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Review

Reference Genes in Plant–Pathogen Interaction: A Bibliometric Analysis

by
Agata Lizzio
1,2,3,*,
Valerio Battaglia
2,
Ernesto Lahoz
2,
Massimo Reverberi
3 and
Milena Petriccione
1,*
1
CREA Council for Agricultural Research and Economics, Olive, Fruit and Citrus Crops (CREA-OFA), 81100 Caserta, Italy
2
CREA Council for Agricultural Research and Economics, Cereal and Industrial Crops (CREA-CI), 81100 Caserta, Italy
3
Department of Environmental Biology, “Sapienza” University of Rome, 00185 Rome, Italy
*
Authors to whom correspondence should be addressed.
Horticulturae 2025, 11(12), 1416; https://doi.org/10.3390/horticulturae11121416
Submission received: 13 October 2025 / Revised: 18 November 2025 / Accepted: 20 November 2025 / Published: 21 November 2025
(This article belongs to the Section Plant Pathology and Disease Management (PPDM))
Browse Figures
Versions Notes

Abstract

Plant–pathogen interactions are complex biological processes characterized by dynamic changes in genes expression. In molecular plant pathology research, RT-qPCR has proven to be a valuable tool for investigating plant–pathogen interactions by examining gene expression changes in both plants and pathogens during infection. The choice of reliable reference genes is crucial, as this directly affects the robustness of normalization and the accuracy of analyzing the expression of genes of interest. A systematic literature search was conducted across relevant academic databases, resulting in the selection of 47 articles (38 on fungi and oomycetes, 7 on bacteria and 2 covering both bacteria, fungi and oomycetes) that evaluated the stability of 190 candidate reference genes. The most used reference genes in plant—fungal and oomycete pathosystems were GAPDH, ACT, TUB and EF, whereas UBQ, TUB, EF and ACT were most used in plant—bacterial pathosystems. Reference genes revealed considerable variability in their stability across different crops, pathogens and experimental conditions. Notably, several classical reference genes, traditionally assumed to maintain stable expression, exhibited considerable variability, supporting concerns regarding their reliability as universal references. Therefore, this review provides important insights for researchers seeking to identify suitable reference genes for their validation studies in plant–pathogen interaction.

1. Introduction

Crop diseases are a major threat to agricultural productivity worldwide, resulting in significant losses. Plant–pathogen interactions are complex biological processes characterized by dynamic changes in gene stability. Understanding the physiological and molecular pathways involved in these interactions is paramount to developing effective strategies for sustainable agriculture [1]. To counteract pathogen invasion, plants rely on a sophisticated immune system that includes both extracellular and intracellular receptors. These receptors mediate two primary layers of immunity: pattern-triggered immunity (PTI), initiated by the recognition of conserved pathogen-associated molecular patterns (PAMPs), and effector-triggered immunity (ETI), activated by the detection of specific pathogen effectors by intracellular nucleotide-binding leucine-rich repeat (NLR) proteins [2].
Significant efforts have been dedicated to elucidating the molecular mechanisms activated during infection and maintained throughout pathogenesis, with the aim of characterizing the complex processes underlying plant–pathogen interactions and identifying the molecular determinants of resistance [3]. Advances in genomic approaches have enabled the identification of numerous genes responsive to biotic stress, as well as the dissection of signaling pathways involved in plant defense mechanisms across diverse host–pathogen systems [4].
Functional genomics studies of plant–pathogen interactions aim to understand the molecular mechanisms underlying plant defense and pathogen virulence strategies. These interactions involve a complex interplay of genes, proteins and biochemical pathways, and researchers use various techniques to dissect and understand these processes. Gene stability analysis is a key experimental strategy [5,6]. In contrast to the gene-by-gene approach of traditional molecular biology techniques, the field of functional genomics uses genome-wide approaches to try to understand the activities and interactions of genes and proteins [7].
Nowadays, several high-throughput technologies have become indispensable tools in the fields of molecular biology and genetics, mainly because of their ability to efficiently and comprehensively assess the quantification of gene expression levels across the entire genome or a significant portion of it, such as microarray, multiplex quantitative PCR, RNA-sequencing and digital droplet PCR, but reverse transcription quantitative real-time PCR (RT-qPCR) remains the most widely used simple, low-cost and efficient method [8,9]. RT-qPCR is known for its good reproducibility, high specificity and sensitivity; furthermore, it allows us to detect the fold change in expression of genes of interest, validate RNA sequencing data and verify the results of differential protein studies of proteomic analysis, providing insights into gene regulation and cellular processes [6,8,9,10]. Furthermore, RT-qPCR is used to assess gene expression under a wide range of experimental conditions, highlighting small dynamic changes between samples [11]. This technique has revolutionized various fields of biological research such as gene expression profiling, biomarker discovery and plant disease diagnosis [12].
In RT-qPCR analysis several steps such as extraction procedures, m-RNA quality, primer design, experimental design, as well as the choice of reference genes (RGs) influence the accuracy and the appropriate interpretation of the results. There are two types of variation that influence the result of gene stability quantification: the first is the biological variation and the second is the inherent technical or experimentally induced variation. Normalization strategies are implemented to eliminate, or reduce as much as possible, technical variations [13].
The use of RGs is currently the main method for normalizing gene of interest expression levels. An RG must be an endogenous gene that is necessary for the organism’s life cycle and has stable expression under specific experimental conditions [14,15] but screening and validation of RGs for specific conditions and for multiple cell types or tissues is required [16,17,18]. Previous studies have shown significant variation in the stability of commonly used RGs. This highlights the lack of a universal RG suitable for internal control in all application scenarios and consequently validation on a case-by-case basis, even within the same species [16,19].
In molecular plant pathology research, RT-qPCR has proven to be a valuable tool for investigating plant–pathogen interactions by examining gene expression changes in both plants and pathogens during infection, thereby providing insight into the molecular mechanisms underlying these interactions [6]. This information is crucial for monitoring changes in the expression levels of plant genes involved in defense responses, such as pathogen recognition receptors, defense-related enzymes, transcription factors and antimicrobial peptides [20,21]. Comparison of gene expression profiles between infected and uninfected plants provides insight into the activation or repression of defense pathways during infection [2,22]. This knowledge helps us understand the strategies used by the pathogen to evade plant defenses and cause disease [23]. Gene expression dynamics assessed at different stages of interaction can help identify key genes that are rapidly induced or repressed upon pathogen attack, providing clues to their role in the defense response [24]. This knowledge will improve our understanding of disease development, facilitate the development of targeted control strategies and contribute to the breeding of resistant crop varieties [25].
The objectives of this review are to thoroughly examine the research carried out over the last 17 years, highlighting the RGs that have been effectively used in the context of plant–pathogen interactions. By analyzing these studies, the review aims to provide insights into the selection and validation of RGs, thereby contributing to improving the reliability and accuracy of gene expression analyses in plant–pathogen interactions.

2. Relevant Studies on Reference Gene Stability for RT-qPCR Normalization in Plant Pathogen Interactions

A systematic literature search was conducted in relevant academic databases, including Science direct (https://www.sciencedirect.com/), Springer (https://link.springer.com/), Wiley (http://onlinelibrary.wiley.com/), PubMed (https://www.ncbi.nlm.nih.gov/pubmed) and Google Scholar (https://scholar.google.com/), focusing on single or combined keywords such as “reference genes” or “housekeeping genes” or “internal control gene” and “host–plant interaction” or “plant–pathogen interaction” and “qRT-PCR” or “qPCR” or “quantitative PCR” or “RT-qPCR” and Boolean operators (AND and OR). Despite the efficiency of the above search algorithms, other noteworthy papers that escaped detection were also evaluated.
In the last 17 years (2008–2024), 47 papers have been published on RGs stability, mainly between 2011 and 2013, reflecting the continued interest and research activity in this important area of plant pathology (Figure 1).
Overall, 54 fungal and oomycete species have been reported in association with 35 crops, whereas 20 bacterial species have been investigated in relation to 10 crops. Specifically, 38 publications are related to fungi and oomycetes, 7 to bacteria and 2 to both fungi and bacteria as reported in Table 1 and Table S1.

