miRNA Prediction Research Articles

An ensemble of stacking classifiers for improved prediction of miRNA–mRNA interactions

MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in regulating gene expression at the post-transcriptional level by binding to potential target sites of messenger RNAs (mRNAs), facilitated by the Argonaute family of proteins. Selecting the conservative candidate target sites (CTS) is a challenging step, considering that most of the existing computational algorithms primarily focus on canonical site types, which is a time-consuming and inefficient utilization of miRNA target site interactions. We developed a stacking classifier algorithm that addresses the CTS selection criteria using feature-encoding techniques that generates feature vectors, including k-mer nucleotide composition, dinucleotide composition, pseudo-nucleotide composition, and sequence order coupling. This innovative stacking classifier algorithm surpassed previous state-of-the-art algorithms in predicting functional miRNA targets. We evaluated the performance of the proposed model on 10 independent test datasets and obtained an average accuracy of 79.77%, which is a significant improvement of 7.26 % over previous models. This improvement shows that the proposed method has great potential for distinguishing highly functional miRNA targets and can serve as a valuable tool in biomedical and drug development research.

Prediction of miRNA-Disease Associations by Cascade Forest Model Based on Stacked Autoencoder.

Numerous pieces of evidence have indicated that microRNA (miRNA) plays a crucial role in a series of significant biological processes and is closely related to complex disease. However, the traditional biological experimental methods used to verify disease-related miRNAs are inefficient and expensive. Thus, it is necessary to design some excellent approaches to improve efficiency. In this work, a novel method (CFSAEMDA) is proposed for the prediction of unknown miRNA-disease associations (MDAs). Specifically, we first capture the interactive features of miRNA and disease by integrating multi-source information. Then, the stacked autoencoder is applied for obtaining the underlying feature representation. Finally, the modified cascade forest model is employed to complete the final prediction. The experimental results present that the AUC value obtained by our method is 97.67%. The performance of CFSAEMDA is superior to several of the latest methods. In addition, case studies conducted on lung neoplasms, breast neoplasms and hepatocellular carcinoma further show that the CFSAEMDA method may be regarded as a utility approach to infer unknown disease-miRNA relationships.

A machine learning method for predicting disease-associated microRNA connections using network internal topology data

Recent studies have shown that aberrations or mutations of microRNAs (miRNA) can cause various diseases. Therefore, identifying disease-associated microRNAs can aid in diagnosing and treating related disorders. However, getting precise correlations through biological research is expensive and time-consuming. This research suggests a machine learning method (HNDLM) based on network internal topology data to anticipate disease-miRNA connections. HNDLM applies the recently described network embedding technique to biological networks rather than creating similar ones. According to the experimental findings, HNDLM performs better in terms of accuracy and Area Under the ROC Curve (AUC) value when compared to the traditional algorithms MIDPE, MIDP, WBSMDA, RLSMDA, Collective Prediction based on Transductive Learning (CPTL), and Human Disease-related MiRNA Prediction (HDMP). The top 30 potential miRNAs suggested by HNDLM can also be verified by earlier research in the case study. HNDLM can identify probable disease-miRNA correlations, which will aid in the investigation of illness path physiology and advance the field of bioinformatics.

An integrated analysis of microRNAs regulating DNA damage response in triple-negative breast cancer

