Abstract

   Red radish microgreens (RRM) have gained considerable attention for
   their promising therapeutic potential. However, the molecular
   mechanisms underlying their bioactivity remain inadequately
   characterized. This study explores the anti-inflammatory, antioxidant,
   and anticancer properties of RRM extract using in silico and in vivo
   Drosophila model analyses. The metabolite profile of the RRM extract
   was characterized using comprehensive metabolomics techniques,
   including Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid
   Chromatography High-Resolution Mass Spectrometry (LC-HRMS).
   Furthermore, in silico analysis utilizing network pharmacology
   identified target proteins of RRM compounds associated with cancer,
   inflammation, and oxidative stress. Concurrently, in vivo experiments
   with Drosophila melanogaster PGRP-LB^Δ (Dm PGRP-LB^Δ) larvae was
   conducted to assess the extract’s impact on immune and oxidative stress
   pathways. In silico analysis revealed that RRM compounds interacted
   with key proteins (AKT1, ESR1, MAPK1, SRC, TP53), modulating pathways
   related to cancer, inflammation, and oxidative stress. Molecular
   dynamics simulations reinforced the docking results by confirming
   robust binding of kaempferitrin to AKT1. In vivo studies showed that
   RRM extract suppressed immune-related genes (dptA, totA) through the
   NFκB and JAK-STAT pathways, reduced ROS levels, and selectively
   regulated antioxidant gene expression by enhancing sod1 while
   decreasing sod2 and cat. These results suggest RRM extract as a
   functional food for managing oxidative stress, inflammation, and
   cancer. Further research in higher organisms and clinical settings is
   needed.

Supplementary Information

   The online version contains supplementary material available at
   10.1038/s41598-025-02999-5.

   Keywords: Red radish microgreens, Functional food, Drosophila, Network
   Pharmacology, Molecular mechanisms

   Subject terms: Pharmaceutics, Target identification

Introduction

   Microgreens, the young shoots of vegetables and herbs, are recognized
   for their rich concentration of bioactive compounds with notable health
   benefits. Among these, red radish (Raphanus sativus var. sativus)
   microgreens have gained attention due to their high levels of bioactive
   compounds with numerous health benefits. Research has demonstrated that
   these compounds modulate key biological pathways related to oxidative
   stress, inflammation, and carcinogenesis, highlighting the potential of
   red radish microgreens (RRM) in functional food development^[46]1.

   Red radish has long been valued for its health benefits, particularly
   due to bioactive compounds like glucosinolates, isothiocyanates, and
   sulforaphane^[47]2. These compounds play crucial roles in detoxifying
   carcinogens, inhibiting tumor growth, and contributing to antioxidant
   activity by scavenging free radicals and reducing oxidative damage, a
   key factor in chronic inflammation and cancer progression. Red radish
   microgreens retain these properties, further establishing their
   potential as a functional food with anti-inflammatory^[48]3 and
   anticancer^[49]4 activities.

   Despite existing findings, significant gaps remain in understanding the
   molecular mechanisms driving the anti-inflammatory and anticancer
   effects of red radish microgreens. Most studies have relied on in vitro
   assays^[50]5–[51]7, with limited use of omics approaches or advanced
   computational methods to predict molecular interactions. This study
   aims to address these gaps by employing a multidisciplinary approach
   that integrates metabolomics, in silico analyses, and preclinical
   validation. Metabolomics will provide a comprehensive profile of
   bioactive compounds in RRM and their roles in metabolic pathways,
   overcoming the limitations of analyzing individual compounds. In silico
   analyses will predict how these compounds interact with antioxidant-,
   inflammation- and cancer-related targets, offering mechanistic
   insights. Finally, preclinical validation using Drosophila models will
   assess the biological relevance of these findings, ensuring their
   practical application in health contexts.

   Computer methods have emerged as an important tool in new drug
   discovery, enabling scientists to screen large compound libraries
   efficiently and predict how they will interact with target proteins.
   Techniques such as molecular docking, virtual screening, and network
   pharmacology simulate drug-target interactions at the molecular level,
   thereby accelerating the discovery and optimization of promising lead
   compounds^[52]8–[53]12. Furthermore, the application of machine
   learning techniques has been employed to enhance these predictions,
   supporting the development of therapies with multiple targets and
   speeding the drug development process^[54]13,[55]14.

   Preclinical drug testing necessitates appropriate model organisms, but
   ethical concerns regarding traditional animals like mice and rats are
   increasing. Drosophila melanogaster, the fruit fly, presents a
   promising alternative with a long history in research, including
   studies on neurodegenerative diseases and metabolic
   syndromes^[56]15,[57]16. Sharing approximately 60% genetic similarity
   with humans, D. melanogaster serves as a valuable model for studying
   human diseases. Up to 75% of human disease-related genes have an
   ortholog in Drosophila^[58]17, enabling researchers to develop disease
   models through genetic manipulation or chemical induction. Specialized
   tools can further predict orthologous genes based on sequence,
   expression patterns, and function, reinforcing Drosophila’s role in
   biomedical research^[59]18. Moreover, Drosophila has been widely used
   to study inflammatory pathways, oxidative stress responses, and
   cancer-related mechanisms due to its conserved immune signaling
   pathways, including Toll, immune deficiency and JAK/STAT, which closely
   resemble those in humans^[60]19,[61]20. It has also served as an
   alternative model organism in nutrigenomics research^[62]21. Studies
   have shown that Drosophila can effectively model chronic inflammation,
   tumor progression, and oxidative stress-related diseases, making it a
   valuable system for evaluating the therapeutic potential of bioactive
   compounds^[63]22.