2.1. Main Keywords in the Analyzed Studies

The network visualization map, generated from the keywords of the analyzed studies, highlights the conceptual structure of research on RGs in plant–pathogen interactions. Six main clusters can be identified, each representing groups of terms with strong co-occurrence relationships (Figure 2). Central nodes such as RGs, gene expression regulation, plant and RT-qPCR indicate core methodological and biological themes that are highly interconnected across studies. The size of these nodes reflects their frequent use, underscoring their pivotal role in this field of research.
Peripheral nodes, linked to specific crops (e.g., Solanum lycopersicum, Oryza), pathogens (Ralstonia solanacearum, Fusarium spp.), or experimental approaches (transcriptome, algorithms, molecular biology), demonstrate how RG studies are contextualized within diverse plant–pathogen systems and methodological frameworks. The presence of multiple inter-cluster connections indicates that RG validation is not confined to isolated research areas but rather intersects with broader topics such as host–pathogen interactions, stress responses and computational approaches.
Overall, the network illustrates both the centrality of RG selection in molecular studies and the multidisciplinary nature of plant–pathogen interaction research, integrating genetics, genomics, pathology and bioinformatics.

2.2. Network Visualization of Academic Journals in the Analyzed Studies

The figure, created using VOSviewer, shows a network of scientific journals where research on validation of RGs has been published. Each node represents a journal, with the size of the node reflecting the number of published articles, while the edges indicate the strength of co-citation links between journals. The color scale corresponds to the average year of publication, ranging from earlier periods (blue) to more recent ones (red). (Figure 3)
The analysis highlights that Scientific Reports and PLOS One have a central position in the network, characterized by a larger number of papers and extensive connections with other journals. This underscores their role as multidisciplinary journal in the field. In contrast, specialized journals, such as Plant Cell Reports, BMC Molecular Biology, and BMC Plant Biology, form smaller clusters, reflecting their thematic focus within molecular and cellular plant sciences. More peripheral positions, such as that of Tropical Plant Pathology and Australasian Plant Pathology, indicate a narrower but still relevant contribution.
The temporal gradient reveals how publication trends have evolved. Journals shown in blue and green (e.g., BMC Research Notes) had greater prominence in earlier years (2010–2014), whereas those in orange and red have gained visibility in more recent years (2018–2024). This dynamic distribution suggests a shift in research attention, with newer or more specialized outlets increasingly contributing to the literature on RG validation.
Overall, the network emphasizes both the central role of high-impact, multidisciplinary journals in disseminating RG validation studies and the continuing relevance of specialized journals that support research in specific subfields.

3. Number of Reference Genes per Study

In gene expression analyses, selecting and validating stable RGs is critical to ensuring accurate and reproducible results. The number of RGs evaluated can vary significantly depending on the organism, the pathogen and the specific experimental conditions. In fungal and oomycetes studies, the number of RGs examined typically ranges from 5 to 26 (Table S1). However, in most cases, gene stability is assessed using a panel from 5 to 10 candidate RGs, balancing the need for thorough evaluation with experimental feasibility. This approach enables researchers to identify the most stable genes without introducing excessive complexity to the analysis.
In contrast, studies focusing on plant–bacteria interactions often report a broader range of evaluated RGs, from 3 to 24. These studies tend to assess a slightly higher number of RGs than fungal and oomycetes studies, with an average of around 10–11 genes tested. This broader evaluation likely reflects the complexity and variability of plant responses to bacterial colonization, including both pathogenic and beneficial interactions. An extensive selection of candidate RGs enhances the reliability of the normalization process by accounting for greater biological variability and ensuring that the selected RGs maintain consistent stability under different conditions. In fungal, oomycetes and bacterial systems, careful validation of RGs is essential for generating trustworthy gene expression data. This highlights the importance of adapting RG selection to the specific biological context of each study.

4. Software Packages Used for Normalization Strategy

In the analyzed studies, several computational tools have been used to assess RG stability and identify the most suitable genes under specific experimental conditions and sample sets (Table S1) as suggested by De Spiegelaere and coworkers [69]. These include GeNorm (version 3.2) [70], NormFinder (version 21) [71], BestKeeper (version 1) [72], the comparative ΔCt method [73] and the integrative tool RefFinder [74].
GeNorm determines expression stability by calculating the average pairwise variation (M-value) of each candidate RG in comparison with all other RGs tested. The algorithm performs a stepwise exclusion of the least stable genes, those with the highest M-values, to generate a stability ranking. Moreover, it calculates a pairwise variation (Vn/Vn + 1), with a commonly accepted cut-off of 0.15, to define the optimal number of RGs required for reliable normalization [70].
NormFinder uses a model-based approach that considers both intra- and inter-group variation to calculate a stability value (SV) for each RG. Genes with lower SVs are considered more stable, with a general threshold of 0.15 for acceptable stability [6,71].
BestKeeper applies a statistical approach based on the calculation of standard deviation (SD), coefficient of variation (CV) and Pearson’s correlation coefficient (r). It computes a BestKeeper Index (BKI) as the geometric mean of Cq values from the most stable RGs (those with low SD and high r values) and then evaluates the correlation of each RG against the BKI. The RGs showing the highest correlation are deemed the most stable [72].
The comparative ΔCt method assesses the relative expression of RG pairs within each sample and identifies the most stable genes by analyzing the consistency of these pairwise differences across all samples [73].
RefFinder, an R-based integrative software tool, consolidates the results from GeNorm, NormFinder, BestKeeper and the ΔCt method to produce a comprehensive ranking of RG stability. It calculates the geometric mean of the stability ranks from each method, assigning the lowest geometric mean as indicative of the most stable RG [75,76].
The data show that most studies used a moderate number of tools for RG normalization with 59% using three algorithms, 20% using four and only 6% going as far as five (Table S1). This suggests that researchers generally aim for a balance between robustness and feasibility. Using multiple algorithms is important because each one is based on different assumptions and statistical approaches. By applying three or four algorithms, it is possible to validate the findings and select the most stable RGs with a high statistical level of confidence.

