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Microorganisms
Volume 9
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Microorganisms, Volume 9, Issue 1 (January 2021) – 204 articles

Cover Story (view full-size image): Microorganisms require iron to grow, develop, and produce metabolites. Genomic analyses of the aflatoxin-producing species Aspergillus flavus revealed both putative reductive and siderophore-mediated iron uptake and utilization pathways. Multiple indels that disable genes in the iron uptake cluster were detected, and in each case, conservation of indels suggests these mutations are fixed in clonal lineages. Climate change is expected to reduce iron concentrations in crop tissues, and this may cause differential abilities to utilize iron to impact competitiveness of aflatoxin producers during crop infection and, as a result, effectiveness of various atoxigenic A. flavus biocontrol genotypes. View this paper
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15 pages, 616 KiB  
Article
Improved Plasmid-Based Inducible and Constitutive Gene Expression in Corynebacterium glutamicum
by Nadja A. Henke, Irene Krahn and Volker F. Wendisch
Microorganisms 2021, 9(1), 204; https://doi.org/10.3390/microorganisms9010204 - 19 Jan 2021
Cited by 19 | Viewed by 6811
Abstract
Corynebacterium glutamicum has been safely used in white biotechnology for the last 60 years and the portfolio of new pathways and products is increasing rapidly. Hence, expression vectors play a central role in discovering endogenous gene functions and in establishing heterologous gene expression. [...] Read more.
Corynebacterium glutamicum has been safely used in white biotechnology for the last 60 years and the portfolio of new pathways and products is increasing rapidly. Hence, expression vectors play a central role in discovering endogenous gene functions and in establishing heterologous gene expression. In this work, new expression vectors were designed based on two strategies: (i) a library screening of constitutive native and synthetic promoters and (ii) an increase of the plasmid copy number. Both strategies were combined and resulted in a very strong expression and overproduction of the fluorescence protein GfpUV. As a second test case, the improved vector for constitutive expression was used to overexpress the endogenous xylulokinase gene xylB in a synthetic operon with xylose isomerase gene xylA from Xanthomonas campestris. The xylose isomerase activity in crude extracts was increased by about three-fold as compared to that of the parental vector. In terms of application, the improved vector for constitutive xylA and xylB expression was used for production of the N-methylated amino acid sarcosine from monomethylamine, acetate, and xylose. As a consequence, the volumetric productivity of sarcosine production was 50% higher as compared to that of the strain carrying the parental vector. Full article
(This article belongs to the Special Issue Genetics and Physiology of Corynebacteria)
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Figure 1
<p>Fluorescence reporter assay of a promoter library in the pECXT99A plasmid background in <span class="html-italic">C. glutamicum</span> WT. Median GfpUV fluorescence intensities were normalized to an autofluorescent control. Mean values and standard deviations of biological triplicates are shown. Measurements were performed 18 h after inoculation in CGXII medium with 4% glucose. Endogenous promoters: P<span class="html-italic">pgk</span>, P<span class="html-italic">ilvC</span>, P<span class="html-italic">sodA</span>, P<span class="html-italic">gapA</span>, and P<span class="html-italic">tuf</span>. Synthetic promoters: P<span class="html-italic">H36</span>, P<span class="html-italic">45</span>, and P<span class="html-italic">syn</span>. Reference: the IPTG-inducible promoter P<span class="html-italic">trc</span> induced with 1 mM IPTG.</p> Full article ">Figure 2
<p>Fluorescence reporter assay of conventional and newly constructed IPTG-inducible expression vectors in <span class="html-italic">C. glutamicum</span> WT. Vector pECXT99A-<span class="html-italic">gfpUV</span> expressed <span class="html-italic">gfpUV</span> from P<span class="html-italic">trc</span>, while P<span class="html-italic">tac</span> was used for vectors pEKEx3-<span class="html-italic">gfpUV,</span> pVWEx1-<span class="html-italic">gfpUV,</span> pVWEx4-<span class="html-italic">gfpUV,</span> and pVWEx6-<span class="html-italic">gfpUV</span>. Median GfpUV fluorescence intensities were normalized to an autofluorescent control. Mean values and standard deviations of biological triplicates are shown. Measurements were performed 20 h after inoculation in CGXII medium with 4% glucose. Newly constructed vectors: pVWEx4 and pVWEx6. Reference vectors: pEKEx3, pECXT99A, and pVWEx1. Concentration of the inductor IPTG is given in mM.</p> Full article ">Figure 3
<p>SDS-PAGE for comparison of GfpUV (27 kDa) protein abundance based on different expression vectors in <span class="html-italic">C. glutamicum</span> WT. 10 µg of crude extracts from the main cultivation in CGXII with 4% glucose was loaded on a 10% SDS-PAGE. Pre-stained protein ladder (26616 from Thermo Scientific) was used as reference standard. Concentration of the inductor IPTG is given in mM.</p> Full article ">Figure 4
<p>Schematic representation of sarcosine production by recombinant <span class="html-italic">C. glutamicum</span> (<b>A</b>), Xylose isomerase (XylA) activities in crude extracts measured in triplicates (<b>B</b>), and sarcosine titers produced by <span class="html-italic">C. glutamicum</span> strains SAR3 and SAR3* measured 20 h after inoculation from triplicates (<b>C</b>). XylA: xylose isomerase; XylB: xylulose kinase; PPP: pentose phosphate pathway; GAP: glyceraldehyde 3-phosphate; AceB: malate synthase; AceA: isocitrate lyase; DpkA: imine reductase; Icd: isocitrate dehydrogenase; MMA: monomethylamine.</p> Full article ">
14 pages, 479 KiB  
Article
Study on Bacteria Isolates and Antimicrobial Resistance in Wildlife in Sicily, Southern Italy
by Delia Gambino, Domenico Vicari, Maria Vitale, Giorgia Schirò, Francesco Mira, Maria La Giglia, Alessandra Riccardi, Antonino Gentile, Susanna Giardina, Anna Carrozzo, Valentina Cumbo, Antonio Lastra and Valeria Gargano
Microorganisms 2021, 9(1), 203; https://doi.org/10.3390/microorganisms9010203 - 19 Jan 2021
Cited by 22 | Viewed by 5061
Abstract
Wild environments and wildlife can be reservoirs of pathogens and antibiotic resistance. Various studies have reported the presence of zoonotic bacteria, resistant strains, and genetic elements that determine antibiotic resistance in wild animals, especially near urban centers or agricultural and zootechnical activities. The [...] Read more.
Wild environments and wildlife can be reservoirs of pathogens and antibiotic resistance. Various studies have reported the presence of zoonotic bacteria, resistant strains, and genetic elements that determine antibiotic resistance in wild animals, especially near urban centers or agricultural and zootechnical activities. The purpose of this study was the analysis, by cultural and molecular methods, of bacteria isolated from wild animals in Sicily, Italy, regarding their susceptibility profile to antibiotics and the presence of antibiotic resistance genes. Bacteriological analyses were conducted on 368 wild animals, leading to the isolation of 222 bacterial strains identified by biochemical tests and 16S rRNA sequencing. The most isolated species was Escherichia coli, followed by Clostridium perfringens and Citrobacter freundii. Antibiograms and the determination of resistance genes showed a reduced spread of bacteria carrying antibiotic resistance among wild animals in Sicily. However, since several wild animals are becoming increasingly close to residential areas, it is important to monitor their health status and to perform microbiological analyses following a One Health approach. Full article
(This article belongs to the Special Issue Wildlife Microbiology 2.0)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Map of Sicily showing where the animals were obtained. Site A: Wildlife Rescue Center of Bosco di Ficuzza; site B: Parco delle Madonie; site C: Parco dei Nebrodi; site D: Wildlife Rescue Center “Stretto di Messina”.</p> Full article ">
15 pages, 1167 KiB  
Article
Impact of Antibiotics Associated with the Development of Toxic Epidermal Necrolysis on Early and Late-Onset Infectious Complications
by Bretislav Lipovy, Jakub Holoubek, Marketa Hanslianova, Michaela Cvanova, Leo Klein, Ivana Grossova, Robert Zajicek, Peter Bukovcan, Jan Koller, Matus Baran, Peter Lengyel, Lukas Eimer, Marie Jandova, Milan Kostal, Pavel Brychta and Petra Borilova Linhartova
Microorganisms 2021, 9(1), 202; https://doi.org/10.3390/microorganisms9010202 - 19 Jan 2021
Cited by 4 | Viewed by 3501
Abstract
Toxic epidermal necrolysis (TEN) is a rare disease, which predominantly manifests as damage to the skin and mucosa. Antibiotics count among the most common triggers of this hypersensitive reaction. Patients with TEN are highly susceptible to infectious complications due to the loss of [...] Read more.
Toxic epidermal necrolysis (TEN) is a rare disease, which predominantly manifests as damage to the skin and mucosa. Antibiotics count among the most common triggers of this hypersensitive reaction. Patients with TEN are highly susceptible to infectious complications due to the loss of protective barriers and immunosuppressant therapy. The aim of this study was to investigate the potential relationship between antibiotics used before the development of TEN and early and late-onset infectious complications in TEN patients. In this European multicentric retrospective study (Central European Lyell syndrome: therapeutic evaluation (CELESTE)), records showed that 18 patients with TEN used antibiotics (mostly aminopenicillins) before the disease development (group 1), while in 21 patients, TEN was triggered by another factor (group 2). The incidence of late-onset infectious complications (5 or more days after the transfer to the hospital) caused by Gram-positive bacteria (especially by Enterococcus faecalis/faecium) was significantly higher in group 1 than in group 2 (82.4% vs. 35.0%, p = 0.007/pcorr = 0.014) while no statistically significant difference was observed between groups of patients with infection caused by Gram-negative bacteria, yeasts, and filamentous fungi (p > 0.05). Patients with post-antibiotic development of TEN are critically predisposed to late-onset infectious complications caused by Gram-positive bacteria, which may result from the dissemination of these bacteria from the primary focus. Full article
(This article belongs to the Section Medical Microbiology)
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<p>Clustering of 39 patients in the Central European Lyell syndrome: therapeutic evaluation (CELESTE) cohort according to the antibiotics used immediately before TEN development, subsequent infectious complications and their combinations, and patient survival.</p> Full article ">Figure 2
<p>The incidence of early-onset (<span class="html-italic">n</span> = 39) and late-onset infectious complications (<span class="html-italic">n</span> = 37; 2 patients died less than 5 days after admission) in patients with toxic epidermal necrolysis (TEN) in the context of the use of antibiotics before TEN development. Fisher’s exact test was used for statistical evaluation. <span class="html-italic">P</span><sub>corr</sub> represents values after the Bonferroni correction for multiple analyses. Corrections were made for repeated analyses in the evaluation of early and late-onset infections. group 1: patients used antibiotics immediately before TEN development; group 2: patients used no antibiotics immediately before TEN development; early-onset, infectious complications developing within less than 5 days of admission to the hospital; late-onset, infectious complications developing 5 or more days after patient’s hospitalization.</p> Full article ">
19 pages, 2870 KiB  
Review
Corynebacterium glutamicum Mechanosensing: From Osmoregulation to L-Glutamate Secretion for the Avian Microbiota-Gut-Brain Axis
by Yoshitaka Nakayama
Microorganisms 2021, 9(1), 201; https://doi.org/10.3390/microorganisms9010201 - 19 Jan 2021
Cited by 16 | Viewed by 7835
Abstract
After the discovery of Corynebacterium glutamicum from avian feces-contaminated soil, its enigmatic L-glutamate secretion by corynebacterial MscCG-type mechanosensitive channels has been utilized for industrial monosodium glutamate production. Bacterial mechanosensitive channels are activated directly by increased membrane tension upon hypoosmotic downshock; thus; the physiological [...] Read more.
After the discovery of Corynebacterium glutamicum from avian feces-contaminated soil, its enigmatic L-glutamate secretion by corynebacterial MscCG-type mechanosensitive channels has been utilized for industrial monosodium glutamate production. Bacterial mechanosensitive channels are activated directly by increased membrane tension upon hypoosmotic downshock; thus; the physiological significance of the corynebacterial L-glutamate secretion has been considered as adjusting turgor pressure by releasing cytoplasmic solutes. In this review, we present information that corynebacterial mechanosensitive channels have been evolutionally specialized as carriers to secrete L-glutamate into the surrounding environment in their habitats rather than osmotic safety valves. The lipid modulation activation of MscCG channels in L-glutamate production can be explained by the “Force-From-Lipids” and “Force-From-Tethers” mechanosensing paradigms and differs significantly from mechanical activation upon hypoosmotic shock. The review also provides information on the search for evidence that C. glutamicum was originally a gut bacterium in the avian host with the aim of understanding the physiological roles of corynebacterial mechanosensing. C. glutamicum is able to secrete L-glutamate by mechanosensitive channels in the gut microbiota and help the host brain function via the microbiota–gut–brain axis. Full article
(This article belongs to the Special Issue Genetics and Physiology of Corynebacteria)
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Figure 1
<p>A scheme of the <span class="html-italic">C. glutamicum</span> L-glutamate secretion triggered by biotin limitation as mechanosensitive channel model. Biotin limitation shifts metabolic flow to produce L-glutamate by inhibiting the 2-oxoglutarate dehydrogenase complex (ODHC) activity and inhibits the acetyl CoA carboxylase (ACCase) complex activity. As a result of fatty acid biosynthesis inhibition, membrane lipids are altered to increase membrane tension. In the ATCC13032 strain, the MscS-like mechanosensitive channel MscCG is activated exclusively as major exporter by increased membrane tension to release L-glutamate. In the industrial strains Z188, ATCC13869, and S9114, another MscS-like mechanosensitive channel, MscCG2, is also activated as a minor exporter. However, the MscL-type mechanosensitive channel CgMscL is not activated in L-glutamate secretion.</p> Full article ">Figure 2
<p>Osmoregulation of <span class="html-italic">C. glutamicum</span> for osmotic down-/up-shift. Turgor pressure is increased by water influx upon osmotic downshift, and the mechanosensitive solute efflux system, which consists of the mechanosensitive channels, MscCG, MscCG2 and CgMscL, is activated to reduce the osmotic gradient within milliseconds. Osmotic upshift causes water efflux, and thus cells intake betaine as major osmolytes from the environment by the activation of the betaine transporter BetP. The cytoplasmic betaine concentration is fine-tuned by the leakage through the mechanosensitive channel MscCG as the pump and leak mechanism.</p> Full article ">Figure 3
<p>Structural features of the mechanosensitive channel MscCG. (<b>A</b>). Secondary structure comparison among <span class="html-italic">E. coli</span> MscS, <span class="html-italic">C. glutamicum</span> MscCG, and MscCG2. Black bars show predicted transmembrane (TM) helices by TOPCONS program (<a href="https://topcons.net/" target="_blank">https://topcons.net/</a>). (<b>B</b>). The cryoEM 3D structure of <span class="html-italic">E. coli</span> MscS embedded in nanodiscs with POPC:POPG = 1:4 (structure was cited from the Protein data bank 6PWP). (<b>C</b>). The domain structure of the MscCG channel and the gain (<b>red</b>)-and loss(<b>blue</b>)-of-function mutations.</p> Full article ">Figure 4
<p>Force-From-Lipids and Force-From-Tethers paradigms for mechanosensing by mechanosensitive channels upon hypoosmotic downshift. Mechanosensitive channels sense transbilayer force profiles in the lipid bilayers and are activated by increased membrane tension. Molecular tethers, such as cytoskeleton and peptidoglycan, also transmit mechanical force to activate mechanosensitive channels (<b>top</b>). In contrast, hydrostatic pressure does not directly activate mechanosensitive channels (<b>bottom</b>). The red, black, and blue, arrows show force to ion channels, turgor pressure, and water influx, respectively.</p> Full article ">Figure 5
<p>A scheme of “Force-From-Lipids/Tethers” activation mechanisms of the mechanosensitive channel MscCG in industrial L-glutamate production. Biotin limitation, adding fatty ester surfactants, oleic acid auxotrophy, and overexpression of cardiolipin synthase, alter cell membranes and mycolic acid layers by changing fatty acid, phospholipid, and mycolic acid biosynthesis. Temperature upshifts and local anesthetics change membrane mechanical properties. Adding ethambutol and penicillin degrades arabinogalactan and peptidoglycan layers in the cell wall, respectively, and activates MscCG channels.</p> Full article ">Figure 6
<p>Physiological significance as soil and gut bacterium for <span class="html-italic">C. glutamicum</span> L-glutamate secretion. In the soil, L-glutamate is an environmental signal to communicate and interact with plants and microbes (<b>left</b>). In the avian gut, L-glutamate produced by bacteria is absorbed in colonocytes and circulated for the microbiota–gut–brain axis (<b>right</b>).</p> Full article ">
18 pages, 3083 KiB  
Article
Production Optimization, Structural Analysis, and Prebiotic- and Anti-Inflammatory Effects of Gluco-Oligosaccharides Produced by Leuconostoc lactis SBC001
by Minhui Kim, Jae-Kweon Jang and Young-Seo Park
Microorganisms 2021, 9(1), 200; https://doi.org/10.3390/microorganisms9010200 - 19 Jan 2021
Cited by 20 | Viewed by 3724
Abstract
Leuconostoc lactis SBC001, isolated from chive, produces glucansucrase and synthesizes oligosaccharides through its enzymatic activity. This study was conducted to optimize oligosaccharide production using response surface methodology, analyze the structure of purified oligosaccharides, and investigate the prebiotic effect on 24 bacterial and yeast [...] Read more.
