This work has been supported by Beijing Academy of Agriculture and Forestry Sciences (KJCX201917, KJCX20180425, and KJCX20180204) to XY and National Natural Science Foundation of China (31621001) to LL.

1. Installation and testing
<ol>
	<li>Download required dependencies: Bowtie222 and RNAfold23. Compiled packages are recommended.
	<ol>
		<li>Download Bowtie2, a read mapping tool, from its home site (<a href="http://bowtie-bio.sourceforge.net/bowtie2/index.shtml" target="_blank">http://bowtie-bio.sourceforge.net/bowtie2/index.shtml</a>).</li>
		<li>Download RNAfold, a tool of the Vienna package used to predict RNA secondary structure, from <a href="http://www.tbi.univie.ac.at/~ivo/RNA/" target="_blank">http://www.tbi.univie.ac.at/~ivo/RNA/</a>.</li>
		<li>Before installing miRDP2, ensure that these two dependencies are correctly installed, and customize the bash environment file (e.g., .bashrc) to set a correct PATH for these two dependencies. 
		NOTE: Other mapping tools such as Bowtie24 are also suitable to miRDP2; either Bowtie or Bowtie2 can be used after version 1.1.3.</li>
	</ol>
	</li>
	<li>To download the miRDP2 package, go to <a href="https://sourceforge.net/projects/mirdp2/files/latest_version/" target="_blank">https://sourceforge.net/projects/mirdp2/files/latest_version/</a> and fetch the tarball files.</li>
	<li>Before installing miRDP2, make sure that Perl is in the PATH. To install miRDP2, extract all contents of the downloaded tarball file into one folder (command lines as in 1.4.2), and then set the folder path into the PATH. 
	NOTE: A computer or computing node with at least 8 GB RAM and 100 GB storage are recommended to run miRDP2.</li>
	<li>Test the MiRDP2 pipeline.
	<ol>
		<li>To test whether miRDP2 has been correctly installed, use the test data and the expected output found in <a href="https://sourceforge.net/projects/mirdp2/files/TestData/" target="_blank">https://sourceforge.net/projects/mirdp2/files/TestData/</a>. Test data contain one formatted GSM sequencing file and one Arabidopsis thaliana genome file.</li>
		<li>Move all downloaded files to the current working directory: 
		mv miRDP2-v*.tar.gz TestData.tar.gz ncRNA_rfam.tar.gz &#60;user_selected_folder&#62; 
		cd &#60;user_selected_folder&#62;</li>
		<li>Extract the compressed tarball files: 
		tar &#8211;xvzf miRDP2-v*.tar.gz 
		tar &#8211;xvzf TestData.tar.gz 
		tar &#8211;xvzf ncRNA_rfam.tar.gz</li>
		<li>Build the Arabidopsis genome reference index: 
		bowtie2-build -f ./TestData/TAIR10_genome.fa ./TestData/TAIR10_genome</li>
		<li>Build the ncRNA reference index: 
		bowtie2-build -f ./ncRNA_rfam.fa ./1.1.3/script/index/rfam_index</li>
		<li>Run the miRDP2 pipeline: 
		bash ./1.1.3/miRDP2-v1.1.3_pipeline.bash &#8211;g ./TestData/TAIR10_genome.fa -i ./ TestData/TAIR 10_genome &#8211;f ./TestData/GSM2094927.fa &#8211;o . 
		NOTE: Linux commands used are in bold and italic fonts, with command line options in italics. *indicates the version of miRDP2 (the current version is 1.1.3). The bowtie2-build command should take roughly 10 minutes, and the miRDP2 pipeline should finish within several minutes</li>
	</ol>
	</li>
	<li>Check testing outputs.
	<ol>
		<li>Note that a folder named &#39;GSM2094927-15-0-10&#39; is automatically generated in &#60;user_selected_folder&#62;, containing all intermediate files and results.</li>
		<li>Check that the tab-delimited output file GSM2094927-15-0-10_filter_P_prediction, the final output of predicted miRNAs, contains columns that indicate chromosome id, strand direction, representative reads id, precursor id, mature miRNA location, precursor location, mature sequence, and precursor sequence. Note the additional bed file derived from this file to facilitate further analysis.</li>
		<li>Check the file &#34;progress_log&#34;, which provides information about finished steps, and the files &#34;script_log&#34; and &#34;script_err&#34;, that contain program output and warnings. 
		NOTE: Currently, we have tested miRDP2 on two Linux platforms, including CentOS release 6.5 on a cluster server, and Cygwin 2.6.0 on PC Windows system, and miRDP2 should work on similar systems that support Perl.</li>
	</ol>
	</li>
</ol>
2. Identifying novel miRNAs
<ol>
	<li>Before running the pipeline, ensure that the input reads are preprocessed into proper format. 
	NOTE: The new version 1.1.3 of miRDP2 can accept original FASTQ format files as inputs, although the process of formatting reads is carried out as in previous versions.
	<ol>
		<li>First, remove adapters from the 5&#39; and 3&#39; ends of the deep sequencing reads (if present).</li>
		<li>Second, parse the deep sequencing reads into FASTA format.</li>
		<li>Third, remove redundancy such that reads with identical sequence are represented with a single and unique FASTA entry.</li>
		<li>Finally, ensure that all of the FASTA identifiers are unique. Each sequence identifier must end with a &#39;_x&#39; and an integer, indicating the copy number of the exact sequence that was retrieved in the deep sequencing datasets. One way to ensure unique FASTA identifier is to include a running number in the ID. For reference, see the file GSM2094927.fa in the test data (<a href="https://sourceforge.net/projects/mirdp2/files/TestData/" target="_blank">https://sourceforge.net/projects/mirdp2/files/TestData/</a>).</li>
		<li>See the following for examples of correctly formatted reads: 
		 
