This review provides a synopsis of transcriptional responses pertaining to interactions between plant viruses and the insect vectors that transmit them in diverse modes. In the process, it attempts to catalog differential gene expression pertinent to virus–vector interactions in vectors such as virus reception, virus cell entry, virus tissue tropism, virus multiplication, and vector immune responses. Whiteflies, leafhoppers, planthoppers, and thrips are the main insect groups reviewed, along with aphids and leaf beetles. Much of the focus on gene expression pertinent to vector–virus interactions has centered around whole-body RNA extraction, whereas data on virus-induced tissue-specific gene expression in vectors is limited. This review compares transcriptional responses in different insect groups following the acquisition of non-persistent, semi-persistent, and persistent (non-propagative and propagative) plant viruses and identifies parallels and divergences in gene expression patterns. Understanding virus-induced changes in vectors at a transcriptional level can aid in the identification of candidate genes for targeting with RNAi and/or CRISPR editing in insect vectors for management approaches.

Zinc (Zn) accumulation and deficiency affect plant response to pests and diseases differently in varying pathosystems. The concentrations of Zn in plants aid in priming defense signaling pathways and help in enhanced structural defenses against plant pathogens. Studies are lacking on how concentrations of Zn in watermelon plants influence defense against two important soil-borne pathogens: Fusarium oxysporum f. sp. niveum (FON) and southern root-knot nematode (RKN, Meloidogyne incognita). In this study a comparative transcriptomics evaluation of watermelon plants in response to high (1.2 ppm) and low (0.2 ppm) levels of Zn were determined. Differential transcript-level responses differed in watermelon plants when infected with FON or RKN or both under high- and low-Zn treatment regimes in a controlled hydroponics system. Higher numbers of differentially expressed genes (DEGs) were observed in high-Zn-treated than in low-Zn-treated non-inoculated plants, in plants inoculated with FON alone and in plants inoculated with RKN alone. However, in the co-inoculated system, low-Zn treatment had higher DEGs as compared to high-Zn treatment. In addition, most DEGs were significantly enriched in hormone signal transduction and MAPK signaling pathway, suggesting an induction of systemic resistance with high-Zn concentrations. Taken together, this study substantially expands transcriptome data resources and suggests a molecular potential framework for watermelon-Zn interaction in FON and RKN.

Center rot of onion is caused by a complex of plant pathogenic Pantoea species, which can lead to significant yield losses in the field and during storage. Conventional growers use foliar protectants such as a mixture of copper bactericides and an ethylene-bis-dithiocarbamate (EBDC) fungicide to manage the disease; however, organic growers have limited management options besides copper-protectants. Biocontrol agents (BCAs) provide an alternative; however, their efficacy could be compromised due in part to their inability to colonize the foliage. We hypothesized that pretreatment with peroxide (OxiDate 2.0: a.i., hydrogen peroxide and peroxyacetic acid) enhances the colonizing ability of the subsequently applied BCAs, leading to effective center rot management. Field trials were conducted in 2020 and 2021 to assess the efficacy of peroxide, BCAs (Serenade ASO: Bacillus subtilis and BlightBan: Pseudomonas fluorescens), and an insecticide program (tank mix of spinosad and neem oil) to manage center rot. We observed no significant difference in foliar area under the disease progress curve (AUDPC) between the peroxide pretreated P. fluorescens plots and only P. fluorescens-treated plots in 2020 and 2021. Peroxide pretreatment before B. subtilis application significantly reduced the foliar AUDPC as compared with the stand-alone B. subtilis treatment in 2020; however, no such difference was observed in 2021. Similarly, peroxide pretreatment before either of the BCAs did not seem to reduce the incidence of bulb rot as compared with the stand-alone BCA treatment in any of the trials (2020 and 2021). Additionally, our foliar microbiome study showed comparatively higher P. fluorescens retention on peroxide pretreated onion foliage; however, at the end of the growing season, P. fluorescens was drastically reduced and was virtually nonexistent (<0.002% of the total reads). Overall, the pretreatment with peroxide had a limited effect in improving the foliar colonizing ability of BCAs and consequently a limited effect in managing center rot.

