Maria Duk’s research while affiliated with Peter the Great St.Petersburg Polytechnic University and other places

Flowering is initiated in response to environmental cues, with the photoperiod and ambient temperature being the main ones. The regulatory pathways underlying floral transition are well studied in Arabidopsis thaliana but remain largely unknown in legumes. Here, we first applied an in silico approach to infer the regulatory inputs of four FT-like genes of the narrow-leafed lupin Lupinus angustifolius. We studied the roles of FTc1, FTc2, FTa1, and FTa2 in the activation of meristem identity gene AGL8 in response to 8 h and 16 h photoperiods, vernalization, and the circadian rhythm. We developed a set of regression models of AGL8 regulation by the FT-like genes and fitted these models to the recently published gene expression data. The importance of the input from each FT-like gene or their combinations was estimated by comparing the performance of models with one or few FT-like genes turned off, thereby simulating loss-of-function mutations that were yet unavailable in L. angustifolius. Our results suggested that in the early flowering Ku line and intermediate Pal line, the FTc1 gene played a major role in floral transition; however, it acted through different mechanisms under short and long days. Turning off the regulatory input of FTc1 resulted in substantial changes in AGL8 expression associated with vernalization sensitivity and the circadian rhythm. In the wild ku line, we found that both FTc1 and FTa1 genes had an essential role under long days, which was associated with the vernalization response. These results could be applied both for setting up new experiments and for data analysis using the proposed modeling approach.

Chickpea (Cicer arientinum) is an important crop grown in the Middle East, Central Asia, Turkey, India and southern Russia and used in a wide variety of traditional dishes. The decrease in genetic diversity during domestication, as well as the crop's greater sensitivity to abiotic and biotic stresses, provides the idea of using landraces in breeding programs to improve the crop. The new IIIVmrMLM method for genome-wide association search allowed us to identify new variants in the genetic data of the chickpea collection, which were localized within important genes, and to identify landraces best suited to the climate of the two experimental stations.

Flowering time is the most important agronomic trait which is used in breeding and determines the crop performance. Vernalization, or prolonged exposure to cold, accelerates flowering and increases yields in many crops. The molecular mechanisms of vernalization-induced flowering are well studied in Arabidopsis thaliana, but remain largely unknown for legumes. Mathematical modeling is a powerful tool to predict regulatory interactions in gene networks on the basis of gene expression patterns. This review concerns previously developed approaches to modeling gene regulatory networks of the flowering transition process and the prospects for their adaptation with the aim of conducting the analysis of the mechanisms of vernalization requirement in legumes.

The chickpea plays a significant role in global agriculture and occupies an increasing share in the human diet. The main aim of the research was to develop a model for the prediction of two chickpea productivity traits in the available dataset. Genomic data for accessions were encoded in Artificial Image Objects, and a model for the thousand-seed weight (TSW) and number of seeds per plant (SNpP) prediction was constructed using a Convolutional Neural Network, dictionary learning and sparse coding for feature extraction, and extreme gradient boosting for regression. The model was capable of predicting both traits with an acceptable accuracy of 84–85%. The most important factors for model solution were identified using the dense regression attention maps method. The SNPs important for the SNpP and TSW traits were found in 34 and 49 genes, respectively. Genomic prediction with a constructed model can help breeding programs harness genotypic and phenotypic diversity to more effectively produce varieties with a desired phenotype.

Chickpea is a staple crop for many nations worldwide. Modeling genotype-by-environment interactions and assessing the genotype’s ability to contribute adaptive alleles are crucial for chickpea breeding. In this study, we evaluated 12 agronomically important traits of 159 accessions from the N.I. Vavilov All Russian Institute for Plant Genetic Resources collection. These included 145 landraces and 13 cultivars grown in different climatic conditions in Kuban (45°18′ N and 40°52′ E) in both 2016 and 2022, as well as in Astrakhan (46°06′ N and 48°04′ E) in 2022. Using the IIIVmrMLM model in multi-environmental mode, we identified 161 quantitative trait nucleotides (QTNs) with stable genetic effects across different environments. Furthermore, we have observed 254 QTN-by-environment interactions with distinct environment-specific effects. Notably, five of these interactions manifested large effects, with R2 values exceeding 10%, while the highest R2 value for stable QTNs was 4.7%. Within the protein-coding genes and their 1 Kb flanking regions, we have discerned 22 QTNs and 45 QTN-by-environment interactions, most likely tagging the candidate causal genes. The landraces obtained from the N.I Vavilov All Russian Institute for Plant Genetic Resources collection exhibit numerous favorable alleles at quantitative trait nucleotide loci, showing stable effects in the Kuban and Astrakhan regions. Additionally, they possessed a significantly higher number of Kuban-specific favorable alleles of the QTN-by-environment interaction loci compared to the Astrakhan-specific ones. The environment-specific alleles found at the QTN-by-environment interaction loci have the potential to enhance chickpea adaptation to specific climatic conditions.

In many plant species, flowering is promoted by the cold treatment or vernalization. The mechanism of vernalization-induced flowering has been extensively studied in Arabidopsis but remains largely unknown in legumes. The orthologs of the FLC gene, a major regulator of vernalization response in Arabidopsis, are absent or non-functional in the vernalization-sensitive legume species. Nevertheless, the legume integrator genes FT and SOC1 are involved in the transition of the vernalization signal to meristem identity genes, including PIM (AP1 ortholog). However, the regulatory contribution of these genes to PIM activation in legumes remains elusive. Here, we presented the theoretical and data-driven analyses of a feed-forward regulatory motif that includes a vernalization-responsive FT gene and several SOC1 genes, which independently activate PIM and thereby mediate floral transition. Our theoretical model showed that the multiple regulatory branches in this regulatory motif facilitated the elimination of no-sense signals and amplified useful signals from the upstream regulator. We further developed and analyzed four data-driven models of PIM activation in Medicago trancatula in vernalized and non-vernalized conditions in wild-type and fta1-1 mutants. The model with FTa1 providing both direct activation and indirect activation via three intermediate activators, SOC1a, SOC1b, and SOC1c, resulted in the most relevant PIM dynamics. In this model, the difference between regulatory inputs of SOC1 genes was nonessential. As a result, in the M. trancatula model, the cumulative action of SOC1a, SOC1b, and SOC1c was favored. Overall, in this study, we first presented the in silico analysis of vernalization-induced flowering in legumes. The considered vernalization network motif can be supplemented with additional regulatory branches as new experimental data become available.

Chickpea (Cicer arientinum) is an important leguminous crop, which is widely grown especially in the Near East. In wet weather conditions, the susceptibility of chickpeas to fungal diseases such as Ascochyta blight and Fusarium blight increases. Thus, selection of disease-resistant and early-ripening varieties is critically needed. The present study was conducted to investigate genome associations in 171 samples of chickpea plants, grown in two experimental stations in Krasnodar (Kuban experimental station) and Astrakhan (Astrakhan experimental station), examine relationship between genes and 12 phenotypic traits as well as explore the association between genes and 3 hallmarks of resistance to pathogenes: Fusarium blight, Ascochyta blight and Noctuidae. Variants associated with different phenotypic traits were identified using a genome-wide association study (GWAS).

Classical methods for identification of genetic variants associated with certain macroscopic phenotypic traits are, as a rule, limited to analyses of single nucleotide polymorphisms. Copy number variations, and more broadly structural variants may provide a plethora of useful information due to the magnitude of the changes they induce. However, their use in genome-wide association studies is seriously limited mostly due to the uncertainties in their discovery (i.e., failure to resolve an event with nucleotide resolution) by computational algorithms from genomic data. Nevertheless, in certain cases, such analyses are possible and may still yield valuable results. Our recent work has revealed genetic variants (single nucleotide polymorphisms) possibly related to phenotypic traits determining fibre quality. Here, we decided to extend the analyses to structural variants, namely copy number variations. Importantly, we use a novel high-coverage dataset allowing for accurate prediction of copy number variations. Overall, we compiled a list of 41 candidate genes associated with five quantitative phenotypic traits. Furthermore, the genome stability metric developed earlier facilitated stratification of copy number variant loci with regard to their stability. On the whole, our analyses suggest that the genomic regions less resilient to external and internal stresses are more susceptible to changes associated with the studied phenotypic traits.