مجله زیست فناوری گیاهان زراعی
پیاپی 45 (بهار 1403)

Heat stress has detrimental effects on the growth and performance of plants through biochemical, physiological, morphological, and molecular changes. Plants have developed complex mechanisms to balance growth and tolerance to stress, allowing them to effectively defend against more severe stresses by remembering mild stress and forming heat stress memory, known as thermopriming. To investigate the role of thermopriming in inducing the transcription response of HSFA2, HSFA1b and MIPS2 genes, the changes in the transcriptional level of the genes were studied at different times after priming and return stress in canola seedlings using the qRT-PCR technique. The results showed that the expression of these genes was not stable during the recovery period after the initial mild stress (memory phase), while their transcription level immediately after facing the second severe stress was induced at a much higher level in primed plants (P+T treatment) compared to non-primed plants (T treatment) which continued until 48 hours after return stress. Also, morphological analysis of seedlings at 7 and 14 days after release from the second stress showed that thermopriming increase the growth indices and heat tolerance in these plants through strengthening the expression of stress memory genes. Since the HSFA1b, HSFA2 and MIPS2 genes have maintained their expression level until days after the return stress, these genes can be the key components of the transcriptional memory of heat stress and be used in breeding programs and the development of heat tolerant varieties.

Today, using advanced technologies such as the global positioning system (GPS), agricultural drones, satellite mapping, remote sensors, and precision agriculture machinery provides farmers with a lot of big data during production. According to the reports, this can be considered a part of the digital economy in precision agriculture and be economically exploited. The analysis of this data cannot be processed by traditional processing systems due to its complexity. Given the size and complexity of big data, artificial intelligence can transform this data into valuable information through machine learning algorithms. This technology is being used to performance prediction algorithms, reducing agricultural inputs such as fertilizers and poisons, monitoring the growing conditions, pest management, breeding, molecular studies and finally value chain management. Developing programs using artificial intelligence technology will soon be able to manage the time of agricultural products entering the market, in addition to determining the planting time in order to increase productivity. The production of bio fertilizer from agricultural waste can be another achievement of the development of algorithms based on artificial intelligence to reduce the negative environmental effects and increase the economic productivity of the remaining waste from agricultural products. This study discusses the important applications of artificial intelligence in agriculture and its impact on Precision agriculture.

Among cereals, durum wheat (Triticum turgidum L. var. durum) is one of the most important protein and energy sources after bread wheat. It has a significant role in human nutrition in most countries. In this study, to identify and localization of QTLs controlling traits related to starch and Neutral detergent fiber, 118 durum wheat recombinant inbred lines obtained from the cross between the local genotype of Iran-249 originated from the west of Iran and the local cultivar of Zardak from Kermanshah were evaluated in two environments (2014 and 2015) under rainfall conditions using randomized complete block design along with parents in three replications. QTL analysis for each trait was performed using the inclusive composite interval mapping method to identify genomic regions that significantly affected the studied traits. A total of six QTLs were identified for the two traits measured, grain starch content and neutral detergent fiber. Three QTLs were identified on chromosome 7A and one QTL on chromosome 4B for the grain starch content trait. For the neutral detergent fiber trait, one QTL was localization on chromosome 6B and one QTL on chromosome 7A. Identifying and localization QTLs controlling traits related to seed quality characteristics can provide an opportunity to improve these traits through marker-assisted selection. Also, these results can create a basis for identifying candidate genes and map-based cloning and confirming QTL function.

Knowledge on genes effect and action (additive/dominance) is one of the necessities to achieve cultivars with high performance and quality. Estimating the breeding value (additive effect) can be done thanks to molecular markers through best linear unbiased prediction (BLUP). In the present study, 100 oilseed sunflower genotypes were evaluated based on the 10×10 lattice design during two crop years of 1392-1393 under normal and drought stress (irrigation limitation) conditions. The breeding value of 13 traits in 78 genotypes out of 100 was estimated due to having genotyping data with SSR and Retrotransposon based markers in each one of normal and drought stress (irrigation limitation) conditions through BLUP. For this purpose, the kinship matrix was calculated by SSR and Retrotransposon based markers data. According to total ranks of breeding values of all studied traits estimated by molecular data of both markers, in normal conditions, genotypes 47, 11, 8 and 35 and under drought stress (irrigation limitation) conditions, genotypes 8, 11 and 35 showed the highest breeding value. Based on SSR markers data in normal conditions; genotypes 76, 36, 34 and 41 and based on Retrotransposon based markers data; genotypes 61, 78, 72 and 52, and in drought stress (irrigation limitation) conditions based on SSR markers data; genotypes 76, 38, 34, 29 and 70 and based on Retrotransposon based markers data; genotypes 16, 71, 78 and 61 showed the lowest breeding value. Considering both studied conditions and all studied traits and both molecular markers information, genotypes 8, 11 and 35 with high breeding value are introduced as desirable parents for breeding programs.

Salinity is one of the most important environmental stresses that disrupt the natural growth of plants. Plant use different mechanisms to cope with stress conditions, such as salinity, in which changes in protein expression is the most important one at molecular level. Changes in protein expression depends on their physicochemical changes such as half- life, stability index, iso-electric point, molecular weight, extinction coefficient etc. Furtermore, identification of motifs, patterns and protein domains make it possible to predict changes in the conformation, structure and proteins functions. In this research was selected a number of changed protein in expression under salinity stress in wheat based on the previous proteomic studies for further was selected bioinformatic analysis. Study Physicochemical properties of proteins by ProtParam software, identification of domains by InterProScan and CDD, identification patters for prediction of post translational modification by ScanProsite, similarity by Blast, alignment of similar proteins for identification of conserved block was performed by T-Coffee. Out of the 25 proteins associated with salinity stress, 20 proteins have a half-life more than 20 hours. The molecular weight of these proteins was varied between 13 to 117 kDa and 15 protein showed instability index of less than 40 and therefore classified as stable proteins. Investigation of proteins using TMHMM and Protscale softwares, it was found that Aquaporins, Plasma membrane intrinsic proteins, Plasma membrane ATPase and Rust resistance kinase Lr10 are highly hydrophobic proteins, whose major structure located inside the membranes. Out of 25 proteins, 8 proteins were selected and analyzed for identification of patterns, domains, structure and function. α-tubulin as a monomer participates with -tubulin to make α-tubulin dimer. Tubulin create a major part of microtubules that are essential for cell growth and division. This protein consisted of one pattern, Tubulin subunits alpha, beta and gamma signature domain namly PLN00221. For the Triosphosphate isomerase protein, a domain called TIM, which is involved in the catalytic mechanism and for the Calmodulin protein a domain called PTZ00184 was identified which is a calcium binding domain. For the Putative glycine decarbixylase subunit a domain called PRK01202 has been identified that has carboxylase activity. For Cu/Zn superoxide dismutase protein the domain called as SOD is involved in the absorption of superoxide. For Fructose-bisphosphate aldolase protein, the catalytic converter domain was identified as PLV02455 and for Hsp 70- Hsp 90 organizing protein, STI1 domain was identified with ATPase property. For the 2- Cys peroxiredoxin BAS 1 protein, for the PRX-Typ 2 cys domain that plays an important role in regulating oxidation- cell reduction.

Plants use a variety of strategies to cope with abiotic stress, depending on the species and the growth of the plant. Abiotic stresses such as drought is the most important stress that affects yield of agricultural products. In addition, drought stress is one of the main limiting factors in plant growth, it can also inhibit respiration, photosynthesis, and thus affects the growth and physiological metabolism of plants. Plants activate several mechanisms such as morphological and structural changes as well as the expression of drought-resistant genes, the synthesis of hormones and osmotic regulators to reduce drought stress. Drought accelerates grain leaf senescence, altering the expression of thousands of genes and ultimately affecting grain protein content and grain yield. However, the genotypic variability exists for drought induced disruption and tolerance in barley. In this review, the approaches can help for improving barley genotypes in response to drought stress through breeding and physiological traits, genetic engineering, and marker-assisted selection (MAS). We detected genes and proteins involved in response to drought-tolerance using proteomics, transcriptomics and metabolomics approaches. Also, the introduced Quatitatives Traits Loci (QTLs) related to yield and Stay green and physiological traits found in this study can be used for MAS in barley improvement for drought tolerance in the future. In particular, comparative studies of genetically diverse germplasm exposed to adverse conditions such as drought provide valuable insights into plant responses to stress and create information on biochemical pathways involved in adaptation to environmental limitations. Proper evaluation of omics data can help the biomarker discovery.