Elucidating the Functional Role of Predicted miRNAs in Post- Transcriptional Gene Regulation Along with Symbiosis in Medicago truncatula

Deciphering miRNA-lncRNA-mRNA interaction through experimental validation of miRNAs, lncRNAs, and miRNA targets on mRNAs in Cajanus cajan

Pigeon pea (Cajanus cajan) is widely cultivated for its nutritional and medicinal value yet remains an orphan crop as productivity has not been improved because of a lack of genome and non-coding genome information. Non-coding RNAs, like miRNAs and long non-coding RNAs (lncRNAs), are involved in regulation of growth, metabolism, development, and stress response, and have a critical role in post-transcriptional gene regulation (PTGR). We attempted to elucidate the roles of miRNAs and lncRNAs in pigeon pea through experimental validation of computationally predicted miRNAs and lncRNAs and targets of miRNAs on mRNAs. We experimentally validated 20 miRNAs and 11 lncRNAs. We predicted cleavage sites of three miRNA targets: serine/threonine-protein kinase, polygalacturonase, beta-galactosidase. We identified 469 targets of 265 miRNAs and their functional annotations using computational methods. We built a miRNA-mRNA-lncRNA network model, with the miRNAs targeting both mRNAs and lncRNAs, to obtain information on the interplay of these three molecules. A confirmed interaction through experimental validation was established between miRNA, namely cca-miR1535a targeting the mRNA for beta-galactosidase, as well as the lncRNA cca-lnc-020033. Our findings increase knowledge of the non-coding genome of pigeon pea and their roles in PTGR and in improving agronomic traits of this pulse crop.

Deciphering the role of miRNA in reprogramming plant responses to drought stress

Drought is the most prevalent environmental stress that affects plants' growth, development, and crop productivity. However, plants have evolved adaptive mechanisms to respond to the harmful effects of drought. They reprogram their: transcriptome, proteome, and metabolome that alter their cellular and physiological processes and establish cellular homeostasis. One of the crucial regulatory processes that govern this reprogramming is post-transcriptional regulation by microRNAs (miRNAs). miRNAs are small non-coding RNAs, involved in the downregulation of the target mRNA via translation inhibition/mRNA degradation/miRNA-mediated mRNA decay/ribosome drop off/DNA methylation. Many drought-inducible miRNAs have been identified and characterized in plants. Their main targets are regulatory genes that influence growth, development, osmotic stress tolerance, antioxidant defense, phytohormone-mediated signaling, and delayed senescence during drought stress. Overexpression of drought-responsive miRNAs (Osa-miR535, miR160, miR408, Osa-miR393, Osa-miR319, and Gma-miR394) in certain plants has led to tolerance against drought stress indicating their vital role in stress mitigation. Similarly, knock down (miR166/miR398c) or deletion (miR169 and miR827) of miRNAs has also resulted in tolerance to drought stress. Likewise, engineered Arabidopsis plants with miR165, miR166 using short tandem target mimic strategy, exhibited drought tolerance. Since miRNAs regulate the expression of an array of drought-responsive genes, they can act as prospective targets for genetic manipulations to enhance drought tolerance in crops and achieve sustainable agriculture. Further investigations toward functional characterization of diverse miRNAs, and understanding stress-responses regulated by these miRNAs and their utilization in biotechnological applications is highly recommended.

ATGPred-FL: sequence-based prediction of autophagy proteins with feature representation learning

Autophagy plays an important role in biological evolution and is regulated by many autophagy proteins. Accurate identification of autophagy proteins is crucially important to reveal their biological functions. Due to the expense and labor cost of experimental methods, it is urgent to develop automated, accurate and reliable sequence-based computational tools to enable the identification of novel autophagy proteins among numerous proteins and peptides. For this purpose, a new predictor named ATGPred-FL was proposed for the efficient identification of autophagy proteins. We investigated various sequence-based feature descriptors and adopted the feature learning method to generate corresponding, more informative probability features. Then, a two-step feature selection strategy based on accuracy was utilized to remove irrelevant and redundant features, leading to the most discriminative 14-dimensional feature set. The final predictor was built using a support vector machine classifier, which performed favorably on both the training and testing sets with accuracy values of 94.40% and 90.50%, respectively. ATGPred-FL is the first ATG machine learning predictor based on protein primary sequences. We envision that ATGPred-FL will be an effective and useful tool for autophagy protein identification, and it is available for free at http://lab.malab.cn/~acy/ATGPred-FL , the source code and datasets are accessible at https://github.com/jiaoshihu/ATGPred .

Detection of transcription factors binding to methylated DNA by deep recurrent neural network

Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confirmed that some TFs have capability to interact with methylated DNA fragments to further regulate gene expression. Although biochemical experiments could recognize TFs binding to methylated DNA sequences, these wet experimental methods are time-consuming and expensive. Machine learning methods provide a good choice for quickly identifying these TFs without experimental materials. Thus, this study aims to design a robust predictor to detect methylated DNA-bound TFs. We firstly proposed using tripeptide word vector feature to formulate protein samples. Subsequently, based on recurrent neural network with long short-term memory, a two-step computational model was designed. The first step predictor was utilized to discriminate transcription factors from non-transcription factors. Once proteins were predicted as TFs, the second step predictor was employed to judge whether the TFs can bind to methylated DNA. Through the independent dataset test, the accuracies of the first step and the second step are 86.63% and 73.59%, respectively. In addition, the statistical analysis of the distribution of tripeptides in training samples showed that the position and number of some tripeptides in the sequence could affect the binding of TFs to methylated DNA. Finally, on the basis of our model, a free web server was established based on the proposed model, which can be available at https://bioinfor.nefu.edu.cn/TFPM/.

