Browsing by Author "Mechan Llontop, Marco Enrique"

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    Comprehensive characterization of an aspen (Populus tremuloides) leaf litter sample that maintained ice nucleation activity for 48 years
    Vasebi, Yalda; Mechan Llontop, Marco Enrique; Hanlon, Regina; Schmale, David G. III; Schnell, Russell; Vinatzer, Boris A. (European Geosciences Union, 2019-04-24)
    Decaying vegetation was determined to be a potentially important source of atmospheric ice nucleation particles (INPs) in the early 1970s. The bacterium Pseudomonas syringae was the first microorganism with ice nucleation activity (INA) isolated from decaying leaf litter in 1974. However, the ice nucleation characteristics of P. syringae are not compatible with the characteristics of leaf litter-derived INPs since the latter were found to be sub-micron in size, while INA of P. syringae depends on much larger intact bacterial cells. Here we determined the cumulative ice nucleation spectrum and microbial community composition of the historic leaf litter sample 70-S-14 collected in 1970 that conserved INA for 48 years. The majority of the leaf litter-derived INPs were confirmed to be sub-micron in size and to be sensitive to boiling. Culture-independent microbial community analysis only identified Pseudomonas as potential INA. Culture-dependent analysis identified one P. syringae isolate, two isolates of the bacterial species Pantoea ananatis, and one fungal isolate of Mortierella alpina as having INA among 1170 bacterial colonies and 277 fungal isolates, respectively. Both Pa. ananatis and M. alpina are organisms that produce heat-sensitive sub-micron INPs. They are thus both likely sources of the INPs present in sample 70-S-14 and may represent important terrestrial sources of atmospheric INPs, a conclusion that is in line with other recent results obtained in regard to INPs from soil, precipitation, and the atmosphere.
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    Identification, Characterization, and Use of Precipitation-borne and Plant-associated Bacteria
    Mechan Llontop, Marco Enrique (Virginia Tech, 2020-01-10)
    Bacteria are ubiquitously present in every ecosystem on earth. While bacterial communities that reside in specific habitats, called the microbiota, have characteristic compositions, their constituents are exchanged between habitats. To understand the assembly processes and function of a microbial community in an ecosystem, it is thus important to identify its putative sources and sinks. The sources and sinks of the plant leaf microbiome, also called the phyllosphere microbiome, are still under debate. Here, I hypothesized that precipitation is a so far neglected source of the phyllosphere microbiome. Using 16S rRNA amplicon and metagenomic sequencing, I identified the genera Massilia, Sphingomonas, Methylobacterium, Pseudomonas, Acidiphilium, and Pantoea as members of the core rain microbiome in Blacksburg, VA. Further, I used rainwater as a bacterial inoculum to treat tomato plants. I showed that rain-borne bacteria of the genera Chryseobacterium, Enterobacter, Pantoea, Paenibacillus, Duganella, Streptomyces, Massilia, Shinella, Janthinobacterium, Erwinia, and Hyphomicrobium were significantly more abundant in the tomato phyllosphere 7 days post-inoculation, suggesting that these rain-borne bacteria successfully colonized the tomato phyllosphere and had a direct impact on the composition of its microbiome. These results were confirmed by comparing the phyllosphere microbiota of tomato plants grown under greenhouse conditions, and thus never exposed to rain, compared to plants grown outside under environmental conditions, including precipitation. Since a large diversity of bacteria is associated with rain, I also hypothesized that rain-borne bacteria are well adapted to environmental stresses, similar to the stressors microbial biopesticides are exposed to in the field. I thus explored rain as a source of resilient biopesticides to control fire blight, caused by the bacterial pathogen Erwinia amylovora, on apple. In an in-vitro dual culture assay, I identified rain-borne isolates displaying broad-range inhibition against E. amylovora and several other plant pathogens. Two rain-borne isolates, identified as Pantoea agglomerans and P. ananatis, showed the strongest inhibition of E. amylovora. Further experiments showed that these two Pantoea isolates survive under environmental conditions and have a strong protective effect against E. amylovora. However, protection