BackgroundTriple-negative breast cancer (TNBC) remains a clinical challenge due to its aggressive phenotype and limited treatment options for the patients. Many TNBC patients show an inherent defect in the DNA repair capacity primarily by acquiring germline mutations in BRCA1 and BRCA2 genes leading to Homologous Recombination Deficiency (HRD). Epigenetic modifications such as BRCA1 promoter methylation and miRNA expression targeting DNA repair pathway genes have contributed to the HRD phenotype in TNBC. Hence, we aimed to identify microRNAs that are associated with HRD status in the TCGA-BRCA project.Materials and methodsWe implemented a miRNA prediction strategy for identifying miRNAs targeting HR pathway genes using an in silico predicted and experimentally validated list from published literature for their association with genomic instability and factors affecting HRD. In silico analysis was performed to study miRNA expression patterns regulated by DNA methylation and TMB status in the TNBC patients from TCGA-BRCA project. Finally, we analysed selected miRNA expression with immune cell infiltration pattern in the TNBC patient cohort.ResultsOur study identified miRNAs associated with HRD, tumour mutation burden (TMB), and immune cell infiltration. Identified miRNA signatures were associated with the miR-17 ~ 92 cluster, miR-106b ~ 25 cluster, and miR-200b ~ 429 cluster. Pathway analysis of selected miRNAs suggested their association with altered immune cell infiltration in TNBC.ConclusionOur study identified 6 ‘HRD associated miRNAs’ such as miR-106b, miR-93, miR-17, miR-20a, miR-200b, and miR-429 as novel miRNA-based signatures associated with HR deficiency in TNBC.

Genome-Wide Association Study of Lint Percentage in Gossypium hirsutum L. Races.

Lint percentage is one of the most essential yield components and an important economic index for cotton planting. Improving lint percentage is an effective way to achieve high-yield in cotton breeding worldwide, especially upland cotton (Gossypium hirsutum L.). However, the genetic basis controlling lint percentage has not yet been systematically understood. Here, we performed a genome-wide association mapping for lint percentage using a natural population consisting of 189 G. hirsutum accessions (188 accessions of G. hirsutum races and one cultivar TM-1). The results showed that 274 single-nucleotide polymorphisms (SNPs) significantly associated with lint percentage were detected, and they were distributed on 24 chromosomes. Forty-five SNPs were detected at least by two models or at least in two environments, and their 5 Mb up- and downstream regions included 584 makers related to lint percentage identified in previous studies. In total, 11 out of 45 SNPs were detected at least in two environments, and their 550 Kb up- and downstream region contained 335 genes. Through RNA sequencing, gene annotation, qRT-PCR, protein-protein interaction analysis, the cis-elements of the promotor region, and related miRNA prediction, Gh_D12G0934 and Gh_A08G0526 were selected as key candidate genes for fiber initiation and elongation, respectively. These excavated SNPs and candidate genes could supplement marker and gene information for deciphering the genetic basis of lint percentage and facilitate high-yield breeding programs of G. hirsutum ultimately.

English

MicroRNAs are small non coding regulatory RNAs that regulate gene expression after transcription by suppressing translation or degrading mRNA. MiRNAs play an important role in the regulatory systems such as biological processes in plants. By investigating miRNAs in muskmelon, the current study will focus on miRNA prediction, expression, and characterisation. The Cucumis melo ESTs were used in the current investigation to identify 22 novel miRNAs with MFE values ranging from -10.7 to -49.13 kcal/mole. The newly predicted C. melo miRNAs have the greatest number of target genes using psRNA-Target tool connected with GO (Gene Ontology) enrichment analysis. As a result, a total of roughly 78 targets were predicted in response to abiotic and biotic stress. In addition, miR157 has been found to target the receptor, cell signalling, gene expression and transcriptional activation. Consequently, cme-miR2657 targets high mobility group protein while miR2657 and miR5215 participate in positive control of gene silencing post-transcriptionally via RNA for plant hormones.