5. Most Used and Stable Reference Genes

To better understand the selection of RGs, it is crucial to distinguish between those that are most commonly used and those that are most stable across different experimental conditions.
The selection of most used RGs is different between fungi, oomycetes and bacteria, as shown in Figure 4A,B.
In fungal- and oomycetes–plant interaction studies, GAPDH (17%), TUB (16%) and ACT (16%) are among the most frequently employed genes, reflecting their traditional role as reference markers due to their stable expression in many eukaryotic cells. Nevertheless, the use of other genes such as EF (12%), UBQ (11%) and CYP (7%) indicates an increasing diversification in gene selection, likely driven by variability in stability across different fungal species, crops and experimental conditions. Furthermore, genes such as UBC, eIF and 18S, each representing 6% of cases, highlight a growing interest in alternative RGs that may provide enhanced stability or specificity in some experimental contexts.
In contrast, in bacterial–plant interaction studies, few candidate RGs are used. UBQ (23%), TUB (21%) and EF (18%) are the most used and account for over 60% of total usage. This indicates a wide consensus among researchers regarding the selection of RGs in bacterial–plant pathosystems. At a lower percentage, the other RGs used are ACT (14%) and GAPDH (9%), while EXP (8%) and eIF (7%) are used less frequently.
The most stable RGs identified in studies on plant–fungus and plant-oomycetes interactions are summarized here, considering only those reported as stable in at least five different papers. Among the analyzed RGs, ACT (22%) and GAPDH (15%) were most frequently identified as stable, reflecting their widespread use as housekeeping genes in gene expression studies. TUB (14%) and UBQ (13%) also exhibited relatively high stability across different experimental setups. Conversely, genes such as eIF and EF (each 10%), SAND (9%) and UBC (7%) were less frequently validated as stable but may still represent reliable alternatives depending on the specific pathosystem and experimental conditions.
In plant–bacterium pathosystems among the analyzed RGs, 18S and GAPDH were identified as the most stable, being validated in three and two studies, respectively. Other RGs showed stability in a single study, suggesting that their performance may depend on the specific experimental context or species examined.
Ubiquitin is a highly conserved eukaryotic protein, and it is made up of 76 amino acids [77,78]. UBQ’s proteins take part in signaling complex with other proteins, and they are involved in DNA repair, in protein traffic, structure and transcription of chromatin and into endocytosis regulation [77,78,79]. There are two different kinds of UBQ genes: polyubiquitin genes, whose transcripts are found across both mature and immature tissues, as well as ubiquitin genes, which are widely distributed in meristematic tissues [78,80,81].
Several studies have demonstrated that ubiquitins (UBQ and UBQ2) are the most stable RGs in different pathosystems such as in the grapevine/downy mildew [17] and Musa acuminata/Pseudocercospora musae systems [43]. Previously, Chen and coworkers demonstrated that UBQ2 is the most stable gene for the M. acuminata cv. ‘Cavendish’/P. musae pathosystem [45]. Similarly, in Triticum aestivum UBQ has been identified as the most stable RG, during pre-haustorial stages of Puccinia triticina infection [58]. Moreover, UBQ has been recognized as one of the most stable genes in tomato plants infected by R. solanacearum. UBQ is considered the most stable RG in the Oryza sativa/Rhizoctonia solani pathosystem when plants are treated with Pseudomonas saponiphilia, a plant growth-promoting rhizobacterium, that is an inductor of systemic resistance [47].
Ubiquitin-conjugating enzyme (UBC) genes play a crucial role in the ubiquitination process, which is essential for the plant response to abiotic and biotic stress [82,83,84]. These genes are reported as the most stable in several pathosystems, such as Avena fatua/Trichoderma polysporum, Vaccinium myrtilloides and V. angustifolium f. nigrum/Monilinia vaccinii-corymbosi, Piper nigrum/Phytophthora capsci and Rosa hybrida/Diplocarpon rosa [29,51,53,60].
Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is the second most widely studied RG and also ranks as the second most stable gene in several pathosystems. GAPDH is involved in several functions, such as development phases and regulatory mechanisms. It takes part in plant DNA repair, in RNA metabolism and response to stress conditions, such as salinity, heat stress or drought; for these reasons it is defined as a moonlighting protein [85,86]. All GAPDH proteins are characterized by the presence of an acidic catalytic cysteine in the active site. Plants possess multiple GAPDH isoforms which are encoded by different types of genes (gapA, gapB, gapC and gapCp) which are located at different cell compartments [87].
Nine research studies investigating plant-fungal pathosystems have identified GAPDH as one of the most stable genes. It is considered stable in O. sativa following infection by R. solani [47] and after attack by Magnaporthe oryzae [48]. Similarly, GAPDH has been reported as the most stable gene in other plant-fungal interactions, including Musa sp. cv. ‘Prataanã’/Fusarium oxysporum f. sp. cubense [44], V. angustifolium f. nigrum/M. vaccinii-corymbosi [60], P. nigrum/P. capsci [51], Vitis vinifera/Plasmopara viticola [61], Coffea arabica/Hemileia vastatrix [35] and in Fagus sylvatica/P. citricola pathosystems [37]. Additionally, Petriccione and coworkers demonstrated that GAPDH is the most stable RG in Actinidia deliciosa cv. ‘Hayward’ inoculated with P. syringae pv. actinidiae [6].
In contrast, in the pathosystems Elaeis guineensis/Ganoderma boninense [36] and Humulus lupulus/Verticillium albo-atrum [40] GAPDH was reported as the least stable gene.
Actin is a widely distributed protein in the plant cytoskeleton, and it is involved in several subcellular functions that are crucial for plant development, such as cell elongation, cell shape determination and cell division plane localization [88,89,90]. Analyses of ACT genes in plants have revealed that their structure is highly conserved [91]. Among the 29 studies that evaluated ACT as a candidate RG, only eight confirmed it as the most stable gene. For example, in the O. sativa/R. solani pathosystem ACT was found to be the most stable gene [47]. Several studies reported that the stability of ACT is cultivar dependent. Zhang and coworkers analyzed two ACT isoforms (ACT1 and ACT2) in two Musa cultivars, ‘Brazilian’ and ‘Guangfen No. 1’, and showed that after infection with F. oxysporum f. sp. cubense, ACT1 was the most stable only in ‘Brazilian’, while ACT2 was the least stable in this pathosystem [46]. In ‘Guangfen No. 1’, both genes were among the least stable.
In plants, tubulin genes are important for the development of the cytoskeleton, which affects structure, intracellular transport and cell division [92,93]. These genes belong to multigene families comprising several isoforms whose stability can vary by tissue [94]. TUB genes encode proteins composed of α- and β-tubulin subunits, that polymerize to create microtubules, which are an essential component of the cytoskeleton network.
TUB is one of the most investigated genes. Two studies have reported that TUB is one of the most stable in two different crops (Musa spp. and Cucumis melo) following F. oxysporum infection [5,46]. In addition, Saha and Vandemark [41,52] identified β- TUB as one of the most stable RGs in leguminous crops, in Pisum sativum inoculated with Sclerotinia sclerotiorum and in Lens culinaris one day after inoculation with Aphanomyces euteiches. In contrast, α-tubulin exhibits the lowest expression stability among the RGs analyzed in the P. graminis f. sp. avenae/A. sativa pathosystem (oat line Pg4 and cultivar Kasztan) during both compatible and incompatible interactions [27].
Eukaryotic translation is regulated by distinct sets of initiation factors (eIFs) and elongation factors (eEFs) that control the start and progress of protein synthesis. eIFs genes are among the most stable genes identified in plant-fungal pathosystems. These genes play a crucial role in several aspects of plant development in plant defense and response to pathogen invasion and encode initiation factors that are involved in the translation of mRNA into proteins [95,96]. Initiation factors facilitate the correct positioning of ribosomes on mRNA, allowing precise recognition of the start codon and formation of the translation initiation complex [97]. This process is critical for cellular function, stress response and adaptation to environmental changes, including interactions with pathogens [98]. By contrast, translational elongation factors (eEFs) perform essential functions during the elongation cycle of protein biosynthesis. They not only mediate the delivery of aminoacyl-tRNA to the ribosome during polypeptide elongation but also facilitate translocation, the step in which peptidyl-tRNA is shifted from one ribosomal site to another as the mRNA advances through the ribosome [99,100,101].
In A. sativa infected with P. graminis, eukaryotic initiation factor 4A-3 (eIF4A-3) was identified as one of the most suitable RGs for RT-qPCR normalization [27]. Its stability was further confirmed during interactions with Blumeria graminis [26], which reinforces its reliability as an RG in gene expression studies. Similarly, in O. sativa, eIF4A-3 was consistently ranked as one of the most stable genes for RT-qPCR analyses during interactions with both R. solani and the beneficial bacterium P. protegens and in response to M. oryzae infection [47,48]. The eIF gene (TIF) has also been reported to be the most stable RG for normalization studies in leguminous crops including L. culinaris and P. sativum infected by fungal pathogens such as S. sclerotiorum and A. euteiches [41,52].
The elongation factor 1-alpha (eEF1A) gene has also been extensively investigated across several pathosystems. In A. sativa, eEF1A exhibited consistent stability during interactions with P. coronata [28] and P. graminis [27], establishing its status as the most stable RG in these systems. Similarly, Monteiro and coworkers [61] demonstrated that eEF1A was the most stable gene in the V. vinifera cultivars Regent and Trincadeira, which are resistant and susceptible to P. viticola, respectively. eEF1A stability has also been confirmed in other pathosystems, including T. aestivum/P. triticina and Brassica rapa ssp. pekinensis/Peronospora parasitica [30,58].
The SAND gene is conserved across diverse eukaryotic organisms, indicating its role in essential cellular processes [102]. The SAND protein typically localizes in the nucleus, where it is thought to contribute to transcriptional regulation, but it may also be localized in the cytoplasm or plasma membrane, suggesting functional versatility [102,103]. Although its exact role remains unclear, the presence of conserved domains points to involvement in gene regulation, possibly through DNA binding or the assembly of transcription factor complexes [104].
SAND has been reported to be the most stable gene in different cultivars of V. vinifera infected by P. viticola [17,61]. Štajner and coworkers [40] identified SAND as one of the most stable RGs in hop plants of the susceptible cultivar Celeia and the resistant cultivar Wye Target challenged by V. albo-atrum. A study investigating the stability of RGs in five Citrus species inoculated with five different pathogens, two bacteria (Xylella fastidiosa and Candidatus Liberibacter asiaticus), one oomycete (P. parasitica), one virus (Citrus leprosis virus C) and one fungus (Alternaria alternata), found that SAND was the most stable RG across all samples and subsets tested [32].

