PAREameters: a tool for computational inference of plant miRNA-mRNA targeting rules using small RNA and degradome sequencing data

In silico identification of papaya genome-encoded microRNAs to target begomovirus genes in papaya leaf curl disease

Papaya leaf curl disease (PaLCuD) is widespread and classified in the genus begomovirus (Geminiviridae), disseminated by the vector whitefly Bemisia tabaci. RNA interference (RNAi)-based antiviral innate immunity stands as a pivotal defense mechanism and biological process in limiting viral genomes to manage plant diseases. The current study aims to identify and analyze Carica Papaya locus-derived capa-microRNAs with predicted potential for targeting divergent begomovirus species-encoded mRNAs using a 'four integrative in silico algorithms' approach. This research aims to experimentally activate the RNAi catalytic pathway using in silico-predicted endogenous capa-miRNAs and create papaya varieties capable of assessing potential resistance against begomovirus species and monitoring antiviral capabilities. This study identified 48 predicted papaya locus-derived candidates from 23 miRNA families, which were further investigated for targeting begomovirus genes. Premised all the four algorithms combined, capa-miR5021 was the most anticipated miRNA followed by capa-miR482, capa-miR5658, capa-miR530b, capa-miR3441.2, and capa-miR414 'effective' papaya locus-derived candidate capa-miRNA and respected putative binding sites for targets at the consensus nucleotide position. It was predicted to bind and target mostly to AC1 gene of the complementary strand and the AV1 gene of the virion strand of different begomovirus isolates, which were associated with replication-associated protein and encapsidation, respectively, during PaLCuD. These miRNAs were also found targeting betaC1 gene of betasatellite which were associated with retardation in leaf growth and developmental abnormalities with severe symptoms during begomovirus infection. To validate target prediction accuracy, we created an integrated Circos plot for comprehensive visualization of host-virus interaction. In silico-predicted papaya genome-wide miRNA-mediated begomovirus target gene regulatory network corroborated interactions that permit in vivo analysis, which could provide biological material and valuable evidence, leading to the development of begomovirus-resistant papaya plants. The integrative nature of our research positions it at the forefront of efforts to ensure the sustainable cultivation of papaya, particularly in the face of evolving pathogenic threats. As we move forward, the knowledge gained from this study provides a solid foundation for continued exploration and innovation in the field of papaya virology, and to the best of our knowledge, this study represents a groundbreaking endeavor, undertaken for the first time in the context of PaLCuD research.

In Silico Identification of Cassava Genome-Encoded MicroRNAs with Predicted Potential for Targeting the ICMV-Kerala Begomoviral Pathogen of Cassava

Cassava mosaic disease (CMD) is caused by several divergent species belonging to the genus Begomovirus (Geminiviridae) transmitted by the whitefly Bemisia tabaci cryptic species group. In India and other parts of Asia, the Indian cassava mosaic virus-Kerala (ICMV-Ker) is an emergent begomovirus of cassava causing damage that results in reduced yield loss and tuber quality. Double-stranded RNA-mediated interference (RNAi) is an evolutionary conserved mechanism in eukaryotes and highly effective, innate defense system to inhibit plant viral replication and/or translation. The objective of this study was to identify and characterize cassava genome-encoded microRNAs (mes-miRNA) that are predicted to target ICMV-Ker ssDNA-encoded mRNAs, based on four in silico algorithms: miRanda, RNA22, Tapirhybrid, and psRNA. The goal is to deploy the predicted miRNAs to trigger RNAi and develop cassava plants with resistance to ICMV-Ker. Experimentally validated mature cassava miRNA sequences (n = 175) were downloaded from the miRBase biological database and aligned with the ICMV-Ker genome. The miRNAs were evaluated for base-pairing with the cassava miRNA seed regions and to complementary binding sites within target viral mRNAs. Among the 175 locus-derived mes-miRNAs evaluated, one cassava miRNA homolog, mes-miR1446a, was identified to have a predicted miRNA target binding site, at position 2053 of the ICMV-Ker genome. To predict whether the cassava miRNA might bind predicted ICMV-Ker mRNA target(s) that could disrupt viral infection of cassava plants, a cassava locus-derived miRNA-mRNA regulatory network was constructed using Circos software. The in silico-predicted cassava locus-derived mes-miRNA-mRNA network corroborated interactions between cassava mature miRNAs and the ICMV-Ker genome that warrant in vivo analysis, which could lead to the development of ICMV-Ker resistant cassava plants.

