Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses.

Stress Knowledge Map (SKM, https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs describing current knowledge of biochemical, signalling, and regulatory molecular interactions in plants: a highly curated model of plant stress signalling (PSS, 543 reactions) and a large comprehensive knowledge network (CKN, 488,390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entrypoint for plant stress response investigations and the related growth trade-offs as well as interactive exploration of current knowledge. PSS is also formulated as qualitative and quantitative models for systems biology, and thus represents a starting point of a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation, design of validation experiments, or to gain new insights into experimental observations in plant biology.

Cell-type proteomic and metabolomic resolution of early and late grain filling stages of wheat endosperm.

The nutritional value of wheat grains, particularly their protein and metabolite composition, is a result of the grain-filling process, especially in the endosperm. Here, we employ laser microdissection (LMD) combined with shotgun proteomics and metabolomics to generate a cell type-specific proteome and metabolome inventory of developing wheat endosperm at the early (15 DAA) and late (26 DAA) grain-filling stages. We identified 1803 proteins and 41 metabolites from four different cell types (aleurone (AL), sub-aleurone (SA), starchy endosperm (SE) and endosperm transfer cells (ETCs). Differentially expressed proteins were detected, 67 in the AL, 31 in the SA, 27 in the SE and 50 in the ETCs between these two-time points. Cell-type accumulation of specific SUT and GLUT transporters, sucrose converting and starch biosynthesis enzymes correlate well with the respective sugar metabolites, suggesting sugar upload and starch accumulation via nucellar projection and ETC at 15 DAA in contrast to the later stage at 26 DAA. Changes in various protein levels between AL, SA and ETC support this metabolic switch from 15 to 26 DAA. The distinct spatial and temporal abundances of proteins and metabolites revealed a contrasting activity of nitrogen assimilation pathways, e.g. for GOGAT, GDH and glutamic acid, in the different cell types from 15 to 26 DAA, which can be correlated with specific protein accumulation in the endosperm. The integration of cell-type specific proteome and metabolome data revealed a complex metabolic interplay of the different cell types and a functional switch during grain development and grain-filling processes.

Simple prediction of COVID-19 convalescent plasma units with high levels of neutralization antibodies.

BACKGROUND: Hyperimmune convalescent COVID-19 plasma (CCP) containing anti-SARS-CoV-2 neutralizing antibodies (NAbs) was proposed as a therapeutic option for patients early in the new coronavirus disease pandemic. The efficacy of this therapy depends on the quantity of neutralizing antibodies (NAbs) in the CCP units, with titers ≥ 1:160 being recommended. The standard neutralizing tests (NTs) used for determining appropriate CCP donors are technically demanding and expensive and take several days. We explored whether they could be replaced by high-throughput serology tests and a set of available clinical data. METHODS: Our study included 1302 CCP donors after PCR-confirmed COVID-19 infection. To predict donors with high NAb titers, we built four (4) multiple logistic regression models evaluating the relationships of demographic data, COVID-19 symptoms, results of various serological testing, the period between disease and donation, and COVID-19 vaccination status. RESULTS: The analysis of the four models showed that the chemiluminescent microparticle assay (CMIA) for the quantitative determination of IgG Abs to the RBD of the S1 subunit of the SARS-CoV-2 spike protein was enough to predict the CCP units with a high NAb titer. CCP donors with respective results > 850 BAU/ml SARS-CoV-2 IgG had a high probability of attaining sufficient NAb titers. Including additional variables such as donor demographics, clinical symptoms, or time of donation into a particular predictive model did not significantly increase its sensitivity and specificity. CONCLUSION: A simple quantitative serological determination of anti-SARS-CoV-2 antibodies alone is satisfactory for recruiting CCP donors with high titer NAbs.

pISA-tree - a data management framework for life science research projects using a standardised directory tree.

We developed pISA-tree, a straightforward and flexible data management solution for organisation of life science project-associated research data and metadata. pISA-tree was initiated by end-user requirements thus its strong points are practicality and low maintenance cost. It enables on-the-fly creation of enriched directory tree structure (project/Investigation/Study/Assay) based on the ISA model, in a standardised manner via consecutive batch files. Templates-based metadata is generated in parallel at each level enabling guided submission of experiment metadata. pISA-tree is complemented by two R packages, pisar and seekr. pisar facilitates integration of pISA-tree datasets into bioinformatic pipelines and generation of ISA-Tab exports. seekr enables synchronisation with the FAIRDOMHub repository. Applicability of pISA-tree was demonstrated in several national and international multi-partner projects. The system thus supports findable, accessible, interoperable and reusable (FAIR) research and is in accordance with the Open Science initiative. Source code and documentation of pISA-tree are available at https://github.com/NIB-SI/pISA-tree .

Transcriptional deregulation of stress-growth balance in Nicotiana benthamiana biofactories producing insect sex pheromones.

Plant biofactories are a promising platform for sustainable production of high-value compounds, among which are insect sex pheromones, a green alternative to conventional insecticides in agriculture. Recently, we have constructed transgenic Nicotiana benthamiana plants ("Sexy Plants", SxP) that successfully produce a blend of moth (Lepidoptera) sex pheromone compounds (Z)-11-hexadecen-1-ol and (Z)-11-hexadecenyl acetate. However, efficient biosynthesis of sex pheromones resulted in growth and developmental penalty, diminishing the potential for commercial use of SxP in biomanufacturing. To gain insight into the underlying molecular responses, we analysed the whole-genome transcriptome and evaluated it in relation to growth and pheromone production in low- and high-producing transgenic plants of v1.0 and v1.2 SxP lines. In our study, high-producing SxPv1.2 plants accumulated the highest amounts of pheromones but still maintained better growth compared to v1.0 high producers. For an in-depth biological interpretation of the transcriptomic data, we have prepared a comprehensive functional N. benthamiana genome annotation as well as gene translations to Arabidopsis thaliana, enabling functional information transfer by using Arabidopsis knowledge networks. Differential gene expression analysis, contrasting pheromone producers to wild-type plants, revealed that while only a few genes were differentially regulated in low-producing plants, high-producing plants exhibited vast transcriptional reprogramming. They showed signs of stress-like response, manifested as downregulation of photosynthesis-related genes and significant differences in expression of hormonal signalling and secondary metabolism-related genes, the latter presumably leading to previously reported volatilome changes. Further network analyses confirmed stress-like response with activation of jasmonic acid and downregulation of gibberellic acid signalling, illuminating the possibility that the observed growth penalty was not solely a consequence of a higher metabolic burden imposed upon constitutive expression of a heterologous biosynthetic pathway, but rather the result of signalling pathway perturbation. Our work presents an example of comprehensive transcriptomic analyses of disadvantageous stress signalling in N. benthamiana biofactory that could be applied to other bioproduction systems.

PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases.

PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers (microRNA and transcription factors, RNA-binding proteins) to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at https://paintomics.org/.

The Influence of Heterochronic Non-Myeloablative Bone Marrow Transplantation on the Immune System, Frailty, General Health, and Longevity of Aged Murine Recipients.

The stem cell theory of aging postulates that stem cells become inefficient at maintaining the original functions of the tissues. We, therefore, hypothesized that transplanting young bone marrow (BM) to old recipients would lead to rejuvenating effects on immunity, followed by improved general health, decreased frailty, and possibly life span extension. We developed a murine model of non-myeloablative heterochronic BM transplantation in which old female BALB/c mice at 14, 16, and 18(19) months of age received altogether 125.1 ± 15.6 million nucleated BM cells from young male donors aged 7-13 weeks. At 21 months, donor chimerism was determined, and the immune system's innate and adaptive arms were analyzed. Mice were then observed for general health and frailty until spontaneous death, when their lifespan, post-mortem examinations, and histopathological changes were recorded. The results showed that the old mice developed on average 18.7 ± 9.6% donor chimerism in the BM and showed certain improvements in their innate and adaptive arms of the immune system, such as favorable counts of neutrophils in the spleen and BM, central memory Th cells, effector/effector memory Th and Tc cells in the spleen, and B1a and B1b cells in the peritoneal cavity. Borderline enhanced lymphocyte proliferation capacity was also seen. The frailty parameters, pathomorphological results, and life spans did not differ significantly in the transplanted vs. control group of mice. In conclusion, although several favorable effects are obtained in our heterochronic non-myeloablative transplantation model, additional optimization is needed for better rejuvenation effects.