6. Conclusions

This comprehensive review highlights recent advances in the study of RG stability in plant–pathogen interactions. By synthesizing key findings and addressing current challenges, it aims to support researchers in selecting robust RGs for accurate and meaningful gene expression analyses.
In fungal and oomycetes—plant pathosystems, RGs such as GAPDH, TUB and ACT are still widely used, while genes such as EF, UBQ, CYP, UBC, eIF and 18S are increasingly being used to address variability across species, crops and experimental conditions. In bacterial—plant interactions, the choice of RGs is more consistent, with UBQ, TUB and EF predominating, reflecting a broad consensus among researchers.
Across the analyzed studies, UBQ, UBC, GAPDH, eIF, eEF and SAND demonstrate consistently high stability across multiple plant species and pathogen types. In contrast, traditional RGs such as ACT and TUB have shown variable expression depending on the cultivar, tissue or experimental conditions. These results highlight the importance of validating RGs for each experimental context and using multiple validated RGs, as suggested by GeNorm, to improve normalization accuracy and reproducibility. Emerging candidates such as UBC, SAND, eIF4A-3 and eEF1A provide potentially reliable alternatives to classical RGs, as documented in several pathosystems.
Our bibliometric analysis provides an overview of current research, with the substantial number of recent studies reflecting the scientific community’s sustained commitment to unraveling the complexities of plant defense mechanisms against pathogens. The results of studies on RGs in plants exposed to pathogen interactions underscores their pivotal role in ensuring precision and reliability in gene expression studies.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/horticulturae11121416/s1: Table S1: Studies on reference gene expression in plant–fungi and plant–bacteria interactions, including the candidate reference genes assessed, the most stable gene(s) identified, and the analytical tools used.

Author Contributions

Conceptualization, A.L. and M.P.; methodology, A.L.; data curation, A.L.; writing—original draft preparation, A.L., V.B., M.P.; writing—review and editing, M.P., E.L., V.B., M.P., M.R.; supervision, M.P., V.B., M.R.; funding acquisition, M.P., E.L., M.R. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by PNRR Project “National Research Centre for Agricultural Technologies (Agritech)—SPOKE 2 Crop Health: a multidisciplinary system approach to reduce the use of agrochemicals” CUP C23C22000450006 funded by the Italian Ministry for Universities and Research (MUR).

