Endogenous small-noncoding RNAs and potential functions in desiccation tolerance in Physcomitrella patens

Early land plants like moss Physcomitrella patens have developed remarkable drought tolerance. Phytohormone abscisic acid (ABA) protects seeds during water stress by activating genes through transcription factors such as ABSCISIC ACID INSENSITIVE (ABI3). Small noncoding RNA (sncRNA), including microRNAs (miRNAs) and endogenous small-interfering RNAs (endo-siRNAs), are key gene regulators in eukaryotes, playing critical roles in stress tolerance in plants. Combining next-generation sequencing and computational analysis, we profiled and characterized sncRNA species from two ABI3 deletion mutants and the wild type P. patens that were subject to ABA treatment in dehydration and rehydration stages. Small RNA profiling using deep sequencing helped identify 22 novel miRNAs and 6 genomic loci producing trans-acting siRNAs (ta-siRNAs) including TAS3a to TAS3e and TAS6. Data from degradome profiling showed that ABI3 genes (ABI3a/b/c) are potentially regulated by the plant-specific miR536 and that other ABA-relevant genes are regulated by miRNAs and ta-siRNAs. We also observed broad variations of miRNAs and ta-siRNAs expression across different stages, suggesting that they could potentially influence desiccation tolerance. This study provided evidence on the potential roles of sncRNA in mediating desiccation-responsive pathways in early land plants

siRNAs from miRNA sites mediate DNA methylation of target genes

Arabidopsis microRNA (miRNA) genes (MIR) give rise to 20- to 22-nt miRNAs that are generated predominantly by the type III endoribonuclease Dicer-like 1 (DCL1) but do not require any RNA-dependent RNA Polymerases (RDRs) or RNA Polymerase IV (Pol IV). Here, we identify a novel class of non-conserved MIR genes that give rise to two small RNA species, a 20- to 22-nt species and a 23- to 27-nt species, at the same site. Genetic analysis using small RNA pathway mutants reveals that the 20- to 22-nt small RNAs are typical miRNAs generated by DCL1 and are associated with Argonaute 1 (AGO1). In contrast, the accumulation of the 23- to 27-nt small RNAs from the miRNA-generating sites is dependent on DCL3, RDR2 and Pol IV, components of the typical heterochromatic small interfering RNA (hc-siRNA) pathway. We further demonstrate that these MIR-derived siRNAs associate with AGO4 and direct DNA methylation at some of their target loci in trans. In addition, we find that at the miRNA-generating sites, some conserved canonical MIR genes also produce siRNAs, which also induce DNA methylation at some of their target sites. Our systematic examination of published small RNA deep sequencing datasets of rice and moss suggests that this type of dual functional MIRs exist broadly in plants

Statistical Use of Argonaute Expression and RISC Assembly in microRNA Target Identification

MicroRNAs (miRNAs) posttranscriptionally regulate targeted messenger RNAs (mRNAs) by inducing cleavage or otherwise repressing their translation. We address the problem of detecting m/miRNA targeting relationships in homo sapiens from microarray data by developing statistical models that are motivated by the biological mechanisms used by miRNAs. The focus of our modeling is the construction, activity, and mediation of RNA-induced silencing complexes (RISCs) competent for targeted mRNA cleavage. We demonstrate that regression models accommodating RISC abundance and controlling for other mediating factors fit the expression profiles of known target pairs substantially better than models based on m/miRNA expressions alone, and lead to verifications of computational target pair predictions that are more sensitive than those based on marginal expression levels. Because our models are fully independent of exogenous results from sequence-based computational methods, they are appropriate for use as either a primary or secondary source of information regarding m/miRNA target pair relationships, especially in conjunction with high-throughput expression studies

Dissecting Early Differentially Expressed Genes in a Mixture of Differentiating Embryonic Stem Cells

The differentiation of embryonic stem cells is initiated by a gradual loss of pluripotency-associated transcripts and induction of differentiation genes. Accordingly, the detection of differentially expressed genes at the early stages of differentiation could assist the identification of the causal genes that either promote or inhibit differentiation. The previous methods of identifying differentially expressed genes by comparing different cell types would inevitably include a large portion of genes that respond to, rather than regulate, the differentiation process. We demonstrate through the use of biological replicates and a novel statistical approach that the gene expression data obtained without prior separation of cell types are informative for detecting differentially expressed genes at the early stages of differentiation. Applying the proposed method to analyze the differentiation of murine embryonic stem cells, we identified and then experimentally verified Smarcad1 as a novel regulator of pluripotency and self-renewal. We formalized this statistical approach as a statistical test that is generally applicable to analyze other differentiation processes

