Dataset Information

DRNA-Seq Reveals Genomewide TSSs and Noncoding RNAs of Plant Beneficial Rhizobacterium Bacillus amyloliquefaciens FZB42.

ABSTRACT: Bacillus amyloliquefaciens subsp. plantarum FZB42 is a representative of Gram-positive plant-growth-promoting rhizobacteria (PGPR) that inhabit plant root environments. In order to better understand the molecular mechanisms of bacteria-plant symbiosis, we have systematically analyzed the primary transcriptome of strain FZB42 grown under rhizosphere-mimicking conditions using differential RNA sequencing (dRNA-seq). Our analysis revealed 4,877 transcription start sites for protein-coding genes, identified genes differentially expressed under different growth conditions, and corrected many previously mis-annotated genes. We also identified a large number of riboswitches and cis-encoded antisense RNAs, as well as trans-encoded small noncoding RNAs that may play important roles in the gene regulation of Bacillus. Overall, our analyses provided a landscape of Bacillus primary transcriptome and improved the knowledge of rhizobacteria-host interactions.

SUBMITTER: Fan B

PROVIDER: S-EPMC4634765 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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dRNA-Seq Reveals Genomewide TSSs and Noncoding RNAs of Plant Beneficial Rhizobacterium Bacillus amyloliquefaciens FZB42.

Fan Ben B Li Lei L Chao Yanjie Y Förstner Konrad K Vogel Jörg J Borriss Rainer R Wu Xiao-Qin XQ

PloS one 20151105 11

Bacillus amyloliquefaciens subsp. plantarum FZB42 is a representative of Gram-positive plant-growth-promoting rhizobacteria (PGPR) that inhabit plant root environments. In order to better understand the molecular mechanisms of bacteria-plant symbiosis, we have systematically analyzed the primary transcriptome of strain FZB42 grown under rhizosphere-mimicking conditions using differential RNA sequencing (dRNA-seq). Our analysis revealed 4,877 transcription start sites for protein-coding genes, id ...[more]

PMID: 26540162

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Project description:Soybean root rot caused by the oomycete Phytophthora sojae is a serious soilborne disease threatening soybean production in China. Bacillus velezensis FZB42 is a model strain for Gram-positive plant growth-promoting rhizobacteria and is able to produce multiple antibiotics. In this study, we demonstrated that B. velezensis FZB42 can efficiently antagonize P. sojae. The underlying mechanism for the inhibition was then investigated. The FZB42 mutants deficient in the synthesis of lipopeptides (bacillomycin D and fengycin), known to have antifungal activities, and polyketides (bacillaene, difficidin, and macrolactin), known to have antibacterial activities, were not impaired in their antagonism toward P. sojae; in contrast, mutants deficient in bacilysin biosynthesis completely lost their antagonistic activities toward P. sojae, indicating that bacilysin was responsible for the activity. Isolated pure bacilysin confirmed this inference. Bacilysin was previously shown to be antagonistic mainly toward prokaryotic bacteria rather than eukaryotes. Here, we found that bacilysin could severely damage the hyphal structures of P. sojae and lead to the loss of its intracellular contents. A device was invented allowing interactions between P. sojae and B. velezensis FZB42 on nutrient agar. In this manner, the effect of FZB42 on P. sojae was studied by transcriptomics. FZB42 significantly inhibited the expression of P. sojae genes related to growth, macromolecule biosynthesis, pathogenicity, and ribosomes. Among them, the genes for pectate lyase were the most significantly downregulated. Additionally, we showed that bacilysin effectively prevents soybean sprouts from being infected by P. sojae and could antagonize diverse Phytophthora species, such as Phytophthora palmivora, P. melonis, P. capsici, P. litchi, and, most importantly, P. infestans. IMPORTANCE Phytophthora spp. are widespread eukaryotic phytopathogens and often extremely harmful. Phytophthora can infect many types of plants important to agriculture and forestry and thus cause large economic losses. Perhaps due to inappropriate recognition of Phytophthora as a common pathogen in history, research on the biological control of Phytophthora is limited. This study shows that B. velezensis FZB42 can antagonize various Phytophthora species and prevent the infection of soybean seedlings by P. sojae. The antibiotic produced by FZB42, bacilysin, which was already known to have antibacterial effectiveness, is responsible for the inhibitory action against Phytophthora. We further showed that some Phytophthora genes and pathways may be targeted in future biocontrol studies. Therefore, our data provide a basis for the development of new tools for the prevention and control of root and stem rot in soybean and other plant diseases caused by Phytophthora.

