RNA silencing is mediated through RNA interference (RNAi) pathway gene families, i.e., Dicer-Like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) and their cis-acting regulatory elements. The RNAi pathway is also directly connected with the post-transcriptional gene silencing (PTGS) mechanism, and the pathway controls eukaryotic gene regulation during growth, development, and stress response. Nevertheless, genome-wide identification of RNAi pathway gene families such as DCL, AGO, and RDR and their regulatory network analyses related to transcription factors have not been studied in many fruit crop species, including banana (Musa acuminata). In this study, we studied in silico genome-wide identification and characterization of DCL, AGO, and RDR genes in bananas thoroughly via integrated bioinformatics approaches. A genome-wide analysis identified 3 MaDCL, 13 MaAGO, and 5 MaRDR candidate genes based on multiple sequence alignment and phylogenetic tree related to the RNAi pathway in banana genomes. These genes correspond to the Arabidopsis thaliana RNAi silencing genes. The analysis of the conserved domain, motif, and gene structure (exon-intron numbers) for MaDCL, MaAGO, and MaRDR genes showed higher homogeneity within the same gene family. The Gene Ontology (GO) enrichment analysis exhibited that the identified RNAi genes could be involved in RNA silencing and associated metabolic pathways. A number of important transcription factors (TFs), e.g., ERF, Dof, C2H2, TCP, GATA and MIKC_MADS families, were identified by network and sub-network analyses between TFs and candidate RNAi gene families. Furthermore, the cis-acting regulatory elements related to light-responsive (LR), stress-responsive (SR), hormone-responsive (HR), and other activities (OT) functions were identified in candidate MaDCL, MaAGO, and MaRDR genes. These genome-wide analyses of these RNAi gene families provide valuable information related to RNA silencing, which would shed light on further characterization of RNAi genes, their regulatory elements, and functional roles, which might be helpful for banana improvement in the breeding program.

RNA interference (RNAi) regulates a variety of eukaryotic gene expressions that are engaged in response to stress, growth, and the conservation of genomic stability during developmental phases. It is also intimately connected to the post-transcriptional gene silencing (PTGS) process and chromatin modification levels. The entire process of RNA interference (RNAi) pathway gene families mediates RNA silencing. The main factors of RNA silencing are the Dicer-Like (DCL), Argonaute (AGO), and RNA-dependent RNA polymerase (RDR) gene families. To the best of our knowledge, genome-wide identification of RNAi gene families like DCL, AGO, and RDR in sunflower (Helianthus annuus) has not yet been studied despite being discovered in some species. So, the goal of this study is to find the RNAi gene families like DCL, AGO, and RDR in sunflower based on bioinformatics approaches. Therefore, we accomplished an inclusivein silicoinvestigation for genome-wide identification of RNAi pathway gene families DCL, AGO, and RDR through bioinformatics approaches such as (sequence homogeneity, phylogenetic relationship, gene structure, chromosomal localization, PPIs, GO, sub-cellular localization). In this study, we have identified five DCL (HaDCLs), fifteen AGO (HaAGOs), and ten RDR (HaRDRs) in the sunflower genome database corresponding to the RNAi genes of model plantArabidopsis thalianabased on genome-wide analysis and a phylogenetic method. The analysis of the gene structure that contains exon-intron numbers, conserved domain, and motif composition analyses for all HaDCL, HaAGO, and HaRDR gene families indicated almost homogeneity among the same gene family. The protein-protein interaction (PPI) network analysis illustrated that there exists interconnection among identified three gene families. The analysis of the Gene Ontology (GO) enrichment showed that the detected genes directly contribute to the RNA gene-silencing and were involved in crucial pathways. It was observed that thecis-acting regulatory components connected to the identified genes were shown to be responsive to hormone, light, stress, and other functions. That was found in HaDCL, HaAGO, and HaRDR genes associated with the development and growth of plants. Finally, we are able to provide some essential information about the components of sunflower RNA silencing through our genome-wide comparison and integrated bioinformatics analysis, which open the door for further research into the functional mechanisms of the identified genes and their regulatory elements.