From single- to multi-omics: future research trends in medicinal plants

Medicinal plants are the main source of natural metabolites with specialised pharmacological activities and have been widely examined by plant researchers. Numerous omics studies of medicinal plants have been performed to identify molecular markers of species and functional genes controlling key biological traits, as well as to understand biosynthetic pathways of bioactive metabolites and the regulatory mechanisms of environmental responses. Omics technologies have been widely applied to medicinal plants, including as taxonomics, transcriptomics, metabolomics, proteomics, genomics, pangenomics, epigenomics and mutagenomics. However, because of the complex biological regulation network, single omics usually fail to explain the specific biological phenomena. In recent years, reports of integrated multi-omics studies of medicinal plants have increased. Until now, there have few assessments of recent developments and upcoming trends in omics studies of medicinal plants. We highlight recent developments in omics research of medicinal plants, summarise the typical bioinformatics resources available for analysing omics datasets, and discuss related future directions and challenges. This information facilitates further studies of medicinal plants, refinement of current approaches and leads to new ideas.

miR-18a-5p derived from mesenchymal stem cells-extracellular vesicles inhibits ovarian cancer cell proliferation, migration, invasion, and chemotherapy resistance

OBJECTIVE

Ovarian cancer (OC) is a major threat to women's health. Mesenchymal stem cells (MSCs) are key regulators in cellular communication by secreting extracellular vesicles (EVs) that are involved in OC. This study probed into the mechanism of human MSCs derived-EVs (hMSC-EVs) in regulating OC cell growth and chemotherapy resistance.

METHODS

hMSCs and EVs were isolated and identified. After adding EVs, the uptake of EVs by OC CAOV3/ES2 cells (for in vitro studies), and cell proliferation, migration, and invasion were detected. Downregulated miRNAs in hMSC-EVs were screened and miR-18a-5p expression in OC patients was detected. The prognosis of OC patients was analyzed. Binding sites of miR-18a-5p and NACC1 were predicted and validated. NACC1 expression in OC tissues was measured by RT-qPCR, and its correlation with miR-18a-5p was analyzed by Pearson method. AKT/mTOR pathway activation was assessed by WB. The cisplatin sensitivity of EVs-treated CAOV3 cells was evaluated via MTT assay and tested by tumor formation assay in nude mice.

RESULTS

hMSC-EVs suppressed OC cell proliferation, migration, and invasion. miR-18a-5p was downregulated in OC and miR-18a-5p low expression was associated with a poor prognosis. EV-encapsulated miR-18a-5p targeted NACC1. NACC1 was upregulated in OC tissues. miR-18a-5p knockdown and NACC1 overexpression both annulled the inhibition of hMSC-EVs on OC cell growth. AKT and mTOR were elevated in OC and NACC1 activated the AKT/mTOR pathway in OC cells. hMSC-EVs promoted cisplatin sensitivity of OC cells by carrying miR-18a-5p.

CONCLUSION

hMSC-EVs-derived miR-18a-5p inhibits OC cell proliferation, migration, invasion, and chemotherapy resistance.

TRUEE; a bioinformatic pipeline to define the functional microRNA targetome of Arabidopsis

Central to plant microRNA (miRNA) biology is the identification of functional miRNA-target interactions (MTIs). However, the complementarity basis of bioinformatic target prediction results in mostly false positives, and the degree of complementarity does not equate with regulation. Here, we develop a bioinformatic workflow named TRUEE (Targets Ranked Using Experimental Evidence) that ranks MTIs on the extent to which they are subjected to miRNA-mediated cleavage. It sorts predicted targets into high (HE) and low evidence (LE) groupings based on the frequency and strength of miRNA-guided cleavage degradome signals across multiple degradome experiments. From this, each target is assigned a numerical value, termed a Category Score, ranking the extent to which it is subjected to miRNA-mediated cleavage. As a proof-of-concept, the 428 Arabidopsis miRNAs annotated in miRBase were processed through the TRUEE pipeline to determine the miRNA 'targetome'. The majority of high-ranking Category Score targets corresponded to highly conserved MTIs, validating the workflow. Very few Arabidopsis-specific, Brassicaceae-specific, or Conserved-passenger miRNAs had HE targets with high Category Scores. In total, only several hundred MTIs were found to have Category Scores characteristic of currently known physiologically significance MTIs. Although non-exhaustive, clearly the number of functional MTIs is much narrower than many studies claim. Therefore, using TRUEE to numerically rank targets  directly on experimental evidence has given insights into the scope of the functional miRNA targetome of Arabidopsis.

