Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of high-priority transcription factors (TFs), dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We identified distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficient nodes. Dynamic transcriptional regulatory modeling of integrated TF targets and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs, as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that could potentially be used in future genetic crop engineering programs.

Submerged plants can thrive entirely underwater, playing a crucial role in maintaining water quality, supporting aquatic organisms, and enhancing sediment stability. However, they face multiple challenges, including reduced light availability, fluctuating water conditions, and limited nutrient access. Despite these stresses, submerged plants demonstrate remarkable resilience through physiological and biochemical adaptations. Additionally, their interactions with microbial communities are increasingly recognized as pivotal in mitigating these environmental stresses. Understanding the diversity of these microbial communities is crucial for comprehending the complex interactions between submerged plants and their environments. This research aims to identify and screen microbes from submerged plant samples capable of producing 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase and to explore microbial diversity through metagenomic analysis. Microbes were isolated and screened for ACC deaminase production, and metagenomic techniques, including co-occurrence network analysis, were used to examine microbial diversity and interactions within the communities. ACC deaminase-producing microbes can significantly enhance plant metabolism under stress conditions. The identification of the culturable bacteria revealed that most of these microbes belong to the genera Pseudomonas, Bacillus, and Acinetobacter. A total of 177 microbial strains were cultured, with molecular identification revealing 79 reductant, 86 non-reductant, and 12 uncultured strains. Among 162 samples screened for ACC deaminase activity, 50 tested positive. To further understand microbial dynamics, samples were collected from both natural sources and artificial pond reservoirs to assess the impact of the location on flood-associated microbiomes in submerged plants. Metagenomic analysis was conducted on both the epiphytic and endophytic samples. By exploring the overall composition and dynamics of microbial communities associated with submerged plants, this research seeks to deepen our understanding of plant–microbe interactions in aquatic environments. The microbial screening helped to identify the diverse microbes associated with ACC deaminase activity in submerged plants and amplicon sequencing analysis paved the way towards identifying the impact of the location in shaping the microbiome and the diversity associated with endophytic and epiphytic microbes. Co-occurrence network analysis further highlighted the intricate interactions within these microbial communities. Notably, ACC deaminase activity was observed in plant-associated microbes across different locations, with distinct variations between epiphytic and endophytic populations as identified through co-occurrence network analysis.

Plasma treatment has emerged as a promising tool for manipulating plant microbiomes and metabolites. This review explores the diverse applications and effects of plasma on these biological systems. It is hypothesized that plasma treatment will not induce substantial changes in the composition of plant microbiomes or the concentration of plant metabolites. We delve into the mechanisms by which plasma can regulate microbial communities, enhance antimicrobial activity, and recruit beneficial microbes to mitigate stress. Furthermore, we discuss the optimization of plasma parameters for effective microbiome interaction and the role of plasmids in plant–microbe interactions. By characterizing plasmidome responses to plasma exposure and investigating transcriptional and metabolomic shifts, we provide insights into the potential of plasma as a tool for engineering beneficial plant–microbe interactions. The review presented herein demonstrates that plasma treatment induces substantial changes in both microbial community composition and metabolite levels, thereby refuting our initial hypothesis. Finally, we integrate plasmidome, transcriptome, and metabolome data to develop a comprehensive understanding of plasma’s effects on plant biology and explore future perspectives for agricultural applications.

The application of plasma technology in agriculture has emerged as a promising approach to enhance plant health and manage microbial interactions, offering potential solutions for sustainable crop production and disease control. This study contributes to this field by exploring the effects of plasma treatments on plant physiology and microbial dynamics, with a focus on their potential to improve agricultural outcomes. This investigation aims to systematically determine optimal plasma seed treatment parameters for enhancing plant vigor and promoting beneficial microbial associations while minimizing pathogenic interactions in Arabidopsis thaliana. This study focuses on understanding the effects of various plasma treatments on chlorophyll content, root length, microbial growth, and microbial quantification in plants and microbes. The treatments involve the use of an atmospheric jet plasma handheld device, a globe plasma, and a glow discharge plasma chamber with air and argon. These treatments were applied for varying time durations from 10 s to 5 min. The results demonstrated that the globe plasma treatment for 1 min significantly enhanced chlorophyll a extraction and root length, outperforming the other treatments. Additionally, the study examined the impact of plasma on plant–microbe interactions to assess whether plasma treatments affect beneficial microbes. Plasma treatments showed minimal impact on most beneficial microbe activity, though species-specific sensitivities were observed, with Pseudomonas cedrina showing moderate growth inhibition, revealing no significant disruption to their activity. The microbial quantification assays indicated that the globe plasma treatment effectively reduced microbial counts, while combined treatment with plant and microbe plasma together did not yield significant changes. Additionally, the chlorophyll estimation of plasma-treated samples indicated that the globe plasma and atmospheric jet plasma treatments were effective in enhancing chlorophyll content, whereas the combined treatment with both plant and microbe plasma did not yield significant changes. These findings suggest that plasma treatments, especially the globe plasma, are effective in improving plant health and controlling microbial activity. Future research should focus on optimizing plasma conditions, exploring the influence of plasma parameters and the underlying mechanisms, and expanding the scope to include a wider range of plant species and microbial strains to maximize the benefits of plasma technology in agriculture.

Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on the plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of highly-priority transcription factors (TFs) dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We revealed distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficients nodes. Dynamic transcriptional regulatory modeling on integrated TF-target and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that can potentially be used in future genetic crop engineering programs.