Exploring the Nature of the Antimicrobial Metabolites Produced by <i>Paenibacillus ehimensis</i> Soil Isolate MZ921932 Using a Metagenomic Nanopore Sequencing Coupled with LC-Mass Analysis

The continuous emergence of multidrug-resistant (MDR) pathogens poses a global threat to public health. Accordingly, global efforts are continuously conducted to find new approaches to infection control by rapidly discovering antibiotics, particularly those that retain activities against MDR pathogens. In this study, metagenomic nanopore sequence analysis coupled with spectroscopic methods has been conducted for rapid exploring of the various active metabolites produced by Paenibacillus ehimensis soil isolate. Preliminary soil screening resulted in selection of a Gram-positive isolate identified via 16S ribosomal RNA gene sequencing as Paenibacillus ehimensis MZ921932. The isolate showed a broad range of activity against MDR Gram-positive, Gram-negative, and Candida spp. A metagenomics sequence analysis of the soil sample harboring Paenibacillus ehimensis isolate MZ921932 (NCBI GenBank accession PRJNA785410) revealed the presence of conserved biosynthetic gene clusters of petrobactin, tridecaptin, locillomycin (β-lactone), polymyxin, and macrobrevin (polyketides). The liquid chromatography/mass (LC/MS) analysis of the Paenibacillus ehimensis metabolites confirmed the presence of petrobactin, locillomycin, and macrobrevin. In conclusion, Paenibacillus ehimensis isolate MZ921932 is a promising rich source for broad spectrum antimicrobial metabolites. The metagenomic nanopore sequence analysis was a rapid, easy, and efficient method for the preliminary detection of the nature of the expected active metabolites. LC/MS spectral analysis was employed for further confirmation of the nature of the respective active metabolites

Staphylococcal Enterotoxins and Toxic Shock Syndrome Toxin-1 and Their Association among Bacteremic and Infective Endocarditis Patients in Egypt

Purpose. Infective endocarditis (IE) is a major complication in patients with bacteremia of Staphylococcus (S.) aureus infection. Our aim was to determine the association of the major Staphylococcal superantigens (SAgs), including Staphylococcal enterotoxins (SEs) and toxic shock syndrome toxin-1 (TSST-1), among hospitalized patients diagnosed with bacteremia and those with IE. Methods. This study was conducted on 88 patients; of these, 84 (95.5%) had two positive blood cultures. Eighteen out of the 84 patients (21.4%) were diagnosed based on the modified Duke criteria by a cardiologist to have IE. The recovered isolates were screened phenotypically using ELISA followed by molecular analysis of sea, seb, sec, sed, see, and tsst-1, the major SAg coding genes, and the obtained findings were statistically analyzed. Results. Phenotypic screening for SE production of 26 selected Staphylococci (15 isolated from the IE patients (10 S. aureus and 5 coagulase negative staphylococci (CoNS)) and 11 from bacteremic patients (10 S. aureus and 1 CoNS)) using ELISA revealed that 12/26 (46%) isolates were SE producers. PCR analysis showed that 19 (73%) isolates were PCR positive for SAg genes with the highest prevalence of the sea gene (79%), followed by seb (63%) and tsst-1 (21%). The least frequent gene was sed (5.3%). Statistical correlations between bacteremic and IE isolates with respect to prevalence of SAgs showed no significant difference (P value = 0.139, effect size=0.572) indicating no specific association between any of the detected SAgs and IE. Conclusion. There is high prevalence of SEs among clinical isolates of Staphylococci recovered from patients suffering bacteremia and those with IE. No significant difference was found among Staphylococcal isolates recovered from patients with bacteremia or IE regarding both phenotypic and genotypic detection of the tested SAgs

Metagenomic nanopore sequencing versus conventional diagnosis for identification of the dieback pathogens of mango trees

Dieback is one of the most dangerous fungal diseases affecting mango trees. In this study, nanopore metagenome sequencing of the root-soil samples and infected plant tissues was conducted to identify the fungal pathogens present. Soil analysis of the infected mango trees showed the abundance of the Dikarya subkingdom (59%) including Lasiodiplodia theobromae (15%), Alternaria alternata (6%), Ceratocystis huliohia and Colletotrichum gloeosporioides. Analysis of the infected plant tissues revealed the presence of A. alternata (34%). The data were deposited in the National Center of Biotechnology Information (PRJNA767267). In conclusion, nanopore metagenome sequencing analysis was a valuable tool to rapidly identify dieback-associated fungal pathogens

Supplementary material: Metagenomic nanopore sequencing versus conventional diagnosis for identification of the dieback pathogens of mango trees.docx

       Supplemental   Material Figure S1. Features of Dieback Disease observed   in mango trees, A: the shape of the leaf in the early stage of infection, B:   the shape of the leaf in an advanced stage of infection, C, D: symptoms in   the fruit itself Figure   S2. A: Longitudinal section of stem showing vascular tissues in a   healthy tree, B: Longitudinal section   of stem showing brown vascular tissues of dieback diseased tree, C: a picture   of dieback disease in an infected tree. Figure   S3. Mycelial fungal growth on PDA of the recovered fungi from root   soil. A; Fungal isolate-1; B, Fungal isolate-2. Figure S4. Nanopore metagenomic sequence   analysis wheel showing the percentage of Ceratocystis huliohia and Colletotrichum   gloeosporioides present in the sample, but were underrepresented Figure S5. Nanopore metagenomic sequence   analysis wheel showing the 30% of reads classified as bacteria with different   types such as E. coli, Sinorhizobium, Bradyrhizobium and   Rhodoplanes Figure S6. Nanopore metagenomic sequence   analysis wheel showing different species from Pseudomonas such as Pseudomonas stutzeri, Pseudomonas syringae,   and Pseudomonas qingdaonensis Figure S7. Nanopore metagenomic sequence   analysis wheel showing 37 species from actinomycetes group including,  Micrococcales, Micromonosporaceae,   Streptomycetaceae, Pseudonocardiaceae and Propionibacteriales Figure   S8. Nanopore metagenomic sequence analysis wheel of the control   root-soil sample the percentage of bacteria (55%), Archaea (0.3%), Fungi   (0.1%)    Figure   S9. Nanopore metagenomic sequence analysis wheel of a control soil   sample, 100% were of the Dikarya subkingdom including, Ascomycota   (87%), and Basidomycota (13%).        Figure S10.   Nanopore metagenomic sequence analysis bacteria wheel of a control soil   sample showing Proteobacteria (50%), Terrabacteria (33%), PVC   bacterial group (3%) and others.    Table   S1. The parameters used for the statistical Data   analysis of NMS of the detected microbiota of the collected soil samples from   the diseased trees.        Table S2. The parameters used for the statistical Data analysis of NMS of the detected potential pathogens of the dieback   disease of the collected soil samples from the diseased trees. </p