Leuconostoc lactis SBC001, isolated from chive, produces glucansucrase and synthesizes oligosaccharides through its enzymatic activity. This study was conducted to optimize oligosaccharide production using response surface methodology, analyze the structure of purified oligosaccharides, and investigate the prebiotic effect on 24 bacterial and yeast strains and the anti-inflammatory activity using RAW 264.7 macrophage cells. The optimal conditions for oligosaccharide production were a culture temperature of 30 °C and sucrose and maltose concentrations of 9.6% and 7.4%, respectively. Based on 1H-NMR spectroscopic study, the oligosaccharides were identified as gluco-oligosaccharides that consisted of 23.63% α-1,4 glycosidic linkages and 76.37% α-1,6 glycosidic linkages with an average molecular weight of 1137 Da. The oligosaccharides promoted the growth of bacterial and yeast strains, including Lactobacillus plantarum, L. paracasei, L. johnsonii, Leuconostoc mesenteroides, L. rhamnosus, and Saccharomyces cerevisiae. When lipopolysaccharide-stimulated RAW 264.7 cells were treated with the oligosaccharides, the production of nitric oxide was decreased; the expression of inducible nitric oxide synthase, tumor necrosis factor-α, interleukin (IL)-1β, IL-6, and IL-10 was suppressed; and the nuclear factor-kappa B signaling pathway was inhibited. In conclusion, the gluco-oligosaccharides obtained from Leu. lactis SBC001 exhibited a prebiotic effect on six bacterial and yeast strains and anti-inflammatory activity in RAW 264.7 macrophage cells. Full article
(This article belongs to the Section Food Microbiology)
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<p>Three-dimensional response surface plots (<b>A</b>–<b>C</b>) and thin-layer chromatography (TLC) (<b>D</b>) showing the optimization of oligosaccharide production. (<b>A</b>) Response surface plot showing the effect of initial pH (X1) and sucrose concentrations (X2). (<b>B</b>) Response surface plot showing the effect of initial pH (X1) and fermentation temperature (X3). (<b>C</b>) Response surface plot showing the effect of sucrose concentrations (X2) and fermentation temperature (X3). (<b>D</b>) TLC chromatogram of oligosaccharides before and after purification (G, glucose polymers G1–G8; B, before purification; A, after purification by Bio-gel P2).</p> Full article ">Figure 2
<p>Size exclusion high-performance liquid chromatography (HPLC) of the oligosaccharides.</p> Full article ">Figure 3
<p>Carbohydrate composition of the oligosaccharides. (<b>A</b>) high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) chromatogram of 0.1 mg glucose in 10 mL distilled water and 1 mg acid hydrolysate in 10 mL distilled water. (<b>B</b>) TLC chromatogram of the oligosaccharides before and after purification of SBC-oligosaccharides and acid hydrolysate (S, standard; A, oligosaccharides before purification; B, oligosaccharides after purification; C, acid hydrolysates of purified oligosaccharides).</p> Full article ">Figure 4
<p>Enzymatic hydrolysis of SBC-oligosaccharides. G, standard; S, oligosaccharides produced by <span class="html-italic">Leu. lactis</span> SBC001; A, oligosaccharides treated with α-amylase; B, oligosaccharides treated with α-glucosidase; C, oligosaccharides treated with amyloglucosidase; D, oligosaccharides treated with pullulanase M1; E, oligosaccharides treated with β-glucosidase; F, oligosaccharides treated with β-1,3-<span class="html-small-caps">d</span>-glucanase.</p> Full article ">Figure 5
<p>Prebiotic effects of the oligosaccharides produced by <span class="html-italic">Leu. lactis</span> SBC001 on bacterial and yeast strains. (<b>A</b>) <span class="html-italic">L. plantarum</span> KCCM 12116; (<b>B</b>) <span class="html-italic">L. paracasei</span> KCCM 40995; (<b>C</b>) <span class="html-italic">L. johnsonii</span> KCCM 41274; (<b>D</b>) <span class="html-italic">Leu. mesenteroides</span> KCCM 11325; (<b>E</b>) <span class="html-italic">L. rhamnosus</span> KCCM 32405; (<b>F</b>) <span class="html-italic">S. cerevisiae</span> KCCM 50549. Non, glucose-free medium; Glc, 1%(<span class="html-italic">w</span>/<span class="html-italic">v</span>) glucose-containing medium; FOS, glucose-free medium supplemented with 1%(<span class="html-italic">w</span>/<span class="html-italic">v</span>) FOS; SBC, glucose-free medium supplemented with 1%(<span class="html-italic">w</span>/<span class="html-italic">v</span>) oligosaccharides produced by <span class="html-italic">Leu. lactis</span> SBC001. Different letters among groups represent statistically significant differences (<span class="html-italic">p</span> &lt; 0.05). 0, 6, 12, 24, and 48, mean incubation time (h) of bacterial and yeast cells.</p> Full article ">Figure 6
<p>Effects of oligosaccharides on the cell viability and NO production in RAW 264.7 cells. (<b>A</b>) Effects of oligosaccharides on RAW 264.7 cell viability. (<b>B</b>) Effects of oligosaccharides on NO production in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. RAW 264.7 cells were treated with various concentrations of oligosaccharides for 1 h and then treated with 1 μg/mL LPS for 24 h. NO production was evaluated using the Griess reaction assay. Different letters show significant differences among groups at <span class="html-italic">p</span> &lt; 0.05 (n ≥ 3).</p> Full article ">Figure 7
<p>Relative mRNA expression of inducible NO synthase (iNOS) and inflammatory cytokines upon treatment with oligosaccharides in RAW 264.7 cells. RAW 264.7 cells were pretreated with oligosaccharides for 1 h and then stimulated with 1 μg/mL LPS. One-way ANOVA was used to compare group mean values, followed by Duncan’s <span class="html-italic">t</span>-test. Different letters represent significant differences among groups at <span class="html-italic">p</span> &lt; 0.05 (n ≥ 3).</p> Full article ">Figure 8
<p>Effects of the oligosaccharides produced by <span class="html-italic">Leu. lactis</span> SBC001 on the nuclear factor-kappa B (NF-κB) signaling pathway and MAPK signaling pathways in RAW 264.7 cells. RAW 264.7 cells were pretreated with oligosaccharides for 1 h and then treated with 1 μg/mL LPS for 24 h. (<b>A</b>) Western blot images for the expression of genes in the NF-κB signaling pathway. (<b>B</b>) Western blot images for the expression of genes in the MAPK signaling pathways. One-way ANOVA was used for the comparison of group mean values, followed by Duncan’s multiple range test for assessing the significance of individual comparisons (<span class="html-italic">p</span> &lt; 0.05) (n ≥ 3). Different letters represent significant differences among groups. The numbers between blots indicate the relative expression level.</p> Full article ">
27 pages, 5871 KiB  
Article
Fecal Microbiota Transplant from Human to Mice Gives Insights into the Role of the Gut Microbiota in Non-Alcoholic Fatty Liver Disease (NAFLD)
by Sebastian D. Burz, Magali Monnoye, Catherine Philippe, William Farin, Vlad Ratziu, Francesco Strozzi, Jean-Michel Paillarse, Laurent Chêne, Hervé M. Blottière and Philippe Gérard
Microorganisms 2021, 9(1), 199; https://doi.org/10.3390/microorganisms9010199 - 19 Jan 2021
Cited by 45 | Viewed by 7628
Abstract
Non-alcoholic fatty liver diseases (NAFLD) are associated with changes in the composition and metabolic activities of the gut microbiota. However, the causal role played by the gut microbiota in individual susceptibility to NAFLD and particularly at its early stage is still unclear. In [...] Read more.
Non-alcoholic fatty liver diseases (NAFLD) are associated with changes in the composition and metabolic activities of the gut microbiota. However, the causal role played by the gut microbiota in individual susceptibility to NAFLD and particularly at its early stage is still unclear. In this context, we transplanted the microbiota from a patient with fatty liver (NAFL) and from a healthy individual to two groups of mice. We first showed that the microbiota composition in recipient mice resembled the microbiota composition of their respective human donor. Following administration of a high-fructose, high-fat diet, mice that received the human NAFL microbiota (NAFLR) gained more weight and had a higher liver triglycerides level and higher plasma LDL cholesterol than mice that received the human healthy microbiota (HR). Metabolomic analyses revealed that it was associated with lower and higher plasma levels of glycine and 3-Indolepropionic acid in NAFLR mice, respectively. Moreover, several bacterial genera and OTUs were identified as differently represented in the NAFLR and HR microbiota and therefore potentially responsible for the different phenotypes observed. Altogether, our results confirm that the gut bacteria play a role in obesity and steatosis development and that targeting the gut microbiota may be a preventive or therapeutic strategy in NAFLD management. Full article
(This article belongs to the Special Issue The Human Gut Microbiome, Diets and Health)
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<p>Design of the mouse experiment. The experiment was performed on three groups each composed of 12 specific pathogen-free (SPF) C57BL/6J male antibiotic-treated mice: (1) healthy microbiota receiver on control diet (HR_CD) group; (2) healthy microbiota receiver on high-fructose, high-fat diet (HR_2HFD) group; (3) NAFL microbiota receiver on 2HFD (NAFLR_2HFD) group. HR, healthy human microbiota receiver mice; CD, control diet; 2HFD, high-fructose, high-fat diet; NAFLR, NAFL patient microbiota receiver mice; green woman, healthy microbiota donor; purple woman, NAFL microbiota donor; black mice, SPF mice; white mice, antibiotic-treated mice; light green mice, HR_CD mice, HR mice on CD; dark green mice, HR_2HFD mice, HR mice on 2HFD; purple mice, NAFLR_2HFD mice, NAFLR mice on 2HFD. Four brownish dots, mice feces harvest; D, day; D0, mice arrival; D6, basal SPF mice feces harvest; D16, feces harvest after 2 weeks antibiotic treatment; D21-D22, two human fecal microbiota transplants (FMTs) at 24-h intervals; D27, one week after FMT; D55, one month after FMT; D70, 7 weeks of 2HFD treatment, glycemia, insulinemia and oral glucose tolerance test; D84, two months after FMT; D90, 10 weeks of 2HFD treatment; SPF, specific pathogen-free.</p> Full article ">Figure 2
<p>16S rRNA <span class="html-italic">inocula</span>, fecal and caecal microbiome analysis. (<b>a</b>) Observed richness, <span class="html-italic">n</span> = 12 mice/group, *** <span class="html-italic">p</span> &lt; 0.001 for microbiota statistical impact (NAFLR_2HFD vs. HR_2HFD). (<b>b</b>) Unifrac-based PcoA, ANOVA: *** <span class="html-italic">p</span> &lt; 0.001. PcoA, principal coordinate analysis; green star, healthy human microbiota; purple star, NAFL human microbiota; HR, healthy human microbiota receiver mice; CD, control diet; 2HFD, high-fructose, high-fat diet; NAFLR, NAFL patient microbiota receiver mice; HR_CD, HR mice on CD; HR_2HFD, HR mice on 2HFD; NAFLR_2HFD, NAFLR mice on 2HFD; day 27, one week after FMT; day 90, 10 weeks after FMT.</p> Full article ">Figure 3
<p>(<b>a</b>) Food energy intake (kcal/day/mouse), <span class="html-italic">n</span> = 12 mice/group and (<b>b</b>) mice body weight gain (%) follow-up, <span class="html-italic">n</span> = 12 mice/group. HR, healthy human microbiota receiver mice; CD, control diet; 2HFD, high-fructose, high-fat diet; NAFLR, NAFL patient microbiota receiver mice; HR_CD, HR mice on CD; HR_2HFD, HR mice on 2HFD; NAFLR_2HFD, NAFLR mice on 2HFD; green (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001) is used for diet impact (HR + 2HFD vs. HR + CD) statistical comparisons and purple (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001) is used for microbiota impact (NAFLR_2HFD vs. HR_2HFD) statistical comparisons.</p> Full article ">Figure 4
<p>Glycemic and insulin resistance parameters of CD- and 2HFD-fed HR vs. NAFLR mice, <span class="html-italic">n</span> = 9–12 mice/group. (<b>a</b>) Fasting insulinemia at D70, t = 0 min Oral glucose tolerance test (OGTT); (<b>b</b>) fasting glycemia at D70, t = 0 min OGTT; (<b>c</b>) fasting Homeostasis Model Accessment of insuline resistance (HOMA-IR) at D70, t = 0 min OGTT; (<b>d</b>) non-fasting insulinemia at D70, t = 30 min OGTT; (<b>e</b>) non-fasting glycemia at D70, t = 30 min OGTT; (<b>f</b>) non-fasting HOMA-IR at D70, t = 30 min OGTT; (<b>g</b>) blood glucose follow-up during OGTT at D70; green (** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001) is used for diet impact (HR_2HFD vs. HR_CD) and purple (* <span class="html-italic">p</span> &lt; 0.05) is used for microbiota impact (NAFLR_2HFD vs. HR_2HFD) statistical comparisons; (<b>h</b>) AUC of the OGTT at D70; (<b>i</b>) non-fasting insulinemia at D90. HR, healthy human microbiota receiver mice; CD, control diet; 2HFD, high-fructose, high-fat diet; NAFLR, NAFL patient microbiota receiver mice; light green, HR_CD, HR mice on CD; dark green, HR_2HFD, HR mice on 2HFD; purple, NAFLR_2HFD, NAFLR mice on 2HFD; OGTT, oral glucose tolerance test; HOMA-IR, homeostatic model assessment of insulin resistance; AUC, area under the curve. D70, day 70, 7 weeks of 2HFD treatment; D90, day 90, 10 weeks of 2HFD treatment.</p> Full article ">Figure 5
<p>Plasma assay of CD- and 2HFD-fed HR vs. NAFLR mice, <span class="html-italic">n</span> = 11–12 mice/group. (<b>a</b>) Total cholesterol; (<b>b</b>) low-density lipoprotein (LDL) cholesterol; (<b>c</b>) high-density lipoprotein (HDL) cholesterol; (<b>d</b>) leptin; (<b>e</b>) triglycerides; (<b>f</b>) ALT; (<b>g</b>) AST. Light green, HR_CD, HR mouse on CD; dark green, HR_2HFD, HR mouse on 2HFD; purple, NAFLR_2HFD, NAFLR mice on 2HFD; LDL, low-density lipoprotein; HDL, high-density lipoprotein; ALT, alanine transaminase; AST, aspartate transaminase; HR, healthy human microbiota receiver mice; CD, control diet; 2HFD, high-fructose, high-fat diet; NAFLR, NAFL patient microbiota receiver mice; (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p &lt;</span> 0.001) were used for statistical comparisons.</p> Full article ">Figure 6
<p>Liver steatosis analysis of CD- and 2HFD-fed HR vs. NAFLR mice, <span class="html-italic">n</span> = 10–12 mice/group. (<b>a</b>) Liver hematoxylin-eosin staining (HES) histology observation, magnification: 20×; (<b>b</b>) steatosis histological scores; (<b>c</b>) liver triglycerides. HR, healthy human microbiota receiver mice; CD, control diet; 2HFD, high-fructose, high-fat diet; NAFLR, NAFL patient microbiota receiver mice; light green, HR_CD, HR mice on CD; dark green, HR_2HFD, HR mice on 2HFD; purple, NAFLR_2HFD, NAFLR mice on 2HFD; HES, hematoxylin-eosin staining; (* <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001) were used for statistical comparisons.</p> Full article ">Figure 7
<p>Gene expression in the caecum and in the liver, <span class="html-italic">n</span> = 12 mice/group. (<b>a</b>) Caecal permeability; (<b>b</b>) caecal inflammation; (<b>c</b>) liver inflammation score; (<b>d</b>,<b>e</b>) liver genes involved in inflammation. HR, healthy human microbiota receiver mice; CD, control diet; 2HFD, high-fructose, high-fat diet; NAFLR, NAFL patient microbiota receiver mice; light green, HR_CD, HR mice on CD; dark green, HR_2HFD, HR mice on 2HFD; purple, NAFLR_2HFD, NAFLR mice on 2HFD; (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01) were used for statistical comparisons.</p> Full article ">Figure 8
<p>Metabolomic analysis of plasma in HR_2HFD and NAFLR_2HFD mice, <span class="html-italic">n</span> = 12 mice/group. (<b>a</b>) Plasma glycine (µM); (<b>b</b>) plasma 3-IPA (µM). 3-IPA, 3-Indolepropionic acid; HR, healthy human microbiota receiver mice; CD, control diet; 2HFD, high-fructose, high-fat diet; NAFLR, NAFL patient microbiota receiver mice; light green, HR_CD, HR mice on CD; dark green, HR_2HFD, HR mice on 2HFD; purple, NAFLR_2HFD, NAFLR mice on 2HFD; (* <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001) were used for statistical comparisons.</p> Full article ">Figure 9
<p>Hepatic expression of genes involved in lipid and carbohydrate metabolisms. (<b>a</b>) genes whose expression tended to decrease with 2HFD and then tended to increase with NAFL microbiota (<b>b</b>) genes whose expression tended to increase with 2HFD and even more with NAFL microbiota; HR, healthy human microbiota receiver mice; CD, control diet; 2HFD, high-fructose, high-fat diet; NAFLR, NAFL patient microbiota receiver mice; light green, HR_CD, HR mice on CD; dark green, HR_2HFD, HR mice on 2HFD; purple, NAFLR_2HFD, NAFLR mice on 2HFD; (*** <span class="html-italic">p</span> &lt; 0.001) was used for statistical comparisons.</p> Full article ">Figure 10
<p>Heatmap characterization of differentially abundant and prevalent operational taxonomic units (OTUs) all throughout the experiment. CD, control diet; 2HFD, high-fructose, high-fat diet; Mice_CD, mice microbiota at basal state, day 6, on CD; Healthy, healthy human microbiota; NAFL, NAFL patient microbiota; HR, healthy human microbiota receiver mice; NAFLR, NAFL patient microbiota receiver mice; HR_CD, HR on CD regimen; HR_2HFD, HR on 2HFD regimen; NAFLR_2HFD, NAFLR on 2HFD regimen.</p> Full article ">Figure 11
<p>Graphical representation of differentially abundant OTUs, having a large fold change and significant effect size in addition to high relative abundance between (<b>a</b>) healthy, H, and NAFL <span class="html-italic">inocula</span>; (<b>b</b>) HR_CD and HR_2HFD groups on day 90; (<b>c</b>) HR_2HFD and NAFLR_2HFD groups on day 90. Each OTU is represented by a dot and colored according to its taxonomic classification at the family level. Taxonomy at the genus or species level is also indicated, when available, next to each OTU. A logarithmic scale (log-2) was used for the x axis. (<b>d</b>) Core NAFLR_2HFD and Core HR_2HFD abundance through the groups, corresponding, respectively, to the OTUs differentially abundant and transferred from <span class="html-italic">inocula</span> to mice. HR, healthy human microbiota receiver mice on 2HFD; NAFLR, NAFL patient microbiota receiver mice on 2HFD; 2HFD, high-fructose, high-fat diet.</p> Full article ">
11 pages, 722 KiB  
Communication
Effects of Radiation Intensity, Mineral Matrix, and Pre-Irradiation on the Bacterial Resistance to Gamma Irradiation under Low Temperature Conditions
by Vladimir S. Cheptsov, Andrey A. Belov, Elena A. Vorobyova, Anatoli K. Pavlov and Vladimir N. Lomasov
Microorganisms 2021, 9(1), 198; https://doi.org/10.3390/microorganisms9010198 - 19 Jan 2021
Cited by 2 | Viewed by 3757
Abstract
Ionizing radiation is one of the main factors limiting the survival of microorganisms in extraterrestrial conditions. The survivability of microorganisms under irradiation depends significantly on the conditions, in which the irradiation occurs. In particular, temperature, pressure, oxygen and water concentrations are of great [...] Read more.