Bacteria-responsive microRNAs regulate plant innate immunity by modulating plant hormone networks

MicroRNAs (miRNAs) are key regulators of gene expression in development and stress responses in most eukaryotes. We globally profiled plant miRNAs in response to infection of bacterial pathogen Pseudomonas syringae pv. tomato (Pst). We sequenced 13 small-RNA libraries constructed from Arabidopsis at 6 and 14 h post infection of non-pathogenic, virulent and avirulent strains of Pst. We identified 15, 27 and 20 miRNA families being differentially expressed upon Pst DC3000 hrcC, Pst DC3000 EV and Pst DC3000 avrRpt2 infections, respectively. In particular, a group of bacteria-regulated miRNAs targets protein-coding genes that are involved in plant hormone biosynthesis and signaling pathways, including those in auxin, abscisic acid, and jasmonic acid pathways. Our results suggest important roles of miRNAs in plant defense signaling by regulating and fine-tuning multiple plant hormone pathways. In addition, we compared the results from sequencing-based profiling of a small set of miRNAs with the results from small RNA Northern blot and that from miRNA quantitative RT-PCR. Our results showed that although the deep-sequencing profiling results are highly reproducible across technical and biological replicates, the results from deep sequencing may not always be consistent with the results from Northern blot or miRNA quantitative RT-PCR. We discussed the procedural differences between these techniques that may cause the inconsistency

Income Growth Across the U.S. States: An Empirical Analysis

The purpose of this study is to test the convergence hypothesis that prevails the neo-classical economic literature. In light of research on cross-country economic growth, the paper is adopting the same kind of methodology in studying income growth across U.S. states. The paper starts with a survey of literature in the study of growth of convergence. Then it tries to define the term of “convergence” in various economic implications, in particular the notion of “ð-Convergence” versus “ß-Convergence”, “conditional convergence” versus “absolute convergence”, and such popular notion as “club convergence”. The paper then goes into the quantitative analysis of U.S. per capita personal income change in a time series. The time-series data is firstly divided up by U.S. census regions and the pattern of regional income change overtime is carefully identified. ð-Convergence is tested by plotting personal income dispersion across U.S. states in time series from 1958 to 1996. ß-Convergence is tested by doing a simple regression of personal income growth on personal income at its initial level. The issue of alleged divergence since 1980s is carefully addressed and the importance of State Price Index is evaluated for the sake of the accuracy of economic studies on convergence and growth. The paper ended with a multiple-regression analysis aiming at identifying some attributes and determinants of income growth at state level

Research on Russian Electronic Warfare Equipment and Application in Russia-Ukraine Conflict

After implementing the “New Look” military reform, Russia focuses on constructing and developing electronic warfare equipment, which has become an important “asymmetric” weapon for acquiring information dominance to gain battlefield initiative. In order to study the Russian electronic warfare equipment and its operational application in the Russia-Ukraine conflict, the paper summarizes the compilation of Russian electronic warfare force and the technical characteristics of its equipment engaged in the war, and analyzes the application and effect of the electronic warfare equipment since the conflict. The deficiencies of Russian electronic warfare equipment are analyzed from the aspects of electronic warfare concept, electromagnetic spectrum management, systematic application and domestic supply chain and so on, which provides reference to design and develop new electronic warfare equipment and innovate the application of warfare training

A Stochastic Model for Detecting Heterogeneous Link Communities in Complex Networks

Discovery of communities in networks is a fundamental data analysis problem. Most of the existing approaches have focused on discovering communities of nodes, while recent studies have shown great advantages and utilities of the knowledge of communities of links. Stochastic models provides a promising class of techniques for the identification of modular structures, but most stochastic models mainly focus on the detection of node communities rather than link communities. We propose a stochastic model, which not only describes the structure of link communities, but also considers the heterogeneous distribution of community sizes, a property which is often ignored by other models. We then learn the model parameters using a method of maximum likelihood based on an expectation-maximization algorithm. To deal with large complex real networks, we extend the method by a strategy of iterative bipartition. The extended method is not only efficient, but is also able to determine the number of communities for a given network. We test our approach on both synthetic benchmarks and real-world networks including an application to a large biological network, and also compare it with two existing methods. The results demonstrate the superior performance of our approach over the competing methods for detecting link communities