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Microorganisms
Volume 11
Issue 12
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Microorganisms, Volume 11, Issue 12 (December 2023) – 187 articles

Cover Story (view full-size image): Cutaneous leishmaniasis (CL) is the most common clinical presentation of leishmaniasis, a Neglected Tropical Disease (NTD). While other presentations of leishmaniasis can be fatal, CL is mostly non-lethal and despite its prevalence not a priority for drug treatment. Yet, CL can be disfiguring and cause prolonged suffering in afflicted individuals. Several drugs are currently used to treat leishmaniasis, although they often have harsh side effects and long treatment regimens, and drug resistance is emerging across the globe. In search of novel drug therapies, researchers have been using biochemical and computational techniques for early-stage drug discovery efforts. This review focuses on the substantial yet fragmented progress in drug discovery for CL and highlights the common challenges for researchers. View this paper
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25 pages, 2993 KiB  
Review
Medically Significant Vector-Borne Viral Diseases in Iran
by Sarah-Jo Paquette, Ayo Yila Simon, Ara XIII, Gary P. Kobinger and Nariman Shahhosseini
Microorganisms 2023, 11(12), 3006; https://doi.org/10.3390/microorganisms11123006 - 18 Dec 2023
Cited by 2 | Viewed by 2772
Abstract
Vector-borne viral diseases (VBVDs) continue to pose a considerable public health risk to animals and humans globally. Vectors have integral roles in autochthonous circulation and dissemination of VBVDs worldwide. The interplay of agricultural activities, population expansion, urbanization, host/pathogen evolution, and climate change, all [...] Read more.
Vector-borne viral diseases (VBVDs) continue to pose a considerable public health risk to animals and humans globally. Vectors have integral roles in autochthonous circulation and dissemination of VBVDs worldwide. The interplay of agricultural activities, population expansion, urbanization, host/pathogen evolution, and climate change, all contribute to the continual flux in shaping the epidemiology of VBVDs. In recent decades, VBVDs, once endemic to particular countries, have expanded into new regions such as Iran and its neighbors, increasing the risk of outbreaks and other public health concerns. Both Iran and its neighboring countries are known to host a number of VBVDs that are endemic to these countries or newly circulating. The proximity of Iran to countries hosting regional diseases, along with increased global socioeconomic activities, e.g., international trade and travel, potentially increases the risk for introduction of new VBVDs into Iran. In this review, we examined the epidemiology of numerous VBVDs circulating in Iran, such as Chikungunya virus, Dengue virus, Sindbis virus, West Nile virus, Crimean–Congo hemorrhagic fever virus, Sandfly-borne phleboviruses, and Hantavirus, in relation to their vectors, specifically mosquitoes, ticks, sandflies, and rodents. In addition, we discussed the interplay of factors, e.g., urbanization and climate change on VBVD dissemination patterns and the consequent public health risks in Iran, highlighting the importance of a One Health approach to further surveil and to evolve mitigation strategies. Full article
(This article belongs to the Section Virology)
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<p>Different factors and interactions between the vector-borne diseases, vectors, and hosts including animals and humans, and environmental factors, informing the One Health concept.</p> Full article ">Figure 2
<p>Evidence of West Nile virus [<a href="#B48-microorganisms-11-03006" class="html-bibr">48</a>,<a href="#B96-microorganisms-11-03006" class="html-bibr">96</a>,<a href="#B99-microorganisms-11-03006" class="html-bibr">99</a>,<a href="#B107-microorganisms-11-03006" class="html-bibr">107</a>,<a href="#B119-microorganisms-11-03006" class="html-bibr">119</a>,<a href="#B120-microorganisms-11-03006" class="html-bibr">120</a>,<a href="#B121-microorganisms-11-03006" class="html-bibr">121</a>,<a href="#B122-microorganisms-11-03006" class="html-bibr">122</a>], Chikungunya virus [<a href="#B15-microorganisms-11-03006" class="html-bibr">15</a>,<a href="#B46-microorganisms-11-03006" class="html-bibr">46</a>,<a href="#B47-microorganisms-11-03006" class="html-bibr">47</a>,<a href="#B48-microorganisms-11-03006" class="html-bibr">48</a>], Dengue virus [<a href="#B15-microorganisms-11-03006" class="html-bibr">15</a>,<a href="#B48-microorganisms-11-03006" class="html-bibr">48</a>,<a href="#B58-microorganisms-11-03006" class="html-bibr">58</a>,<a href="#B60-microorganisms-11-03006" class="html-bibr">60</a>,<a href="#B62-microorganisms-11-03006" class="html-bibr">62</a>], Sindbis virus [<a href="#B75-microorganisms-11-03006" class="html-bibr">75</a>] and Rift valley fever virus [<a href="#B109-microorganisms-11-03006" class="html-bibr">109</a>] in Iran in either humans, animals, or mosquitoes by indirect and direct evidence.</p> Full article ">Figure 3
<p>Evidence of Crimean–Congo Hemorrhagic Fever virus [<a href="#B23-microorganisms-11-03006" class="html-bibr">23</a>,<a href="#B132-microorganisms-11-03006" class="html-bibr">132</a>,<a href="#B137-microorganisms-11-03006" class="html-bibr">137</a>,<a href="#B139-microorganisms-11-03006" class="html-bibr">139</a>,<a href="#B172-microorganisms-11-03006" class="html-bibr">172</a>], Zahedan Rhabdovirus [<a href="#B165-microorganisms-11-03006" class="html-bibr">165</a>], and Tick-borne encephalitis virus [<a href="#B171-microorganisms-11-03006" class="html-bibr">171</a>] in Iran in either humans, animals, or ticks by indirect and direct evidences.</p> Full article ">Figure 4
<p>Evidence of Sandfly fever virus [<a href="#B2-microorganisms-11-03006" class="html-bibr">2</a>,<a href="#B27-microorganisms-11-03006" class="html-bibr">27</a>,<a href="#B28-microorganisms-11-03006" class="html-bibr">28</a>,<a href="#B173-microorganisms-11-03006" class="html-bibr">173</a>,<a href="#B180-microorganisms-11-03006" class="html-bibr">180</a>,<a href="#B186-microorganisms-11-03006" class="html-bibr">186</a>,<a href="#B187-microorganisms-11-03006" class="html-bibr">187</a>] in Iran in either humans, animals, or sandflies by indirect and direct evidences.</p> Full article ">Figure 5
<p>Evidence of Hantavirus [<a href="#B33-microorganisms-11-03006" class="html-bibr">33</a>,<a href="#B197-microorganisms-11-03006" class="html-bibr">197</a>,<a href="#B199-microorganisms-11-03006" class="html-bibr">199</a>] in Iran in humans by indirect and direct evidence.</p> Full article ">
31 pages, 4864 KiB  
Review
Microevolution and Its Impact on Hypervirulence, Antimicrobial Resistance, and Vaccine Escape in Neisseria meningitidis
by August Mikucki and Charlene M. Kahler
Microorganisms 2023, 11(12), 3005; https://doi.org/10.3390/microorganisms11123005 - 18 Dec 2023
Viewed by 2020
Abstract
Neisseria meningitidis is commensal of the human pharynx and occasionally invades the host, causing the life-threatening illness invasive meningococcal disease. The meningococcus is a highly diverse and adaptable organism thanks to natural competence, a propensity for recombination, and a highly repetitive genome. These [...] Read more.
Neisseria meningitidis is commensal of the human pharynx and occasionally invades the host, causing the life-threatening illness invasive meningococcal disease. The meningococcus is a highly diverse and adaptable organism thanks to natural competence, a propensity for recombination, and a highly repetitive genome. These mechanisms together result in a high level of antigenic variation to invade diverse human hosts and evade their innate and adaptive immune responses. This review explores the ways in which this diversity contributes to the evolutionary history and population structure of the meningococcus, with a particular focus on microevolution. It examines studies on meningococcal microevolution in the context of within-host evolution and persistent carriage; microevolution in the context of meningococcal outbreaks and epidemics; and the potential of microevolution to contribute to antimicrobial resistance and vaccine escape. A persistent theme is the idea that the process of microevolution contributes to the development of new hyperinvasive meningococcal variants. As such, microevolution in this species has significant potential to drive future public health threats in the form of hypervirulent, antibiotic-resistant, vaccine-escape variants. The implications of this on current vaccination strategies are explored. Full article
(This article belongs to the Special Issue Microorganisms Associated with Infectious Disease 2.0)
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<p>Lifestyles of the pathogenic <span class="html-italic">Neisseria</span>. (<b>A</b>) Lifestyle of <span class="html-italic">N. gonorrhoeae</span> (Ngo). (i) Initial attachment between Ngo and the urogenital epithelium is mediated by the type IV pilus. (ii) More intimate attachment is mediated by the adhesins including the opacity proteins and results in microcolony formation. (iii) Ngo crosses the epithelial barrier by transcytosis. Immune activation of resident macrophages and dendritic cells results in the release of cytokines. (iv) Neutrophils are attracted to the site of infection by cytokines and chemokines. Ngo initiates “silent” uptake into neutrophils and establishes a replicative niche inside neutrophils. (v) Infected neutrophils migrate to the epithelial surface. A neutrophil-rich purulent exudate facilitates further transmission to other hosts. (<b>B</b>) Lifestyle of <span class="html-italic">N. meningitidis</span> (Nme). (i) Initial attachment between Ngo and the urogenital epithelium is mediated by the type IV pilus. (ii) More intimate attachment is mediated by the adhesins including the opacity proteins and results in microcolony formation. (iii) Microcolonies mature, and dispersal of Nme results in transmission to other hosts through contact with aerosolised droplets. (iv) Occasionally, Nme will cross the epithelial barrier by transcytosis, stimulating a local immune response. (v) If a given strain of Nme can resist killing by the host immune system, it may enter the systemic circulation and cause invasive meningococcal disease.</p> Full article ">Figure 2
<p>Capsule polysaccharide structure and genetic organisation of the <span class="html-italic">cps</span> island in the six clinically relevant meningococcal serogroups. The genetic organisation of the <span class="html-italic">cps</span> islands in representative closed genomes from MenA (Z2491), MenB (H44/76), MenC (FAM18), MenW (EXNM741), MenX (KL11168), and MenY (M23580) are shown along with the organisation of the <span class="html-italic">cnl</span> locus (α14). Genes are coloured by genetic region. Region A encodes polysaccharide synthesis, region B encodes capsule translocation, region C encodes capsule transport, region D encodes genes for LOS synthesis, region D′ is a degenerate copy of region D, and region E encodes the <span class="html-italic">tex</span> locus and two methyltransferase pseudogenes. Polysaccharide structures are drawn using the IUPAC Symbol Nomenclature for Glycans. Glc = glucose; Gal = galactose; GlcNAc = <span class="html-italic">N</span>-acetylglucosamine; ManNAc = <span class="html-italic">N</span>-acetylmannosamine; Neu5Ac = <span class="html-italic">N</span>-acetylneuraminic acid, sialic acid.</p> Full article ">Figure 3
<p>Mechanisms producing genetic variation in <span class="html-italic">N. meningitidis</span>. (<b>A</b>) Natural competence allows uptake of related DNA through the type IV pilus machinery. This DNA can undergo recombination with the chromosome to aid in DNA repair or to introduce novel genetic variants. (<b>B</b>) Phase variation in <span class="html-italic">Neisseria</span> occurs primarily by slipped-stranded mispairing of repetitive DNA elements during replication. As the repeat element is replicated, the newly synthesised strand may dissociate from the template strand and reanneal incorrectly, resulting in either contraction or expansion of the repeat element. Such changes in open reading frames result in the phasing of gene expression on or off completely, whereas changes in promoter regions may modulate the level of expression for the gene. (<b>C</b>) Hypervariable loci are genes which undergo frequent recombination, either with an array of silent pseudogenes (as is the case for the pilin gene <span class="html-italic">pilE</span>) or by recombination between homologous loci either within the same genome or on DNA acquired by natural transformation from other <span class="html-italic">Neisseria</span> spp. or closely related strains (as is the case for the <span class="html-italic">opa</span> loci). (<b>D</b>) Loss-of-function mutation of the DNA mismatch repair system MutSL results in a hypermutator phenotype which rapidly generates many novel variants. Eventually, a variant with an adaptive advantage may undergo reversion of the mutator phenotype and continue to expand within the population.</p> Full article ">Figure 4
<p>Clonal structure of the meningococcal population. A neighbour-joining phylogeny of 550 <span class="html-italic">N. meningitidis</span> isolates from 11 clonal complexes based on the phylogeny of 1735 core genes. Clonal complexes are each indicated by a different colour. Used with permission from Mullally, Mikucki, Wise, and Kahler [<a href="#B40-microorganisms-11-03005" class="html-bibr">40</a>].</p> Full article ">Figure 5
<p>Microevolution in <span class="html-italic">N. meningitidis</span>. (<b>A</b>) Within-host evolution. (i) Colonisation of a new host presents a genetic bottleneck through which only some meningococcal variants will survive. (ii) Once colonised, microevolution events result in the production of multiple variants within the host. (iii) Some variants are fit in the wider population and will transmit to other hosts. (iv) Some variants have a survival advantage within a given host despite being less fit for transmission. (v) Occasionally, host-adapted variants will be coincidentally adapted for survival in the systemic circulation and go on to cause disease. (<b>B</b>) Microevolution within outbreaks/epidemics. (i) A high-fitness meningococcal variant enters a host population and becomes established as the dominant genotype. (ii) Microevolution events produce a ‘cloud’ of variants with a transient survival advantage in a small number of hosts due to immune-escape or colonisation bottlenecks. These variants are usually less fit and do not cause outbreaks of their own. (iii) Occasionally, a novel variant will have a broader survival advantage and overtake the previous dominant variant. (iv) Novel variants may be transmitted to new host populations, where they will become dominant through the founder effect or because they are adapted to the new host population.</p> Full article ">
25 pages, 1473 KiB  
Review
Oral Antibiotics for Bacteremia and Infective Endocarditis: Current Evidence and Future Perspectives
by Gerasimos Eleftheriotis, Markos Marangos, Maria Lagadinou, Sanjay Bhagani and Stelios F. Assimakopoulos
Microorganisms 2023, 11(12), 3004; https://doi.org/10.3390/microorganisms11123004 - 18 Dec 2023
Cited by 1 | Viewed by 4591
Abstract
Bacteremia and endocarditis are two clinical syndromes that, for decades, were managed exclusively with parenteral antimicrobials, irrespective of a given patient’s clinical condition, causative pathogen, or its antibiotic susceptibility profile. This clinical approach, however, was based on low-quality data and outdated expert opinions. [...] Read more.
Bacteremia and endocarditis are two clinical syndromes that, for decades, were managed exclusively with parenteral antimicrobials, irrespective of a given patient’s clinical condition, causative pathogen, or its antibiotic susceptibility profile. This clinical approach, however, was based on low-quality data and outdated expert opinions. When a patient’s condition has improved, gastrointestinal absorption is not compromised, and an oral antibiotic regimen reaching adequate serum concentrations is available, a switch to oral antibacterials can be applied. Although available evidence has reduced the timing of the oral switch in bacteremia to three days/until clinical improvement, there are only scarce data regarding less than 10-day intravenous antibiotic therapy in endocarditis. Many standard or studied oral antimicrobial dosages are smaller than the approved doses for parenteral administration, which is a risk factor for treatment failure; in addition, the gastrointestinal barrier may affect drug bioavailability, especially when the causative pathogen has a minimum inhibitory concentration that is close to the susceptibility breakpoint. A considerable number of patients infected by such near-breakpoint strains may not be potential candidates for oral step-down therapy to non-highly bioavailable antibiotics like beta-lactams; different breakpoints should be determined for this setting. This review will focus on summarizing findings about pathogen-specific tailoring of oral step-down therapy for bacteremia and endocarditis, but will also present laboratory and clinical data about antibiotics such as beta-lactams, linezolid, and fosfomycin that should be studied more in order to elucidate their role and optimal dosage in this context. Full article
(This article belongs to the Special Issue Bacterial Pathogens Associated with Bacteremia)
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<p>Oral antibiotic options for bacteremia and endocarditis according to causative pathogen, provided that PK/PD targets can be achieved given the MIC. * Only combined with another active antibiotic. † Only as an adjunct treatment against <span class="html-italic">E. faecalis.</span> List of abbreviations: TMP/SMX, trimethoprim/sulfamethoxazole; PK/PD, pharmacokinetic/pharmacodynamic; MIC, minimum inhibitory concentration.</p> Full article ">Figure 2
<p>Chemical structure of certain commonly used oral antibiotics (adapted from PubChem): (<b>A</b>), amoxicillin; (<b>B</b>), cloxacillin sodium; (<b>C</b>), cefalexin; (<b>D</b>), cefuroxime axetil; (<b>E</b>), cefixime; (<b>F</b>), ciprofloxacin hydrochloride; (<b>G</b>), levofloxacin hemihydrate; (<b>H</b>), moxifloxacin hydrochloride; (<b>I</b>) sulfamethoxazole/trimethoprim; (<b>J</b>), clindamycin; (<b>K</b>) linezolid; (<b>L</b>) fosfomycin trometamol [<a href="#B180-microorganisms-11-03004" class="html-bibr">180</a>,<a href="#B181-microorganisms-11-03004" class="html-bibr">181</a>,<a href="#B182-microorganisms-11-03004" class="html-bibr">182</a>,<a href="#B183-microorganisms-11-03004" class="html-bibr">183</a>,<a href="#B184-microorganisms-11-03004" class="html-bibr">184</a>,<a href="#B185-microorganisms-11-03004" class="html-bibr">185</a>,<a href="#B186-microorganisms-11-03004" class="html-bibr">186</a>,<a href="#B187-microorganisms-11-03004" class="html-bibr">187</a>,<a href="#B188-microorganisms-11-03004" class="html-bibr">188</a>,<a href="#B189-microorganisms-11-03004" class="html-bibr">189</a>,<a href="#B190-microorganisms-11-03004" class="html-bibr">190</a>,<a href="#B191-microorganisms-11-03004" class="html-bibr">191</a>].</p> Full article ">
14 pages, 7150 KiB  
Article
Comparison and Three-Dimensional Fluorescence Spectrum Analysis of Activated Sludge Treatment with Fenton and UV-Fenton
by Jiamei Wang, Tian Chai and Xin Chen
Microorganisms 2023, 11(12), 3003; https://doi.org/10.3390/microorganisms11123003 - 18 Dec 2023
Viewed by 896
Abstract
This study investigated the effects of single Fenton and Fenton and UV combined processes on the cracking degree of anaerobic sludge under the same conditions. The optimal experimental conditions were obtained by repeated determination of Fe2+ dosage, H2O2 dosage [...] Read more.