Ionizing radiation is one of the main factors limiting the survival of microorganisms in extraterrestrial conditions. The survivability of microorganisms under irradiation depends significantly on the conditions, in which the irradiation occurs. In particular, temperature, pressure, oxygen and water concentrations are of great influence. However, the influence of factors such as the radiation intensity (in low-temperature conditions) and the type of mineral matrix, in which microorganisms are located, has been practically unstudied. It has been shown that the radioresistance of bacteria can increase after their exposure to sublethal doses and subsequent repair of damage under favorable conditions, however, such studies are also few and the influence of other factors of extraterrestrial space (temperature, pressure) was not studied in them. The viability of bacteria Arthrobacter polychromogenes, Kocuria rosea and Xanthomonas sp. after irradiation with gamma radiation at a dose of 1 kGy under conditions of low pressure (1 Torr) and low temperature (−50 °C) at different radiation intensities (4 vs. 0.8 kGy/h) with immobilization of bacteria on various mineral matrices (montmorillonite vs. analogue of lunar dust) has been studied. Native, previously non-irradiated strains, and strains that were previously irradiated with gamma radiation and subjected to 10 passages of cultivation on solid media were irradiated. The number of survived cells was determined by culturing on a solid medium. It has been shown that the radioresistance of bacteria depends significantly on the type of mineral matrix, on which they are immobilized, wherein montmorillonite contributes to an increased survivability in comparison with a silicate matrix. Survivability of the studied bacteria was found to increase with decreasing radiation intensity, despite the impossibility of active reparation processes under experimental conditions. Considering the low intensity of radiation on various space objects in comparison with radiobiological experiments, this suggests a longer preservation of the viable microorganisms outside the Earth than is commonly believed. An increase in bacterial radioresistance was revealed even after one cycle of irradiation of the strains and their subsequent cultivation under favourable conditions. This indicates the possibility of hypothetical microorganisms on Mars increasing their radioresistance. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Decrease in the number (logarithmic reduction) of viable bacteria under different conditions of irradiation (subfigures (<b>a</b>,<b>b</b>) are based on the same data, combined in different ways for comparison convenience). Montm.—variants immobilized on montmorillonite; Moon—variants immobilized on lunar dust analog; NI—non-pre-irradiated strains; PI—pre-irradiated strains. ND—no data.</p> Full article ">
47 pages, 647 KiB  
Review
Fungal Endophytes as Efficient Sources of Plant-Derived Bioactive Compounds and Their Prospective Applications in Natural Product Drug Discovery: Insights, Avenues, and Challenges
by Archana Singh, Dheeraj K. Singh, Ravindra N. Kharwar, James F. White and Surendra K. Gond
Microorganisms 2021, 9(1), 197; https://doi.org/10.3390/microorganisms9010197 - 19 Jan 2021
Cited by 114 | Viewed by 11971
Abstract
Fungal endophytes are well-established sources of biologically active natural compounds with many producing pharmacologically valuable specific plant-derived products. This review details typical plant-derived medicinal compounds of several classes, including alkaloids, coumarins, flavonoids, glycosides, lignans, phenylpropanoids, quinones, saponins, terpenoids, and xanthones that are produced [...] Read more.
Fungal endophytes are well-established sources of biologically active natural compounds with many producing pharmacologically valuable specific plant-derived products. This review details typical plant-derived medicinal compounds of several classes, including alkaloids, coumarins, flavonoids, glycosides, lignans, phenylpropanoids, quinones, saponins, terpenoids, and xanthones that are produced by endophytic fungi. This review covers the studies carried out since the first report of taxol biosynthesis by endophytic Taxomyces andreanae in 1993 up to mid-2020. The article also highlights the prospects of endophyte-dependent biosynthesis of such plant-derived pharmacologically active compounds and the bottlenecks in the commercialization of this novel approach in the area of drug discovery. After recent updates in the field of ‘omics’ and ‘one strain many compounds’ (OSMAC) approach, fungal endophytes have emerged as strong unconventional source of such prized products. Full article
(This article belongs to the Section Plant Microbe Interactions)
10 pages, 559 KiB  
Communication
The Stable Matching Problem in TBEV Enzootic Circulation: How Important Is the Perfect Tick-Virus Match?
by Katrin Liebig, Mathias Boelke, Domenic Grund, Sabine Schicht, Malena Bestehorn-Willmann, Lidia Chitimia-Dobler, Gerhard Dobler, Klaus Jung and Stefanie C. Becker
Microorganisms 2021, 9(1), 196; https://doi.org/10.3390/microorganisms9010196 - 19 Jan 2021
Cited by 6 | Viewed by 2677
Abstract
Tick-borne encephalitis virus (TBEV), like other arthropod-transmitted viruses, depends on specific vectors to complete its enzootic cycle. It has been long known that Ixodes ricinus ticks constitute the main vector for TBEV in Europe. In contrast to the wide distribution of the TBEV [...] Read more.
Tick-borne encephalitis virus (TBEV), like other arthropod-transmitted viruses, depends on specific vectors to complete its enzootic cycle. It has been long known that Ixodes ricinus ticks constitute the main vector for TBEV in Europe. In contrast to the wide distribution of the TBEV vector, the occurrence of TBEV transmission is focal and often restricted to a small parcel of land, whereas surrounding areas with seemingly similar habitat parameters are free of TBEV. Thus, the question arises which factors shape this focal distribution of TBEV in the natural habitat. To shed light on factors driving TBEV-focus formation, we used tick populations from two TBEV-foci in Lower Saxony and two TBEV-foci from Bavaria with their respective virus isolates as a showcase to analyze the impact of specific virus isolate-tick population relationships. Using artificial blood feeding and field-collected nymphal ticks as experimental means, our investigation showed that the probability of getting infected with the synonymous TBEV isolate as compared to the nonsynonymous TBEV isolate was elevated but significantly higher only in one of the four TBEV foci. More obviously, median viral RNA copy numbers were significantly higher in the synonymous virus–tick population pairings. These findings may present a hint for a coevolutionary adaptation of virus and tick populations. Full article
(This article belongs to the Special Issue Tick-Borne Encephalitis)
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<p>Virus loads measured in synonymous and nonsynonymous virus-tick population pairings. Outliers are represented by circles.</p> Full article ">
13 pages, 1878 KiB  
Communication
Genomic Features of MCR-1 and Extended-Spectrum β-Lactamase-Producing Enterobacterales from Retail Raw Chicken in Egypt
by Mustafa Sadek, José Manuel Ortiz de la Rosa, Mohamed Abdelfattah Maky, Mohamed Korashe Dandrawy, Patrice Nordmann and Laurent Poirel
Microorganisms 2021, 9(1), 195; https://doi.org/10.3390/microorganisms9010195 - 19 Jan 2021
Cited by 28 | Viewed by 3378
Abstract
Colistin is considered as a last resort agent for treatment of severe infections caused by carbapenem-resistant Enterobacterales (CRE). Recently, plasmid-mediated colistin resistance genes (mcr type) have been reported, mainly corresponding to mcr-1 producers. Those mcr-1-positive Enterobacterales have been identified not only [...] Read more.
Colistin is considered as a last resort agent for treatment of severe infections caused by carbapenem-resistant Enterobacterales (CRE). Recently, plasmid-mediated colistin resistance genes (mcr type) have been reported, mainly corresponding to mcr-1 producers. Those mcr-1-positive Enterobacterales have been identified not only from human isolates, but also from food samples, from animal specimens and from environmental samples in various parts of the world. Our study focused on the occurrence and characterization of mcr-1-positive Enterobacterales recovered from retail raw chicken in Egypt. From the 345 retail chicken carcasses collected, a total of 20 samples allowed to recover mcr-1-positive isolates (Escherichia coli, n = 19; Citrobacter freundii, n = 1). No mcr-2- to mcr-10-positive isolate was identified from those samples. The colistin resistance trait was confirmed for all those 20 isolates with a positivity of the Rapid Polymyxin NP (Nordmann-Poirel) test. Minimum inhibitory concentrations (MICs) of colistin for all MCR-1-producing isolates ranged between 4 and 16 μg/mL. Noticeably, 9 out of the 20 mcr-1-positive isolates produced an extended-spectrum β-lactamase (ESBL), respectively producing CTX-M-9 (n = 2), CTX-M-14 (n = 4), CTX-M-15 (n = 2), and SHV-12 (n = 1). Noteworthy, the fosA4 gene encoding resistance to fosfomycin was found in a single mcr-1-positive E. coli isolate, in which both genes were located on different conjugative plasmids. The pulsed-field gel electrophoresis (PFGE) patterns were identified, corresponding to 10 different sequence types (STs), highlighting the genetic diversity of those different E. coli. Whole-genome sequencing revealed three major types of mcr-1-bearing plasmids, corresponding to IncI2, IncX4, and IncHI2 scaffolds. The occurrence of MCR-1-producing multidrug-resistant Enterobacterales in retail raw chicken is of great concern, considering the possibility of transmission to humans through the food chain. Full article
(This article belongs to the Special Issue Antimicrobial Resistance in the Food Production Chain )
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<p>Circular map of <span class="html-italic">mcr-1</span>-positive plasmids compared to other reported similar plasmids. Panel (<b>A</b>) IncX4 plasmids, panel (<b>B</b>) IncI2, panel (<b>C</b>) IncHI2. Red outer ring: plasmid used as reference for the alignment; size of the reference indicated in the middle of each panel. The different arrows indicate the positions, directions of transcription, and predicted function of the ORFs. The <span class="html-italic">mcr-1</span> gene and IS<span class="html-italic">Apl1</span> are marked in red and blue, respectively. The circular maps were generated using the BRIG tool.</p> Full article ">Figure 1 Cont.
<p>Circular map of <span class="html-italic">mcr-1</span>-positive plasmids compared to other reported similar plasmids. Panel (<b>A</b>) IncX4 plasmids, panel (<b>B</b>) IncI2, panel (<b>C</b>) IncHI2. Red outer ring: plasmid used as reference for the alignment; size of the reference indicated in the middle of each panel. The different arrows indicate the positions, directions of transcription, and predicted function of the ORFs. The <span class="html-italic">mcr-1</span> gene and IS<span class="html-italic">Apl1</span> are marked in red and blue, respectively. The circular maps were generated using the BRIG tool.</p> Full article ">
11 pages, 269 KiB  
Review
Detection and Potential Virulence of Viable but Non-Culturable (VBNC) Listeria monocytogenes: A Review
by Nathan E. Wideman, James D. Oliver, Philip Glen Crandall and Nathan A. Jarvis
Microorganisms 2021, 9(1), 194; https://doi.org/10.3390/microorganisms9010194 - 19 Jan 2021
Cited by 50 | Viewed by 6579
Abstract
The detection, enumeration, and virulence potential of viable but non-culturable (VBNC) pathogens continues to be a topic of discussion. While there is a lack of definitive evidence that VBNC Listeria monocytogenes (Lm) pose a public health risk, recent studies suggest that Lm in [...] Read more.
The detection, enumeration, and virulence potential of viable but non-culturable (VBNC) pathogens continues to be a topic of discussion. While there is a lack of definitive evidence that VBNC Listeria monocytogenes (Lm) pose a public health risk, recent studies suggest that Lm in its VBNC state remains virulent. VBNC bacteria cannot be enumerated by traditional plating methods, so the results from routine Lm testing may not demonstrate a sample’s true hazard to public health. We suggest that supplementing routine Lm testing methods with methods designed to enumerate VBNC cells may more accurately represent the true level of risk. This review summarizes five methods for enumerating VNBC Lm: Live/Dead BacLightTM staining, ethidium monoazide and propidium monoazide-stained real-time polymerase chain reaction (EMA- and PMA-PCR), direct viable count (DVC), 5-cyano-2,3-ditolyl tetrazolium chloride-4′,6-diamidino-2-phenylindole (CTC-DAPI) double staining, and carboxy-fluorescein diacetate (CDFA) staining. Of these five supplementary methods, the Live/Dead BacLightTM staining and CFDA-DVC staining currently appear to be the most accurate for VBNC Lm enumeration. In addition, the impact of the VBNC state on the virulence of Lm is reviewed. Widespread use of these supplemental methods would provide supporting data to identify the conditions under which Lm can revert from its VBNC state into an actively multiplying state and help identify the environmental triggers that can cause Lm to become virulent. Highlights: Rationale for testing for all viable Listeria (Lm) is presented. Routine environmental sampling and plating methods may miss viable Lm cells. An overview and comparison of available VBNC testing methods is given. There is a need for resuscitation techniques to recover Lm from VBNC. A review of testing results for post VBNC virulence is compared Full article
(This article belongs to the Special Issue An Update on Listeria monocytogenes)
13 pages, 5918 KiB  
Article
The Role of Polyphosphate in Motility, Adhesion, and Biofilm Formation in Sulfolobales
by Alejandra Recalde, Marleen van Wolferen, Shamphavi Sivabalasarma, Sonja-Verena Albers, Claudio A. Navarro and Carlos A. Jerez
Microorganisms 2021, 9(1), 193; https://doi.org/10.3390/microorganisms9010193 - 18 Jan 2021
Cited by 12 | Viewed by 3615
Abstract
Polyphosphates (polyP) are polymers of orthophosphate residues linked by high-energy phosphoanhydride bonds that are important in all domains of life and function in many different processes, including biofilm development. To study the effect of polyP in archaeal biofilm formation, our previously described Sa. [...] Read more.
Polyphosphates (polyP) are polymers of orthophosphate residues linked by high-energy phosphoanhydride bonds that are important in all domains of life and function in many different processes, including biofilm development. To study the effect of polyP in archaeal biofilm formation, our previously described Sa. solfataricus polyP (−) strain and a new polyP (−) S. acidocaldarius strain generated in this report were used. These two strains lack the polymer due to the overexpression of their respective exopolyphosphatase gene (ppx). Both strains showed a reduction in biofilm formation, decreased motility on semi-solid plates and a diminished adherence to glass surfaces as seen by DAPI (4′,6-diamidino-2-phenylindole) staining using fluorescence microscopy. Even though arlB (encoding the archaellum subunit) was highly upregulated in S. acidocardarius polyP (−), no archaellated cells were observed. These results suggest that polyP might be involved in the regulation of the expression of archaellum components and their assembly, possibly by affecting energy availability, phosphorylation or other phenomena. This is the first evidence indicating polyP affects biofilm formation and other related processes in archaea. Full article
(This article belongs to the Section Environmental Microbiology)
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<p>Effect of the lack of polyphosphates (polyP) in biofilm formation in <span class="html-italic">Sa. solfataricus</span> and <span class="html-italic">S. acidocaldarius</span> on days 2 and 3 of growth. Microtitrate assay of background strains (M16 and MW001), cells transformed with plasmid but no induction of <span class="html-italic">ppx</span> (M16-PPX and MW001-PPX) and polyP (−) cells from each species. The biofilm mass corresponds to Cristal violet attached to the biofilm measured at 570 nm. ANOVA test: **** indicating <span class="html-italic">p</span> ≤ 0.0001, *** <span class="html-italic">p</span> ≤ 0.001, ** <span class="html-italic">p</span> ≤ 0.01, * <span class="html-italic">p</span> ≤ 0.05 and ns: no significative.</p> Full article ">Figure 2
<p>Confocal Laser Microscopy showing differences between wild type (WT) and polyP (−) strains biofilms in day 3. Biofilms were stained with DAPI (4′,6-diamidino-2-phenylindole), ConA and IB4. Biofilms from (<b>A</b>) M16 and polyP (−) strains of <span class="html-italic">Sa. solfataricus</span> and (<b>C</b>) MW001 and polyP (−) of <span class="html-italic">S. acidocaldarius</span> in DAPI channel (blue), merge of ConA (green) and IB4 (red) channels (extracellular polymeric substances (EPS)) and merge of all three channels. (<b>B</b>,<b>D</b>) Z stack of WT and polyP (−) strains in both species, merge with all three channels.</p> Full article ">Figure 3
<p>Effect of the lack of polyP in <span class="html-italic">Sa. solfataricus</span> and <span class="html-italic">S. acidocaldarius</span> cells attachment to a glass surface after 24 h. (<b>A</b>) Microscopical image of MW001 and polyP (−) strains from <span class="html-italic">S. acidocaldarius</span> attached to glass slides. Cells were fixed with formaldehyde and stained with DAPI. Phase contrast (PhC) and DAPI channels are shown. (<b>B</b>) Mean of the percentage of cell numbers per field. Each value was calculated in function of the average in the number of cells per field in the respective background strain (<span class="html-italic">Sa. solfataricus</span> M16 or <span class="html-italic">S. acidocaldarius</span> MW001). ANOVA test: * <span class="html-italic">p</span> ≤ 0.05 and ** <span class="html-italic">p</span> ≤ 0.01.</p> Full article ">Figure 4
<p>Motility assays of polyP (−) and WT strains of <span class="html-italic">Sulfolobales</span>. (<b>A</b>) <span class="html-italic">Sa. solfataricus</span> M16 and (<b>B</b>,<b>C</b>) <span class="html-italic">S. acidocaldarius</span> MW001. The results represent an average of at least 10 spots. ANOVA test: * <span class="html-italic">p</span> ≤ 0.05 and *** <span class="html-italic">p</span> ≤ 0.001.</p> Full article ">Figure 5
<p>Relative gene expression of <span class="html-italic">arl</span>B, <span class="html-italic">aapE</span> and <span class="html-italic">abfR1</span> genes as measured by qRTPCR in polyP (−) versus WT in planktonic cells. (<b>A</b>) <span class="html-italic">Sa. solfataricus</span> (<b>B</b>) <span class="html-italic">S. acidocaldarius</span>. Wilcoxon Signed Rank Tests where *** <span class="html-italic">p</span> ≤ 0.001, ** <span class="html-italic">p</span> ≤ 0.01 and ns: no significant.</p> Full article ">Figure 6
<p>Transmission Electron Microscopy of <span class="html-italic">S. acidocaldarius</span> cells after starving conditions for archaellum induction. (<b>A</b>) MW001 (<b>B</b>) PolyP (−). Red arrows point to archaellum filaments. Blue arrows point to pili. The scale bars are 500 nm.</p> Full article ">Figure 7
<p>Cartoon showing the possible role of polyP in motility, adhesion, and biofilm formation in <span class="html-italic">Sulfolobales</span>.</p> Full article ">
14 pages, 4835 KiB  
Article
Identification of an Acidic Amino Acid Permease Involved in d-Aspartate Uptake in the Yeast Cryptococcus humicola
by Daiki Imanishi, Yoshio Kera and Shouji Takahashi
Microorganisms 2021, 9(1), 192; https://doi.org/10.3390/microorganisms9010192 - 18 Jan 2021
Cited by 3 | Viewed by 3067
Abstract
d-aspartate oxidase (DDO) catalyzes the oxidative deamination of acidic d-amino acids, and its production is induced by d-Asp in several eukaryotes. The yeast Cryptococcus humicola strain UJ1 produces large amounts of DDO (ChDDO) only in the presence of d-Asp. [...] Read more.
