The interaction of mitochondria with cellular components evolved differently in plants and mammals; in plants, the organelle contains proteins such as ALTERNATIVE OXIDASES (AOXs), which, in conjunction with internal and external ALTERNATIVE NAD(P)H DEHYDROGENASES, allow canonical oxidative phosphorylation (OXPHOS) to be bypassed. Plant mitochondria also contain UNCOUPLING PROTEINS (UCPs) that bypass OXPHOS. Recent work revealed that OXPHOS bypass performed by AOXs and UCPs is linked with new mechanisms of mitochondrial retrograde signaling. AOX is functionally associated with the NO APICAL MERISTEM transcription factors, which mediate mitochondrial retrograde signaling, while UCP1 can regulate the plant oxygen-sensing mechanism via the PRT6 N-Degron. Here, we discuss the crosstalk or the independent action of AOXs and UCPs on mitochondrial retrograde signaling associated with abiotic stress responses. We also discuss how mitochondrial function and retrograde signaling mechanisms affect chloroplast function. Additionally, we discuss how mitochondrial inner membrane transporters can mediate mitochondrial communication with other organelles. Lastly, we review how mitochondrial metabolism can be used to improve crop resilience to environmental stresses. In this respect, we particularly focus on the contribution of Brazilian research groups to advances in the topic of mitochondrial metabolism and signaling.

Understanding the evolutionary capacity of populations to adapt to novel environments is one of the major pursuits in genetics. Moreover, for plant breeding, maladaptation is the foremost barrier to capitalizing on intraspecific variation in order to develop new breeds for future climate scenarios in agriculture. Using a unique study design, we simultaneously dissected the population and quantitative genomic basis of short-term evolution in a tropical landrace of maize that was translocated to a temperate environment and phenotypically selected for adaptation in flowering time phenology. Underlying 10 generations of directional selection, which resulted in a 26-day mean decrease in female-flowering time, $60\text{\%}$ of the heritable variation mapped to $14\text{\%}$ of the genome, where, overall, alleles shifted in frequency beyond the boundaries of genetic drift in the expected direction given their flowering time effects. However, clustering these non-neutral alleles based on their profiles of frequency change revealed transient shifts underpinning a transition in genotype–phenotype relationships across generations. This was distinguished by initial reductions in the frequencies of few relatively large positive effect alleles and subsequent enrichment of many rare negative effect alleles, some of which appear to represent allelic series. With these genomic shifts, the population reached an adapted state while retaining $99\text{\%}$ of the standing molecular marker variation in the founding population. Robust selection and association mapping tests highlighted several key genes driving the phenotypic response to selection. Our results reveal the evolutionary dynamics of a finite polygenic architecture conditioning a capacity for rapid environmental adaptation in maize.

Over the past decades, advances in plant biotechnology have allowed the development of genetically modified maize varieties that have significantly impacted agricultural management and improved the grain yield worldwide. To date, genetically modified varieties represent 30% of the world’s maize cultivated area and incorporate traits such as herbicide, insect and disease resistance, abiotic stress tolerance, high yield, and improved nutritional quality. Maize transformation, which is a prerequisite for genetically modified maize development, is no longer a major bottleneck. Protocols using morphogenic regulators have evolved significantly towards increasing transformation frequency and genotype independence. Emerging technologies using either stable or transient expression and tissue culture-independent methods, such as direct genome editing using RNA-guided endonuclease system as an in vivo desired-target mutator, simultaneous double haploid production and editing/haploid-inducer-mediated genome editing, and pollen transformation, are expected to lead significant progress in maize biotechnology. This review summarises the significant advances in maize transformation protocols, technologies, and applications and discusses the current status, including a pipeline for trait development and regulatory issues related to current and future genetically modified and genetically edited maize varieties.

Recent advances in genome editing have enormously enhanced the effort to develop biotechnology crops for more sustainable food production. CRISPR/Cas, the most versatile genome-editing tool, has shown the potential to create genome modifications that range from gene knockout and gene expression pattern modulations to allele-specific changes in order to design superior genotypes harboring multiple improved agronomic traits. However, a frequent bottleneck is the delivery of CRISPR/Cas to crops that are less amenable to transformation and regeneration. Several technologies have recently been proposed to overcome transformation recalcitrance, including HI-Edit/IMGE and ectopic/transient expression of genes encoding morphogenic regulators. These technologies allow the eroding of the barriers that make crops inaccessible for genome editing. In this review, we discuss the advances in genome editing in crops with a particular focus on the use of technologies to improve complex traits such as water use efficiency, drought stress, and yield in maize.

The recalcitrance exhibited by many maize (Zea mays) genotypes to traditional genetic transformation protocols poses a significant challenge to the large-scale application of genome editing (GE) in this major crop species. Although a few maize genotypes are widely used for genetic transformation, they prove unsuitable for agronomic tests in field trials or commercial applications. This challenge is exacerbated by the predominance of transformable maize lines adapted to temperate geographies, despite a considerable proportion of maize production occurring in the tropics. Ectopic expression of morphogenic regulators (MRs) stands out as a promising approach to overcome low efficiency and genotype dependency, aiming to achieve ’universal’ transformation and GE capabilities in maize. Here, we report the successful GE of agronomically relevant tropical maize lines using a MR-based, Agrobacterium-mediated transformation protocol previously optimized for the B104 temperate inbred line. To this end, we used a CRISPR/Cas9-based construct aiming at the knockout of the VIRESCENT YELLOW-LIKE (VYL) gene, which results in an easily recognizable phenotype. Mutations at VYL were verified in protoplasts prepared from B104 and three tropical lines, regardless of the presence of a single nucleotide polymorphism (SNP) at the seed region of the VYL target site in two of the tropical lines. Three out of five tropical lines were amenable to transformation, with efficiencies reaching up to 6.63%. Remarkably, 97% of the recovered events presented indels at the target site, which were inherited by the next generation. We observed off-target activity of the CRISPR/Cas9-based construct towards the VYL paralog VYL-MODIFIER, which could be partly due to the expression of the WUSCHEL (WUS) MR. Our results demonstrate efficient GE of relevant tropical maize lines, expanding the current availability of GE-amenable genotypes of this major crop.

Quantum computers use the properties of quantum physics to perform information storage and processing operations. The operation of these computers involves concepts such as entanglement and superposition, which endow them with a great processing power that even surpasses that of the most powerful current supercomputers, while consuming significantly lower amounts of energy. The different studies analyzed in this review article suggest that quantum computing will have a deep impact in areas such as finance, logistics, transportation, space and automotive technology, materials science, energy, pharmaceutical and healthcare industry, cybersecurity, and agriculture. In digital agriculture, several applications that could be executed more efficiently in quantum computers for data processing and understanding of biological processes were identified and exemplified. These applications are grouped here into the following four areas: bioinformatics, remote sensing, climate modeling, and smart farming. This article also explores the strategic importance of mastering quantum computing, highlights some advantages in relation to classical computing, and presents a mapping of the services already available, enabling institutions to undertake strategic planning for the incorporation of quantum computing into their development processes. Finally, the challenges for the implementation of quantum computing are highlighted, along with some ongoing initiatives aimed at furthering research at the forefront of knowledge in this area applied to digital agriculture.