In silico analysis identified bZIP transcription factors genes responsive to abiotic stress in Alfalfa (Medicago sativa L.).

BACKGROUND: Alfalfa (Medicago sativa L.) is the most cultivated forage legume around the world. Under a variety of growing conditions, forage yield in alfalfa is stymied by biotic and abiotic stresses including heat, salt, drought, and disease. Given the sessile nature of plants, they use strategies including, but not limited to, differential gene expression to respond to environmental cues. Transcription factors control the expression of genes that contribute to or enable tolerance and survival during periods of stress. Basic-leucine zipper (bZIP) transcription factors have been demonstrated to play a critical role in regulating plant growth and development as well as mediate the responses to abiotic stress in several species, including Arabidopsis thaliana, Oryza sativa, Lotus japonicus and Medicago truncatula. However, there is little information about bZIP transcription factors in cultivated alfalfa. RESULT: In the present study, 237 bZIP genes were identified in alfalfa from publicly available sequencing data. Multiple sequence alignments showed the presence of intact bZIP motifs in the identified sequences. Based on previous phylogenetic analyses in A. thaliana, alfalfa bZIPs were similarly divided and fell into 10 groups. The physico-chemical properties, motif analysis and phylogenetic study of the alfalfa bZIPs revealed high specificity within groups. The differential expression of alfalfa bZIPs in a suite of tissues indicates that bZIP genes are specifically expressed at different developmental stages in alfalfa. Similarly, expression analysis in response to ABA, cold, drought and salt stresses, indicates that a subset of bZIP genes are also differentially expressed and likely play a role in abiotic stress signaling and/or tolerance. RT-qPCR analysis on selected genes further verified these differential expression patterns. CONCLUSIONS: Taken together, this work provides a framework for the future study of bZIPs in alfalfa and presents candidate bZIPs involved in stress-response signaling.

Validation of UAV-based alfalfa biomass predictability using photogrammetry with fully automatic plot segmentation.

Alfalfa is the most widely cultivated forage legume, with approximately 30 million hectares planted worldwide. Genetic improvements in alfalfa have been highly successful in developing cultivars with exceptional winter hardiness and disease resistance traits. However, genetic improvements have been limited for complex economically important traits such as biomass. One of the major bottlenecks is the labor-intensive phenotyping burden for biomass selection. In this study, we employed two alfalfa fields to pave a path to overcome the challenge by using UAV images with fully automatic field plot segmentation for high-throughput phenotyping. The first field was used to develop the prediction model and the second field to validate the predictions. The first and second fields had 808 and 1025 plots, respectively. The first field had three harvests with biomass measured in May, July, and September of 2019. The second had one harvest with biomass measured in September of 2019. These two fields were imaged one day before harvesting with a DJI Phantom 4 pro UAV carrying an additional Sentera multispectral camera. Alfalfa plot images were extracted by GRID software to quantify vegetative area based on the Normalized Difference Vegetation Index. The prediction model developed from the first field explained 50-70% (R Square) of biomass variation in the second field by incorporating four features from UAV images: vegetative area, plant height, Normalized Green-Red Difference Index, and Normalized Difference Red Edge Index. This result suggests that UAV-based, high-throughput phenotyping could be used to improve the efficiency of the biomass selection process in alfalfa breeding programs.