d-aspartate oxidase (DDO) catalyzes the oxidative deamination of acidic d-amino acids, and its production is induced by d-Asp in several eukaryotes. The yeast Cryptococcus humicola strain UJ1 produces large amounts of DDO (ChDDO) only in the presence of d-Asp. In this study, we analyzed the relationship between d-Asp uptake by an amino acid permease (Aap) and the inducible expression of ChDDO. We identified two acidic Aap homologs, named “ChAap4 and ChAap5,” in the yeast genome sequence. ChAAP4 deletion resulted in partial growth defects on d-Asp as well as l-Asp, l-Glu, and l-Phe at pH 7, whereas ChAAP5 deletion caused partial growth defects on l-Phe and l-Lys, suggesting that ChAap4 might participate in d-Asp uptake as an acidic Aap. Interestingly, the growth of the Chaap4 strain on d- or l-Asp was completely abolished at pH 10, suggesting that ChAap4 is the only Aap responsible for d- and l-Asp uptake under high alkaline conditions. In addition, ChAAP4 deletion significantly decreased the induction of DDO activity and ChDDO transcription in the presence of d-Asp. This study revealed that d-Asp uptake by ChAap4 might be involved in the induction of ChDDO expression by d-Asp. Full article
(This article belongs to the Section Molecular Microbiology and Immunology)
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<p>Construction of Chaap4 and Chaap5 strains. (<b>A</b>) Schematic representation of <span class="html-italic">ChAAP4</span> and <span class="html-italic">ChAAP5</span> gene disruptions by homologous recombination using <span class="html-italic">ChURA3</span>. (<b>B</b>) PCR analysis of <span class="html-italic">ChAAP4</span> gene disruption. The numbers above the box indicate the distance (bp) from the start codon ATG of the chromosomal <span class="html-italic">ChAAP4</span>. <span class="html-italic">ChURA3</span> was inserted, in the opposite direction, into the <span class="html-italic">ChAAP4</span> between 117 and 309 bp. Lanes 1 and 2, the negative control amplification of the upstream and the downstream regions, respectively, in the wild-type strain (strain UJ1); lanes 3 and 4, the amplification of the upstream and downstream regions in <span class="html-italic">Chaap4</span> strain. (<b>C</b>) PCR analysis of <span class="html-italic">ChAAP5</span> gene disruption. The numbers above the box indicate the distance (bp) from the start codon ATG of the chromosomal <span class="html-italic">ChAAP5</span>. <span class="html-italic">ChURA3</span> was inserted, in the opposite direction, into the genomic <span class="html-italic">ChAAP5</span> between 716 and 1368 bp. Lanes 1 and 2, the negative control amplification of the upstream and the downstream regions, respectively, in the wild-type strain (strain UJ1); lanes 3 and 4, the amplification of the upstream and the downstream regions in <span class="html-italic">Chaap5</span> strain. PCR was performed using <span class="html-italic">Chaap4-</span> or <span class="html-italic">Chaap5</span>-specific primer pairs in <a href="#app1-microorganisms-09-00192" class="html-app">Table S1</a>.</p> Full article ">Figure 2
<p>Phylogenetic relationship of Aaps homologs of <span class="html-italic">C. humicola</span> strain UJ1 and YATs. The phylogenetic tree was constructed by the neighbor-joining method with 1000 bootstrap trials using MEGA version 7.0. The numbers at nodes indicate bootstrap value as percentages. The asterisks indicate Aap homologs of strain UJ1. Accession numbers of the amino acid sequences used for the analysis were as follows: <span class="html-italic">C. neoformans</span> CnAap1 (CNAG_02539), CnAap2 (CNAG_07902), CnAap3 (CNAG_1118), CnAap4 (CNAG_00597), CnAap5 (CNAG_07367), CnAap6 (CNAG_07449), CnAap7 (CNAG_05345), and CnAap8 (CNAG_00574); <span class="html-italic">Saccharomyces cerevisiae</span> Tat1p (Uniprot: P38085), Tat2p (P38967), Tat3p (A4UZ28), Gap1p (P19145), Hip1p (P06775), Gnp1p (P48813), Agp1p (P25376), Agp2p (P38090), Agp3p (P43548), Bap2p (P38084), Bap3p (P41815), Sam3p (Q08986), Mmp1p (Q12372), Lyp1p (P32487), Alp1p (P38971), Can1p (P04817), Dip5p (P53388), Put4p (P15380), and Ssy1p (Q03770); <span class="html-italic">Aspergillus nidulans</span> AgtA (B2M1L6) and PrnB (P18696); <span class="html-italic">Uromyces fabae</span> Aat1 (Q96TU9), Aat2 (O00062), and Aat3 (Q700T6).</p> Full article ">Figure 3
<p>Comparison of amino acid sequences at transmembrane (TM) region 1 and 6 of ChAap4 and ChAap5 with <span class="html-italic">E. coli</span> AdiC (UniProKB: P60061) and three yeast dicarboxylic amino acid permeases: Dip5p (<span class="html-italic">S. cerevisiae</span>), PcDip5 (<span class="html-italic">P. chrysogenum</span>), and AgtA (<span class="html-italic">A. nidulans</span>). Transmembrane (TM) regions were predicted by Phobius (<a href="http://phobius.sbc.su.se/" target="_blank">http://phobius.sbc.su.se/</a>). GXG and (F/Y)(S/A/T)(F/Y)XGXE motifs (where X is any amino acid) are boxed in red and blue, respectively.</p> Full article ">Figure 4
<p>Effect of medium pH on the growth of <span class="html-italic">Chaap</span> strains on amino acids. The wild<span class="html-small-caps">-</span>type (strain UJ1) (open circles), <span class="html-italic">Chaap4</span> (open squares), and <span class="html-italic">Chaap5</span> (open triangles) strains were cultivated at 30 °C in a medium containing 10 mM NH<sub>4</sub>Cl or each amino acid as the sole nitrogen source. Initial pH of the media was adjusted to 3.0 (<b>A</b>,<b>D</b>,<b>G</b>,<b>J</b>,<b>M</b>), 7.0 (<b>B</b>,<b>E</b>,<b>H</b>,<b>K</b>,<b>N</b>), or 10.0 (<b>C</b>,<b>F</b>,<b>I</b>,<b>L</b>,<b>O</b>).</p> Full article ">Figure 5
<p>The expression of <span class="html-italic">ChDDO</span> gene in <span class="html-italic">Chaap</span> strains grown on <span class="html-small-caps">d-</span>Asp at different pHs. (<b>A</b>) DDO activity in the extracts from the cells grown in a synthetic medium containing (<span class="html-small-caps">d-</span>Asp or <span class="html-small-caps">l</span>-Asp) or not containing (None). For this, 60 mM <span class="html-small-caps">d-</span>Asp or <span class="html-small-caps">l</span>-Asp was used as the sole nitrogen and carbon sources at different pHs at 30 °C. The enzyme activity is expressed as a percentage of that of the wild<span class="html-small-caps">-</span>type strain at pH 3.0. Statistical differences were ascertained by Student’s <span class="html-italic">t</span>-test, * <span class="html-italic">p</span> &lt; 5 × 10<sup>−4</sup>, ** <span class="html-italic">p</span> &lt; 5 × 10<sup>−6</sup>, and *** <span class="html-italic">p</span> &lt; 1 × 10<sup>−6</sup>. (<b>B</b>) Transcription of <span class="html-italic">ChDDO</span> gene. The gene transcription was analyzed by qRT-PCR using total RNA from cells grown under the same conditions as in the analysis of DDO activity, normalized to <span class="html-italic">TAF10</span> gene transcription, and expressed as a relative ratio of that of the wild<span class="html-small-caps">-</span>type strain at pH 3.0. Statistical differences were ascertained by Welch’s <span class="html-italic">t</span>-test, † <span class="html-italic">p</span> &lt; 5 × 10<sup>−4</sup>, †† <span class="html-italic">p</span> &lt; 5 × 10<sup>−7</sup>, and ††† <span class="html-italic">p</span> &lt; 1×10<sup>−7</sup>. The values are the means of three independent experiments, and the error bars are the standard deviations.</p> Full article ">Figure 6
<p>Electrostatic potential of substrate-binding pockets of AdiC (<b>A</b>), ChAap4 (<b>B</b>), and ChAap5 (<b>C</b>). The electrostatic potential was calculated using the software PyMOL 2.3x and is showed by a gradient of blue (positive charge) and red (negative charge) colors. The carbon atoms of <span class="html-small-caps">l</span>-Arg in AdiC is colored in black. Oxygen and nitrogen atoms are shown in red and blue, respectively. GXG and (F/Y)(S/A/T)(F/Y)XGXE motifs are displayed in light pink and light blue, respectively.</p> Full article ">Figure 7
<p>Substrate-binding site of AdiC (<b>A</b>) and ChAap4 (<b>B</b>). Trp293 in TM8 and Asn101 in TM3 in AdiC and Tyr145 and Ile148 in TM3 in ChAap4 potentially involved in the substrate preference are displayed in light green.</p> Full article ">Figure 8
<p>Relationship between <span class="html-small-caps">d</span>-Asp uptake by ChAap4 and <span class="html-italic">ChDDO</span> gene expression in the yeast <span class="html-italic">C. humicola</span> strain UJ1. In acidic and neutral environments, <span class="html-small-caps">d</span>-Asp molecules (red circles) are transported (grey arrows) by ChAap4 (red proteins) and unknown Aaps (probably Gaps, blue proteins). In high alkaline environments, the <span class="html-small-caps">d</span>-Asp uptake by Gaps is abolished but not by ChAap4. Intracellular <span class="html-small-caps">d</span>-Asp induces <span class="html-italic">ChDDO</span> gene expression via an unknown signaling pathway (grey dotted arrows).</p> Full article ">
14 pages, 3097 KiB  
Article
Core and Accessory Genome Analysis of Vibrio mimicus
by Iliana Guardiola-Avila, Leonor Sánchez-Busó, Evelia Acedo-Félix, Bruno Gomez-Gil, Manuel Zúñiga-Cabrera, Fernando González-Candelas and Lorena Noriega-Orozco
Microorganisms 2021, 9(1), 191; https://doi.org/10.3390/microorganisms9010191 - 18 Jan 2021
Cited by 6 | Viewed by 4014
Abstract
Vibrio mimicus is an emerging pathogen, mainly associated with contaminated seafood consumption. However, little is known about its evolution, biodiversity, and pathogenic potential. This study analyzes the pan-, core, and accessory genomes of nine V. mimicus strains. The core genome yielded 2424 genes [...] Read more.
Vibrio mimicus is an emerging pathogen, mainly associated with contaminated seafood consumption. However, little is known about its evolution, biodiversity, and pathogenic potential. This study analyzes the pan-, core, and accessory genomes of nine V. mimicus strains. The core genome yielded 2424 genes in chromosome I (ChI) and 822 genes in chromosome II (ChII), with an accessory genome comprising an average of 10.9% of the whole genome for ChI and 29% for ChII. Core genome phylogenetic trees were obtained, and V. mimicus ATCC-33654 strain was the closest to the outgroup in both chromosomes. Additionally, a phylogenetic study of eight conserved genes (ftsZ, gapA, gyrB, topA, rpoA, recA, mreB, and pyrH), including Vibrio cholerae, Vibrio parilis, Vibrio metoecus, and Vibrio caribbenthicus, clearly showed clade differentiation. The main virulence genes found in ChI corresponded with type I secretion proteins, extracellular components, flagellar proteins, and potential regulators, while, in ChII, the main categories were type-I secretion proteins, chemotaxis proteins, and antibiotic resistance proteins. The accessory genome was characterized by the presence of mobile elements and toxin encoding genes in both chromosomes. Based on the genome atlas, it was possible to characterize differential regions between strains. The pan-genome of V. mimicus encompassed 3539 genes for ChI and 2355 genes for ChII. These results give us an insight into the virulence and gene content of V. mimicus, as well as constitute the first approach to its diversity. Full article
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<p>Genome BLAST atlas of both chromosomes of <span class="html-italic">V. mimicus</span> using <span class="html-italic">V. mimicus</span> MB451 as a reference. Order of the genomes from the inner dark circle: <span class="html-italic">V. mimicus</span> VM573, <span class="html-italic">V. mimicus</span> SX-4, <span class="html-italic">V. mimicus</span> CAIM-602<sup>T</sup>, <span class="html-italic">V. mimicus</span> ATCC-33654, <span class="html-italic">V. mimicus</span> VM603, <span class="html-italic">V. mimicus</span> VM223, <span class="html-italic">V. mimicus</span> CAIM-1882, <span class="html-italic">V. mimicus</span> CAIM-1883, <span class="html-italic">V. cholerae</span> 16961, and <span class="html-italic">V. cholerae</span> O395. Genomic regions unique to the reference strain that are not present in the other strains are without color (no blast hit). The outside numbers (1 to 10 in Ch I and 1 to 13 in ChII), corresponds to the variable regions identified.</p> Full article ">Figure 2
<p>Phylogenetic tree of the core genes of <span class="html-italic">V. mimicus</span> MB451, <span class="html-italic">V. mimicus</span> VM573, <span class="html-italic">V. mimicus</span> SX4, <span class="html-italic">V. mimicus</span> CAIM-602<sup>T</sup>, <span class="html-italic">V. mimicus</span> ATCC-33654, <span class="html-italic">V. mimicus</span> VM603, <span class="html-italic">V. mimicus</span> VM223, <span class="html-italic">V. mimicus</span> CAIM-1882, <span class="html-italic">V. mimicus</span> CAIM-1883, and two <span class="html-italic">V. cholerae</span> (Vc O395 and Vc 16961). (<b>A</b>): Maximum likelihood (ML) tree of ChI. (<b>B</b>): ML tree of ChII.</p> Full article ">Figure 3
<p>Presence-absence matrix of the genes of the accessory genome of the nine <span class="html-italic">V. mimicus</span> strains (<span class="html-italic">V. mimicus</span> MB451, <span class="html-italic">V. mimicus</span> VM573, <span class="html-italic">V. mimicus</span> SX-4, <span class="html-italic">V. mimicus</span> CAIM-602<sup>T</sup>, <span class="html-italic">V. mimicus</span> ATCC-33654, <span class="html-italic">V. mimicus</span> VM603, <span class="html-italic">V. mimicus</span> VM223, <span class="html-italic">V. mimicus</span> CAIM-1882, and <span class="html-italic">V. mimicus</span> CAIM-1883) for each chromosome. Where red color indicates gene presence</p> Full article ">Figure 4
<p>Classification of virulence genes in the core and accessory genome of <span class="html-italic">V. mimicus</span> by category.</p> Full article ">Figure 5
<p>ML phylogenetic tree of eight housekeeping genes <span class="html-italic">(ftsZ</span>, <span class="html-italic">gapA</span>, <span class="html-italic">gyrB</span>, <span class="html-italic">topA</span>, <span class="html-italic">rpoA</span>, <span class="html-italic">recA</span>, <span class="html-italic">mreB</span>, and <span class="html-italic">pyrH</span>) of the nine strains of <span class="html-italic">V. mimicus</span>, <span class="html-italic">V. cholerae</span> 16961, <span class="html-italic">V. parilis (</span>RC586), <span class="html-italic">V. metoecus</span> (RC341), and <span class="html-italic">V. caribbeanicus</span> ATCC BAA-2122.</p> Full article ">
17 pages, 1264 KiB  
Review
Interactions between Anaerobic Fungi and Methanogens in the Rumen and Their Biotechnological Potential in Biogas Production from Lignocellulosic Materials
by Yuqi Li, Zhenxiang Meng, Yao Xu, Qicheng Shi, Yuping Ma, Min Aung, Yanfen Cheng and Weiyun Zhu
Microorganisms 2021, 9(1), 190; https://doi.org/10.3390/microorganisms9010190 - 17 Jan 2021
Cited by 37 | Viewed by 8790
Abstract
Anaerobic fungi in the digestive tract of herbivores are one of the critical types of fiber-degrading microorganisms present in the rumen. They degrade lignocellulosic materials using unique rhizoid structures and a diverse range of fiber-degrading enzymes, producing metabolic products such as H2 [...] Read more.