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Kumari, R.; Kumar, A.; Kumar, P.; Jakhar, D.S. Advances in plant-pathogen interactions in terms of biochemical and molecular aspects. In Plant-Microbe Interaction-Recent Advances in Molecular and Biochemical Approaches; Swapnil, P., Meena, M., Harish, Marwal, A., Vijayalakshmi, S., Zehra, A., Eds.; Elsevier Inc.: Cambridge, UK, 2023; Volume 1, pp. 111–122. [Google Scholar] [CrossRef]
  2. Saraiva, M.; Ściślak, M.E.; Ascurra, Y.T.; Ferrando, T.M.; Zic, N.; Henard, C.; van West, P.; Trusch, F.; Vleeshouwers, V.G. The molecular dialog between oomycete effectors and their plant and animal hosts. Fungal Biol. Rev. 2023, 43, 100289. [Google Scholar] [CrossRef]
  3. Wieczorek, K.; Denis, E.; Lynch, O.; and Osek, J. Molecular characterization and antibiotic resistance profiling of Campylobacter isolated from cattle in polish slaughterhouses. Food Microbiol. 2013, 34, 130–136. [Google Scholar] [CrossRef]
  4. Boava, L.P.; Laia, M.L.; Jacob, T.R.; Dabbas, K.M.; Goncalves, J.F.; Ferro, J.A.; Ferro, M.I.; and Furtado, E.L. Selection of endogenous genes for gene expression studiesin Eucalyptus under biotic (Puccinia psidii) and abiotic (acibenzolar-S-methyl) stresses using RT-qPCR. BMC Res. Notes 2010, 3, 43. [Google Scholar] [CrossRef]
  5. Kong, Q.; Yuan, J.; Gao, L.; Zhao, S.; Jiang, W.; Huang, Y.; Bie, Z. Identification of suitable reference genes for gene expression normalization in qRT-PCR analysis in watermelon. PLoS ONE 2014, 9, e90612. [Google Scholar] [CrossRef]
  6. Petriccione, M.; Mastrobuoni, F.; Zampella, L.; Scortichini, M. Reference gene selection for normalization of RT-qPCR gene expression data from Actinidia deliciosa leaves infected with Pseudomonas syringae pv. actinidiae. Sci. Rep. 2015, 5, 16961. [Google Scholar] [CrossRef] [PubMed]
  7. Bunnik, E.M.; Le Roch, K.G. An introduction to functional genomics and systems biology. Adv. Wound Care 2013, 2, 490–498. [Google Scholar] [CrossRef]
  8. Pfaffl, M.W. Relative quantification. In Real-Time PCR; Dorak, M.T., Ed.; Taylor & Francis: New York, NY, USA, 2006; pp. 63–82. [Google Scholar]
  9. Kundu, A.; Patel, A.; Pal, A. Defining reference genes for qPCR normalization to study biotic and abiotic stress responses in Vigna mungo. Plant Cell Rep. 2013, 32, 1647–1658. [Google Scholar] [CrossRef]
  10. Petriccione, M.; Magri, A.; Gaudiano, M.; Scortichini, M. Selection and validation of reference genes for gene expression studies in Xanthomonas arboricola pv. juglandis subjected to abiotic stress. Plant Pathol. 2021, 70, 1455–1466. [Google Scholar] [CrossRef]
  11. Chapman, J.R.; Waldenström, J. With reference to reference genes: A systematic review of endogenous controls in gene expression studies. PLoS ONE 2015, 10, e0141853. [Google Scholar] [CrossRef]
  12. Lal, P.; Tiwari, R.K.; Kumar, A.; Altaf, M.A.; Alsahli, A.A.; Lal, M.K.; Kumar, R. Bibliometric analysis of real-time PCR-based pathogen detection in plant protection research: A comprehensive study. Front. Plant Sci. 2023, 14, 1129714. [Google Scholar] [CrossRef] [PubMed]
  13. Derveaux, S.; Vandesompele, J.; Hellemans, J. How to do successful gene expression analysis using real-time PCR. Methods 2010, 50, 227–230. [Google Scholar] [CrossRef] [PubMed]
  14. Orrego, A.; Gavilán, M.C.; Arévalos, A.; Ortíz, B.; Gaete Humada, B.; Pineda-Fretez, A.; Romero-Rodríguez, M.C.; Flores Giubi, M.E.; Kohli, M.M.; Iehisa, J.C.M. Identification of reference genes and their validation for gene expression analysis in phytopathogenic fungus Macrophomina phaseolina. PLoS ONE 2022, 17, e0272603. [Google Scholar] [CrossRef]
  15. Bustin, S.A.; Benes, V.; Garson, J.A.; Hellemans, J.; Huggett, J.; Kubista, M.; Mueller, R.; Nolan, T.; Pfaffl, M.W.; Shipley, G.L.; et al. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 2009, 55, 611–622. [Google Scholar] [CrossRef] [PubMed]
  16. Bustin, S.A.; Benes, V.; Garson, J.; Hellemans, J.; Huggett, J.; Kubista, M.; Mueller, R.; Nolan, T.; Pfaffl, M.W.; Shipley, G.; et al. The need for transparency and good practices in the qPCR literature. Nat. Methods 2013, 10, 1063–1067. [Google Scholar] [CrossRef]
  17. Selim, M.; Legay, S.; Berkelmann-Löhnertz, B.; Langen, G.; Kogel, K.H.; Evers, D. Identification of suitable reference genes for real-time RT-PCR normalization in the grapevine-downy mildew pathosystem. Plant Cell Rep. 2012, 31, 205–216. [Google Scholar] [CrossRef] [PubMed]
  18. Radonić, A.; Thulke, S.; Mackay, I.M.; Landt, O.; Siegert, W.; Nitsche, A. Guideline to reference gene selection for quantitative real-time PCR. Biochem. Biophys. Res. Commun. 2004, 313, 856–862. [Google Scholar] [CrossRef]
  19. Kalushkov, P.; and Hodek, I. The effects of thirteen species of aphids on some life history parameters of the ladybird Coccinella septempunctata. Biol. Control 2004, 49, 21–32. [Google Scholar] [CrossRef]
  20. Couto, D.; Zipfel, C. Regulation of pattern recognition receptor signalling in plants. Nat. Rev. Immunol. 2016, 16, 537–552. [Google Scholar] [CrossRef]
  21. Tang, Y.; Bao, X.; Liu, K.; Wang, J.; Zhang, J.; Feng, Y.; Wang, Y.; Lin, L.; Feng, J.; Li, C. Genome-wide identification and expression profiling of the auxin response factor (ARF) gene family in physic nut. PLoS ONE 2018, 13, e0201024. [Google Scholar] [CrossRef]
  22. Andersen, E.J.; Ali, S.; Byamukama, E.; Yen, Y.; Nepal, M.P. Disease resistance mechanisms in plants. Genes 2018, 9, 339. [Google Scholar] [CrossRef]
  23. Wang, M.R.; Bi, W.L.; Bettoni, J.C.; Zhang, D.; Volk, G.M.; Wang, Q.C. Shoot tip cryotherapy for plant pathogen eradication. Plant Pathol. 2022, 71, 1241–1254. [Google Scholar] [CrossRef]
  24. Backes, A.; Charton, S.; Planchon, S.; Esmaeel, Q.; Sergeant, K.; Hausman, J.F.; Renaut, J.; Barka, E.A.; Jacquard, C.; Guerriero, G. Gene expression and metabolite analysis in barley inoculated with net blotch fungus and plant growth-promoting rhizobacteria. Plant Physiol. Biochem. 2021, 168, 488–500. [Google Scholar] [CrossRef]
  25. Pandit, M.A.; Kumar, J.; Gulati, S.; Bhandari, N.; Mehta, P.; Katyal, R.; Rawat, C.D.; Mishra, V.; Kaur, J. Major Biological Control Strategies for Plant Pathogens. Pathogens 2022, 1, 273. [Google Scholar] [CrossRef]
  26. Sowa, S.; Toporowska, J.; Paczos-Grzęda, E. Identification of reference genes for gene expression assessment in Avena sativa under biotic stress triggered by Blumeria graminis. Sci. Rep. 2024, 14, 30006. [Google Scholar] [CrossRef] [PubMed]
  27. Sowa, S.; Sozoniuk, M.; Toporowska, J.; Kowalczyk, K.; Paczos-Grzęda, E. Reference genes expression stability in Avena sativa L. during compatible and incompatible interactions with Puccinia graminis. Sci. Rep. 2022, 12, 18369. [Google Scholar] [CrossRef]
  28. Sowa, S.; Sozoniuk, M.; Toporowska, J.; Kowalczyk, K.; Paczos-Grzęda, E. Validation of reference genes as an internal control for studying Avena sativa-Puccinia coronata interaction by RT-qPCR. Sci. Rep. 2022, 12, 14601. [Google Scholar] [CrossRef] [PubMed]
  29. Zhu, H.; Ma, Y.; Guo, Q. Expression stability of internal reference gene in response to Trichoderma polysporum infection in Avena fatua L. Curr. Genet. 2021, 67, 909–918. [Google Scholar] [CrossRef]
  30. Qi, J.; Yu, S.; Zhang, F.; Shen, X.; Zhao, X.; Yu, Y.; Zhang, D. Reference gene selection for real-time quantitative polymerase chain reaction of mRNA transcript levels in Chinese cabbage (Brassica rapa L. ssp. pekinensis). Plant Mol. Biol. Rep. 2010, 28, 597–604. [Google Scholar] [CrossRef]
  31. Zhu, X.; Li, X.; Chen, W.; Chen, J.; Lu, W.; Chen, L.; Fu, D. Evaluation of new reference genes in papaya for accurate transcript normalization under different experimental conditions. PLoS ONE 2012, 7, e44405. [Google Scholar] [CrossRef]
  32. Mafra, V.; Kubo, K.S.; Alves-Ferreira, M.; Ribeiro-Alves, M.; Stuart, R.M.; Boava, L.P.; Rodrigues, C.M.; Machado, M.A. Reference genes for accurate transcript normalization in citrus genotypes under different experimental conditions. PLoS ONE 2012, 7, e31263. [Google Scholar] [CrossRef]
