1 Introduction

Rice (Oryza sativa L.) is a staple food for more than half of the world's population, making it a critical crop for global food security. Increased rice production plays an extremely important role in ensuring food security and people's living standards. However, rice yields are highly susceptible to environmental stresses, particularly drought, which is one of the most severe limitations on rice productivity (Hamzelou et al., 2020). Drought stress affects approximately 50% of the world's rice production, leading to significant yield losses (Sircar and Parekh, 2018). Understanding the molecular mechanisms underlying drought tolerance in rice is essential for developing drought-resistant rice varieties that can withstand water-deficit conditions. Studying its response to drought stress can provide valuable insights into the adaptive mechanisms and potential targets for genetic improvement (Agrawal et al., 2016).

Proteomics is the science of studying the protein composition of cells, tissues or organisms. With the development of science and technology, proteomics methods have changed to high-throughput methods such as tissue microarray (TMA), protein pathway array and mass spectrometry (Chandramouli et al., 2009). However, Protein-Protein Interaction Network Analysis (PPI Network Analysis) is one of the important research components of proteomics. PPI networks are crucial for understanding the complex biological processes that control cellular responses to environmental stresses. PPIs facilitate the coordination of various cellular functions by enabling proteins to interact and form functional complexes. For drought stress, PPI networks can reveal key regulatory proteins and pathways involved in stress response, signal transduction (Paul et al., 2015; Usman et al., 2020). Proteomic approaches, such as two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and mass spectrometry (MS), have advanced our ability to analyze these networks, providing a comprehensive view to understand the proteomic changes under drought conditions (Salekdeh et al., 2002). Nowadays by integrating bioinformatics tools, researchers can analyze PPI networks to identify critical proteins that interactions that contribute to drought tolerance.

This review combines findings from proteomics and bioinformatics studies to comprehensively analyze the PPI network of rice under drought stress, summarizes the current knowledge on the effects of drought stress on rice at the proteomic level, clarifies the construction and analysis of the PPI network under drought stress conditions, identifies key proteins and interactions involved in the drought stress response of rice, and discusses the potential application of these findings to breeding programs and biotechnological interventions to enhance drought resistance. Through this review, we hope to bridge the gap between proteomics data and practical applications in rice improvement and provide directions for future research and development in this critical area.

2 Drought Stress Responses in Rice

2.1 Physiological and biochemical responses to drought stress

Drought stress significantly impacts the physiological and biochemical processes in rice. One of the primary physiological responses is the reduction of leaf water content, which can lead to stomatal closure to minimize water loss through transpiration. This process, however, also limits CO₂ uptake, thereby reducing photosynthesis and growth (Maksup et al., 2014). Additionally, drought stress induces the accumulation of osmo protectants such as proline and soluble sugars, which help maintain cell turgor and protect cellular structures (Hamzelou et al., 2020). In response to drought stress, plants accumulate organic and inorganic solute, achieve osmotic adaptations by accumulating osmoprotectants and increase antioxidant activity for scavenging Reactive Oxygen Species (ROS) to improve drought tolerance.

2.2 Molecular mechanisms responses to drought stress

At the molecular level, drought stress triggers a complex network of gene expression changes. Key molecular mechanisms is the upregulated ABA (abscisic acid)-dependent signaling pathway with energy metabolic processes (Sircar and Parekh, 2019; Hsu et al., 2021). Various transcription factors (TFs) such as bHLH (basic helix-loop-helix) and bZIP (basic leucine zipper), MYB (myeloblastosis), are involved in the regulation of ABAdependent signaling pathways and play a major role in the stress response by regulating the expression of many downstream drought-responsive genes (Peleg et al., 2011; Soltanpour et al., 2022). Furthermore, drought stress induces the expression of heat shock proteins (HSPs), late embryogenesis abundant (LEAs), calmodulin-like protein (CML) and other stress-related proteins that help in protein folding, protection, and repair. Proteomic studies have identified actin depolymerizing factor and S-like RNase homologues proteins, that are differentially expressed under drought conditions, suggesting their roles in maintaining cellular structure and function during drought stress (Hong et al., 2016; Pant et al., 2022).

