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This study aimed to investigate the fungal diversity and its temporal and spatial dynamics in the rhizosphere soil of healthy cotton by high-throughput sequencing. We studied species richness, composition, and distribution of cotton rhizosphere fungal community with respect to location (Alaer, Kuerle, Tumushuke, Hami, Shihezi, Wusu, and Jinghe) and plant growth period (seedling stage, bud stage, flowering stage, and boll-opening stage) using the methods of PCR-based high-throughput sequencing and real-time quantitative PCR. A total of 1,838,454 fungal nuclear ribosomal internal transcribed spacer region sequences (rRNA ITS) were obtained from all cotton plants sampled at different growth stages in the seven locations in Xinjiang. The most abundant fungal group in the cotton rhizosphere was the Ascomycota (78.72%), followed by the Zygomycota (9.56%) and Basidiomycota (2.77%). These sequences revealed an enormous number of operational taxonomic units (OTUs) in cotton (1802 unique OTUs), with 67-464 OTUs in a single cotton sample, at a 3% threshold and a sequencing depth of 30,000 sequences. We identified 33 classes and 389 genera from the resulting 1,800,714 sequences. Sordariomycetes was the most frequent class in all samples, followed by Leotiomycetes and Eurotiomycetes. There were some differences in OTUs among different growth stages, but the differences were not significant, with 382 OTUs (14.66%) being common to each of the stages. A marked difference in the diversity of fungi in the rhizosphere soil of cotton was evident among the different locations, with the highest number of OTUs being detected in Jinghe (1084 OTUs) and clusters of OTUs representative of northern and eastern Xinjiang being detected. There were significantly more tags of Mortierella in Jinghe and Wusu than in the other sampling sites. The dynamics of the rhizosphere fungal communities were influenced by sampling sites. To the best of our knowledge, the current study is the first application of PCR-based Illumina to characterize and compare the fungal biodiversity in multiple rhizosphere soil samples from cotton.

Endogenous bacteria are important for maintaining the health and other ecologically relevant functions of cotton plants. However, little is known about the community structures and diversity of endophytic bacteria in cotton plants. In our study, we used the Illumina amplicon sequencing technology to study the endophytic bacteria found in cotton root tissue in Xinjiang, China. A total of 60.84 × 106 effective sequences of the 16S rRNA gene in the V5–V6 variable region revealed a large number of operational taxonomic units (OTUs), namely 81–338 OTUs, at a cut-off level of 3% and a sequencing depth of 50 000 sequences. Among the 23 classes identified, Gammaproteobacteria was the dominant group, followed by Alphaproteobacteria, Actinobacteria, and Bacillus. The diversity of endogenous bacteria differed at different growth periods, with the most OTUs detected in seedlings (654), followed by the budding stage (381), flowering stage (350), and flocking stage (351). A total of 217 OTUs were common to all four stages. Pantoea tags were more common to the Shihezi region, whereas Erwinia labels were more common to the Hami region. These results suggest that the dynamics of endophytic bacterial communities are affected by plant growth stage. This highlights the relevance of microbial diversity studies in improving our understanding of endophyte communities.

This study details the introduction of a gfp marker into an endophytic bacterial strain (Achromobacter marplatensis strain 17, isolated from sugar beet) to monitor its colonization of sugar beet (Beta. vulgaris L.). Stability of the plasmid encoding the gfp was confirmed in vitro for at least 72 h of bacterial growth and after the colonization of tissues, under nonselective conditions. The colonization was observed using fluorescence microscopy and enumeration of culturable endophytes in inoculated sugar beet plants that grew for 10 or 20 days. gfp-Expressing strains were re-isolated from the inner tissues of surface-sterilized roots and stems of inoculated plants, and the survival of the Achromobacter marplatensis 17:gfp strain in plants 20 days after inoculation, even in the absence of selective pressure, suggests that it is good colonizer. These results also suggest that this strain could be a useful tool for the delivery of enzymes or other proteins into plants. In addition, the study highlights that sugar beet plants can be used effectively for detailed in vitro studies on the interactions between A. marplatensis strain 17 and its host, particularly if a gfp-tagged strain of the pathogen is used.

We studied species richness, composition and distribution of sugar beet endophytic fungi with location, plant species and plant growth period using the methods of PCR-based 454 pyrosequencing and terminal-restriction fragment length polymorphism. In total 4345 fungal nuclear ribosomal internal transcribed spacer region sequences (rDNA ITS) obtained from all sugar beet grown in two locations (Shihezi and Changji) on the north slope of Tianshan Mountain, China. The most abundant fungal groups in all sugar beet were Basidiomycota (97.26%). The other dominant phyla were from Ascomycota (0.61%) and Glomeromycota (2.13%). There was a marked difference in diversity of endophytic fungi in sugar beet for different growth stages. The greatest number of Operational T-RFLP Units was detected during sucrose accumulation (84) and tuber growth (74). Endophytic fungal diversity was reduced during seedling growth (18 OTUs) and rosette formation (58 OTUs). One OTU was common to all four periods of growth. There were more OTUs in Shihezi than in Changji. The dynamics of endophytic fungal communities were influenced by plant genotype and plant growth stage. Results indicated that the three factors were significantly related (α = 0.05) to the distribution of endophytic fungi, with host species the most important, followed by sampling dates and sampling locations. To our knowledge, this study is the first application of 454 pyrosequencing and T-RFLP to characterize and compare multiple sugar beet samples.