In silico identification of sugarcane (Saccharum officinarum L.) genome encoded microRNAs targeting sugarcane bacilliform virus

Computational identification and experimental validation of novel Saccharum officinarum microRNAs along with their targets through RT-PCR approach

Various metabolic and cell signaling processes impact the functions of sugarcane plant cells. MicroRNAs (miRNAs) play critical regulatory roles in enhancing yield and providing protection against various stressors. This study seeks to identify and partially characterize several novel miRNAs in sugarcane using in silico tools, while also offering a preliminary assessment of their functions. This was accomplished by predicting novel conserved miRNAs in sugarcane plants using a variety of genomics-based techniques like BLASTn, MFOLD, psRNA Target, sequence logo, Weblogo, primer-3, etc. and annotated using miRBase and NCBI. For validation, RT-PCR method was used along with agarose gel. After the preparation of fourteen randomly chosen primers, they were validated by RT-PCR. Accordingly, they contain fifty specific targeted proteins with substantial targets in the structural, transcriptional protein, etc. Furthermore, the sof-miR5025a directs the heat repeat protein while the voltage-dependent anion is governed by sof-miR8005a. Similarly, the sof-miR7768b and sof-miR6249b monitor the pathogenesis-related protein and zinc finger, C2H2 type protein, which assist as transcription factors. Thus, the novel sugarcane miRNAs target a wide range of important genes help regulate the environment for sugarcane to generate a higher-quality crop.

An Integrative Computational Approach for Identifying Cotton Host Plant MicroRNAs with Potential to Abate CLCuKoV-Bur Infection

Cotton leaf curl Kokhran virus-Burewala (CLCuKoV-Bur) has a circular single-stranded ssDNA genome of 2759 nucleotides in length and belongs to the genus Begomovirus (family, Geminiviridae). CLCuKoV-Bur causes cotton leaf curl disease (CLCuD) and is transmitted by the whitefly Bemisis tabaci cryptic species. Monopartite begomoviruses encode five open reading frames (ORFs). CLCuKoV-Bur replicates through a dsDNA intermediate. Five open reading frames (ORFs) are organized in the small circular, single-stranded (ss)-DNA genome of CLCuKoV-Bur (2759 bases). RNA interference (RNAi) is a naturally occurring process that has revolutionized the targeting of gene regulation in eukaryotic organisms to combat virus infection. The aim of this study was to elucidate the potential binding attractions of cotton-genome-encoded microRNAs (Gossypium hirsutum-microRNAs, ghr-miRNAs) on CLCuKoV-Bur ssDNA-encoded mRNAs using online bioinformatics target prediction tools, RNA22, psRNATarget, RNAhybrid, and TAPIR. Using this suite of robust algorithms, the predicted repertoire of the cotton microRNA-binding landscape was determined for a CLCuKoV-Bur consensus genome sequence. Previously experimentally validated cotton (Gossypium hirsutum L.) miRNAs (n = 80) were selected from a public repository miRNA registry miRBase (v22) and hybridized in silico into the CLCuKoV-Bur genome (AM421522) coding and non-coding sequences. Of the 80 ghr-miRNAs interrogated, 18 ghr-miRNAs were identified by two to four algorithms evaluated. Among them, the ghr-miR399d (accession no. MIMAT0014350), located at coordinate 1747 in the CLCuKoV-Bur genome, was predicted by a consensus or "union" of all four algorithms and represents an optimal target for designing an artificial microRNA (amiRNA) silencing construct for in planta expression. Based on all robust predictions, an in silico ghr-miRNA-regulatory network was developed for CLCuKoV-Bur ORFs using Circos software version 0.6. These results represent the first predictions of ghr-miRNAs with the therapeutic potential for developing CLCuD resistance in upland cotton plants.

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.

Developing new sugarcane varieties suitable for mechanized production in China: principles, strategies and prospects

The sugar industry, which relates to people's livelihood, is strategic and fundamental in the development of agricultural economy. In China, sugar derived from sugarcane accounts for approximately 85% of total sugar production. Mechanization is the "flower" of sugarcane industry. As the saying goes "when there are blooming flowers, there will be sweet honey." However, due to limitations in land resources, technology, equipment, organization, and management, mechanization throughout the sugarcane production process has not yet brought about the economic benefits that a mechanized system should provide and has not reached an ideal yield through the integration of agricultural machinery and agronomic practice. This paper briefly describes how to initiate the mechanization of Chinese sugarcane production to promote the sound, healthy, and rapid development of the sugarcane industry, and how to ultimately achieve the transformation of sugarcane breeding in China and the modernization of the sugarcane industry from three perspectives, namely, requirements of mechanized production for sugarcane varieties, breeding strategies for selecting new sugarcane varieties suitable for mechanized production, and screening for sugarcane varieties that are suitable for mechanization and diversification in variety distribution or arrangement in China. We also highlight the current challenges surrounding this topic and look forward to its bright prospects.