  33. Maximo, H.J.; Dalio, R.J.; Rodrigues, C.M.; Breton, M.C.; Machado, M.A. Reference genes for RT-qPCR analysis in Citrus and Poncirus infected by zoospores of Phytophthora parasitica. Trop. Plant Path. 2017, 42, 76–85. [Google Scholar] [CrossRef]
  34. Figueiredo, A.; Loureiro, A.; Batista, D.; Monteiro, F.; Várzea, V.; Pais, M.S.; Gichuru, E.K.; Silva, M.C. Validation of reference genes for normalization of qPCR gene expression data from Coffea spp. hypocotyls inoculated with Colletotrichum kahawae. BMC Res. Notes 2013, 6, 388. [Google Scholar] [CrossRef]
  35. Barsalobres-Cavallari, C.F.; Severino, F.E.; Maluf, M.P.; Maia, I.G. Identification of suitable internal control genes for expression studies in Coffea arabica under different experimental conditions. BMC Mol. Biol. 2009, 10, 1. [Google Scholar] [CrossRef] [PubMed]
  36. Kwan, Y.M.; Meon, S.; Ho, C.L.; Wong, M.Y. Selection of reference genes for quantitative real-time PCR normalization in Ganoderma-infected oil palm (Elaeis guineensis) seedlings. Australas. Plant Pathol. 2016, 45, 261–268. [Google Scholar] [CrossRef]
  37. Olbrich, M.; Gerstner, E.; Welzl, G.; Fleischmann, F.; Oßwald, W.; Bahnweg, G.; Ernst, D. Quantification of mRNAs and housekeeping gene selection for quantitative real-time RT-PCR normalization in European beech (Fagus sylvatica L.) during abiotic and biotic stress. Z. Naturforsch. C 2008, 63, 574–582. [Google Scholar] [CrossRef][Green Version]
  38. Bansal, R.; Mittapelly, P.; Cassone, B.J.; Mamidala, P.; Redinbaugh, M.G.; Michel, A. Recommended reference genes for quantitative PCR analysis in soybean have variable stabilities during diverse biotic stresses. PLoS ONE 2015, 10, e0134890. [Google Scholar] [CrossRef] [PubMed]
  39. Hirschburger, D.; Müller, M.; Voegele, R.T.; Link, T. Reference genes in the pathosystem Phakopsora pachyrhizi/soybean suitable for normalization in transcript profiling. Int. J. Mol. Sci. 2015, 16, 23057–23075. [Google Scholar] [CrossRef] [PubMed]
  40. Štajner, N.; Cregeen, S.; Javornik, B. Evaluation of reference genes for RT-qPCR expression studies in hop (Humulus lupulus L.) during infection with vascular pathogen Verticillium albo-atrum. PLoS ONE 2013, 8, e68228. [Google Scholar] [CrossRef]
  41. Saha, G.C.; Vandemark, G.J. Stability of expression of reference genes among different lentil (Lens culinaris) genotypes subjected to cold stress, white mold disease, and Aphanomyces root rot. Plant Mol. Biol. Rep. 2013, 31, 1109–1115. [Google Scholar] [CrossRef]
  42. Prakash, H.; Shukla, P.; Ramesha, A.; Arunakumar, G.S.; Doss, S.G.; Ponnuvel, K.M. Evaluation of reference genes for accurate normalization of qPCR data under biotic stresses in mulberry (Morus indica L.). Sci. Hortic. 2024, 323, 112507. [Google Scholar] [CrossRef]
  43. Rego, E.C.S.; Pinheiro, T.D.M.; Antonino, J.D.; Alves, G.S.C.; Cotta, M.G.; Fonseca, F.C.D.A.; Miller, R.N.G. Stable reference genes for RT-qPCR analysis of gene expression in the Musa acuminata-Pseudocercospora musae interaction. Sci. Rep. 2019, 9, 14592. [Google Scholar] [CrossRef]
  44. de Castro Costa, É.; Bastos, L.S.; Gomes, T.G.; Miller, R.N.G. Reference genes for RT-qPCR analysis in Musa acuminata genotypes contrasting in resistance to Fusarium oxysporum f. sp. cubense subtropical race 4. Sci. Rep. 2024, 14, 16578. [Google Scholar] [CrossRef]
  45. Chen, L.; Zhong, H.Y.; Kuang, J.F.; Li, J.G.; Lu, W.J.; Chen, J.Y. Validation of reference genes for RT-qPCR studies of gene expression in banana fruit under different experimental conditions. Planta 2011, 234, 377–390. [Google Scholar] [CrossRef] [PubMed]
  46. Zhang, L.; Liu, L.; Cheng, P.; Shen, H.; Rong, B.; Liu, W.; Yu, G. Identification and validation of reference genes for RT-qPCR analysis in banana (Musa spp.) under Fusarium wilt resistance induction conditions. J. Phytopathol. 2017, 165, 746–754. [Google Scholar] [CrossRef]
  47. Hashemipetroudi, S.H.; Ghorbani, H.; Rostami, M.; Rezaei, A.; Goodwin, P.H.; Kuhlmann, M. Selection of reference genes for RT-qPCR analysis of rice with Rhizoctonia solani infection and biocontrol PGPR/KSi application. Mol. Biol. Rep. 2023, 50, 4225–4237. [Google Scholar] [CrossRef] [PubMed]
  48. Bevitori, R.; Oliveira, M.B.; Grossi-de-Sá, M.F.; Lanna, A.C.; da Silveira, R.D.; Petrofeza, S. Selection of optimized candidate reference genes for qRT-PCR normalization in rice (Oryza sativa L.) during Magnaporthe oryzae infection and drought. Genet. Mol. Res. 2014, 13, 9795–9805. [Google Scholar] [CrossRef]
  49. Narsai, R.; Ivanova, A.; Ng, S.; Whelan, J. Defining reference genes in Oryza sativa using organ, development, biotic and abiotic transcriptome datasets. BMC Plant Biol. 2010, 10, 56. [Google Scholar] [CrossRef]
  50. Borges, A.; Tsai, S.M.; Caldas, D.G.G. Validation of reference genes for RT-qPCR normalization in common bean during biotic and abiotic stresses. Plant Cell Rep. 2012, 31, 827–838. [Google Scholar] [CrossRef]
  51. Umadevi, P.; Suraby, E.J.; Anandaraj, M.; Nepolean, T. Identification of stable reference gene for transcript normalization in black pepper-Phytophthora capsici pathosystem. Physiol. Mol. Biol. Plants 2019, 25, 945–952. [Google Scholar] [CrossRef]
  52. Saha, G.C.; Vandemark, G.J. Evaluation of expression stability of candidate references genes among green and yellow pea cultivars (Pisum sativum L.) subjected to abiotic and biotic stress. Am. J. Plant Sci. 2012, 3, 235–242. [Google Scholar] [CrossRef][Green Version]
  53. Klie, M.; Debener, T. Identification of superior reference genes for data normalisation of expression studies via quantitative PCR in hybrid roses (Rosa hybrida). BMC Res. Notes 2011, 4, 518. [Google Scholar] [CrossRef]
  54. Huang, N.; Ling, H.; Liu, F.; Su, Y.; Su, W.; Mao, H.; Zhang, X.; Wang, L.; Chen, R.; Que, Y. Identification and evaluation of PCR reference genes for host and pathogen in sugarcane-Sporisorium scitamineum interaction system. BMC Genom. 2018, 19, 479. [Google Scholar] [CrossRef]
  55. Wei, L.; Miao, H.; Zhao, R.; Han, X.; Zhang, T.; Zhang, H. Identification and testing of reference genes for Sesame gene expression analysis by quantitative real-time PCR. Planta 2013, 237, 873–889. [Google Scholar] [CrossRef] [PubMed]
  56. Bai, S.; Wang, X.; Guo, M.; Cheng, G.; Khan, A.; Yao, W.; Gao, Y.; Li, J. Selection and Evaluation of Reference Genes for Quantitative Real-Time PCR in Tomato (Solanum lycopersicum L.) Inoculated with Oidium neolycopersici. Agronomy 2022, 12, 3171. [Google Scholar] [CrossRef]
  57. Pinheiro, T.T.; Litholdo, C.G., Jr.; Sereno, M.L.; Leal, G.A., Jr.; Albuquerque, P.S.B.D.; Figueira, A. Establishing references for gene expression analyses by RT-qPCR in Theobroma cacao tissues. Genet. Mol. Res. 2011, 10, 3291–3305. [Google Scholar] [CrossRef]
  58. Prasad, P.; Savadi, S.; Bhardwaj, S.C.; Gangwar, O.P.; Khan, H.; Kumar, S. Evaluation of candidate reference genes for normalization of RT-qPCR data in wheat during pre-haustorial stages of Puccinia triticina infection. Trop. Plant Path. 2020, 45, 96–101. [Google Scholar] [CrossRef]
  59. Scholtz, J.J.; Visser, B. Reference gene selection for qPCR gene expression analysis of rust-infected wheat. Physiol. Mol. Plant Pathol. 2013, 81, 22–25. [Google Scholar] [CrossRef]
  60. Jose, S.; Abbey, J.; Jaakola, L.; Percival, D. Selection and validation of reliable reference genes for gene expression studies from Monilinia vaccinii-corymbosi infected wild blueberry phenotypes. Sci. Rep. 2020, 10, 11688. [Google Scholar] [CrossRef] [PubMed]
  61. Monteiro, F.; Sebastiana, M.; Pais, M.S.; Figueiredo, A. Reference gene selection and validation for the early responses to downy mildew infection in susceptible and resistant Vitis vinifera cultivars. PLoS ONE 2013, 8, e72998. [Google Scholar] [CrossRef] [PubMed]
  62. Alves Oliveira, D.; Tang, J.D.; Warburton, M.L. Reference gene selection for RT-qPCR analysis in Maize Kernels inoculated with Aspergillus flavus. Toxins 2021, 13, 386. [Google Scholar] [CrossRef]
  63. Joseph, J.T.; Poolakkalody, N.J.; Shah, J.M. Screening internal controls for expression analyses involving numerous treatments by combining statistical methods with reference gene selection tools. Physiol. Mol. Biol. Plants 2019, 25, 289–301. [Google Scholar] [CrossRef] [PubMed]
