Weixiong Zhang

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

BackgroundSmall 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.ResultsIn 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.ConclusionDeep sequencing proved to be an effective strategy that allowed the discovery of 23 low-abundance new miRNAs and 40 candidate miRNAs in rice.

Characterization and Identification of MicroRNA Core Promoters in Four Model Species

MicroRNAs are short, noncoding RNAs that play important roles in post-transcriptional gene regulation. Although many functions of microRNAs in plants and animals have been revealed in recent years, the transcriptional mechanism of microRNA genes is not well-understood. To elucidate the transcriptional regulation of microRNA genes, we study and characterize, in a genome scale, the promoters of intergenic microRNA genes in Caenorhabditis elegans, Homo sapiens, Arabidopsis thaliana, and Oryza sativa. We show that most known microRNA genes in these four species have the same type of promoters as protein-coding genes have. To further characterize the promoters of microRNA genes, we developed a novel promoter prediction method, called common query voting (CoVote), which is more effective than available promoter prediction methods. Using this new method, we identify putative core promoters of most known microRNA genes in the four model species. Moreover, we characterize the promoters of microRNA genes in these four species. We discover many significant, characteristic sequence motifs in these core promoters, several of which match or resemble the known cis-acting elements for transcription initiation. Among these motifs, some are conserved across different species while some are specific to microRNA genes of individual species.

Identification of cold-inducible microRNAs in plants by transcriptome analysis

MicroRNAs are approximately 21-nt long, non-coding RNAs that play critical roles in post-transcriptional gene regulation. Even though a large number of miRNAs have been identified, annotating their functions remains a challenge. We develop a computational, transcriptome-based approach to annotating stress-inducible microRNAs in plants. With this approach, we find that nineteen microRNA genes of eleven microRNA families in Arabidopsis thaliana are up-regulated by cold stress. Our experiments validate that among the eleven microRNAs, eight are differentially induced and three are constantly expressed under low temperature. Our result expands the number of cold-inducible microRNAs from four to eight. A promoter analysis further reveals that the cold-responsive microRNA genes contain many known stress-related cis-regulatory elements in their promoters. Our analysis also indicates that many signaling pathways, such as auxin pathways, may be affected by cold-inducible microRNAs. Our approach can be applied to plant microRNAs responding to other abiotic and biotic stresses. The research demonstrates that machine learning methods, augmented by wet-lab analysis, hold a great promise for functional annotation of microRNAs.

Distributed stochastic search and distributed breakout: properties, comparison and applications to constraint optimization problems in sensor networks

This research is motivated by some distributed scheduling problems in distributed sensor networks, in which computational and communication resources are scarce. We first cast these problems as distributed constraint satisfaction problems (DisCSPs) and distributed constraint optimization problems (DisCOPs) and model them as distributed graph coloring. To cope with limited resources and restricted real-time requirement, it is imperative to use distributed algorithms that have low overhead on resource consumption and high-quality anytime performance. To meet these requirements, we study two existing DisCSP algorithms, distributed stochastic search algorithm (DSA) and distributed breakout algorithm (DBA), for solving DisCOPs and the distributed scheduling problems. We experimentally show that DSA has a phase-transition or threshold behavior, in that its solution quality degenerates abruptly and dramatically when the degree of parallel executions of distributed agents increases beyond some critical value. We also consider the completeness and complexity of DBA for distributed graph coloring. We show that DBA is complete on coloring acyclic graphs, coloring an acyclic graph of n nodes in O(n^2) steps. However, on a cyclic graph, DBA may never terminate. To improve DBAs performance on coloring cyclic graphs, we propose two stochastic variations. Finally, we directly compare DSA and DBA for solving distributed graph coloring and distributed scheduling problems in sensor networks. The results show that DSA is superior to DBA when controlled properly, having better or competitive solution quality and significantly lower communication cost than DBA. Therefore, DSA is the algorithm of choice for our distributed scheduling problems and other distributed problems of similar properties.