Comprehensive insights into the regulatory mechanisms of lncRNA in alkaline-salt stress tolerance in rice

BACKGROUND

Alkaline-salt is one of the abiotic stresses that slows plant growth and developmental processes and threatens crop yield. Long non-coding RNAs (lncRNAs) are endogenous RNA found in plants that engage in a variety of cellular functions and stress responses.

METHOD

lncRNAs act as competing endogenous RNAs (ceRNA) and constitute a new set of gene control. The precise regulatory mechanism by which lncRNAs function as ceRNAs in response to alkaline-salt stress remains unclear. We identified alkaline-salt responsive lncRNAs using transcriptome-wide analysis of two varieties including alkaline-salt tolerant [WD20342 (WD)] and alkaline-salt sensitive [Caidao (CD)] rice cultivar under control and alkaline-salt stress treated [WD20342 (WDT, and Caidao (CDT)] conditions.

RESULTS

Investigating the competitive relationships between mRNAs and lncRNAs, we next built a ceRNA network involving lncRNAs based on the ceRNA hypothesis. Expression profiles revealed that a total of 65, 34, and 1549 differentially expressed (DE) lncRNAs, miRNAs, and mRNAs were identified in alkaline-salt tolerant WD (Control) vs. WDT (Treated). Similarly, 75 DE-lncRNAs, 34 DE-miRNAs, and 1725 DE-mRNAs (including up-regulated and down-regulated) were identified in alkaline-salt sensitive CD (Control) vs. CDT (Treated), respectively. An alkaline-salt stress ceRNA network discovered 321 lncRNA-miRNA-mRNA triplets in CD and CDT, with 32 lncRNAs, 121 miRNAs, and 111 mRNAs. Likewise, 217 lncRNA-miRNA-mRNA triplets in WD and WDT revealed the NONOSAT000455-osa_miR5809b-LOC_Os11g01210 triplet with the highest degree as a hub node with the most significant positive correlation in alkaline-salt stress response.

CONCLUSION

The results of our investigation indicate that osa-miR5809b is dysregulated and plays a part in regulating the defense response of rice against alkaline-salt stress. Our study highlights the regulatory functions of lncRNAs acting as ceRNAs in the mechanisms underlying alkaline-salt resistance in rice.

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.

Emerging and Re-Emerging Diseases Caused by Badnaviruses

New and emerging plant diseases are caused by different pathogens including viruses that often cause significant crop losses. Badnaviruses are pararetroviruses that contain a single molecule of ds DNA genome of 7 to 9 kb in size and infect a large number of economically important crops such as banana and plantains, black pepper, cacao, citrus, grapevine, pineapple, sugarcane, sweet potato, taro, and yam, causing significant yield losses. Many of the species in the genus have a restricted host range and several of them are known to infect a single crop. Combined infections of different virus species and strains offer conditions that favor the development of new strains via recombination, especially in vegetatively propagated crops. The primary spread of badnaviruses is through vegetative propagating materials while for the secondary spread, they depend on insects such as mealybugs and aphids. Disease emerges as a consequence of the interactions between host and pathogens under favorable environmental conditions. The viral genome of the pararetroviruses is known to be integrated into the chromosome of the host and a few plants with integrants when subjected to different kinds of abiotic stress will give rise to episomal forms of the virus and cause disease. Attempts have been made to develop management strategies for badnaviruses both conventionally and using precision breeding techniques such as genome editing. Until 2016 only 32 badnavirus species infecting different crops were known, but in a span of six years, this number has gone up to 68. The current review highlights the emerging disease problems and management options for badnaviruses infecting economically important crops.

Prediction of Site Directed miRNAs as Key Players of Transcriptional Regulators Against Influenza C Virus Infection Through Computational Approaches

MicroRNAs (miRNAs) are small non-coding RNAs that play critical roles in gene expression, cell differentiation, and immunity against viral infections. In this study, we have used the computational tools, RNA22, RNAhybrid, and miRanda, to predict the microRNA-mRNA binding sites to find the putative microRNAs playing role in the host response to influenza C virus infection. This computational research screened the following four miRNAs: hsa-mir-3155a, hsa-mir-6796-5p, hsa-mir-3194-3p and hsa-mir-4673, which were further investigated for binding site prediction to the influenza C genome. Moreover, multiple sites in protein-coding region (HEF, CM2, M1-M2, NP, NS1- NS2, NSF, P3, PB1 and PB2) were predicted by RNA22, RNAhybrid and miRanda. Furthermore, 3D structures of all miRNAs and HEF were predicted and checked for their binding potential through molecular docking analysis. The comparative results showed that among all proteins, HEF is higher in prevalence throughout the analysis as a potential (human-derived) microRNAs target. The target-site conservation results showed that core nucleotide sequence in three different strains is responsible for potential miRNA binding to different viral strains. Further steps to use these microRNAs may lead to new therapeutic insights on fighting influenza virus infection.