High-Throughput Sequencing Analysis of the Bacterial Community in Stone Fruit Phloem Tissues Infected by "Candidatus Phytoplasma prunorum"

Bacterial community shifts of commercial apples, oranges, and peaches at different harvest points across multiple growing seasons

Assessing the microbes present on tree fruit carpospheres as the fruit enters postharvest processing could have useful applications, as these microbes could have a major influence on spoilage, food safety, verification of packing process controls, or other aspects of processing. The goal of this study was to establish a baseline profile of bacterial communities associated with apple (pome fruit), peach (stone fruit), and Navel orange (citrus fruit) at harvest. We found that commercial peaches had the greatest bacterial richness followed by oranges then apples. Time of harvest significantly changed bacterial diversity in oranges and peaches, but not apples. Shifts in diversity varied by fruit type, where 70% of the variability in beta diversity on the apple carposphere was driven by the gain and loss of species (i.e., nestedness). The peach and orange carposphere bacterial community shifts were driven by nearly an even split between turnover (species replacement) and nestedness. We identified a small core microbiome for apples across and between growing seasons that included only Methylobacteriaceae and Sphingomonadaceae among the samples, while peaches had a larger core microbiome composed of five bacterial families: Bacillaceae, Geodermtophilaceae, Nocardioidaceae, Micrococcaeceae, and Trueperaceae. There was a relatively diverse core microbiome for oranges that shared all the families present on apples and peaches, except for Trueperaceae, but also included an additional nine bacterial families not shared including Oxalobacteraceae, Cytophagaceae, and Comamonadaceae. Overall, our findings illustrate the important temporal dynamics of bacterial communities found on major commercial tree fruit, but also the core bacterial families that constantly remain with both implications being important entering postharvest packing and processing.

Molecular Characterization of 'Candidatus Phytoplasma prunorum' in the Czech Republic and Susceptibility of Apricot Rootstocks to the Two Most Abundant Haplotypes

'Candidatus Phytoplasma prunorum' is one of the most destructive pathogens of Prunus species, where susceptible species render unproductive several years after infection. In epidemiology, the molecular characterization of phytoplasmas is based on sequence analysis of variable nonribosomal genes. In this study aceF, pnp, imp and secY genes were used for characterization of the 'Ca. P. prunorum' genotypes present in the Czech Republic. In total, 56 plant and 33 vector (Cacopsylla pruni) samples positive to 'Ca. P. prunorum' collected in seven localities were used in the study. Based on sequence analysis, four aceF, two pnp, six imp, and three secY genotypes were identified in analyzed samples. The most abundant in both plant and insect samples were the A6, P2, I4, and S2 genotypes. Most of the Czech 'Ca. P. prunorum' haplotypes clustered together in the haplotype network analysis. Next, two isolates representing the two most abundant Czech haplotypes (A6-P2-I4-S2 and A5-P2-I4-S2) were used in the susceptibility test of three apricot rootstock types (St. Julien A, M-VA-1, GF-305). Susceptibility was analyzed by phytoplasma quantification using quantitative real-time PCR and evaluation of symptom manifestation. Based on the results, the influence of the rootstock type on the phytoplasma titer and symptom manifestation was greater than of the phytoplasma isolate, while the year of analysis had no influence on the results. The results also showed that the phytoplasma titer is increasing in plant tissues during the vegetation period.