PVT1 alleviates hypoxia-induced endothelial apoptosis by enhancing autophagy via the miR-15b-5p/ATG14 and miR-424-5p/ATG14 axis

Endothelial dysfunction plays a crucial role in the pathogenesis of vascular disease. Long noncoding RNA (lncRNA) and microRNA (miRNA) play important roles in various cellular processes and are involved in several vascular endothelial cells (VECs) biological processes, including cell growth, migration, autophagy, and apoptosis. The functions of plasmacytoma variant translocation 1 (PVT1) in VECs have been progressively investigated in recent years, mainly with regard to proliferation and migration of endothelial cells (ECs). However, the mechanism underlying the regulation of autophagy and apoptosis in human umbilical vein endothelial cells (HUVEC) by PVT1 remains unclear. The present study showed that PVT1 knockdown accelerated apoptosis induced by oxygen and glucose deprivation (OGD) through suppression of cellular autophagy. Bioinformatic prediction of PVT1 target miRNAs revealed that PVT1 interacts with miR-15b-5p and miR-424-5p. The study further showed that miR-15b-5p and miR-424-5p inhibit the functions of autophagy related 14 (ATG14) and suppress cellular autophagy. The results showed that PVT1 can function as a competing endogenous RNA (ceRNA) of miR-15b-5p and miR-424-5p and promote cellular autophagy by competitive binding, which down-regulates apoptosis. The results showed that PVT1 can function as a competing endogenous RNA (ceRNA) of miR-15b-5p and miR-424-5p and promote cellular autophagy through competitive binding, which down-regulates apoptosis. The study provides insight into a novel therapeutic target that may be explored in the future for the treatment of cardiovascular disease.

Prediction of small molecule drug-miRNA associations based on GNNs and CNNs.

MicroRNAs (miRNAs) play a crucial role in various biological processes and human diseases, and are considered as therapeutic targets for small molecules (SMs). Due to the time-consuming and expensive biological experiments required to validate SM-miRNA associations, there is an urgent need to develop new computational models to predict novel SM-miRNA associations. The rapid development of end-to-end deep learning models and the introduction of ensemble learning ideas provide us with new solutions. Based on the idea of ensemble learning, we integrate graph neural networks (GNNs) and convolutional neural networks (CNNs) to propose a miRNA and small molecule association prediction model (GCNNMMA). Firstly, we use GNNs to effectively learn the molecular structure graph data of small molecule drugs, while using CNNs to learn the sequence data of miRNAs. Secondly, since the black-box effect of deep learning models makes them difficult to analyze and interpret, we introduce attention mechanisms to address this issue. Finally, the neural attention mechanism allows the CNNs model to learn the sequence data of miRNAs to determine the weight of sub-sequences in miRNAs, and then predict the association between miRNAs and small molecule drugs. To evaluate the effectiveness of GCNNMMA, we implement two different cross-validation (CV) methods based on two different datasets. Experimental results show that the cross-validation results of GCNNMMA on both datasets are better than those of other comparison models. In a case study, Fluorouracil was found to be associated with five different miRNAs in the top 10 predicted associations, and published experimental literature confirmed that Fluorouracil is a metabolic inhibitor used to treat liver cancer, breast cancer, and other tumors. Therefore, GCNNMMA is an effective tool for mining the relationship between small molecule drugs and miRNAs relevant to diseases.

Paternal Western diet causes transgenerational increase in food consumption in Drosophila with parallel alterations in the offspring brain proteome and microRNAs.

Several lines of evidence indicate that ancestral diet might play an important role in determining offspring's metabolic traits. However, it is not yet clear whether ancestral diet can affect offspring's food choices and feeding behavior. In the current study, taking advantage of Drosophila model system, we demonstrate that paternal Western diet (WD) increases offspring food consumption up to the fourth generation. Paternal WD also induced alterations in F1 offspring brain proteome. Using enrichment analyses of pathways for upregulated and downregulated proteins, we found that upregulated proteins had significant enrichments in terms related to translation and translation factors, whereas downregulated proteins displayed enrichments in small molecule metabolic processes, TCA cycles, and electron transport chain (ETC). Using MIENTURNET miRNA prediction tool, dme-miR-10-3p was identified as the top conserved miRNA predicted to target proteins regulated by ancestral diet. RNAi-based knockdown of miR-10 in the brain significantly increased food consumption, implicating miR-10 as a potential factor in programming feeding behavior. Together, these findings suggest that ancestral nutrition may influence offspring feeding behavior through alterations in miRNAs.