From Causal Networks to Adverse Outcome Pathways: A Developmental Neurotoxicity Case Study.

The last decade has seen the adverse outcome pathways (AOP) framework become one of the most powerful tools in chemical risk assessment, but the development of new AOPs remains a slow and manually intensive process. Here, we present a faster approach for AOP generation, based on manually curated causal toxicological networks. As a case study, we took a recently published zebrafish developmental neurotoxicity network, which contains causally connected molecular events leading to neuropathologies, and developed two new adverse outcome pathways: Inhibition of Fyna (Src family tyrosine kinase A) leading to increased mortality via decreased eye size (AOP 399 on AOP-Wiki) and GSK3beta (Glycogen synthase kinase 3 beta) inactivation leading to increased mortality via defects in developing inner ear (AOP 410). The approach consists of an automatic separation of the toxicological network into candidate AOPs, filtering the AOPs according to available evidence and length as well as manual development of new AOPs and weight-of-evidence evaluation. The semiautomatic approach described here provides a new opportunity for fast and straightforward AOP development based on large network resources.

Direct regulation of shikimate, early phenylpropanoid, and stilbenoid pathways by Subgroup 2 R2R3-MYBs in grapevine.

The stilbenoid pathway is responsible for the production of resveratrol in grapevine (Vitis vinifera L.). A few transcription factors (TFs) have been identified as regulators of this pathway but the extent of this control has not been deeply studied. Here we show how DNA affinity purification sequencing (DAP-Seq) allows for the genome-wide TF-binding site interrogation in grape. We obtained 5190 and 4443 binding events assigned to 4041 and 3626 genes for MYB14 and MYB15, respectively (approximately 40% of peaks located within −10 kb of transcription start sites). DAP-Seq of MYB14/MYB15 was combined with aggregate gene co-expression networks (GCNs) built from more than 1400 transcriptomic datasets from leaves, fruits, and flowers to narrow down bound genes to a set of high confidence targets. The analysis of MYB14, MYB15, and MYB13, a third uncharacterized member of Subgroup 2 (S2), showed that in addition to the few previously known stilbene synthase (STS) targets, these regulators bind to 30 of 47 STS family genes. Moreover, all three MYBs bind to several PAL, C4H, and 4CL genes, in addition to shikimate pathway genes, the WRKY03 stilbenoid co-regulator and resveratrol-modifying gene candidates among which ROMT2-3 were validated enzymatically. A high proportion of DAP-Seq bound genes were induced in the activated transcriptomes of transient MYB15-overexpressing grapevine leaves, validating our methodological approach for delimiting TF targets. Overall, Subgroup 2 R2R3-MYBs appear to play a key role in binding and directly regulating several primary and secondary metabolic steps leading to an increased flux towards stilbenoid production. The integration of DAP-Seq and reciprocal GCNs offers a rapid framework for gene function characterization using genome-wide approaches in the context of non-model plant species and stands up as a valid first approach for identifying gene regulatory networks of specialized metabolism.

RNA Sequencing Analyses for Deciphering Potato Molecular Responses.

Understanding the molecular mechanisms of potato development and responses to environmental stressors is of utmost importance for achieving stable crop yields. RNA sequencing (RNA-Seq) provides an insight into responses of all of the organism genes to the environmental and developmental cues and thus provides insights into underlying modes of action. In this chapter, we guide a researcher through some of the most important steps in the analysis of transcriptomics data. The initial topic of experimental design is followed by a more wet-lab-oriented section on RNA-Seq sample preparation. Next, we present intermediate steps of data retrieval, quality control, mapping, and differential expression of the dataset and a section on how to expose your data to the public (i.e., public repositories) and make it findable, accessible, interoperable, and reusable (FAIR). In the last four sections, we describe specific tools or Web applications, which ease the exploration of generated results in the context of their gene function and network-based visualizations, specifically GoMapMan, GSEA, DiNAR, and Biomine Explorer. All sections are accompanied by potato dataset examples and include general hints and tricks, as well as potato specificities that one should be aware of.