This study investigated the effects of single Fenton and Fenton and UV combined processes on the cracking degree of anaerobic sludge under the same conditions. The optimal experimental conditions were obtained by repeated determination of Fe2+ dosage, H2O2 dosage and reaction time, so as to achieve the maximum cracking of sludge. In addition, this study applied three-dimensional fluorescence spectrum analysis technology to analyze the organic matter leached from the treated sludge, and different regions of the three-dimensional fluorescence spectra were analyzed and compared for each treatment condition. Repeated experiments showed that the optimal conditions for Fenton are a pH of 3, reaction time of 40 min, 1.4 g/L of Fe2+ and 9 g/L of H2O2. The Fenton process cracking yielded a protein concentration of 0.66 mg/L and sCOD of 5489 mg/L, and the UV-Fenton pretreatment yielded a protein concentration of 0.74 mg/L and sCOD of 5856 mg/L. The sludge particle size was reduced from the original 54.52 mm to 40.30 mm and 36.37 mm, respectively. In addition to these parameters, it was also demonstrated that the Fenton process has a strong cracking effect on sludge by indicators such as the SEM and sludge water content and that UV irradiation can play a role in assisting and helping sludge cracking. Full article
(This article belongs to the Special Issue Microbial Applications for Sustainable Resource and Energy Recovery)
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Figure 1
<p>UV-Fenton reaction device. 1—Magnetic stirrer; 2—Light shield; 3—UV lamp; 4—Test sludge and light repellent bottle.</p> Full article ">Figure 2
<p>Effect of Fe<sup>2+</sup> concentration on protein concentration and TOC in the sludge during the Fenton reaction.</p> Full article ">Figure 3
<p>Effect of H<sub>2</sub>O<sub>2</sub> dosage on protein concentration and TOC in the sludge during the Fenton reaction.</p> Full article ">Figure 4
<p>Effect of the Fenton reaction time on protein concentration and TOC in sludge.</p> Full article ">Figure 5
<p>Effect of UV-Fenton reaction time on protein concentration and TOC in sludge.</p> Full article ">Figure 6
<p>The content of protein, humic acid and polysaccharide in sludge following different pretreatment methods. Initial sludge is sludge that has not been pretreated.</p> Full article ">Figure 7
<p>sCOD and TOC content in sludge following different pretreatment methods.</p> Full article ">Figure 8
<p>3D-EEM of sludge after different pretreatment methods.</p> Full article ">Figure 9
<p>Sludge field-emission scanning electron microscope analysis after different pretreatments: (<b>a</b>) pH 3, (<b>b</b>) UV, (<b>c</b>) Fenton, (<b>d</b>) UV-Fenton.</p> Full article ">Figure 9 Cont.
<p>Sludge field-emission scanning electron microscope analysis after different pretreatments: (<b>a</b>) pH 3, (<b>b</b>) UV, (<b>c</b>) Fenton, (<b>d</b>) UV-Fenton.</p> Full article ">Figure 10
<p>Trends of laser particle size changes.</p> Full article ">Figure 11
<p>Comparative plots of sludge water content and average particle size.</p> Full article ">
11 pages, 4660 KiB  
Communication
Soil Bacterial Diversity Responds to Long-Term Establishment of Perennial Legumes in Warm-Season Grassland at Two Soil Depths
by Adesuwa Sylvia Erhunmwunse, Victor Alonso Guerra, Jung-Chen Liu, Cheryl L. Mackowiak, Ann Rachel Soffes Blount, José Carlos Batista Dubeux, Jr. and Hui-Ling Liao
Microorganisms 2023, 11(12), 3002; https://doi.org/10.3390/microorganisms11123002 - 18 Dec 2023
Cited by 1 | Viewed by 1066
Abstract
The introduction of rhizoma peanut (RP Arachis glabrata Benth) into bahiagrass (Paspalum notatum Flüggé) may require time to develop stable plant–soil microbe interactions as the microbial legacy of the previous plant community may be long-lasting. A previous study showed that <2 years of introducing [...] Read more.
The introduction of rhizoma peanut (RP Arachis glabrata Benth) into bahiagrass (Paspalum notatum Flüggé) may require time to develop stable plant–soil microbe interactions as the microbial legacy of the previous plant community may be long-lasting. A previous study showed that <2 years of introducing rhizoma peanut into bahiagrass pastures minimally affected soil bacterial diversity and community composition. In this study, we compared the effects of the long-term inclusion of rhizoma peanut (>8 years) into bahiagrass on soil bacterial diversity and community composition against their monocultures at 0 to 15 and 15 to 30 cm soil depths using next-generation sequencing to target bacterial 16S V3–V4 regions. We observed that a well-established RP–bahiagrass mixed stand led to a 36% increase in bacterial alpha diversity compared to the bahiagrass monoculture. There was a shift from a soil bacterial community dominated by Proteobacteria (~26%) reported in other bahiagrass and rhizoma peanut studies to a soil bacterial community dominated by Firmicutes (39%) in our study. The relative abundance of the bacterial genus Crossiella, known for its antimicrobial traits, was enhanced in the presence of RP. Differences in soil bacterial diversity and community composition were substantial between 0 to 15 and 15 to 30 cm soil layers, with N2-fixing bacteria belonging to the phylum Proteobacteria concentrated in 0 to 15 cm. Introducing RP into bahiagrass pastures is a highly sustainable alternative to mineral N fertilizer inputs. Our results provide evidence that this system also promotes greater soil microbial diversity and is associated with unique taxa that require further study to better understand their contributions to healthy pastures. Full article
(This article belongs to the Special Issue Research on Plant—Bacteria Interactions)
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<p>Relative abundance (&gt;1%) of major bacterial phyla under three forage treatments (Arg, Arg-Eco, and Eco) at two soil depths (0 to 15 and 15 to 30 cm).</p> Full article ">Figure 2
<p>Differential abundant bacterial genera among (<b>A</b>) forage treatments and (<b>B</b>) soil depths based on Lefse analysis. Differences in abundance among forage species (<b>A</b>) and soil depth (<b>B</b>) are coded by color (see legends). The linear discriminant analysis effect size (LEfSe) of microbial communities with LDA scores greater than 2.0 was considered.</p> Full article ">
15 pages, 5289 KiB  
Article
Differences in Bacterial Co-Occurrence Networks and Ecological Niches at the Surface Sediments and Bottom Seawater in the Haima Cold Seep
by Song Zhong, Jingchun Feng, Jie Kong, Yongji Huang, Xiao Chen and Si Zhang
Microorganisms 2023, 11(12), 3001; https://doi.org/10.3390/microorganisms11123001 - 18 Dec 2023
Cited by 2 | Viewed by 1175
Abstract
Cold seeps are highly productive chemosynthetic ecosystems in the deep-sea environment. Although microbial communities affected by methane seepage have been extensively studied in sediments and seawater, there is a lack of investigation of prokaryotic communities at the surface sediments and bottom seawater. We [...] Read more.
Cold seeps are highly productive chemosynthetic ecosystems in the deep-sea environment. Although microbial communities affected by methane seepage have been extensively studied in sediments and seawater, there is a lack of investigation of prokaryotic communities at the surface sediments and bottom seawater. We revealed the effect of methane seepage on co-occurrence networks and ecological niches of prokaryotic communities at the surface sediments and bottom seawater in the Haima cold seep. The results showed that methane seepage could cause the migration of Mn and Ba from the surface sediments to the overlying seawater, altering the elemental distribution at seepage sites (IS) compared with non-seepage sites (NS). Principal component analysis (PCA) showed that methane seepage led to closer distances of bacterial communities between surface sediments and bottom seawater. Co-occurrence networks indicated that methane seepage led to more complex interconnections at the surface sediments and bottom seawater. In summary, methane seepage caused bacterial communities in the surface sediments and bottom seawater to become more abundant and structurally complex. This study provides a comprehensive comparison of microbial profiles at the surface sediments and bottom seawater of cold seeps in the South China Sea (SCS), illustrating the impact of seepage on bacterial community dynamics. Full article
(This article belongs to the Special Issue Microbial Communities Involved in the Methane Cycle)
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<p>Location and landscapes of the sampling sites. (<b>a</b>) Map of the sampling sites with water depth. The box on the right is a zoom-in of the “Haima” area with the locations of four sites. (<b>b</b>) Landscapes of the seepage sites (ROV1 and ROV2). (<b>c</b>) Landscapes of the non-seepage sites (ROV3 and ROV5).</p> Full article ">Figure 2
<p>The physicochemical characteristics of seawater and sediment at the surface sediments and bottom seawater. (<b>a</b>) CH<sub>4</sub>, TOC and SO<sub>4</sub><sup>2−</sup> concentration in seawater and sediment. (<b>b</b>) Metals ion and Cl<sup>−</sup> concentration in seawater and sediment. “ns” means no significant difference between groups; “*” means significant difference between groups (<span class="html-italic">p</span> &lt; 0.05; multiple comparison with ANOVA tests).</p> Full article ">Figure 3
<p>Compositions of bacterial communities at the surface sediments and bottom seawater. (<b>a</b>) Microbial alpha diversity in vertical profile. (<b>b</b>) PCA of microorganisms at the surface sediments and bottom seawater. (<b>c</b>) Graph of differences in microbial composition at the phylum level. (<b>d</b>) Graph of differences in microbial composition at the family level. “ns” means no significant difference between groups; “*” means significant difference between groups (<span class="html-italic">p</span> &lt; 0.05; multiple comparison with ANOVA tests).</p> Full article ">Figure 4
<p>Structures and functions of bacterial communities at the surface sediments and bottom seawater. (<b>a</b>) Graph of differences in microbial composition at the genera level. (<b>b</b>) Bacterial function predictions based on the FAPROTAX tool.</p> Full article ">Figure 5
<p>RDA of bacterial community structure and environmental factors.</p> Full article ">Figure 6
<p>Co-occurrence networks and stability of bacterial communities in the NS and IS groups. (<b>a</b>) The nodes are colored based on the phylum level of prokaryotic microorganisms. A connection indicates a strong (Spearman’s ρ &gt; 0.9) and significant (<span class="html-italic">p</span> &lt; 0.01) correlation. The size of each node is proportional to the degree of ASV. (<b>b</b>) The relationship between bacterial ASV richness and average variation degree (AVD) of assembled bacterial communities. “ns” means no significant difference between groups; “*” means the significant difference between groups (<span class="html-italic">p</span> &lt; 0.05; multiple comparisons using ANOVA tests).</p> Full article ">Figure 7
<p>Mechanisms of bacterial assembly at the surface sediments and bottom seawater. (<b>a</b>) Niche breadth of bacterial communities at the surface sediments and bottom seawater. (<b>b</b>) Assessment of community structure based on the Raup–Crick index. (<b>c</b>) Assessment of the relative importance of deterministic and stochastic processes in community assembly based on the neutral community models. The solid blue line represents the best-fit value of the neutral community models, the dashed blue line represents the 95% confidence interval of the model, ASVs with a higher or lower frequency than predicted by the neutral community models are shown in different colours. “ns” means no significant difference between groups; “*” means significant difference between groups (<span class="html-italic">p</span> &lt; 0.05; multiple comparison with ANOVA tests).</p> Full article ">
11 pages, 1387 KiB  
Article
Tigecycline Sensitivity Reduction in Escherichia coli Due to Widely Distributed tet(A) Variants
by Shan Zhang, Mingquan Cui, Dejun Liu, Bo Fu, Tingxuan Shi, Yang Wang, Chengtao Sun and Congming Wu
Microorganisms 2023, 11(12), 3000; https://doi.org/10.3390/microorganisms11123000 - 18 Dec 2023
Viewed by 1291
Abstract
Despite scattered studies that have reported mutations in the tet(A) gene potentially linked to tigecycline resistance in clinical pathogens, the detailed function and epidemiology of these tet(A) variants remains limited. In this study, we analyzed 64 Escherichia coli isolates derived from [...] Read more.
Despite scattered studies that have reported mutations in the tet(A) gene potentially linked to tigecycline resistance in clinical pathogens, the detailed function and epidemiology of these tet(A) variants remains limited. In this study, we analyzed 64 Escherichia coli isolates derived from MacConkey plates supplemented with tigecycline (2 μg/mL) and identified five distinct tet(A) variants that account for reduced sensitivity to tigecycline. In contrast to varied tigecycline MICs (0.25 to 16 μg/mL) of the 64 tet(A)-variant-positive E. coli isolates, gene function analysis confirmed that the five tet(A) variants exhibited a similar capacity to reduce tigecycline sensitivity in DH5α carrying pUC19. Among the observed seven non-synonymous mutations, the V55M mutation was unequivocally validated for its positive role in conferring tigecycline resistance. Interestingly, the variability in tigecycline MICs among the E. coli strains did not correlate with tet(A) gene expression. Instead, a statistically significant reduction in intracellular tigecycline concentrations was noted in strains displaying higher MICs. Genomic analysis of 30 representative E. coli isolates revealed that tet(A) variants predominantly resided on plasmids (n = 14) and circular intermediates (n = 13). Within China, analysis of a well-characterized E. coli collection isolated from pigs and chickens in 2018 revealed the presence of eight tet(A) variants in 103 (4.2%, 95% CI: 3.4–5.0%) isolates across 13 out of 17 tested Chinese provinces or municipalities. Globally, BLASTN analysis identified 21 tet(A) variants in approximately 20.19% (49,423/244,764) of E. coli genomes in the Pathogen Detection database. These mutant tet(A) genes have been widely disseminated among E. coli isolates from humans, food animals, and the environment sectors, exhibiting a growing trend in tet(A) variants over five decades. Our findings underscore the urgency of addressing tigecycline resistance and the underestimated role of tet(A) mutations in this context. Full article
(This article belongs to the Section Veterinary Microbiology)
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<p>The number of tigecycline MICs observed among the 64 <span class="html-italic">tet</span>(A)-variant-harboring <span class="html-italic">E. coli</span> (<b>A</b>); Tigecycline MIC of <span class="html-italic">tet</span>(A) variants in the transformants (<b>B</b>).</p> Full article ">Figure 2
<p>Comparison of the mean value of tigecycline accumulation in the presence (<b>A</b>) and absence (<b>B</b>) of CCCP in 23 <span class="html-italic">tet</span>(A)v1-positive <span class="html-italic">E. coli</span> at 15 min of exposure. The error bars represent the standard error of the mean. * <span class="html-italic">p</span> &lt; 0.05; ns means not significant.</p> Full article ">Figure 3
<p>The similarity of 21 identified <span class="html-italic">tet</span>(A) variant proteins (<b>A</b>); Growing tendency of <span class="html-italic">tet</span>(A)-variant-harboring <span class="html-italic">E. coli</span> over time (<b>B</b>). The dots are the presence of <span class="html-italic">tet</span>(A) variant in different years, the solid line is a fitted linear regression line, and the red region within the dashed lines indicates 95% confidence interval for the regression line in (<b>B</b>).</p> Full article ">
18 pages, 1687 KiB  
Systematic Review
The Role of Bifidobacterium in Liver Diseases: A Systematic Review of Next-Generation Sequencing Studies
by Gabriel Henrique Hizo and Pabulo Henrique Rampelotto
Microorganisms 2023, 11(12), 2999; https://doi.org/10.3390/microorganisms11122999 - 17 Dec 2023
Cited by 4 | Viewed by 1532
Abstract
The physiopathology of liver diseases is complex and can be caused by various factors. Bifidobacterium is a bacterial genus commonly found in the human gut microbiome and has been shown to influence the development of different stages of liver diseases significantly. This study [...] Read more.