Impaired angiogenesis in diabetic critical limb ischemia is mediated by a miR-130b/INHBA signaling axis.

Patients with peripheral artery disease (PAD) and diabetes compose a high-risk population for development of critical limb ischemia (CLI) and amputation, although the underlying mechanisms remain poorly understood. Comparison of dysregulated microRNAs from diabetic patients with PAD and diabetic mice with limb ischemia revealed the conserved microRNA, miR-130b-3p. In vitro angiogenic assays demonstrated that miR-130b rapidly promoted proliferation, migration, and sprouting in endothelial cells (ECs), whereas miR-130b inhibition exerted antiangiogenic effects. Local delivery of miR-130b mimics into ischemic muscles of diabetic mice (db/db) following femoral artery ligation (FAL) promoted revascularization by increasing angiogenesis and markedly improved limb necrosis and amputation. RNA-Seq and gene set enrichment analysis from miR-130b-overexpressing ECs revealed the BMP/TGF-β signaling pathway as one of the top dysregulated pathways. Accordingly, overlapping downregulated transcripts from RNA-Seq and miRNA prediction algorithms identified that miR-130b directly targeted and repressed the TGF-β superfamily member inhibin-β-A (INHBA). miR-130b overexpression or siRNA-mediated knockdown of INHBA induced IL-8 expression, a potent angiogenic chemokine. Lastly, ectopic delivery of silencer RNAs (siRNA) targeting Inhba in db/db ischemic muscles following FAL improved revascularization and limb necrosis, recapitulating the phenotype of miR-130b delivery. Taken together, a miR-130b/INHBA signaling axis may provide therapeutic targets for patients with PAD and diabetes at risk of developing CLI.

Pinostrobin induces acute leukemia cell apoptosis via the regulation of miR-410-5p and SFRP5

AimsThis study attempted to explore the mechanisms involved in pinostrobin (PN)-mediated acute leukemia cell apoptosis regulated by miR-410-5p. Material and methodsNB4 and MOLT-4 cells were cultured and treated with PN at the IC50 concentration. Apoptosis was examined by Annexin V-FITC/PI staining. RT–qPCR was used to measure the expression of caspase-3, BAK, BCL-W, and MCL-1. The target protein of PN was identified using LC–MS/MS followed by bioinformatic analysis. TargetScan, DIANA, and miRDB were used for the prediction of miRNAs involved in the PN-induced apoptosis mechanism. miRNA mimic transfection, RT–qPCR, and western blot analysis were performed to evaluate the regulatory effect of miRNA on its target and the involvement of miRNA in apoptosis induction by PN. In addition, the synergistic effect of PN and daunorubicin (DNR) were investigated by using the MTT assay. Key findingsThe results showed that PN reduced cell viability and induced apoptosis in both leukemia cell lines. From the LC–MS/MS and bioinformatics analysis, SFRP5 and miR-410-5p were selected as a potential PN target protein and miRNA, respectively. After miRNA mimic transfection, miR-410-5p, which is an onco-miRNA, was decreased and led to increased apoptosis in both cell lines, indicating that this miRNA is involved in PN-mediated apoptosis mechanisms. Moreover, PN demonstrated a synergistic effect with DNR, suggesting that PN may be used in combination with conventional chemotherapy drugs. SignificancePN regulates the expression of miR-410-5p and SFRP5 to promote apoptosis in acute leukemia cells. It could be developed as an alternative treatment for leukemia in the future.

