due to its resistance to disinfection, the tendency to form clumps, and attachment to surfaces in biofilms. Additionally, there are no guidelines for management of M. avium in drinking water, and one risk assessment study suggested a low risk of infection. The role of tap water in the transmission of the other opportunistic pathogens is less clear and, in many cases, actions to manage L. pneumophila (e.g., maintenance of a disinfectant residual, flushing, cleaning of storage tanks, etc.) will also be beneficial in helping to manage these organisms as well. Full article
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<p>Etiology of drinking water associated outbreaks (<span class="html-italic">n</span> = 928) by year, US, 1971–2014. Source: Benedict et al. [<a href="#B9-microorganisms-12-00916" class="html-bibr">9</a>].</p>
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<p>Examples of biofilms in water systems. From: LeChevallier [<a href="#B33-microorganisms-12-00916" class="html-bibr">33</a>].</p>
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<p>Average and standard deviation of water temperatures in treated water storages within a distribution system in Australia. Data from 2013–2022.</p>
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<p>Percentage of chlorine residuals greater than 0.2 mg/L in three regions of an Australian water system.</p>
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<p>AOC levels in 94 North American water systems. From Volk and LeChevallier [<a href="#B29-microorganisms-12-00916" class="html-bibr">29</a>].</p>
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<p>BDOC levels in 30 North American water systems. From Volk and LeChevallier [<a href="#B29-microorganisms-12-00916" class="html-bibr">29</a>].</p>
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<p>Decision tree for coliform occurrences in drinking water. Abbreviations: T °C, temperature in degrees Celsius, AOC, assimilable organic carbon. From Volk and LeChevallier [<a href="#B29-microorganisms-12-00916" class="html-bibr">29</a>].</p>
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<p>Legionella cases in Victoria, 2012 to 2020.</p>
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<p>Comparison of Legionnaires’ Disease (LD) and Pontiac Fever (PF) Cases 2006–2017. Adapted from Hamilton et al. [<a href="#B120-microorganisms-12-00916" class="html-bibr">120</a>].</p>
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<p>Concentration of culturable Legionella during outbreaks (red-orange) and routine monitoring (green). The black line is the 5 × 10<sup>4</sup> cfu/L action level as a break between sporadic cases and outbreaks. From NASEM [<a href="#B111-microorganisms-12-00916" class="html-bibr">111</a>].</p>
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<p>Relationship between <span class="html-italic">L. pneumophila</span> concentration and free chlorine residual. From LeChevallier [<a href="#B34-microorganisms-12-00916" class="html-bibr">34</a>].</p>
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<p>Relationship between temperature and concentration of <span class="html-italic">L. pneumophila</span>. From LeChevallier [<a href="#B34-microorganisms-12-00916" class="html-bibr">34</a>].</p>
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<p>Impact of distribution system flushing at site 824 to control the occurrence of culturable <span class="html-italic">L. pneumophila</span>. Symbols: d, downstream f site 824; u, upstream of site 824. From LeChevallier [<a href="#B34-microorganisms-12-00916" class="html-bibr">34</a>].</p>
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<p>Projected NTM cases in Queensland, Australia from 2020 to 2040. From Ratnatunga et al. [<a href="#B194-microorganisms-12-00916" class="html-bibr">194</a>].</p>
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<p>Occurrence of <span class="html-italic">M. avium</span> and <span class="html-italic">M. intracellulare</span> in distribution system biofilm samples (N = 55). Site # - site number. From Falkinham et al. [<a href="#B50-microorganisms-12-00916" class="html-bibr">50</a>].</p>
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<p>Disinfection of mycobacteria by free chlorine. Experimental conditions: pH 7.0, 25 °C, initial free chlorine concentration 0.5 mg/L.Adapted from Le Dantec et al. [<a href="#B216-microorganisms-12-00916" class="html-bibr">216</a>].</p>
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<p>Impact of pipe composition on biofilm growth of <span class="html-italic">M. avium</span>. Adapted from LeChevallier et al. [<a href="#B231-microorganisms-12-00916" class="html-bibr">231</a>].</p>
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<p>Seasonal occurrence of P. aeruginosa in Croatia. Adapted from Vukić Lušić et al. [<a href="#B246-microorganisms-12-00916" class="html-bibr">246</a>].</p>
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24 pages, 15946 KiB  
Article
Characterization of a Novel Species of Legionella Isolated from a Healthcare Facility: Legionella resiliens sp. nov
by Sandra Cristino, Maria Rosaria Pascale, Federica Marino, Carlo Derelitto, Silvano Salaris, Massimiliano Orsini, Stefano Squarzoni, Antonella Grottola and Luna Girolamini
Pathogens 2024, 13(3), 250; https://doi.org/10.3390/pathogens13030250 - 14 Mar 2024
Cited by 1 | Viewed by 2149
Abstract
Two Legionella-like isolates, 8cVS16T and 9fVS26, were isolated from a water distribution system (WDS) in a healthcare facility. Cells were Gram- and Ziehl Neelsen-stain-negative, rod-shaped, motile, and exhibited a blue-white fluorescence under Wood’s lamp at 365 nm. The strains grew in [...] Read more.