The physiopathology of liver diseases is complex and can be caused by various factors. Bifidobacterium is a bacterial genus commonly found in the human gut microbiome and has been shown to influence the development of different stages of liver diseases significantly. This study investigated the relationship between the Bifidobacterium genus and liver injury. In this work, we performed a systematic review in major databases using the key terms “Bifidobacterium”, “ALD”, “NAFLD”, “NASH”, “cirrhosis”, and “HCC” to achieve our purpose. In total, 31 articles were selected for analysis. In particular, we focused on studies that used next-generation sequencing (NGS) technologies. The studies focused on assessing Bifidobacterium levels in the diseases and interventional aimed at examining the therapeutic potential of Bifidobacterium in the mentioned conditions. Overall, the abundance of Bifidobacterium was reduced in hepatic pathologies. Low levels of Bifidobacterium were associated with harmful biochemical and physiological parameters, as well as an adverse clinical outcome. However, interventional studies using different drugs and treatments were able to increase the abundance of the genus and improve clinical outcomes. These results strongly support the hypothesis that changes in the abundance of Bifidobacterium significantly influence both the pathophysiology of hepatic diseases and the related clinical outcomes. In addition, our critical assessment of the NGS methods and related statistical analyses employed in each study highlights concerns with the methods used to define the differential abundance of Bifidobacterium, including potential biases and the omission of relevant information. Full article
(This article belongs to the Special Issue Gut Microbiota in Disease, Second Edition)
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<p>According to the workflow, 31 studies were included out of the initial 648 following a rigorous filtering process.</p> Full article ">Figure 2
<p>Overview of the methods used to detect and quantify <span class="html-italic">Bifidobacterium</span> genus in liver lesions and their upregulation or downregulation status. (<b>A</b>) Circular barplot with sequencing type, database, method, and next-generation sequencing (NGS) technology information. (<b>B</b>) <span class="html-italic">Bifidobacterium</span> levels in untreated disease groups [<a href="#B4-microorganisms-11-02999" class="html-bibr">4</a>,<a href="#B15-microorganisms-11-02999" class="html-bibr">15</a>,<a href="#B16-microorganisms-11-02999" class="html-bibr">16</a>,<a href="#B17-microorganisms-11-02999" class="html-bibr">17</a>,<a href="#B18-microorganisms-11-02999" class="html-bibr">18</a>,<a href="#B19-microorganisms-11-02999" class="html-bibr">19</a>,<a href="#B20-microorganisms-11-02999" class="html-bibr">20</a>,<a href="#B21-microorganisms-11-02999" class="html-bibr">21</a>,<a href="#B22-microorganisms-11-02999" class="html-bibr">22</a>,<a href="#B23-microorganisms-11-02999" class="html-bibr">23</a>,<a href="#B24-microorganisms-11-02999" class="html-bibr">24</a>,<a href="#B25-microorganisms-11-02999" class="html-bibr">25</a>,<a href="#B26-microorganisms-11-02999" class="html-bibr">26</a>,<a href="#B27-microorganisms-11-02999" class="html-bibr">27</a>,<a href="#B28-microorganisms-11-02999" class="html-bibr">28</a>,<a href="#B29-microorganisms-11-02999" class="html-bibr">29</a>,<a href="#B30-microorganisms-11-02999" class="html-bibr">30</a>], see <a href="#microorganisms-11-02999-t001" class="html-table">Table 1</a>. (<b>C</b>) <span class="html-italic">Bifidobacterium</span> levels in treated disease groups [<a href="#B31-microorganisms-11-02999" class="html-bibr">31</a>,<a href="#B32-microorganisms-11-02999" class="html-bibr">32</a>,<a href="#B33-microorganisms-11-02999" class="html-bibr">33</a>,<a href="#B34-microorganisms-11-02999" class="html-bibr">34</a>,<a href="#B35-microorganisms-11-02999" class="html-bibr">35</a>,<a href="#B36-microorganisms-11-02999" class="html-bibr">36</a>,<a href="#B37-microorganisms-11-02999" class="html-bibr">37</a>,<a href="#B38-microorganisms-11-02999" class="html-bibr">38</a>,<a href="#B39-microorganisms-11-02999" class="html-bibr">39</a>,<a href="#B40-microorganisms-11-02999" class="html-bibr">40</a>,<a href="#B41-microorganisms-11-02999" class="html-bibr">41</a>,<a href="#B42-microorganisms-11-02999" class="html-bibr">42</a>,<a href="#B43-microorganisms-11-02999" class="html-bibr">43</a>,<a href="#B44-microorganisms-11-02999" class="html-bibr">44</a>], see <a href="#microorganisms-11-02999-t002" class="html-table">Table 2</a>. Blue circles denote upregulation, and red circles indicate downregulation.</p> Full article ">Figure 3
<p>Risk of bias. (<b>A</b>) shows the risk of bias in case-control studies [<a href="#B4-microorganisms-11-02999" class="html-bibr">4</a>,<a href="#B19-microorganisms-11-02999" class="html-bibr">19</a>,<a href="#B28-microorganisms-11-02999" class="html-bibr">28</a>,<a href="#B30-microorganisms-11-02999" class="html-bibr">30</a>,<a href="#B34-microorganisms-11-02999" class="html-bibr">34</a>,<a href="#B35-microorganisms-11-02999" class="html-bibr">35</a>], (<b>B</b>) in cohort studies [<a href="#B20-microorganisms-11-02999" class="html-bibr">20</a>,<a href="#B33-microorganisms-11-02999" class="html-bibr">33</a>,<a href="#B34-microorganisms-11-02999" class="html-bibr">34</a>,<a href="#B43-microorganisms-11-02999" class="html-bibr">43</a>], and (<b>C</b>) in cross-sectional studies [<a href="#B18-microorganisms-11-02999" class="html-bibr">18</a>,<a href="#B29-microorganisms-11-02999" class="html-bibr">29</a>]. Domain “D” represents the items in the JBI checklist. “Overall” represents the study’s risk of bias based on judgment criteria.</p> Full article ">Figure 4
<p>A graphical scheme of the progression and stages of liver diseases. Starting from a healthy liver until the development of hepatocellular carcinoma.</p> Full article ">
19 pages, 14382 KiB  
Article
Temporal Profiling of Host Proteome against Different M. tuberculosis Strains Reveals Delayed Epigenetic Orchestration
by Prabhakar Babele, Mukul K. Midha, Kanury V. S. Rao and Ajay Kumar
Microorganisms 2023, 11(12), 2998; https://doi.org/10.3390/microorganisms11122998 - 16 Dec 2023
Viewed by 1287
Abstract
Apart from being preventable and treatable, tuberculosis is the deadliest bacterial disease afflicting humankind owing to its ability to evade host defence responses, many of which are controlled by epigenetic mechanisms. Here, we report the temporal dynamics of the proteome of macrophage-like host [...] Read more.
Apart from being preventable and treatable, tuberculosis is the deadliest bacterial disease afflicting humankind owing to its ability to evade host defence responses, many of which are controlled by epigenetic mechanisms. Here, we report the temporal dynamics of the proteome of macrophage-like host cells after infecting them for 6, 18, 30, and 42 h with two laboratory strains (H37Ra and H37Rv) and two clinical strains (BND433 and JAL2287) of Mycobacterium tuberculosis (MTB). Using SWATH-MS, the proteins characterized at the onset of infection broadly represented oxidative stress and cell cytoskeleton processes. Intermediary and later stages of infection are accompanied by a reshaping of the combination of proteins implicated in histone stability, gene expression, and protein trafficking. This study provides strain-specific and time-specific variations in the proteome of the host, which might further the development of host-directed therapeutics and diagnostic tools against the pathogen. Also, our findings accentuate the importance of proteomic tools in delineating the complex recalibration of the host defence enabled as an effect of MTB infection. To the best of our knowledge, this is the first comprehensive proteomic account of the host response to avirulent and virulent strains of MTB at different time periods of the life span of macrophage-like cells. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE repository with the dataset identifier PXD022352. Full article
(This article belongs to the Special Issue Immunometabolism in Mycobacterium tuberculosis (M.tb) Infection)
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<p>Label-free DIA-SWATH analysis of macrophage-like cell proteome after infection with different strains of <span class="html-italic">M. tuberculosis</span> at 18 h of infection. Host cells were harvested and subjected to LC-MS/MS analysis following protein extraction and trypsin digestion. Left panel: The number of differentially expressed proteins detected in each dataset. The differentially expressed proteins were grouped into common virulent proteins, if they were shared between any of the three virulent strains (H37Rv, BND433, and JAL2287), and unique unshared proteins, if they were not shared among them. Right panel: Unsupervised hierarchical clustering and principal component analysis of the protein profiling of THP1 cells. Volcano plots with up- and down-regulated proteins (up/down ratio in parentheses) for each dataset are also shown. Con: control; Ra: H37Ra; Rv: H37Rv; BND: BND433; JAL: JAL2287.</p> Full article ">Figure 2
<p>Biological processes were predicted for the differentially expressed proteins of macrophage-like cells after infection with different strains of <span class="html-italic">M. tuberculosis</span> at 18 h of infection. The differentially expressed proteins were grouped into common virulent proteins, if they were shared between any of the three virulent strain infections (H37Rv, BND433, and JAL2287), and unique unshared proteins, if they were not shared among them. Left panel: up-regulated proteins; right panel: down-regulated proteins. Ra: H37Ra; Rv: H37Rv; BND: BND433; JAL: JAL2287.</p> Full article ">Figure 3
<p>Cellular components, molecular functions, and KEGG pathways were predicted for the common differentially expressed proteins of the host that were shared between any of the three virulent strains (H37Rv, BND433, and JAL2287) after infection at 18 h of incubation. Upper left panel: up-regulated proteins; upper right panel: down-regulated proteins. Lower panel: protein–protein interaction (PPI) networks of protein interactions with Markov clustering in circles were also constructed. Nodes in the diagram represent proteins, and edges represent protein–protein interactions between the nodes. PPI: protein–protein interaction.</p> Full article ">
15 pages, 4455 KiB  
Article
Development and Validation of a Highly Sensitive Multiplex Immunoassay for SARS-CoV-2 Humoral Response Monitorization: A Study of the Antibody Response in COVID-19 Patients with Different Clinical Profiles during the First and Second Waves in Cadiz, Spain
by Lucia Olvera-Collantes, Noelia Moares, Ricardo Fernandez-Cisnal, Juan P. Muñoz-Miranda, Pablo Gonzalez-Garcia, Antonio Gabucio, Carolina Freyre-Carrillo, Juan de Dios Jordan-Chaves, Teresa Trujillo-Soto, Maria P. Rodriguez-Martinez, Maria I. Martin-Rubio, Eva Escuer, Manuel Rodriguez-Iglesias, Cecilia Fernandez-Ponce and Francisco Garcia-Cozar
Microorganisms 2023, 11(12), 2997; https://doi.org/10.3390/microorganisms11122997 - 16 Dec 2023
Viewed by 1435
Abstract
There is still a long way ahead regarding the COVID-19 pandemic, since emerging waves remain a daunting challenge to the healthcare system. For this reason, the development of new preventive tools and therapeutic strategies to deal with the disease have been necessary, among [...] Read more.
There is still a long way ahead regarding the COVID-19 pandemic, since emerging waves remain a daunting challenge to the healthcare system. For this reason, the development of new preventive tools and therapeutic strategies to deal with the disease have been necessary, among which serological assays have played a key role in the control of COVID-19 outbreaks and vaccine development. Here, we have developed and evaluated an immunoassay capable of simultaneously detecting multiple IgG antibodies against different SARS-CoV-2 antigens through the use of Bio-PlexTM technology. Additionally, we have analyzed the antibody response in COVID-19 patients with different clinical profiles in Cadiz, Spain. The multiplex immunoassay presented is a high-throughput and robust immune response monitoring tool capable of concurrently detecting anti-S1, anti-NC and anti-RBD IgG antibodies in serum with a very high sensitivity (94.34–97.96%) and specificity (91.84–100%). Therefore, the immunoassay proposed herein may be a useful monitoring tool for individual humoral immunity against SARS-CoV-2, as well as for epidemiological surveillance. In addition, we show the values of antibodies against multiple SARS-CoV-2 antigens and their correlation with the different clinical profiles of unvaccinated COVID-19 patients in Cadiz, Spain, during the first and second waves of the pandemic. Full article
(This article belongs to the Special Issue Coronaviruses: Past, Present, and Future)
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<p>Schematic representation of the multiplex immunoassay developed for anti-S1, anti-RBD and anti-NC IgG antibody detection. Anti-S1 IgG (<b>A</b>), anti-RBD IgG (<b>B</b>) and/or anti-NC IgG (<b>C</b>) antibodies present in patients’ sera will bind to the magnetic beads functionalized with each antigen, and were differentially color-coded. Human antibodies captured onto each bead by the corresponding antigen would be recognized by the biotinylated antihuman IgG detection antibody, that will in turn bind fluorescently labeled streptavidin (STV-PE). PE fluorescence corresponding to the amount of antibodies captured by each antigen will be assigned by electronically gating each fluorescently coded bead, thus allowing for the simultaneous detection of IgG antibodies recognizing S1, NC and RBD by the Bio-Plex<sup>TM</sup> array reader.</p> Full article ">Figure 2
<p>ROC analysis of the performed multiplex immunoassay. ROC curve representation (Black line) for the detection of anti-S1 IgG antibodies (<b>A</b>), anti-RBD IgG antibodies (<b>B</b>) and anti-NC IgG antibodies (<b>C</b>).</p> Full article ">Figure 3
<p>Comparison of antibody values between samples from prepandemic and COVID-19 patients. Scatter plot represents the MFI values for anti-RBD (<b>A</b>), anti-S1 (<b>B</b>) and anti-NC IgG (<b>C</b>). Bold lines represent the assay positivity cut-off value. The <span class="html-italic">p</span>-value returned from the Mann–Whitney U test carried out between both groups is shown as asterisks (***), for <span class="html-italic">p</span>-values ≤0.001.</p> Full article ">Figure 4
<p>SARS-CoV-2 IgG antibody values by age groups. Scatter plot represents the MFI values for IgG anti-S1 (<b>A</b>), anti-RBD (<b>B</b>) and anti-NC IgG (<b>C</b>) antibodies. Bold lines represent the assay positivity cut-off. No statistically significant differences are represented with ‘ns’.</p> Full article ">Figure 5
<p>Scatter plot showing the IgG antibody values against SARS-CoV-2 obtained in the multiplex immunoassay according to sex. Scatter plot represents the MFI values for the detection of anti-S1 (<b>A</b>), anti-RBD (<b>B</b>) and anti-NC IgG (<b>C</b>) antibodies. Bold lines represent the assay positivity cut-off. No statistically significant differences are represented with ‘ns’.</p> Full article ">Figure 6
<p>Dynamics of the SARS-CoV-2 antibody values according to the days since a positive PCR. Scatter plot representing the MFI values for the detection of anti-S1 (<b>A</b>), anti-RBD (<b>B</b>) and anti-NC IgG (<b>C</b>) antibodies. Bold lines represent the assay positivity cut-off. Statistical significance returned from the Mann–Whitney U test for each comparison is shown with asterisks (*), (**) or (***), for <span class="html-italic">p</span>-values ≤ 0.05, ≤0.01 and ≤0.001, respectively. No statistically significant differences are represented with ‘ns’.</p> Full article ">Figure 7
<p>Comparison of the antibody values according to COVID-19 severity. Scatter plot represents the MFI values for anti-S1 (<b>A</b>), anti-RBD (<b>B</b>) and anti-NC IgG (<b>C</b>) antibodies. Bold lines represent the assay positivity cut-off. The <span class="html-italic">p</span>-values returned from the Mann–Whitney U test for each comparison is shown as asterisks (*) and (***), for <span class="html-italic">p</span>-values ≤ 0.05 and ≤0.001, respectively. No statistically significant differences are represented with ‘ns’.</p> Full article ">Figure 8
<p>Comparison of different SARS-CoV-2 IgG antibody profiles by body mass index. Scatter plot represents the MFI values for anti-S1 (<b>A</b>), anti-RBD (<b>B</b>) and anti-NC IgG (<b>C</b>) antibodies. Bold lines represent the assay positivity cut-off. The <span class="html-italic">p</span>-values returned from the Mann–Whitney U test for each comparison is shown as asterisks (*), for <span class="html-italic">p</span>-values ≤ 0.05. No statistically significant differences are represented with ‘ns’.</p> Full article ">Figure 9
<p>Comparison of antibody values according to comorbidities. Scatter plot represents the MFI values for anti-S1 (<b>A</b>), anti-RBD (<b>B</b>) and anti-NC IgG (<b>C</b>) antibodies. Bold lines represent the assay positivity cut-off. The <span class="html-italic">p</span>-values returned from the Mann–Whitney U test for each comparison is shown as asterisks (*), for <span class="html-italic">p</span>-values ≤ 0.05. No statistically significant differences are represented with ‘ns’.</p> Full article ">
17 pages, 1763 KiB  
Review
Rocahepevirus ratti as an Emerging Cause of Acute Hepatitis Worldwide
by Sara Benavent, Silvia Carlos and Gabriel Reina
Microorganisms 2023, 11(12), 2996; https://doi.org/10.3390/microorganisms11122996 - 16 Dec 2023
Cited by 7 | Viewed by 2045
Abstract
The hepatitis E virus (HEV) is a widespread human infection that causes mainly acute infection and can evolve to a chronic manifestation in immunocompromised individuals. In addition to the common strains of hepatitis E virus (HEV-A), known as Paslahepevirus balayani, pathogenic to [...] Read more.