		&#62;read0_x29909 
		TTTGGATTGAAGGGAGCTCTA 
		&#62;read1_x36974 
		TTCCACAGCTTTCTTGAACTG 
		&#62;read2_x32635 
		TTCCACAGCTTTCTTGAACTT</li>
	</ol>
	</li>
	<li>Build reference indices.
	<ol>
		<li>For the genome reference, to save time, download Bowtie2 index files from the iGenomes website (<a href="https://support.illumina.com/sequencing/sequencing_software/igenome.html" target="_blank">https://support.illumina.com/sequencing/sequencing_software/igenome.html</a>) if the genome sequences of the species of interest have been indexed. Otherwise, users index reference sequences and keep the index file for a while till the project is finished since the genome sequence might need to be re-indexed. Details on how to index a genome reference are included in bowtie2 manual (<a href="http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml" target="_blank">http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml</a>).</li>
		<li>Another non-miRNA ncRNA index is also needed to filter out noisy sequences from other non-coding RNA fragments. The file is a collection of main ncRNA sequences from Rfam, including rRNA, tRNA, snRNA, and snoRNA. To build this index, please refer to part 1.4, as the index should be placed and named correctly, i.e. &#60;miRDP2_version&#62;/script/index/rfam_index.</li>
	</ol>
	</li>
	<li>Run miRDP2.
	<ol>
		<li>To use miRDP2 to detect new miRNAs from deep sequencing data, run the bash script in the package to start the analysis pipeline (An example can be found in step 1.4): 
		&#60;path_to_miRDP2_folder&#62;/miRDP2-v*.*_pipeline.bash &#8211;g &#60;genome_file&#62; -i &#60;path_to_index/index_prefix&#62; -f &#60;seq_file &#62; -o &#60;output_folder&#62; 
		where * indicates the version of the pipeline bash script. There are three parameters that can be modified: 1) the number of different locations a read could be mapped to, 2) the mismatch number for running bowtie2, and 3) the threshold of RPM (Reads Per Million). Modify these using the &#8211;L, -M, and &#8211;R options, respectively. A detailed explanation is in section 3.1.</li>
	</ol>
	</li>
	<li>Check the miRDP2 outputs.
	<ol>
		<li>Note that the output folder will be automatically generated under &#60;output_folder&#62;, and named &#39;&#60;seq_file_name&#62;-15-0-10&#39;; the last 3 numbers indicate the values (default in this case) for parameters 1, 2, and 3, respectively. The file &#60;seq_file_name&#62;_filter_P_prediction contains information of the final predicted miRNAs satisfying the newly updated plant miRNA annotation criteria. Details on the format of output file are described in part 1.4.</li>
	</ol>
	</li>
</ol>
3. Modifications and caution using miRDP2
<ol>
	<li>Parameters that can be modified
	<ol>
		<li>Use the &#39;-L&#39; option to set the limit of how many locations a read could be mapped to (parameter 1). Read mapping to too many sites are possibly associated with repeat sequences, and are not likely to miRNAs. The default setting is 15. For specific species, if there are miRNA families with many members, the first parameter may be increased manually to adapt to the genome landscape.</li>
		<li>Use the &#39;-M&#39; option to set the allowed mismatches for bowtie (parameter 2). The default setting is 0.</li>
		<li>Use the &#39;-R&#39; option to set the threshold for reads potentially corresponding to mature miRNAs (parameter 3). To reduce time consumption and false-positives, filter reads by RPM. Only reads exceeding a certain RPM threshold may represent mature sequences of miRNAs rather than background noise, and would be kept for further analysis. The default setting is 10 RPM.</li>
		<li>Note that changing these parameters can potentially affect performance and time consumption. In general, an increase of parameter 1 and 2 and a decrease of parameter 3 would generate a less stringent result and longer running time and vice versa.</li>
	</ol>
	</li>
	<li>Redundancy and miRNA*
	<ol>
		<li>Note that the output miRNAs from miRDP2 may differ from the known miRNAs. We found that this is mainly due to one of two reasons: heterogeneity of the mature miRNAs or the relative abundance of miRNA and miRNA*. We found that this does not impact the optimal length selection of precursors and the profiling of known miRNA genes.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