Two Legionella-like isolates, 8cVS16T and 9fVS26, were isolated from a water distribution system (WDS) in a healthcare facility. Cells were Gram- and Ziehl Neelsen-stain-negative, rod-shaped, motile, and exhibited a blue-white fluorescence under Wood’s lamp at 365 nm. The strains grew in a range of 32–37 °C on BCYE with L-cysteine (Cys+), GVPC, and MWY agar medium, with a positive reaction for oxidase, catalase, and gelatinase. The dominant fatty acids were summed features 3 (C16:1ω7c/C16:1ω6c) (27.7%), C16:0 iso (17.5%), and C16:0 (16.3%), and Q13 as the major ubiquinone. The mip and rpoB gene sequences showed a similarity of 96.7% and 92.4%, with L. anisa (ATCC 35292T). The whole genomes sequencing (WGS) performed displayed a GC content of 38.21 mol% for both. The digital DNA-DNA hybridization (dDDH) analysis demonstrated the separation of the two strains from the phylogenetically most related L. anisa (ATCC 35292T), with ≤43% DNA-DNA relatedness. The Average Nucleotide Identity (ANI) between the two strains and L. anisa (ATCC 35292T) was 90.74%, confirming that the two isolates represent a novel species of the genus Legionella. The name proposed for this species is Legionella resiliens sp. nov., with 8cVS16T (=DSM 114356T = CCUG 76627T) as the type strain. Full article
(This article belongs to the Section Bacterial Pathogens)
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<p>Single colony of 8cVS16<sup>T</sup> growth on BCYE Cys+ medium for 48 h at 35 °C. Image acquired using a Heerbrugg Wild M38 Professional Optical Stereo Binocular Microscope with Volpi Intralux 4000 Light Source (90 W). Magnification ×10 and continuous zoom magnification ×4.5.</p>
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<p>Strain 8cVS16<sup>T</sup> growth on BCYE Cys+ for 48 h at 35 °C and 2.5% CO<sub>2</sub> (<b>A</b>) and under Wood’s lamp (long-wavelength UV light at 365 nm) (<b>B</b>).</p>
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<p>Dendrogram based on whole-cell MALDI-TOF mass spectra (Maldi Byotyper, Bruker<sup>®</sup>) of strains 8cVS16<sup>T</sup> and 9fVS26 (in bold type) and other <span class="html-italic">Legionella</span> strains present in the instrument data base.</p>
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<p>Scanning electron microscopy (SEM) images of strain 8cVS16T grown on BCYE Cys+ agar for 48 h at 35 °C with 2.5% CO<sub>2</sub>. View of (<b>A</b>) aflagellate form of strain grown on BCYE Cys+ (Bar 2 μm), (<b>B</b>) flagellate form of strain grown on BCYE Cys+, and (<b>C</b>) fragment (5 × 5 mm) of BCYE Cys + medium on which the strain grew. Bar (<b>A</b>) 2 µm and (<b>B</b>,<b>C</b>) 1 μm. Magnification: (<b>A</b>) ×15,000 and (<b>B</b>,<b>C</b>) ×30,000.</p>
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<p>Comparative WGS relationship among 8cVS16<sup>T</sup> and 9fVS26 and the other 62 <span class="html-italic">Legionella</span> species annotated in NCBI. Branch labels display the substitutions per site. The bootstrap values are 100, using the “Rapid” bootstrapping option of RaxML [<a href="#B56-pathogens-13-00250" class="html-bibr">56</a>]. Bar 0.2 substitution per nucleotide position; 8cVS16<sup>T</sup> and 9fVS26 are in red.</p>
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<p>Visualization of the pangenome analysis of the strains 8cVS16<sup>T</sup> and 9fVS26 obtained with Roary software (v. 3.13.0) and the close phylogenetic relative strains (<span class="html-italic">L. anisa</span>, <span class="html-italic">L. bozemanae</span>, <span class="html-italic">L. parisiensis</span>, and <span class="html-italic">L. tucsonensis</span>). Using the presence or absence of core genes, the whole genomes of the strains were clustered. The blue color indicated the presence of the gene, whereas the white color indicated the absence of the gene. The bottom part of the figure represents the genetic frequency of the pangenome.</p>