The hepatitis E virus (HEV) is a widespread human infection that causes mainly acute infection and can evolve to a chronic manifestation in immunocompromised individuals. In addition to the common strains of hepatitis E virus (HEV-A), known as Paslahepevirus balayani, pathogenic to humans, a genetically highly divergent rat origin hepevirus (RHEV) can cause hepatitis possessing a potential risk of cross-species infection and zoonotic transmission. Rocahepevirus ratti, formerly known as Orthohepevirus C, is a single-stranded RNA virus, recently reassigned to Rocahepevirus genus in the Hepeviridae family, including genotypes C1 and C2. RHEV primarily infects rats but has been identified as a rodent zoonotic virus capable of infecting humans through the consumption of contaminated food or water, causing both acute and chronic hepatitis cases in both animals and humans. This review compiles data concluding that 60% (295/489) of RHEV infections are found in Asia, being the continent with the highest zoonotic and transmission potential. Asia not only has the most animal cases but also 16 out of 21 human infections worldwide. Europe follows with 26% (128/489) of RHEV infections in animals, resulting in four human cases out of twenty-one globally. Phylogenetic analysis and genomic sequencing will be employed to gather global data, determine epidemiology, and assess geographical distribution. This information will enhance diagnostic accuracy, pathogenesis understanding, and help prevent cross-species transmission, particularly to humans. Full article
(This article belongs to the Special Issue Emerging Pathogens Causing Acute Hepatitis)
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<p>Phylogenetic tree of members of the family <span class="html-italic">Hepeviridae,</span> subfamily <span class="html-italic">Orthohepevirinae</span> (ORF1 polyprotein residues 1–450) (<a href="https://ictv.global/report/chapter/hepeviridae/hepeviridae" target="_blank">https://ictv.global/report/chapter/hepeviridae/hepeviridae</a>, accessed on 31 October 2023).</p> Full article ">Figure 2
<p>Selection of studies about <span class="html-italic">Rocahepevirus ratti</span>, including its epidemiology, 2000–2023. This selection was made for inclusion of studies in the bibliographic review. It was necessary to consider the fact that <span class="html-italic">Orthohepevirus</span> C has been renowned as <span class="html-italic">Rocahepevirus ratti</span> in recent studies (2022 and 2023).</p> Full article ">Figure 3
<p>Hepatitis E virus (HEV-A) genome structure, including its open reading frames (ORFs) and proteins. Contains a single-stranded positive-sense RNA molecule approximately 7.2 kb long and is presented in the genome of the virus of HEV-A. It displays a 7-methylguanosine RNA at the 5′ end and poly-A at the 3′ terminus. All HEV strains contain three open reading frames (ORFs): ORF1, ORF2, and ORF3 (an additional ORF4(?) region is found which functionality is not totally understood). ORF1 produces non-structural polyproteins for viral replication and transcription such as (Met), Y-domain, (PCP), (HVR), (Hel), and (RdRp). ORF2 encodes the capsid protein, while ORF3 encodes a multifunctional phosphoprotein, known as VP13. These proteins from ORF2 and ORF3 exhibit partial overlap and are translated from a subgenomic RNA with 2.2 kb in length. Additionally, ORF4 gives rise to an internal ribosome entry site-like protein (IRES) to respond to the stress generated in the endoplasmic reticulum (ER). ORF4 is an enhancer of viral replication. (GenBank accession number AF444002.1) [<a href="#B10-microorganisms-11-02996" class="html-bibr">10</a>,<a href="#B13-microorganisms-11-02996" class="html-bibr">13</a>,<a href="#B14-microorganisms-11-02996" class="html-bibr">14</a>].</p> Full article ">Figure 4
<p>Map of animal infections caused by <span class="html-italic">Rocahepevirus ratti</span> in different countries. The darker the color, the greater the number of infections.</p> Full article ">
8 pages, 1323 KiB  
Case Report
A Large-Scale Outbreak of Trichinellosis from Infected Wild Boar Meat in Croatia and the Role of Real-Time PCR Assays in Confirming the Source of the Disease
by Davor Balić, Tomislav Dijanić, Marija Agičić, Josip Barić, Maria Kaltenbrunner, Hrvoje Krajina, Rupert Hochegger, Mario Škrivanko and Karlo Kožul
Microorganisms 2023, 11(12), 2995; https://doi.org/10.3390/microorganisms11122995 - 16 Dec 2023
Cited by 1 | Viewed by 1202
Abstract
Background: Trichinellosis in Croatia posed a significant health concern during the 1990s, followed by a notable improvement in the epidemiological situation. However, in 2017, there was a resurgence, with 37 recorded cases in 3 outbreaks and 3 sporadic cases. The source of this [...] Read more.
Background: Trichinellosis in Croatia posed a significant health concern during the 1990s, followed by a notable improvement in the epidemiological situation. However, in 2017, there was a resurgence, with 37 recorded cases in 3 outbreaks and 3 sporadic cases. The source of this epidemic was homemade meat products derived from wild boar meat, leading to 26 infections. Methods: At the beginning of the outbreak and during the treatment of the patients, the medical and epidemiological records prepared throughout the investigation and over the course of patient treatment were reviewed. The recovery of the first-stage (L1) larvae from suspect meat products was achieved by artificial digestion. The molecular identification of the isolated larvae was performed by multiplex PCR. The molecular identification of the meat used to prepare the meat products was performed by real-time PCR assays. Results: The epidemic started in early 2017. In total, 71 exposed persons were documented: 26 with clinical symptoms and 3 hospitalised in two cities in different counties. The L1 burden in three different meat products was from 5.25 to 7.08 larvae per gram (LPG), and T. spiralis was determined as the aetiological agent of the outbreak. The molecular and biological identification confirmed that implicated meat products were made solely from wild boar meat. Conclusions: Although trichinellosis is no longer a frequent occurrence in Croatia, several cases are still registered nearly every year. Wild boar meat poses an important risk factor for human health if compulsory testing is not conducted before consumption, especially if the meat products are consumed without proper thermal processing. Full article
(This article belongs to the Special Issue Detection and Epidemiology of Vector-, Food- and Water-Borne Pathogens with a Focus on One Health Approaches)
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<p>The number of affected people in two counties (in red—trichinellosis endemic counties).</p> Full article ">Figure 2
<p>Number of registered cases by gender.</p> Full article ">Figure 3
<p>Number of registered cases by age groups.</p> Full article ">
20 pages, 4732 KiB  
Article
Microbial Diversity and Community Structure of Wastewater-Driven Microalgal Biofilms
by Olga Blifernez-Klassen, Julia Hassa, Diana L. Reinecke, Tobias Busche, Viktor Klassen and Olaf Kruse
Microorganisms 2023, 11(12), 2994; https://doi.org/10.3390/microorganisms11122994 - 16 Dec 2023
Cited by 1 | Viewed by 1642
Abstract
Dwindling water sources increase the need for efficient wastewater treatment. Solar-driven algal turf scrubber (ATS) system may remediate wastewater by supporting the development and growth of periphytic microbiomes that function and interact in a highly dynamic manner through symbiotic interactions. Using ITS and [...] Read more.
Dwindling water sources increase the need for efficient wastewater treatment. Solar-driven algal turf scrubber (ATS) system may remediate wastewater by supporting the development and growth of periphytic microbiomes that function and interact in a highly dynamic manner through symbiotic interactions. Using ITS and 16S rRNA gene amplicon sequencing, we profiled the microbial communities of four microbial biofilms from ATS systems operated with municipal wastewater (mWW), diluted cattle and pig manure (CattleM and PigM), and biogas plant effluent supernatant (BGE) in comparison to the initial inocula and the respective wastewater substrates. The wastewater-driven biofilms differed significantly in their biodiversity and structure, exhibiting an inocula-independent but substrate-dependent establishment of the microbial communities. The prokaryotic communities were comparable among themselves and with other microbiomes of aquatic environments and were dominated by metabolically flexible prokaryotes such as nitrifiers, polyphosphate-accumulating and algicide-producing microorganisms, and anoxygenic photoautotrophs. Striking differences occurred in eukaryotic communities: While the mWW biofilm was characterized by high biodiversity and many filamentous (benthic) microalgae, the agricultural wastewater-fed biofilms consisted of less diverse communities with few benthic taxa mainly inhabited by unicellular chlorophytes and saprophytes/parasites. This study advances our understanding of the microbiome structure and function within the ATS-based wastewater treatment process. Full article
(This article belongs to the Special Issue Microbial Ecosystems in Water and Wastewater Treatment)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Biodiversity of eukaryotic and prokaryotic communities of initial inocula, different ATS biofilms, and wastewater substrates. Results are presented as the boxes’ bounds and lines representing maxima, medians, and minima; n = 3.</p> Full article ">Figure 2
<p>Nonmetric multidimensional scaling (nMDS) of Bray–Curtis distances in eukaryotic and prokaryotic communities of different ATS biofilms, wastewater substrates, and initial inocula. Principal component analyses are based on the (<b>a</b>) presence/absence of and (<b>b</b>) normalized relative abundance of ASVs and presented as unweighted and weighted UniFrac distance plots, respectively.</p> Full article ">Figure 3
<p>Taxonomic profiles on the phyla level of the initial inocula, the different ATS systems, and their respective wastewater substrates as deduced from 16S rRNA and ITS gene amplicon data. Phyla with a maximal relative abundance of less than 3% were summarized as “miscellaneous”. Taxa that were not taxonomically assigned at the phyla level were summarized as “unclassified”.</p> Full article ">Figure 4
<p>Taxonomic profiles on the class level of the initial inocula and different ATS systems and their respective wastewater substrates as deduced from (<b>a</b>) ITS and (<b>b</b>) 16S rRNA gene amplicon data. Classes with a maximal relative abundance of less than 1.5% were summarized as “miscellaneous”. Class-level taxa that could not be taxonomically assigned were classified as “unassigned“.</p> Full article ">Figure 5
<p>Heatmap of most abundant taxa on genus level of the initial inocula, the different ATS systems, and their respective wastewater substrates. Genera with a maximal relative abundance higher than 2.5% in at least one condition are shown as deduced from (<b>a</b>) ITS and (<b>b</b>) 16S rRNA amplicons. Detailed information about the statistical analyses is provided in <a href="#app1-microorganisms-11-02994" class="html-app">Tables S3 and S4</a>. Squares indicate very low abundance appearance (&lt;0.01) of the respective taxa.</p> Full article ">Figure 6
<p>Morphological appearance and grouping of filamentous and unicellular microalgae within the ATS periphytons. Shown are the average biomass productivities of the biofilms from the ATS systems (during two weeks before harvest) operated with municipal wastewater and diluted cattle and pig manure, as well as supernatant from biogas effluent (mWW ATS, CattleM ATS, PigM ATS, and BGE ATS), respectively. Optical microscope images (the scale bar refers to 50 μm length) of the periphytic communities were taken during the sampling for sequencing analysis. The morphological grouping of microbiome members was estimated based on the relative abundance of detected taxa via ITS and 16S rRNA amplicon sequencing results, optical appearance and in agreement with observations from the literature. The grouping results for (E) Eukaryota and (B) Bacteria are presented as bar graphs; SD, n = 3. Error bars for biomass productivity represent SE, n = 7.</p> Full article ">
14 pages, 1101 KiB  
Article
Enterotoxigenic and Antimicrobic Susceptibility Profile of Staphylococcus aureus Isolates from Fresh Cheese in Croatia
by Ivana Ljevaković-Musladin, Lidija Kozačinski, Marija Krilanović, Marina Vodnica Martucci, Mato Lakić, Luca Grispoldi and Beniamino T. Cenci-Goga
Microorganisms 2023, 11(12), 2993; https://doi.org/10.3390/microorganisms11122993 - 15 Dec 2023
Viewed by 1021
Abstract
Certain Staphylococcus aureus strains harbour staphylococcal enterotoxin genes and hence can produce enterotoxin during their growth in food. Therefore, food can be a source of staphylococcal food poisoning, one of the most common food-borne diseases worldwide. Epidemiological data show that S. aureus is [...] Read more.
Certain Staphylococcus aureus strains harbour staphylococcal enterotoxin genes and hence can produce enterotoxin during their growth in food. Therefore, food can be a source of staphylococcal food poisoning, one of the most common food-borne diseases worldwide. Epidemiological data show that S. aureus is often present in raw milk cheeses, and consequently, cheeses are often the source of staphylococcal food poisoning outbreaks. The aim of this study was to determine the phenotypic characteristics of S. aureus isolates from fresh cheese, including antibiotic susceptibility; the presence of classical sea-see enterotoxin genes through molecular methods; and the isolate’s ability to produce SEA-SEE enterotoxins in vitro through reversed passive latex agglutination. A total of 180 coagulase-positive staphylococci were isolated from 18 out of 30 cheese samples, and 175 were confirmed as S. aureus through latex agglutination and API STAPH tests. All isolates possessed phenotypic characteristics typical for S. aureus, with certain variations in the egg yolk reaction (18.3% of the isolates showed a weak reaction and 28% no reaction at all) and haemolysis pattern (36.6% of the isolates produced double-haemolysis and 4.6% were non-haemolytic). Antibiotic resistance was observed in 1.1% of the isolates and to mupirocin only. Real-time PCR detected the sec gene in 34 (19.4%) isolates, but most isolates (80.6%) were not enterotoxigenic. For all 34 (19.4%) strains that carried the sec gene, the RPLA method detected the production of the SEC enterotoxin in vitro. For those enterotoxigenic strains, the possibility of enterotoxin production in fresh cheese could not be ruled out. Full article
(This article belongs to the Special Issue Microorganisms and Fermented Foods 2.0)
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<p>Cluster dendrogram for 175 <span class="html-italic">S. aureus</span> isolates.</p> Full article ">Figure 2
<p>Cluster dendrogram for 34 SEC-positive <span class="html-italic">S. aureus</span> isolates.</p> Full article ">
11 pages, 317 KiB  
Article
The Presence of Legionella in Water Used for Car Washing: Implications for Public Health
by Pasqualina Laganà, Alessio Facciolà, Roberta Palermo, Osvalda De Giglio, Santi Antonino Delia and Maria Eufemia Gioffrè
Microorganisms 2023, 11(12), 2992; https://doi.org/10.3390/microorganisms11122992 - 15 Dec 2023
Viewed by 1213
Abstract
Although today all of the aspects of Legionella are better understood than in the past, in many countries the interest is still mainly focused on healthcare and tourism facilities. Other at-risk areas are less explored, such as those where workers are often in [...] Read more.
Although today all of the aspects of Legionella are better understood than in the past, in many countries the interest is still mainly focused on healthcare and tourism facilities. Other at-risk areas are less explored, such as those where workers are often in contact with water during their activities. In reality, any water system capable of producing aerosols can be considered a potential source of Legionella transmission, including car washes, where a large number of users work and flow through annually. From January to May 2022, 120 samples were carried out in 30 car washes located in Messina (Italy): 60 samples of water and 60 of aerosols. The aim of this investigation was to evaluate the risk of legionellosis in car washing workers exposed to potentially contaminated aerosols. To increase the probability of finding Legionella, the sample collections were organized on different days of the week. Of the total samples taken, 10 (8.3%) were positive for Legionella: seven (11.7%) water (range 100–1000 CFU) and three (5%) aerosol (range 10–150 CFU) samples. Detected serogroups were L. pneumophila sgr 1, 7, 10 and Legionella gormanii. Given the results obtained, preventative measures should be implemented in such facilities in order to protect the health of users and car wash operators. Full article
(This article belongs to the Special Issue Clinical and Environmental Surveillance for the Prevention of Legionellosis)
14 pages, 312 KiB  
Article
Antimicrobial Resistance Profiles of Enterococcus faecium and Enterococcus faecalis Isolated from Healthy Dogs and Cats in South Korea
by Bo-Youn Moon, Md. Sekendar Ali, Ji-Hyun Choi, Ye-Eun Heo, Yeon-Hee Lee, Hee-Seung Kang, Tae-Sun Kim, Soon-Seek Yoon, Dong-Chan Moon and Suk-Kyung Lim
Microorganisms 2023, 11(12), 2991; https://doi.org/10.3390/microorganisms11122991 - 15 Dec 2023
Cited by 1 | Viewed by 1863
Abstract
Enterococcus spp. are typically found in the gastrointestinal tracts of humans and animals. However, they have the potential to produce opportunistic infections that can be transmitted to humans or other animals, along with acquired antibiotic resistance. In this study, we aimed to investigate [...] Read more.
Enterococcus spp. are typically found in the gastrointestinal tracts of humans and animals. However, they have the potential to produce opportunistic infections that can be transmitted to humans or other animals, along with acquired antibiotic resistance. In this study, we aimed to investigate the antimicrobial resistance profiles of Enterococcus faecium and Enterococcus faecalis isolates obtained from companion animal dogs and cats in Korea during 2020–2022. The resistance rates in E. faecalis towards most of the tested antimicrobials were relatively higher than those in E. faecium isolated from dogs and cats. We found relatively higher resistance rates to tetracycline (65.2% vs. 75.2%) and erythromycin (39.5% vs. 49.6%) in E. faecalis isolated from cats compared to those from dogs. However, in E. faecium, the resistance rates towards tetracycline (35.6% vs. 31.5%) and erythromycin (40.3% vs. 35.2%) were comparatively higher for dog isolates than cats. No or very few E. faecium and E. faecalis isolates were found to be resistant to daptomycin, florfenicol, tigecycline, and quinupristin/dalfopristin. Multidrug resistance (MDR) was higher in E. faecalis recovered from cats (44%) and dogs (33.9%) than in E. faecium isolated from cats (24.1%) and dogs (20.5%). Moreover, MDR patterns in E. faecalis isolates from dogs (27.2%) and cats (35.2%) were shown to encompass five or more antimicrobials. However, E. faecium isolates from dogs (at 13.4%) and cats (at 14.8%) were resistant to five or more antimicrobials. Taken together, the prevalence of antimicrobial-resistant enterococci in companion animals presents a potential public health concern. Full article
(This article belongs to the Section Antimicrobial Agents and Resistance)
23 pages, 1834 KiB  
Review
One Health Perspectives on Food Safety in Minimally Processed Vegetables and Fruits: From Farm to Fork
by Maria Isabel Santos, Madalena Grácio, Mariana Camoesas Silva, Laurentina Pedroso and Ana Lima
Microorganisms 2023, 11(12), 2990; https://doi.org/10.3390/microorganisms11122990 - 15 Dec 2023
Cited by 7 | Viewed by 3398
Abstract
While food markets and food production chains are experiencing exponential growth, global attention to food safety is steadily increasing. This is particularly crucial for ready-to-eat products such as fresh-cut salads and fruits, as these items are consumed raw without prior heat treatment, making [...] Read more.
