An improved bacterial mRNA enrichment strategy in dual RNA sequencing to unveil the dynamics of plant-bacterial interactions.

BACKGROUND: Dual RNA sequencing is a powerful tool that enables a comprehensive understanding of the molecular dynamics underlying plant-microbe interactions. RNA sequencing (RNA-seq) poses technical hurdles in the transcriptional analysis of plant-bacterial interactions, especially in bacterial transcriptomics, owing to the presence of abundant ribosomal RNA (rRNA), which potentially limits the coverage of essential transcripts. Therefore, to achieve cost-effective and comprehensive sequencing of the bacterial transcriptome, it is imperative to devise efficient methods for eliminating rRNA and enhancing the proportion of bacterial mRNA. In this study, we modified a strand-specific dual RNA-seq method with the goal of enriching the proportion of bacterial mRNA in the bacteria-infected plant samples. The enriched method involved the sequential separation of plant mRNA by poly A selection and rRNA removal for bacterial mRNA enrichment followed by strand specific RNA-seq library preparation steps. We assessed the efficiency of the enriched method in comparison to the conventional method by employing various plant-bacterial interactions, including both host and non-host resistance interactions with pathogenic bacteria, as well as an interaction with a beneficial rhizosphere associated bacteria using pepper and tomato plants respectively. RESULTS: In all cases of plant-bacterial interactions examined, an increase in mapping efficiency was observed with the enriched method although it produced a lower read count. Especially in the compatible interaction with Xanthmonas campestris pv. Vesicatoria race 3 (Xcv3), the enriched method enhanced the mapping ratio of Xcv3-infected pepper samples to its own genome (15.09%; 1.45-fold increase) and the CDS (8.92%; 1.49-fold increase). The enriched method consistently displayed a greater number of differentially expressed genes (DEGs) than the conventional RNA-seq method at all fold change threshold levels investigated, notably during the early stages of Xcv3 infection in peppers. The Gene Ontology (GO) enrichment analysis revealed that the DEGs were predominantly enriched in proteolysis, kinase, serine type endopeptidase and heme binding activities. CONCLUSION: The enriched method demonstrated in this study will serve as a suitable alternative to the existing RNA-seq method to enrich bacterial mRNA and provide novel insights into the intricate transcriptomic alterations within the plant-bacterial interplay.

Beyond NGS data sharing for plant ecological resilience and improvement of agronomic traits.

Decoding complex plant omics is essential for advancing our understanding of plant biology, evolution, and breeding as well as for practical applications in agriculture, conservation, and biotechnology. The advent of Next-Generation Sequencing (NGS) has revolutionized global plant genomic research, offering high-throughput, cost-effective, and accurate methods for generating genomic data. However, challenges still exist that suggest an entirely unresolved genome characterized by high heterozygosity, extensive repetitive sequences, and complex ploidy features. In addition, individual investigation of genomic information from various genetic resources is essential for omics research, as there are differences in traits within a single breed beyond a species due to the uniqueness of sequence variation. This article provides high-quality genomic and transcriptomic insights targeted at the agronomical background.

Optimized Replication of ADC-Based Particle Counting Algorithm with Reconfigurable Multi-Variables in Pseudo-Supervised Digital Twining of Reference Dust Sensor Systems.

As the application fields for digital twins have expanded, various studies have been conducted with the objective of optimizing the costs. Among these studies, research on low-power and low-performance embedded devices has been implemented at a low cost by replicating the performance of existing devices. In this study, we attempt to obtain similar particle count results in a single-sensing device replicated from the particle count results in a multi-sensing device without knowledge of the particle count acquisition algorithm of the multi-sensing device. Through filtering, we suppressed the noise and baseline movements of the raw data of the device. In addition, in the process of determining the multi-threshold for obtaining the particle counts, the existing complex particle count determination algorithm was simplified to make it possible to utilize the look-up table. The proposed simplified particle count calculation algorithm reduced the optimal multi-threshold search time by 87% on average and the root mean square error by 58.5% compared to existing method. In addition, it was confirmed that the distribution of particle count from optimal multi-thresholds has a similar shape to that from multi-sensing devices.