Anaerobic fungi in the digestive tract of herbivores are one of the critical types of fiber-degrading microorganisms present in the rumen. They degrade lignocellulosic materials using unique rhizoid structures and a diverse range of fiber-degrading enzymes, producing metabolic products such as H2/CO2, formate, lactate, acetate, and ethanol. Methanogens in the rumen utilize some of these products (e.g., H2 and formate) to produce methane. An investigation of the interactions between anaerobic fungi and methanogens is helpful as it provides valuable insight into the microbial interactions within the rumen. During the last few decades, research has demonstrated that anaerobic fungi stimulate the growth of methanogens and maintain methanogenic diversity. Meanwhile, methanogens increase the fiber-degrading capability of anaerobic fungi and stimulate metabolic pathways in the fungal hydrogenosome. The ability of co-cultures of anaerobic fungi and methanogens to degrade fiber and produce methane could potentially be a valuable method for the degradation of lignocellulosic materials and methane production. Full article
(This article belongs to the Special Issue Unleashing the Hidden Potential of Anaerobic Fungi)
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<p>Glucose and xylose metabolism by anaerobic fungi. The main path is indicated by bold arrows. The proposed metabolites are indicated in italics. AC, aconitase; ACH, acetyl-CoA hydrolase; ADH, alkoholdehydrogenase; CS, citrate synthase; E, enolase; FBA, fructosebisphosphate aldolase; F, fumarase; FR, fumarate reductase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GPI, glucose-6-phosphate isomerase; HK, hexokinase; ICD, isocitrate dehydrogenase; LDH, lactatedehydrogenase; MDH, malate dehydrogenase; ME, malic enzyme; NADHD, NADH dehydrogenase; SCS, succinyl-CoA synthetase; TPI, triosephosphate isomerase; XK, xylulokinase; XI, xylose isomerase; PFK, phosphofructokinase; PFL, pyruvate formate lyase; PGK, 3-phosphoglycerate kinase; PGM, phosphoglucomutase; PK, pyruvate kinase; PEPCK, phosphoenolpyruvate carboxykinase.</p> Full article ">Figure 2
<p>The CAZymes in anaerobic fungal strain <span class="html-italic">Pecoramyces</span> sp. F1. The identified complement of (<b>a</b>) CAZymes; (<b>b</b>) Carbohydrate binding modules (CBM); (<b>c</b>) glycoside hydrolases (GH); (<b>d</b>) Carbohydrate esterases (CE); (<b>e</b>) Polysaccharide lyases (PL); (<b>f</b>) Glycosyl transferases (GT).</p> Full article ">Figure 3
<p>Proposed metabolic pathway for glucose by anaerobic fungi co-cultured with methanogens. The red and green arrows indicate stimulated and inhibited pathways. The proposed metabolites are indicated in italics. AC, aconitase; ACH, acetyl-CoA hydrolase; ADH, alkoholdehydrogenase; CS, citrate synthase; E, enolase; F, fumarase; FBA, fructosebisphosphate aldolase; FDH, formate dehydrogenase; FR, fumarate reductase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GPI, glucose-6-phosphate isomerase; HK, hexokinase; ICD, isocitrate dehydrogenase; LDH, lactatedehydrogenase; MCR, methyl coenzyme-M reductase; MDH, malate dehydrogenase; ME, malic enzyme; NADHD, NADH dehydrogenase; SCS, succinyl-CoA synthetase; TPI, triosephosphate isomerase; XK, xylulokinase; XI, xylose isomerase; PFK, phosphofructokinase; PFL, pyruvate formate lyase; PGK, 3-phosphoglycerate kinase; PGM, phosphoglucomutase; PK, pyruvate kinase; PEPCK, phosphoenolpyruvate carboxykinase.</p> Full article ">
15 pages, 3140 KiB  
Article
Microbiome Signatures in a Fast- and Slow-Progressing Gastric Cancer Murine Model and Their Contribution to Gastric Carcinogenesis
by Prerna Bali, Joanna Coker, Ivonne Lozano-Pope, Karsten Zengler and Marygorret Obonyo
Microorganisms 2021, 9(1), 189; https://doi.org/10.3390/microorganisms9010189 - 17 Jan 2021
Cited by 17 | Viewed by 3599
Abstract
Gastric cancer is the third most common cause of death from cancer in the world and infection with Helicobacterpylori (H. pylori) is the main cause of gastric cancer. In addition to Helicobacter infection, the overall stomach microbiota has recently emerged [...] Read more.
Gastric cancer is the third most common cause of death from cancer in the world and infection with Helicobacterpylori (H. pylori) is the main cause of gastric cancer. In addition to Helicobacter infection, the overall stomach microbiota has recently emerged as a potential factor in gastric cancer progression. Previously we had established that mice deficient in myeloid differentiation primary response gene 88 (MyD88, Myd88−/−) rapidly progressed to neoplasia when infected with H. felis. Thus, in order to assess the role of the microbiota in this fast-progressing gastric cancer model we investigated changes of the gastric microbiome in mice with different genotypic backgrounds: wild type (WT), MyD88-deficient (Myd88−/−), mice deficient in the Toll/interleukin-1 receptor (TIR) domain-containing adaptor-inducing interferon-β (TRIF, TrifLps2), and MyD88- and TRIF-deficient (Myd88−/−/TrifLps2, double knockout (DKO)) mice. We compared changes in alpha diversity, beta diversity, relative abundance, and log-fold differential of relative abundance ratios in uninfected and Helicobacter infected mice and studied their correlations with disease progression to gastric cancer in situ. We observed an overall reduction in microbial diversity post-infection with H. felis across all genotypes. Campylobacterales were observed in all infected mice, with marked reduction in abundance at 3 and 6 months in Myd88−/− mice. A sharp increase in Lactobacillales in infected Myd88−/− and DKO mice at 3 and 6 months was observed as compared to TrifLps2 and WT mice, hinting at a possible role of these bacteria in gastric cancer progression. This was further reinforced upon comparison of Lactobacillales log-fold differentials with histological data, indicating that Lactobacillales are closely associated with Helicobacter infection and gastric cancer progression. Our study suggests that differences in genotypes could influence the stomach microbiome and make it more susceptible to the development of gastric cancer upon Helicobacter infection. Additionally, increase in Lactobacillales could contribute to faster development of gastric cancer and might serve as a potential biomarker for the fast progressing form of gastric cancer. Full article
(This article belongs to the Special Issue Helicobacter pylori and Gastric Carcinogenesis)
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<p>Changes in microbial diversity in the stomach across four different genotypes, with infection and time. Shannon diversity index (<b>A</b>) and Pielou’s evenness (<b>B</b>) values for each gastric community, divided by genotype and month. Top and bottom plots represent the same data, alpha diversity values, with lines on the bottom plots denoting average values for infected and uninfected communities over time. Statistical significance on bottom plots refers to differences between infected and uninfected, months 1–6 combined. (<b>C</b>) Principal component analysis of robust Aitchison distance values between communities, months 1–6 combined. Biplot arrows indicate operational taxonomic units (OTUs) driving separation between samples, with arrows labeled with the genus of the OTU. Arrows and genus labels are matched by color. All diversity metrics were calculated using QIIME2. Statistical significance determined by Student’s <span class="html-italic">t</span>-test for alpha diversity and permutational multivariate analysis of variance (PERMANOVA) with Benjamini-Hochberg FDR correction for beta diversity (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.005).</p> Full article ">Figure 2
<p>Relative abundance of different phyla across four genotypes. Relative abundance of individual phyla from WT, <span class="html-italic">Myd88<sup>−/−</sup>, Trif</span><sup>Lps2</sup>, and double knockout (DKO) genotypes. The top eight phyla are shown in the legend. Samples are grouped into <span class="html-italic">Helicobacter</span>-infected and non-<span class="html-italic">Helicobacter</span>-infected and divided by time point. Sequencing data were processed in QIIME2, then plotted in PhyloSeq.</p> Full article ">Figure 3
<p>Relative abundance of different orders across four genotypes. Relative abundance of the top 15 orders from WT, <span class="html-italic">Myd88<sup>−/−</sup>, Trif</span><sup>Lps2</sup>, and DKO genotypes. Remaining phyla are grouped into “Other”. Samples are grouped into <span class="html-italic">Helicobacter</span>-infected and non-<span class="html-italic">Helicobacter</span>-infected and divided by time point. Sequencing data were processed in QIIME2, then plotted in PhyloSeq.</p> Full article ">Figure 4
<p>Log ratios between relevant orders across four genotypes. Log ratios of the Lactobacillales/Rickettsiales (<b>A</b>,<b>B</b>) and Clostridiales/Rickettsiales (<b>C</b>,<b>D</b>) relative abundance ratios between samples, months 1–6 combined. (<b>A</b>,<b>C</b>) show ratios by infection status, all genotypes combined. (<b>B</b>,<b>D</b>) show ratios by genotype and infection status. Log ratios were calculated and processed using Songbird and Qurro. Statistical significance was determined by analysis of variance (ANOVA) ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.005).</p> Full article ">Figure 5
<p>Histopathological scoring for mucous metaplasia. Following infection with <span class="html-italic">H. felis</span> for 1 month (<b>A</b>), 3 months (<b>B</b>) and 6 months (<b>C</b>), H&amp;E-stained stomach sections from each mouse (WT and <span class="html-italic">Myd88</span><sup>−/−</sup>, <span class="html-italic">Trif</span><sup>Lps2</sup>and DKO) were evaluated for indications of pathology. Mucous metaplasia was scored by a blinded comparative pathologist according to the criteria described in Materials and Methods. A <span class="html-italic">p</span> value of 0.05 was considered statistically significant. (<b>A</b>) 1month post infection, <span class="html-italic">n</span> = 14 for WT, <span class="html-italic">n</span> = 15 for <span class="html-italic">Myd88</span><sup>−/−</sup>, <span class="html-italic">n</span> = 14 for <span class="html-italic">Trif</span><sup>Lps2</sup>, and <span class="html-italic">n</span> = 12 for DKO mice; (<b>B</b>) 3 months post infection, <span class="html-italic">n</span> = 16 for WT, <span class="html-italic">n</span> = 16 for Myd88<sup>−/−</sup>, <span class="html-italic">n</span> = 16 for Trif<sup>Lps2</sup>, <span class="html-italic">n</span> = 13 for DKO mice; (<b>C</b>) 6 months post infection, <span class="html-italic">n</span> = 12 for WT, <span class="html-italic">n</span> = 16 for Myd88<sup>−/−</sup>, <span class="html-italic">n</span> = 16 for Trif<sup>Lps2</sup>, <span class="html-italic">n</span> = 12 for DKO mice. Statistical significance was determined by Mann-Whitney test, *** <span class="html-italic">p</span> &lt; 0.005).</p> Full article ">Figure 6
<p>Predictive relationship between Lactobacillales and mucous metaplasia from <span class="html-italic">Helicobacter</span> infection. (<b>A</b>,<b>B</b>) Ordinal logistic regression analysis of log ratios of Lactobacillales/Rickettsiales (<b>A</b>), and Clostridiales/Rickettsiales (<b>B</b>) Relative abundance and gastric mucous histology score. The black circle marks the average of each category. Ordinal logistic regression was calculated using the polr command in R (*** <span class="html-italic">p</span> &lt; 0.001). (<b>C</b>–<b>E</b>) ROC curve for log ratios of Campylobacterales, Lactobacillales, and Clostridiales to Rickettsiales. The blue line represents the performance of each ratio log fold-differential in predicting <span class="html-italic">Helicobacter</span> infection. The red line represents the result expected for a metric with a 50% chance of predicting infection. The area under the curve (AUC) value refers to the area under the blue line. Receiver operating characteristic (ROC) plots were constructed in Prism7 using log fold-differentials from Songbird and Qurro.</p> Full article ">
19 pages, 933 KiB  
Review
Metagenomics Approaches for the Detection and Surveillance of Emerging and Recurrent Plant Pathogens
by Edoardo Piombo, Ahmed Abdelfattah, Samir Droby, Michael Wisniewski, Davide Spadaro and Leonardo Schena
Microorganisms 2021, 9(1), 188; https://doi.org/10.3390/microorganisms9010188 - 16 Jan 2021
Cited by 62 | Viewed by 9637
Abstract
Globalization has a dramatic effect on the trade and movement of seeds, fruits and vegetables, with a corresponding increase in economic losses caused by the introduction of transboundary plant pathogens. Current diagnostic techniques provide a useful and precise tool to enact surveillance protocols [...] Read more.
Globalization has a dramatic effect on the trade and movement of seeds, fruits and vegetables, with a corresponding increase in economic losses caused by the introduction of transboundary plant pathogens. Current diagnostic techniques provide a useful and precise tool to enact surveillance protocols regarding specific organisms, but this approach is strictly targeted, while metabarcoding and shotgun metagenomics could be used to simultaneously detect all known pathogens and potentially new ones. This review aims to present the current status of high-throughput sequencing (HTS) diagnostics of fungal and bacterial plant pathogens, discuss the challenges that need to be addressed, and provide direction for the development of methods for the detection of a restricted number of related taxa (specific surveillance) or all of the microorganisms present in a sample (general surveillance). HTS techniques, particularly metabarcoding, could be useful for the surveillance of soilborne, seedborne and airborne pathogens, as well as for identifying new pathogens and determining the origin of outbreaks. Metabarcoding and shotgun metagenomics still suffer from low precision, but this issue can be limited by carefully choosing primers and bioinformatic algorithms. Advances in bioinformatics will greatly accelerate the use of metagenomics to address critical aspects related to the detection and surveillance of plant pathogens in plant material and foodstuffs. Full article
(This article belongs to the Section Plant Microbe Interactions)
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<p>Possible applications of shotgun and amplicon-based metagenomics.</p> Full article ">
16 pages, 3889 KiB  
Review
The HrpG/HrpX Regulon of Xanthomonads—An Insight to the Complexity of Regulation of Virulence Traits in Phytopathogenic Bacteria
by Doron Teper, Sheo Shankar Pandey and Nian Wang
Microorganisms 2021, 9(1), 187; https://doi.org/10.3390/microorganisms9010187 - 16 Jan 2021
Cited by 28 | Viewed by 4684
Abstract
Bacteria of the genus Xanthomonas cause a wide variety of economically important diseases in most crops. The virulence of the majority of Xanthomonas spp. is dependent on secretion and translocation of effectors by the type 3 secretion system (T3SS) that is controlled by [...] Read more.
Bacteria of the genus Xanthomonas cause a wide variety of economically important diseases in most crops. The virulence of the majority of Xanthomonas spp. is dependent on secretion and translocation of effectors by the type 3 secretion system (T3SS) that is controlled by two master transcriptional regulators HrpG and HrpX. Since their discovery in the 1990s, the two regulators were the focal point of many studies aiming to decipher the regulatory network that controls pathogenicity in Xanthomonas bacteria. HrpG controls the expression of HrpX, which subsequently controls the expression of T3SS apparatus genes and effectors. The HrpG/HrpX regulon is activated in planta and subjected to tight metabolic and genetic regulation. In this review, we cover the advances made in understanding the regulatory networks that control and are controlled by the HrpG/HrpX regulon and their conservation between different Xanthomonas spp. Full article
(This article belongs to the Special Issue Bacterial Protein Secretion Systems and Toxins: Toolkits for Environmental Fitness)
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<p>Genetic organization of the hypersensitive reaction and pathogenicity (<span class="html-italic">hrp</span>) gene cluster and the <span class="html-italic">hrpG/hrpX</span> coding regions in <span class="html-italic">Xanthomonas</span> spp. Schematic representation of the gene clusters of group 2 xanthomonads <span class="html-italic">X</span>. <span class="html-italic">citri</span> subsp. <span class="html-italic">citri</span> (<span class="html-italic">Xcci,</span> strain 306), <span class="html-italic">X. euvesicatoria</span> (<span class="html-italic">Xeu</span>, strain 85-10), <span class="html-italic">X. oryzae</span> pv. <span class="html-italic">oryzae</span> (<span class="html-italic">Xoo</span>, strain KACC10331), and <span class="html-italic">X. campestris</span> pv. <span class="html-italic">campestris</span> (<span class="html-italic">Xcc</span>, strain 8004), and group 1 Xanthomonas <span class="html-italic">X</span>. <span class="html-italic">translucens</span> pv. <span class="html-italic">undulosa</span> (<span class="html-italic">Xtu</span>, strain 4699). The location of the indicated regions within their respective genomes is stated.</p> Full article ">Figure 2
<p>Schematic representation of the metabolic and genetic regulation of <span class="html-italic">hrpG</span> and <span class="html-italic">hrpX</span> in <span class="html-italic">Xanthomonas</span>. Metabolites and environmental cues are represented in rectangles. Arrows and “⊣” signs indicate that a protein promotes or inhibits the target, respectively, based on transcriptional or functional analyses. If the analysis was based on data derived from specific <span class="html-italic">Xanthomonas</span> species, it is marked at the bottom of the represented protein names. Data represent information derived from <span class="html-italic">X. euvesicatoria</span>, <span class="html-italic">X</span>. <span class="html-italic">citri</span> subsp. <span class="html-italic">citri</span> (<span class="html-italic">Xcci</span>), <span class="html-italic">X. oryzae</span> pv. <span class="html-italic">oryzae</span> (<span class="html-italic">Xoo</span>), <span class="html-italic">X. oryzae</span> pv. <span class="html-italic">oryzicola</span> (<span class="html-italic">Xoc</span>), <span class="html-italic">X. axonopodis</span> pv. <span class="html-italic">glycines</span> and <span class="html-italic">X. campestris</span> pv. <span class="html-italic">campestris</span> (<span class="html-italic">Xcc</span>).</p> Full article ">Figure 3
<p>Schematic representation of direct transcriptional and post-transcriptional regulation of HrpG and HrpX. Red “⊣” represents negative transcriptional regulation of <span class="html-italic">hrpG</span> and <span class="html-italic">hrpX</span> based on transcriptional expression and promoter binding analyses. The Blue arrow represents positive transcriptional regulation of <span class="html-italic">hrpX</span> based on transcriptional expression and promoter-binding analyses. Black “⊣” and arrows represent negative and positive post-transcriptional regulation of HrpG, respectively, based on biochemical and functional analyses. Black circled “P” represents protein phosphorylation. Data represent information derived from <span class="html-italic">X</span>. <span class="html-italic">citri</span> subsp. <span class="html-italic">citri</span> (<span class="html-italic">Xcci</span>), <span class="html-italic">X. oryzae</span> pv. <span class="html-italic">oryzae</span> (<span class="html-italic">Xoo</span>), and <span class="html-italic">X. campestris</span> pv. <span class="html-italic">campestris</span> (<span class="html-italic">Xcc</span>).</p> Full article ">
27 pages, 3852 KiB  
Article
Comparative Genomics of Marine Bacteria from a Historically Defined Plastic Biodegradation Consortium with the Capacity to Biodegrade Polyhydroxyalkanoates
by Fons A. de Vogel, Cathleen Schlundt, Robert E. Stote, Jo Ann Ratto and Linda A. Amaral-Zettler
Microorganisms 2021, 9(1), 186; https://doi.org/10.3390/microorganisms9010186 - 16 Jan 2021
Cited by 15 | Viewed by 6462 | Correction
Abstract
Biodegradable and compostable plastics are getting more attention as the environmental impacts of fossil-fuel-based plastics are revealed. Microbes can consume these plastics and biodegrade them within weeks to months under the proper conditions. The biobased polyhydroxyalkanoate (PHA) polymer family is an attractive alternative [...] Read more.
