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Author: search.filters.author.Ramegowda, VenkategowdaStart date: 2020

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Now showing 1 - 6 of 6
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    An artificial neural network–based deep learning model to predict combined stress impact and interaction in plants
    (John Wiley & Sons, 2026) Priya, Piyush; Pandey, Prachi; Jain, Rubi; Kandpal, Manu; Jain, Shradha; Chaudhury, Rim; Ramegowda, Venkategowda; Senthil-Kumar, Muthappa
    Premise: Plants are frequently exposed to combinations of abiotic and biotic stresses that pose a greater threat to yield and productivity than individual stresses. However, knowledge of the impact of many stress combinations in numerous plants is limited due to the lack of experimental data, which could take decades to generate. To overcome this limitation, we utilized existing literature data from various plant species and stress combinations to derive biological inferences, thereby gaining a comprehensive understanding of plant responses through a computational tool. Methods: Public databases were used to gather literature on the impact of various abiotic and biotic stress combinations. Then, a composite artificial neural network (ANN)–based multi-target classification and regression deep learning model was developed using machine learning algorithms. Results: The model predicted the impact of stress interactions in plants, including the morphological parameters affected and percentage changes in those parameters, with an overall accuracy of 76.33%. Predicted reductions in yield were validated in rice under combined drought and heat stress. Discussion: The ANN-based model developed in this study is a valuable resource for plant researchers seeking to understand the impact of stress combinations. The tool can make use of multivariate and complex combined stress datasets.
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    Stress combinations and their interactions in crop plants
    (Springer Nature Publishing AG, 2024) Ramegowda, Venkategowda; Senthil, Alagarswamy; Senthil‑Kumar, Muthappa
    Combined stresses are a common occurrence in agricultural felds. There is a pressing need for empirical understanding of the plant responses and fnd ways to develop stress tolerant plants and stress management strategies to tackle combined stresses in the feld conditions. Here a comprehensive overview of the current understating and recent research on combined stress interactions in plants are presented. Here we comprehend the fndings from various studies focusing on diferent aspects of combined stress, including abiotic-abiotic, abiotic-biotic, and biotic-biotic stress interactions. In general, the studies discussed here highlight the escalating impact of climate change on plants, emphasizing the need for a deeper understanding of plant responses to concurrent abiotic and biotic stresses. Key fndings from the articles published in this issue, include the adverse efects of combined drought and high-temperature stress on crop growth and yield, the exacerbation of pathogen impacts under abiotic stresses, and the potential for melatonin and salicylic acid to mitigate stress-induced damage. Additionally, use of model systems for quicker understanding of combined stress responses and development of methods and technologies which can be extrapolated to crop plants are discussed. Overall, fndings from the articles from this special issue underscore the complexity of combined stress interactions in plants and highlight the importance of interdisciplinary research eforts to address the challenges posed by climate change and ensure global food security.
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    Drought attenuates plant responses to multiple rhizospheric pathogens: A study on a dry root rot-associated disease complex in chickpea fields
    (Elsevier B.V., 2023) Chilakala, Aswin Reddy; Pandey, Prachi; Durgadevi, Athimoolam; Kandpal, Manu; Patil, Basavanagouda S.; Rangappa, Krishnappa; Reddy, Puli Chandra Obul; Ramegowda, Venkategowda; Senthil-Kumar, Muthappa
    Context or problem: Root rots, a major factor contributing to yield loss in chickpea, often occur in disease complexes. Objective or research question: Plant responses to disease complexes are not well elucidated. We sought a clear understanding of a newly identified disease complex in chickpea, dry root rot (DRR)–wilt disease complex, in the field and studied the effect of drought on the severity of the complex and its effect on yield. We compared plant responses to DRR alone and the disease complex under drought and determined the phytohormones involved in plant defense against the disease complex. Methods: We compared the effect of 14 environments (two soil moisture regimes at seven locations) on the incidence of the disease complex and yield loss in four chickpea genotypes. We also studied the effect of drought on rhizospheric and root endo-microbial communities by whole-genome and metagenomic sequencing and performed LC-MS-based phytohormonal profiling of chickpea roots. Results: Soil moisture and plant genetic variability were critical in modulating disease incidence in field conditions. DRR was the primary driver of the disease complex under drought stress. Drought aggravated the yield reductions caused by the disease complex from 35% to 60% in susceptible genotypes. Further, drought-tolerant genotypes performed better under combined disease complex infection and drought stress and exhibited lesser yield losses than susceptible genotypes. Pathogenic fungi such as Macrophomina phaseolina, Fusarium oxysporum, and Rhizoctonia solani were enriched in the chickpea rhizosphere, and M. phaseolina was predominant in infected chickpea roots under both well-watered and drought conditions. Symbiotic associations of chickpea with nitrogen-fixing bacteria were suppressed under drought stress. Abscisic acid, jasmonic acid, and salicylic acid were found to be involved in defense against the disease complex across various stages of plant growth. Implications or significance: We highlight the interaction between drought and soil pathogens affecting chickpea yield and suggest the utilization of drought-tolerant root traits as donor traits for improving combined stress resistance. We also demonstrate growth stage–dependent phytohormonal responses elicited by DRR and the DRR–wilt disease complex. The identification and management of root rots is essential, and our findings offer valuable new insights into a lesser-known but highly significant disease complex of chickpea. Data availability statement: Manuscript data is available at Supplementary File S1. The soil microbe whole-genome and metagenome and root-microbe 16 S and ITS sequencing data are available at NCBI PRJNA871091 and PRJNA895851.