While food markets and food production chains are experiencing exponential growth, global attention to food safety is steadily increasing. This is particularly crucial for ready-to-eat products such as fresh-cut salads and fruits, as these items are consumed raw without prior heat treatment, making the presence of pathogenic microorganisms quite frequent. Moreover, many studies on foodborne illnesses associated with these foods often overlook the transmission links from the initial contamination source. The prevention and control of the dissemination of foodborne pathogens should be approached holistically, involving agricultural production, processing, transport, food production, and extending to final consumption, all while adopting a One Health perspective. In this context, our objective is to compile available information on the challenges related to microbiological contamination in minimally handled fruits and vegetables. This includes major reported outbreaks, specific bacterial strains, and associated statistics throughout the production chain. We address the sources of contamination at each stage, along with issues related to food manipulation and disinfection. Additionally, we provide potential solutions to promote a healthier approach to fresh-cut fruits and vegetables. This information will be valuable for both researchers and food producers, particularly those focused on ensuring food safety and quality. Full article
(This article belongs to the Topic Food Hygiene and Food Safety)
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<p>Phases of minimally processed fruit and vegetable processing, from the farmland to packaging. Adapted from [<a href="#B39-microorganisms-11-02990" class="html-bibr">39</a>,<a href="#B40-microorganisms-11-02990" class="html-bibr">40</a>,<a href="#B41-microorganisms-11-02990" class="html-bibr">41</a>,<a href="#B42-microorganisms-11-02990" class="html-bibr">42</a>,<a href="#B43-microorganisms-11-02990" class="html-bibr">43</a>].</p> Full article ">Figure 2
<p>Sources of contamination in vegetables crops during production and post-harvest; source: adapted from [<a href="#B87-microorganisms-11-02990" class="html-bibr">87</a>].</p> Full article ">Figure 3
<p>Distribution (%) of estimated cases of foodborne illness and deaths by food category in the USA in the period 1998–2008; source: [<a href="#B48-microorganisms-11-02990" class="html-bibr">48</a>,<a href="#B115-microorganisms-11-02990" class="html-bibr">115</a>].</p> Full article ">Figure 4
<p>Schematic representation of the entry and permanence of pathogenic microorganisms in the plant life cycle. Adapted from: [<a href="#B52-microorganisms-11-02990" class="html-bibr">52</a>].</p> Full article ">
12 pages, 16466 KiB  
Article
Association between the Presence of Resistance Genes and Sanitiser Resistance of Listeria monocytogenes Isolates Recovered from Different Food-Processing Facilities
by Yue Cheng, Zeinabossadat Ebrahimzadeh Mousavi, Vincenzo Pennone, Daniel Hurley and Francis Butler
Microorganisms 2023, 11(12), 2989; https://doi.org/10.3390/microorganisms11122989 - 15 Dec 2023
Cited by 1 | Viewed by 1154
Abstract
Sanitisers are widely used in cleaning food-processing facilities, but their continued use may cause an increased resistance of pathogenic bacteria. Several genes have been attributed to the increased sanitiser resistance ability of L. monocytogenes. This study determined the presence of sanitiser resistance [...] Read more.
Sanitisers are widely used in cleaning food-processing facilities, but their continued use may cause an increased resistance of pathogenic bacteria. Several genes have been attributed to the increased sanitiser resistance ability of L. monocytogenes. This study determined the presence of sanitiser resistance genes in Irish-sourced L. monocytogenes isolates and explored the association with phenotypic sanitiser resistance. The presence of three genes associated with sanitiser resistance and a three-gene cassette (mdrL, qacH, emrE, bcrABC) were determined in 150 L. monocytogenes isolates collected from Irish food-processing facilities. A total of 23 isolates contained bcrABC, 42 isolates contained qacH, one isolate contained emrE, and all isolates contained mdrL. Additionally, 47 isolates were selected and grouped according to the number and type of resistance genes, and the minimal inhibitory concentration (MIC) of these isolates for benzalkonium chloride (BAC) was determined experimentally using the broth microdilution method. The BAC resistance of the strain carrying the bcrABC gene cassette was significantly higher than that of strains lacking the gene cassette, and the BAC resistance of the strain carrying the qacH gene was significantly higher than that of strains lacking the qacH gene (p < 0.05). Isolates harbouring both the qacH and bcrABC genes did not show higher BAC resistance. With respect to environmental factors, there was no significant difference in MIC values for isolates recovered from different processing facilities. In summary, this investigation highlights the prevalence of specific sanitiser resistance genes in L. monocytogenes isolates from Irish food-processing settings. While certain genes correlated with increased resistance to benzalkonium chloride, the combination of multiple genes did not necessarily amplify this resistance. Full article
(This article belongs to the Special Issue An Update on Listeria monocytogenes 2.0)
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<p>The distribution of MIC values for the 47 isolates analysed in this study.</p> Full article ">Figure 2
<p>The distribution in MIC values for isolates with or without the <span class="html-italic">bcrABC</span> gene cassette.</p> Full article ">Figure 3
<p>The distribution of MIC values for isolates with or without the <span class="html-italic">qacH</span> gene.</p> Full article ">Figure 4
<p>MIC distribution of isolates harbouring either the <span class="html-italic">bcrABC</span> gene cassette or the <span class="html-italic">qacH</span> gene (presence/absence indicated as 1/0. A small random element has been introduced to distinguish individual test results).</p> Full article ">Figure 5
<p>Phylogenetic tree of 13 ST121 <span class="html-italic">L. monocytogenes</span> isolates tested for MIC.</p> Full article ">Figure 6
<p>Amino acid sequences encoded in the <span class="html-italic">qacH</span> gene of 13 ST121 <span class="html-italic">L. monocytogenes</span> isolates (amino acid positions 1–120 are shown). The symbol “*” on the bottom line means there is no difference in the amino acid at this position. The symbol“:” on the bottom line means there is a difference in the amino acid at this position.</p> Full article ">Figure 7
<p>Variation in the percentage of isolates harbouring different resistance genes depending on the process facility.</p> Full article ">
16 pages, 4860 KiB  
Article
Isolation and Evaluation of Streptomyces melanogenes YBS22 with Potential Application for Biocontrol of Rice Blast Disease
by Luyang Song, Fei Wang, Chuang Liu, Zhengzhe Guan, Mengjiao Wang, Rongrong Zhong, Huijun Xi, Ying Zhao and Caiyi Wen
Microorganisms 2023, 11(12), 2988; https://doi.org/10.3390/microorganisms11122988 - 15 Dec 2023
Cited by 1 | Viewed by 1292
Abstract
Plant diseases caused by pathogenic fungi pose a significant threat to agricultural production. This study reports on a strain YBS22 with broad-spectrum antifungal activity that was isolated and identified, and its active metabolites were purified and systematically studied. Based on a whole genome [...] Read more.
Plant diseases caused by pathogenic fungi pose a significant threat to agricultural production. This study reports on a strain YBS22 with broad-spectrum antifungal activity that was isolated and identified, and its active metabolites were purified and systematically studied. Based on a whole genome sequence analysis, the new strain YBS22 was identified as Streptomyces melanogenes. Furthermore, eight gene clusters were predicted in YBS22 that are responsible for the synthesis of bioactive secondary metabolites. These clusters have homologous sequences in the MIBiG database with a similarity of 100%. The antifungal effects of YBS22 and its crude extract were evaluated in vivo and vitro. Our findings revealed that treatment with the strain YBS22 and its crude extract significantly reduced the size of necrotic lesions caused by Magnaporthe oryzae on rice leaves. Further analysis led to the isolation and purification of an active compound from the crude extract of the strain YBS22, identified as N-formylantimycin acid methyl ester, an analog of antimycin, characterized by NMR and MS analyses. Consistently, the active compound can significantly inhibit the germination and development of M. oryzae spores in a manner that is both dose- and time-dependent. As a result, we propose that the strain YBS22 could serve as a novel source for the development of biological agents aimed at controlling rice blast disease. Full article
(This article belongs to the Section Plant Microbe Interactions)
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<p>Phenotypic characteristics and biochemical properties of the strain YBS22. (<b>A</b>); Characteristics and micromorphology of the strain YBS22 on Gause’s medium. (<b>a</b>) Front. (<b>b</b>) Back. (<b>c</b>) Single colony. (<b>d</b>) Aerial hyphae (SEM3500×). (<b>e</b>) Spore producing chains (SEM 6000×). (<b>f</b>) Spores (SEM 6000×). (<b>B</b>); Physiological and biochemical characteristics of the strain YBS22. “+” indicates positive reaction, “-” indicates negative reaction.</p> Full article ">Figure 2
<p>Phylogenetic analysis. (<b>A</b>); Phylogenetic tree based on 16S rRNA sequences obtained by the maximum likelihood (ML) method with 1000 replicates. (<b>B</b>); A ML phylogenetic tree was constructed using selected bacterial genome sequences. The values next to the tree indicate ANI and AAI values. Circle at nodes indicate the levels of bootstrap values (1000 replicates).</p> Full article ">Figure 3
<p>Genome features of the <span class="html-italic">S. melanogenes</span> YBS22 chromosome. Rings represent the following features labeled from inside to outside: ring 1, scale; ring 2, GC skew, green, and purple correspond to above- and below-average GC skew, respectively; rings 3, GC content; ring 4 and ring 7 represent the COG to which each CDS belongs; rings 5 to 6, blocks correspond to the positions of CDS, tRNA, and rRNA on the genome.</p> Full article ">Figure 4
<p>Gene clusters for secondary metabolism in the YBS22 chromosome. (<b>A</b>,<b>B</b>); Gene cluster structure analysis of the predicted compound desferrioxamin (<b>B</b>). (<b>C</b>,<b>D</b>); Gene cluster structure analysis of the predicted compound antimycin.</p> Full article ">Figure 5
<p>The antifungal effects of YBS22 and its crude extract were evaluated in vivo and vitro. (<b>A</b>); Antifungal activity of crude extract of the strain YBS22. (<b>B</b>); Antifungal activity of crude extract against <span class="html-italic">M. oryzae</span> and mycelial morphology. (<b>a</b>) Control group treated with methanol solvent; (<b>b</b>) Treatment with 200 μg/mL crude extract. (<b>C</b>,<b>D</b>); The length of lesions in different treatment. (<b>a</b>) Control: sterile water treatment; (<b>b</b>) Treatment with sterile water after <span class="html-italic">M. oryzae</span> inoculated; (<b>c</b>) Treatment with 200 μg/mL crude extract of YBS22 after <span class="html-italic">M. oryzae</span> inoculated, * ANOVA, <span class="html-italic">p</span> = 0.017; (<b>d</b>) Treatment with culture supernatant of YBS22 after <span class="html-italic">M. oryzae</span> inoculated, * ANOVA, <span class="html-italic">p</span> = 0.019. Significant different at the <span class="html-italic">p</span> &lt; 0.05 level.</p> Full article ">Figure 6
<p>Conidial germination inhibition assays of compound <b>1</b>. Note: Mock: Sterile water control group; Treatment 1: Compound 1 is 50 μg/mL; Treatment 2: Compound <b>1</b> is 25 μg/mL; Treatment 3: Compound <b>1</b> is 5 μg/mL; Scale: 20 μm.</p> Full article ">
22 pages, 929 KiB  
Review
State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs
by Mikhail A. Kulyashov, Semyon K. Kolmykov, Tamara M. Khlebodarova and Ilya R. Akberdin
Microorganisms 2023, 11(12), 2987; https://doi.org/10.3390/microorganisms11122987 - 14 Dec 2023
Cited by 2 | Viewed by 1708
Abstract
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key [...] Read more.
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Biology, Diversity, and Ecology of Methanotrophic Bacteria)
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<p>Development stages of a genome-scale metabolic model of any metabolic process (created with BioRender.com). A key step in constraint-based modeling is the construction of the GSM model which is represented at the pyramid’s base. This part of the pyramid briefly illustrates the main approaches (bottom-up: from in vitro data via enzymatic reactions to a metabolic map. Top-down: from omics data to a metabolic map) for GSM models reconstruction. The next block demonstrates an equally important stage—the modification and expansion/reduction of the original GSM model. The block preceding the vertex reflects the model simulation and further visualization of the obtained in silico results using metabolic maps. At the top of the pyramid is a relatively new stage that provides a significant refinement of the model’s predictions through the integration of omics data into the original GSM model for the reconstruction of context-specific models (CS models). Tools developed using the Python programming language are highlighted in pink, while software packages written in MATLAB are highlighted in blue.</p> Full article ">
13 pages, 1798 KiB  
Article
The Genomic Characteristics of an Arthritis-Causing Salmonella pullorum
by Zhiyuan Lu, Jiaqi Huang, Peiyong Li, Mengze Song, Ben Liu, Wenli Tang and Shuhong Sun
Microorganisms 2023, 11(12), 2986; https://doi.org/10.3390/microorganisms11122986 - 14 Dec 2023
Cited by 1 | Viewed by 1248
Abstract
Salmonella enterica subsp. enterica serovar Gallinarum biovar pullorum (Salmonella pullorum) is an avian-specific pathogen that has caused considerable economic losses to the poultry industry. High endemicity, poor implementation of hygiene measures, and lack of effective vaccines hinder the prevention and control of [...] Read more.
Salmonella enterica subsp. enterica serovar Gallinarum biovar pullorum (Salmonella pullorum) is an avian-specific pathogen that has caused considerable economic losses to the poultry industry. High endemicity, poor implementation of hygiene measures, and lack of effective vaccines hinder the prevention and control of this disease in intensively maintained poultry flocks. In recent years, the incidence of arthritis in chicks caused by Salmonella pullorum infection has increased. In this study, four Salmonella pullorum strains were identified from the livers, spleens, and joint fluids of Qingjiaoma chicken breeders with arthritis clinical signs, and an arthritis model of chicks was successfully established using SP206-2. Whole genome sequencing of the SP206-2 strain showed that the genome was 4,730,579 bp, 52.16% GC content, and contained 5007 genes, including 4729 protein-coding regions. The genomic analysis of four arthritis-causing isolates and three diarrhea-causing isolates showed that the genome of arthritis-causing isolates was subject to nonsynonymous mutations, shift mutations, and gene copy deletions. An SNP phylogenetic tree analysis showed that arthritis-causing isolates are located in a different evolutionary branch from diarrhea-causing isolates. Further differential genes analysis showed that the genome of arthritis-causing isolates had missense mutations in genes related to substance metabolism and substance transport, as a result of adaptive evolution. Full article
(This article belongs to the Special Issue Poultry Pathogens and Poultry Diseases)
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<p>Complete genome of the arthritic <span class="html-italic">Salmonella</span> pullorum isolate SP206-2. The first circle from outside to inside is the genome sequence information. In the second circle, the GC content curve of the genomic sequence was sliding across the genome with a sliding window of 2000 bp, and the average GC content was calculated. The dashed line shows the average GC content of the reference genome. In the third circle, the GC skew curve of the genomic sequence was slid across the genome with a sliding window of 2000 bp, and the average GC content was calculated. The dashed line shows the reference line with GC skew 0. In the fourth circle, the depth and coverage information of next-generation sequencing were slid on the genome with 2000 bp as a sliding window, and the average sequencing depth was calculated to reflect the coverage of reads in different regions. The dashed line shows the average reads coverage at the overall level. In the fifth circle, the third-generation sequencing depth and coverage information were slid on the genome with 2000 bp as a sliding window, and the average sequencing depth was calculated to reflect the coverage of reads in different regions. The dashed line shows the average reads coverage at the overall level. The sixth circle shows the CDs and non-coding RNA regions (rRNA, tRNA) in the reference genome, which are represented by two inner and outer layers, the outer layer represents the plus strand, and the inner layer represents the minus strand.</p> Full article ">Figure 2
<p>(<b>a</b>) Heat map of ANI analysis of <span class="html-italic">S</span>. pullorum. The depth of the color represents the value of the similarity. (<b>b</b>) Phylogenetic tree-based SNP.</p> Full article ">Figure 3
<p>(<b>a</b>) Relative expression of <span class="html-italic">treZ</span>, <span class="html-italic">qorB</span>, and <span class="html-italic">ccmF-1</span> in SP206-2 and CVCC526. (<b>b</b>) Growth curves of SP206-2 and CVCC526 in LB broth medium. (<b>c</b>) Growth curves of SP206-2 and CVCC526 in M9 medium. *, <span class="html-italic">p</span> &lt; 0.05 (One-way ANOVA); ***, <span class="html-italic">p</span> &lt; 0.001; ****, <span class="html-italic">p</span>&lt; 0.0001; ns, no significance.</p> Full article ">Figure 4
<p>(<b>a</b>) MIC of ampicillin against CVCC526, SP206-2 and SP-A100. (<b>b</b>) MIC of tetracycline against CVCC526, SP206-2, and SP-A100. (<b>c</b>) MIC of gentamicin against CVCC526, SP206-2, and SP-A100. (<b>d</b>) MIC of ciprofloxacin against CVCC526, SP206-2, and SP-A100.</p> Full article ">
21 pages, 2033 KiB  
Review
AAV Immunotoxicity: Implications in Anti-HBV Gene Therapy
by Ridhwaanah Jacobs, Makafui Dennis Dogbey, Njabulo Mnyandu, Keila Neves, Stefan Barth, Patrick Arbuthnot and Mohube Betty Maepa
Microorganisms 2023, 11(12), 2985; https://doi.org/10.3390/microorganisms11122985 - 14 Dec 2023
Cited by 1 | Viewed by 1858
Abstract
Hepatitis B virus (HBV) has afflicted humankind for decades and there is still no treatment that can clear the infection. The development of recombinant adeno-associated virus (rAAV)-based gene therapy for HBV infection has become important in recent years and research has made exciting [...] Read more.