Prunus persica plant endogenous peptides PpPep1 and PpPep2 cause PTI-like transcriptome reprogramming in peach and enhance resistance to Xanthomonas arboricola pv. pruni.

BACKGROUND: Rosaceae species are economically highly relevant crops. Their cultivation systems are constrained by phytopathogens causing severe losses. Plants respond to invading pathogens through signaling mechanisms, a component of which are of them being plant elicitor peptides (Peps). Exogenous application of Peps activates defense mechanisms and reduces the symptoms of pathogen infection in various pathosystems. We have previously identified the Rosaceae Peps and showed, in an ex vivo system, that their topical application efficiently enhanced resistance to the bacterial pathogen Xanthomonas arboricola pv. pruni (Xap). RESULTS: Here we demonstrate the effectiveness of Prunus persica peptides PpPep1 and PpPep2 in protecting peach plants in vivo at nanomolar doses, with 40% reduction of the symptoms following Xap massive infection. We used deep sequencing to characterize the transcriptomic response of peach plants to preventive treatment with PpPep1 and PpPep2. The two peptides induced highly similar massive transcriptomic reprogramming in the plant. One hour, 1 day and 2 days after peptide application there were changes in expression in up to 8% of peach genes. We visualized the transcriptomics dynamics in a background knowledge network and detected the minor variations between plant responses to PpPep1 and PpPep2, which might explain their slightly different protective effects. By designing a P. persica Pep background knowledge network, comparison of our data and previously published immune response datasets was possible. CONCLUSIONS: Topical application of P. persica Peps mimics the PTI natural response and protects plants against massive Xap infection. This makes them good candidates for deployment of natural, targeted and environmental-friendly strategies to enhance resistance in Prunus species and prevent important biotic diseases.

New Cross-Talks between Pathways Involved in Grapevine Infection with 'Candidatus Phytoplasma solani' Revealed by Temporal Network Modelling.

Understanding temporal biological phenomena is a challenging task that can be approached using network analysis. Here, we explored whether network reconstruction can be used to better understand the temporal dynamics of bois noir, which is associated with 'Candidatus Phytoplasma solani', and is one of the most widespread phytoplasma diseases of grapevine in Europe. We proposed a methodology that explores the temporal network dynamics at the community level, i.e., densely connected subnetworks. The methodology offers both insights into the functional dynamics via enrichment analysis at the community level, and analyses of the community dissipation, as a measure that accounts for community degradation. We validated this methodology with cases on experimental temporal expression data of uninfected grapevines and grapevines infected with 'Ca. P. solani'. These data confirm some known gene communities involved in this infection. They also reveal several new gene communities and their potential regulatory networks that have not been linked to 'Ca. P. solani' to date. To confirm the capabilities of the proposed method, selected predictions were empirically evaluated.

Differential Response of Grapevine to Infection with 'Candidatus Phytoplasma solani' in Early and Late Growing Season through Complex Regulation of mRNA and Small RNA Transcriptomes.

Bois noir is the most widespread phytoplasma grapevine disease in Europe. It is associated with 'Candidatus Phytoplasma solani', but molecular interactions between the causal pathogen and its host plant are not well understood. In this work, we combined the analysis of high-throughput RNA-Seq and sRNA-Seq data with interaction network analysis for finding new cross-talks among pathways involved in infection of grapevine cv. Zweigelt with 'Ca. P. solani' in early and late growing seasons. While the early growing season was very dynamic at the transcriptional level in asymptomatic grapevines, the regulation at the level of small RNAs was more pronounced later in the season when symptoms developed in infected grapevines. Most differentially expressed small RNAs were associated with biotic stress. Our study also exposes the less-studied role of hormones in disease development and shows that hormonal balance was already perturbed before symptoms development in infected grapevines. Analysis at the level of communities of genes and mRNA-microRNA interaction networks revealed several new genes (e.g., expansins and cryptdin) that have not been associated with phytoplasma pathogenicity previously. These novel actors may present a new reference framework for research and diagnostics of phytoplasma diseases of grapevine.