High-Resolution Melting (HRM) Genotyping.

High-resolution melting (HRM) analysis is a simple, fast, and inexpensive real-time polymerase chain reaction (PCR)-based method used to identify genetic variation between populations and detect single-nucleotide polymorphisms (SNPs) in nucleic acid sequences. HRM is a powerful technique that detects the differences between SNP allele melting temperatures by using a fluorescent dye inserted into the duplex deoxyribonucleic acid (DNA) structure. Prior to performing HRM analysis, optimizing the primer design, PCR mixture, and software settings is essential to obtain accurate and reliable results. In this chapter, we describe a detailed SNP genotyping method that includes primer design and the analysis of the shapes and positions of the melt curve of the luminescence intensity of the fluorescent dye attached to amplified DNA using software of qPCR instruments. This protocol is applicable for genotyping germplasm, genetic mapping, and marker-assisted breeding in plants.

Universal gene co-expression network reveals receptor-like protein genes involved in broad-spectrum resistance in pepper (Capsicum annuum L.).

Receptor-like proteins (RLPs) on plant cells have been implicated in immune responses and developmental processes. Although hundreds of RLP genes have been identified in plants, only a few RLPs have been functionally characterized in a limited number of plant species. Here, we identified RLPs in the pepper (Capsicum annuum) genome and performed comparative transcriptomics coupled with the analysis of conserved gene co-expression networks (GCNs) to reveal the role of core RLP regulators in pepper-pathogen interactions. A total of 102 RNA-seq datasets of pepper plants infected with four pathogens were used to construct CaRLP-targeted GCNs (CaRLP-GCNs). Resistance-responsive CaRLP-GCNs were merged to construct a universal GCN. Fourteen hub CaRLPs, tightly connected with defense-related gene clusters, were identified in eight modules. Based on the CaRLP-GCNs, we evaluated whether hub CaRLPs in the universal GCN are involved in the biotic stress response. Of the nine hub CaRLPs tested by virus-induced gene silencing, three genes (CaRLP264, CaRLP277, and CaRLP351) showed defense suppression with less hypersensitive response-like cell death in race-specific and non-host resistance response to viruses and bacteria, respectively, and consistently enhanced susceptibility to Ralstonia solanacearum and/or Phytophthora capsici. These data suggest that key CaRLPs are involved in the defense response to multiple biotic stresses and can be used to engineer a plant with broad-spectrum resistance. Together, our data show that generating a universal GCN using comprehensive transcriptome datasets can provide important clues to uncover genes involved in various biological processes.

Leaf-to-Whole Plant Spread Bioassay for Pepper and Ralstonia solanacearum Interaction Determines Inheritance of Resistance to Bacterial Wilt for Further Breeding.

Bacterial wilt (BW) disease from Ralstonia solanacearum is a serious disease and causes severe yield losses in chili peppers worldwide. Resistant cultivar breeding is the most effective in controlling BW. Thus, a simple and reliable evaluation method is required to assess disease severity and to investigate the inheritance of resistance for further breeding programs. Here, we developed a reliable leaf-to-whole plant spread bioassay for evaluating BW disease and then, using this, determined the inheritance of resistance to R. solanacearum in peppers. Capsicum annuum 'MC4' displayed a completely resistant response with fewer disease symptoms, a low level of bacterial cell growth, and significant up-regulations of defense genes in infected leaves compared to those in susceptible 'Subicho'. We also observed the spreading of wilt symptoms from the leaves to the whole susceptible plant, which denotes the normal BW wilt symptoms, similar to the drenching method. Through this, we optimized the evaluation method of the resistance to BW. Additionally, we performed genetic analysis for resistance inheritance. The parents, F1 and 90 F2 progenies, were evaluated, and the two major complementary genes involved in the BW resistance trait were confirmed. These could provide an accurate evaluation to improve resistant pepper breeding efficiency against BW.