Hepatitis B virus (HBV) has afflicted humankind for decades and there is still no treatment that can clear the infection. The development of recombinant adeno-associated virus (rAAV)-based gene therapy for HBV infection has become important in recent years and research has made exciting leaps. Initial studies, mainly using mouse models, showed that rAAVs are non-toxic and induce minimal immune responses. However, several later studies demonstrated rAAV toxicity, which is inextricably associated with immunogenicity. This is a major setback for the progression of rAAV-based therapies toward clinical application. Research aimed at understanding the mechanisms behind rAAV immunity and toxicity has contributed significantly to the inception of approaches to overcoming these challenges. The target tissue, the features of the vector, and the vector dose are some of the determinants of AAV toxicity, with the latter being associated with the most severe adverse events. This review discusses our current understanding of rAAV immunogenicity, toxicity, and approaches to overcoming these hurdles. How this information and current knowledge about HBV biology and immunity can be harnessed in the efforts to design safe and effective anti-HBV rAAVs is discussed. Full article
(This article belongs to the Special Issue Adeno-Associated Virus Biology and AAV Vector-Mediated Gene Therapy)
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<p>Commonly used tools in HBV gene therapy development. To silence HBV gene expression, the RNAi pathway is activated exogenously by artificial microRNAs (miRNAs), for example. A microRNA duplex is incorporated into an RNA silencing complex (RISC) before the selection of one strand that will guide the RISC to the target HBV messenger RNAs. This results in HBV RNA degradation or translation suppression. Transcription activator-like endonucleases (TALENs) and zinc finger nucleases (ZFNs) work in pairs and require a right TALEN or right ZFN and left TALEN or left ZFN, conjugated to a cleavage domain, for double-strand cleavage to occur. TALENs consist of tandem repeats comprising 33–35 amino acids, whereas ZFNs consist of only 3–6 ZFs. The single guide RNA (sgRNA) associate and directs Cas 9 to the DNA sequence of interest. The Cas 9 enzyme creates double-stranded DNA breaks. Once cleavage has occurred, a double-strand DNA break occurs and recruits host machinery for repair. The error-prone non-homologous end joining (NHEJ) repair pathway is favored, which leads to insertions and deletions (indels) within the cccDNA/rcDNA (created with <a href="http://Biorender.com" target="_blank">Biorender.com</a>, accessed on 29 November 2023).</p> Full article ">Figure 2
<p>Development of AAVs for anti-HBV gene delivery. The wild-type AAV <span class="html-italic">rep</span> and <span class="html-italic">cap</span> ORFs are replaced with the anti-HBV sequence, most preferably driven by a liver-specific promoter (LSP). For primary microRNA (pri-miRNA) or short hairpin RNA (shRNA) and zinc finger nucleases (ZFNs), the self-complementary AAVs (scAAVs) are commonly used, whereas single-stranded AAVs (ssAAVs) are used for transcription activator-like endonucleases (TALENs) and RNA-guided clustered regulatory interspaced short palindromic repeats (CRISPR) and CRISPR associated (Cas) sequences. sgRNA expression is commonly driven by a polymerase III non-liver-specific promoter (NLSP). The AAV-mediated delivery of RNAi activators targeting <span class="html-italic">Surface</span> and <span class="html-italic">X</span> ORFs has been well characterized. AAVs carrying ZNFs targeting <span class="html-italic">Surface</span>, <span class="html-italic">Polymerase</span>, and <span class="html-italic">Core</span> ORFs have been reported. Although dual ssAAVs are promising for TALEN delivery, no anti-HBV TALEN expressing AAV has been reported. AAVs have been used to deliver CRISPR/Cas sequences against all HBV ORFs. The colored arrows on the HBV genome indicate the four open HBV reading frames, with all seven start codons and encoded proteins indicated (created with <a href="http://Biorender.com" target="_blank">Biorender.com</a>, accessed on 29 November 2023).</p> Full article ">Figure 3
<p>Treg response modulation by AAV and HBV in the liver. Liver infection with AAV increases IL-10 production by KCs and enhances Foxp3<sup>+</sup>CD4<sup>+</sup> Tregs differentiation and expansion. Secreted IL-10 can also mediate the conversion of antigen-specific CD4<sup>+</sup> T cells into Foxp3<sup>+</sup>CD4<sup>+</sup> Tregs and inhibit CD4<sup>+</sup> T cell activity. Hepatitis, caused by HBV infection, stimulates MHC-II expression by hepatocytes. This enhances antigen presentation to naïve CD4<sup>+</sup> T cells and results in their activation and differentiation to form Foxp3<sup>+</sup>CD4<sup>+</sup> Tregs (created with <a href="http://BioRender.com" target="_blank">BioRender.com</a> accessed on 21 November 2023).</p> Full article ">
12 pages, 1865 KiB  
Article
Characterisation of Soil Bacterial Communities That Exhibit Chemotaxis to Root Exudates from Phosphorus-Limited Plants
by Katherine V. Weigh, Bruna D. Batista, Huong Hoang and Paul G. Dennis
Microorganisms 2023, 11(12), 2984; https://doi.org/10.3390/microorganisms11122984 - 14 Dec 2023
Viewed by 1410
Abstract
The ability to sense and direct movement along chemical gradients is known as ‘chemotaxis’ and is a common trait among rhizosphere microorganisms, which are attracted to organic compounds released from plant roots. In response to stress, the compounds released from roots can change [...] Read more.
The ability to sense and direct movement along chemical gradients is known as ‘chemotaxis’ and is a common trait among rhizosphere microorganisms, which are attracted to organic compounds released from plant roots. In response to stress, the compounds released from roots can change and may recruit symbionts that enhance host stress tolerance. Decoding this language of attraction could support the development of microbiome management strategies that would enhance agricultural production and sustainability. In this study, we employ a culture-independent bait-trap chemotaxis assay to capture microbial communities attracted to root exudates from phosphorus (P)-sufficient and P-deficient Arabidopsis thaliana Col-0 plants. The captured populations were then enumerated and characterised using flow cytometry and phylogenetic marker gene sequencing, respectively. Exudates attracted significantly more cells than the control but did not differ between P treatments. Relative to exudates from P-sufficient plants, those collected from P-deficient plants attracted a significantly less diverse bacterial community that was dominated by members of the Paenibacillus, which is a genus known to include powerful phosphate solubilisers and plant growth promoters. These results suggest that in response to P deficiency, Arabidopsis exudates attract organisms that could help to alleviate nutrient stress. Full article
(This article belongs to the Special Issue Microbial Ecology of Soil Microorganisms, Networks with Plants and Ecosystem Services)
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<p>Mean number of cells captured (per gram of soil) in the chemotaxis assays by treatment after 30 min incubation. Different letters above bars represent statistically significant differences as determined by Tukey’s HSD (<span class="html-italic">p</span> &lt; 0.05). Error bars represent the standard error of the mean.</p> Full article ">Figure 2
<p>(<b>A</b>) Relative and (<b>B</b>) absolute (cell count-corrected relatives) abundances of bacterial communities captured in control, P-sufficient, and P-deficient treatments at the phylum and class levels.</p> Full article ">Figure 3
<p>Distance-based redundancy analyses (db-RDA) summarising the compositional similarity of bacterial communities in control and exudate-baited traps as represented by Hellinger-transformed (<b>A</b>) relative, and (<b>B</b>) absolute abundances. In the top right of each panel are the results of PERMANOVA models in which treatment was a categorical predictor variable. Dominant operational taxonomic units (OTUs) identified at the genus level are shown in blue text with the OTU IDs in square brackets. These IDs are consistent between figures.</p> Full article ">Figure 4
<p>Heatmap summarising the relative and absolute-estimated abundances of bacterial OTUs present at ≥2% mean relative abundance in any treatment. Numbers in square brackets represent OTU IDs and are consistent with other figures. Red letters indicate the results of Tukey’s HSD post hoc tests for individual OTUs. Within OTUs, treatments sharing the same letter are not significantly different. Rows with no letters all shared the same letter.</p> Full article ">Figure 5
<p>The alpha diversity of bacterial communities, as represented by Shannon’s Diversity Index, in each treatment. Error bars are standard errors, and the letters represent the results of Tukey’s HSD post hoc analysis, where treatments with the same letter are not significantly different (<span class="html-italic">p</span> &lt; 0.05).</p> Full article ">
33 pages, 23892 KiB  
Article
Bacillus subtilis 26D Triggers Induced Systemic Resistance against Rhopalosiphum padi L. by Regulating the Expression of Genes AGO, DCL and microRNA in Bread Spring Wheat
by Sergey D. Rumyantsev, Svetlana V. Veselova, Guzel F. Burkhanova, Valentin Y. Alekseev and Igor V. Maksimov
Microorganisms 2023, 11(12), 2983; https://doi.org/10.3390/microorganisms11122983 - 14 Dec 2023
Cited by 2 | Viewed by 1696
Abstract
Bacillus subtilis 26D is a plant growth-promoting endophytic bacteria capable of inducing systemic resistance through the priming mechanism, which includes plant genome reprogramming and the phenomenon of RNA interference (RNAi) and microRNA (miRNAs). The phloem-feeding insect bird cherry-oat aphid Rhopalosiphum padi L. is [...] Read more.
Bacillus subtilis 26D is a plant growth-promoting endophytic bacteria capable of inducing systemic resistance through the priming mechanism, which includes plant genome reprogramming and the phenomenon of RNA interference (RNAi) and microRNA (miRNAs). The phloem-feeding insect bird cherry-oat aphid Rhopalosiphum padi L. is a serious pest that causes significant damage to crops throughout the world. However, the function of plant miRNAs in the response to aphid infestation remains unclear. The results of this work showed that B. subtilis 26D stimulated aphid resistance in wheat plants, inducing the expression of genes of hormonal signaling pathways ICS, WRKY13, PR1, ACS, EIN3, PR3, and ABI5. In addition, B. subtilis 26D activated the RNAi mechanism and regulated the expression of nine conserved miRNAs through activation of the ethylene, salicylic acid (SA), and abscisic acid (ABA) signaling pathways, which was demonstrated by using treatments with phytohormones. Treatment of plants with SA, ethylene, and ABA acted in a similar manner to B. subtilis 26D on induction of the expression of the AGO4, AGO5 and DCL2, DCL4 genes, as well as the expression of nine conserved miRNAs. Different patterns of miRNA expression were found in aphid-infested plants and in plants treated with B. subtilis 26D or SA, ethylene, and ABA and infested by aphids, suggesting that miRNAs play multiple roles in the plant response to phloem-feeding insects, associated with effects on hormonal signaling pathways, redox metabolism, and the synthesis of secondary metabolites. Our study provides new data to further elucidate the fine mechanisms of bacterial-induced priming. However, further extensive work is needed to fully unravel these mechanisms. Full article
(This article belongs to the Special Issue Advances in Microbial and Plant Biotechnology)
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<p>The scheme of the experiment showing the process of growing bacteria in laboratory conditions and measuring the titer of bacterial cells (<b>A</b>); sterilization, treatment, and germination of seeds, as well as plant growth conditions (<b>B</b>); treatment of plants with phytohormones ABA, SA, and ethephon (<b>C</b>).</p> Full article ">Figure 2
<p>The scheme of bioassay of the different types of resistance to aphids—antibiosis (<b>A</b>) and endurance (<b>B</b>).</p> Full article ">Figure 3
<p>Influence of <span class="html-italic">B. subtilis</span> 26D strain (Bs26D) on the hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) content (<b>A</b>), peroxidase activity (POD) (<b>B</b>), and catalase activity (CAT) (<b>C</b>) of wheat plants infested with bird cherry-oat aphid <span class="html-italic">R. padi</span>. The samples are indicated as follows: Control—non-bacterized plants and unpopulated with aphids; Bs26D—plants treated with the <span class="html-italic">B. subtilis</span> 26D strain before sowing; <span class="html-italic">R. padi</span>—plants populated with aphids; Bs26D + <span class="html-italic">R. padi</span>—bacterized plants and populated with aphids. Figures present means ± SE (<span class="html-italic">n</span> = 6). Columns of each histogram marked with different letters represent the mean values that are statistically different from each other according to Duncan’s test (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 4
<p>Influence of <span class="html-italic">B. subtilis</span> 26D strain (Bs26D) on the relative expression of genes involved in the SA biosynthesis, <span class="html-italic">TaICS</span> (<b>A</b>), the JA biosynthesis, <span class="html-italic">TaLOX</span> (<b>B</b>), the ethylene biosynthesis, <span class="html-italic">TaACS</span> (<b>C</b>) and the ABA biosynthesis, <span class="html-italic">TaNCED</span> (<b>D</b>) in wheat plants infested with bird cherry-oat aphid <span class="html-italic">R. padi</span>. The samples are indicated as follows: Control—non-bacterized plants and unpopulated with aphids; Bs26D—plants treated with the <span class="html-italic">B. subtilis</span> 26D strain before sowing; <span class="html-italic">R. padi</span>—plants populated with aphids; Bs26D + <span class="html-italic">R. padi</span>—bacterized plants and populated with aphids. Figures present means ± SE (<span class="html-italic">n</span> = 6). Columns of each histogram marked with different letters represent the mean values that are statistically different from each other according to Duncan’s test (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 5
<p>Influence of the <span class="html-italic">B. subtilis</span> 26D strain (Bs26D) on the relative expression of genes the SA signaling pathway, <span class="html-italic">TaWRKY13</span> (<b>A</b>), the JA signaling pathway, <span class="html-italic">TaERF1</span> (<b>B</b>), the ethylene signaling pathway, <span class="html-italic">TaEIN3</span> (<b>C</b>) and the ABA signaling pathway, <span class="html-italic">TaABI5</span> (<b>D</b>) in wheat plants infested with bird cherry-oat aphid <span class="html-italic">R. padi</span>. The samples are indicated as follows: Control—non-bacterized plants and unpopulated with aphids; Bs26D—plants treated with the <span class="html-italic">B. subtilis</span> 26D strain before sowing; <span class="html-italic">R. padi</span>—plants populated with aphids; Bs26D + <span class="html-italic">R. padi</span>—bacterized plants and populated with aphids. Figures present means ± SE (<span class="html-italic">n</span> = 6). Columns of each histogram marked with different letters represent the mean values that are statistically different from each other according to Duncan’s test (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 6
<p>Influence of the <span class="html-italic">B. subtilis</span> 26D strain (Bs26D) on the relative expression of SA-dependent genes <span class="html-italic">TaPR1</span> (<b>A</b>) and <span class="html-italic">TaPR2</span> (<b>B</b>), ethylene-dependent gene <span class="html-italic">TaPR3</span> (<b>C</b>), and JA-dependent gene <span class="html-italic">TaPR6</span> (<b>D</b>) in wheat plants infested with bird cherry-oat aphid <span class="html-italic">R. padi</span>. The samples are indicated as follows: Control—non-bacterized plants and unpopulated with aphids; Bs26D—plants treated with the <span class="html-italic">B. subtilis</span> 26D strain before sowing; <span class="html-italic">R. padi</span>—plants populated with aphids; Bs26D + <span class="html-italic">R. padi</span>—bacterized plants and populated with aphids. Figures present means ± SE (<span class="html-italic">n</span> = 6). Columns of each histogram marked with different letters represent the mean values that are statistically different from each other according to Duncan’s test (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 7
<p>Influence of the <span class="html-italic">B. subtilis</span> 26D strain (Bs26D) on the relative expression of genes encoding RNAi enzymes <span class="html-italic">AGO1</span> and <span class="html-italic">AGO2</span> (<b>A</b>), <span class="html-italic">AGO4</span> and <span class="html-italic">AGO5</span> (<b>B</b>), <span class="html-italic">DCL2</span> and <span class="html-italic">DCL4</span> (<b>C</b>) in wheat plants infested with bird cherry-oat aphid <span class="html-italic">R. padi</span>. The samples are indicated as follows: Control—non-bacterized plants and unpopulated with aphids; Bs26D—plants treated with the <span class="html-italic">B. subtilis</span> 26D strain before sowing; <span class="html-italic">R. padi</span>—plants populated with aphids; Bs26D + <span class="html-italic">R. padi</span>—bacterized plants and populated with aphids. Figures present means ± SE (<span class="html-italic">n</span> = 6). Columns of each histogram marked with different letters represent the mean values that are statistically different from each other according to Duncan’s test (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 8
<p>Influence of the <span class="html-italic">B. subtilis</span> 26D strain (Bs26D) and phytohormones salicylic acid (SA), abscisic acid (ABA), and ethephone (ET) on the relative expression miRNAs miR393 (<b>A</b>), miR164 (<b>B</b>) in wheat plants infested with bird cherry-oat aphid <span class="html-italic">R. padi</span>. The samples are indicated as follows: Control—non-bacterized plants and unpopulated with aphids; Bs26D—plants treated with the <span class="html-italic">B. subtilis</span> 26D strain before sowing; <span class="html-italic">R. padi</span>—non-bacterized and untreated plants populated with aphids; SA—plants treated with salicylic acid; ET—plants treated with ethephone; ABA—plants treated with abscisic acid. Figures present means ± SE (<span class="html-italic">n</span> = 6). Columns of each histogram marked with different letters represent the mean values that are statistically different from each other according to Duncan’s test (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 9
<p>Influence of the <span class="html-italic">B. subtilis</span> 26D strain (Bs26D) and phytohormones salicylic acid (SA), abscisic acid (ABA), and ethephone (ET) on the relative expression miRNAs miR156 (<b>A</b>), miR159 (<b>B</b>), miR160 (<b>C</b>), and miR166a (<b>D</b>) in wheat plants infested with bird cherry-oat aphid <span class="html-italic">R. padi</span>. The samples are indicated as follows: Control—non-bacterized plants and unpopulated with aphids; Bs26D—plants treated with the <span class="html-italic">B. subtilis</span> 26D strain before sowing; <span class="html-italic">R. padi</span>—non-bacterized and untreated plants populated with aphids; SA—plants treated with salicylic acid; ET—plants treated with ethephone; ABA—plants treated with abscisic acid. Figures present means ± SE (<span class="html-italic">n</span> = 6). Columns of each histogram marked with different letters represent the mean values that are statistically different from each other according to Duncan’s test (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">Figure 10
<p>Influence of the <span class="html-italic">B. subtilis</span> 26D strain (Bs26D) and phytohormones salicylic acid (SA), abscisic acid (ABA), and ethephone (ET) on the relative expression miRNAs miR396d (<b>A</b>), miR398 (<b>B</b>), and miR408 (<b>C</b>) in wheat plants infested with bird cherry-oat aphid <span class="html-italic">R. padi</span>. The samples are indicated as follows: Control—non-bacterized plants and unpopulated with aphids; Bs26D—plants treated with the <span class="html-italic">B. subtilis</span> 26D strain before sowing; <span class="html-italic">R. padi</span>—non-bacterized and untreated plants populated with aphids; SA—plants treated with salicylic acid; ET—plants treated with ethephone; ABA—plants treated with abscisic acid. Figures present means ± SE (<span class="html-italic">n</span> = 6). Columns of each histogram marked with different letters represent the mean values that are statistically different from each other according to Duncan’s test (<span class="html-italic">p</span> ≤ 0.05).</p> Full article ">
21 pages, 5128 KiB  
Article
Effect of Antimicrobial Use in Conventional Versus Natural Cattle Feedlots on the Microbiome and Resistome
by Catrione Lee, Rahat Zaheer, Krysty Munns, Devin B. Holman, Gary Van Domselaar, Athanasios Zovoilis and Tim A. McAllister
Microorganisms 2023, 11(12), 2982; https://doi.org/10.3390/microorganisms11122982 - 14 Dec 2023
Cited by 1 | Viewed by 1645
Abstract
Antimicrobial use (AMU) in the livestock industry has been associated with increased levels of antimicrobial resistance. Recently, there has been an increase in the number of “natural” feedlots in the beef cattle sector that raise cattle without antibiotics. Shotgun metagenomics was employed to [...] Read more.
