Evaluation and application of tools for the identification of known microRNAs in plants.
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ABSTRACT:
SUBMITTER: Li Q
PROVIDER: S-EPMC8027368 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MicroRNAs (miRNAs) are a class of small endogenous RNAs that play important regulatory roles in plants by negatively affecting gene expression. Studies on the identification of miRNAs and their functions in various plant species and organs have significantly contributed to plant development research. In the current study, we utilized high-throughput sequencing to detect the miRNAs in the root, stem, and leaf tissues of raspberry (Rubus idaeus). A total of more than 35 million small RNA reads ranging in size from 18 to 35 nucleotides were obtained, with 147 known miRNAs and 542 novel miRNAs identified among the three organs. Sequence verification and the relative expression profiles of the six known miRNAs were investigated by stem-loop quantitative real-time PCR. Furthermore, the potential target genes of the known and novel miRNAs were predicted and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway annotation. Enrichment analysis of the GO-associated biological processes and molecular functions revealed that these target genes were potentially involved in a wide range of metabolic pathways and developmental processes. Moreover, the miRNA target prediction revealed that most of the targets predicted as transcription factor-coding genes are involved in cellular and metabolic processes. This report is the first to identify miRNAs in raspberry. The detected miRNAs were analyzed by cluster analysis according to their expression, which revealed that these conservative miRNAs are necessary for plant functioning. The results add novel miRNAs to the raspberry transcriptome, providing a useful resource for the further elucidation of the functional roles of miRNAs in raspberry growth and development.
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Project description:MicroRNAs (miRNAs) are an extensive class of ~22-nucleotide noncoding RNAs thought to regulate gene expression in metazoans. We find that miRNAs are also present in plants, indicating that this class of noncoding RNA arose early in eukaryotic evolution. In this paper 16 Arabidopsis miRNAs are described, many of which have differential expression patterns in development. Eight are absolutely conserved in the rice genome. The plant miRNA loci potentially encode stem-loop precursors similar to those processed by Dicer (a ribonuclease III) in animals. Mutation of an Arabidopsis Dicer homolog, CARPEL FACTORY, prevents the accumulation of miRNAs, showing that similar mechanisms direct miRNA processing in plants and animals. The previously described roles of CARPEL FACTORY in the development of Arabidopsis embryos, leaves, and floral meristems suggest that the miRNAs could play regulatory roles in the development of plants as well as animals.
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Project description:MicroRNAs are endogenous small non-coding RNAs that negatively regulate mRNAs, mainly at the post-transcriptional level, and play an important role in resistance response of plants. To date, there are few reports on resistance response of strawberry miRNAs to pathogens. In this study, using high-throughput sequencing, 134 conserved and 35 novel miRNAs were identified in six libraries within the treatment of Botrytis cinerea. A total 497 potential target genes were predicted using Fragaria vesca genome. Most of the differential expressed miRNAs in strawberry fruits were up-regulated in early libraries and down-regulated in late libraries. PIRL, the target gene of miR5290a, showed the opposite expressed trend compared with miR5290 from T1 to T3 libraries, and functional analysis of the PIRL gene shows that it has obvious resistance to B. cinerea in the strawberry fruits with overexpressed PIRL gene. We speculate that miR5290a negatively regulates its target gene PIRL to increase resistance to pathogen infection, and further analysis of PIRL function is meaningful for studying the plant-pathogen relationship and improving strawberry fruit quality and yield.
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Project description:Recently, a novel class of global gene regulators called microRNAs (miRNAs), were identified in both plants and animals. MiRNAs can reduce protein levels of their target genes with a minor impact on the target genes' mRNAs. Accumulating evidence demonstrates the importance of miRNAs in cancer. MiRNAs that are overexpressed in cancer may function as oncogenes, and miRNAs with tumour suppressor activity in normal tissue may be downregulated in cancer. Although major advances have been achieved in our understanding of cancer biology, as well as in the development of new targeted therapies, the progress in developing improved early diagnosis and screening tests has been inadequate. This results in most cancers being diagnosed in advanced stages, delaying timely treatment and leading to poor outcomes. There is intense research seeking specific molecular changes that are able to identify patients with early cancer or precursor lesions. MiRNA expression data in various cancers demonstrate that cancer cells have different miRNA profiles compared with normal cells, thus underscoring the tremendous diagnostic and therapeutic potential of miRNAs in cancer. These unique properties of miRNAs make them extremely useful potential agents for clinical diagnostics as well as in personalised care for individual patients in the future.