2.3 Importance of proteomics in understanding drought stress responses

Proteomics provides a comprehensive approach to understanding the drought stress responses, which offering insights that are not apparent from transcriptomic or genomic studies alone. Proteomic analyses have revealed that drought stress leads to significant changes in the abundance of proteins involved in photosynthesis, carbohydrate metabolism, and protein synthesis and other metabolic pathways (Wu et al., 2016; Hamzelou et al., 2020). Wu et al. (2016) characterized a new ClpD1 protease, indicating a shift in metabolic priorities to cope with stress, which downregulated of photosynthetic proteins and upregulated of stress-related proteins in drought-tolerant rice varieties. Furthermore, the field of proteomics has been instrumental in uncovering previously unknown proteins that respond to drought stress. These proteins play vital roles in cell defense and energy metabolism, making them promising candidates for serving as biomarkers in the development of drought-tolerant crops (Maksup et al., 2014; Agrawal et al., 2016). The integration of proteomic data with transcriptomics and metabolomics can provide a system understanding for the complex regulatory networks involved in drought stress responses (Shu et al., 2011; Yun et al., 2022). By leveraging proteomic technologies, researchers can identify key proteins and pathways that contribute to drought tolerance, thereby facilitating the development of more resilient crop varieties through genetic engineering and breeding programs.

3 Proteomics Approaches in Studying Drought Stress

3.1 Overview of proteomics techniques

Proteomics techniques have become indispensable in studying plant responses to drought stress (Liu et al., 2019). High-performance liquid chromatography attached with tandem mass spectrometry (HPLC-MS/MS) and 2D-PAGE are the most commonly used methods. HPLC-MS/MS allows for the high-throughput identification and quantification of proteins, providing detailed insights into the proteome's composition and dynamics under stress conditions. similarly, 2D-PAGE method can separate proteins based on their isoelectric point and molecular weight, enable the detection of post-translational modifications and change in protein abundance. These techniques, combined with bioinformatics tools, facilitate the comprehensive analysis of protein expression and function in response to drought stress.

3.2 Quantitative proteomics in drought stress research

Quantitative proteomics has been instrumental in understanding the molecular mechanisms underlying drought tolerance in rice. Techniques such as label-free quantification and tandem mass tag (TMT) multiplexing have been used to accurately measure changes in protein abundance. A study using label-free quantitative proteomics identified significant alterations in the rice roots proteome under drought conditions, highlighting the upregulation of stress-related proteins and the downregulation of photosynthetic machinery (Wu et al., 2016; Shi et al., 2018). Similarly, TMT-based approaches have revealed the differential expression of proteins (DEPs) involved in various metabolic pathways, providing insights into the adaptive responses of rice to water deficit (Mirzaei et al., 2012).

3.3 Identification of drought-responsive proteins in rice

Several studies have identified proteins that respond to drought stress in rice. Paul et al. (2015) and Agrawal et al. (2016) performed proteomic analysis of rice leaves under drought conditions identified proteins involved in energy metabolism, cell defense, and signal transduction as being differentially expressed. Notably, proteins namely actin depolymerizing factor, chloroplastic glutathione-dependent dehydroascorbate reductase, and caffeoyl-CoAO-methyltransferase have been reported to increase in abundance during drought stress (Salekdeh et al., 2002; Ali and Komatsu, 2006). Additionally, dehydrogenase and pyruvate dehydrogenase proteins related to carbohydrate and energy metabolism, have been implicated in enhancing drought tolerance. These findings underscore the complex network of protein interactions and regulatory mechanisms that enable rice to adapt to drought stress.

4 PPI Networks

4.1 Definition and significance of PPI networks

PPI networks are conceptualized as maps of the physical and functional interactions between proteins within a cell. These networks are of great importance for the comprehension of the intricate biological processes and pathways that regulate cellular functions. In the context of rice subjected to drought stress, PPI networks can elucidate the mechanisms through which proteins interact to mediate stress responses, thereby potentially identifying key proteins that contribute to drought tolerance. One of the study on Brachy podium distachyon roots and leaves under drought stress demonstrated the significance of PPI networks in generating synergistic responses to stress (Bian et al., 2017). Similarly, the hybrid protein interactome in rice has been showed to contribute to heterosis, with specific PPIs potentially enhancing stress resilience (Li et al., 2019).

