Advances in sequencing technologies and bioinformatics tools have fueled a renewed interest in whole genome sequencing efforts in many organisms. The growing availability of multiple genome sequences has advanced our understanding of the within-species diversity, in the form of a pangenome. Pangenomics has opened new avenues for future research such as allowing dissection of complex molecular mechanisms and increased confidence in genome mapping. To comprehensively capture the genetic diversity for improving plant performance, the pangenome concept is further extended from species to genus level by the inclusion of wild species, constituting a super-pangenome. Characterization of pangenome has implications for both basic and applied research. The concept of pangenome has transformed the way biological questions are addressed. From understanding evolution and adaptation to elucidating host–pathogen interactions, finding novel genes or breeding targets to aid crop improvement to design effective vaccines for human prophylaxis, the increasing availability of the pangenome has revolutionized several aspects of biological research. The future availability of high-resolution pangenomes based on reference-level near-complete genome assemblies would greatly improve our ability to address complex biological problems.

Sheath blight (ShB) is the most serious disease of rice (Oryza sativa L.), caused by the soil-borne fungus Rhizoctonia solani Kühn (R. solani). It poses a significant threat to global rice productivity, resulting in approximately 50% annual yield loss. Managing ShB is particularly challenging due to the broad host range of the pathogen, its necrotrophic nature, the emergence of new races, and the limited availability of highly resistant germplasm. In this study, we conducted QTL mapping using an F₂ population derived from a cross between a partially resistant accession (IRGC81941A) of Oryza nivara and the susceptible rice cultivar Punjab rice 121 (PR121). Our analysis identified 29 QTLs for ShB resistance, collectively explaining a phenotypic variance ranging from 4.70 to 48.05%. Notably, a cluster of four QTLs (qRLH1.1, qRLH1.2, qRLH1.5, and qRLH1.8) on chromosome 1 consistently exhibit a resistant response against R. solani. These QTLs span from 0.096 to 420.1 Kb on the rice reference genome and contain several important genes, including Ser/Thr protein kinase, auxin-responsive protein, protease inhibitor/seed storage/LTP family protein, MLO domain-containing protein, disease-responsive protein, thaumatin-like protein, Avr9/Cf9-eliciting protein, and various transcription factors. Additionally, simple sequence repeats (SSR) markers RM212 and RM246 linked to these QTLs effectively distinguish resistant and susceptible rice cultivars, showing great promise for marker-assisted selection programs. Furthermore, our study identified pre-breeding lines in the advanced backcrossed population that exhibited superior agronomic traits and sheath blight resistance compared to the recurrent parent. These promising lines hold significant potential for enhancing the sheath blight resistance in elite cultivars through targeted improvement efforts.

Maize is a highly versatile crop holding significant importance in global food, feed and nutritional security. Grain yield is a complex trait and difficult to improve without targeting the improvement of grain yield attributing traits, which are relatively less complex in nature. Hence, considering the erosion in genetic diversity, there is an urgent need to use wild relatives for genetic diversification and unravel the genomic regions for grain yield attributing traits in maize. Thus, the current study aimed to identify quantitative trait loci (QTLs) linked with grain yield and yield attributing traits. Two BC₂F₂ populations developed from the cross of LM13 with Zea parviglumis (population 1) and LM14 with Zea parviglumis (population 2) were genotyped and phenotyped in field conditions in the kharif season. BC₂F_2:3 lines in both populations were phenotyped again for grain yield and attributing traits in the spring season. In total, three QTLs each for ear height (EH), two QTLs for flag leaf length (FLL) and one QTL each for ear diameter (ED), plant height, flag leaf length (FLL), flag leaf width and 100 kernel-weight were identified in population 1. In population 2, two QTLs for kernel row per ear (KRPE) and one QTL for FLL were detected in. QTLs for EH, FLL and KPRE showed consistency across seasons. Among the identified QTLs, six QTLs were found to be co-localized near identified genomic regions in previous studies, validating their potential in contributing to trait expression. The identified QTLs can be utilized for marker assisted selection, transferring favorable alleles from wild relatives in modern maize.