With the advent of NGS, a large number of miRNA loci have been identified from an ever-increasing amount of sRNA sequencing data in diverse species29,30. In the centralized community database miRBase21, the deposited miRNA items have increased almost 100 times in the last decade. However, in comparison to miRNAs in animals, plant miRNAs have many unique features that make the identification/annotation more complicated13</...

One of the most exciting discoveries in the last two decades in biology is the proliferating role of sRNA species in regulating diverse functions of the genome1. In particular, miRNAs constitute an important class of 20- to 24-nt sRNAs in eukaryotes, and mainly function at post-transcriptional level as prominent gene regulators throughout life cycle development stages as well as in stimulus and stress responses2,3. In plants, miRNAs arise from primary transcripts called pri-miRNAs, which are generally transcribed by RNA polymerase II as individual transcription units4,5. Processed by evolutionarily conserved cellular machinery (Drosha RNase III in animals, DICER-like in plants), pri-miRNAs are excised into the immediate miRNA precursors, pre-miRNAs, which contain sequences forming intra-molecular stem-loop structures6,7. Pre-miRNAs are then processed into double-stranded intermediates, namely miRNA duplexes, consisting of the functional strand, mature miRNA, and the less frequently functional partner, miRNA*2,8. After loaded into the RNA-induced silencing complex (RISC), the mature miRNAs could recognize their mRNA targets based on sequence complementarity, resulting in a negative regulatory function2,8. miRNAs could either destabilize their target transcripts or prevent target translation but the former manner is dominated in plants8,9.
Since the fortuitous discovery of the first miRNA in the nematode Caenorhabditis elegans10,11, much research has been committed to miRNA identification and its functional analysis, especially after the availability of NGS method. The wide application of the NGS method has greatly promoted the utilization of computational tools that were designed to capture the unique feature of miRNAs, such as the stem-loop structure of precursors and their preferential accumulation of sequence reads on mature miRNA and miRNA*. As a result, researchers have achieved remarkable success in identifying miRNAs in diverse species. Based on a previously described probability model12, we developed miRDeep-P13, which was the first computational tool for discovering plant miRNAs from NGS data. miRDeep-P was specifically aimed at conquering the challenges of decoding plant miRNAs featuring more variable precursor length and large paralogous families13,14,15. After its release, this program has been downloaded thousands of times and used to annotate miRNA transcriptomes in more than 40 plant species16. Propelled by NGS-based tools like miRDeep-P, there has been a dramatic increase in the number of registered miRNAs in the public miRNA repository miRBase17, where over 38,000 miRNA items are currently hosted (release 22.1) in comparison to only ~500 miRNA items (release 2.0) in 200818.