Biodegradable and compostable plastics are getting more attention as the environmental impacts of fossil-fuel-based plastics are revealed. Microbes can consume these plastics and biodegrade them within weeks to months under the proper conditions. The biobased polyhydroxyalkanoate (PHA) polymer family is an attractive alternative due to its physicochemical properties and biodegradability in soil, aquatic, and composting environments. Standard test methods are available for biodegradation that employ either natural inocula or defined communities, the latter being preferred for standardization and comparability. The original marine biodegradation standard test method ASTM D6691 employed such a defined consortium for testing PHA biodegradation. However, the taxonomic composition and metabolic potential of this consortium have never been confirmed using DNA sequencing technologies. To this end, we revived available members of this consortium and determined their phylogenetic placement, genomic sequence content, and metabolic potential. The revived members belonged to the Bacillaceae, Rhodobacteraceae, and Vibrionaceae families. Using a comparative genomics approach, we found all the necessary enzymes for both PHA production and utilization in most of the members. In a clearing-zone assay, three isolates also showed extracellular depolymerase activity. However, we did not find classical PHA depolymerases, but identified two potentially new extracellular depolymerases that resemble triacylglycerol lipases. Full article
(This article belongs to the Special Issue Microbes on Plastics, Close Encounters of the Fourth Kind)
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<p>General overview of the polyhydroxyalkanoate (PHA) cycle with examples of enzymes catalyzing the reactions. Enzymes shown are: AacS—acetoacetyl-CoA synthetase, Bdh—3-hydroxybutyrate dehydrogenase, BktB—β-ketothiolase, FabG—3-oxoacyl-[acyl-carrier-protein] reductase, FadB—multifunctional enoyl-CoA hydratase and hydroxyacyl-CoA dehydrogenase, Hbd—3-hydroxybutyryl-CoA dehydrogenase, Hpd—3-hydroxypropionate dehydrogenase, PhaA—acetyl-CoA acetyltransferase, PhaB—acetoacetyl-CoA reductase, PhaC—PHA synthase, PhaG—hydroxyacyl-CoA-[acyl-carrier-protein] transferase, PhaJ—(R)-specific enoyl-CoA hydratase, PhaY—PHA oligomer hydrolase, PhaZ—PHA depolymerase, and ScoA/ScoB—3-oxoacid CoA-transferase subunit A and B, with CoA = coenzyme A and ACP = acyl carrier protein.</p> Full article ">Figure 2
<p>Maximum Likelihood inferred phylogenetic analysis of the NTK consortium isolates and close relatives, based on the 16S rRNA marker gene sequence. Bootstrap support values above 50% are shown on the nodes and GenBank accession numbers are noted next to the strains. Information on the right provides the following: type strain information, isolation source (marine versus non-marine), genome assembly level available, and representative genome status for a given taxon. The evolutionary distance is indicated by the scale bar.</p> Full article ">Figure 3
<p>Genome comparison of the three sequenced NTK <span class="html-italic">Bacillus</span> spp. genomes (NTK034, NTK071 and NTK074B) in blue, with closely related neighbors in grey. The semicircles show gene presence (dark color) and absence (light color). Alignment of the genomes is based on gene clustering, with the genome order based on the gene cluster presence/absence tree, shown in the upper right corner. The dendrogram in the center represents the hierarchy in gene clustering using Euclidean distance and Ward linkage. Genome properties shown: “Number gene clusters” represents the total number of gene clusters found in the genome; “Singleton gene clusters” represents the number of genes found in only one genome, “Completion” in (%) is calculated based on single-copy genes, “GC-content” shows the average guanine and cytosine nucleotide content and “Total length” is the genome length in base pairs.</p> Full article ">Figure 4
<p>Genome comparison of isolate <span class="html-italic">Rhodobacter</span> sp. NTK016B, highlighted in pink, with <span class="html-italic">Rhodobacteraceae</span> neighbors with sequenced genomes in grey. The semicircles show gene presence (dark color) and absence (light color). Gene clustering and genome alignment, order, and properties are presented as in <a href="#microorganisms-09-00186-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 5
<p>Genome comparison of <span class="html-italic">V. proteolyticus</span> NBRC 13287 and <span class="html-italic">V. alginolyticus</span> ATCC 33787 (in green), with closely related neighbors (in grey). The semicircles show gene presence (dark color) and absence (light color). Gene clustering and genome alignment, order, and properties are presented as in <a href="#microorganisms-09-00186-f003" class="html-fig">Figure 3</a>.</p> Full article ">Figure 6
<p>Alignment of the conserved features in known extracellular PHA depolymerases and closely related lipases, together with those from the extracellular depolymerase candidates of the NTK biodegradation consortium members (marked by ►◄). Shown are the oxyanion hole (blue), lipase box (yellow), and amino acid residues of the catalytic triad (red). Proteins from species with experimentally validated structural features are marked bold. Highlighted amino acids in the alignment at specific positions have similar physicochemical properties.</p> Full article ">
10 pages, 856 KiB  
Article
Detection of SARS-CoV-2 and Other Infectious Agents in Lower Respiratory Tract Samples Belonging to Patients Admitted to Intensive Care Units of a Tertiary-Care Hospital, Located in an Epidemic Area, during the Italian Lockdown
by Adriana Calderaro, Mirko Buttrini, Sara Montecchini, Giovanna Piccolo, Monica Martinelli, Maria Loretana Dell'Anna, Alan Di Maio, Maria Cristina Arcangeletti, Clara Maccari, Flora De Conto and Carlo Chezzi
Microorganisms 2021, 9(1), 185; https://doi.org/10.3390/microorganisms9010185 - 16 Jan 2021
Cited by 8 | Viewed by 2728
Abstract
The aim of this study was the detection of infectious agents from lower respiratory tract (LRT) samples in order to describe their distribution in patients with severe acute respiratory failure and hospitalized in intensive care units (ICU) in an Italian tertiary-care hospital. LRT [...] Read more.
The aim of this study was the detection of infectious agents from lower respiratory tract (LRT) samples in order to describe their distribution in patients with severe acute respiratory failure and hospitalized in intensive care units (ICU) in an Italian tertiary-care hospital. LRT samples from 154 patients admitted to ICU from 27 February to 10 May 2020 were prospectively examined for respiratory viruses, including SARS-CoV-2, bacteria and/or fungi. SARS-CoV-2 was revealed in 90 patients (58.4%, 72 males, mean age 65 years). No significant difference was observed between SARS-CoV-2 positives and SARS-CoV-2 negatives with regard to sex, age and bacterial and/or fungal infections. Nonetheless, fungi were more frequently detected among SARS-CoV-2 positives (44/54, 81.4%, p = 0.0053). Candida albicans was the overall most frequently isolated agent, followed by Enterococcus faecalis among SARS-CoV-2 positives and Staphylococcus aureus among SARS-CoV-2 negatives. Overall mortality rate was 40.4%, accounting for 53 deaths: 37 among SARS-CoV-2 positives (mean age 69 years) and 16 among SARS-CoV-2 negatives (mean age 63 years). This study highlights the different patterns of infectious agents between the two patient categories: fungi were prevalently involved among SARS-CoV-2-positive patients and bacteria among the SARS-CoV-2-negative patients. The different therapies and the length of the ICU stay could have influenced these different patterns of infectious agents. Full article
(This article belongs to the Special Issue COVID-19: Focusing on Epidemiologic, Virologic, and Clinical Studies)
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<p>Temporal distribution of SARS-CoV-2 results among the 154 patients admitted in intensive care units.</p> Full article ">Figure 2
<p>Distribution of bacteria (<b>A</b>) and fungi (<b>B</b>) among SARS-CoV-2-positive and -negative patients.</p> Full article ">
13 pages, 946 KiB  
Article
Blastocystis sp. Prevalence and Subtypes Distribution amongst Syrian Refugee Communities Living in North Lebanon
by Salma Khaled, Nausicaa Gantois, Aisha Ayoubi, Gaël Even, Manasi Sawant, Jinane El Houmayraa, Mathieu Nabot, Sadia Benamrouz-Vanneste, Magali Chabé, Gabriela Certad, Dima El Safadi, Fouad Dabboussi, Monzer Hamze and Eric Viscogliosi
Microorganisms 2021, 9(1), 184; https://doi.org/10.3390/microorganisms9010184 - 16 Jan 2021
Cited by 21 | Viewed by 3482
Abstract
Molecular data concerning the prevalence and subtype (ST) distribution of the intestinal parasite Blastocystis sp. remain scarce in the Middle East. Accordingly, we performed the first molecular epidemiological survey ever conducted in the Syrian population. A total of 306 stool samples were collected [...] Read more.
Molecular data concerning the prevalence and subtype (ST) distribution of the intestinal parasite Blastocystis sp. remain scarce in the Middle East. Accordingly, we performed the first molecular epidemiological survey ever conducted in the Syrian population. A total of 306 stool samples were collected from Syrian refugees living in 26 informal tented settlements (ITS) subjected or not to water, sanitation, and hygiene (WASH) interventions in North Lebanon, then screened for the presence of Blastocystis sp. by real-time polymerase chain reaction followed by subtyping. The overall prevalence of the parasite was shown to reach 63.7%. Blastocystis sp. colonization was not significantly associated with gender, age, symptomatic status, abdominal pain or diarrhea. In contrast, WASH intervention status of ITS was identified as a risk factor for infection. Among a total of 164 subtyped isolates, ST3 was predominant, followed by ST1, ST2, and ST10. No particular ST was reported to be associated with age, gender, symptomatic status, digestive disorders, or WASH intervention status of ITS. Intra-ST diversity of ST1 to ST3 was low suggesting large-scale anthroponotic transmission. Moreover, comparative analysis of ST1 to ST3 genotypes revealed that the circulation of the parasite between Syrian refugees and the host population was likely limited. Full article
(This article belongs to the Section Parasitology)
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<p>Location of the 26 ITS in the governorate of Akkar in the North Lebanon region screened for the presence of <span class="html-italic">Blastocystis</span> sp.</p> Full article ">Figure 2
<p>Alignment of partial SSU rDNA gene sequences from <span class="html-italic">Blastocystis</span> sp. ST1 (<b>A</b>), ST2 (<b>B</b>), and ST3 (<b>C</b>) isolates colonizing Syrian refugees. Positions of variable nucleotides in comparison to reference sequences (genotypes ST1-1, ST2-1 and ST3-1) are indicated above the alignment (vertical numbering). Genotypes identified within each ST are indicated on the left of the alignments. Nucleotides identical to those of the reference sequences are represented by dashes. On the right of each alignment are reported the total number and percentage of isolates identified in Syrian refugees for each genotype.</p> Full article ">
16 pages, 1899 KiB  
Article
Novel Models of Streptococcus canis Colonization and Disease Reveal Modest Contributions of M-Like (SCM) Protein
by Ingrid Cornax, Jacob Zulk, Joshua Olson, Marcus Fulde, Victor Nizet and Kathryn A Patras
Microorganisms 2021, 9(1), 183; https://doi.org/10.3390/microorganisms9010183 - 16 Jan 2021
Cited by 4 | Viewed by 3758
Abstract
Streptococcus canis is a common colonizing bacterium of the urogenital tract of cats and dogs that can also cause invasive disease in these animal populations and in humans. Although the virulence mechanisms of S. canis are not well-characterized, an M-like protein, SCM, has [...] Read more.
Streptococcus canis is a common colonizing bacterium of the urogenital tract of cats and dogs that can also cause invasive disease in these animal populations and in humans. Although the virulence mechanisms of S. canis are not well-characterized, an M-like protein, SCM, has recently identified been as a potential virulence factor. SCM is a surface-associated protein that binds to host plasminogen and IgGs suggesting its possible importance in host-pathogen interactions. In this study, we developed in vitro and ex vivo blood component models and murine models of S. canis vaginal colonization, systemic infection, and dermal infection to compare the virulence potential of the zoonotic S. canis vaginal isolate G361 and its isogenic SCM-deficient mutant (G361∆scm). We found that while S. canis establishes vaginal colonization and causes invasive disease in vivo, the contribution of the SCM protein to virulence phenotypes in these models is modest. We conclude that SCM is dispensable for invasive disease in murine models and for resistance to human blood components ex vivo, but may contribute to mucosal persistence, highlighting a potential contribution to the recently appreciated genetic diversity of SCM across strains and hosts. Full article
(This article belongs to the Special Issue Epidemiology and Pathogenicity of Animal-Adapted Streptococci)
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<p>SCM deficiency minimally impacts <span class="html-italic">S. canis</span> growth, hemolytic activity, and biofilm formation. Growth curves of <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm</span> in Todd-Hewitt broth (THB, (<b>A</b>)) or RPMI-1640 (RPMI, (<b>B</b>)) as measured by optical density (OD<sub>600</sub>). (<b>C</b>) Biofilm formation of <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm</span> or GAS 5448 or 5448Δ<span class="html-italic">emm1</span> in THB or RPMI quantified by SYTO 13 fluorescence and expressed as the percent fluorescence of the WT strain. (<b>D</b>) Representative images (two per condition) of <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm</span> biofilms grown for 48 h in THB or RPMI and stained with SYTO 13. Symbols represent individual experimental replicates (<b>A</b>–<b>C</b>) with lines indicating interquartile ranges. Representative images are of independent experimental replicates, scale bar = 200 μm (<b>D</b>). Data were analyzed by two-way ANOVA with Sidak’s multiple comparisons post-test (<b>A</b>–<b>C</b>). *, <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>SCM does not alter <span class="html-italic">S. canis</span> survival, reactive oxygen species release, cytokine production, nor induce antigenic activity in human sera. (<b>A</b>) Percent survival of <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm</span> after 30 min of exposure to canine DH82 macrophages, MOI = 1. (<b>B</b>) Reactive oxygen species production by DH82 macrophages infected with <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm,</span> MOI = 10, and normalized to uninfected cells. (<b>C</b>) Percent survival of <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm</span> after 30 min of exposure to human THP-1 differentiated macrophages, MOI = 1. (<b>D</b>) THP-1 cells were infected with <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm,</span> MOI = 1, and cell supernatant added to HEK-Blue cells. Alkaline phosphatase activity was measured colorimetrically at OD620 and background signal (uninfected cell supernatant) was deducted. Fold IL-1β release was calculated versus GAS across four independent experiments. (<b>E</b>) Percent survival of <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm</span> after 30 or 60 min of infection in human whole blood. (<b>F</b>) Percent survival of <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm</span> after 30 or 60 min of infection in isolated human neutrophils, MOI = 1. (<b>G</b>) Quantification of human IgG titers, expressed as relative fluorescent units (RFU), for a purified truncated form of SCM (<span class="html-italic">n</span> = 20 donors) via modified ELISA using diluted human sera, positive control: recombinant human IgG, negative control: buffer only. Symbols represent independent experimental replicates (<b>A</b>–<b>D</b>), biological replicates ((<b>E</b>), <span class="html-italic">n</span> = 6/group, (<b>F</b>), <span class="html-italic">n</span> = 5/group), or the results of one independent experiment (<b>G</b>), performed twice independently), with lines indicating medians and interquartile ranges. Data were analyzed by Wilcoxon matched-pairs signed rank test (<b>A</b>), two-way ANOVA with Sidak’s multiple comparisons post-test (<b>B</b>,<b>E</b>,<b>F</b>), or Friedman test with Dunn’s multiple comparisons test (<b>C</b>,<b>D</b>) and determined not significant.</p> Full article ">Figure 3
<p><span class="html-italic">S. canis</span> is highly virulent in mouse models of systemic and dermal infection, yet SCM does not contribute to virulence in these models. (<b>A</b>) Percent survival of <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm</span> after 30 min of infection in murine whole blood collected from CD1 mice. (<b>B</b>) CD1 male and female mice were infected subcutaneously with 1 × 10<sup>8</sup> CFU of WT <span class="html-italic">S. canis</span> G361 or G361Δ<span class="html-italic">scm</span> and skin lesion size measured daily. (<b>C</b>) Representative image of skin lesions three days post subcutaneous infection with WT <span class="html-italic">S. canis</span> G361 (left) or G361Δ<span class="html-italic">scm</span> (right). (<b>D</b>) CD1 male and female mice were infected intraperitoneally with 5  ×  10<sup>7</sup> CFU of WT <span class="html-italic">S. canis</span> G361, G361Δ<span class="html-italic">scm</span>, or <span class="html-italic">S. pyogenes</span> 5448 and survival monitored over 3 days. Symbols represent biological replicates ((<b>A</b>), <span class="html-italic">n</span> = 5/group, (<b>B</b>), <span class="html-italic">n</span> = 20/group, and (<b>D</b>), <span class="html-italic">n</span> = 10-21/group) with lines indicating medians and interquartile ranges (<b>A</b>,<b>B</b>) or percentage survival (<b>D</b>). Data were analyzed by Wilcoxon matched-pairs signed rank test (<b>A</b>), two-way ANOVA with Sidak’s multiple comparisons post-test (<b>B</b>) or Log rank Mantel-Cox test (<b>D</b>) and determined not significant.</p> Full article ">Figure 4
<p><span class="html-italic">S. canis</span> adheres to vaginal epithelial cells and persists in a murine model of vaginal colonization, and SCM confers a fitness advantage in this environment. (<b>A</b>) Percent adherence of <span class="html-italic">S. canis</span> G361, G361Δ<span class="html-italic">scm</span>, or GBS COH1 to VK2 cells after 30 min of infection, MOI = 1. CD1 female mice were vaginally administered 1  ×  10<sup>7</sup> CFU of WT <span class="html-italic">S. canis</span> G361, G361Δ<span class="html-italic">scm</span>, or WT GBS COH1, or PBS as a control. (<b>B</b>) Mice were vaginally swabbed daily, and the levels of bacterial CFU recovered from swabs are shown. (<b>C</b>) Cells collected from day 3 post-inoculation were analyzed for surface markers via flow cytometry. Total cell counts of each population recovered on the swabs are shown. (<b>D</b>) Vaginal epithelial tissues were fixed, sectioned, and stained with H&amp;E. Histological examination revealed keratinized epithelium (top images) and neutrophil infiltration (bottom images) similarly across treatment groups. Magnification = 200X. (<b>E</b>) CD1 female mice were vaginally administered 1  ×  10<sup>7</sup> CFU each of WT <span class="html-italic">S. canis</span> G361 and G361Δ<span class="html-italic">scm</span> in competition. Mice were vaginally swabbed daily, and the levels of bacterial CFU recovered from swabs are shown. Symbols represent biological replicates ((<b>B</b>), <span class="html-italic">n</span> = 18/group, (<b>C</b>), <span class="html-italic">n</span> = 12–18/group, and (<b>E</b>), <span class="html-italic">n</span> = 20/group) or the means of four independent experimental replicates (<b>B</b>), performed in technical duplicate) with lines indicating medians and interquartile ranges. Dotted line in (<b>B</b>,<b>E</b>) indicates limit of detection. Data were analyzed by Kruskal-Wallis test with Dunn’s multiple comparisons test (<b>A</b>), two-way ANOVA with Sidak’s multiple comparisons post-test (<b>B</b>,<b>C</b>) or Wilcoxon matched-pairs signed rank test (<b>E</b>). ***, <span class="html-italic">p</span> &lt; 0.001; **, <span class="html-italic">p</span> &lt; 0.01; *, <span class="html-italic">p</span> &lt; 0.05; ns, not significant.</p> Full article ">
16 pages, 1781 KiB  
Article
Tracing the Trophic Plasticity of the Coral–Dinoflagellate Symbiosis Using Amino Acid Compound-Specific Stable Isotope Analysis
by Christine Ferrier-Pagès, Stephane Martinez, Renaud Grover, Jonathan Cybulski, Eli Shemesh and Dan Tchernov
Microorganisms 2021, 9(1), 182; https://doi.org/10.3390/microorganisms9010182 - 16 Jan 2021
Cited by 26 | Viewed by 6226
Abstract
The association between corals and photosynthetic dinoflagellates is one of the most well-known nutritional symbioses, but nowadays it is threatened by global changes. Nutritional exchanges are critical to understanding the performance of this symbiosis under stress conditions. Here, compound-specific δ15N and [...] Read more.