Prediction of disease-related miRNAs by voting with multiple classifiers

There is strong evidence to support that mutations and dysregulation of miRNAs are associated with a variety of diseases, including cancer. However, the experimental methods used to identify disease-related miRNAs are expensive and time-consuming. Effective computational approaches to identify disease-related miRNAs are in high demand and would aid in the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In this study, we develop an ensemble learning framework to reveal the potential associations between miRNAs and diseases (ELMDA). The ELMDA framework does not rely on the known associations when calculating miRNA and disease similarities and uses multi-classifiers voting to predict disease-related miRNAs. As a result, the average AUC of the ELMDA framework was 0.9229 for the HMDD v2.0 database in a fivefold cross-validation. All potential associations in the HMDD V2.0 database were predicted, and 90% of the top 50 results were verified with the updated HMDD V3.2 database. The ELMDA framework was implemented to investigate gastric neoplasms, prostate neoplasms and colon neoplasms, and 100%, 94%, and 90%, respectively, of the top 50 potential miRNAs were validated by the HMDD V3.2 database. Moreover, the ELMDA framework can predict isolated disease-related miRNAs. In conclusion, ELMDA appears to be a reliable method to uncover disease-associated miRNAs.

Prediction of Early- and Late-Onset Pre-Eclampsia in the Preclinical Stage via Placenta-Specific Extracellular miRNA Profiling.

Pre-eclampsia (PE) is one of the severe complications of pregnancy in 3-8% of all cases and is one of the leading causes of maternal and perinatal mortality. The fundamental role in the pathogenesis of PE is assigned to maternal and/or placental factors, whereby the combination and manifestation of which determines the time of onset of the clinical symptoms of PE (before or after 34 weeks of gestation) and their severity. It is known that the expression level of miRNAs, the regulators of signaling cascades in the cell, depends on gestational age. In the present study, we focused on the identification of the placenta-specific miRNAs that differentiate between early- and late-onset pre-eclampsia (ePE and lPE) throughout pregnancy, from the first to the third trimester. A total of 67 patients were analyzed using small RNA deep sequencing and real-time quantitative PCR, which resulted in a core list of miRNAs (let-7b-5p, let-7d-3p, let-7f-5p, let-7i-5p, miR-22-5p, miR-451a, miR-1246, miR-30e-5p, miR-20a-5p, miR-1307-3p, and miR-320e), which in certain combinations can predict ePE or lPE with 100% sensitivity and 84-100% specificity in the 1st trimester of pregnancy. According to the literature data, these miRNA predictors of PE control trophoblast proliferation, invasion, migration, syncytialization, the endoplasmic reticulum unfolded protein response, immune tolerance, angiogenesis, and vascular integrity. The simultaneous detection of let-7d-3p, miR-451a, and miR-1307-3p, resistant to the repeated freezing/thawing of blood serum samples, in combination with biochemical (b-hCG and PAPP-A) and ultrasound (UAPI) parameters, allowed us to develop a universal model for the prediction of ePE and lPE onset (FPR = 15.7% and FNR = 9.5%), which was validated using a test cohort of 48 patients and demonstrated false-positive results in 26.7% of cases and false negatives in 5.6% of cases. For comparison, the use of the generally accepted Astraia program in the analysis of the test cohort of patients led to worse results: FPR = 62.1% and FNR = 33.3%.

Sequencing and bioinformatics analysis of miRNA from rat endplate chondrogenic exosomes.

Exosomes have a key role in various diseases, such as arthritis, heart disease and respiratory disease. Exosomes from various sources have also been indicated to improve intervertebral disc degeneration. However, the role of endplate chondrogenic exosomes in intervertebral disc degeneration has remained largely elusive. The aim of the present study was to compare exosomal microRNA (miRNA) expression patterns in endplate chondrocytes before and after degeneration, and their potential roles in the pathogenesis of intervertebral disc degeneration (IVDD). Endplate chondrocytes were extracted from rats and cultured to obtain pre- and post-degeneration chondrocytes. Exosomes were obtained from the chondrocytes by centrifugation. The two groups of exosomes were subjected to small RNA sequencing, miRNA identification, novel miRNA prediction, quantitative analysis of miRNA expression and differentially expressed (DE) miRNA screening, in addition to miRNA target gene (TG) prediction and TG functional annotation and enrichment analysis. The percentage of miRNAs isolated from the exosomes before and after degeneration was found to differ. A total of 58 DE miRNAs were analyzed, the expression levels of which were significantly different post-degeneration compared with pre-degeneration. Cell experiments were also performed, in which the exosomes were co-cultured with nucleus pulposus (NP) cells. The results indicated that the chondrocyte-derived exosomes were taken up by the NP cells and influenced the expression of aggrecan and collagen 1A and 2A, suggesting that they may inhibit IVDD via their action on NP cells. The specific miRNAs present in exosomes during IVDD may be used to develop new targets for the treatment and diagnosis of this condition. DE exosomal miRNAs derived from endplate cartilage pre- and post-degeneration may be associated with the risk of IVDD and could help to distinguish patients with IVDD. Furthermore, the expression of certain miRNAs may be associated with disease progression, which may contribute to understanding the pathophysiology of IVDD from an epigenetic perspective.

NeRNA: A negative data generation framework for machine learning applications of noncoding RNAs

Many supervised machine learning based noncoding RNA (ncRNA) analysis methods have been developed to classify and identify novel sequences. During such analysis, the positive learning datasets usually consist of known examples of ncRNAs and some of them might even have weak or strong experimental validation. On the contrary, there are neither databases listing the confirmed negative sequences for a specific ncRNA class nor standardized methodologies developed to generate high quality negative examples. To overcome this challenge, a novel negative data generation method, NeRNA (negative RNA), is developed in this work. NeRNA uses known examples of given ncRNA sequences and their calculated structures for octal representation to create negative sequences in a manner similar to frameshift mutations but without deletion or insertion. NeRNA is tested individually with four different ncRNA datasets including microRNA (miRNA), transfer RNA (tRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA). Furthermore, a species-specific case analysis is performed to demonstrate and compare the performance of NeRNA for miRNA prediction. The results of 1000 fold cross-validation on Decision Tree, Naïve Bayes and Random Forest classifiers, and deep learning algorithms such as Multilayer Perceptron, Convolutional Neural Network, and Simple feedforward Neural Networks indicate that models obtained by using NeRNA generated datasets, achieves substantially high prediction performance. NeRNA is released as an easy-to-use, updatable and modifiable KNIME workflow that can be downloaded with example datasets and required extensions. In particular, NeRNA is designed to be a powerful tool for RNA sequence data analysis.

Identification of Long Intergenic Noncoding RNAs in Rhizoctonia cerealis following Inoculation of Wheat.

Wheat sharp eyespot caused by Rhizoctonia cerealis is primarily a severe threat to worldwide wheat production. Currently, there are no resistant wheat cultivars, and the use of fungicides is the primary method for controlling this disease. Elucidating the mechanisms of R. cerealis pathogenicity can accelerate the pace of the control of this disease. Long intergenic noncoding RNAs (lincRNAs) that function in plant-pathogen interactions might provide a new perspective. We systematically analyzed lincRNAs and identified a total of 1,319 lincRNAs in R. cerealis. We found that lincRNAs are involved in various biological processes, as shown by differential expression analysis and weighted correlation network analysis (WGCNA). Next, one of nine hub lincRNAs in the blue module that was related to infection and growth processes, MSTRG.4380.1, was verified to reduce R. cerealis virulence on wheat by a host-induced gene silencing (HIGS) assay. Following that, RNA sequencing (RNA-Seq) analysis revealed that the significantly downregulated genes in the MSTRG.4380.1 knockdown lines were associated mainly with infection-related processes, including hydrolase, transmembrane transporter, and energy metabolism activities. Additionally, 23 novel microRNAs (miRNAs) were discovered during small RNA (sRNA) sequencing (sRNA-Seq) analysis of MSTRG.4380.1 knockdown, and target prediction of miRNAs suggested that MSTRG.4380.1 does not act as a competitive endogenous RNA (ceRNA). This study performed the first genome-wide identification of R. cerealis lincRNAs and miRNAs. It confirmed the involvement of a lincRNA in the infection process, providing new insights into the mechanism of R. cerealis infection and offering a new approach for protecting wheat from R. cerealis. IMPORTANCE Rhizoctonia cerealis, the primary causal agent of wheat sharp eyespot, has caused significant losses in worldwide wheat production. Since no resistant wheat cultivars exist, chemical control is the primary method. However, this approach is environmentally unfriendly and costly. RNA interference (RNAi)-mediated pathogenicity gene silencing has been proven to reduce the growth of Rhizoctonia and provides a new perspective for disease control. Recent studies have shown that lincRNAs are involved in various biological processes across species, such as biotic and abiotic stresses. Therefore, verifying the function of lincRNAs in R. cerealis is beneficial for understanding the infection mechanism. In this study, we reveal that lincRNAs could contribute to the virulence of R. cerealis, which provides new insights into controlling this pathogen.

