Radiotolerant endophytic bacteria and analysis of the effects of 137Cesium on the metabolome of Pantoea sp.

Some bacteria have developed mechanisms to withstand the stress caused by ionizing radiation. The ability of these radioresistant microorganisms to survive high levels of radiation is primarily attributed to their DNA repair mechanisms and the production of protective metabolites. To determine the effect of irradiation on bacterial growth, we propose to compare the metabolites produced by the irradiated isolates to those of the control (non-irradiated isolates) using mass spectrometry, molecular networking, and chemometric analysis. We identified the secondary metabolites produced by these bacteria and observed variations in growth following irradiation. Notably, after 48 h of exposure to radiation, Pantoea sp. bacterial cells exhibited a significant 6-log increase compared to non-irradiated cells. Non-irradiated cells produce exclusively Pyridindolol, 1-hydroxy-4-methylcarbostyril, N-alkyl, and N-2-alkoxyethyl diethanolamine, while 5'-methylthioadenosine was detected only in irradiated cells. These findings suggest that the metabolic profile of Pantoea sp. remained relatively stable. The results obtained from this study have the potential to facilitate the development of innovative strategies for harnessing the capabilities of endophytic bacteria in radiological protection and bioremediation of radionuclides.

Multi-trait and multi-environment genomic prediction for flowering traits in maize: a deep learning approach.

Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize.