Co-expression networks provide insights into molecular mechanisms of postharvest temperature modulation of apple fruit to reduce superficial scald

Honaas, Loren; Hargarten, Heidi; Ficklin, Stephen P.; Hadish, John A.; Wafula, Eric; dePamphilis, Claude W.; Mattheis, James P.; Rudell, David R.

doi:10.1016/j.postharvbio.2018.09.016

Cited by 20 publications

(18 citation statements)

References 52 publications

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“…Total RNA was extracted from cryopreserved tissue of the 80 biological samples (described above) as described in Honaas and Kahn (2017) . RNA purity, quantity, and integrity was evaluated as described in Honaas et al (2019) . High quality total RNA (RIN ≥ 8.0) was used as input for Illumina TruSeq 1 (RS-122-2103, Illumina San Diego, CA United States) library preparation at the Penn State Genomics Core facility (University Park, PA, United States) as described in Honaas et al (2019) .…”

Section: Methodsmentioning

confidence: 99%

Transcriptomics of Differential Ripening in ‘d’Anjou’ Pear (Pyrus communis L.)

et al. 2021

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Estimating maturity in pome fruits is a critical task that directs virtually all postharvest supply chain decisions. This is especially important for European pear (Pyrus communis) cultivars because losses due to spoilage and senescence must be minimized while ensuring proper ripening capacity is achieved (in part by satisfying a fruit chilling requirement). Reliable methods are lacking for accurate estimation of pear fruit maturity, and because ripening is maturity dependent it makes predicting ripening capacity a challenge. In this study of the European pear cultivar ‘d’Anjou’, we sorted fruit at harvest based upon on-tree fruit position to build contrasts of maturity. Our sorting scheme showed clear contrasts of maturity between canopy positions, yet there was substantial overlap in the distribution of values for the index of absorbance difference (IAD), a non-destructive spectroscopic measurement that has been used as a proxy for pome fruit maturity. This presented an opportunity to explore a contrast of maturity that was more subtle than IAD could differentiate, and thus guided our subsequent transcriptome analysis of tissue samples taken at harvest and during storage. Using a novel approach that tests for condition-specific differences of co-expressed genes, we discovered genes with a phased character that mirrored our sorting scheme. The expression patterns of these genes are associated with fruit quality and ripening differences across the experiment. Functional profiles of these co-expressed genes are concordant with previous findings, and also offer new clues, and thus hypotheses, about genes involved in pear fruit quality, maturity, and ripening. This work may lead to new tools for enhanced postharvest management based on activity of gene co-expression modules, rather than individual genes. Further, our results indicate that modules may have utility within specific windows of time during postharvest management of ‘d’Anjou’ pear.

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Section: Methodsmentioning

confidence: 99%

Transcriptomics of Differential Ripening in ‘d’Anjou’ Pear (Pyrus communis L.)

et al. 2021

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“…Most recommendations for postharvest handling have focused on rapid cooling to reduce ripening and senescence processes [116]. Of interest to researchers in mitigating postharvest disorders include non-chemical treatments, such as CA storage, dynamic CA, and temperature conditioning-namely intermittent warming during storage, which can be as effective as conventional practices [117].…”

Section: Effects: Reduction Of Scald Incidence Bymentioning

confidence: 99%

“…More recently, insights into the molecular mechanisms of this treatment were achieved by Honaas et al [117] by analysis of transcriptional responses of 'Granny Smith' fruit peel during early phases of long-term storage and after different warming treatments. The authors described a list of candidate genes, namely MDP0000265728, MDP0000164538, MDP0000170826, and MDP0000284624 involved in promotion as well as mitigation of this disorder [117]. According to the authors, this genetic traceability is potentially useful for the development of novel postharvest technology.…”

Section: Effects: Reduction Of Scald Incidence Bymentioning

confidence: 99%

Strategies to Preserve Postharvest Quality of Horticultural Crops and Superficial Scald Control: From Diphenylamine Antioxidant Usage to More Recent Approaches

et al. 2020

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Horticultural crops are vulnerable to several disorders, which affect their physiological and organoleptic quality. For about forty years, the control of physiological disorders (such as superficial scald) in horticultural crops, particularly in fruit, was achieved through the application of the antioxidant diphenylamine (DPA), usually combined with controlled atmosphere (CA) conditions. However, identification of DPA residues and metabolites in treated fruits, associated with their toxicity, banned the use of this antioxidant in Europe. This triggered the urgent need for novel and, ideally, natural and sustainable alternatives, combined with adequate storage conditions to protect cultivars from harmful agents. This review systematizes the state-of-the-art DPA application on several fresh cultivars, such as apples, pears, and vegetables (potatoes, spinach, etc.), as well as the possible mechanisms of the action and effects of DPA, emphasizing its antioxidant properties. Alternative methods to DPA are also discussed, as well as respective effects and limitations. Recent research on scald development molecular pathways are highlighted to open new non-chemical strategies opportunities. This appraisal shows that most of the current solutions have not lead to satisfactory commercial results; thus, further research aimed to understand the mechanisms underlying postharvest disorders and to design sustainable and safe solutions to improve horticultural products storage is needed.

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“…KINCv1 has been used to find conditionspecific gene relationships (i.e. graph edges) in large and diverse cancer datasets [4], [5] and post-harvest scald in apples [6].…”

Section: B Gene Co-expression Networkmentioning

confidence: 99%

GPU Implementation of Pairwise Gaussian Mixture Models for Multi-Modal Gene Co-Expression Networks

et al. 2019

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Gene co-expression networks (GCNs) are widely used in bioinformatics research to perform system-level analyses of organisms based on the pairwise correlation between all expressed genes. For large datasets which contain samples from multiple sources, gene pairs can exhibit multiple modes of coexpression which confound typical correlation approaches. A clustering method such as Gaussian Mixture Models (GMMs) may be used to separate the modes of each gene pair in an unsupervised manner, prior to computing the correlation of each mode. However, pairwise clustering significantly increases the computational cost of constructing a GCN, as several clustering models must be evaluated for each gene pair, and the number of gene pairs grows rapidly with the number of genes. In this paper, we present a heterogeneous, high-throughput multi-CPU/GPU software package for multi-modal GCN construction, implemented in version 3 of the Knowledge Independent Network Construction (KINC) software. We determine the optimal values for several execution parameters of the GPU implementation, and we benchmark our CPU and GPU implementations for up to 8 CPUs/GPUs. Our GPU implementation achieves a 167x speedup over the corresponding CPU implementation, as well as a 500x speedup over KINCv1. INDEX TERMS Bioinformatics, Gaussian mixture model, gene co-expression network, gene expression matrix, GPU computing, high-performance computing, high-throughput computing.

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Co-expression networks provide insights into molecular mechanisms of postharvest temperature modulation of apple fruit to reduce superficial scald

Cited by 20 publications

References 52 publications

Transcriptomics of Differential Ripening in ‘d’Anjou’ Pear (Pyrus communis L.)

Transcriptomics of Differential Ripening in ‘d’Anjou’ Pear (Pyrus communis L.)

Strategies to Preserve Postharvest Quality of Horticultural Crops and Superficial Scald Control: From Diphenylamine Antioxidant Usage to More Recent Approaches

GPU Implementation of Pairwise Gaussian Mixture Models for Multi-Modal Gene Co-Expression Networks

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