Cultivar-specific transcriptome and pan-transcriptome reconstruction of tetraploid potato.

Although the reference genome of Solanum tuberosum Group Phureja double-monoploid (DM) clone is available, knowledge on the genetic diversity of the highly heterozygous tetraploid Group Tuberosum, representing most cultivated varieties, remains largely unexplored. This lack of knowledge hinders further progress in potato research. In conducted investigation, we first merged and manually curated the two existing partially-overlapping DM genome-based gene models, creating a union of genes in Phureja scaffold. Next, we compiled available and newly generated RNA-Seq datasets (cca. 1.5 billion reads) for three tetraploid potato genotypes (cultivar Désirée, cultivar Rywal, and breeding clone PW363) with diverse breeding pedigrees. Short-read transcriptomes were assembled using several de novo assemblers under different settings to test for optimal outcome. For cultivar Rywal, PacBio Iso-Seq full-length transcriptome sequencing was also performed. EvidentialGene redundancy-reducing pipeline complemented with in-house developed scripts was employed to produce accurate and complete cultivar-specific transcriptomes, as well as to attain the pan-transcriptome. The generated transcriptomes and pan-transcriptome represent a valuable resource for potato gene variability exploration, high-throughput omics analyses, and breeding programmes.

Enabling reusability of plant phenomic datasets with MIAPPE 1.1.

Enabling data reuse and knowledge discovery is increasingly critical in modern science, and requires an effort towards standardising data publication practices. This is particularly challenging in the plant phenotyping domain, due to its complexity and heterogeneity. We have produced the MIAPPE 1.1 release, which enhances the existing MIAPPE standard in coverage, to support perennial plants, in structure, through an explicit data model, and in clarity, through definitions and examples. We evaluated MIAPPE 1.1 by using it to express several heterogeneous phenotyping experiments in a range of different formats, to demonstrate its applicability and the interoperability between the various implementations. Furthermore, the extended coverage is demonstrated by the fact that one of the datasets could not have been described under MIAPPE 1.0. MIAPPE 1.1 marks a major step towards enabling plant phenotyping data reusability, thanks to its extended coverage, and especially the formalisation of its data model, which facilitates its implementation in different formats. Community feedback has been critical to this development, and will be a key part of ensuring adoption of the standard.

Physiological and Proteomic Signatures Reveal Mechanisms of Superior Drought Resilience in Pearl Millet Compared to Wheat.

Presently, pearl millet and wheat are belonging to highly important cereal crops. Pearl millet, however, is an under-utilized crop, despite its superior resilience to drought and heat stress in contrast to wheat. To investigate this in more detail, we performed comparative physiological screening and large scale proteomics of drought stress responses in drought-tolerant and susceptible genotypes of pearl millet and wheat. These chosen genotypes are widely used in breeding and farming practices. The physiological responses demonstrated large differences in the regulation of root morphology and photosynthetic machinery, revealing a stay-green phenotype in pearl millet. Subsequent tissue-specific proteome analysis of leaves, roots and seeds led to the identification of 12,558 proteins in pearl millet and wheat under well-watered and stress conditions. To allow for this comparative proteome analysis and to provide a platform for future functional proteomics studies we performed a systematic phylogenetic analysis of all orthologues in pearl millet, wheat, foxtail millet, sorghum, barley, brachypodium, rice, maize, Arabidopsis, and soybean. In summary, we define (i) a stay-green proteome signature in the drought-tolerant pearl millet phenotype and (ii) differential senescence proteome signatures in contrasting wheat phenotypes not capable of coping with similar drought stress. These different responses have a significant effect on yield and grain filling processes reflected by the harvest index. Proteome signatures related to root morphology and seed yield demonstrated the unexpected intra- and interspecies-specific biochemical plasticity for stress adaptation for both pearl millet and wheat genotypes. These quantitative reference data provide tissue- and phenotype-specific marker proteins of stress defense mechanisms which are not predictable from the genome sequence itself and have potential value for marker-assisted breeding beyond genome assisted breeding.

Multiomics analysis of tolerant interaction of potato with potato virus Y.

