Rhizoctonia solani, causing Rhizoctonia crown and root rot, is a major risk to sugar beet (Beta vulgaris L.) cultivation. The development of resistant varieties accelerated by marker-assisted selection is a priority of breeding programs. We report the identification of a single-nucleotide polymorphism (SNP) marker linked to Rhizoctonia resistance using restriction site-associated DNA (RAD) sequencing of two geographically discrete sets of plant materials with different degrees of resistance/susceptibility to enable a wider selection of superior genotypes. The variant calling pipeline utilized SAMtools for variant calling and the resulting raw SNPs from RAD sequencing (15,988 and 22,439 SNPs) were able to explain 13.40% and 25.45% of the phenotypic variation in the two sets of material from different sources of origin, respectively. An association analysis was carried out independently on both the datasets and mutually occurring significant SNPs were filtered depending on their contribution to the phenotype using principal component analysis (PCA) biplots. To provide a ready-to-use marker for the breeding community, a systematic molecular validation of significant SNPs distributed across the genome was undertaken to combine high-resolution melting, Sanger sequencing, and rhAmp SNP genotyping. We report that RsBv1 located on Chromosome 6 (9,000,093 bp) is significantly associated with Rhizoctonia resistance (p < 0.01) and able to explain 10% of the phenotypic disease variance. The related SNP assay is thus ready for marker-assisted selection in sugar beet breeding for Rhizoctonia resistance.

Late blight, caused by oomycetes Phytophthora infestans is one of the most challenging fungal diseases to manage in tomato plants (Solanum lycopersicum L.). Toward managing the disease, conventional breeding has successfully introgressed genetic loci conferring disease resistance from various wild relatives of tomato into commercial varieties. The cataloging of disease-associated SNP markers and a deeper understanding of disease-resistance mechanisms are needed to keep up with the demand for commercial varieties resistant against emerging pathogen strains. To this end, we performed transcriptome sequencing to evaluate the gene expression dynamics of tomato varieties, resistant and susceptible to Phytophthora infection. Further integrating the transcriptome dataset with large-scale public genomic data of varieties with known disease phenotypes, a panel of single nucleotide polymorphism (SNP) markers correlated with disease resistance was identified. These SNPs were then validated on 31 lines with contrasting phenotypes for late blight. The identified SNPs are located on genes coding for a putative cysteine-rich transmembrane module (CYSTM), Solyc09g098310, and a nucleotide-binding site–leucine-rich repeat protein, Solyc09g098100, close to the well-studied Ph-3 resistance locus known to have a role in plant immunity against fungal infections. The panel of SNPs generated by this study using transcriptome sequencing showing correlation with disease resistance across a broad set of plant material can be used as markers for molecular screening in tomato breeding.

Interactions among plant roots, soil, and microorganisms in the rhizosphere regulate nutrient cycling, plant health, and ecosystem resilience. Recent advances in DNA sequencing and multi-omics are contributing to a shift from primarily descriptive surveys toward more mechanistic and predictive frameworks. This review synthesizes methodological developments and conceptual insights spanning microbial ecology, functional genomics, and agricultural applications. We first summarize DNA-based approaches—marker-gene sequencing, shotgun metagenomics, and quantitative nucleic acid assays—and then complementary omics layers, including metatranscriptomics, metaproteomics, metabolomics, epigenomics, ionomics, and phenomics. We next outline computational advances in data integration, network modeling, and visualization that help represent complex multi-layered datasets as biologically interpretable systems. Applications relevant to climate resilience and sustainable agriculture are discussed, including the design of synthetic microbial communities, the identification of biomarkers for soil health and stress tolerance, and case studies in which rhizosphere multi-omics informs crop breeding and soil management strategies. Overall, these developments underscore the potential of treating microbes as functional and, to some extent, manageable components of the plant holobiont. Looking ahead, we identify key research gaps involving standardized workflows, cross-scale causal inference, and real-time monitoring pipelines that integrate molecular diagnostics with remote sensing and edge–cloud analytics. By linking ecological mechanisms with translational practice, multi-omics frameworks may support the development of more sustainable, data-driven agriculture that better aligns productivity with environmental stewardship.

The molecular and phenotypic effects of a brown seaweed extract (BSE) were assessed in sugar beet (Beta vulgaris L.). Transcript levels of BSE-treated and untreated plants were studied by RNA-seq and validated by quantitative real-time PCR analysis (RT-qPCR). Root morphology, sugar yield, and processing quality traits were also analyzed to better elucidate the treatment effects. RNA-seq revealed 1019 differentially expressed genes (DEGs) between the BSE-treated and untreated plants. An adjusted p-value < 0.1 and an absolute value of log2 (fold change) greater than one was used as criteria to select the DEGs. Gene ontology (GO) identified hormone pathways as an enriched biological process. Six DEGs involved in auxin and ABA pathways were validated using RT-qPCR. The phenotypic characterization indicated that BSE treatment led to a significant increase (p < 0.05) in total root length and the length of fine roots of plants grown under hydroponics conditions. The sugar yield of plants grown under field conditions was higher (p < 0.05) in the treated field plots compared with the control treatment, without impacting the processing quality. Our study unveiled the relevant effects of BSE application in regulating auxin- and ABA-related gene expression and critical traits related to sugar beet development and yield.

The characterization of genetic diversity in elite breeding stocks is crucial for the registration and protection of new varieties. Moreover, experimental population structure analysis and information about the genetic distinctiveness of commercial materials are essential for crop breeding programs. The purpose of our research was to assess the genetic relationships of 32 endive (Cichorium endivia L.) breeding lines, 18 from var. latifolium (escarole) and 14 from var. crispum (curly), using heterologous Cichorium intybus-derived simple sequence repeats (SSR) markers and single-nucleotide polymorphisms (SNP) markers. We found that 14 out of 29 SSR markers were successfully amplified, but only 8 of them were related to polymorphic loci. To overcome the limitation of the low number of informative SSR marker loci, an alternative SNP-based approach was employed. The 4621 SNPs produced by a restriction site-associated DNA marker sequencing approach were able to fully discriminate the 32 endive accessions; most importantly, as many as 50 marker loci were found to distinguish the curly group from the escarole group. Interestingly, 24 of the marker loci mapped within a peripheral segment of chromosome 8 of lettuce (Lactuca sativa L.), spanning a chromosomal region of 49.6 Mb. Following Sanger sequencing-based validation, three genes were determined to carry nonsynonymous SNPs, and one of them matched a putative ortholog of AtELP1, subunit 1 of the Elongator complex. Considering that several previously characterized Elongator complex subunit mutants exhibited elongated and/or curly leaf phenotypes, this gene should be taken into consideration for a better understanding of the underlying mechanism controlling leaf shape in endive.

Sulfur is an essential plant macronutrient, and its adequate supply allows an efficient root storage and sugar extractability in sugar beets (Beta vulgaris L.). In this study, we investigated the effect of changes in sulfur availability on the endophytic community structure of sugar beets. Plants were hydroponically grown in a complete nutrient solution (S-supplied), a nutrient solution without MgSO₄ (S-deprived), and a nutrient solution without MgSO₄ for six days and resupplied with 100 μM MgSO₄ for 48 h (S-resupplied). The sulfur status was monitored by inductively coupled plasma ICP–OES, and combustion analysis together with the evaluation of microRNA395 as a biomarker for sulfate status. Metabarcoding of the bacterial 16S rRNA gene was carried out in order to determine leaf endophytic community structure. The Shannon diversity index significantly differed (p < 0.05) between sulfate-supplied and sulfate-deprived seedlings. Validation by Real-Time PCR showed a significant increase (p < 0.05) of Burkholderia spp. in sulfate-deprived plants as compared to sulfate-supplied ones. The study sheds new light on the effects of nutrient deficiency on the microbiome of sugar beet plants.