However, two new challenges have arisen from plant miRNA annotation. First, high ratios of false-positives have heavily impacted the quality of plant miRNA annotations16,19 for the following reasons: 1) a deluge of endogenous short interfering RNAs (siRNAs) from NGS sRNA libraries were erroneously annotated as miRNAs due to lacking of a stringent miRNA annotation criteria; 2) for species without a priori miRNA information, false-positives predicted based on NGS data are difficult to eliminate. Using miRBase as an example, Taylor et al.20 found one third of plant miRNA entries in the public repository21 (release 21) lacked convincing supporting evidence and even three-fourths of plant miRNA families were questionable. Second, it becomes an extremely time-consuming process for predicting plant miRNAs with large and complex genomes16. To overcome these challenges, we updated miRDeep-P by adding a new filtering strategy, overhauling the scoring algorithm and integrating new criteria for plant miRNA annotation, and released the new version miRDP2. In addition, we tested miRDP2 using NGS sRNA datasets with gradually increasing genome sizes: Arabidopsis, rice, tomato, maize and wheat. Compared to other five widely used tools and its old version, miRDP2 parsed these sRNA data and analyzed miRNA transcriptomes faster with improved accuracy and sensitivity.
Contents of the miRDP2 package 
The miRDP2 package consists of six documented Perl scripts that should be run sequentially by the prepared bash script. Of the six scripts, three (convert_bowtie_to_blast.pl, filter_alignments.pl, and excise_candidate.pl) are inherited from miRDeep-P. The other scripts are modified from the original version. Functions of the six scripts are described in the following:
preprocess_reads.pl filters input reads, including reads that are too long or too short (&#60;19 nt or &#62;25 nt), and reads correlated with Rfam ncRNA sequences, as well as reads with RPM (Reads Per Million) less than 5. The script then retrieves reads correlated to known miRNA mature sequences. The input files are original reads in FASTA/FASTQ format and bowtie2 output of reads mapping to miRNA and ncRNA sequences.
The formula for calculating RPM is as the following:
<img alt="Equation 1" src="/files/ftp_upload/59864/59864equ01.jpg" />
convert_bowtie_to_blast.pl changes the bowtie format into BLAST-parsed format. BLAST-parsed format is a custom tabular separated format derived from standard NCBI BLASToutput format.
filter_alignments.pl filters the alignments of deep sequencing reads to a genome. It filters partial alignments as well as multi-aligned reads (user-specified frequency cutoff). The basic input is a file in BLAST-parsed format.
excise_candidate.pl cuts out potential precursor sequences from a reference sequence using aligned reads as guidelines. The basic input is a file in BLAST-parsed format and a FASTA file. The output is all potential precursor sequences in FASTA format.
mod-miRDP.pl needs two input files, signature file and structure file, which is modified from the core miRDeep-P algorithm by changing the scoring system with plant specific parameters. The input files are dot-bracket precursor structure file and reads distribution signature file.
mod-rm_redundant_meet_plant.pl needs three input files: chromosome_length, precursors and original_prediction generated by mod-miRDP.pl. It generates two output files, non-redundant predicted file and predicted file filtered by newly updated plant miRNA criteria. Details on the format of output file are described in section 1.4.

