Identification and characterization of endogenous small interfering RNAs from rice

RNA silencing-mediated small interfering RNAs (siRNAs) and microRNAs (miRNAs) have diverse natural roles, ranging from regulation of gene expression and heterochromatin formation to genome defense against transposons and viruses. Unlike miRNAs, endogenous siRNAs are generally not conserved between species; consequently, their identification requires experimental approaches. Thus far, endogenous siRNAs have not been reported from rice, which is a model species for monocotyledonous plants. We identified a large set of putative endogenous siRNAs from root, shoot and inflorescence small RNA cDNA libraries of rice. Most of these siRNAs are from intergenic regions, although a substantial proportion (22%) originates from the introns and exons of protein-coding genes. Northern and RT–PCR analysis revealed that the expression of some of the siRNAs is tissue specific or developmental stage specific. A total of 25 transposons and 21 protein-coding genes were predicted to be cis-targets of some of the siRNAs. Based on sequence homology, we also predicted 111 putative trans-targets for 44 of the siRNAs. Interestingly, ∼46% of the predicted trans-targets are transposable elements, which suggests that endogenous siRNAs may play an important role in the suppression of transposon proliferation. Using RNA ligase-mediated-5′ rapid amplification of cDNA end assays, we validated three of the predicted targets and provided evidence for both cis- and trans-silencing of target genes by siRNAs-guided mRNA cleavage

Bioinformatics analysis suggests base modifications of tRNAs and miRNAs in Arabidopsis thaliana

<p>Abstract</p> <p>Background</p> <p>Modifications of RNA bases have been found in some mRNAs and non-coding RNAs including rRNAs, tRNAs, and snRNAs, where modified bases are important for RNA function. Little is known about RNA base modifications in <it>Arabidopsis thaliana</it>.</p> <p>Results</p> <p>In the current work, we carried out a bioinformatics analysis of RNA base modifications in tRNAs and miRNAs using large numbers of cDNA sequences of small RNAs (sRNAs) generated with the 454 technology and the massively parallel signature sequencing (MPSS) method. We looked for sRNAs that map to the genome sequence with one-base mismatch (OMM), which indicate candidate modified nucleotides. We obtained 1,187 sites with possible RNA base modifications supported by both 454 and MPSS sequences. Seven hundred and three of these sites were within tRNA loci. Nucleotide substitutions were frequently located in the T arm (substitutions from A to U or G), upstream of the D arm (from G to C, U, or A), and downstream of the D arm (from G to U). The positions of major substitution sites corresponded with the following known RNA base modifications in tRNAs: N1-methyladenosine (m¹A), N2-methylguanosine (m²G), and N2-N2-methylguanosine (m²₂G).</p> <p>Conclusion</p> <p>These results indicate that our bioinformatics method successfully detected modified nucleotides in tRNAs. Using this method, we also found 147 substitution sites in miRNA loci. As with tRNAs, substitutions from A to U or G and from G to C, U, or A were common, suggesting that base modifications might be similar in tRNAs and miRNAs. We suggest that miRNAs contain modified bases and such modifications might be important for miRNA maturation and/or function.</p

Distinctive Core Histone Post-Translational Modification Patterns in Arabidopsis thaliana

Post-translational modifications of histones play crucial roles in the genetic and epigenetic regulation of gene expression from chromatin. Studies in mammals and yeast have found conserved modifications at some residues of histones as well as non-conserved modifications at some other sites. Although plants have been excellent systems to study epigenetic regulation, and histone modifications are known to play critical roles, the histone modification sites and patterns in plants are poorly defined. In the present study we have used mass spectrometry in combination with high performance liquid chromatography (HPLC) separation and phospho-peptide enrichment to identify histone modification sites in the reference plant, Arabidopsis thaliana. We found not only modifications at many sites that are conserved in mammalian and yeast cells, but also modifications at many sites that are unique to plants. These unique modifications include H4 K20 acetylation (in contrast to H4 K20 methylation in non-plant systems), H2B K6, K11, K27 and K32 acetylation, S15 phosphorylation and K143 ubiquitination, and H2A K144 acetylation and S129, S141 and S145 phosphorylation, and H2A.X S138 phosphorylation. In addition, we found that lysine 79 of H3 which is highly conserved and modified by methylation and plays important roles in telomeric silencing in non-plant systems, is not modified in Arabidopsis. These results suggest distinctive histone modification patterns in plants and provide an invaluable foundation for future studies on histone modifications in plants