The association between corals and photosynthetic dinoflagellates is one of the most well-known nutritional symbioses, but nowadays it is threatened by global changes. Nutritional exchanges are critical to understanding the performance of this symbiosis under stress conditions. Here, compound-specific δ15N and δ13C values of amino acids (δ15NAA and δ13CAA) were assessed in autotrophic, mixotrophic and heterotrophic holobionts as diagnostic tools to follow nutritional interactions between the partners. Contrary to what was expected, heterotrophy was mainly traced through the δ15N of the symbiont’s amino acids (AAs), suggesting that symbionts directly profit from host heterotrophy. The trophic index (TP) ranged from 1.1 to 2.3 from autotrophic to heterotrophic symbionts. In addition, changes in TP across conditions were more significant in the symbionts than in the host. The similar δ13C-AAs signatures of host and symbionts further suggests that symbiont-derived photosynthates are the main source of carbon for AAs synthesis. Symbionts, therefore, appear to be a key component in the AAs biosynthetic pathways, and might, via this obligatory function, play an essential role in the capacity of corals to withstand environmental stress. These novel findings highlight important aspects of the nutritional exchanges in the coral–dinoflagellates symbiosis. In addition, they feature δ15NAA as a useful tool for studies regarding the nutritional exchanges within the coral–symbiodiniaceae symbiosis. Full article
(This article belongs to the Section Plant Microbe Interactions)
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<p>A schematic summarizing of previous research on amino acids (AA) and how they can be synthesized/obtained within the coral holobiont. Dashed lines are studies that used coral, and solid lines are those that used <span class="html-italic">Aiptasia</span>. Ellipses in the middle of the diagram are AA that can be synthesized/obtained by both host and algal symbionts: black hashed ellipses represent the pioneering work of Fitzgerald and Szmant; and red text indicates AA uptake through osmotrophy.</p> Full article ">Figure 2
<p>Changes in the δ<sup>15</sup>N value of the (<b>a</b>) glutamic acid and (<b>b</b>) phenylalanine according to the nutritional state of the coral holobiont: autotrophic host and symbionts are represented in blue, the mixotrophic host and symbionts in red and the heterotrophic host and symbionts in green.</p> Full article ">Figure 3
<p>The value of the trophic position index according to the nutritional state of the coral holobiont: the autotrophic host and symbionts represented in blue, the mixotrophic host and symbionts in red and the heterotrophic host and symbionts in green.</p> Full article ">Figure 4
<p>Changes in the δ<sup>13</sup>C value of the essential amino acids according to the nutritional state of the coral holobiont: the autotrophic host and symbionts are represented in blue, the mixotrophic host and symbionts in red and the heterotrophic host and symbionts in green.</p> Full article ">Figure 5
<p>MDS performed with the δ<sup>13</sup>C value of the essential amino acids (Valine, Leucine, Isoleucine, Methionine, Phenylalanine) of autotrophic (blue symbols) and heterotrophic (green symbols) coral holobionts: host (circles) and symbionts (triangles). These data therefore include two nutritional treatments with two fractions, or 24 data points.</p> Full article ">
19 pages, 415 KiB  
Review
Listeria monocytogenes Biofilms in the Food Industry: Is the Current Hygiene Program Sufficient to Combat the Persistence of the Pathogen?
by Tina Mazaheri, Brayan R. H. Cervantes-Huamán, Maria Bermúdez-Capdevila, Carolina Ripolles-Avila and José Juan Rodríguez-Jerez
Microorganisms 2021, 9(1), 181; https://doi.org/10.3390/microorganisms9010181 - 15 Jan 2021
Cited by 92 | Viewed by 9185
Abstract
Biofilms contain microbial cells which are protected by a self-produced matrix and they firmly attach themselves to many different food industry surfaces. Due to this protection, microorganisms within biofilms are much more difficult to eradicate and therefore to control than suspended cells. A [...] Read more.
Biofilms contain microbial cells which are protected by a self-produced matrix and they firmly attach themselves to many different food industry surfaces. Due to this protection, microorganisms within biofilms are much more difficult to eradicate and therefore to control than suspended cells. A bacterium that tends to produce these structures and persist in food processing plants is Listeria monocytogenes. To this effect, many attempts have been made to develop control strategies to be applied in the food industry, although there seems to be no clear direction on how to manage the risk the bacteria poses. There is no standardized protocol that is applied equally to all food sectors, so the strategies for the control of this pathogen depend on the type of surface, the nature of the product, the conditions of the food industry environment, and indeed the budget. The food industry performs different preventive and corrective measures on possible L. monocytogenes-contaminated surfaces. However, a critical evaluation of the sanitization methods applied must be performed to discern whether the treatment can be effective in the long-term. This review will focus on currently used strategies to eliminate biofilms and control their formation in processing facilities in different food sectors (i.e., dairy, meat, fish, chilled vegetables, and ready-to-eat products). The technologies employed for their control will be exemplified and discussed with the objective of understanding how L. monocytogenes can be improved through food safety management systems. Full article
(This article belongs to the Special Issue An Update on Listeria monocytogenes)
11 pages, 1066 KiB  
Review
Chronic Active Epstein–Barr Virus Infection: The Elucidation of the Pathophysiology and the Development of Therapeutic Methods
by Ayako Arai
Microorganisms 2021, 9(1), 180; https://doi.org/10.3390/microorganisms9010180 - 15 Jan 2021
Cited by 21 | Viewed by 7127
Abstract
Chronic active Epstein–Barr virus infection (CAEBV) is a disease where Epstein–Barr virus (EBV)-infected T- or NK-cells are activated and proliferate clonally. The symptoms of this dual-faced disease include systemic inflammation and multiple organ failures caused by the invasion of infected cells: inflammation and [...] Read more.
Chronic active Epstein–Barr virus infection (CAEBV) is a disease where Epstein–Barr virus (EBV)-infected T- or NK-cells are activated and proliferate clonally. The symptoms of this dual-faced disease include systemic inflammation and multiple organ failures caused by the invasion of infected cells: inflammation and neoplasm. At present, the only effective treatment strategy to eradicate EBV-infected cells is allogeneic stem cell transplantation. Lately, the investigation into the disease’s pathogenic mechanism and pathophysiology has been advancing. In this review, I will evaluate the new definition in the 2017 WHO classification, present the advancements in the study of CAEBV, and unfold the future direction. Full article
(This article belongs to the Special Issue Pathogenic Role of Virus Infection in Head and Neck Tumors)
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<p>The results of the nationwide fact-finding research of chronic active Epstein–Barr virus infection (CAEBV) treatment in Japan [<a href="#B4-microorganisms-09-00180" class="html-bibr">4</a>]. (<b>a</b>) Sexual specificity by age; (<b>b</b>) survival rate by age; (<b>c</b>) survival rate by treatment method.</p> Full article ">Figure 1 Cont.
<p>The results of the nationwide fact-finding research of chronic active Epstein–Barr virus infection (CAEBV) treatment in Japan [<a href="#B4-microorganisms-09-00180" class="html-bibr">4</a>]. (<b>a</b>) Sexual specificity by age; (<b>b</b>) survival rate by age; (<b>c</b>) survival rate by treatment method.</p> Full article ">
18 pages, 7489 KiB  
Article
Adjunct Culture of Non-Starter Lactic Acid Bacteria for the Production of Provola Dei Nebrodi PDO Cheese: In Vitro Screening and Pilot-Scale Cheese-Making
by Cinzia Lucia Randazzo, Luigi Liotta, Maria De Angelis, Giuseppe Celano, Nunziatina Russo, Koenraad Van Hoorde, Vincenzo Chiofalo, Alessandra Pino and Cinzia Caggia
Microorganisms 2021, 9(1), 179; https://doi.org/10.3390/microorganisms9010179 - 15 Jan 2021
Cited by 20 | Viewed by 3677
Abstract
The present study aimed at selecting non-starter lactic acid bacteria strains, with desirable technological and enzymatic activities, suitable as adjunct culture for the Provola dei Nebrodi cheese production. One hundred and twenty-one lactic acid bacteria, isolated from traditional Provola dei Nebrodi cheese samples, [...] Read more.
The present study aimed at selecting non-starter lactic acid bacteria strains, with desirable technological and enzymatic activities, suitable as adjunct culture for the Provola dei Nebrodi cheese production. One hundred and twenty-one lactic acid bacteria, isolated from traditional Provola dei Nebrodi cheese samples, were genetically identified by Rep-PCR genomic fingerprinting, using the (GTG)5-primer, and by matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS). Twenty-seven strains, included in the qualified presumption of safety (QPS) list, were tested for technological and proteinase/peptidase activities. Results showed that technological features and flavour formation abilities were strain-dependent. Among the selected strains, Lacticaseibacillus paracasei PN 76 and Limosilactobacillus fermentum PN 101 were used as adjunct culture in pilot-scale cheese-making trials. Data revealed that adjunct cultures positively affected the flavour development of cheese, starting from 30 days of ripening, contributing to the formation of key flavour compounds. The volatile organic compound profiles of experimental cheeses was significantly different from those generated in the controls, suggesting that the selected adjunct strains were able to accelerate the flavour development, contributing to a unique profile of Provola dei Nebrodi cheese. Full article
(This article belongs to the Special Issue Microbial Populations of Fermented Foods)
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<p>UPGMA dendrogram of the Rep-PCR and matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF MS) analyses of presumptive non-starter lactic acid bacteria (NSLAB) isolated from semi-hard Provola dei Nebrodi PDO (PN) cheese samples. Node values indicate the average percentage of similarity based on (GTG)<sub>5</sub>-PCR and MALDI-TOF MS profiles. The tree was made with BioNumerics version 5.1.</p> Full article ">Figure 2
<p>Acidification activity of the selected NSLAB strains isolated from artisanal semi-hard Provola dei Nebrodi PDO cheese samples. Results are reported as mean and standard deviation of three replicates. For each species, bars with different lowercase letters (<b>a</b>–<b>e</b>) are significantly different at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 3
<p>Grouping of samples based on the volatile organic compounds (VOCs) identified in experimental (ECh) and control (CCh) cheeses at 0, 30 and 60 days of ripening. The different batch of cheeses are indicated as I, II and III.</p> Full article ">
13 pages, 4119 KiB  
Article
Elicitation of Antimicrobial Active Compounds by Streptomyces-Fungus Co-Cultures
by Matthieu Nicault, Ali Zaiter, Stéphane Dumarcay, Patrick Chaimbault, Eric Gelhaye, Pierre Leblond and Cyril Bontemps
Microorganisms 2021, 9(1), 178; https://doi.org/10.3390/microorganisms9010178 - 15 Jan 2021
Cited by 17 | Viewed by 4631
Abstract
The bacteria of the genus Streptomyces and Basidiomycete fungi harbor many biosynthetic gene clusters (BGCs) that are at the origin of many bioactive molecules with medical or industrial interests. Nevertheless, most BGCs do not express in standard lab growth conditions, preventing the full [...] Read more.
The bacteria of the genus Streptomyces and Basidiomycete fungi harbor many biosynthetic gene clusters (BGCs) that are at the origin of many bioactive molecules with medical or industrial interests. Nevertheless, most BGCs do not express in standard lab growth conditions, preventing the full metabolic potential of these organisms from being exploited. Because it generates biotic cues encountered during natural growth conditions, co-culture is a means to elicit such cryptic compounds. In this study, we explored 72 different Streptomyces-fungus interaction zones (SFIZs) generated during the co-culture of eight Streptomyces and nine fungi. Two SFIZs were selected because they showed an elicitation of anti-bacterial activity compared to mono-cultures. The study of these SFIZs showed that co-culture had a strong impact on the metabolic expression of each partner and enabled the expression of specific compounds. These results show that mimicking the biotic interactions present in this ecological niche is a promising avenue of research to explore the metabolic capacities of Streptomyces and fungi. Full article
(This article belongs to the Special Issue Natural Products from Streptomyces)
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<p><span class="html-italic">Streptomyces</span>-fungus co-culture setup and screening. (<b>A</b>) Illustration of a <span class="html-italic">Streptomyces</span>-fungus co-culture after 14 days of growth. (<b>B</b>) Screening of bioactive molecules elicited during the co-culture. The compound extracts of mono-cultures of fungi and <span class="html-italic">Streptomyces</span> (rows and columns named “control”) in GA medium were compared with the extracts resulting from their co-culture in a bioassay experiment against the growth of <span class="html-italic">B. subtilis</span> ATCC6633. The values in the table indicate the percentage of inhibition of <span class="html-italic">B. subtilis</span> ATCC6633 after a 24 h growth period in comparison with a control grown in absence of the extract. The two co-cultures between <span class="html-italic">S. commune</span> 6601-A with S1D4-11 and S1D4-23 (highlighted in blue) were selected as they presented a significant (<span class="html-italic">t</span>-test <span class="html-italic">p</span> value &lt; 0.05) impact on the growth of <span class="html-italic">B. subtilis</span> ATCC6633 in comparison with the controls.</p> Full article ">Figure 2
<p>Antimicrobial activities of S1D4-11-FIZ and S1D4-23-FIZ against different <span class="html-italic">Bacilli</span>. Inhibition was quantified as a percentage of growth inhibition in comparison with a control without extract. The growth was measured by spectrometry at OD 600 nm after 24 h of growth. The different Bacilli strains are (<b>A</b>) <span class="html-italic">Bacillus subtilis</span> ATCC6633, (<b>B</b>) <span class="html-italic">Bacillus</span> sp. RB2-2 N12, (<b>C</b>) <span class="html-italic">Bacillus</span> sp. RB2-2 N10, and (<b>D</b>) <span class="html-italic">Bacillus</span> sp. RB2-1 N16. Statistical difference was assessed with a <span class="html-italic">t</span>-test. *** = <span class="html-italic">p</span> value &lt; 0.005 for both comparisons between fungus and <span class="html-italic">Streptomyces</span>-fungus interaction zone (SFIZ) and <span class="html-italic">Streptomyces</span> and SFIZ.</p> Full article ">Figure 3
<p>Gas chromatography-mass spectrometry (GC-MS) spectrum comparison. (<b>A</b>) Partial least square-discriminant analysis (PLS-DA)comparison of GC-MS spectra of mono- and co-cultures. (<b>B</b>) Heat-map of the first 750 discriminant features (26%) revealed by PLS-DA. The scale indicates the relative abundance of features calculated by centered-reduced of initial intensity.</p> Full article ">Figure 4
<p>Distribution of common and specific features between SFIZs and controls. LC-MS metabolite profiles were recorded and analyzed with GNPS. The Venn diagram compares specific and common features between each SFIZ and its controls as well as SFIZ features between the two experiments. The number of SFIZ specific features for each experiment is indicated in the dashed square. +m: positive mode; −m: negative mode; ft.: features.</p> Full article ">Figure 5
<p>Molecular networking of ions produced during mono- and co-cultures. Data were obtained byLC-MS/MS in both positive and negative modes and were analyzed through GNPS. Each spectrum is shown as a node and related metabolites are linked by edges. Node color represents fractions by a chromatographic separation (C18 reverse phase) <span class="html-italic">prior</span> MS/MS detection. For both SFIZ extracts, the activity against the indicator strain was only found in two fractions (N° 22 and 23). To identify potential candidates induced during the studied interactions, we selected compounds found in fractions 22 and 23 and in SFIZs but not in the controls (<a href="#microorganisms-09-00178-f006" class="html-fig">Figure 6</a>). As control extracts were solubilized in dimethyl sulfoxid (DMSO) (see previous paragraph), we restricted this analysis to SFIZ extract molecules found in both DMSO and methanol, recognizing that some methanol-soluble and DMSO-insoluble candidates could be omitted.</p> Full article ">Figure 6
<p>Identification of specific compounds in bioactive fractions. LC-MS spectra of fractions N° 22 and N° 23 (named S1D4-11-FIZ F22 and S1D4-23-FIZ F23, respectively) with anti-<span class="html-italic">Bacillus</span> activity were compared with single culture controls and with total extracts of SFIZ in DMSO, which also has similar activity. Compounds with potential activities (highlighted in red) are those at the intersection of the total SFIZ extract and in the fraction considered. The number of features found in each condition is indicated. Features were analyzed and compared with DEREPLICATOR+ in order to build the Venn diagram.</p> Full article ">
13 pages, 1652 KiB  
Article
Performance of a Four-Antigen Staphylococcus aureus Vaccine in Preclinical Models of Invasive S. aureus Disease
by Ingrid L. Scully, Yekaterina Timofeyeva, Arthur Illenberger, Peimin Lu, Paul A. Liberator, Kathrin U. Jansen and Annaliesa S. Anderson
Microorganisms 2021, 9(1), 177; https://doi.org/10.3390/microorganisms9010177 - 15 Jan 2021
Cited by 19 | Viewed by 5421
Abstract
A Staphylococcus aureus four-antigen vaccine (SA4Ag) was designed for the prevention of invasive disease in surgical patients. The vaccine is composed of capsular polysaccharide type 5 and type 8 CRM197 conjugates, a clumping factor A mutant (Y338A-ClfA) and manganese transporter subunit C [...] Read more.