Genetic Control of Human Brain Transcript Expression in Alzheimer Disease

We recently surveyed the relationship between the human brain transcriptome and genome in a series of neuropathologically normal postmortem samples. We have now analyzed additional samples with a confirmed pathologic diagnosis of late-onset Alzheimer disease (LOAD; final n = 188 controls, 176 cases). Nine percent of the cortical transcripts that we analyzed had expression profiles correlated with their genotypes in the combined cohort, and approximately 5% of transcripts had SNP-transcript relationships that could distinguish LOAD samples. Two of these transcripts have been previously implicated in LOAD candidate-gene SNP-expression screens. This study shows how the relationship between common inherited genetic variants and brain transcript expression can be used in the study of human brain disorders. We suggest that studying the transcriptome as a quantitative endo-phenotype has greater power for discovering risk SNPs influencing expression than the use of discrete diagnostic categories such as presence or absence of disease.

An Iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots

MOTIVATION Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to its difficulty in modeling. Although, several dynamic programming algorithms exist for the prediction of pseudoknots using thermodynamic approaches, they are neither reliable nor efficient. On the other hand, comparative methods are more reliable, but are often done in an ad hoc manner and require expert intervention. Maximum weighted matching, an algorithm for pseudoknot prediction with comparative analysis, suffers from low-prediction accuracy in many cases. RESULTS Here we present an algorithm, iterated loop matching, for reliably and efficiently predicting RNA secondary structures including pseudoknots. The method can utilize either thermodynamic or comparative information or both, thus is able to predict pseudoknots for both aligned and individual sequences. We have tested the algorithm on a number of RNA families. Using 8-12 homologous sequences, the algorithm correctly identifies more than 90% of base-pairs for short sequences and 80% overall. It correctly predicts nearly all pseudoknots and produces very few spurious base-pairs for sequences without pseudoknots. Comparisons show that our algorithm is both more sensitive and more specific than the maximum weighted matching method. In addition, our algorithm has high-prediction accuracy on individual sequences, comparable with the PKNOTS algorithm, while using much less computational resources. AVAILABILITY The program has been implemented in ANSI C and is freely available for academic use at http://www.cse.wustl.edu/~zhang/projects/rna/ilm/ SUPPLEMENTARY INFORMATION http://www.cse.wustl.edu/~zhang/projects/rna/ilm/

UV-B responsive microRNA genes in Arabidopsis thaliana

MicroRNAs (miRNAs) are small, non‐coding RNAs that play critical roles in post‐transcriptional gene regulation. In plants, mature miRNAs pair with complementary sites on mRNAs and subsequently lead to cleavage and degradation of the mRNAs. Many miRNAs target mRNAs that encode transcription factors; therefore, they regulate the expression of many downstream genes. In this study, we carry out a survey of Arabidopsis microRNA genes in response to UV‐B radiation, an important adverse abiotic stress. We develop a novel computational approach to identify microRNA genes induced by UV‐B radiation and characterize their functions in regulating gene expression. We report that in A. thaliana, 21 microRNA genes in 11 microRNA families are upregulated under UV‐B stress condition. We also discuss putative transcriptional downregulation pathways triggered by the induction of these microRNA genes. Moreover, our approach can be directly applied to miRNAs responding to other abiotic and biotic stresses and extended to miRNAs in other plants and metazoans.

Identifying network communities with a high resolution

Community structure is an important property of complex networks. The automatic discovery of such structure is a fundamental task in many disciplines, including sociology, biology, engineering, and computer science. Recently, several community discovery algorithms have been proposed based on the optimization of a modularity function (Q) . However, the problem of modularity optimization is NP-hard and the existing approaches often suffer from a prohibitively long running time or poor quality. Furthermore, it has been recently pointed out that algorithms based on optimizing Q will have a resolution limit; i.e., communities below a certain scale may not be detected. In this research, we first propose an efficient heuristic algorithm QCUT, which combines spectral graph partitioning and local search to optimize Q . Using both synthetic and real networks, we show that QCUT can find higher modularities and is more scalable than the existing algorithms. Furthermore, using QCUT as an essential component, we propose a recursive algorithm HQCUT to solve the resolution limit problem. We show that HQCUT can successfully detect communities at a much finer scale or with a higher accuracy than the existing algorithms. We also discuss two possible reasons that can cause the resolution limit problem and provide a method to distinguish them. Finally, we apply QCUT and HQCUT to study a protein-protein interaction network and show that the combination of the two algorithms can reveal interesting biological results that may be otherwise undetected.