  64. Pombo, M.A.; Ramos, R.N.; Zheng, Y.; Fei, Z.; Martin, G.B.; Rosli, H.G. Transcriptome-based identification and validation of reference genes for plant-bacteria interaction studies using Nicotiana benthamiana. Sci. Rep. 2019, 9, 1632. [Google Scholar] [CrossRef]
  65. Sagun, C.M.; Grandmottet, F.; Suachaowna, N.; Sujipuli, K.; Ratanasut, K. Validation of suitable reference genes for normalization of quantitative reverse transcriptase-polymerase chain reaction in rice infected by Xanthomonas oryzae pv. oryzae. Plant Gene 2020, 21, 100217. [Google Scholar] [CrossRef]
  66. Albuquerque, G.M.R.; Fonseca, F.C.A.; Boiteux, L.S.; Borges, R.C.F.; Miller, R.N.G.; Lopes, C.A.; Souza, E.B.; Fonseca, M.E.N. Stability analysis of reference genes for RT-qPCR assays involving compatible and incompatible Ralstonia solanacearum-tomato ‘Hawaii 7996’ interactions. Sci. Rep. 2021, 11, 18719. [Google Scholar] [CrossRef]
  67. Ghareeb, H.; Bozsó, Z.; Ott, P.G.; Wydra, K. Silicon and Ralstonia solanacearum modulate expression stability of housekeeping genes in tomato. Physiol. Mol. Plant Pathol. 2011, 75, 176–179. [Google Scholar] [CrossRef]
  68. Pombo, M.A.; Zheng, Y.; Fei, Z.; Martin, G.B.; Rosli, H.G. Use of RNA-seq data to identify and validate RT-qPCR reference genes for studying the tomato-Pseudomonas pathosystem. Sci. Rep. 2017, 7, 44905. [Google Scholar] [CrossRef]
  69. De Spiegelaere, W.; Dern-Wieloch, J.; Weigel, R.; Schumacher, V.; Schorle, H.; Nettersheim, D.; Bergmann, M.; Brehm, R.; Kliesch, S.; Vandekerckhove, L.; et al. Reference gene validation for RT-qPCR, a note on different available software packages. PLoS ONE 2015, 10, e0122515. [Google Scholar] [CrossRef] [PubMed]
  70. Vandesompele, J.; De Preter, K.; Pattyn, F.; Poppe, B.; Van Roy, N.; De Paepe, A.; Speleman, F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3, research0034-1. [Google Scholar] [CrossRef]
  71. Andersen, C.L.; Jensen, J.L.; Orntoft, T.F. Normalization of real-time quantitative reverse transcription-PCR data: A model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004, 64, 5245–5250. [Google Scholar] [CrossRef]
  72. Pfaffl, M.W.; Tichopad, A.; Prgomet, C.; Neuvians, T.P. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotechnol. Lett. 2004, 26, 509–515. [Google Scholar] [CrossRef]
  73. Silver, N.; Best, S.; Jiang, J.; Thein, S.L. Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol. Biol. 2006, 7, 33. [Google Scholar] [CrossRef] [PubMed]
  74. Available online: https://github.com/fulxie/RefFinder (accessed on 15 November 2025).
  75. Duan, M.; Wang, J.; Zhang, X.; Yang, H.; Wang, H.; Qiu, Y.; Song, J.; Guo, Y.; Li, X. Identification of Optimal Reference Genes for Expression Analysis in Radish (Raphanus sativus L.) and Its Relatives Based on Expression Stability. Front. Plant Sci. 2017, 8, 1605. [Google Scholar] [CrossRef]
  76. Xie, F.; Wang, J.; Zhang, B. RefFinder: A web-based tool for comprehensively analyzing and identifying reference genes. Funct. Integr. Genomics 2023, 23, 125. [Google Scholar] [CrossRef]
  77. Auler, P.A.; Benitez, L.C.; do Amaral, M.N.; Vighi, I.L.; Dos Santos Rodrigues, G.; da Maia, L.C.; Braga, E.J. Evaluation of stability and validation of reference genes for RT-qPCR expression studies in rice plants under water deficit. J. Appl. Genet. 2017, 58, 163–177. [Google Scholar] [CrossRef]
  78. Sun, C.W.; Griffen, S.; Callis, J. A model for the evolution of polyubiquitin genes from the study of Arabidopsis thaliana ecotypes. Plant Mol. Biol. 1997, 34, 745–758. [Google Scholar] [CrossRef] [PubMed]
  79. Hernandez-Garcia, C.M.; Martinelli, A.P.; Bouchard, R.A.; Finer, J.J. A soybean (Glycine max) polyubiquitin promoter gives strong constitutive expression in transgenic soybean. Plant Cell Rep. 2009, 28, 837–849. [Google Scholar] [CrossRef]
  80. Binet, M.N.; Weil, J.H.; Tessier, L.H. Structure and expression of sunflower ubiquitin genes. Plant Mol. Biol. 1991, 17, 395–407. [Google Scholar] [CrossRef] [PubMed]
  81. Callis, J.; Raasch, J.A.; Vierstra, R.D. Ubiquitin extension proteins of Arabidopsis thaliana. Structure, localization, and expression of their promoters in transgenic tobacco. J. Biol. Chem. 1990, 265, 12486–12493. [Google Scholar] [CrossRef] [PubMed]
  82. Gao, W.; Zhang, L.; Zhang, Y.; Zhang, P.; Shahinnia, F.; Chen, T.; Yang, D. Genome-wide identification and expression analysis of the UBC gene family in wheat (Triticum aestivum L.). BMC Plant Biol. 2024, 24, 341. [Google Scholar] [CrossRef]
  83. Jeon, E.H.; Pak, J.H.; Kim, M.J.; Kim, H.J.; Shin, S.H.; Lee, J.H.; Kim, D.H.; Oh, J.S.; Oh, B.J.; Jung, H.W.; et al. Ectopic expression of ubiquitin-conjugating enzyme gene from wild rice, OgUBC1, confers resistance against UV-B radiation and Botrytis infection in Arabidopsis thaliana. Biochem. Biophys. Res. Commun. 2012, 427, 309–314. [Google Scholar] [CrossRef]
  84. Pickart, C.M. Mechanisms underlying ubiquitination. Annu. Rev. Biochem. 2001, 70, 503–533. [Google Scholar] [CrossRef]
  85. Kim, S.C.; Guo, L.; Wang, X. Nuclear moonlighting of cytosolic glyceraldehyde-3-phosphate dehydrogenase regulates Arabidopsis response to heat stress. Nat. Commun. 2020, 11, 3439. [Google Scholar] [CrossRef]
  86. Zhang, H.; Zhao, Y.; Zhou, D.-X. Rice NAD+ dependent histone deacetylase OsSRT1 represses glycolysis function of GAPDH as a transcriptional activator of glycolytic genes. Nucleic Acids Res. 2017, 45, 12241–12255. [Google Scholar] [CrossRef] [PubMed]
  87. Zaffagnini, M.; Fermani, S.; Costa, A.; Lemaire, S.; Trost, P. Plant cytoplasmic GAPDH: Redox post-translational modifications and moonlighting properties. Front. Plant Sci. 2013, 4, 450. [Google Scholar] [CrossRef]
  88. McDowell, J.M.; Huang, S.; McKinney, E.C.; Yong-Qiang, A.; Meaghed, R.B. Structure evolution of the actin gene family in Arabidopsis thaliana. Genetics 1996, 142, 587–602. [Google Scholar] [CrossRef]
  89. Eleftheriou, E.P.; Palevitz, B.A. The effect of cytochalasin D on preprophase band organization in root tip cells of Allium. J. Cell Sci. 1992, 103, 989–998. [Google Scholar] [CrossRef]
  90. Wernicke, W.; Jung, G. Role of cytoskeleton in cell shaping of developing mesophyll of wheat (Triticum aestivum L.). Eur. J. Cell Biol. 1992, 57, 88–94. [Google Scholar] [PubMed]
  91. Šlajcherová, K.; Fišerová, J.; Fischer, L.; Schwarzerová, K. Multiple Actin Isotypes in Plants: Diverse Genes for Diverse Roles? Front. Plant Sci. 2012, 3, 226. [Google Scholar] [CrossRef] [PubMed]
  92. Radchuk, V.V. The transcriptome of the tubulin gene family in plants. In The Plant Cytoskeleton: A Key Tool for Agro-Biotechnology, 2nd ed.; Blume, Y.B., Baird, W.V., Yemets, A.I., Breviario, D., Eds.; Springer: Dordrecht, The Netherland, 2008; pp. 219–241. [Google Scholar] [CrossRef]
  93. Silflow, C.D.; Oppenheimer, D.G.; Kopozak, S.D.; Ploense, S.E.; Ludwig, S.R.; Haas, N.; Peter Snustad, D. Plant tubulin genes: Structure and differential expression during development. Dev. Genet. 1987, 8, 435–460. [Google Scholar] [CrossRef]
  94. Gardiner, J.; Overall, R.; Marc, J. Plant microtubule cytoskeleton complexity: Microtubule arrays as fractals. J. Exp. Bot. 2012, 63, 635–642. [Google Scholar] [CrossRef][Green Version]
  95. Dutt, S.; Parkash, J.; Mehra, R.; Sharma, N.; Singh, B.; Raigond, P.; Joshi, A.; Chopra, S.; Singh, B.P. Translation initiation in plants: Roles and implications beyond protein synthesis. Biol. Plant. 2015, 59, 401–412. [Google Scholar] [CrossRef]
  96. Shopan, J.; Liu, C.; Hu, Z.; Zhang, M.; Yang, J. Identification of eukaryotic translation initiation factors and the temperature-dependent nature of Turnip mosaic virus epidemics in allopolyploid Brassica juncea. 3Biotech 2020, 10, 75. [Google Scholar] [CrossRef]
  97. Raabe, K.; Honys, D.; Michailidis, C. The role of eukaryotic initiation factor 3 in plant translation regulation. Plant Physiol. Biochem. 2019, 145, 75–83. [Google Scholar] [CrossRef] [PubMed]