Distribution characteristics of soil AM fungi community in soft sandstone area

To explore the diversity and distribution characteristics of soil arbuscular mycorrhizae fungi (AMF) communities in the soft sandstone area, thirteen arsenic sandstone rock samples were collected from three planting plots (SI, SII and SIII) and one bare control plot (CK), separately. The sampling locations are as follows: the top of the slope (denoted by the number 1), sunny slope (2), shady slope (3) and gully bottom (4). These samples were then tested with an Illumina HiSeq PE250 high-throughput sequencing platform. Experimental results show that the SIII4 sample (from the gully bottom of the SIII plot) has the highest moisture content of 9.1%, while the CK sample in the control plot has lowest moisture content. SI2 has the highest pH of 9.58 and CK has the lowest pH of 8.73. SII1 has the highest available phosphorus (AP) content of 9.61 mg/kg, while SII3 has the lowest AP content of 2.29 mg/kg. Furthermore, SI2 has the highest NH₄-N content of 11.24 mg/kg, while SII1 has the lowest NH₄-N of 4.09 mg/kg. SII1 has the highest available potassium (AK) content of 48.92 mg/kg and CK has the lowest AK content of 1.82 mg/kg. In the observed-species index reflecting AMF genetic diversity, SI1 differences significantly from SII4 and SIII3 (P < 0.05). In the Shannon index, SI1 is significantly different from SI2, SI3, SI4; SII2 is significantly different from SII3; SI2, SI4, SII1 and SII3 are quite different from CK (P < 0.05). The dominant genera of AMF in these plots include Glomus (17.24%-65.53%), Scutellospora (0.04%-67.38%), Septoglomus (2.83%-43.03%) and Kamienskia (0.64%-46.38%). The dominant genera of AMF vary significantly between sunny slope and shady slope. Positive correlation exists between soil NH₄-N and the AM fungal community structure. There are prominent positive correlations exist among genetic diversity index chao1, observed-species, pH and AP (P < 0.05), and obviously negative correlation between observed species and AK (P < 0.05). The research findings on the distribution characteristics of AM fungus community in the arsenic sandstone plot and their relationship with environmental factors can help with arsenic sandstone management in other similar areas.

A novel PCR-clamping assay reducing plant host DNA amplification significantly improves prokaryotic endo-microbiome community characterization

Due to the sequence homology between the bacterial 16S rRNA gene and plant chloroplast and mitochondrial DNA, the taxonomic characterization of plant microbiome using amplicon-based high throughput sequencing often results in the overwhelming presence of plant-affiliated reads, preventing the thorough description of plant-associated microbial communities. In this work we developed a PCR blocking primer assay targeting the taxonomically informative V5-V6 region of the 16S rRNA gene in order to reduce plant DNA co-amplification, and increase diversity coverage of associated prokaryotic communities. Evaluation of our assay on the characterization of the prokaryotic endophytic communities of Zea mays, Pinus taeda and Spartina alternifora leaves led to significantly reducing the proportion of plant reads, yielded 20 times more prokaryotic reads and tripled the number of detected OTUs compared to a commonly used V5-V6 PCR protocol. To expand the application of our PCR-clamping assay across a wider taxonomic spectrum of plant hosts, we additionally provide an alignment of chloroplast and mitochondrial DNA sequences encompassing more than 200 terrestrial plant families as a supporting tool for customizing our blocking primers.

A Tripartite Microbial-Environment Network Indicates How Crucial Microbes Influence the Microbial Community Ecology

Current technologies could identify the abundance and functions of specific microbes, and evaluate their individual effects on microbial ecology. However, these microbes interact with each other, as well as environmental factors, in the form of complex network. Determination of their combined ecological influences remains a challenge. In this study, we developed a tripartite microbial-environment network (TMEN) analysis method that integrates microbial abundance, metabolic function, and environmental data as a tripartite network to investigate the combined ecological effects of microbes. Applying TMEN to analyzing the microbial-environment community structure in the sediments of Hangzhou Bay, one of the most seriously polluted coastal areas in China, we found that microbes were well-organized into 4 bacterial communities and 9 archaeal communities. The total organic carbon, sulfate, chemical oxygen demand, salinity, and nitrogen-related indexes were detected as crucial environmental factors in the microbial-environmental network. With close interactions with these environmental factors, Nitrospirales and Methanimicrococcu were identified as hub microbes with connection advantage. Our TMEN method could close the gap between lack of efficient statistical and computational approaches and the booming of large-scale microbial genomic and environmental data. Based on TMEN, we discovered a potential microbial ecological mechanism that crucial species with significant influence on the microbial community ecology would possess one or two of the community advantages for enhancing their ecological status and essentiality, including abundance advantage and connection advantage.