Identification of novel and candidate miRNAs in rice by high throughput sequencing

<p>Abstract</p> <p>Background</p> <p>Small RNA-guided gene silencing at the transcriptional and post-transcriptional levels has emerged as an important mode of gene regulation in plants and animals. Thus far, conventional sequencing of small RNA libraries from rice led to the identification of most of the conserved miRNAs. Deep sequencing of small RNA libraries is an effective approach to uncover rare and lineage- and/or species-specific microRNAs (miRNAs) in any organism.</p> <p>Results</p> <p>In order to identify new miRNAs and possibly abiotic-stress regulated small RNAs in rice, three small RNA libraries were constructed from control rice seedlings and seedlings exposed to drought or salt stress, and then subjected to pyrosequencing. A total of 58,781, 43,003 and 80,990 unique genome-matching small RNAs were obtained from the control, drought and salt stress libraries, respectively. Sequence analysis confirmed the expression of most of the conserved miRNAs in rice. Importantly, 23 new miRNAs mostly each derived from a unique locus in rice genome were identified. Six of the new miRNAs are conserved in other monocots. Additionally, we identified 40 candidate miRNAs. Allowing not more than 3 mis-matches between a miRNA and its target mRNA, we predicted 20 targets for 9 of the new miRNAs.</p> <p>Conclusion</p> <p>Deep sequencing proved to be an effective strategy that allowed the discovery of 23 low-abundance new miRNAs and 40 candidate miRNAs in rice.</p

Effect of ω-3 Fatty Acid on Gastrointestinal Motility after Abdominal Operation in Rats

Objective. To investigate whether ω-3 fatty acid could stimulate gastrointestinal motility after abdominal operation. Method. Wistar rats were randomly divided into 3 group (normal saline group, intralipid group, and ω-3 fatty acid group, n = 18/group) after partial caecectomy and gastrostomosis, each group was divided into 3 groups (POD1, POD3, and POD6, n = 6/group). Serum gastrin (GAS), motilin (MTL), interleukin-1 (IL-1), interleukin-6 (IL-6), tissue necrosis factor-α (TNF-α), cyclooxygenase-2 (COX-2), gastric emptying rate, and small bowel propulsion rate were measured. Results. On POD 3, gastric emptying rate and small bowel propulsion rate in ω-3 fatty acid group were higher than those in normal saline group and intralipid group. Serum GAS and MTL levels in ω-3 fatty acid group were higher than those in normal saline group, but serum IL-1, IL-6, TNF-α, and COX-2 levels were lower than those in normal saline group and intralipid group. Conclusion. ω-3 fatty acid could accelerate the recovery of gastrointestinal mobility after abdominal operation in rats, mainly by relieving postoperative inflammation

PiCoCo: Pixelwise Contrast and Consistency Learning for Semisupervised Building Footprint Segmentation

Building footprint segmentation from high-resolution remote sensing (RS) images plays a vital role in urban planning, disaster response, and population density estimation. Convolutional neural networks (CNNs) have been recently used as a workhorse for effectively generating building footprints. However, to completely exploit the prediction power of CNNs, large-scale pixel-level annotations are required. Most state-of-the-art methods based on CNNs are focused on the design of network architectures for improving the predictions of building footprints with full annotations, while few works have been done on building footprint segmentation with limited annotations. In this article, we propose a novel semisupervised learning method for building footprint segmentation, which can effectively predict building footprints based on the network trained with few annotations (e.g., only 0.0324 km2 out of 2.25-km2 area is labeled). The proposed method is based on investigating the contrast between the building and background pixels in latent space and the consistency of predictions obtained from the CNN models when the input RS images are perturbed. Thus, we term the proposed semisupervised learning framework of building footprint segmentation as PiCoCo, which is based on the enforcement of Pixelwise Contrast and Consistency during the learning phase. Our experiments, conducted on two benchmark building segmentation datasets, validate the effectiveness of our proposed framework as compared to several state-of-the-art building footprint extraction and semisupervised semantic segmentation methods