Antimicrobial use (AMU) in the livestock industry has been associated with increased levels of antimicrobial resistance. Recently, there has been an increase in the number of “natural” feedlots in the beef cattle sector that raise cattle without antibiotics. Shotgun metagenomics was employed to characterize the impact of AMU in feedlot cattle on the microbiome, resistome, and mobilome. Sequenced fecal samples identified a decline (q < 0.01) in the genera Methanobrevibacter and Treponema in the microbiome of naturally vs. conventionally raised feedlot cattle, but this difference was not (q > 0.05) observed in catch basin samples. No differences (q > 0.05) were found in the class-level resistome between feedlot practices. In fecal samples, decreases from conventional to natural (q < 0.05) were noted in reads for the antimicrobial-resistant genes (ARGs) mefA, tet40, tetO, tetQ, and tetW. Plasmid-associated ARGs were more common in feces from conventional than natural feedlot cattle. Interestingly, more chromosomal- than plasmid-associated macrolide resistance genes were observed in both natural and conventional feedlots, suggesting that they were more stably conserved than the predominately plasmid-associated tetracycline resistance genes. This study suggests that generationally selected resistomes through decades of AMU persist even after AMU ceases in natural production systems. Full article
(This article belongs to the Section Veterinary Microbiology)
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<p>The abundance of phyla (&gt;1%) in fecal (<b>A</b>) and catch basin water (<b>B</b>) samples normalized with the TMM (trimmed mean of m-values) method across conventional and natural feedlots.</p> Full article ">Figure 2
<p>Boxplots of normalized TMM (trimmed mean m-value) abundance of prevalent (&gt;1%) fecal genera within order Methanobacteriales (analysis of compositions of microbiomes with bias correction with adjusted <span class="html-italic">p</span>-value significance via Benjamini–Hochberg method; q &gt; 0.05 = ns; q &lt; 0.0001 = ****).</p> Full article ">Figure 3
<p>Boxplots of normalized TMM (trimmed mean m-value) abundance of prevalent (&gt;1%) fecal genera within order Spirochaetales (analysis of compositions of microbiomes with bias correction with adjusted <span class="html-italic">p</span>-value significance via Benjamini–Hochberg method; q &gt; 0.05 = ns; q &lt; 0.01 = **; q &lt; 0.001 = ***; q &lt; 0.0001 = ****).</p> Full article ">Figure 4
<p>Boxplots of trimmed mean of m-value (TMM)-normalized gene abundance of resistance for abundant resistance classes (&gt;25,000 normalized counts) in fecal samples (<b>A</b>); antimicrobial drug resistance classes in catch basin water samples (<b>B</b>); and biocide and metal resistance classes in catch basin water samples (<b>C</b>) (MLS = macrolide, lincosamide, streptogramin; analysis of the composition of microbiomes with bias correction and adjusted <span class="html-italic">p</span>-value significance used the Benjamini–Hochberg method; q &gt; 0.05 = ns).</p> Full article ">Figure 5
<p>Heatmap of antimicrobial resistance groups from fecal samples stratified into resistance mechanism comparing conventional and natural feedlots.</p> Full article ">Figure 6
<p>Heatmap of antimicrobial resistance groups in catch basin water samples stratified into resistance mechanism comparing conventional and natural feedlots.</p> Full article ">Figure 7
<p>Fecal resistance profiles of ARG-carrying contigs from (<b>A</b>) 102 plasmids (<span class="html-italic">n</span> = 24 samples) and (<b>B</b>) 342 chromosomes (<span class="html-italic">n</span> = 60 samples). AMG = aminoglycoside; BLA = β-lactam; CHL = chloramphenicol; MLS = macrolide, lincosamide, and streptogramin; SUL = sulfonamide; TET = tetracycline; TMP = trimethoprim. With regard to chromosomal ARGs, the most abundant resistance class was tetracycline (41.5%) followed by MLS (28.7%), β-lactam (14.3%), chloramphenicol (3.5%), aminoglycoside (1.8%), and trimethoprim (0.3%; (<b>A</b>)). In most cases, no two ARGs belonging to the same class were associated with a contig, with the exception of two instances of two aminoglycoside ARGs, <span class="html-italic">ant</span>(6)-<span class="html-italic">Ia</span> and <span class="html-italic">aph</span>(3’)-<span class="html-italic">III</span>, and one instance of two tetracycline ARGs, <span class="html-italic">tet</span>(40) and <span class="html-italic">tet</span>(O).</p> Full article ">Figure 8
<p>Sankey diagram depicting contigs with antimicrobial resistance genes (ARGs) and antimicrobial classes associated with their respective contig localizations and feedlot type.</p> Full article ">Figure 9
<p>Chord diagram of associations between antimicrobial resistant gene classes in conventional vs. natural feedlots, and association with chromosomes or plasmids. Associations within the same antimicrobial resistance gene (ARG) reflect that there are multiple ARGs on the same contig that confer resistance to a single antimicrobial class. (MDR = multi-drug resistance from a single antimicrobial resistance gene).</p> Full article ">
10 pages, 1935 KiB  
Article
Diversity of Fecal Indicator Enterococci among Different Hosts: Importance to Water Contamination Source Tracking
by Soichiro Tamai and Yoshihiro Suzuki
Microorganisms 2023, 11(12), 2981; https://doi.org/10.3390/microorganisms11122981 - 14 Dec 2023
Cited by 7 | Viewed by 1344
Abstract
Enterococcus spp. are common bacteria present in the intestinal tracts of animals and are used as fecal indicators in aquatic environments. On the other hand, enterococci are also known as opportunistic pathogens. Elucidating their composition in the intestinal tracts of domestic animals can [...] Read more.
Enterococcus spp. are common bacteria present in the intestinal tracts of animals and are used as fecal indicators in aquatic environments. On the other hand, enterococci are also known as opportunistic pathogens. Elucidating their composition in the intestinal tracts of domestic animals can assist in estimating the sources of fecal contamination in aquatic environments. However, information on the species and composition of enterococci in animal hosts (except humans) is still lacking. In this study, enterococci were isolated from the feces of cattle, pigs, birds, and humans using selective media. Enterococcal species were identified using mass spectrometry technology, and each host was characterized by diversity and cluster analysis. The most dominant species were E. hirae in cattle, E. faecium in birds, and E. faecalis in pigs and humans. Cattle had the highest alpha diversity, with high interindividual and livestock farm diversity. The dominant enterococcal species in pigs and humans were identical, and cluster analysis showed that the majority of the two hosts’ species clustered together. Full article
(This article belongs to the Special Issue Water Microorganisms Associated with Human Health)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Alpha diversity analysis of each host based on the Shannon index.</p> Full article ">Figure 2
<p>Beta diversity analysis of each host based on the Bray–Curtis distance.</p> Full article ">Figure 3
<p>Cluster analysis based on the Bray–Curtis distance.</p> Full article ">Figure 4
<p>MALDI-TOF MS results showing the relative abundance of enterococcal species in the feces of each host: (<b>a</b>) cattle, (<b>b</b>) pigs, (<b>c</b>) birds, and (<b>d</b>) humans. The <span class="html-italic">x</span> axis indicates the individual number and farm. Farm A, A; Farm B, B; Farm C, C; Farm D, D; Farm E, E.</p> Full article ">
15 pages, 821 KiB  
Article
Satureja hortensis L. and Calendula officinalis L., Two Romanian Plants, with In Vivo Antiparasitic Potential against Digestive Parasites of Swine
by Mihai-Horia Băieş, Vlad-Dan Cotuţiu, Marina Spînu, Attila Mathe, Anamaria Cozma-Petruț, Vlad I. Bocǎneţ and Vasile Cozma
Microorganisms 2023, 11(12), 2980; https://doi.org/10.3390/microorganisms11122980 - 13 Dec 2023
Viewed by 1346
Abstract
Internal parasitic diseases of swine constitute a major welfare and health concern in low-input livestock farming. Due to an increase in chemical resistance, phytotherapeutic remedies have become an alternative for the prophylaxis and therapy of digestive parasitosis, albeit few remedies have been subjected [...] Read more.
Internal parasitic diseases of swine constitute a major welfare and health concern in low-input livestock farming. Due to an increase in chemical resistance, phytotherapeutic remedies have become an alternative for the prophylaxis and therapy of digestive parasitosis, albeit few remedies have been subjected to scientific validation. Low-input swine farming in Romania has adopted the traditional use of phytotherapy for controlling pathogens in livestock. The current study aimed to assess the antiparasitic potential of Calendula officinalis and Satureja hortensis against digestive parasites of swine in two low-input farms. The fecal samples were collected from sows, fatteners, and weaners, and were tested using the following coproparasitological methods: centrifugal sedimentation, flotation (Willis, McMaster egg counting technique), Ziehl–Neelsen stain modified by Henricksen, modified Blagg method, and in vitro nematode larvae/protozoan oocyst cultures. Six species of digestive parasites were diagnosed, namely Ascaris suum, Trichuris suis, Oesophagostomum spp., Balantioides coli, Eimeria spp., and Cryptosporidium spp., in various combinations, dependent on the swine category. A dose of 140 mg/kg bw/day of C. officinalis and 100 mg/kg bw/day of S. hortensis powders administered for 10 consecutive days revealed a strong antiprotozoal and anthelmintic activity on the aforementioned parasites. The curative efficacy can be attributed to the presence of polyphenols, sterols, tocopherols, and methoxylated flavones. In conclusion, our results indicate that S. hortensis and C. officinalis are promising alternatives to the commercially available antiparasitics, enabling their use as natural antiparasitic products against gastrointestinal parasites in pigs. Full article
(This article belongs to the Special Issue Parasitic Diseases in Livestock)
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Figure 1
<p>Evolution of parasite count over time for each parasite-treatment pair: EG—experimental group, C—control group; ES = <span class="html-italic">Eimeria</span> spp., BC = <span class="html-italic">B. coli</span>, AS = <span class="html-italic">A. suum</span>, TS = <span class="html-italic">T. suis</span>, OE = <span class="html-italic">Oesophagostomum</span> spp., CO = <span class="html-italic">Calendula officinalis</span>, SH = <span class="html-italic">Satureja hortensis</span>.</p> Full article ">Figure 1 Cont.
<p>Evolution of parasite count over time for each parasite-treatment pair: EG—experimental group, C—control group; ES = <span class="html-italic">Eimeria</span> spp., BC = <span class="html-italic">B. coli</span>, AS = <span class="html-italic">A. suum</span>, TS = <span class="html-italic">T. suis</span>, OE = <span class="html-italic">Oesophagostomum</span> spp., CO = <span class="html-italic">Calendula officinalis</span>, SH = <span class="html-italic">Satureja hortensis</span>.</p> Full article ">
14 pages, 3560 KiB  
Article
Biodegradation of Cholesterol by Enterococcus faecium YY01
by Ruimin Yang, Shahbaz Ahmad, Hongyan Liu, Qianqian Xu, Chunhua Yin, Yang Liu, Haiyang Zhang and Hai Yan
Microorganisms 2023, 11(12), 2979; https://doi.org/10.3390/microorganisms11122979 - 13 Dec 2023
Viewed by 1160
Abstract
Cholesterol (CHOL) is one of the risk factors causing the blockage of the arterial wall, atherosclerosis, coronary heart disease, and other serious cardiovascular diseases. Here, a promising bacterial strain for biodegrading CHOL was successfully isolated from the gut of healthy individuals and identified [...] Read more.
Cholesterol (CHOL) is one of the risk factors causing the blockage of the arterial wall, atherosclerosis, coronary heart disease, and other serious cardiovascular diseases. Here, a promising bacterial strain for biodegrading CHOL was successfully isolated from the gut of healthy individuals and identified as Enterococcus faecium YY01 with an analysis of the 16S rDNA sequence. An initial CHOL of 1.0 g/L was reduced to 0.5 g/L in 5 days, and glucose and beef extract were found to be optimal carbon and nitrogen sources for the rapid growth of YY01, respectively. To gain further insight into the mechanisms underlying CHOL biodegradation, the draft genome of YY01 was sequenced using Illumina HiSeq. Choloylglycine hydrolase, acyltransferase, and alkyl sulfatase was encoded by gene0586, gene1890, and gene2442, which play crucial roles in converting 3α, 7α, 12α-trihydroxy-5β-choranic acid to choline-CoA and then choline-CoA to bile acid. Notably, choloylglycine hydrolase was closely related to the biosynthesis of both primary and secondary bile acid. The findings of this study provide valuable insights into the metabolism pathway of CHOL biodegradation by YY01 and offer a potential avenue for the development of bacterioactive drugs against hypercholesterolemia. Full article
(This article belongs to the Section Microbial Biotechnology)
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Figure 1
<p><span class="html-italic">E. faecium</span> YY01 colonies (<b>a</b>) and morphology (<b>b</b>).</p> Full article ">Figure 2
<p>Phylogenetic tree of <span class="html-italic">E</span>. <span class="html-italic">faecium</span> YY01 based on the 16S rDNA sequence. The phylogenetic relationships between the species were assessed at the genomic level using average nucleotide identity (ANI) analysis. Through this analysis, the identity between strain YY01 and <span class="html-italic">E</span>. <span class="html-italic">faecium</span> is 84%.</p> Full article ">Figure 3
<p>CHOL biodegradation kinetic curve by <span class="html-italic">E</span>. <span class="html-italic">faecium</span> YY01. CHOL was biodegraded by YY01 from an initial concentration of 1 g/L to 0.5 g/L. The results shown are the mean values from three replicate experiments.</p> Full article ">Figure 4
<p>Effects of carbon source (<b>a</b>), nitrogen source (<b>b</b>), C/N (<b>c</b>), initial pH (<b>d</b>), and temperature (<b>e</b>) on YY01 growth.</p> Full article ">Figure 5
<p>Response surface plot for 24 h of YY01 incubation (C/N (<b>A</b>), initial pH (<b>B</b>), temperature (<b>C</b>), and inoculation amount (<b>D</b>)).</p> Full article ">Figure 5 Cont.
<p>Response surface plot for 24 h of YY01 incubation (C/N (<b>A</b>), initial pH (<b>B</b>), temperature (<b>C</b>), and inoculation amount (<b>D</b>)).</p> Full article ">Figure 6
<p>COG (<b>a</b>), GO (<b>b</b>), KEEG (<b>c</b>) annotation classification of <span class="html-italic">E</span>. <span class="html-italic">faecium</span> YY01.</p> Full article ">Figure 7
<p>CAZy classification results of <span class="html-italic">E. faecium</span> YY01.</p> Full article ">Figure 8
<p>The metabolism pathway for biodegrading CHOL with <span class="html-italic">E</span>. <span class="html-italic">faecium</span> YY01.</p> Full article ">
29 pages, 5077 KiB  
Article
Nutrient and Microbiome-Mediated Plant–Soil Feedback in Domesticated and Wild Andropogoneae: Implications for Agroecosystems
by Amanda Quattrone, Yuguo Yang, Pooja Yadav, Karrie A. Weber and Sabrina E. Russo
Microorganisms 2023, 11(12), 2978; https://doi.org/10.3390/microorganisms11122978 - 13 Dec 2023
Viewed by 1443
Abstract
Plants influence the abiotic and biotic environment of the rhizosphere, affecting plant performance through plant–soil feedback (PSF). We compared the strength of nutrient and microbe-mediated PSF and its implications for plant performance in domesticated and wild grasses with a fully crossed greenhouse PSF [...] Read more.
