Selection and validation of reliable reference genes for quantitative real-time PCR in Barnyard millet (Echinochloa spp.) under varied abiotic stress conditions.

Quantitative real-time polymerase chain reaction (RT-qPCR) using a stable reference gene is widely used for gene expression research. Barnyard millet (Echinochloa spp.) is an ancient crop in Asia and Africa that is widely cultivated for food and fodder. It thrives well under drought, salinity, cold, and heat environmental conditions, besides adapting to any soil type. To date, there are no gene expression studies performed to identify the potential candidate gene responsible for stress response in barnyard millet, due to lack of reference gene. Here, 10 candidate reference genes, Actin (ACT), α-tubulin (α-TUB), ß-tubulin (ß-TUB), RNA pol II (RP II), elongation factor-1 alpha (EF-1α), adenine phosphoribosyltransferase (APRT), TATA-binding protein-like factor (TLF), ubiquitin-conjugating enzyme 2 (UBC2), ubiquitin-conjugating enzyme E2L5 (UBC5) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH), were selected from mRNA sequences of E. crus-galli and E. colona var frumentacea. Five statistical algorithms (geNorm, NormFinder, BestKeeper, ΔCt, and RefFinder) were applied to determine the expression stabilities of these genes in barnyard millet grown under four different abiotic stress (drought, salinity, cold and heat) exposed at different time points. The UBC5 and ɑ-TUB in drought, GAPDH in salinity, GAPDH and APRT in cold, and EF-1α and RP II in heat were the most stable reference genes, whereas ß-TUB was the least stable irrespective of stress conditions applied. Further Vn/Vn + 1 analysis revealed two reference genes were sufficient to normalize gene expression across all sample sets. The suitability of identified reference genes was validated with Cu-ZnSOD (SOD1) in the plants exposed to different abiotic stress conditions. The results revealed that the relative quantification of the SOD1 gene varied according to reference genes and the number of reference genes used, thus highlighting the importance of the choice of a reference gene in such experiments. This study provides a foundational framework for standardizing RT-qPCR analyses, enabling accurate gene expression profiling in barnyard millet.

Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches.

Introduction: Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively. Results: The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R2 of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2). Discussion: Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.

Altering Stomatal Density for Manipulating Transpiration and Photosynthetic Traits in Rice through CRISPR/Cas9 Mutagenesis.

Stomata regulates conductance, transpiration and photosynthetic traits in plants. Increased stomatal density may contribute to enhanced water loss and thereby help improve the transpirational cooling process and mitigate the high temperature-induced yield losses. However, genetic manipulation of stomatal traits through conventional breeding still remains a challenge due to problems involved in phenotyping and the lack of suitable genetic materials. Recent advances in functional genomics in rice identified major effect genes determining stomatal traits, including its number and size. Widespread applications of CRISPR/Cas9 in creating targeted mutations paved the way for fine tuning the stomatal traits for enhancing climate resilience in crops. In the current study, attempts were made to create novel alleles of OsEPF1 (Epidermal Patterning Factor), a negative regulator of stomatal frequency/density in a popular rice variety, ASD 16, using the CRISPR/Cas9 approach. Evaluation of 17 T0 progenies identified varying mutations (seven multiallelic, seven biallelic and three monoallelic mutations). T0 mutant lines showed a 3.7-44.3% increase in the stomatal density, and all the mutations were successfully inherited into the T1 generation. Evaluation of T1 progenies through sequencing identified three homozygous mutants for one bp insertion. Overall, T1 plants showed 54-95% increased stomatal density. The homozygous T1 lines (# E1-1-4, # E1-1-9 and # E1-1-11) showed significant increase in the stomatal conductance (60-65%), photosynthetic rate (14-31%) and the transpiration rate (58-62%) compared to the nontransgenic ASD 16. Results demonstrated that the genetic alterations in OsEPF1 altered the stomatal density, stomatal conductance and photosynthetic efficiency in rice. Further experiments are needed to associate this technology with canopy cooling and high temperature tolerance.

Plant growth-promoting rhizobacteria Shewanella putrefaciens and Cronobacter dublinensis enhance drought tolerance of pearl millet by modulating hormones and stress-responsive genes.

Drought is a major abiotic stress that affects crop productivity. Endophytic bacteria have been found to alleviate the adverse effects of drought on plants. In the present study, we evaluated the effects of two endophytic bacteria Shewanella putrefaciens strain MCL-1 and Cronobacter dublinensis strain MKS-1 on pearl millet (Pennisetum glaucum (L.) R. Br.) under drought stress conditions. Pearl millet plants were grown under three water levels: field capacity (FC), mild drought stress (MD), and severe drought stress (SD). The effects of inoculation on plant growth, physiological attributes, phytohormone content, and drought stress-responsive genes were assessed. The inoculation of pearl millet seeds with endophytes significantly improved shoot and root dry weight and root architecture of plants grown under FC and drought stress conditions. There was a significant increase in relative water content and proline accumulation in the inoculated plants. Among the phytohormones analyzed, the content of ABA and IAA was significantly higher in endophyte-treated plants under all moisture regimes than in uninoculated plants. C. dublinensis-inoculated plants had higher GA content than uninoculated plants under all moisture regimes. The expression level of genes involved in phytohormone biosynthesis (SbNCED, SbGA20oX, and SbYUC) and coding drought-responsive transcription factors (SbAP2, SbSNAC1 and PgDREB2A) was significantly higher under SD in endophyte-inoculated plants than in uninoculated plants. Thus, these endophytic bacteria presumably enhanced the tolerance of pearl millet to drought stress by modulating root growth, plant hormones, physiology and the expression of genes involved in drought tolerance.