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<p>Amino acid composition heatmap (AAI data) of 8cVS16<sup>T</sup> and 9fVS26 strains (in bold type) and the other 63 <span class="html-italic">Legionella</span> species genome presented in NCBI. The heatmap is based on a comparison of amino acid composition, expressed as a percentage, among genomes of <span class="html-italic">Legionella</span> species. <a href="#app1-pathogens-13-00250" class="html-app">Table S1</a> contains a list of the <span class="html-italic">Legionella</span> species used. The heatmap colors represent the percentage of similarity, from white (highest value) to dark red (lowest value).</p>
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13 pages, 679 KiB  
Article
ReporType: A Flexible Bioinformatics Tool for Targeted Loci Screening and Typing of Infectious Agents
by Helena Cruz, Miguel Pinheiro and Vítor Borges
Int. J. Mol. Sci. 2024, 25(6), 3172; https://doi.org/10.3390/ijms25063172 - 9 Mar 2024
Cited by 2 | Viewed by 1835
Abstract
In response to the pressing need for continuous monitoring of emergence and circulation of pathogens through genomics, it is imperative to keep developing bioinformatics tools that can help in their rapid characterization and classification. Here, we introduce ReporType, a versatile bioinformatics pipeline designed [...] Read more.
In response to the pressing need for continuous monitoring of emergence and circulation of pathogens through genomics, it is imperative to keep developing bioinformatics tools that can help in their rapid characterization and classification. Here, we introduce ReporType, a versatile bioinformatics pipeline designed for targeted loci screening and typing of infectious agents. Developed using the snakemake workflow manager, ReporType integrates multiple software for read quality control and de novo assembly, and then applies ABRicate for locus screening, culminating in the production of easily interpretable reports for the identification of pathogen genotypes and/or screening of specific genomic loci. The pipeline accommodates a range of input formats, from Illumina or Oxford Nanopore Technology (ONT) reads (FASTQ) to Sanger sequencing files (AB1), or FASTA files, making it flexible for application in multiple pathogens and with different purposes. ReporType is released with pre-prepared databases for some viruses and bacteria, yet it remains easily configurable to handle custom databases. ReporType performance and functionality were validated through proof-of-concept exercises, encompassing diverse pathogenic species, including viruses such as measles, Newcastle disease virus (NDV), Dengue virus (DENV), influenza, hepatitis C virus (HCV) and Human T-Cell Lymphotropic virus type 1 (HTLV-1), as well as bacteria like Chlamydia trachomatis and Legionella pneumophila. In summary, ReporType emerges as a simple, dynamic and pan-pathogen tool, poised to evolve in tandem with the ever-changing needs of the fields of pathogen genomics, infectious disease epidemiology, and one health bioinformatics. ReporType is freely available at GitHub. Full article
(This article belongs to the Special Issue Machine Learning Applications in Bioinformatics and Biomedicine)
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<p>Schematic representation of ReporType data processing and analysis.</p>
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<p>Summary of the number of sequences and input format covered in ReporType proof-of-concept exercises, per pathogen. Details are provided in <a href="#app1-ijms-25-03172" class="html-app">Tables S3 and S4</a>.</p>
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19 pages, 3799 KiB  
Article
Microbial Community Establishment, Succession, and Temporal Dynamics in an Industrial Semi-Synthetic Metalworking Fluid Operation: A 50-Week Real-Time Tracking
by Renuka Kapoor, Suresh Babu Selvaraju, Venkataramanan Subramanian and Jagjit S. Yadav
Microorganisms 2024, 12(2), 267; https://doi.org/10.3390/microorganisms12020267 - 26 Jan 2024
Cited by 1 | Viewed by 1028
Abstract
Microorganisms colonizing modern water-based metalworking fluids (MWFs) have been implicated in various occupational respiratory health hazards to machinists. An understanding of the exposure risks from specific microbial groups/genera/species (pathogenic or allergenic) and their endotoxins and the need for strategies for effective, timely fluid [...] Read more.
Microorganisms colonizing modern water-based metalworking fluids (MWFs) have been implicated in various occupational respiratory health hazards to machinists. An understanding of the exposure risks from specific microbial groups/genera/species (pathogenic or allergenic) and their endotoxins and the need for strategies for effective, timely fluid management warrant real-time extended tracking of the establishment of microbial diversity and the prevailing fluid-related factors. In the current study, the microbial community composition, succession, and dynamics of a freshly recharged industrial semi-synthetic MWF operation was tracked in real-time over a period of 50 weeks, using a combination of microbiological and molecular approaches. Substantial initial bacterial count (both viable and non-viable) even in the freshly recharged MWF pointed to the inefficiency of the dumping, cleaning, and recharge (DCR) process. Subsequent temporal analysis using optimized targeted genus/group-specific qPCR confirmed the presence of Pseudomonads, Enterics, Legionellae, Mycobacteria (M. immunogenum), Actinomycetes, and Fungi. In contrast, selective culturing using commercial culture media yielded non-specific isolates and collectively revealed Gram-negative (13 genera representing 19 isolates) and Gram-positive (2 genera representing 6 isolates) bacteria and fungi but not mycobacteria. Citrobacter sp. and Bacillus cereus represented the most frequent Gram-negative and Gram-positive isolates, respectively, across different media and Nectria haematococca isolation as the first evidence of this fungal pathogen colonizing semi-synthetic MWF. Unbiased PCR-DGGE analysis revealed a more diverse whole community composition revealing 22 bacterial phylotypes and their succession. Surges in the endotoxin level coincided with the spikes in Gram-negative bacterial population and biocide additions. Taken together, the results showed that semi-synthetic MWF is conducive for the growth of a highly diverse microbial community including potential bacterial and fungal pathogens, the current DCR practices are inefficient in combating microbial reestablishment, and the practice of periodic biocide additions facilitates the build-up of endotoxins and non-viable bacterial population. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>(<b>A</b>): Population dynamic changes in the microbial population in a semi-synthetic metalworking fluid (MWF) operation tracked for 50 weeks based on microscopic and culturable counts on the temporally-drawn MWF samples. The sampling schedule is detailed in the <a href="#sec2-microorganisms-12-00267" class="html-sec">Section 2</a>. Abbreviations: TC (total count); V (viable count); NV (nonviable count); C (culturable count); VBNC (viable but nonculturable count). The bacterial numbers are expressed as microscopic count (TC, V, NV, and VBNC) or cfu (C) per ml of the fluid. (<b>B</b>): Microbial population dynamic changes (total and viable bacterial counts) in relation to changes in fluid parameters in the semi-synthetic MWF operation. The samples were drawn from the semi-synthetic MWF operation over a 50-week period during which time it was “top-loaded” or “recharged” with fresh fluid, or treated with biocides at different time intervals. The fluid sampling schedule is described under <a href="#sec2-microorganisms-12-00267" class="html-sec">Section 2</a> and the fluid’s physical/chemical parameters are described in <a href="#app1-microorganisms-12-00267" class="html-app">Supplementary Table S1</a>. The figure indicates- recharge of the pit/reservoir with semi-synthetic MWF fluid (solid arrow), top-loading of the pit/reservoir with semi-synthetic MWF fluid (striped arrow), and addition of biocides Kathon or Grotan (blank arrow). The two-headed arrow indicates a 2-week temporary shut-down of the operation after week 25.</p>
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<p>(<b>A</b>): Population dynamic changes in the microbial population in a semi-synthetic metalworking fluid (MWF) operation tracked for 50 weeks based on microscopic and culturable counts on the temporally-drawn MWF samples. The sampling schedule is detailed in the <a href="#sec2-microorganisms-12-00267" class="html-sec">Section 2</a>. Abbreviations: TC (total count); V (viable count); NV (nonviable count); C (culturable count); VBNC (viable but nonculturable count). The bacterial numbers are expressed as microscopic count (TC, V, NV, and VBNC) or cfu (C) per ml of the fluid. (<b>B</b>): Microbial population dynamic changes (total and viable bacterial counts) in relation to changes in fluid parameters in the semi-synthetic MWF operation. The samples were drawn from the semi-synthetic MWF operation over a 50-week period during which time it was “top-loaded” or “recharged” with fresh fluid, or treated with biocides at different time intervals. The fluid sampling schedule is described under <a href="#sec2-microorganisms-12-00267" class="html-sec">Section 2</a> and the fluid’s physical/chemical parameters are described in <a href="#app1-microorganisms-12-00267" class="html-app">Supplementary Table S1</a>. The figure indicates- recharge of the pit/reservoir with semi-synthetic MWF fluid (solid arrow), top-loading of the pit/reservoir with semi-synthetic MWF fluid (striped arrow), and addition of biocides Kathon or Grotan (blank arrow). The two-headed arrow indicates a 2-week temporary shut-down of the operation after week 25.</p>
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<p>Selective culture-based quantification of the targeted groups in temporally drawn MWF samples. The quantitative levels of a test group/genus are expressed as CFU per ml of the fluid, determined as described in the <a href="#sec2-microorganisms-12-00267" class="html-sec">Section 2</a>.</p>
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<p>Real-time qPCR-based quantification of the targeted groups/genera in temporally drawn MWF samples. The quantitative levels of a test group/genus are expressed as counts per ml (<b>A</b>–<b>D</b>) or microgram DNA per ml (<b>E</b>,<b>F</b>) of the fluid, calculated as described in the <a href="#sec2-microorganisms-12-00267" class="html-sec">Section 2</a>.</p>
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<p>Global microbial community composition and dynamics as revealed by Dice/UPGMA clustering of DGGE fingerprints of semi-synthetic MWF samples. The samples were grouped into 8 clusters. The band tolerance of 5% was used.</p>
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<p><b>Endotoxin concentration pattern in temporally drawn MWF samples.</b> The sampling schedule spanned a period of 50 weeks as described in the <a href="#sec2-microorganisms-12-00267" class="html-sec">Section 2</a>.</p>
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17 pages, 1457 KiB  
Article
Exploring Pathogenic and Zoonotic Bacteria from Wild Rodents, Dogs, and Humans of the Ngorongoro District in Tanzania Using Metagenomics Next-Generation Sequencing
by Amina Ramadhani Issae, Abdul Selemani Katakweba, Rose Peter Kicheleri, Augustino Alfred Chengula, Marco van Zwetselaar and Christopher Jacob Kasanga
Zoonotic Dis. 2023, 3(3), 226-242; https://doi.org/10.3390/zoonoticdis3030019 - 1 Sep 2023
Viewed by 2295
Abstract
Globally, zoonoses have serious consequences due to their socioeconomic impacts. Ngorongoro District is home to a diverse range of wildlife and domestic animals, including rodents and dogs, which often coexist in close proximity with humans. The aim of the study was to identify [...] Read more.
Globally, zoonoses have serious consequences due to their socioeconomic impacts. Ngorongoro District is home to a diverse range of wildlife and domestic animals, including rodents and dogs, which often coexist in close proximity with humans. The aim of the study was to identify the zoonotic bacteria present in wild rodents, domestic dogs, and humans using metagenomics next-generation sequencing technology. A cross-sectional study was conducted in 2022. This study used both Illumina and Oxford Nanopore sequencing technologies to identify bacteria in 530 blood samples collected from humans (n = 200), wild rodents (n = 230), and dogs (n = 100). Several zoonotic airborne/contagious bacteria, including Mycobacterium spp., Mycoplasma spp., Bordetella spp., and Legionella spp., were detected in wild rodents, domestic dogs, and humans. Arthropod-borne zoonotic bacteria such as Bartonella spp., Borrelia spp., and Rickettsia spp. were detected in all three hosts, while Orientia spp. was found in wild rodents and domestic dogs. Yersinia pestis, Streptobacillus spp. and Anaplasma spp. were found only in wild rodents. Other zoonotic bacteria found shared among wild rodents, domestic dogs, and humans are Leptospira spp., Brucella spp., and Salmonella spp. Generally, wild rodents had the highest prevalence of zoonotic bacterial species when compared to domestic dogs and humans. The detection of zoonotic bacteria in rodents, dogs, and humans supports the hypothesis that infections can spread between animals and humans sharing the same environment. Full article
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<p>The map of Tanzania and the Ngorongoro District showing the study villages. The map was developed using QGIS software version 3.26.1 and shapefiles from DIVA-GIS and The Humanitarian Data Exchange (HDX), freely accessible at <a href="https://www.diva-gis.org/datadown" target="_blank">https://www.diva-gis.org/datadown</a> (accessed on 3 July 2023) and <a href="https://data.humdata.org/dataset/cod-ab-tza" target="_blank">https://data.humdata.org/dataset/cod-ab-tza</a> (accessed on 3 July 2023), respectively.</p>
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<p>Tree diagram indicating the names of the bacterial families as well as genera and their overall reads (abundances).</p>
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11 pages, 535 KiB  
Article
Pre-Pandemic Distribution of Bacterial Species in Nasopharyngeal Swab Specimens from Pediatric and Adult Patients Detected via RT-PCR Using the Allplex Respiratory Panel
by Dong-Hyun Lee, Young-Jin Choi, Jieun Kim, Eunhee Han and Mi-Hyun Bae
Life 2023, 13(9), 1840; https://doi.org/10.3390/life13091840 - 30 Aug 2023
Cited by 1 | Viewed by 1281
Abstract
Background: Recently, panel-based molecular diagnostics for the simultaneous detection of respiratory viruses and bacteria in nasopharyngeal swab (NPS) specimens have been highlighted. We identified the distribution of bacterial species in NPS specimens collected from pediatric and adult patients by employing RT-PCR (Allplex respiratory [...] Read more.
Background: Recently, panel-based molecular diagnostics for the simultaneous detection of respiratory viruses and bacteria in nasopharyngeal swab (NPS) specimens have been highlighted. We identified the distribution of bacterial species in NPS specimens collected from pediatric and adult patients by employing RT-PCR (Allplex respiratory panel 4, RP4, Seegene) to estimate its applicability in a panel-based assay for detecting respiratory viruses. Methods: We used 271 and 173 NPS specimens from pediatric and adult patients, respectively. The results of the Allplex RP4 panel using NPS (NPS-RP4) from adult patients were compared with those of the Seeplex PneumoBacter ACE Detection assay (Seegene), which used sputum for testing (sputum-Seeplex). Results: A total of 147 specimens (54.2%) were positive for the NPS-RP4 panel in pediatric patients. There were 94, 77, 10, 3, 3, and 2 specimens that were positive for Haemophilus influenzae (HI), Streptococcus pneumoniae (SP), Mycoplasma pneumoniae (MP), Chlamydia pneumoniae (CP), Bordetella pertussis (BP), and B. parapertussis (BPP), respectively. Among 173 adult patients, 39 specimens (22.5%) were positive in the NPS-RP4. Thirty specimens were positive for HI, and 13 were positive for SP. One specimen tested positive for both MP and Legionella pneumophila (LP). CP, BP, and BPP results were all negative. However, 126 specimens (72.8%) had positive results with sputum-Seeplex (99 SP, 59 HI, three LP, and two MP), and the overall percentage of agreement between the two assays was 39.3% in the adult patients. Conclusions: Bacterial species in NPS from more than half of pediatric patients were detected. Performing the Allplex RP4 assay with NPS revealed additional respiratory bacteria that are not detected in current clinical practices, which do not include bacterial testing, demanding the use of sputum specimens. However, the use of NPS showed low agreement with standard assays using sputum in adult patients. Thus, more research is needed to develop a reliable RT-PCR method using NPS specimens in adult patients. Full article
(This article belongs to the Section Microbiology)
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<p>Prevalence of bacterial species in the different age groups of pediatric patients. Overall prevalence was the highest in the 2–4-year age group (70.3%). The most prevalent species in the &lt;2-year age group was <span class="html-italic">S. pneumoniae</span> (25.5%) and <span class="html-italic">H. influenzae</span> in the 2–4-year (47.3%) and 5–17-year age groups (37.2%). The prevalence of <span class="html-italic">M. pneumoniae</span> was the highest in the 5–17-year age group (6.4%).</p>
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