Project description:The most prominent RNA modification - N6-methyladenosine (m6A) - affects gene regulation and cancer progression. The extent and effect of m6A on long non-coding RNAs (lncRNAs) is, however, still not clear. The most established method for m6A detection is methylated RNA immunoprecipitation and sequencing (MeRIP-seq). However, Oxford Nanopore Technologies recently developed direct RNA-seq (dRNA-seq) method, allowing m6A identification at higher resolution and in its native form. We performed whole transcriptome sequencing of the glioblastoma cell line U87-MG with both MeRIP-seq and dRNA-seq. For MeRIP-seq, m6A peaks were identified using nf-core/chipseq, and for dRNA-seq - EpiNano pipeline. MeRIP-seq analysis revealed 5086 lncRNAs transcripts, while dRNA-seq identified 336 lncRNAs transcripts from which 556 and 198 were found to be m6A modified, respectively. While 24 lncRNAs with m6A overlapped between two methods. Gliovis database analysis revealed that the expression of the major part of identified overlapping lncRNAs was associated with glioma grade or patient survival prognosis. We found that the frequency of m6A occurrence in lncRNAs varied more than 9-fold throughout the provided list of 24 modified lncRNAs. The highest m6A frequency was detected in MIR1915HG, THAP9-AS1, MALAT1, NORAD1, and NEAT1 (49-88nt), while MIR99AHG, SNHG3, LOXL1-AS1, ILF3-DT showed the lowest m6A frequency (445-261nt). Taken together, (1) we provide a high accuracy list of 24 m6A modified lncRNAs of U87-MG cells; (2) we conclude that MeRIP-seq is more suitable for an initial m6A screening study, due to its higher lncRNA coverage, whereas dRNA-seq is most useful when more in-depth analysis of m6A quantity and precise location is of interest.Abbreviations: (dRNA-seq) direct RNA-seq, (GBM) glioblastoma, (LGG) low-grade glioma, (lncRNAs) long non-coding RNAs, (m6A) N6-methyladenosine, (MeRIP-seq) methylated RNA immunoprecipitation and sequencing, (ncRNA) non-coding RNA, (ONT) Oxford Nanopore Technologi; Lietuvos Mokslo Taryba.

Project description:BACKGROUND: Differential RNA sequencing (dRNA-seq) is a high-throughput screening technique designed to examine the architecture of bacterial operons in general and the precise position of transcription start sites (TSS) in particular. Hitherto, dRNA-seq data were analyzed by visualizing the sequencing reads mapped to the reference genome and manually annotating reliable positions. This is very labor intensive and, due to the subjectivity, biased. RESULTS: Here, we present TSSAR, a tool for automated de novo TSS annotation from dRNA-seq data that respects the statistics of dRNA-seq libraries. TSSAR uses the premise that the number of sequencing reads starting at a certain genomic position within a transcriptional active region follows a Poisson distribution with a parameter that depends on the local strength of expression. The differences of two dRNA-seq library counts thus follow a Skellam distribution. This provides a statistical basis to identify significantly enriched primary transcripts.We assessed the performance by analyzing a publicly available dRNA-seq data set using TSSAR and two simple approaches that utilize user-defined score cutoffs. We evaluated the power of reproducing the manual TSS annotation. Furthermore, the same data set was used to reproduce 74 experimentally validated TSS in H. pylori from reliable techniques such as RACE or primer extension. Both analyses showed that TSSAR outperforms the static cutoff-dependent approaches. CONCLUSIONS: Having an automated and efficient tool for analyzing dRNA-seq data facilitates the use of the dRNA-seq technique and promotes its application to more sophisticated analysis. For instance, monitoring the plasticity and dynamics of the transcriptomal architecture triggered by different stimuli and growth conditions becomes possible.The main asset of a novel tool for dRNA-seq analysis that reaches out to a broad user community is usability. As such, we provide TSSAR both as intuitive RESTful Web service ( http://rna.tbi.univie.ac.at/TSSAR) together with a set of post-processing and analysis tools, as well as a stand-alone version for use in high-throughput dRNA-seq data analysis pipelines.

Dataset Information

DRNA-Seq Reveals Genomewide TSSs and Noncoding RNAs of Plant Beneficial Rhizobacterium Bacillus amyloliquefaciens FZB42.

Publications

dRNA-Seq Reveals Genomewide TSSs and Noncoding RNAs of Plant Beneficial Rhizobacterium Bacillus amyloliquefaciens FZB42.

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