4.2 Techniques for constructing PPI networks

A variety of experimental techniques are employed in the construction of PPI networks. The yeast two-hybrid (Y2H) assay is a method used to detects physical interactions between two proteins. This is achieved by reconstituting a functional TF in yeast. It has been employed extensively for the mapping of large-scale PPI networks. Co-Immunoprecipitation (Co-IP) employs antibodies to precipitate a protein of interest along with its interacting partners from a cell lysate, thereby facilitating the identification of protein complexes. The label-free shotgun proteomics can be employed to identify and quantify proteins and their interactions under different conditions. For example, this approach has been used to examine the effects of drought stress in rice (Hamzelou et al., 2020; Bai et al., 2021).

4.3 Bioinformatics tools and databases for PPI analysis

The utilisation of bioinformatics tools and databases is a fundamental aspect of the analysis and visualisation of PPI networks. STRING is a database of known and predicted PPIs that integrates data from a variety of sources, including experimental data, computational prediction methods, and public text collections. It offers a comprehensive overview of protein interactions and is widely utilized tool in the field of PPI network analysis (Zainal-Abidin et al., 2022). CYTOSCAPE is an open-source software platform designed for the visualisation of complex networks and the integration of diverse attribute data. It is particularly useful for visualizing PPI networks and the subsequent analysis of their topological properties (Figure 1) (Zainal-Abidin et al., 2022). GO analysis is a method of categorizing proteins within PPI network based on their biological processes, cellular components, and molecular functions. This approach was employed to identify drought-responsive proteins in rice (Hamzelou et al., 2020). By integrating experimental data with bioinformatics tools, researchers can construct detailed PPI networks that provide insights into the molecular mechanisms underlying drought stress responses in rice. The networks can be employed to identify potential targets for genetic improvement and breeding programs, with the objective of enhancing drought tolerance in rice.

Figure 1 Bioinformatics workflow for the construction of protein–protein interaction network (PPI) (Adopted from Zainal-Abidin et al., 2022)

5 Insights from Proteomics and Bioinformatics Analysis

5.1 Identification of key drought-responsive proteins in rice

Proteomic studies have identified a number of key proteins that respond to drought stress in rice. For example, a study of diverse rice genotypes demonstrated that eight proteins were consistently induced across all genotypes under drought conditions, indicating their potential role in drought tolerance mechanisms (Hamzelou et al., 2020). Furthermore, the overexpression of specific proteins such as late embryogenesis abundant (LEA) proteins, has been demonstrated to enhance drought resistance in rice, thereby underscoring their pivotal role in stress response (Xiao et al., 2007). Another study identified 38 co-upregulated proteins related to drought tolerance in weedy rice, with six proteins exhibiting a significant association with drought tolerance (Han et al., 2020). These findings highlight the significance of these proteins in enhancing drought resilience in rice.

5.2 Construction and analysis of PPI networks under drought stress

The construction and analysis of PPI networks under drought stress provide insights into the complex molecular interactions that underpin drought tolerance. Proteomic approaches have enabled the identification of numerous proteins whose abundance is altered in response to drought, thus facilitating the construction of PPI networks. Salekdeh et al. (2002) employed 2D-PAGE and MS methods to detect over 1,000 proteins, with 42 exhibiting significant alterations in abundance in response to drought stress. These proteins can be mapped into PPI networks to understand their interactions and regulatory roles. Besides, bioinformatics tools have been utilized to analyze these networks, thereby elucidating the key pathways and molecular functions involved in drought response (Jangam et al., 2016).

5.3 Functional annotation and pathway enrichment analysis of PPI networks

The functional annotation and pathway enrichment analysis of PPI networks facilitate the elucidation of the biological processes and pathways involved in drought stress response. Transcriptome analysis has demonstrated that a considerable number of drought-responsive genes are regulated in a manner that is a specific to the tissue or development stage-express, indicating the presence of a complex regulatory network (Wang et al., 2011). Gene ontology (GO) analysis and pathway enrichment studies have identified several critical pathways, including plant hormone signal transduction, protein processing in the endoplasmic reticulum, and the mitogen-activated protein kinase (MAPK) signaling pathways, which are enriched in drought-responsive proteins (Hao et al., 2022)., The integration of proteomic and bioinformatics data facilitates a comprehensive understanding of the molecular mechanisms that underpin drought tolerance in rice.

