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.

Kindlin supports platelet integrin αIIbß3 activation by interacting with paxillin.

Kindlins play an important role in supporting integrin activation by cooperating with talin; however, the mechanistic details remain unclear. Here, we show that kindlins interacted directly with paxillin and that this interaction could support integrin αIIbß3 activation. An exposed loop in the N-terminal F0 subdomain of kindlins was involved in mediating the interaction. Disruption of kindlin binding to paxillin by structure-based mutations significantly impaired the function of kindlins in supporting integrin αIIbß3 activation. Both kindlin and talin were required for paxillin to enhance integrin activation. Interestingly, a direct interaction between paxillin and the talin head domain was also detectable. Mechanistically, paxillin, together with kindlin, was able to promote the binding of the talin head domain to integrin, suggesting that paxillin complexes with kindlin and talin to strengthen integrin activation. Specifically, we observed that crosstalk between kindlin-3 and the paxillin family in mouse platelets was involved in supporting integrin αIIbß3 activation and in vivo platelet thrombus formation. Taken together, our findings uncover a novel mechanism by which kindlin supports integrin αIIbß3 activation, which might be beneficial for developing safer anti-thrombotic therapies.

Sharpin suppresses ß1-integrin activation by complexing with the ß1 tail and kindlin-1.

BACKGROUND: Previously sharpin has been identified as an endogenous inhibitor of ß1-integrin activation by directly binding to a conserved region in the cytoplasmic tails (CTs) of the integrin ß1-associated α subunits. METHODS: Here we employed biochemical approaches and cellular analyses to evaluate the function and molecular mechanism of the sharpin-kindlin-1 complex in regulating ß1-integrin activation. RESULTS: In this study, we found that although the inhibition of sharpin on ß1-integrin activation could be confirmed, sharpin had no apparent effect on integrin αIIbß3 activation in CHO cell system. Notably, a direct interaction between sharpin and the integrin ß1 CT was detected, while the interaction of sharpin with the integrin αIIb and the ß3 CTs were substantially weaker. Importantly, sharpin was able to inhibit the talin head domain binding to the integrin ß1 CT, which can mechanistically contribute to inhibiting ß1-integrin activation. Interestingly, we also found that sharpin interacted with kindlin-1, and the interaction between sharpin and the integrin ß1 CT was significantly enhanced when kindlin-1 was present. Consistently, we observed that instead of acting as an activator, kindlin-1 actually suppressed the talin head domain mediated ß1-integrin activation, indicating that kindlin-1 may facilitate recruitment of sharpin to the integrin ß1 CT. CONCLUSION: Taken together, our findings suggest that sharpin may complex with both kindlin-1 and the integrin ß1 CT to restrict the talin head domain binding, thus inhibiting ß1-integrin activation.

Long Noncoding RNA NEAT1 Contributes to the Tumorigenesis of Colorectal Cancer Through Regulating SLC38A1 Expression by Sponging miR-138.

Background: Colorectal cancer (CRC), a malignant tumor, has become a highly relevant social problem. Nuclear paraspeckle assembly transcript 1 (NEAT1) was reported as an oncogenic long noncoding RNA in diverse tumors, including CRC. Nevertheless, the mechanism of NEAT1 in CRC remains unknown. Materials and Methods: The expression levels of NEAT1 and solute carrier family 38 member 1 (SLC38A1) in CRC tissues and cells were detected by real-time quantitative polymerase chain reaction. The protein levels of p62, microtubule-associated protein light (LC3-I), LC3-II, and SLC38A1 were examined by Western blot assay. Cell proliferation, apoptosis, and invasion were measured by 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-H-tetrazolium bromide (MTT), and flow cytometry and transwell assays, respectively. The interaction between miR-138 and NEAT1 or SLC38A1 was predicted by StarBase or TargetScan, and verified by the dual-luciferase reporter assay. The effect of NEAT1 on tumor growth was determined in CRC mice model. Results: The expression of NEAT1 and SLC38A1 was upregulated in CRC tissues and cells. NEAT1 knockdown or SLC38A1 downregulation restrained cell proliferation and invasion, and accelerated cell apoptosis and autophagy of CRC cells. NEAT1 acted as a sponge of miR-138 to regulate SLC38A1 expression. Furthermore, NEAT1 deficiency suppressed tumor growth in vivo. Conclusion: These studies disclosed that NEAT1 knockdown inhibited CRC progression by miR-138/SLC38A1 axis, providing an underlying target for CRC treatment.

Rcirc: An R Package for circRNA Analyses and Visualization.

Circular RNA (circRNA), which has a closed-loop structure, is a special type of endogenous RNA that plays important roles in many biological processes. With improvements in next-generation sequencing technology and bioinformatics methods, some tools have been published for circRNA detection based on RNA-seq. Here, we developed the R package "Rcirc" for further analyses of circRNA after its detection. Rcirc identifies the coding ability of circRNA and visualizes various aspects of this feature. It also provides general visualization for both single circRNAs and meta-features of thousands of circRNAs. Rcirc was designed as a user-friendly tool that provides many highly automated functions without requiring the user to perform many complicated processes. It is available on GitHub (https://github.com/PSSUN/Rcirc) under the license GPL 3.0.

CircCode: A Powerful Tool for Identifying circRNA Coding Ability.

Circular RNAs (circRNAs), which play vital roles in many regulatory pathways, are widespread in many species. Although many circRNAs have been discovered in plants and animals, the functions of these RNAs have not been fully investigated. In addition to the function of circRNAs as microRNA (miRNA) decoys, the translation potential of circRNAs is important for the study of their functions; yet, few tools are available to identify their translation potential. With the development of high-throughput sequencing technology and the emergence of ribosome profiling technology, it is possible to identify the coding ability of circRNAs with high sensitivity. To evaluate the coding ability of circRNAs, we first developed the CircCode tool and then used CircCode to investigate the translation potential of circRNAs from humans and Arabidopsis thaliana. Based on the ribosome profile databases downloaded from NCBI, we found 3,610 and 1,569 translated circRNAs in humans and A. thaliana, respectively. Finally, we tested the performance of CircCode and found a low false discovery rate and high sensitivity for identifying circRNA coding ability. CircCode, a Python 3-based framework for identifying the coding ability of circRNAs, is also a simple and powerful command line-based tool. To investigate the translation potential of circRNAs, the user can simply fill in the given configuration file and run the Python 3 scripts. The tool is freely available at https://github.com/PSSUN/CircCode.