Plants influence the abiotic and biotic environment of the rhizosphere, affecting plant performance through plant–soil feedback (PSF). We compared the strength of nutrient and microbe-mediated PSF and its implications for plant performance in domesticated and wild grasses with a fully crossed greenhouse PSF experiment using four inbred maize genotypes (Zea mays ssp. mays b58, B73-wt, B73-rth3, and HP301), teosinte (Z. mays ssp. parviglumis), and two wild prairie grasses (Andropogon gerardii and Tripsacum dactyloides) to condition soils for three feedback species (maize B73-wt, teosinte, Andropogon gerardii). We found evidence of negative PSF based on growth, phenotypic traits, and foliar nutrient concentrations for maize B73-wt, which grew slower in maize-conditioned soil than prairie grass-conditioned soil. In contrast, teosinte and A. gerardii showed few consistent feedback responses. Both rhizobiome and nutrient-mediated mechanisms were implicated in PSF. Based on 16S rRNA gene amplicon sequencing, the rhizosphere bacterial community composition differed significantly after conditioning by prairie grass and maize plants, and the final soil nutrients were significantly influenced by conditioning, more so than by the feedback plants. These results suggest PSF-mediated soil domestication in agricultural settings can develop quickly and reduce crop productivity mediated by PSF involving changes to both the soil rhizobiomes and nutrient availability. Full article
(This article belongs to the Special Issue Rhizosphere Microbial Community 2.0)
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<p>Fully crossed plant–soil feedback (PSF) experimental design. We performed a fully crossed greenhouse plant–soil feedback study involving two phases. An initial (pre-conditioned) soil community was conditioned for three months by seven plant genotypes representing two functional groups (prairie grasses and maize). The grass functional group consisted of two wild prairie grass species—<span class="html-italic">Andropogon gerardii</span> (12 pots) and <span class="html-italic">Tripsacum dactyloides</span> (12 pots), and the maize functional group consisted of five maize genotypes—<span class="html-italic">Zea mays B73</span>-<span class="html-italic">wt</span> (12 pots), <span class="html-italic">B73</span>-<span class="html-italic">rth3</span> (12 pots), <span class="html-italic">b58</span> (12 pots), <span class="html-italic">HP301</span> (12 pots), and <span class="html-italic">Zea mays</span> ssp. <span class="html-italic">parviglumis</span> (10 pots). Samples of the pre-conditioned soil, unconditioned soil (not shown), and conditioned soils (after removing the conditioning plants) were analyzed to characterize the microbial (bacterial and archaeal) community. The conditioned soil for each pot was split and potted into three replicate pots, one for each of the three feedback species (<span class="html-italic">A. gerardii</span>, <span class="html-italic">Zea mays B73</span>-<span class="html-italic">wt</span>, and <span class="html-italic">Zea mays</span> ssp. <span class="html-italic">parviglumis</span>). Since the soil was not homogenized across pots within a conditioning genotype, each feedback plant could be mapped to one of each conditioning replicate. Feedback plants were grown for one month, harvested, and phenotyped for leaf, root, and growth traits. The soil and leaves were analyzed for carbon, nitrogen, phosphorus, potassium, magnesium, and calcium.</p> Full article ">Figure 2
<p>Analysis map for the plant–soil feedback (PSF) experiment. The conceptual diagram shows the PSF mechanisms, the research questions (Q) addressing them (refer to the Introduction for a description of the research questions), and the variables measured. Soil conditioning effects caused by plant conditioning genotypes are indicated by the light blue shaded box, which includes two specific PSF mechanisms examined in this study: effects on soil nutrients and effects on the soil microbial community in the rhizosphere. Phenotypes of the feedback plants grown in the conditioned soil depend both on the genotype of the feedback plant (feedback genotype, green box) and on the effects on soil caused by the conditioning genotype (green arrow). The yellow dashed box indicates measurements of the phenotypes of the feedback plants (<a href="#microorganisms-11-02978-t001" class="html-table">Table 1</a>). Solid lines indicate mechanisms specifically addressed in our study, and dashed lines indicate mechanisms that were not addressed by this study. Circles and ellipses indicate variables quantified in this study. The final soil nutrient concentrations are a result of both the effects of soil conditioning (post-conditioning soil nutrients) and the effects of feedback genotypes.</p> Full article ">Figure 3
<p>Effects of conditioning genotypes on rhizosphere microbial community structure. Principal coordinate analyses (PCoA) using the (<b>A</b>) Bray–Curtis dissimilarity index for abundance-weighted analysis of the amplicon sequence variant (ASV) composition and (<b>B</b>) Jaccard presence–absence dissimilarity matrix for the ASV composition. Colors and symbols indicate conditioning genotypes and functional groups, respectively. The prairie grass species (circles) are pink (<span class="html-italic">Andropogon gerardii</span>; ‘and’) and red (<span class="html-italic">Tripsacum dactyloides</span>; ‘tri’). Maize genotypes are triangles, and the <span class="html-italic">Z. mays</span> ssp. <span class="html-italic">parviglumis</span> are light orange (teosinte, ‘teo’), while the <span class="html-italic">Z. mays</span> ssp. <span class="html-italic">mays</span> genotypes are in cooler colors: <span class="html-italic">b58</span> is light green, <span class="html-italic">B73</span>-<span class="html-italic">wt</span> is dark green, <span class="html-italic">B73</span>-<span class="html-italic">rth3</span> (‘rth’) is dark blue, and <span class="html-italic">HP301</span> (‘pop’) is purple. Ellipses represent the 95% confidence ellipse based on the standard deviation around the centroid. The corresponding permutational analysis of variance (perMANOVA) and tests of dispersion can be found in <a href="#microorganisms-11-02978-t002" class="html-table">Table 2</a>.</p> Full article ">Figure 4
<p>Variations in the conditioning genotype and functional group effects of the plant performance of each feedback genotype. Total biomass growth rates were assessed for the conditioning genotype effect (<b>A</b>–<b>C</b>) and the conditioning genotype nested within the conditioning functional groups (prairie grass species versus maize genotypes) (<b>D</b>–<b>F</b>) using type III analysis of variance (ANOVAs) of separate linear mixed models for <span class="html-italic">A. gerardii</span> (<b>A</b>,<b>D</b>), maize <span class="html-italic">B73</span>-<span class="html-italic">wt</span> (<b>B</b>,<b>E</b>), and teosinte feedback plants (<b>C</b>,<b>F</b>). Colors indicate conditioning genotypes and conditioning functional groups. The conditioning functional groups in the key for parts (<b>A</b>–<b>C</b>) and the boxplots in parts (<b>D</b>–<b>F</b>) were colored so the prairie grass spp. group (<span class="html-italic">A. gerardii</span> and <span class="html-italic">T. dactyloides</span>) coordinated with a red–orange hue, while the maize genotypes groups (<span class="html-italic">Z. mays</span> ssp., <span class="html-italic">parviglumis</span>, <span class="html-italic">Z. mays</span> ssp. <span class="html-italic">mays b58</span>, <span class="html-italic">B73</span>-<span class="html-italic">wt</span>, <span class="html-italic">B73</span>-<span class="html-italic">rth3</span>, and <span class="html-italic">HP301</span>) coordinated with a light blue hue. The prairie grass species are pink (<span class="html-italic">Andropogon gerardii</span>; ‘and’) and red (<span class="html-italic">Tripsacum dactyloides</span>; ‘tri’). Maize genotypes of the <span class="html-italic">Z. mays</span> ssp. <span class="html-italic">parviglumis</span> are light orange (teosinte, ‘teo’) and the <span class="html-italic">Z. mays</span> ssp. <span class="html-italic">mays</span> genotypes are in cooler colors: <span class="html-italic">b58</span> is light green, <span class="html-italic">B73</span>-<span class="html-italic">wt</span> is dark green, <span class="html-italic">B73</span>-<span class="html-italic">rth3</span> (‘rth’) is dark blue, and <span class="html-italic">HP301</span> (‘pop’) is purple. The conditioning functional groups in parts (<b>D</b>–<b>F</b>) were colored so the prairie grass spp. group (<span class="html-italic">A. gerardii</span> and <span class="html-italic">T. dactyloides</span>) coordinated with a red–orange hue while the maize genotypes groups (<span class="html-italic">Z. mays</span> ssp., <span class="html-italic">parviglumis</span>, <span class="html-italic">Z. mays</span> ssp. <span class="html-italic">mays b58</span>, <span class="html-italic">B73</span>-<span class="html-italic">wt</span>, <span class="html-italic">B73</span>-<span class="html-italic">rth3</span>, and <span class="html-italic">HP301</span>) coordinated with a light blue hue. Lowercase letters indicate significant differences between conditioning genotypes or nested functional groups. The significance between conditioning genotypes (<b>A</b>–<b>C</b>) was deduced from post hoc pairwise comparisons using Benjamini–Hochberg correction. Refer to <a href="#microorganisms-11-02978-t003" class="html-table">Table 3</a> for the omnibus tests.</p> Full article ">Figure 5
<p>Variation in phenotypic traits and foliar nutrient concentrations of feedback plants among the conditioning genotypes. Phenotypic traits and leaf nutrient concentrations were assessed for a conditioning genotype effect (<b>A</b>–<b>K</b>) and a conditioning genotype nested within conditioning functional groups (prairie grass species versus maize genotypes) (<b>L</b>–<b>V</b>) using type III analysis of variance (ANOVAs) of separate linear mixed models for phenotypic measurements that significantly differed within each feedback genotype, including the root length ratio in <span class="html-italic">A. gerardii</span> feedback plants (<b>A</b>,<b>L</b>), average specific leaf area (<b>B</b>,<b>M</b>), average leaf area (<b>C</b>,<b>N</b>), root mass ratio (<b>D</b>,<b>O</b>), leaf nitrogen (<b>E</b>,<b>P</b>), leaf potassium (<b>F</b>,<b>Q</b>), leaf calcium (<b>G</b>,<b>R</b>), and leaf magnesium (<b>H</b>,<b>S</b>) in maize <span class="html-italic">B73</span>-<span class="html-italic">wt</span> feedback plants(<b>B</b>–<b>H</b>,<b>M</b>–<b>S</b>), along with leaf potassium (<b>I</b>,<b>T</b>), leaf calcium (<b>J</b>,<b>U</b>), and leaf magnesium (<b>K</b>,<b>V</b>) in teosinte feedback plants (<b>I</b>–<b>K</b>,<b>T</b>–<b>V</b>). Differences between conditioning genotypes in parts (<b>A</b>–<b>K</b>) were determined based on the post hoc pairwise comparisons using Benjamini–Hochberg correction between the conditioning genotypes for each significant (<span class="html-italic">p</span> &lt; 0.05) phenotypic trait in the type III ANOVA omnibus tests (<a href="#microorganisms-11-02978-t003" class="html-table">Table 3</a>) across the soil conditioning genotypes. Colors indicate the conditioning genotypes in (<b>A</b>–<b>K</b>) and the conditioning functional groups in the (<b>L</b>–<b>V</b>) plots. See the in-figure legend for the correspondence of the colors with the plant genotypes and functional groups. The significance between the conditioning genotypes (<b>A</b>–<b>K</b>) was determined from post hoc pairwise comparisons using Benjamini–Hochberg correction. Lowercase letters within each figure indicate significant (<span class="html-italic">p</span> ≤ 0.05) pairwise comparisons between groups after correction for multiple comparisons. Refer to <a href="#microorganisms-11-02978-t003" class="html-table">Table 3</a> for the omnibus tests.</p> Full article ">Figure 6
<p>Effects of feedback genotype and conditioning genotype on the final soil nutrient composition. Non-metric multidimensional scaling (NMDS) analysis using Gower’s distance on scaled soil nutrient concentrations was the multivariate response variable parsed into (<b>A</b>) <span class="html-italic">Andropogon gerardii</span>, (<b>B</b>) <span class="html-italic">Zea mays</span> ssp. <span class="html-italic">parviglumis</span>, and (<b>C</b>) <span class="html-italic">Zea mays</span> ssp. <span class="html-italic">mays B73</span>-<span class="html-italic">wt</span> feedback seedlings. The composition of the soil nutrient concentrations included carbon and nitrogen percentages in soil, along with milligrams of phosphorus, potassium, calcium, and magnesium per kilogram of soil analyzed. Colors and symbols indicate the conditioning genotypes and functional groups, respectively. The prairie grass species (circles) are pink (<span class="html-italic">Andropogon gerardii</span>; ‘and’) and red (<span class="html-italic">Tripsacum dactyloides</span>; ‘tri’). Maize genotypes are triangles, and the <span class="html-italic">Z. mays</span> ssp. <span class="html-italic">parviglumis</span> are light orange (teosinte, ‘teo’), while the <span class="html-italic">Z. mays</span> ssp. <span class="html-italic">mays</span> genotypes are in cooler colors: <span class="html-italic">b58</span> is light green, <span class="html-italic">B73</span>-<span class="html-italic">wt</span> is dark green, <span class="html-italic">B73</span>-<span class="html-italic">rth3</span> (‘rth’) is dark blue, and <span class="html-italic">HP301</span> (‘pop’) is purple. Ellipses represent the 95% confidence ellipse based on the standard deviation around the centroid. Refer to <a href="#app1-microorganisms-11-02978" class="html-app">Table S4</a> for the corresponding statistical analyses. The corresponding permutational analysis of variance (perMANOVA) and tests of dispersion can be found in <a href="#microorganisms-11-02978-t004" class="html-table">Table 4</a>.</p> Full article ">Figure 7
<p>Plant soil feedback effects in monoculture. A synthesis of the conditioning and feedback effects for (<b>A</b>) <span class="html-italic">Andropogon gerardii</span>, (<b>B</b>) <span class="html-italic">Zea mays</span> ssp. <span class="html-italic">parviglumis</span>, and (<b>C</b>) <span class="html-italic">Zea mays</span> ssp. <span class="html-italic">mays B73</span>-<span class="html-italic">wt</span> feedback seedlings in self-conditioned soils—from the same conditioning genotype or conditioning functional group—relative to soils conditioned by ‘other’ genotypes, across generations of growth. Colors and arrows refer to negative (red), nonsignificant or zero (black), and positive (green) effects between plants and rhizobiomes (arrows leading from plants to microbes in the same column) and feedback effects on successive generations of plant growth in monoculture (arrows spanning across columns). The text refers to the specific processes identified in the results of this study for each feedback genotype. This figure was created using <a href="http://biorendr.com" target="_blank">biorendr.com</a>.</p> Full article ">
18 pages, 3225 KiB  
Article
HLA-DQ2/8 and COVID-19 in Celiac Disease: Boon or Bane
by Aaron Lerner, Carina Benzvi and Aristo Vojdani
Microorganisms 2023, 11(12), 2977; https://doi.org/10.3390/microorganisms11122977 - 13 Dec 2023
Viewed by 1512
Abstract
The SARS-CoV-2 pandemic continues to pose a global threat. While its virulence has subsided, it has persisted due to the continual emergence of new mutations. Although many high-risk conditions related to COVID-19 have been identified, the understanding of protective factors remains limited. Intriguingly, [...] Read more.
The SARS-CoV-2 pandemic continues to pose a global threat. While its virulence has subsided, it has persisted due to the continual emergence of new mutations. Although many high-risk conditions related to COVID-19 have been identified, the understanding of protective factors remains limited. Intriguingly, epidemiological evidence suggests a low incidence of COVID-19-infected CD patients. The present study explores whether their genetic background, namely, the associated HLA-DQs, offers protection against severe COVID-19 outcomes. We hypothesize that the HLA-DQ2/8 alleles may shield CD patients from SARS-CoV-2 and its subsequent effects, possibly due to memory CD4 T cells primed by previous exposure to human-associated common cold coronaviruses (CCC) and higher affinity to those allele’s groove. In this context, we examined potential cross-reactivity between SARS-CoV-2 epitopes and human-associated CCC and assessed the binding affinity (BA) of these epitopes to HLA-DQ2/8. Using computational methods, we analyzed sequence similarity between SARS-CoV-2 and four distinct CCC. Of 924 unique immunodominant 15-mer epitopes with at least 67% identity, 37 exhibited significant BA to HLA-DQ2/8, suggesting a protective effect. We present various mechanisms that might explain the protective role of HLA-DQ2/8 in COVID-19-afflicted CD patients. If substantiated, these insights could enhance our understanding of the gene–environment enigma and viral–host relationship, guiding potential therapeutic innovations against the ongoing SARS-CoV-2 pandemic. Full article
(This article belongs to the Section Virology)
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Figure 1
<p>A graphical representation of the workflow for searching sequence similarity and HLA-DQ2/HLA-DQ8 binding affinity. Data Aggregation: SARS-CoV-2 epitopes were extracted from IEDB. UniProt was searched to retrieve proteins sequences of four CCC strains, OC43, HKU1, NL63, and 229E. Sequence Alignment: Emboss Matcher was employed; 924 similar sequences were found with a cut-off ≥11 identical AAs on 15-mer sequences. Data Validation: NetMHCIIpan-4.2 method was employed on the 924 sequences, and 37 were found to have a significant BA to HLA-DQ2/DQ8. Created with BioRender (accessible via <a href="https://www.BioRender.com/" target="_blank">https://www.BioRender.com/</a> on 5 November 2023).</p> Full article ">Figure 2
<p>Homology between SARS-CoV-2 and CCC epitope sequences, in relation to HLA-DQ2/8. Each panel represents a specific protein: (<b>A</b>) Spike glycoprotein, (<b>B</b>) NSP3, (<b>C</b>) NSP12, and (<b>D</b>) NSP13. The dark blue lines indicate the percentage of homology between SARS-CoV-2 and the highest matching CCC (among 229E, NL63, HKU1, and OC43). Only similarities of 40% or above are shown. Regions displaying significant binding to HLA-DQ2/HLA-DQ8 alleles are highlighted in red.</p> Full article ">Figure 3
<p>A schematic representation of 15-mer CCC epitopes with a minimum of 67% sequence identity to SARS-CoV-2 and strong binding to the celiac-associated HLA-DQ2/DQ8. (<b>A</b>) Exposure <b>to CCC</b>. Epitopes are presented, particularly on HLA-DQ2/8, to naïve CD4 T cells, leading to activation and proliferation, initiating an immune response. (<b>B</b>) Exposure to SARS-CoV-2. Some 15-mer epitopes have a minimum of 67% sequence identity to CCC and a significant BA- to CD-associated HLA-DQ2/8 (<b>C</b>,<b>D</b>). Those are presented to memory CD4 T cells, activating B cells, and CD8 T cells.</p> Full article ">
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