Transcriptional Analysis of Distant Signaling Induced by Insect Elicitors and Mechanical Wounding in Zea mays

When plants are under insect herbivore attack defensive measures are activated not only locally, but also in distant and systemic tissues. While insect elicitors (IE) abundant in the oral secretions of the attacking herbivore are essential in the regulation of induced defenses, little is known about their effects on systemic defense signaling in maize (Zea mays). The goal of this study was therefore to identify genetic markers that can be used to further characterize local and systemic signaling events induced by IE or mechanical wounding (MW). We selected genes for this study based on their putative involvement in signaling (allene oxide synthase), regulation of gene expression (transcription factor MYC7), and in direct defenses (ribosome inactivating protein) and analyzed their expression in different sections of the treated leaf as well as in systemic parts of the same plant. We found the most significant transcript accumulation of the selected genes after treatment with insect elicitors in those parts with increased JA levels. Additionally, treatment with IE did also induce the accumulation of MYC7 transcripts in basal parts of the treated leaf and systemically. MW, in contrast, did induce RIP and AOS only locally, but not MYC7. This local suppression of MYC7 was further studied by adding glutathione (GSH) as an electron donor to MW plants to quench putative α, β-unsaturated carbonyls, which build up to significant levels around the damage site. Indeed, GSH-treated MW plants accumulated MYC7 at the damage site and also produced more volatiles, suggesting a putative redox-regulatory element being involved in the suppression of MYC7. The results presented herein provide evidence for the specific induction of distant signaling events triggered by IE, most likely through electric signaling. Additionally, a putative role for MW-induced α, β-unsaturated carbonyls in the transcriptional regulation of defense genes was discovered

Fungal volatile organic compounds: emphasis on their plant growth-promoting

Fungal volatile organic compounds (VOCs) commonly formed bioactive interface between plants and countless of microorganisms on the above- and below-ground plant-fungus interactions. Fungal-plant interactions symbolize intriguingly biochemical complex and challenging scenarios that are discovered by metabolomic approaches. Remarkably secondary metabolites (SMs) played a significant role in the virulence and existence with plant-fungal pathogen interaction; only 25% of the fungal gene clusters have been functionally identified, even though these numbers are too low as compared with plant secondary metabolites. The current insights on fungal VOCs are conducted under lab environments and to apply small numbers of microbes; its molecules have significant effects on growth, development, and defense system of plants. Many fungal VOCs supported dynamic processes, leading to countless interactions between plants, antagonists, and mutualistic symbionts. The fundamental role of fungal VOCs at field level is required for better understanding, so more studies will offer further constructive scientific evidences that can show the cost-effectiveness of ecofriendly and ecologically produced fungal VOCs for crop welfare

An enigma in the genetic responses of plants to salt stresses

Soil salinity is one of the main factors restricting crop production throughout the world. Various salt tolerance traits and the genes controlling these traits are responsible for coping with salinity stress in plants. These coping mechanisms include osmotic tolerance, ion exclusion, and tissue tolerance. Plants exposed to salinity stress sense the stress conditions, convey specific stimuli signals, and initiate responses against stress through the activation of tolerance mechanisms that include multiple genes and pathways. Advances in our understanding of the genetic responses of plants to salinity and their connections with yield improvement are essential for attaining sustainable agriculture. Although a wide range of studies have been conducted that demonstrate genetic variations in response to salinity stress, numerous questions need to be answered to fully understand plant tolerance to salt stress. This chapter provides an overview of previous studies on the genetic control of salinity stress in plants, including signaling, tolerance mechanisms, and the genes, pathways, and epigenetic regulators necessary for plant salinity tolerance

noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimizing technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches

noisyR: Enhancing biological signal in sequencing datasets by characterising random technical noise

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs samplespecific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimising technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches