Phased small-interfering RNAs (phasiRNAs) are a special class of small RNAs, which are generated in 21-or 24-nt intervals from transcripts of precursor RNAs. Although phasiRNAs have been found in a range of organisms, their biological functions in plants have yet to be uncovered. Here we show that phasiRNAs generated by the photopheriod-sensetive genic male sterility 1 (Pms1) locus were associated with photoperiod-sensitive male sterility (PSMS) in rice, a germplasm that started the two-line hybrid rice breeding. The Pms1 locus encodes a long-noncoding RNA PMS1T that was preferentially expressed in young panicles. PMS1T was targeted by miR2118 to produce 21-nt phasiRNAs that preferentially accumulated in the PSMS line under long-day conditions. A single nucleotide polymorphism in PMS1T nearby the miR2118 recognition site was critical for fertility change, likely leading to differential accumulation of the phasiRNAs. This result suggested possible roles of phasiRNAs in reproductive development of rice, demonstrating the potential importance of this RNA class as regulators in biological processes.photoperiod-sensitive male sterility | long-noncoding RNA | phasiRNA

The highly conserved plant microRNA, miR156, is an essential regulator for plant development. In Arabidopsis (Arabidopsis thaliana), miR156 modulates phase changing through its temporal expression in the shoot. In contrast to the gradual decrease over time in the shoot (or whole plant), we found that the miR156 level in rice (Oryza sativa) gradually increased from young leaf to old leaf after the juvenile stage. However, the miR156-targeted rice SQUAMOSA-promoter binding-like (SPL) transcription factors were either dominantly expressed in young leaves or not changed over the time of leaf growth. A comparison of the transcriptomes of early-emerged old leaves and later-emerged young leaves from wild-type and miR156 overexpression (miR156-OE) rice lines found that expression levels of 3,008 genes were affected in miR156-OE leaves. Analysis of temporal expression changes of these genes suggested that miR156 regulates gene expression in a leaf age-dependent manner, and miR156-OE attenuated the temporal changes of 2,660 genes. Interestingly, seven conserved plant microRNAs also showed temporal changes from young to old leaves, and miR156-OE also attenuated the temporal changes of six microRNAs. Consistent with global gene expression changes, miR156-OE plants resulted in dramatic changes including precocious leaf maturation and rapid leaf/tiller initiation. Our results indicate that another gradient of miR156 is present over time, a gradual increase during leaf growth, in addition to the gradual decrease during shoot growth. Gradually increased miR156 expression in the leaf might be essential for regulating the temporal expression of genes involved in leaf development.

Photoperiod-sensitive male sterility (PSMS) is a valuable germplasm for hybrid rice breeding. Recently, we cloned pms3, a locus controlling PSMS, which encodes a long non-coding RNA called LDMAR required for normal male fertility of the rice plant under long-day conditions. Increased methylation in the promoter of LDMAR in the PSMS rice (Nongken 58S) relative to the wild-type (Nongken 58) reduced expression of LDMAR leading to male sterility under long-day conditions. In this study, we identified a siRNA named Psi-LDMAR in the LDMAR promoter region that was more abundant in Nongken 58S than in Nongken 58. We showed that Psi-LDMAR was likely derived from AK111270, a transcript obtained from the sense strand of the LDMAR promoter with its 3'-end having a 110-base overlap with the 5'-end of LDMAR. Overexpressing AK111270 in Nongken 58S greatly enriched Psi-LDMAR, which induced RNA-directed DNA methylation in the LDMAR promoter and repressed the expression of LDMAR. Reduction of LDMAR in Nongken 58S changed the critical day length for fertility recovery and delayed the fertility recovery under short-day conditions. This result added to our understanding of the molecular mechanism for PSMS.

MicroRNAs are a class of endogenous small RNA molecules (20-24 nucleotides) that have pivotal roles in regulating gene expression mostly at posttranscriptional levels in plants. Plant microRNAs have been implicated in the regulation of diverse biological processes including growth and stress responses. However, the information about microRNAs in regulating abiotic stress responses in rice is limited. We optimized a one-tube stem-loop reverse transcription quantitative PCR (ST-RT qPCR) for high-throughput expression profiling analysis of microRNAs in rice under normal and stress conditions. The optimized ST-RT qPCR method was as accurate as small RNA gel blotting and was more convenient and time-saving than other methods in quantifying microRNAs. With this method, 41 rice microRNAs were quantified for their relative expression levels after drought, salt, cold, and abscisic acid (ABA) treatments. Thirty-two microRNAs showed induced or suppressed expression after stress or ABA treatment. Further analysis suggested that stress-responsive cis-elements were enriched in the promoters of stress-responsive microRNA genes. The expressions of five and seven microRNAs were significantly affected in the rice plant with defects in stress tolerance regulatory genes OsSKIPa and OsbZIP23, respectively. Some of the predicted target genes of these microRNAs were also related to abiotic stresses. We conclude that ST-RT qPCR is an efficient and reliable method for expression profiling of microRNAs and a significant portion of rice microRNAs participate in abiotic stress response and regulation.

The TaskTracer system seeks to help multi-tasking users manage the resources that they create and access while carrying out their work activities. It does this by associating with each user-defined activity the set of files, folders, email messages, contacts, and web pages that the user accesses when performing that activity. The initial TaskTracer system relies on the user to notify the system each time the user changes activities. However, this is burdensome, and users often forget to tell TaskTracer what activity they are working on. This paper introduces TaskPredictor, a machine learning system that attempts to predict the user's current activity. TaskPredictor has two components: one for general desktop activity and another specifically for email. TaskPredictor achieves high prediction precision by combining three techniques: (a) feature selection via mutual information, (b) classification based on a confidence threshold, and (c) a hybrid design in which a Naive Bayes classifier estimates the classification confidence but where the actual classification decision is made by a support vector machine. This paper provides experimental results on data collected from TaskTracer users.