A Staphylococcus aureus four-antigen vaccine (SA4Ag) was designed for the prevention of invasive disease in surgical patients. The vaccine is composed of capsular polysaccharide type 5 and type 8 CRM197 conjugates, a clumping factor A mutant (Y338A-ClfA) and manganese transporter subunit C (MntC). S. aureus pathogenicity is characterized by an ability to rapidly adapt to the host environment during infection, which can progress from a local infection to sepsis and invasion of distant organs. To test the protective capacity of the SA4Ag vaccine against progressive disease stages of an invasive S. aureus infection, a deep tissue infection mouse model, a bacteremia mouse model, a pyelonephritis model, and a rat model of infectious endocarditis were utilized. SA4Ag vaccination significantly reduced the bacterial burden in deep tissue infection, in bacteremia, and in the pyelonephritis model. Complete prevention of infection was demonstrated in a clinically relevant endocarditis model. Unfortunately, these positive preclinical findings with SA4Ag did not prove the clinical utility of SA4Ag in the prevention of surgery-associated invasive S. aureus infection. Full article
(This article belongs to the Special Issue Staphylococcal Infections (Host and Pathogenic Factors))
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<p>SA4Ag antigens are immunogenic in preclinical species. Immune responses against SA4Ag antigens CP5, CP8, ClfA, and MntC were measured before (pre) and after (PD) immunization. Rodents were immunized three times subcutaneously with SA4Ag + AlPO<sub>4</sub> prior to sample collection post-dose 3 (PD3). Non-human primates (NHP) were immunized a single time with SA4Ag without adjuvant. Anti-capsular immune responses were measured by the OPA assay for CP5 (<b>A</b>) and CP8 (<b>B</b>). Anti-protein immune responses were measured by cLIA for ClfA (<b>C</b>) and MntC (<b>D</b>). Human responses to a single unadjuvanted dose of SA4Ag are included as a comparator, adapted from [<a href="#B27-microorganisms-09-00177" class="html-bibr">27</a>].</p> Full article ">Figure 1 Cont.
<p>SA4Ag antigens are immunogenic in preclinical species. Immune responses against SA4Ag antigens CP5, CP8, ClfA, and MntC were measured before (pre) and after (PD) immunization. Rodents were immunized three times subcutaneously with SA4Ag + AlPO<sub>4</sub> prior to sample collection post-dose 3 (PD3). Non-human primates (NHP) were immunized a single time with SA4Ag without adjuvant. Anti-capsular immune responses were measured by the OPA assay for CP5 (<b>A</b>) and CP8 (<b>B</b>). Anti-protein immune responses were measured by cLIA for ClfA (<b>C</b>) and MntC (<b>D</b>). Human responses to a single unadjuvanted dose of SA4Ag are included as a comparator, adapted from [<a href="#B27-microorganisms-09-00177" class="html-bibr">27</a>].</p> Full article ">Figure 2
<p>Immunization with SA4Ag reduces bacterial burden in a <span class="html-italic">S. aureus</span> pyelonephritis model. Female CD-1 mice (<span class="html-italic">n</span> = 5) were vaccinated at weeks 0, 3, and 6, with SA4Ag or with vehicle alone. Two weeks after the final vaccination animals were challenged with ~2 × 10<sup>8</sup> <span class="html-italic">S. aureus</span> Reynolds. Two days post-challenge, the mice were euthanized and kidneys collected. Bacteria in kidneys were enumerated (colony-forming unit [CFU]/kidney). <span class="html-italic">p</span> value was calculated by Student’s <span class="html-italic">t</span>-test.</p> Full article ">
12 pages, 2511 KiB  
Article
RNA Interference by Cyanobacterial Feeding Demonstrates the SCSG1 Gene Is Essential for Ciliogenesis during Oral Apparatus Regeneration in Stentor
by Wei Wei, Chuanqi Jiang, Xiaocui Chai, Juyuan Zhang, Cheng-Cai Zhang, Wei Miao and Jie Xiong
Microorganisms 2021, 9(1), 176; https://doi.org/10.3390/microorganisms9010176 - 15 Jan 2021
Cited by 5 | Viewed by 2827
Abstract
In the giant ciliate Stentor coeruleus, oral apparatus (OA) regeneration is an experimentally tractable regeneration paradigm that occurs via a series of morphological steps. OA regeneration is thought to be driven by a complex regulatory system that orchestrates the temporal expression of [...] Read more.
In the giant ciliate Stentor coeruleus, oral apparatus (OA) regeneration is an experimentally tractable regeneration paradigm that occurs via a series of morphological steps. OA regeneration is thought to be driven by a complex regulatory system that orchestrates the temporal expression of conserved and specific genes. We previously identified a S. coeruleus-specific gene (named SCSG1) that was significantly upregulated during the ciliogenesis stages of OA regeneration, with an expression peak at the stage of the first OA cilia appearance. We established a novel RNA interference (RNAi) method through cyanobacteria Synechocystis sp. PCC6803 feeding in S. coeruleus. The expression of SCSG1 gene was significantly knocked down by using this method and induced abnormal ciliogenesis of OA regeneration in S. coeruleus, suggesting that SCSG1 is essential for OA regeneration in S. coeruleus. This novel RNAi method by cyanobacterial feeding has potential utility for studying other ciliates. Full article
(This article belongs to the Section Molecular Microbiology and Immunology)
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<p>pSCT3C vector map. A DNA fragment of the target gene was amplified and cloned into the pSCT3C double-P<span class="html-italic"><sub>cpcB</sub></span> vector, which has two <span class="html-italic">cpcB</span> promoters in opposite orientations flanking the multiple cloning site. The pSCT3C vector consists of the mobilization genes required for conjugative transfer and the origin for vegetative replication. The pSCT3C vector also has a selectable marker that confers Km resistance. Cloned plasmids are transformed into DH10B, an <span class="html-italic">E. coli</span> strain which can transfer exogenous DNA into <span class="html-italic">Synechocystis</span> PCC6803 via bacterial conjugation.</p> Full article ">Figure 2
<p>Verification of plasmid transformation into <span class="html-italic">Synechocystis</span> PCC6803 by bacterial colony PCR and of exogenous <span class="html-italic">SCSG1</span> and <span class="html-italic">Mob1</span> gene expression in <span class="html-italic">Synechocystis</span> PCC6803 transformants using RT-PCR. No DNA fragments were amplified in all controls (data not shown). (<b>A</b>) Bacterial colony PCR of <span class="html-italic">Mob1</span> transformants generated DNA fragments of the expected sizes (633 bp, 317 bp, and 347 bp, respectively). (<b>B</b>) Bacterial colony PCR of <span class="html-italic">SCSG1</span> transformants generated DNA fragments of the expected sizes (597 bp, 299 bp, and 298 bp, respectively). (<b>C</b>) Bacterial colony PCR of a negative control transformant carrying the empty pSCT3C plasmid showed the 1305 bp DNA fragment. (<b>D</b>) RT-PCR of <span class="html-italic">Mob1</span> transformants showed DNA fragments of the expected sizes (633 bp, 317 bp, and 347 bp, respectively). (<b>E</b>) RT-PCR of <span class="html-italic">SCSG1</span> transformants showed DNA fragments of the expected sizes (597 bp, 299 bp, and 298 bp, respectively).</p> Full article ">Figure 3
<p>Ingestion of <span class="html-italic">Synechocystis</span> PCC6803 by <span class="html-italic">S. coeruleus</span>. (<b>A</b>) Brightfield microscopy image of a live <span class="html-italic">S. coeruleus</span> cell with <span class="html-italic">Synechocystis</span> PCC6803 stored in its food vacuoles. The red arrow indicates a food vacuole. Scale bar: 100 μm. (<b>B</b>) Autofluorescence microscopy image of live <span class="html-italic">Synechocystis</span> PCC6803 cells. Scale bar: 50 μm. (<b>C</b>) Autofluorescence microscopy image of a live <span class="html-italic">S. coeruleus</span> cell with <span class="html-italic">Synechocystis</span> PCC6803 stored in its food vacuoles. The white arrow indicates a food vacuole. Scale bar: 100 μm.</p> Full article ">Figure 4
<p><span class="html-italic">Mob1</span> RNAi resulted in aberrant cell polarity in <span class="html-italic">S. coeruleus</span>. (<b>A</b>,<b>B</b>) Representative brightfield microscopy images of cells treated with control RNAi (<b>A</b>) and <span class="html-italic">Mob1</span> RNAi (<b>B</b>) by <span class="html-italic">Synechocystis</span> 6803 feeding for 10 days. <span class="html-italic">Mob1</span> RNAi resulted in the development of two ectopic posterior poles (red arrows) in cell under normal growth conditions. Scale bars: 100 μm (<b>A</b>) and 50 μm (<b>B</b>). (<b>C</b>) Representative brightfield microscopy image of a cell treated with <span class="html-italic">Mob1</span> RNAi by <span class="html-italic">Synechocystis</span> 6803 feeding for 14 days. <span class="html-italic">Mob1</span> RNAi resulted in the development of an ectopic posterior pole (red arrow) during OA regeneration. Scale bars: 100 μm.</p> Full article ">Figure 5
<p>(<b>A</b>) The clustering analysis of 532 upregulated DEGs with maximum expression at the stage of the first OA cilia appearance during OA regeneration in <span class="html-italic">S. coeruleus</span>. Five expression patterns (No. 21, 24, 45, 37, and 13) of genes showed statistically significant difference (<span class="html-italic">p</span> &lt; 0.00001) (colored boxes). (<b>B</b>) The time-series analysis of 51 upregulated DEGs in No. 45 pattern. The x-axis shows the time points, and the y-axis shows the time series of gene expression levels. (<b>C</b>) Gene expression profiles of <span class="html-italic">SCSG1 gene</span> during OA regeneration in <span class="html-italic">S. coeruleus</span>. The x-axis shows the time points, and the y-axis shows the time series of gene expression levels.</p> Full article ">Figure 6
<p><span class="html-italic">SCSG1</span> RNAi resulted in aberrant cell morphology during OA regeneration in <span class="html-italic">S. coeruleus</span>. (<b>A</b>) RNA-seq and qRT-PCR data showing down-regulated expression of <span class="html-italic">SCSG1</span> normalized to <span class="html-italic">18S</span> expression in control and <span class="html-italic">SCSG1</span> RNAi cells, respectively. (<b>B</b>,<b>C</b>) Brightfield microscopy images of <span class="html-italic">S. coeruleus</span> cells treated with control and <span class="html-italic">SCSG1</span> RNAi, showing normal (<b>B</b>) and aberrant (<b>C</b>) cell morphology without regenerated OA (red arrows). Scale bars: 100 μm (<b>B</b>) and 50 μm (<b>C</b>).</p> Full article ">
16 pages, 1999 KiB  
Article
Self-Crossing Leads to Weak Co-Variation of the Bacterial and Fungal Communities in the Rice Rhizosphere
by Jingjing Chang, Shaohua Shi, Lei Tian, Marcio F. A. Leite, Chunling Chang, Li Ji, Lina Ma, Chunjie Tian and Eiko E. Kuramae
Microorganisms 2021, 9(1), 175; https://doi.org/10.3390/microorganisms9010175 - 15 Jan 2021
Cited by 10 | Viewed by 3511
Abstract
The rhizomicrobial community is influenced by plant genotype. However, the potential differences in the co-assembly of bacterial and fungal communities between parental lines and different generations of rice progenies have not been examined. Here we compared the bacterial and fungal communities in the [...] Read more.
The rhizomicrobial community is influenced by plant genotype. However, the potential differences in the co-assembly of bacterial and fungal communities between parental lines and different generations of rice progenies have not been examined. Here we compared the bacterial and fungal communities in the rhizomicrobiomes of female parent Oryza rufipogon wild rice; male parent Oryza sativa cultivated rice; their F1 progeny; and the F2, F3 and F4 self-crossing generations. Our results showed that the bacterial and fungal α-diversities of the hybrid F1 and self-crossing generations (F2, F3, F4) were closer to one of the two parental lines, which may indicate a role of the parental line in the diversity of the rhizosphere microbial community assembly. Self-crossing from F1 to F4 led to weak co-variation of the bacterial and fungal communities and distinct rhizosphere microbiomes. In the parental and self-crossing progenies, the reduction of community dissimilarity was higher for the fungal community than for the bacterial community. Full article
(This article belongs to the Special Issue Microbial Interactions in Soil)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The α-diversity of the (<b>a</b>,<b>b</b>) bacterial and (<b>c</b>,<b>d</b>) fungal communities in bulk soil (BS) and the rhizospheres of the female parent <span class="html-italic">Oryza rufipogon</span> (Or) wild rice; male parent <span class="html-italic">Oryza sativa</span> (Os) cultivated rice; hybrid generation (F1); and the generations (F2, F3 and F4) obtained by self-crossing. Data with the same letters within each column indicate no significant difference by one-way ANOVA with LSD tests at <span class="html-italic">p</span> &lt; 0.05.</p> Full article ">Figure 2
<p>Between-class analysis (BCA) of (<b>a</b>) bacterial and (<b>b</b>) fungal communities in bulk soil (BS) and the rhizospheres of the female parent <span class="html-italic">Oryza rufipogon</span> (Or) wild rice; male parent <span class="html-italic">Oryza sativa</span> (Os) cultivated rice; hybrid generation (F1); and the generations F2, F3 and F4 obtained by self-crossing. Each rice generation is represented by a circle, the size of each circle represents the 95% confidence interval.</p> Full article ">Figure 3
<p>Co-inertia analysis of bacterial and fungal communities in bulk soil (BS) and the rhizospheres of the female parent <span class="html-italic">Oryza rufipogon</span> (Or) wild rice; male parent <span class="html-italic">Oryza sativa</span> (Os) cultivated rice; hybrid generation (F1); and the generations F2, F3 and F4 obtained by self-crossing. The arrows represent the co-variation of both communities within the generations (the shorter the arrow, the higher the covariance between bacterial and fungal community). The arrows origins represent the bacterial communities and the arrow heads represent the positions of the fungal communities in the co-inertia space.</p> Full article ">Figure 4
<p>Network based on SparCC correlation coefficients (SparCC’s rho cut-off = 0.8, <span class="html-italic">p</span> &lt; 0.01) showing the co-occurrence patterns of parental-affected groups of bacteria and fungi in different rice generations. Red and blue lines represent significant positive and negative (<span class="html-italic">p</span> &lt; 0.01) linear relationships, respectively. The rhizospheres of the hybrid generation (<b>F1</b>) and the progenies obtained by self-crossing (<b>F2</b>, <b>F3</b>, <b>F4</b>) were analyzed. Number of nodes (<b>a</b>), number of correlations (<b>b</b>), average degree (<b>c</b>) and connectance (<b>d</b>) of network for rhizosphere microbial communities for the rice progenies F1–F4.</p> Full article ">Figure 5
<p>Increasing community dissimilarity of the bacterial (red), fungal (green) and total (bacteria and fungi) (blue) communities according to the generations of rice self-crossing as given by the generalized dissimilarity model. The maximum height reached by each curve reveals the total amount of compositional turnover associated with the generations of self-crossing. The shape of each function indicates the rate of compositional turnover.</p> Full article ">
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