Non-coding RNAs as Novel Biomarkers in Cancer Drug Resistance

Chemotherapy is often the primary and most effective anticancer treatment; however, drug resistance remains a major obstacle to it being curative. Recent studies have demonstrated that non-coding RNAs (ncRNAs), especially microRNAs and long non-coding RNAs, are involved in drug resistance of tumor cells in many ways, such as modulation of apoptosis, drug efflux and metabolism, epithelial-to-mesenchymal transition, DNA repair, and cell cycle progression. Exploring the relationships between ncRNAs and drug resistance will not only contribute to our understanding of the mechanisms of drug resistance and provide ncRNA biomarkers of chemoresistance, but will also help realize personalized anticancer treatment regimens. Due to the high cost and low efficiency of biological experimentation, many researchers have opted to use computational methods to identify ncRNA biomarkers associated with drug resistance. In this review, we summarize recent discoveries related to ncRNA-mediated drug resistance and highlight the computational methods and resources available for ncRNA biomarkers involved in chemoresistance.

Detection of circRNA Biomarker for Acute Myocardial Infarction Based on System Biological Analysis of RNA Expression

Acute myocardial infarction (AMI) is myocardial necrosis caused by the persistent interruption of myocardial blood supply, which has high incidence rate and high mortality in middle-aged and elderly people in the worldwide. Biomarkers play an important role in the early diagnosis and treatment of AMI. Recently, more and more researches confirmed that circRNA may be a potential diagnostic biomarker and therapeutic target for cardiovascular diseases. In this paper, a series of biological analyses were performed to find new effective circRNA biomarkers for AMI. Firstly, the expression levels of circRNAs in blood samples of patients with AMI and those with mild coronary stenosis were compared to reveal circRNAs which were involved in AMI. Then, circRNAs which were significant expressed abnormally in the blood samples of patients with AMI were selected from those circRNAs. Next, a ceRNA network was constructed based on interactions of circRNA, miRNA and mRNA through biological analyses to detect crucial circRNA associated with AMI. Finally, one circRNA was selected as candidate biomarker for AMI. To validate effectivity and efficiency of the candidate biomarker, fluorescence in situ hybridization, hypoxia model of human cardiomyocytes, and knockdown and overexpression analyses were performed on candidate circRNA biomarker. In conclusion, experimental results demonstrated that the candidate circRNA was an effective biomarker for diagnosis and therapy of AMI.

The miRNA: a small but powerful RNA for COVID-19

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a severe and rapidly evolving epidemic. Now, although a few drugs and vaccines have been proved for its treatment and prevention, little systematic comments are made to explain its susceptibility to humans. A few scattered studies used bioinformatics methods to explore the role of microRNA (miRNA) in COVID-19 infection. Combining these timely reports and previous studies about virus and miRNA, we comb through the available clues and seemingly make the perspective reasonable that the COVID-19 cleverly exploits the interplay between the small miRNA and other biomolecules to avoid being effectively recognized and attacked from host immune protection as well to deactivate functional genes that are crucial for immune system. In detail, SARS-CoV-2 can be regarded as a sponge to adsorb host immune-related miRNA, which forces host fall into dysfunction status of immune system. Besides, SARS-CoV-2 encodes its own miRNAs, which can enter host cell and are not perceived by the host's immune system, subsequently targeting host function genes to cause illnesses. Therefore, this article presents a reasonable viewpoint that the miRNA-based interplays between the host and SARS-CoV-2 may be the primary cause that SARS-CoV-2 accesses and attacks the host cells.

Full-length transcriptome sequencing provides insights into the evolution of apocarotenoid biosynthesis in Crocus sativus

Crocus sativus, containing remarkably amounts of crocin, picrocrocin and safranal, is the source of saffron with tremendous medicinal, economic and cultural importance. Here, we present a high-quality full-length transcriptome of the sterile triploid C. sativus, using the PacBio SMRT sequencing technology. This yields 31,755 high-confidence predictions of protein-coding genes, with 50.1% forming paralogous gene pairs. Analysis on distribution of Ks values suggests that the current genome of C. sativus is probably a product resulting from at least two rounds of whole-genome duplication (WGD) events occurred at ~28 and ~114 million years ago (Mya), respectively. We provide evidence demonstrating that the recent β WGD event confers a major impact on family expansion of secondary metabolite genes, possibly leading to an enhanced accumulation of three distinct compounds: crocin, picrocrocin and safranal. Phylogenetic analysis unravels that the founding member (CCD2) of CCD enzymes necessary for the biosynthesis of apocarotenoids in C. sativus might be evolved from the CCD1 family via the β WGD event. Based on the gene expression profiling, CCD2 is found to be expressed at an extremely high level in the stigma. These findings may shed lights on further genomic refinement of the characteristic biosynthesis pathways and promote germplasm utilization for the improvement of saffron quality.