6 Case Studies

6.1 Specific PPI networks involving TFs and signaling proteins

A number of studies have emphasized the significance of TFs and signaling proteins in the PPI networks of rice in response to drought stress. Wang et al. (2020a) discovered the TFs (OsNAC006) is the positive regulators and can be target for genome editing in rice, knockout of OsNAC006 results in drought sensitivity. Usman et al. (2020) reported that ABA receptors (OsPYL9) also positive regulated the drought tolerance mechanisms. The OsPYL9 is mutagenized through CRISPR/Cas9 system. The OsPYL9 mutants exhibited higher ABA levels, antioxidant activity and accumulated more waxy crystals on the leaf epidermis and showed decreased vascular bundles (Figure 2) under drought conditions, which were markedly superior to those observed in the wild type. Finally, the OsPYL9 mutants show an increase in grain yield under both well watered field and drought conditions.

Figure 2 Microscopic analysis of wild type (WT) and mutant line GXU16-9 leaf surfaces (Adopted from Usman et al., 2020) Image caption: Scanning electron microscope (SEM) images of cuticular wax on adaxial leaf surface (A) and number and distance of vascular bundles (B) of the WT and mutant line GXU16-9 (Adopted from Usman et al., 2020)

The observed phenotypic about an increase in wax accumulation and change in vascular structure, represent crucial adaptations process for mutant plants to enhance their stress responses. And proteomic analysis revealed that proteins such as GIGANTEA, Adagio-like, and Pseudo-response regulator proteins exhibited higher interaction within the PPI network, thereby indicating their significant roles in circadian rhythm and drought response. Additionally, another study focuses on edited the Grain Size 3 (GS3) gene also for obtaining valuable and stable long-grain rice mutants. The mutants’ plant of PPI networks found that proteins related to DNA damage-binding, ubiquitin-40S ribosomal, and cysteine proteinase inhibitor showed a higher degree of interaction (Usman et al., 2021). These study demonstrated the successful application of CRISPR/Cas9 technology in enhancing rice drought resistance and yield through targeted gene editing and regulation of specific TFs and signaling proteins involved in the PPI network. Furthermore, OsbHLH148, a bHLH protein, has been observed to interact with OsJAZ proteins within the jasmonate signaling pathway, which plays a pivotal role in the development of drought tolerance. This interaction indicated the presence of a complex regulatory mechanism involving multiple signaling pathways (Seo et al., 2011).

6.2 Interaction networks related to osmoprotectants and stress-related proteins

It is evident that osmoprotectants and stress-related proteins play a pivotal role in the drought stress response in rice. One study demonstrated the interaction of the F-box protein OsFBX257 with protein kinases and phosphatases, which modulated root architecture and drought stress tolerance. This protein constituted part of the SCF complex and interacts with 14-3-3 rice proteins, thereby indicating its role in the regulation of osmoprotectants and stress-related proteins. Soltanpour et al. (2022) identified major protein in the meta-QTL areas which are mitochondrial transcription termination factor (MTERF), ABA-insensitive protein 5 (ABI5), the G-box binding factor 4 (GBF4), protein kinase PINOID (PID), protein related to autophagy 18A (ATG18A), histidi ne kinase 2 (AHK2), inositol-tetrakisphosphate 1-kinase 2 (ITPK2) and protein detoxification 48 (DTX48). These proteins are regulatory proteins involved in the regulation of signal transduction and gene expression that respond to drought stress.

6.3 Comparative analysis of PPI networks in different rice varieties under drought stress

Comparative proteomic studies have revealed significant differences in the PPI networks of various rice genotypes under the conditions of drought stress. A study on eight diverse rice genotypes demonstrated that while some proteins were commonly induced in response to drought stress, others were unique to specific genotypes, indicating diversity in metabolic responses (Hamzelou et al., 2020). In another study, a network-based computational approach was employed to analyze seven drought-tolerant rice genotypes, with the objective of identifying co-expressed gene modules and potential candidate genes related to drought resistance. This study emphasised the pivotal function of the ABA signaling pathway and the distinct regulation of jasmonic acid (JA) phytohormones, within the PPI networks of different genotypes (Sircar and Parekh, 2018). The integration of these findings facilitates a more nuanced comprehension of the intricate dynamics of PPI networks in rice under drought stress. This, in turn, can inform the development of more effective breeding strategies and genetic engineering approaches to enhance drought tolerance in rice.