Comprehensive Analysis of the INDETERMINATE DOMAIN (IDD) Gene Family and Their Response to Abiotic Stress in Zea mays

Transcription factors (TFs) are important regulators of numerous gene expressions due to their ability to recognize and combine cis-elements in the promoters of target genes. The INDETERMINATE DOMAIN (IDD) gene family belongs to a subfamily of C2H2 zinc finger proteins and has been identified only in terrestrial plants. Nevertheless, little study has been reported concerning the genome-wide analysis of the IDD gene family in maize. In total, 22 ZmIDD genes were identified, which can be distributed on 8 chromosomes in maize. On the basis of evolutionary relationships and conserved motif analysis, ZmIDDs were categorized into three clades (1, 2, and 3), each owning 4, 6, and 12 genes, respectively. We analyzed the characteristics of gene structure and found that 3 of the 22 ZmIDD genes do not contain an intron. Cis-element analysis of the ZmIDD promoter showed that most ZmIDD genes possessed at least one ABRE or MBS cis-element, and some ZmIDD genes owned the AuxRR-core, TCA-element, TC-rich repeats, and LTR cis-element. The Ka:Ks ratio of eight segmentally duplicated gene pairs demonstrated that the ZmIDD gene families had undergone a purifying selection. Then, the transcription levels of ZmIDDs were analyzed, and they showed great differences in diverse tissues as well as abiotic stresses. Furthermore, regulatory networks were constructed through the prediction of ZmIDD-targeted genes and miRNAs, which can inhibit the transcription of ZmIDDs. In total, 6 ZmIDDs and 22 miRNAs were discovered, which can target 180 genes and depress the expression of 9 ZmIDDs, respectively. Taken together, the results give us valuable information for studying the function of ZmIDDs involved in plant development and climate resilience in maize.

ASmiR: a machine learning framework for prediction of abiotic stress-specific miRNAs in plants.

Abiotic stresses have become a major challenge in recent years due to their pervasive nature and shocking impacts on plant growth, development, and quality. MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of specific abiotic stress-responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning-based computational model for prediction of miRNAs associated with four specific abiotic stresses such as cold, drought, heat and salt. The pseudo K-tuple nucleotide compositional features of Kmer size 1 to 5 were used to represent miRNAs in numeric form. Feature selection strategy was employed to select important features. With the selected feature sets, support vector machine (SVM) achieved the highest cross-validation accuracy in all four abiotic stress conditions. The highest cross-validated prediction accuracies in terms of area under precision-recall curve were found to be 90.15, 90.09, 87.71, and 89.25% for cold, drought, heat and salt respectively. Overall prediction accuracies for the independent dataset were respectively observed 84.57, 80.62, 80.38 and 82.78%, for the abiotic stresses. The SVM was also seen to outperform different deep learning models for prediction of abiotic stress-responsive miRNAs. To implement our method with ease, an online prediction server "ASmiR" has been established at https://iasri-sg.icar.gov.in/asmir/ . The proposed computational model and the developed prediction tool are believed to supplement the existing effort for identification of specific abiotic stress-responsive miRNAs in plants.