Lectin receptor-like kinases (LecRLKs) are a significant subgroup of the receptor-like kinases (RLKs) protein family. They play crucial roles in plant growth, development, immune responses, signal transduction, and stress tolerance. However, the genome-wide identification and characterization of LecRLK genes and their regulatory elements have not been explored in a major cereal crop, barley (Hordeum vulgare L.). Therefore, in this study, integrated bioinformatics tools were used to identify and characterize the LecRLK gene family in barley. Based on the phylogenetic tree and domain organization, a total of 113 LecRLK genes were identified in the barley genome (referred to as HvlecRLK) corresponding to the LecRLK genes of Arabidopsis thaliana. These putative HvlecRLK genes were classified into three groups: 62 G-type LecRLKs, 1 C-type LecRLK, and 50 L-type LecRLKs. They were unevenly distributed across eight chromosomes, including one unknown chromosome, and were predominantly located in the plasma membrane (G-type HvlecRLK (96.8%), C-type HvlecRLK (100%), and L-type HvlecRLK (98%)). An analysis of motif composition and exon-intron configuration revealed remarkable homogeneity with the members of AtlecRLK. Notably, most of the HvlecRLKs (27 G-type, 43 L-type) have no intron, suggesting their rapid functionality. The Ka/Ks and syntenic analysis demonstrated that HvlecRLK gene pairs evolved through purifying selection and gene duplication was the major factor for the expansion of the HvlecRLK gene family. Exploration of gene ontology (GO) enrichment indicated that the identified HvlecRLK genes are associated with various cellular processes, metabolic pathways, defense mechanisms, kinase activity, catalytic activity, ion binding, and other essential pathways. The regulatory network analysis identified 29 transcription factor families (TFFs), with seven major TFFs including bZIP, C2H2, ERF, MIKC_MADS, MYB, NAC, and WRKY participating in the regulation of HvlecRLK gene functions. Most notably, eight TFFs were found to be linked to the promoter region of both L-type HvleckRLK64 and HvleckRLK86. The promoter cis-acting regulatory element (CARE) analysis of barley identified a total of 75 CARE motifs responsive to light responsiveness (LR), tissue-specific (TS), hormone responsiveness (HR), and stress responsiveness (SR). The maximum number of CAREs was identified in HvleckRLK11 (25 for LR), HvleckRLK69 (17 for TS), and HvleckRLK80 (12 for HR). Additionally, HvleckRLK14, HvleckRLK16, HvleckRLK33, HvleckRLK50, HvleckRLK52, HvleckRLK56, and HvleckRLK110 were predicted to exhibit higher responses in stress conditions. In addition, 46 putative miRNAs were predicted to target 81 HvlecRLK genes and HvlecRLK13 was the most targeted gene by 8 different miRNAs. Protein-protein interaction analysis demonstrated higher functional similarities of 63 HvlecRLKs with 7 Arabidopsis STRING proteins. Our overall findings provide valuable information on the LecRLK gene family which might pave the way to advanced research on the functional mechanism of the candidate genes as well as to develop new barley cultivars in breeding programs.

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.

Lectins are sugar-binding proteins found abundantly in plants. Lectin superfamily members have diverse roles, including plant growth, development, cellular processes, stress responses, and defense against microbes. However, the genome-wide identification and functional analysis of lectin genes in sweet orange (Citrus sinensis L.) remain unexplored. Therefore, we used integrated bioinformatics approaches (IBA) for in-depth genome-wide identification, characterization, and regulatory factor analysis of sweet orange lectin genes. Through genome-wide comparative analysis, we identified a total of 141 lectin genes distributed across 10 distinct gene families such as 68 CsB-Lectin, 13 CsLysin Motif (LysM), 4 CsChitin-Bind1, 1 CsLec-C, 3 CsGal-B, 1 CsCalreticulin, 3 CsJacalin, 13 CsPhloem, 11 CsGal-Lec, and 24 CsLectinlegB.This classification relied on characteristic domain and phylogenetic analysis, showing significant homology with Arabidopsis thaliana’s lectin gene families. A thorough analysis unveiled common similarities within specific groups and notable variations across different protein groups. Gene Ontology (GO) enrichment analysis highlighted the predicted genes’ roles in diverse cellular components, metabolic processes, and stress-related regulation. Additionally, network analysis of lectin genes with transcription factors (TFs) identified pivotal regulators like ERF, MYB, NAC, WRKY, bHLH, bZIP, and TCP. The cis-acting regulatory elements (CAREs) found in sweet orange lectin genes showed their roles in crucial pathways, including light-responsive (LR), stress-responsive (SR), hormone-responsive (HR), and more. These findings will aid in the in-depth molecular examination of these potential genes and their regulatory elements, contributing to targeted enhancements of sweet orange species in breeding programs.

Identifying stock is key to sustainable fisheries management and conservation. Using traditional morphometrics (TMR) and image-based truss network analysis (ITNA), we evaluated the stock structure of the endangered queen loach, Botia dario. The study was carried out in the following five stocks in Bangladesh’s northern rivers: the Atrai, Dhorala, Danu, Jamuna, and Padma. The inventory regarding stock structure was investigated using a total of nine traditional morphometrics, 11 ratios, and 23 truss measurements for each individual. To generate 23 ITNA, 12 landmarks were used. To assess variations among the stocks, a principal component analysis (PCA), factor analysis (FA), canonical variate analysis (CVA), and cluster analysis (CA) were performed. Six principal components explained 91.50% of the variation in TMR, while seven principal components explained 73.425% of the variation in ITNA. CVA, using traditional methods and ratios were correctly classified as 65.0%, 42.0%, 64.2%, 89.3%, and 77.5% for Danu, Padma, Jamuna, Dhorala, and Atrai River stocks, respectively, based on original grouped classes. CVA using ITNA was correctly classified as 90.0%, 80.0%, 77.4%, 94.6%, and 98.6% for Danu, Padma, Jamuna, Dhorala, and Atrai River stocks, respectively, based on original grouped classes. CVA analysis based on TMR and ITNA showed that cannonical variates (CV1 to CV3) are related to the whole-body shape. Both TMR and ITNA formed two clusters. In the first cluster, the Jamuna and Atrai River stocks combinedly formed a separate stock based on (TMR). In ITNA, the Dhorala and Atrai River formed as separate stocks from the other four stocks. According to this study, combining TMR and ITNA analysis aids in the differentiation of various B. dario stocks. The stock separation of this species was supposed to be geographic disconnection, waterway nature, and temperature variations. The B. dario stocks are heavily exploited and the species is an ideal nominee for species variation to boost the aquaculture yield. Within-stock distinctions were revealed in this study, necessitating the identification of gene pools and molecular studies to achieve a deeper understanding of the stocks. Through a more scientific approach, this stock structure study may aid in the development of conservation programs for this endangered species.