from disease in an orchard was inconsistent, suggesting that the timing of application and formulations must be improved for field applications. Using a UV-mutagenesis screen and whole-genome sequencing, I found that a phenazine antibiotic produced by the P. agglomerans isolate was the likely active molecule that inhibited E. amylovora. Bacterial communities are constantly released as aerosols into the atmosphere from plant, soil, and aquatic sources. When in the atmosphere, bacteria may play crucial roles in geochemical processes, including the formation of precipitation. To understand the potential role of decaying vegetation as a source of atmospheric Ice Nucleation Particles (INPs), I analyzed a historic leaf litter sample collected in 1970 that had maintained Ice Nucleation Activity (INA) for 48 years. A culture-dependent analysis identified the bacterial species Pantoea ananatis and the fungal species Mortierella alpina to have INA and to be present in the leaf litter sample. Further, I determined that both P. ananatis and M. alpina produced heat-sensitive sub-micron INPs that may contribute to atmospheric INPs. The development of new sequencing technologies has facilitated our understanding of microbial community composition, assembly, and function. Most research in bacterial community composition is based on the sequencing of a single region of the 16S rRNA gene. Here, I tested the potential of culture-independent 16S rRNA sequencing of the phyllosphere microbiome for disease diagnosis. I compared the community composition of the microbiome of the aerial parts of cheddar pinks (Dianthus gratianopolitanus) that showed disease symptoms with the microbiome of healthy plants to identify the causative agent. However, I found that the pathogen is probably ubiquitous on cheddar pinks since it was present at similar abundance levels in symptomatic as well as healthy plants. Moreover, the low-resolution of 16S rRNA sequencing did not allow to identify the pathogen at the species or strain level. In summary, in this thesis, I found support for the hypothesis that rain is one of the sources of the phyllosphere microbiome, that rain is a promising source of biopesticides to control plant diseases in the field, that leaf litter is a source of atmospheric INPs, and that 16S rRNA sequencing is not well suited for pathogen identification in support of plant disease diagnosis. Finally, in additional research to which I contributed but that is not included in this thesis, I found that metagenomic sequencing can identify pathogens at the species and strain level and can overcome the limitations of 16S rRNA sequencing.
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    Strain-level identification of bacterial tomato pathogens directly from metagenomic sequences
    Mechan Llontop, Marco Enrique; Sharma, Parul; Aguilera Flores, Marcela; Yang, Shu; Pollock, Jill; Tian, Long; Huang, Chengjie; Rideout, Steven L.; Heath, Lenwood S.; Li, Song; Vinatzer, Boris A. (Scientific Societies, 2019-12-12)
    Routine strain-level identification of plant pathogens directly from symptomatic tissue could significantly improve plant disease control and prevention. Here we tested the Oxford Nanopore Technologies (ONT) MinIONTM sequencer for metagenomic sequencing of tomato plants either artificially inoculated with a known strain of the bacterial speck pathogen Pseudomonas syringae pv. tomato (Pto), or collected in the field and showing bacterial spot symptoms caused by either one of four Xanthomonas species. After species-level identification using ONT's WIMP software and the third party tools Sourmash and MetaMaps, we used Sourmash and MetaMaps with a custom database of representative genomes of bacterial tomato pathogens to attempt strain-level identification. In parallel, each metagenome was assembled and the longest contigs were used as query with the genome-based microbial identification Web service LINbase. Both the read-based and assembly-based approaches correctly identified Pto strain T1 in the artificially inoculated samples. The pathogen strain in most field samples was identified as a member of Xanthomonas perforans group 2. This result was confirmed by whole genome sequencing of colonies isolated from one of the samples. Although in our case, metagenome-based pathogen identification at the strain-level was achieved, caution still needs to be exerted when interpreting strain-level results because of the challenges inherent to assigning reads to specific strains and the error rate of nanopore sequencing.