Allicin tolerance ( alt ) clusters in phytopathogenic bacteria, which provide resistance to thiosulfinates like allicin, are challenging to find using conventional approaches due to their varied architecture and the paradox of being vertically maintained within genera despite likely being horizontally transferred. This results in significant sequential diversity that further complicates their identification. Natural language processing (NLP) - like techniques, such as those used in DeepBGC, offers a promising solution by treating gene clusters like a language, allowing for identifying and collecting gene clusters based on patterns and relationships within the sequences. We curated and validated alt -like clusters in Pantoea ananatis 97-1R (PA), Burkholderia gladioli pv. gladioli FDAARGOS 389 (BG), and Pseudomonas syringae pv. tomato DC3000 –(PTO). Leveraging sequences from the RefSeq bacterial database, we conducted comparative analyses of gene synteny, gene/protein sequences, protein structures, and predicted protein interactions. This approach enabled the discovery of several novel alt -like clusters previously undetectable by other methods, which were further validated experimentally. Our work highlights the effectiveness of NLP-like techniques for identifying underrepresented gene clusters and expands our understanding of the diversity and utility of alt -like clusters in diverse bacterial genera. This work demonstrates the potential of these techniques to simplify the identification process and enhance the applicability of biological data in real-world scenarios. Significance Statement: Thiosulfinates, like allicin, are potent antifeedants and antimicrobials produced by Allium species and pose a challenge for phytopathogenic bacteria. Phytopathogenic bacteria have been shown to utilize an allicin tolerance ( alt ) gene cluster to circumvent this host response, leading to economically significant yield losses. Due to the complexity of mining these clusters, we applied techniques akin to natural language processing to analyze Pfam domains and gene proximity. This approach led to the identification of novel alt -like gene clusters, showcasing the potential of artificial intelligence to reveal elusive and underrepresented genetic clusters and enhance our understanding of their diversity and role across various bacterial genera.

Allicin tolerance ( alt ) clusters in phytopathogenic bacteria, which provide resistance to thiosulfinates like allicin, are challenging to find using conventional approaches due to their varied architecture and the paradox of being vertically maintained within genera despite likely being horizontally transferred. This results in significant sequential diversity that further complicates their identification. Natural language processing (NLP), like techniques such as those used in DeepBGC, offers a promising solution by treating gene clusters like a language, allowing for identifying and collecting gene clusters based on patterns and relationships within the sequences. We curated and validated alt -like clusters in Pantoea ananatis 97-1R, Burkholderia gladioli pv. gladioli FDAARGOS 389, and Pseudomonas syringae pv. tomato DC3000. Leveraging sequences from the RefSeq bacterial database, we conducted comparative analyses of gene synteny, gene/protein sequences, protein structures, and predicted protein interactions. This approach enabled the discovery of several novel alt -like clusters previously undetectable by other methods, which were further validated experimentally. Our work highlights the effectiveness of NLP-like techniques for identifying underrepresented gene clusters and expands our understanding of the diversity and utility of alt -like clusters in diverse bacterial genera. This work demonstrates the potential of these techniques to simplify the identification process and enhance the applicability of biological data in real-world scenarios. IMPORTANCE: Thiosulfinates, like allicin, are potent antifeedants and antimicrobials produced by Allium species and pose a challenge for phytopathogenic bacteria. Phytopathogenic bacteria have been shown to utilize an allicin tolerance ( alt ) gene cluster to circumvent this host response, leading to economically significant yield losses. Due to the complexity of mining these clusters, we applied techniques akin to natural language processing to analyze Pfam domains and gene proximity. This approach led to the identification of novel alt -like gene clusters, showcasing the potential of artificial intelligence to reveal elusive and underrepresented genetic clusters and enhance our understanding of their diversity and role across various bacterial genera.