Leaf blight disease of maize was observed during a field survey of Karnataka and Maharashtra in 580 and 360 ha areas, respectively, conducted in the Kharif season of 2020 at V3 to V7 growth stages. The symptomatic plants showed grey to brown spindle-shaped spots. Four fungal isolates (Ero_DWD-27, Ero_DWD-20, Ero_Mya-1 and Ero_Rri-1) were obtained. Isolation used standard tissue culture on PDA, followed by single spore purification (Johnston and Booth 1983). Microscopic observations of pure cultures revealed spore characteristics resembling Exserohilum rostratum. The isolation process was repeated on the same symptomatic leaves from the original locations, confirming morphological and cultural similarity to E. rostratum despite typical turcicum leaf blight symptoms. Pathogenicity was confirmed via whole-plant inoculation on susceptible maize, fulfilling Koch's postulates. The characteristic symptoms on leaves first appear as yellowish, water-soaked spots, progressing to spindle-shaped necrotic lesions. Single-spore cultures' morphological and cultural characteristics revealed their resemblance to E. rostratum. Conidia were dark brown, straight to slightly curved (48.28-65.35 x 12.24-15.84 µm), with six to seven septa and a prominent hilum. Conidia developed with six to seven septa, including two terminal dark and thick septa (Lin et al. 2011). The conidiophores were septate, light to dark brown, geniculate, single or in groups of 2-6, and measured about 140-350 x 6-14 µm in size. Colony color varied on PDA after 14 days, from dark grey to greyish white with circular or filamentous growth forms and mostly regular margins. Molecular identification used ITS, β-tubulin, and LSU regions amplified with standard primers (White et al. 1990; Kroon et al. 2004; Fliegerová et al. 2006; Manzar et al. 2022; Anwer et al. 2022). Sequences were deposited in GenBank (NCBI) under accession numbers PQ394595-PQ394596 and PQ408655-PQ408656 (ITS), PQ432870-PQ432871 and PQ438739-PQ438740 (β-tubulin), and PQ300560, PQ373037, PQ373042, and PQ375114 (LSU). BLAST analysis confirmed the high similarity of isolates Ero_DWD-27, Ero_DWD-20, Ero_Mya-1, and Ero_Rri-1 to E. rostratum. A maximum-likelihood phylogenetic tree based on combined ITS, β-tubulin, and LSU sequences corroborated this identification. Based on colony and morphological characters, pathogenicity, and confirmation at the molecular level, the isolated fungal isolates were identified as E. rostratum. Although E. rostratum has been reported to cause the leaf spot on maize in Henan, China (Xie et al. 2022). To our knowledge, this is the first study of E. rostratum causing maize leaf blight in India. The study underscores the need for accurate pathogen identification and management, as maize is India's third major cereal.

Maydis leaf blight (MLB) is a prevalent disease affecting maize worldwide, caused by the necrotrophic plant pathogen Bipolaris maydis (Nisikado and Miyake). Depending on environmental conditions, MLB can lead to yield losses of up to 40% or more. To combat this disease, various chemical and biocontrol/botanical agents have been developed and proven effective. This study aimed to assess the efficacy of different combinations of disease management components as an alternative approach. The effectiveness of three modules, namely organic, chemical, and IDM, was tested in hot spot locations, namely Ludhiana, Karnal, and Delhi, during 2019 and 2020. Results indicated that the chemical module demonstrated superior disease control, achieving percentages of 54.16 and 52.92 at Ludhiana and Karnal, respectively. The IDM module also showed promising results, with disease control percentages of 45.87 and 44.69 at Ludhiana and Karnal, respectively. Conversely, the organic module exhibited the least effectiveness. Notably, at the Delhi location, the standard control (Foliar spray with Mancozeb 75 WP @ 2.5 g / l water) proved most effective, achieving a disease control percentage of 64.29, followed by the IDM module at 50.00. The chemical module exhibited the highest percent increase in yield (PIY), with figures of 86.47 and 52.92 at Ludhiana and Karnal, respectively, compared to other treatments. This study highlights the superior efficacy of the chemical and IDM modules in comparison to the positive control (check). Consequently, these modules present alternative strategies for effectively managing MLB.