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    Combined drought and heat stress influences the root water relation and determine the dry root rot disease development under field conditions: A study using contrasting chickpea genotypes
    (Frontiers Media S.A., 2022) Chilakala, Aswin Reddy; Mali, Komal Vitthalrao; Irulappan, Vadivelmurugan; Patil, Basavanagouda S.; Pandey, Prachi; Rangappa, Krishnappa; Ramegowda, Venkategowda; Kumar, M. Nagaraj; Puli, Chandra Obul Reddy; Mohan-Raju, Basavaiah; Senthil-Kumar, Muthappa
    Abiotic stressors such as drought and heat predispose chickpea plants to pathogens of key importance leading to significant crop loss under field conditions. In this study, we have investigated the influence of drought and high temperature on the incidence and severity of dry root rot disease (caused by Macrophomina phaseolina) in chickpea, under extensive on- and off-season field trials and greenhouse conditions. We explored the association between drought tolerance and dry root rot resistance in two chickpea genotypes, ICC 4958 and JG 62, with contrasting resistance to dry root rot. In addition, we extensively analyzed various patho-morphological and root architecture traits altered by combined stresses under field and greenhouse conditions in these genotypes. We further observed the role of edaphic factors in dry root rot incidence under field conditions. Altogether, our results suggest a strong negative correlation between the plant water relations and dry root rot severity in chickpeas, indicating an association between drought tolerance and dry root rot resistance. Additionally, the significant role of heat stress in altering the dynamics of dry root rot and the importance of combinatorial screening of chickpea germplasm for dry root rot resistance, drought, and heat stress have been revealed.
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    High-throughput analysis of gene function under multiple abiotic stresses using leaf disks from silenced plants
    (Springer Nature Publishing AG, 2022) Yamunarani, Ramegowda; Ramegowda, Venkategowda; Senthil-Kumar, Muthappa; Mysore, Kirankumar S.
    The high throughputness and affordability of “omics” technologies is leading to the identification of a large number of abiotic stress genes, with many of them responsive to multiple stresses. In vivo functional characterization of these genes under multiple stresses is challenging but essential to develop resilient crops for the changing climate. Here we describe a high-throughput Virus-Induced Gene Silencing-based methodology for functional analysis of genes under multiple abiotic stresses using leaf disks. Leaves with maximal silencing, which is localized to only a few leaves and to a short period, can be effectively used for multiple stress imposition and stress affect quantification.
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    Low soil moisture predisposes field-grown chickpea plants to dry root rot disease: evidence from simulation modeling and correlation analysis
    (Springer Nature Publishing AG, 2021) Sinha, Ranjita; Irulappan, Vadivelmurugan; Patil, Basavanagouda S.; Reddy, Puli Chandra Obul; Ramegowda, Venkategowda; Mohan‑Raju, Basavaiah; Rangappa, Krishnappa; Singh, Harvinder Kumar; Bhartiya, Sharad; Senthil-Kumar, Muthappa
    Rhizoctonia bataticola causes dry root rot (DRR), a devastating disease in chickpea (Cicer arietinum). DRR incidence increases under water defcit stress and high temperature. However, the roles of other edaphic and environmental factors remain unclear. Here, we performed an artifcial neural network (ANN)-based prediction of DRR incidence considering DRR incidence data from previous reports and weather factors. ANN-based prediction using the backpropagation algorithm showed that the combination of total rainfall from November to January of the chickpea-growing season and average maximum temperature of the months October and November is crucial in determining DRR occurrence in chickpea felds. The prediction accuracy of DRR incidence was 84.6% with the validation dataset. Field trials at seven diferent locations in India with combination of low soil moisture and pathogen stress treatments confrmed the impact of low soil moisture on DRR incidence under diferent agroclimatic zones and helped in determining the correlation of soil factors with DRR incidence. Soil phosphorus, potassium, organic carbon, and clay content were positively correlated with DRR incidence, while soil silt content was negatively correlated. Our results establish the role of edaphic and other weather factors in chickpea DRR disease incidence. Our ANN-based model will allow the location-specifc prediction of DRR incidence, enabling efcient decision-making in chickpea cultivation to minimize yield loss.