PCirc: random forest-based plant circRNA identification software.

BACKGROUND: Circular RNA (circRNA) is a novel type of RNA with a closed-loop structure. Increasing numbers of circRNAs are being identified in plants and animals, and recent studies have shown that circRNAs play an important role in gene regulation. Therefore, identifying circRNAs from increasing amounts of RNA-seq data is very important. However, traditional circRNA recognition methods have limitations. In recent years, emerging machine learning techniques have provided a good approach for the identification of circRNAs in animals. However, using these features to identify plant circRNAs is infeasible because the characteristics of plant circRNA sequences are different from those of animal circRNAs. For example, plants are extremely rich in splicing signals and transposable elements, and their sequence conservation in rice, for example is far less than that in mammals. To solve these problems and better identify circRNAs in plants, it is urgent to develop circRNA recognition software using machine learning based on the characteristics of plant circRNAs. RESULTS: In this study, we built a software program named PCirc using a machine learning method to predict plant circRNAs from RNA-seq data. First, we extracted different features, including open reading frames, numbers of k-mers, and splicing junction sequence coding, from rice circRNA and lncRNA data. Second, we trained a machine learning model by the random forest algorithm with tenfold cross-validation in the training set. Third, we evaluated our classification according to accuracy, precision, and F1 score, and all scores on the model test data were above 0.99. Fourth, we tested our model by other plant tests, and obtained good results, with accuracy scores above 0.8. Finally, we packaged the machine learning model built and the programming script used into a locally run circular RNA prediction software, Pcirc ( https://github.com/Lilab-SNNU/Pcirc ). CONCLUSION: Based on rice circRNA and lncRNA data, a machine learning model for plant circRNA recognition was constructed in this study using random forest algorithm, and the model can also be applied to plant circRNA recognition such as Arabidopsis thaliana and maize. At the same time, after the completion of model construction, the machine learning model constructed and the programming scripts used in this study are packaged into a localized circRNA prediction software Pcirc, which is convenient for plant circRNA researchers to use.

Identification and Characterization of circRNAs Responsive to Methyl Jasmonate in Arabidopsis thaliana.

Circular RNAs (circRNAs) are endogenous noncoding RNAs with covalently closed continuous loop structures that are formed by 3'-5' ligation during splicing. These molecules are involved in diverse physiological and developmental processes in eukaryotic cells. Jasmonic acid (JA) is a critical hormonal regulator of plant growth and defense. However, the roles of circRNAs in the JA regulatory network are unclear. In this study, we performed high-throughput sequencing of Arabidopsis thaliana at 24 h, 48 h, and 96 h after methyl JA (MeJA) treatment. A total of 8588 circRNAs, which were distributed on almost all chromosomes, were identified, and the majority of circRNAs had lengths between 200 and 800 bp. We identified 385 differentially expressed circRNAs (DEcircRNAs) by comparing data between MeJA-treated and untreated samples. Gene Ontology (GO) enrichment analysis of the host genes that produced the DEcircRNAs showed that the DEcircRNAs are mainly involved in response to stimulation and metabolism. Additionally, some DEcircRNAs were predicted to act as miRNA decoys. Eight DEcircRNAs were validated by qRT-PCR with divergent primers, and the junction sites of five DEcircRNAs were validated by PCR analysis and Sanger sequencing. Our results provide insight into the potential roles of circRNAs in the MeJA regulation network.

Soil bacteria are more sensitive than fungi in response to nitrogen and phosphorus enrichment.

Anthropogenic activities have dramatically increased nitrogen (N) and phosphorous (P) enrichments in terrestrial ecosystems. However, it is still unclear on how bacterial and fungal communities would respond to the simultaneously increased N and P enrichment. In this study, we used a field experiment to simulate N and P input, and examined the effects of N and P additions on the abundance, alpha-diversity, and community composition of soil bacteria and fungi in a riparian forest. Six nutrient-addition treatments, including low N (30 kg N ha-1 year-1), high N (150 kg N ha -1 year-1), low P (30 kg P2O5 ha-1 year-1), high P (150 kg P2O5 ha -1 year-1), low N+P, high N+P, and a control (CK) treatment were set up. We found that the N and P additions significantly affected bacterial abundance, community composition, but not the alpha diversity. Specifically, 16S, nirK, and nirS gene copy numbers were significantly reduced after N and P additions, which were correlated with decreases in soil pH and NO- 3-N, respectively; Co-additions of N and P showed significantly antagonistic interactions on bacterial gene copies; Nutrient additions significantly increased the relative abundance of Proteobacteria while reduced the relative abundance of Chloroflexi. Mantel's test showed that the alteration in bacterial composition was associated with the changes in soil pH and NO- 3-N. The nutrient additions did not show significant effects on fungal gene copy numbers, alpha diversity, and community composition, which could be due to non-significant alterations in soil C/N and total P concentration. In conclusion, our results suggest that soil bacteria are more sensitive than fungi in response to N and P enrichment; the alterations in soil pH and NO- 3-N explain the effects of N and P enrichment on bacterial communities, respectively; and the co-addition of N and P reduces the negative effects of these two nutrients addition in alone. These findings improve our understanding of microbial response to N and P addition, especially in the context of simultaneous enrichment of anthropogenic nutrient inputs.

GreenCircRNA: a database for plant circRNAs that act as miRNA decoys.

Circular RNAs (circRNAs) are endogenous non-coding RNAs that form a covalently closed continuous loop, are widely distributed and play important roles in a series of developmental processes. In plants, an increasing number of studies have found that circRNAs can regulate plant metabolism and are involved in plant responses to biotic or abiotic stress. Acting as miRNA decoys is a critical way for circRNAs to perform their functions. Therefore, we developed GreenCircRNA-a database for plant circRNAs acting as miRNA decoys that is dedicated to providing a plant-based platform for detailed exploration of plant circRNAs and their potential decoy functions. This database includes over 210 000 circRNAs from 69 species of plants; the main data sources of circRNAs in this database are NCBI, EMBL-EBI and Phytozome. To investigate the function of circRNAs as competitive endogenous RNAs, the possibility of circRNAs from 38 plants to act as miRNA decoys was predicted. Moreover, we provide basic information for the circRNAs in the database, including their locations, host genes and relative expression levels, as well as full-length sequences, host gene GO (Gene Ontology) numbers and circRNA visualization. GreenCircRNA is the first database for the prediction of circRNAs that act as miRNA decoys and contains the largest number of plant species. Database URL: http://greencirc.cn.

Prevotella and Klebsiella proportions in fecal microbial communities are potential characteristic parameters for patients with major depressive disorder.

BACKGROUND: The diagnosis of major depression disorder (MDD) and other mental disorders were depended on some subjective survey scales. There are confirmed relationship between the gut flora and the mental states of MDD patients. METHODS: The V3-V4 region of the 16S rRNA gene was extracted from the fecal microbial communities in MDD patients, PCR amplified and sequenced on the Illumina Miseq platform. RESULTS: More phylum Firmicutes, less Bacteroidetes, and more genus Prevotella, Klebsiella, Streptococcus and Clostridium XI were found in MDD patients. The changes of the proportion of Prevotella and Klebsiella were consistent with Hamilton depression rating scale. LIMITATIONS: The conclusion was limited by small sample sizes and potential uncontrollable influence factors on fecal microbiota. DISCUSSION: Prevotella and Klebsiella proportion in fecal microbial communities should be concerned in the diagnosis and therapeutic monitoring of MDD in future.