Pantoea ananatis is a member of a Pantoea spp. complex that causes center rot of onion, which significantly affects onion yield and quality. This pathogen does not have typical virulence factors like type II or type III secretion systems but appears to require a biosynthetic gene-cluster, HiVir/PASVIL (located chromosomally), for a phosphonate secondary metabolite, and the onion-virulence regions, OVR (localized on a megaplasmid), for onion pathogenicity and virulence, respectively. We conducted a deep pan-genome-wide association study (pan-GWAS) to predict additional genes associated with pathogenicity in P. ananatis using a panel of diverse strains ( n = 81). We utilized a red-onion scale necrosis assay as an indicator of pathogenicity. Based on this assay, we differentiated pathogenic ( n = 51)- vs. non-pathogenic ( n = 30)-strains phenotypically. Pan-GWAS revealed a large core genome of 3,153 genes and a flexible accessory genome of ≤5,065 genes. Phylogenomic analysis using pan-GWAS and presence and absence variants (PAVs) distinguished red-scale necrosis inducing (pathogenic) strains from non-scale necrosis inducing (non-pathogenic) strains of P. ananatis . The pan-GWAS also predicted 42 genes, including 14 from the previously identified HiVir/PASVIL cluster associated with pathogenicity, and 28 novel genes that were not previously associated with pathogenicity in onion. Of the 28 novel genes identified, eight have annotated functions of site-specific tyrosine kinase, N-acetylmuramoyl-L-alanine amidase, TraR/DksA family transcriptional regulator, and HTH-type transcriptional regulator. The remaining 20 genes are currently hypothetical. This is the first report of using pan-GWAS on P. ananatis for the prediction of novel genes contributing to pathogenicity in onion, which will be utilized for further functional analyses. Pan-genomic differences (using PAVs) differentiated onion pathogenic from non-pathogenic strains of P. ananatis , which has been difficult to achieve using single or multiple gene-based phylogenetic analyses. The pan-genome analysis also allowed us to evaluate the presence and absence of HiVir/PASVIL genes and 11 megaplasmid-borne OVR-A genes regarded as the ‘ alt ’ cluster that aid in P. ananatis colonization in onion bulbs. We concluded that HiVir/PASVIL genes are associated with pathogenic P. ananatis strains and the alt gene cluster alone is not responsible for pathogenicity on onion. The pan-genome also provides clear evidence of constantly evolving accessory genes in P. ananatis that may contribute to host-range expansion and niche-adaptation. Author summary: Pantoea ananatis is a major bacterial pathogen that causes center rot of onion and diseases of a number of other plant species. In order to understand the genome architecture and identify genes responsible for pathogenicity in onion, a pan-genome analysis was performed. We used 81 strains of P. ananatis collected over 20 years from different regions of the state of Georgia, USA. The pan-genome study identified a core genome with a conserved set of genes and an accessory genome that displayed variation among strains. We conducted pan-GWAS (pan-genome-wide association study) using presence and absence variants (PAVs) in the genomes and associated onion-pathogenic phenotypes based on a red-onion scale necrosis assay. The study resulted in identification of genes, including a cluster of chromosomal HiVir/PASVIL genes, that are significantly associated with the onion pathogenic phenotype. In addition, we identified 28 novel genes, a majority of which ( n = 20) have hypothetical functions. We concluded and further substantiated earlier findings that a cluster of genes is responsible for pathogenicity on onion. The pan-genome analysis also allowed us to evaluate the presence and absence of HiVir/PASVIL genes and 11 megaplasmid-borne OVR-A genes regarded as the ‘ alt ’ cluster that aid in bacterial colonization of onion bulbs by P. ananatis strains. We concluded that HiVir/PASVIL genes are associated with onion-pathogenic strains, and the alt gene cluster alone is not responsible for pathogenicity on onion. This study also provides potential evidence of constantly evolving accessory genes in P. ananatis which may help in host range expansion and adaptation to diverse niches.