MicroRNA-125a/b-5p promotes malignant behavior in multiple myeloma cells and xenograft tumor growth by targeting DIS3

Multiple myeloma (MM) is a hematological malignancy with a high prevalence and is characterized by the clonal expansion of malignant plasma cells. As a new tumor suppressor, defective in sister chromatid joining (DIS3) was reported to be a gene closely related to MM. This study elucidated the biological functions and underlying mechanisms of DIS3 in MM. DIS3 mRNA and protein levels were detected using RT-qPCR and western blotting, respectively. Methyl thiazolyl tetrazolium assays, flow cytometry analyses, Transwell assays, and wound healing assays were performed to detect the proliferation, apoptosis, invasion, and migration of MM cells. The binding relationship between miR-125a/b-5p and DIS3 was verified using luciferase reporter assays and RNA pulldown assays. Xenograft tumor models were established in nude mice to investigate the effects of miR-125a/b-5p and DIS3 on tumor growth in vivo. DIS3 levels were downregulated in MM cells, and DIS3 upregulation inhibited the malignant behaviors of MM cells. Mechanistically, miR-125a/b-5p directly targeted the 3' untranslated region of DIS3. The expression of miR-125a/b-5p was upregulated in MM cells, miR-125a/b-5p knockdown inhibited the malignant behaviors of MM cells, and the inhibitory effect was reversed by DIS3 downregulation. The results of in vivo experiments indicated that miR-125a/b-5p promoted tumor growth by downregulating DIS3. Overall, miR-125a/b-5p promotes MM cellular processes and xenograft tumor growth by targeting DIS3.

The Multiverse of Plant Small RNAs: How Can We Explore It?

Plant small RNAs (sRNAs) are a heterogeneous group of noncoding RNAs with a length of 20–24 nucleotides that are widely studied due to their importance as major regulators in various biological processes. sRNAs are divided into two main classes—microRNAs (miRNAs) and small interfering RNAs (siRNAs)—which differ in their biogenesis and functional pathways. Their identification and enrichment with new structural variants would not be possible without the use of various high-throughput sequencing (NGS) techniques, allowing for the detection of the total population of sRNAs in plants. Classifying sRNAs and predicting their functional role based on such high-performance datasets is a nontrivial bioinformatics task, as plants can generate millions of sRNAs from a variety of biosynthetic pathways. Over the years, many computing tools have been developed to meet this challenge. Here, we review more than 35 tools developed specifically for plant sRNAs over the past few years and explore some of their basic algorithms for performing tasks related to predicting, identifying, categorizing, and quantifying individual sRNAs in plant samples, as well as visualizing the results of these analyzes. We believe that this review will be practical for biologists who want to analyze their plant sRNA datasets but are overwhelmed by the number of tools available, thus answering the basic question of how to choose the right one for a particular study.

MicroRNA-mediated bioengineering for climate-resilience in crops

Global projections on the climate change and the dynamic environmental perturbations indicate severe impacts on food security in general, and crop yield, vigor and the quality of produce in particular. Sessile plants respond to environmental challenges such as salt, drought, temperature, heavy metals at transcriptional and/or post-transcriptional levels through the stress-regulated network of pathways including transcription factors, proteins and the small non-coding endogenous RNAs. Amongs these, the miRNAs have gained unprecedented attention in recent years as key regulators for modulating gene expression in plants under stress. Hence, tailoring of miRNAs and their target pathways presents a promising strategy for developing multiple stress-tolerant crops. Plant stress tolerance has been successfully achieved through the over expression of microRNAs such as Os-miR408, Hv-miR82 for drought tolerance; OsmiR535A and artificial DST miRNA for salinity tolerance; and OsmiR535 and miR156 for combined drought and salt stress. Examples of miR408 overexpression also showed improved efficiency of irradiation utilization and carbon dioxide fixation in crop plants. Through this review, we present the current understanding about plant miRNAs, their roles in plant growth and stress-responses, the modern toolbox for identification, characterization and validation of miRNAs and their target genes including in silico tools, machine learning and artificial intelligence. Various approaches for up-regulation or knock-out of miRNAs have been discussed. The main emphasis has been given to the exploration of miRNAs for development of bioengineered climate-smart crops that can withstand changing climates and stressful environments, including combination of stresses, with very less or no yield penalties.

noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimizing technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches.

Recent development of bioinformatics tools for microRNA target prediction

MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

NATpare: a pipeline for high-throughput prediction and functional analysis of nat-siRNAs

Natural antisense transcript-derived small interfering RNAs (nat-siRNAs) are a class of functional small RNA (sRNA) that have been found in both plant and animals kingdoms. In plants, these sRNAs have been shown to suppress the translation of messenger RNAs (mRNAs) by directing the RNA-induced silencing complex (RISC) to their sequence-specific mRNA target(s). Current computational tools for classification of nat-siRNAs are limited in number and can be computationally infeasible to use. In addition, current methods do not provide any indication of the function of the predicted nat-siRNAs. Here, we present a new software pipeline, called NATpare, for prediction and functional analysis of nat-siRNAs using sRNA and degradome sequencing data. Based on our benchmarking in multiple plant species, NATpare substantially reduces the time required to perform prediction with minimal resource requirements allowing for comprehensive analysis of nat-siRNAs in larger and more complex organisms for the first time. We then exemplify the use of NATpare by identifying tissue and stress specific nat-siRNAs in multiple Arabidopsis thaliana datasets.