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Project description:Genome editing tools have the potential to change the genomic architecture of a genome at precise locations, with desired accuracy. These tools have been efficiently used for trait discovery and for the generation of plants with high crop yields and resistance to biotic and abiotic stresses. Due to complex genomic architecture, it is challenging to edit all of the genes/genomes using a particular genome editing tool. Therefore, to overcome this challenging task, several genome editing tools have been developed to facilitate efficient genome editing. Some of the major genome editing tools used to edit plant genomes are: Homologous recombination (HR), zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), pentatricopeptide repeat proteins (PPRs), the CRISPR/Cas9 system, RNA interference (RNAi), cisgenesis, and intragenesis. In addition, site-directed sequence editing and oligonucleotide-directed mutagenesis have the potential to edit the genome at the single-nucleotide level. Recently, adenine base editors (ABEs) have been developed to mutate A-T base pairs to G-C base pairs. ABEs use deoxyadeninedeaminase (TadA) with catalytically impaired Cas9 nickase to mutate A-T base pairs to G-C base pairs.
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Project description:ObjectivePoint-of-care tools (PoCTs) provide evidence-based information on patient care and procedures at the time of need. Registered nurses have unique practice needs, and many PoCTs are marketed to support their practice. However, there is little reported evidence in the literature about evaluating nursing-focused PoCTs.Case presentationThe investigators developed a rubric containing evaluation criteria based on content, coverage of nursing topics, transparency of the evidence, user perception, and customization of PoCTs for supporting nursing practice. The investigators selected five PoCTs cited in the literature and of interest to local nursing leadership: ClinicalKey for Nursing, DynaMed, Lippincott's Advisor and Procedures, Nursing Reference Center Plus, and UpToDate. Application of the rubric found Lippincott had the highest coverage of diagnoses, while ClinicalKey for Nursing had strong content focused on interventions and outcomes. Nursing Reference Center Plus provided the most well-rounded coverage of nursing terminology and topics. DynaMed and UpToDate were more transparent with indicating conflict of interest, but both had lower coverage of nursing terminology, content, and care processes.ConclusionNone of the five PoCTs strongly met all of the evaluated criteria. The rubric developed for this study highlights each PoCT's strengths and weaknesses that can then be used to inform the decision-making process based on priorities and budget. The investigators recommend licensing a nursing PoCT and a PoCT like DynaMed or UpToDate to provide comprehensive, evidence-based, patient care coverage and to meet the diverse information needs of nurses.
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Project description:The brown planthopper (BPH) is the most devastating insect pest of rice. The rice gene BPH15 confers resistance to BPH. MicroRNAs (miRNAs) regulate a spectrum of development and defense response processes in plants. In this study, we analyzed six miRNA profiles of a BPH15 introgression line (P15) and a susceptible recipient line (PC) at three time points (0?h, 6?h and 48?h) after BPH attack, and identified 464 known miRNAs and 183 potential novel miRNAs. Before the BPH feeding, we identified 23 miRNAs differentially expressed in P15 and PC. We speculated that the resistant plant is in a priming state by the regulation of miRNAs. After the BPH feeding, 104 miRNAs were found to be expressed differentially in P15 (68 in P15-6/P15-0, 36 in P15-48/P15-0), and 80 miRNAs were found expressed differentially in PC (32 in PC-6/PC-0, 48 in PC-48/PC-0), which illustrated that miRNA expression is activated upon attack. These miRNAs regulate different pathways that contribute to the basal defense and specific resistance of rice to the BPH. Our study provides additional data for scientists to further explore the mechanism of plant defense against insect attack and to find a way for efficient insect control.