Deciphering the role of Andrographis paniculata micro-RNAs in regulation of cancer

Background and objectiveAndrographis paniculata belonging to Acanthaceae family is well-known potential hepatoprotective herb. The role of diet on human health and diseases including Hepatocellular Carcinoma has been demonstrated by innumerable studies which have also testified that plant-derived micro-RNAs, transferred to animals via oral consumption wherein it alters host gene expression and this provides the evidence for inter-species gene regulation. Herein, we aim to explore the role of predicted miRNAs of A. paniculata on human health. Methods and resultsIn the present study, A. paniculata miRNAs have been predicted and their impact on human transcriptome is uncovered by employing computational approach. By implementing miRNA prediction on A. paniculata genomic data and plant miRNA sequences present in miRBase repository, we have identified 20 miRNAs which are confined to 16 miR families. 197 human target genes were predicted for the identified miRNAs, which were further subjected to Gene Ontology analysis and network analysis revealing their significant role in various biological processes and signaling pathways. The identified hub proteins HGF, NRP2 and CCND1 show their significant involvement in cancerous pathways including STAT3, MAPK, TGF-β, mTOR and VEGFR pathways. Deregulation of these pathways has been reported as oncogenic response involved in Hepatocellular Carcinoma, cell proliferation and metastasis. ConclusionThe findings of our study suggest potentially beneficiary role of A. paniculata miRNAs in regulating cancer via cross-species genetic regulation. Conclusively, our study sustains the promising theory of inter-species gene regulation.

SNHG 12 and hsa-miR-140-5P may play an important role in the ceRNA network related to hypertrophic cardiomyopathy.

Competing endogenous RNA (ceRNA) networks play important roles in the mechanism and development of a variety of diseases. This study aimed to construct a ceRNA network of hypertrophic cardiomyopathy (HCM). We searched the Gene Expression Omnibus (GEO) database and then analyzed the RNAs of 353 samples to explore differentially expressed lncRNAs (DELs), microRNAs (miRNAs; DEMs), and messenger RNAs (DEmRNAs) during the progression of HCM. Weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and transcription factor (TF) prediction of miRNAs were also performed, and the GO terms, KEGG pathway terms, protein-protein interaction (PPI) network, and Pearson correlation network of the DEGs were visualized with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and through Pearson analysis. In addition, a ceRNA network related to HCM was constructed on the basis of the DELs, DEMs, and DEs. Finally, the function of the ceRNA network was explored via GO and KEGG enrichment analyses. Through our analysis, 93 DELs (77 upregulated and 16 downregulated), 163 DEMs (91 upregulated and 72 downregulated), and 432 DEGs (238 upregulated and 194 downregulated) were screened. The functional enrichment analysis results for miRNAs showed that the miRNAs were mainly related to the VEGFR signaling network and the INFr pathway and were mainly regulated by TFs such as SOX1, TEAD1, and POU2F1. Gene set enrichment analysis (GSEA), GO analysis, and KEGG enrichment analysis showed that the DEGs were enriched in the Hedgehog signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. In addition, a ceRNA network including 8 lncRNAs (e.g., LINC00324, SNHG12, and ALMS1-IT1), 7 miRNAs (e.g., hsa-miR-217, hsa-miR-184, and hsa-miR-140-5p), and 52 mRNAs (e.g., IGFBP5, TMED5, and MAGT1) was constructed. The results revealed that SNHG12, hsa-miR-140-5p, hsa-miR-217, TFRC, HDAC4, TJP1, IGFBP5, and CREB5 may form an important network involved in the pathology of HCM. The novel ceRNA network that we have demonstrated will provide new research points about molecular mechanisms of HCM.