  98. Dinkova, T.D.; Martinez-Castilla, L.; Cruz-Espíndola, M.A. The Diversification of eIF4E Family Members in Plants and Their Role in the Plant-Virus Interaction. In Evolution of the Protein Synthesis Machinery and Its Regulation; Hernández, G., Jagus, R., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 187–205. [Google Scholar] [CrossRef]
  99. Cocco, E.; Farci, D.; Guadalupi, G.; Manconi, B.; Maxia, A.; Piano, D. The elongation factor 1-alpha as storage reserve and environmental sensor in Nicotiana tabacum L. seeds. Plant Sci. 2024, 345, 112113. [Google Scholar] [CrossRef] [PubMed]
  100. Xu, L.; Zhang, L.; Liu, Y.; Sod, B.; Li, M.; Yang, T.; Gao, T.; Yang, Q.; Long, R. Overexpression of the elongation factor MtEF1A1 promotes salt stress tolerance in Arabidopsis thaliana and Medicago truncatula. BMC Plant Biol. 2023, 23, 138. [Google Scholar] [CrossRef]
  101. Fu, J.; Momčilović, I.; Prasad, P.V.V. Roles of protein synthesis elongation factor EF-TU in heat tolerance in plants. J. Bot. 2012, 2012, 835836. [Google Scholar] [CrossRef]
  102. Cottage, A.; Edwards, Y.J.; Elgar, G. SAND, a new protein family: From nucleic acid to protein structure and function prediction. Comp. Funct. Genom. 2001, 2, 226–235. [Google Scholar] [CrossRef]
  103. Carles, C.C.; Fletcher, J.C. Missing links between histones and RNA Pol II arising from SAND? Epigenetics 2010, 5, 381–385. [Google Scholar] [CrossRef]
  104. Francois, M.; Donovan, P.; Fontaine, F. Modulating transcription factor activity: Interfering with protein-protein interaction networks. Semin. Cell Dev. Biol. 2020, 99, 12–19. [Google Scholar] [CrossRef]
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Figure 1. Distribution of the 47 studies included in this review by year of publication (2008–2024) and by type of pathosystem investigated.
Figure 1. Distribution of the 47 studies included in this review by year of publication (2008–2024) and by type of pathosystem investigated.
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Figure 2. Overlay visualization map generated with VOSviewer (version 1.6.20), showing the most frequently used keywords in studies on reference genes in plant–pathogen interactions from 2008 to 2024.
Figure 2. Overlay visualization map generated with VOSviewer (version 1.6.20), showing the most frequently used keywords in studies on reference genes in plant–pathogen interactions from 2008 to 2024.
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Figure 3. Network generated with VOSviewer showing the most frequently used journals publishing research on reference genes in plant–pathogen interactions from 2008 to 2024.
Figure 3. Network generated with VOSviewer showing the most frequently used journals publishing research on reference genes in plant–pathogen interactions from 2008 to 2024.
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Figure 4. Frequencies of the most used reference genes in fungal or oomycete (A) and bacterial (B) plant pathosystem. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH); Actin (ACT); Tubulin (TUB); Elongation factor (EF); Ubiquitin (UBQ); Cyclophilin (CYP); Ubiquitin-conjugating enzyme (UBC); Eukaryotic translation initiation factor (eIF); 18S ribosomal RNA (18S); Expressed protein (EXP).
Figure 4. Frequencies of the most used reference genes in fungal or oomycete (A) and bacterial (B) plant pathosystem. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH); Actin (ACT); Tubulin (TUB); Elongation factor (EF); Ubiquitin (UBQ); Cyclophilin (CYP); Ubiquitin-conjugating enzyme (UBC); Eukaryotic translation initiation factor (eIF); 18S ribosomal RNA (18S); Expressed protein (EXP).
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Table 1. Studies on candidate reference genes stability in plant/fungi and oomycetes or bacteria pathosystems.
Table 1. Studies on candidate reference genes stability in plant/fungi and oomycetes or bacteria pathosystems.
Plant Pathogen Reference
Fungi and Oomycetes
Avena sativaBlumeria graminis f.sp. avenae
Puccinia graminis f.sp. avenae
Puccinia coronata f.sp. avenae		[26]
[27]
[28]
Avena fatuaTrichoderma polysporum[29]
Brassica rapaPeronospora parasitica
(Hyaloperonospora parasitica)		[30]
Carica papayaColletotrichum gloeosporioides[31]
Citrus × clementinaAlternaria alternata
Phytophthora parasitica
(Phytophthora nicotianae)		[32]
Citrus reshniAlternaria alternata
Phytophthora parasitica
(Phytophthora nicotianae)		[32]
Citrus reticulataAlternaria alternata
Phytophthora parasitica
(Phytophthora nicotianae)		[32]
Citrus sinensisAlternaria alternata
Phytophthora parasitica
(Phytophthora nicotianae)		[32]
Citrus sunkiPhytophthora parasitica
(Phytophthora nicotianae)
Alternaria alternata		[33]
[32]
Coffea arabicaColletotrichum kahawae
Hemileia vastatrix		[34]
[35]
Cucumis meloFusarium oxysporum f.sp. melonis[5]
Elaeis guineensisGanoderma boninense[36]
Eucalyptus grandis × 
Eucalyptus urophylla		Puccinia psidii
(Austropuccinia psidii)		[4]
Fagus sylvaticaPhytophthora citricola[37]
Glycine maxMicrosphaera diffusa 
(Erysiphe diffusa)
Phakopsora pachyrhizi		[38]
[39]
Humulus lupulusVerticillium alboatrum[40]
Lens culinarisSclerotinia sclerotiorum 
Aphanomyces euteiches		[41]
Morus indicaFusarium solani 
Lasiodiplodia theobromae 
Phyllactinia corylea 
(Phyllactinia guttata)		[42]
Musa acuminataPseudocercospora musae 
Fusarium oxysporum f.sp. cubense 
Colletotrichum musae		[43]
[44]
[45]
Musa spp. Fusarium oxysporum f.sp. cubense[46]
Oryza sativaRhizoctonia solani 
Magnaporthe oryzae 
(Pyricularia oryzae)
Magnaporthe grisea
(Pyricularia grisea)		[47]
[48]
[49]
Phaseolus vulgarisColletotrichum lindemuthianum[50]
Piper nigrumPhytophthora capsici[51]
Pisum sativumSclerotinia sclerotiorum[52]
Poncirus trifoliataPhytophthora parasitica
(Phytophthora nicotianae)
Alternaria alternata		[33]
[32]
Rosa hybridaDiplocarpon rosae[53]
Saccharum officinarumSporisorium scitamineum[54]
Sesamum indicumFusarium oxysporum[55]
Solanum lycopersicumOidium neolycopersici
(Erysiphe neolycopersici)		[56]
Theobroma cacaoMoniliophthora perniciosa[57]
Triticum aestivumPuccinia triticina
(Puccinia recondita)
Puccinia striiformis 
Puccinia graminis f.sp. tritici		[58]
[59]
Vaccinium angustifolium f. nigrumMonilinia vaccinii-corymbosi[60]
Vaccinium myrtilloidesMonilinia vaccinii-corymbosi[60]
Vitis viniferaPlasmopara viticola[17,61]
Zea maysAspergillus flavus[62]
Bacteria
Actinidia deliciosaPseudomonas syringae pv. actinidiae[6]
Arabidopsis thalianaAgrobacterium tumefaciens[63]
Citrus clementinaXylella fastidiosa 
Candidatus Liberibacter asiaticus 		[32]
Citrus reshniXylella fastidiosa 
Candidatus Liberibacter asiaticus 		[32]
Citrus reticulataXylella fastidiosa 
Candidatus Liberibacter asiaticus 		[32]
Citrus sinensisXylella fastidiosa 
Candidatus Liberibacter asiaticus 		[32]
Citrus sunkiXylella fastidiosa 
Candidatus Liberibacter asiaticus 		[32]
Nicotiana benthamianaPseudomonas fluorescens[64]
Oryza sativaXantomonas oryzae pv. oryzae 
Xantomonas oryzae pv. oryzicola		[65]
[49]
Poncirus trifoliataXylella fastidiosa 
Candidatus Liberibacter asiaticus 		[32]
Solanum lycopersicumRalstonia solanacearum 
Xantomonas syringae pv. tomato 
Pseudomonas syringae pv. tomato		[66]
[67]
[68]
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Lizzio, A.; Battaglia, V.; Lahoz, E.; Reverberi, M.; Petriccione, M. Reference Genes in Plant–Pathogen Interaction: A Bibliometric Analysis. Horticulturae 2025, 11, 1416. https://doi.org/10.3390/horticulturae11121416

AMA Style

Lizzio A, Battaglia V, Lahoz E, Reverberi M, Petriccione M. Reference Genes in Plant–Pathogen Interaction: A Bibliometric Analysis. Horticulturae. 2025; 11(12):1416. https://doi.org/10.3390/horticulturae11121416

Chicago/Turabian Style

Lizzio, Agata, Valerio Battaglia, Ernesto Lahoz, Massimo Reverberi, and Milena Petriccione. 2025. "Reference Genes in Plant–Pathogen Interaction: A Bibliometric Analysis" Horticulturae 11, no. 12: 1416. https://doi.org/10.3390/horticulturae11121416

APA Style

Lizzio, A., Battaglia, V., Lahoz, E., Reverberi, M., & Petriccione, M. (2025). Reference Genes in Plant–Pathogen Interaction: A Bibliometric Analysis. Horticulturae, 11(12), 1416. https://doi.org/10.3390/horticulturae11121416

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