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Legumes are the most important crop plants in agriculture, contributing 27% of the world's primary food production. However, productivity and production of Legumes is reduced due to increasing environmental stress. Hence, there is a pressing need to understand the molecular mechanism involved in stress response and legumes adaptation. Proteomics provides an important molecular approach to investigate proteins involved in stress response. Both the gel-based and gel-free-based techniques have significantly contributed to understanding the proteome regulatory network in leguminous plants. In the present review, we have discussed the role of different proteomic approaches (2-DE, 2 D-DIGE, ICAT, iTRAQ, etc.) in the identification of various stress-responsive proteins in important leguminous crops, including soybean, chickpea, cowpea, pigeon pea, groundnut, and common bean under variable abiotic stresses including heat, drought, salinity, waterlogging, frost, chilling and metal toxicity. The proteomic analysis has revealed that most of the identified differentially expressed proteins in legumes are involved in photosynthesis, carbohydrate metabolism, signal transduction, protein metabolism, defense, and stress adaptation. The proteomic approaches provide insights in understanding the molecular mechanism of stress tolerance in legumes and have resulted in the identification of candidate genes used for the genetic improvement of plants against various environmental stresses. Identifying novel proteins and determining their expression under different stress conditions provide the basis for effective engineering strategies to improve stress tolerance in crop plants through marker-assisted breeding. [ABSTRACT FROM AUTHOR]

Micronutrient deficiency is a widespread food-related health problem around the world. The present study was conducted to evaluate a set of 63 advanced breeding lines of bread wheat (Triticum aestivum L.) for grain iron (GFe) and grain zinc (GZn) concentrations, and to characterise the germplasm set via simple sequence repeat (SSR) markers (both genic and random). Substantial variation was found for both micronutrients. GFe concentration ranged from 28.9 to 67.4 mg kg-1 and GZn from 26.3 to 56.6 mg kg-1.Molecular characterisation with six genic and 20 random SSR markers detected 168 alleles with an average of 3.170 alleles per locus. Analysis of genotypic data based on division into two subpopulations revealed 165 alleles with an average of 3.113 alleles per locus in the low GFe--GZn subpopulation, whereas in the high GFe--GZn subpopulation, 149 alleles with an average of 2.811 alleles per locus were detected. Genic SSRs detected a higher average number of alleles (3.273 alleles per locus) than random SSRs (3.143 alleles per locus). Hierarchical clustering using genic markers alone clustered the whole germplasm set into two distinct groups: one possessing low GFe--GZn genotypes, the other with high GFe--GZn genotypes. Study of marker--trait associations (MTAs) identified seven new MTAs (six for GZn and one for GFe) and validated one MTA for GZn concentration. The promising genotypes andMTAs identified during the study will prove useful in wheat bio-fortification programs in the future. [ABSTRACT FROM AUTHOR]

Fusarium wilt (FW) caused by Fusarium oxysporum f. sp. ciceri is a devastating disease of chickpea ( Cicer arietinum ). To identify promising resistant genotypes and genomic loci for FW resistance, a core set of 179 genotypes of chickpea was tested for FW reactions at the seedling and reproductive stages under field conditions and controlled conditions in the greenhouse. Our results revealed that at the seedling stage, most of the genotypes were resistant, whereas at the reproductive stage, most of the genotypes were susceptible. Genotyping using a 50K Axiom ® Cicer SNP Array and trait data of FW together led to the identification of 26 significant ( P ≤ E-05) marker-trait associations (MTAs) for FW resistance. Among the 26 MTAs, 12 were identified using trait data recorded in the field (three at the seedling and nine at the reproductive stage), and 14 were identified using trait data recorded under controlled conditions in the greenhouse (six at the seedling and eight at the reproductive stage). The phenotypic variation explained by these MTAs varied from 11.75 to 15.86%, with an average of 13.77%. Five MTAs were classified as major, explaining more than 15% of the phenotypic variation for FW, and two were declared stable, being identified in two environments. One of the promising stable and major MTAs (Affx_123280060) detected in field conditions at the reproductive stage was also detected in greenhouse conditions at the seedling and reproductive stages. The stable and major (>15% PVE) MTAs can be used in chickpea breeding programs., Competing Interests: The author(s) declare no conflict of interest.