Article Online

Articles Online (Volume 13, Issue 3)

Review Article

Long Non-coding RNAs and Their Biological Roles in Plants

Xue Liu, Lili Hao, Dayong Li, Lihuang Zhu, Songnian Hu

With the development of genomics and bioinformatics, especially the extensive applications of high-throughput sequencing technology, more transcriptional units with little or no protein-coding potential have been discovered. Such RNA molecules are called non-protein-coding RNAs (npcRNAs or ncRNAs). Among them, long npcRNAs or ncRNAs (lnpcRNAs or lncRNAs) represent diverse classes of transcripts longer than 200 nucleotides. In recent years, the lncRNAs have been considered as important regulators in many essential biological processes. In plants, although a large number of lncRNA transcripts have been predicted and identified in few species, our current knowledge of their biological functions is still limited. Here, we have summarized recent studies on their identification, characteristics, classification, bioinformatics, resources, and current exploration of their biological functions in plants.
随着基因组学和生物信息学的发展，特别是高通量测序技术的应用，越来越多的没有或者很少有蛋白编码功能的转录单位被发现，这样的RNA分子被称为非蛋白编码RNA nonprotein-coding RNAs (npcRNAs or ncRNAs)。在这些转录单位中，长非蛋白编码RNA（lnpcRNAs or lncRNAs）是指其转录本长度大于200nt。近几年来，长非蛋白编码RNA的研究已经越来越受到人们的关注，他们被认为在许多重要的生物学功能中起到调控作用。在植物中，虽然大量的长链非编码RNA被预测并鉴定出来，但是他们的生物学功能我们还知之甚少。此文总结了近年来在植物中关于长链非编码RNA的鉴定，以及他们的生物学特性、分类、生物信息和功能等方面的研究。
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Page 137-147

Review Article

Metagenomic Surveys of Gut Microbiota

Rahul Shubhra Mandal, Sudipto Saha, Santasabuj Das

Gut microbiota of higher vertebrates is host-specific. The number and diversity of the organisms residing within the gut ecosystem are defined by physiological and environmental factors, such as host genotype, habitat, and diet. Recently, culture-independent sequencing techniques have added a new dimension to the study of gut microbiota and the challenge to analyze the large volume of sequencing data is increasingly addressed by the development of novel computational tools and methods. Interestingly, gut microbiota maintains a constant relative abundance at operational taxonomic unit (OTU) levels and altered bacterial abundance has been associated with complex diseases such as symptomatic atherosclerosis, type 2 diabetes, obesity, and colorectal cancer. Therefore, the study of gut microbial population has emerged as an important field of research in order to ultimately achieve better health. In addition, there is a spontaneous, non-linear, and dynamic interaction among different bacterial species residing in the gut. Thus, predicting the influence of perturbed microbe–microbe interaction network on health can aid in developing novel therapeutics. Here, we summarize the population abundance of gut microbiota and its variation in different clinical states, computational tools available to analyze the pyrosequencing data, and gut microbe–microbe interaction networks.
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Page 148-158

Original Research

Regulatory MicroRNA Networks: Complex Patterns of Target Pathways for Disease-related and Housekeeping MicroRNAs

Sachli Zafari, Christina Backes, Petra Leidinger, Eckart Meese, Andreas Keller

Blood-based microRNA (miRNA) signatures as biomarkers have been reported for various pathologies, including cancer, neurological disorders, cardiovascular diseases, and also infections. The regulatory mechanism behind respective miRNA patterns is only partially understood. Moreover, “preserved” miRNAs, i.e., miRNAs that are not dysregulated in any disease, and their biological impact have been explored to a very limited extent. We set out to systematically determine their role in regulatory networks by defining groups of highly-dysregulated miRNAs that contribute to a disease signature as opposed to preserved housekeeping miRNAs. We further determined preferential targets and pathways of both dysregulated and preserved miRNAs by computing multi-layer networks, which were compared between housekeeping and dysregulated miRNAs. Of 848 miRNAs examined across 1049 blood samples, 8 potential housekeepers showed very limited expression variations, while 20 miRNAs showed highly-dysregulated expression throughout the investigated blood samples. Our approach provides important insights into miRNAs and their role in regulatory networks. The methodology can be applied to systematically investigate the differences in target genes and pathways of arbitrary miRNA sets.

Page 159-168

Original Research

Competing Risks Data Analysis with High-dimensional Covariates: An Application in Bladder Cancer

Leili Tapak, Massoud Saidijam, Majid Sadeghifar, Jalal Poorolajal, Hossein Mahjub

Analysis of microarray data is associated with the methodological problems of high dimension and small sample size. Various methods have been used for variable selection in high-dimension and small sample size cases with a single survival endpoint. However, little effort has been directed toward addressing competing risks where there is more than one failure risks. This study compared three typical variable selection techniques including Lasso, elastic net, and likelihood-based boosting for high-dimensional time-to-event data with competing risks. The performance of these methods was evaluated via a simulation study by analyzing a real dataset related to bladder cancer patients using time-dependent receiver operator characteristic (ROC) curve and bootstrap .632+ prediction error curves. The elastic net penalization method was shown to outperform Lasso and boosting. Based on the elastic net, 33 genes out of 1381 genes related to bladder cancer were selected. By fitting to the Fine and Gray model, eight genes were highly significant (P < 0.001). Among them, expression of RTN4, SON, IGF1R, SNRPE, PTGR1, PLEK, and ETFDH was associated with a decrease in survival time, whereas SMARCAD1 expression was associated with an increase in survival time. This study indicates that the elastic net has a higher capacity than the Lasso and boosting for the prediction of survival time in bladder cancer patients. Moreover, genes selected by all methods improved the predictive power of the model based on only clinical variables, indicating the value of information contained in the microarray features.

Page 169-176

Method

Correlating Bladder Cancer Risk Genes with Their Targeting MicroRNAs Using MMiRNA-Tar

Yang Liu, Steve Baker, Hui Jiang, Gary Stuart, Yongsheng Bai