<table><tbody><tr><td>Computer/computing node</td><td>N/A</td><td>N/A</td><td>Perl is required; at least 8 GB RAM and 100 GB storage are recommended</td></tr></tbody></table>

a bioinformatics pipeline to accurately and efficiently analyze the microrna transcriptomes in plants

MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in regulating gene expression at the post-transcriptional level. Sequencing sRNA libraries by Next Generation Sequencing (NGS) methods has been widely employed to identify and analyze miRNA transcriptomes in the last decade, resulting in a rapid increase of miRNA discovery. However, two major challenges arise in plant miRNA annotation due to increasing depth of sequenced sRNA libraries as well as the size and complexity of plant genomes. First, many other types of sRNAs, in particular, short interfering RNAs (siRNAs) from sRNA libraries, are erroneously annotated as miRNAs by many computational tools. Second, it becomes an extremely time-consuming process for analyzing miRNA transcriptomes in plant species with large and complex genomes. To overcome these challenges, we recently upgraded miRDeep-P (a popular tool for miRNA transcriptome analyses) to miRDeep-P2 (miRDP2 for short) by employing a new filtering strategy, overhauling the scoring algorithm and incorporating newly updated plant miRNA annotation criteria. We tested miRDP2 against sequenced sRNA populations in five representative plants with increasing genomic complexity, including Arabidopsis, rice, tomato, maize and wheat. The results indicate that miRDP2 processed these tasks with very high efficiency. In addition, miRDP2 outperformed other prediction tools regarding sensitivity and accuracy. Taken together, our results demonstrate miRDP2 as a fast and accurate tool for analyzing plant miRNA transcriptomes, therefore a useful tool in helping the community better annotate miRNAs in plants.

The miRNA annotation pipeline, miRDP2, described herein is applied to 10 public sRNA-seq libraries from 5 plant species with gradually increased genome length, including Arabidopsis thaliana, Oryza sativa (rice), Solanum lycopersicum (tomato), Zea mays (maize) and Triticum aestivum (wheat) (Figure 1A). Overall, for each species, 2 representative sRNA libraries from different tissues (collapsed into unique reads, details in the pro...

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A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

A bioinformatics pipeline, namely miRDeep-P2 (miRDP2 for short), with updated plant miRNA criteria and an overhauled algorithm, could accurately and efficiently analyze microRNA transcriptomes in plants, especially for species with complex and large genomes.

MicroRNAs (miRNAs) are 20- to 24-nucleotide (nt) endogenous small RNAs (sRNAs) extensively existing in plants and animals that play potent roles in ...

a-bioinformatics-pipeline-to-accurately-efficiently-analyze-microrna

Research

JoVE Journal

Genetics

8.3K Views.  Beijing Academy of Agriculture and Forestry Sciences. miRDeep2 kan worden gebruikt om plantaardige microRNAs die belangrijke transcriptieregelgevers zijn in de ontwikkeling van installaties nauwkeurig te identificeren en cruciaal zijn voor het reageren op milieuproblemen. miRDeep2 vereist een duidelijk korte looptijd en vertoont een uitstekende prestatie in zowel gevoeligheid als nauwkeurigheid, vooral voor het voorspellen van microRNA in planten met een groot genoom. Valse positieven en lange verwerkingstijd zijn uitdagingen in plantaardige mic...

Video: A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs

MicroRNAs (miRNAs) are a class of small noncoding RNAs that post-transcriptionally regulate cellular gene expression. MiRNAs bind to the 3&#39; untranslated region (UTR) of target mRNA to inhibit protein translation or in some instances cause mRNA degradation. The binding of the miRNA to the 3&#39; UTR of the target mRNA is mediated by a 2&#8211;8 nucleotide seed sequence at the 5&#39; end of miRNA. While the role of miRNAs as cellular regulatory molecules is well established, identification of the target mRNAs with functional relevance remains a challenge. Bioinformatic tools have been employed to predict sequences within the 3&#39; UTR of mRNAs as potential targets for miRNA binding. These tools have also been utilized to determine the evolutionary conservation of such sequences among related species in an attempt to predict functional role. However, these computational methods often generate false positive results and are limited to predicting canonical interaction between miRNA and mRNA. Therefore, experimental procedures that measure direct binding of miRNA to its mRNA target are necessary to establish functional interaction. In this report, we describe a sensitive method for validating direct interaction between the cellular miRNA miR-125b and the 3&#39; UTR of PARP-1 mRNA. We elaborate a protocol in which synthetic biotinylated-miRNA mimics were transfected into mammalian cells and the miRNA-mRNA complex in the cellular lysate was pulled down with streptavidin-coated magnetic beads. Finally, the target mRNA in the pulled-down nucleic acid complex was quantified using a qPCR-based strategy.