7 Application of PPI Network Analysis

7.1 Potential targets for genetic engineering and breeding for drought tolerance

PPI networks offer a comprehensive insight into the molecular mechanisms that underpin drought tolerance in rice. By identifying key proteins and their interactions, researchers can pinpoint potential targets for genetic engineering and breeding programs aimed at enhancing drought tolerance. ABA is a premier signal for rice to response to drought, which triggers a variety of physiological processes such as stomatal closure, root system modulation, post-transcriptional gene expression, and metabolic alterations. Under drought stress, recent studies also have highlighted the ABA levels rise and restrict the activity of PP2C (protein phosphatase 2C) by binding the respective promotor Pyrabactin resistance (PYR)/PYR-like (PYL)/ regulatory components of ABA receptors (RCARs) and triggering the activity of SnRK2 (sucrose non-fermenting 1-related protein kinase 2), thus promoting the ABA dependent physiological and molecular response (Dong et al., 2015; Chen et al., 2020). So, the ABA receptors (OsPYL9) have demonstrated potential for improving drought tolerance and grain yield when edited using CRISPR/Cas9 technology (Usman et al., 2020). Furthermore, proteins involved in cell defense, bioenergy, and metabolism, such as malate dehydrogenase and succinyl-CoA, have been identified as pivotal for drought adaptation (Agrawal et al., 2016). These findings indicate that ABA and these proteins are crucial to plant's survive under drought, the CRISPR/Cas9 editing tools also open the new era and extend the research to enhance the yield potential and develop new stress tolerant genotypes.

7.2 Integration of PPI network analysis with other omics data

The integration PPI network analysis with genomics and metabolomics omics data, can facilitate a more comprehensive understanding of the drought response in rice. Because the integration PPI data with gene co-expression networks and mitogen-activated protein kinase (MAPK) signaling pathway, calcium signaling pathway can offer a systems-level perspective of intricate drought-responsive processes (Sircar and Parekh, 2018; Aslam et al., 2022). Whole-genome mining and meta-QTL analysis have identified a multitude of candidate genes and molecular markers associated with drought tolerance.These can be further validated and prioritized using PPI networks (Yang et al., 2020; Selamat and Nadarajah, 2021). Proteomic approaches have also revealed significant alterations in protein abundance and post-translational modifications (PTMs) in reponse to drought stress (Liu et al., 2019), providing additional insights that can be integrated with PPI data to identify key regulatory proteins and pathways. This integrative approach can facilitate a deeper comprehension of the molecular mechanisms underlying drought tolerance, thereby enabling the advancement of more resilient rice varieties.

7.3 Implications for sustainable agriculture and water management

The insights gained from PPI network analysis and its integration with other omics data have significant implications for sustainable agriculture and water management. The identification and engineering of drought-tolerant rice varieties will facilitate a reduction in the reliance on water-intensive agricultural practices and an improvement in crop yields under water-limited conditions. The development of drought-tolerant rice varieties can contribute to the implementation of more sustainable agricultural practices, as it will result in a reduction of water usage and an increase in the resilience of crops to environmental stressors (Jogaiah et al., 2013). Furthermore, understanding the molecular mechanisms of drought tolerance can inform the development of water management strategies, such as optimizing irrigation practices to enhance the drought resilience of crops. In conclusion, the application of PPI network analysis in rice research has the potential to significantly advance sustainable agriculture and improving food security in the face of global climate challenges.

8 Future Perspectives

8.1 Advances in proteomics and bioinformatics technologies

The recent advancements in proteomics and bioinformatics have significantly enhanced our understanding of PPI networks in rice under drought stress. Techniques such as label-free shotgun proteomics and 2D-PAGE have enabled the identification and quantification of drought-responsive proteins across various rice genotypes (Hamzelou et al., 2020). Furthermore, the integration of MS-based interactome analysis has provided a more comprehensive view of PPI networks, facilitating the discovery of novel interacting proteins and their regulatory mechanisms. The utilization of CRISPR/Cas9 for precise gene editing, as exemplified by the OsPYL9 and GS3, has also paved the way for new avenues for enhancing drought tolerance and grain yield in rice by regulating circadian rhythms and stress-responsive proteins (Usman et al., 2020; Usman et al., 2021).