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Project description:BackgroundViruses, including bacteriophages, are important components of environmental and human associated microbial communities. Viruses can act as extracellular reservoirs of bacterial genes, can mediate microbiome dynamics, and can influence the virulence of clinical pathogens. Various targeted metagenomic analysis techniques detect viral sequences, but these methods often exclude large and genome integrated viruses. In this study, we evaluate and compare the ability of nine state-of-the-art bioinformatic tools, including Vibrant, VirSorter, VirSorter2, VirFinder, DeepVirFinder, MetaPhinder, Kraken 2, Phybrid, and a BLAST search using identified proteins from the Earth Virome Pipeline to identify viral contiguous sequences (contigs) across simulated metagenomes with different read distributions, taxonomic compositions, and complexities.ResultsOf the tools tested in this study, VirSorter achieved the best F1 score while Vibrant had the highest average F1 score at predicting integrated prophages. Though less balanced in its precision and recall, Kraken2 had the highest average precision by a substantial margin. We introduced the machine learning tool, Phybrid, which demonstrated an improvement in average F1 score over tools such as MetaPhinder. The tool utilizes machine learning with both gene content and nucleotide features. The addition of nucleotide features improves the precision and recall compared to the gene content features alone.Viral identification by all tools was not impacted by underlying read distribution but did improve with contig length. Tool performance was inversely related to taxonomic complexity and varied by the phage host. For instance, Rhizobium and Enterococcus phages were identified consistently by the tools; whereas, Neisseria prophage sequences were commonly missed in this study.ConclusionThis study benchmarked the performance of nine state-of-the-art bioinformatic tools to identify viral contigs across different simulation conditions. This study explored the ability of the tools to identify integrated prophage elements traditionally excluded from targeted sequencing approaches. Our comprehensive analysis of viral identification tools to assess their performance in a variety of situations provides valuable insights to viral researchers looking to mine viral elements from publicly available metagenomic data.
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Project description:MicroRNAs (miRNAs) are a group of non-coding RNAs that have functions in post-transcriptional gene regulation in plants. Although the most important economic component of peach trees (Prunus persica) is the fruit, not much is known about miRNAs in this organ. In this study, miRNAs and their targets were identified and characterized from libraries of small RNAs of peach fruit through Solexa based-sequencing and bioinformatics approaches. A total of 557 known peach miRNAs belonging to 34 miRNA families were identified, and some of these miRNAs were found to be highly conserved in at least four other plant species. Using the most current criteria for miRNA annotation, 275 putative novel miRNAs were predicted, and the sequencing frequencies of these novel miRNAs were less than those of the conserved miRNAs. In total, 3959 and 1614 target genes for 349 known and 193 novel miRNAs, respectively, were predicted with the criteria that a single target gene can be targeted by different miRNAs and that a single miRNA can also have a large number of target genes. Three targets were even found to be targeted by 13 novel miRNAs that contained the same complete miRNA sequence at different locations and had different scaffolds. The proteins predicted to be targeted by the miRNAs identified in this study encompass a wide range of transcription factors and are involved in many biological processes and pathways, including development, metabolism, stress responses and signal transduction. A total of 115 and 101 target genes were identified to be cleaved by 60 known miRNAs and 27 novel miRNAs through degradome sequencing, respectively. These miRNAs induce cleavage of their targets precisely at the position between nucleotides 10 and 11 of the miRNA sequences from the 5' to the 3' end. Thirty conserved miRNAs and 19 novel miRNAs exhibited differential expression profiles in the peach, and the expression patterns of some miRNAs appeared to be tissue- or developmental stage-specific. The findings of this study provide an important basis for the analysis of miRNAs, their targets and the functions of these targets in peach fruit.
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Project description:High-throughput RNA sequencing unveiled the complexity of transcriptome and significantly increased the records of long noncoding RNAs (lncRNAs), which were reported to participate in a variety of biological processes. Identification of lncRNAs is a key step in lncRNA analysis, and a bunch of bioinformatics tools have been developed for this purpose in recent years. While these tools allow us to identify lncRNA more efficiently and accurately, they may produce inconsistent results, making selection a confusing issue. We compared the performance of 41 analysis models based on 14 software packages and different data sets, including high-quality data and low-quality data from 33 species. In addition, computational efficiency, robustness, and joint prediction of the models were explored. As a practical guidance, key points for lncRNA identification under different situations were summarized. In this investigation, no one of these models could be superior to others under all test conditions. The performance of a model relied to a great extent on the source of transcripts and the quality of assemblies. As general references, FEELnc_all_cl, CPC, and CPAT_mouse work well in most species while COME, CNCI, and lncScore are good choices for model organisms. Since these tools are sensitive to different factors such as the species involved and the quality of assembly, researchers must carefully select the appropriate tool based on the actual data. Alternatively, our test suggests that joint prediction could behave better than any single model if proper models were chosen. All scripts/data used in this research can be accessed at http://bioinfo.ihb.ac.cn/elit.
| S-EPMC7749630 | biostudies-literature