Deep sequencing of small RNA libraries reveals dynamic regulation of conserved and novel microRNAs and microRNA-stars during silkworm development

BackgroundIn eukaryotes, microRNAs (miRNAs) have emerged as critical regulators of gene expression. The Silkworm (Bombyx mori L.) is one of the most suitable lepidopteran insects for studying the molecular aspects of metamorphosis because of its large size, availability of mutants and genome sequence. Besides, this insect also has been amply studied from a physiological and biochemical perspective. Deep sequencing of small RNAs isolated from different stages of silkworm is a powerful tool not only for measuring the changes in miRNA profile but also for discovering novel miRNAs.ResultsWe generated small RNA libraries from feeding larvae, spinning larvae, pupae and adults of B. mori and obtained ~2.5 million reads of 18-30 nt. Sequence analysis identified 14 novel and 101 conserved miRNAs. Most novel miRNAs are preferentially expressed in pupae, whereas more than 95% of the conserved miRNAs are dynamically regulated during different developmental stages. Remarkably, the miRNA-star (miR*) of four miRNAs are expressed at much higher levels than their corresponding miRNAs, and their expression profiles are distinct from their corresponding miRNA profiles during different developmental stages. Additionally, we detected two antisense miRNA loci (miR-263-S and miR-263-AS; miR-306-S and miR-306-AS) that are expressed in sense and antisense directions. Interestingly, miR-263 and miR-306 are preferentially and abundantly expressed in pupae and adults, respectively.ConclusionsWe identified 101 homologs of conserved miRNAs, 14 species-specific and two antisense miRNAs in the silkworm. Our results provided deeper insights into changes in conserved and novel miRNA and miRNA* accumulation during development.

Deep sequencing of small RNAs from human skin reveals major alterations in the psoriasis miRNAome

Psoriasis is a chronic and complex inflammatory skin disease with lesions displaying dramatically altered mRNA expression profiles. However, much less is known about the expression of small RNAs. Here, we describe a comprehensive analysis of the normal and psoriatic skin miRNAome with next-generation sequencing in a large patient cohort. We generated 6.7 × 10(8) small RNA reads representing 717 known and 284 putative novel microRNAs (miRNAs). We also observed widespread expression of isomiRs and miRNA*s derived from known and novel miRNA loci, and a low frequency of miRNA editing in normal and psoriatic skin. The expression and processing of selected novel miRNAs were confirmed with qRT-PCR in skin and other human tissues or cell lines. Eighty known and 18 novel miRNAs were 2-42-fold differentially expressed in psoriatic skin. Of particular significance was the 2.7-fold upregulation of a validated novel miRNA derived from the antisense strand of the miR-203 locus, which plays a role in epithelial differentiation. Other differentially expressed miRNAs included hematopoietic-specific miRNAs such as miR-142-3p and miR-223/223*, and angiogenic miRNAs such as miR-21, miR-378, miR-100 and miR-31, which was the most highly upregulated miRNA in psoriatic skin. The functions of these miRNAs are consistent with the inflammatory and hyperproliferative phenotype of psoriatic lesions. In situ hybridization of differentially expressed miRNAs revealed stratified epidermal expression of an uncharacterized keratinocyte-derived miRNA, miR-135b, as well as the epidermal infiltration of the hematopoietic-specific miRNA, miR-142-3p, in psoriatic lesions. This study lays a critical framework for functional characterization of miRNAs in healthy and diseased skin.