8.2 Challenges and limitations in PPI network studies

Despite these advancements, a number of challenges and constraints remain in the study of PPI networks. A significant challenge is the complexity and dynamic nature of PPIs, which can vary significantly under different environmental conditions and developmental stages (Li et al., 2019). Similarly, the high-throughput nature of proteomic data presents additional challenges in data analysis and interpretation. Recent advancements in sophisticated bioinformatics tools and resources are required to identify bona fide interactions (Kattan et al., 2023). Therefore,, the functional characterization of numerous drought-responsive proteins is incomplete, necessitating additional experimental validation to elucidate their roles in stress response.

8.3 Future research directions and potential breakthroughs

Future research should focus on the following directions to overcome these challenges and achieve potential breakthroughs. Combining proteomics with genomics, transcriptomics, and metabolomics can provide a holistic view of the molecular mechanisms underlying drought stress response in rice. This integrative approach can help identify key regulatory networks and potential biomarkers for drought tolerance (Jangam et al., 2016; Bian et al., 2017). The development of more sophisticated bioinformatics tools and algorithms for PPI network analysis will be crucial. These tools should be able to handle large datasets, identify novel interactions, and provide insights into the functional significance of these interactions (Kattan et al., 2023). One study of epistatic interactions between proteins from different parental genomes in rice can provide insights into the genetic basis of heterosis and its contribution to drought tolerance. This can be achieved through in silico PPI predictions and experimental validation (Li et al., 2019). Experimental validation of candidate proteins identified by proteomic and bioinformatic analyses is essential. Therefore, currently available options of marker-assisted selection (MAS) breeding, transgenic techniques and genome editing should be employed at a rapid pace. These techniques can significantly contribute in enhancing drought tolerance in rice in coming years. By addressing these research directions and interrelate the plant genetics, physiology, osmotic adjustments, stomatal conductance, field performance, we can advance our understanding of PPI networks in rice under drought stress and develop innovative strategies to improve crop resilience to environmental challenges.

9 Concluding Remarks

PPI network studies in rice under drought stress have revealed several critical insights. Co-expression networks have proved invaluable in identifying gene-pair associations and tightly coupled clusters that represent coordinated biological processes. For instance, the ABA signaling pathway has been identified as a central process in drought response, with significant crosstalk with energy metabolic processes.

Additionally, proteomic analyses have demonstrated that drought stress results in the differential regulation of proteins involved in photosynthesis, growth, development, and protein synthesis, which are often downregulated during drought conditions. Furthermore, the identification of drought-responsive proteins, such as those involved in the ABA signaling pathway, calcium signaling, and cell wall metabolism, has provided potential biomarkers for drought tolerance. The integration of proteomics and bioinformatics has proven to be an effective interdisciplinary approach for elucidating the intricate mechanisms underlying drought stress in rice. Proteomic techniques, including label-free shotgun proteomics and 2D-PAGE, have enabled the identification and quantification of drought-responsive proteins across different rice genotypes. Bioinformatics tools, including GO analysis and KEGG pathway analysis, have further elucidated the functional roles of these proteins in drought tolerance. This interdisciplinary approach has not only enhanced our understanding of the molecular mechanisms underlying drought stress but also facilitated the identification of potential candidate genes for genetic improvement.

The future of drought tolerance studies in rice will depend on the continued integration of advanced proteomic and bioinformatic techniques. The identification of key proteins that respond to drought conditions and their associated pathways provides a foundation for developing drought-tolerant rice varieties through genetic engineering and breeding programs. As climate change continues to pose significant challenges to global rice production, interdisciplinary research combining proteomics, bioinformatics, and genetic engineering will be crucial in developing resilient rice varieties capable of withstanding drought stress.

Acknowledgments

We extend our sincere thanks to two anonymous peer reviewers for their invaluable feedback on the initial draft of this paper, whose critical evaluations and constructive suggestions have greatly contributed to the improvement of our manuscript.

Funding

This work was supported by grants from the Central Leading Local Science and Technology Development Project (grant nos. 202207AA110010) and the Key and Major Science and Technology Projects of Yunnan (grant nos. 202202AE09002102).

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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