This report describes a fast and reliable method for validating mRNA targets of cellular miRNAs. The method uses synthetic biotinylated Locked Nucleic Acid (LNA)-based miRNA mimics to capture target mRNA. Subsequently, streptavidin-coated magnetic beads are employed to pulldown the target mRNA for quantification by qPCR polymerase chain reaction.

MicroRNAs (miRNAs) are a class of small noncoding RNAs that post-transcriptionally regulate cellular gene expression. MiRNAs bind to the 3&#39; ...

Analysis of Combinatorial miRNA Treatments to Regulate Cell Cycle and Angiogenesis

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Similar to other cancer cells, a fundamental characteristic of LC cells is unregulated proliferation and cell division. Inhibition of proliferation by halting cell cycle progression has been shown to be a promising approach for cancer treatment, including LC. 
miRNA therapeutics have emerged as important post-transcriptional gene regulators and are increasingly being studied for use in cancer treatment. In recent work, we utilized two miRNAs, miR-143 and miR-506, to regulate cell cycle progression. A549 non-small cell lung cancer (NSCLC) cells were transfected, gene expression alterations were analyzed, and apoptotic activity due to the treatment was finally analyzed. Downregulation of cyclin-dependent kinases (CDKs) were detected (i.e., CDK1, CDK4 and CDK6), and cell cycle halted at the G1/S and G2/M phase transitions. Pathway analysis indicated potential antiangiogenic activity of the treatment, which endows the approach with multifaceted activity. Here, described are the methodologies used to identify miRNA activity regarding cell cycle inhibition, induction of apoptosis, and effects of treatment on endothelial cells by inhibition of angiogenesis. It is hoped that the methods presented here will support future research on miRNA therapeutics and corresponding activity and that the representative data will guide other researchers during experimental analyses.

miRNA therapeutics have significant potential in regulating cancer progression. Demonstrated here are analytical approaches used for identification of the activity of a combinatorial miRNA treatment in halting cell cycle and angiogenesis.

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Similar to other cancer cells, a fundamental characteristic of LC cells is ...

Cancer Research

A Complete Pipeline for Isolating and Sequencing MicroRNAs, and Analyzing Them Using Open Source Tools

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in disease states and provide therapeutic targets and biomarkers. Here, we detail how to accurately quantify microRNAs. Briefly, this method describes isolating microRNAs, ligating them to adaptors suitable for high-throughput sequencing, amplifying the final products, and preparing a sample library. Then, we explain how to align the obtained sequencing reads to microRNA hairpins, and quantify, normalize, and calculate their differential expression. Versatile and robust, this combined experimental workflow and bioinformatic analysis enables users to begin with tissue extraction and finish with microRNA quantification.

Here, we describe a step-by-step strategy for isolating small RNAs, enriching for microRNAs, and preparing samples for high-throughput sequencing. We then describe how to process sequence reads and align them to microRNAs, using open source tools.

Half of all human transcripts are thought to be regulated by microRNAs. Therefore, quantifying microRNA expression can reveal underlying mechanisms in ...

Potato Virus X-Based microRNA Silencing (VbMS) In Potato.

Virus-based microRNA silencing (VbMS) is a rapid and efficient tool for functional characterization of microRNAs (miRNAs) in plants. The VbMS system has been developed and applied for various plant species including Nicotiana benthamiana, tomato, Arabidopsis, cotton, and monocot plants such as wheat and maize. Here, we describe a detailed protocol using PVX-based VbMS vectors to silence endogenous miRNAs in potato. To knock down the expression of a specific miRNA, target mimic (TM) molecules of miRNA of interest are designed, integrated into plant virus vectors, and expressed in potato by Agrobacterium infiltration to bind directly to the endogenous miRNA of interest and block its function.

Potato Virus X-Based microRNA Silencing VbMS In Potato.

We present a detailed protocol for potato virus X (PVX)-based microRNA silencing (VbMS) system to functionally characterize endogenous microRNAs (miRNAs) in potato. Target mimic (TM) molecules of miRNA of interest are integrated into the PVX vector and transiently expressed in potato to silence the target miRNA or miRNA family.