Potato virus Y (PVY) is the most economically important viral pathogen of potato worldwide. Different potato cultivars react to the pathogen differently, resulting in resistant, tolerant or disease outcome of the interaction. Here we focus on tolerant interaction between potato cv. Désirée and PVYNTN. To capture the response in its full complexity, we analyzed the dynamic changes on multiple molecular levels, including transcriptomics, sRNAomics, degradomics, proteomics and hormonomics. The analysis was complemented by the measurements of viral accumulation, photosynthetic activity and phenotypisation of the symptoms. Besides cv. Désirée we also studied its transgenic counterpart depleted for the accumulation of salicylic acid (NahG-Désirée). This multiomics analysis provides better insights into the mechanisms leading to tolerant response of potato to viral infection and can be used as a base in further studies of plant immunity regulation.

Interactive exploration of heterogeneous biological networks with Biomine Explorer.

SUMMARY: Biomine Explorer is a web application that enables interactive exploration of large heterogeneous biological networks constructed from selected publicly available biological knowledge sources. It is built on top of Biomine, a system which integrates cross-references from several biological databases into a large heterogeneous probabilistic network. Biomine Explorer offers user-friendly interfaces for search, visualization, exploration and manipulation as well as public and private storage of discovered subnetworks with permanent links suitable for inclusion into scientific publications. A JSON-based web API for network search queries is also available for advanced users. AVAILABILITY AND IMPLEMENTATION: Biomine Explorer is implemented as a web application, which is publicly available at https://biomine.ijs.si. Registration is not required but registered users can benefit from additional features such as private network repositories.

DiNAR: revealing hidden patterns of plant signalling dynamics using Differential Network Analysis in R.

BACKGROUND: Progress in high-throughput molecular methods accompanied by more complex experimental designs demands novel data visualisation solutions. To specifically answer the question which parts of the specifical biological system are responding in particular perturbation, integrative approach in which experimental data are superimposed on a prior knowledge network is shown to be advantageous. RESULTS: We have developed DiNAR, Differential Network Analysis in R, a user-friendly application with dynamic visualisation that integrates multiple condition high-throughput data and extensive biological prior knowledge. Implemented differential network approach and embedded network analysis allow users to analyse condition-specific responses in the context of topology of interest (e.g. immune signalling network) and extract knowledge concerning patterns of signalling dynamics (i.e. rewiring in network structure between two or more biological conditions). We validated the usability of software on the Arabidopsis thaliana and Solanum tuberosum datasets, but it is set to handle any biological instances. CONCLUSIONS: DiNAR facilitates detection of network-rewiring events, gene prioritisation for future experimental design and allows capturing dynamics of complex biological system. The fully cross-platform Shiny App is hosted and freely available at https://nib-si.shinyapps.io/DiNAR. The most recent version of the source code is available at https://github.com/NIB-SI/DiNAR/ with a DOI 10.5281/zenodo.1230523 of the archived version in Zenodo.

Network Modeling Unravels Mechanisms of Crosstalk between Ethylene and Salicylate Signaling in Potato.

To develop novel crop breeding strategies, it is crucial to understand the mechanisms underlying the interaction between plants and their pathogens. Network modeling represents a powerful tool that can unravel properties of complex biological systems. In this study, we aimed to use network modeling to better understand immune signaling in potato (Solanum tuberosum). For this, we first built on a reliable Arabidopsis (Arabidopsis thaliana) immune signaling model, extending it with the information from diverse publicly available resources. Next, we translated the resulting prior knowledge network (20,012 nodes and 70,091 connections) to potato and superimposed it with an ensemble network inferred from time-resolved transcriptomics data for potato. We used different network modeling approaches to generate specific hypotheses of potato immune signaling mechanisms. An interesting finding was the identification of a string of molecular events illuminating the ethylene pathway modulation of the salicylic acid pathway through Nonexpressor of PR Genes1 gene expression. Functional validations confirmed this modulation, thus supporting the potential of our integrative network modeling approach for unraveling molecular mechanisms in complex systems. In addition, this approach can ultimately result in improved breeding strategies for potato and other sensitive crops.