Backgorund Environmental stresses including abiotic stresses and biotic stresses limit yield of plants. Stress-tolerant breeding is an efficient way to improve plant yield under stress conditions. Genome editing by CRISPR/Cas9 can be used in molecular breeding to improve agronomic traits in crops, but in most cases, with fitness costs. The plant hormone ABA regulates plant responses to abiotic stresses via signaling transduction. We previously identified AITRs as a family of novel transcription factors that play a role in regulating plant responses to ABA and abiotic stresses. We found that abiotic stress tolerance was increased in the single, double and triple aitr mutants. However, it is unclear if the increased abiotic stress tolerance in the mutants may have fitness costs. Results We report here the characterization of AITRs as suitable candidate genes for CRISPR/Cas9 editing to improve plant stress tolerance. By using CRISPR/Cas9 to target AITR3 and AITR4 simultaneously in the aitr256 triple and aitr1256 quadruple mutants respectively, we generated Cas9-free aitr23456 quintuple and aitr123456 sextuple mutants. We found that reduced sensitivities to ABA and enhanced tolerance to drought and salt were observed in these mutants. Most importantly, plant growth and development was not affected even in the aitr123456 sextuple mutants, in whom the entire AITR family genes have been knocked out, and the aitr123456 sextuple mutants also showed a wild type response to the pathogen infection. Conclusions Our results suggest that knockout of the AITR family genes in Arabidopsis enhanced abiotic stress tolerance without fitness costs. Considering that knock-out a few AITRs will lead to enhanced abiotic stress tolerance, that AITRs are widely distributed in angiosperms with multiple encoding genes, AITRs may be targeted for molecular breeding to improve abiotic stress tolerance in plants including crops.

Both seed size and abiotic stress tolerance are important agronomic traits in crops. In Arabidopsis, two closely related transcription repressors DPA4 (Development-Related PcG Target in the APEX4)/NGAL3 and SOD7 (Suppressor of da1-1)/NGAL2 (NGATHA-like protein) function redundantly to regulate seed size, which was increased in the dpa4 sod7 double mutants. Whereas ABA-induced transcription repressors (AITRs) are involved in the regulation of ABA signaling and abiotic stress tolerance, Arabidopsis aitr2 aitr5 aitr6 (aitr256) triple mutant showed enhanced tolerance to drought and salt. Here we performed CRISPR/Cas9 genome editing to disrupt DPA4 and SOD7 in aitr256 mutant, trying to integrate seed size and abiotic stress tolerance traits in Arabidopsis, and also to examine whether DPA4 and SOD7 may regulate other aspects of plant growth and development. Indeed, seed size was increased in the dpa4 sod7 aitr256 quintuple mutants, and enhanced tolerance to drought was observed in the mutants. In addition, we found that shoot branching was affected in the dpa4 sod7 aitr256 mutants. The mutant plants failed to produce secondary branches, and flowers/siliques were distributed irregularly on the main stems of the plants. Floral organ number and fertility were also affected in the dpa4 sod7 aitr256 mutant plants. To examine if these phenotypes were dependent on loss-of-function of AITRs, dpa4 sod7 double mutants were generated in Col wild type background, and we found that the dpa4 sod7 mutant plants showed a phenotype similar to the dpa4 sod7 aitr256 quintuple mutants. Taken together, our results indicate that the integration of seed size and abiotic stress tolerance traits by CRISPR/Cas9 editing was successful, and our results also revealed a role of DPA4 and SOD7 in the regulation of inflorescence architecture in Arabidopsis.

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Abstract-Regression testing is performed to see if anychanges introduced in software will not affect the rest of functional software parts. It is inefficient to re-execute all test cases every time the changes are made. In this regard test cases are prioritized by following some criteria to perform efficient testing while meeting limited testing resources. In our research we have proposed value based particle swarm intelligence algorithm for test case prioritization. The aim of our research is to detect maximum faults earlier in testing life cycle.

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