Virus-based microRNA silencing (VbMS) is a rapid and efficient tool for functional characterization of microRNAs (miRNAs) in plants. The VbMS system has ...

Environment

RNA Blot Analysis for the Detection and Quantification of Plant MicroRNAs

MicroRNAs (miRNAs) are a class of endogenously expressed non-coding, ~21 nt small RNAs involved in the regulation of gene expression in both plants and animals. Most miRNAs act as negative switches of gene expression targeting key genes. In plants, primary miRNAs (pri-miRNAs) transcripts are generated by RNA polymerase II, and they form varying lengths of stable stem-loop structures called pre-miRNAs. An endonuclease, Dicer-like1, processes the pre-miRNAs into miRNA-miRNA* duplexes. One of the strands from miRNA-miRNA* duplex is selected and loaded onto Argonaute 1 protein or its homologs to mediate the cleavage of target mRNAs. Although miRNAs are key signaling molecules, their detection is often carried out by less than optimal PCR-based methods instead of a sensitive northern blot analysis. We describe a simple, reliable, and extremely sensitive northern method that is ideal for the quantification of miRNA levels with very high sensitivity, literally from any plant tissue. Additionally, this method can be used to confirm the size, stability and the abundance of miRNAs and their precursors.

This method demonstrates use of the northern hybridization technique to detect miRNAs from total RNA extract.

MicroRNAs (miRNAs) are a class of endogenously expressed non-coding, ~21 nt small RNAs involved in the regulation of gene expression in both plants and ...

Biochemistry

Lung microRNA Profiling Across the Estrous Cycle in Ozone-exposed Mice

MicroRNA (miRNA) profiling has become of interest to researchers working in various research areas of biology and medicine. Current studies show a promising future of using miRNAs in the diagnosis and care of lung diseases. Here, we define a protocol for miRNA profiling to measure the relative abundance of a group of miRNAs predicted to regulate inflammatory genes in the lung tissue from of an ozone-induced airway inflammation mouse model. Because it has been shown that circulating sex hormone levels can affect the regulation of lung innate immunity in females, the purpose of this method is to describe an inflammatory miRNA profiling protocol in female mice, taking into consideration the estrous cycle stage of each animal at the time of ozone exposure. We also address applicable bioinformatics approaches to miRNA discovery and target identification methods using limma, an R/Bioconductor software, and functional analysis software to understand the biological context and pathways associated with differential miRNA expression.

Here we describe a method to assess lung expression of miRNAs that are predicted to regulate inflammatory genes using mice exposed to ozone or filtered air at different stages of the estrous cycle.

MicroRNA (miRNA) profiling has become of interest to researchers working in various research areas of biology and medicine. Current studies show a ...

Immunology and Infection

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Luminescent Identification of Functional Elements in 3&#8217;UTRs (3&#8217;LIFE) allows the rapid identification of targets of specific miRNAs within an array of hundreds of queried 3&#8217;UTRs. Target identification is based on the dual-luciferase assay, which detects binding at the mRNA level by measuring translational output, giving a functional readout of miRNA targeting. 3&#8217;LIFE uses non-proprietary buffers and reagents, and publically available reporter libraries, making genome-wide screens feasible and cost-effective. 3&#8217;LIFE can be performed either in a standard lab setting or scaled up using liquid handling robots and other high-throughput instrumentation. We illustrate the approach using a dataset of human 3&#8217;UTRs cloned in 96-well plates, and two test miRNAs, let-7c and miR-10b. We demonstrate how to perform DNA preparation, transfection, cell culture and luciferase assays in 96-well format, and provide tools for data analysis. In conclusion 3&#39;LIFE is highly reproducible, rapid, systematic, and identifies high confidence targets.

Luminescent identification of functional elements in 3&#8217; untranslated regions (3&#8217;UTRs) (3&#8217;LIFE) is a technique to identify functional regulation in 3&#8217;UTRs by miRNAs or other regulatory factors. This protocol utilizes high-throughput methodology such as 96-well transfection and luciferase assays to screen hundreds of putative interactions for functional repression.

Luminescent Identification of Functional Elements in 3&#8217;UTRs (3&#8217;LIFE) allows the rapid identification of targets of specific miRNAs within an ...

Biology

An Easy Method for Plant Polysome Profiling

Translation of mRNA to protein is a fundamental and highly regulated biological process. Polysome profiling is considered as a gold standard for the analysis of translational regulation. The method described here is an easy and economical way for fractionating polysomes from various plant tissues. A sucrose gradient is made without the need for a gradient maker by sequentially freezing each layer. Cytosolic extracts are then prepared in a buffer containing cycloheximide and chloramphenicol to immobilize the cytosolic and chloroplastic ribosomes to mRNA and are loaded onto the sucrose gradient. After centrifugation, six fractions are directly collected from the bottom to the top of the gradient, without piercing the ultracentrifugation tube. During collection, the absorbance at 260 nm is read continuously to generate a polysome profile that gives a snapshot of global translational activity. Fractions are then pooled to prepare three different mRNA populations: the polysomes, mRNAs bound to several ribosomes; the monosomes, mRNAs bound to one ribosome; and mRNAs that are not bound to ribosomes. mRNAs are then extracted. This protocol has been validated for different plants and tissues including Arabidopsis thaliana seedlings and adult plants, Nicotiana benthamiana, Solanum lycopersicum, and Oryza sativa leaves.

This protocol describes an easy method to extract and fractionate transcripts from plant tissues on the basis of the number of bound ribosomes. It allows a global estimate of translation activity and the determination of the translational status of specific mRNAs.

Translation of mRNA to protein is a fundamental and highly regulated biological process. Polysome profiling is considered as a gold standard for the ...

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the prediction of family functions based on several features, such as the presence of sequence motifs or of particular sites for post-translational modification and the expression profile of family members in different conditions. This work describes a detailed protocol for gene family characterization. Here, the procedure is applied to the characterization of the Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligase family in grapevine. The methods include the genome-wide identification of family members, the characterization of gene localization, structure, and duplication, the analysis of conserved protein motifs, the prediction of protein localization and phosphorylation sites as well as gene expression profiling across the family in different datasets. Such procedure, which could be extended to further analyses depending on experimental purposes, could be applied to any gene family in any plant species for which genomic data are available, and it provides valuable information to identify interesting candidates for functional studies, giving insights into the molecular mechanisms of plant adaptation to their environment.

This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligases.

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the ...

mirMachine: A One-Stop Shop for Plant miRNA Annotation

Of different types of noncoding RNAs, microRNAs (miRNAs) have arguably been in the spotlight over the last decade. As post-transcriptional regulators of gene expression, miRNAs play key roles in various cellular pathways, including both development and response to a/biotic stress, such as drought and diseases. Having high-quality reference genome sequences enabled identification and annotation of miRNAs in several plant species, where miRNA sequences are highly conserved. As computational miRNA identification and annotation processes are mostly error-prone processes, homology-based predictions increase prediction accuracy. We developed and have improved the miRNA annotation pipeline, SUmir, in the last decade, which has been used for several plant genomes since then. 
This study presents a fully automated, new miRNA pipeline, mirMachine (miRNA Machine), by (i) adding an additional filtering step on the secondary structure predictions, (ii) making it fully automated, and (iii) introducing new options to predict either known miRNA based on homology or novel miRNAs based on small RNA sequencing reads using the previous pipeline. The new miRNA pipeline, mirMachine, was tested using The Arabidopsis Information Resource, TAIR10, release of the Arabidopsis genome and the International Wheat Genome Sequencing Consortium (IWGSC) wheat reference genome v2.

Herein, we present a new and fully automated miRNA pipeline, mirMachine that 1) can identify known and novel miRNAs more accurately and 2) is fully automated and freely available. Users can now execute a short submission script to run the fully automated mirMachine pipeline.

Of different types of noncoding RNAs, microRNAs (miRNAs) have arguably been in the spotlight over the last decade. As post-transcriptional regulators of ...

A Bioinformatics Pipeline to Accurately and Efficiently Analyze the MicroRNA Transcriptomes in Plants

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