   In this study, the anti-inflammatory potential of RRM extract was
   tested in vivo using the PGRP-LB^∆ mutant of D. melanogaster. The aim
   is to evaluate the anti-inflammatory, antioxidant, and anticancer
   properties of RRM by identifying bioactive compounds, elucidating their
   mechanisms of action, and validating their therapeutic potential for
   disease prevention and therapy.

Results

Chemical composition and multi-target gene network analysis of RRM bioactive
compounds

   LC-HRMS and GC-MS analyses reveal a diverse chemical profile of RRM
   compounds. Specifically, 40 compounds were detected via GC-MS and 191
   via LC-MS, yielding a total of 231 compounds. After applying filtering
   criteria, 141 compounds remained. These were then categorized into
   various chemical groups (Fig. [64]1): lipids represented the largest
   group (24.7%), followed by phenolic compounds (16.0%) and amino acids
   with their derivatives (12.3%). Additionally, heterocyclic compounds
   (11.1%), fatty acyls (6.2%), and smaller fractions of vitamins,
   terpenes, alkaloids, and other classes (ranging from 2.5 to 4.9%)
   further underscore the chemical diversity.

Fig. 1.

   [65]Fig. 1
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   Classification of RRM compounds identified via LC-HRMS and GC-MS
   analyses, categorized based on their chemical classes.

   A total of 141 filtered compounds were subsequently utilized to target
   genes associated with cancer, inflammation, and oxidative stress. The
   analysis identified 7,645 target genes corresponding to the bioactive
   compounds in RRM, which were then refined to 1,173 unique genes after
   removing duplicates. From the GeneCards database, 30,554 cancer-related
   genes, 16,357 inflammation-associated genes, and 14,285 oxidative
   stress-related genes were retrieved. After merging these datasets and
   eliminating redundancies, a total of 35,910 unique genes implicated in
   these biological processes were obtained. A Venn diagram analysis
   identified 1,154 overlapping genes that serve as common targets of RRM
   compounds and are associated with cancer, inflammation, and oxidative
   stress. This significant overlap underscores the multitargeting
   potential of RRM bioactive compounds, highlighting their ability to
   modulate interconnected pathways involved in these conditions. These
   overlapping genes were prioritized for further investigation, as
   illustrated in Fig. [67]2.

Fig. 2.

   Fig. 2
   [68]Open in a new tab

   Venn diagram illustrating the overlapping target genes between RRM
   compounds and genes associated with cancer, inflammation, and oxidative
   stress.

   Protein-protein interaction (PPI) analysis was conducted for the 1,154
   target proteins using a stringent interaction score threshold of 0.900
   to ensure high-confidence results. The resulting network, visualized in
   Supplementary File 1, comprises 869 targets interconnected by 3,760
   interactions. Further network evaluation included centrality analyses,
   calculating degree, betweenness, closeness, and stress centralities to
   determine key regulatory nodes. The CytoHubba plugin, utilizing maximum
   neighborhood component (MNC) and maximal clique centrality (MCC)
   algorithms, identified 32 key RRM-targeted genes implicated in cancer,
   inflammation, and oxidative stress. The integrated network (Fig. [69]3)
   and centrality rankings (Table [70]1) highlight significant targets
   based on multiple topological metrics. Among these, TP53 emerged as the
   most central node, exhibiting the highest stress centrality
   (1,575,862), degree (97), and betweenness (0.16580), reinforcing its
   crucial role in cancer and oxidative stress pathways.

Fig. 3.

   [71]Fig. 3
   [72]Open in a new tab

   Network of the top 32 genes identified using MNC and MCC methods,
   highlighting key interactions among genes associated with cancer,
   inflammation, and oxidative stress. Central nodes, including TP53, SRC,
   MAPK1, and AKT1, play crucial roles in cell cycle regulation, signal
   transduction, and stress responses. This network visualization
   emphasizes the interconnected roles of these genes and their potential
   as therapeutic targets of RRM compounds.

Table 1.

   Top 32 RRM-targeted proteins associated with cancer, inflammation, and
   oxidative stress, identified using MNC and MCC methods.
   Gene symbol Full gene name Betweenness Closeness Degree Stress
   TP53 Tumor protein p53 0.16580 0.36032 97 1,575,862
   SRC Proto-oncogene tyrosine-protein kinase 0.05630 0.33269 65 584,680
   MAPK1 Mitogen-activated protein kinase 1 0.03301 0.34363 57 446,066
   ESR1 Estrogen receptor 1 0.03521 0.34026 47 443,166
   AKT1 AKT serine/threonine kinase 1 0.03302 0.34499 60 434,276
   MAPK3 Mitogen-activated protein kinase 3 0.02931 0.34119 55 400,168
   EP300 E1A binding protein p300 0.04708 0.33946 42 396,576
   HSP90AA1 Heat shock protein 90 alpha family class A member 1 0.03881
   0.34066 56 374,750
   JUN Jun proto-oncogene 0.03058 0.34444 40 337,184
   STAT3 Signal transducer and activator of transcription 0.02892 0.33787
   48 327,664
   PIK3CA Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit
   alpha 0.01884 0.32473 61 305,404
   JAK2 Janus kinase 2 0.01664 0.29020 33 304,780
   CTNNB1 Catenin beta-1 0.02517 0.33282 43 248,356
   PIK3R1 Phosphoinositide-3-kinase regulatory subunit 1 0.01303 0.31167
   61 239,400
   HDAC1 Histone deacetylase 1 0.01999 0.31358 41 218,734
   PTK2 Protein tyrosine kinase 2 0.01665 0.31912 42 208,326
   EGFR Epidermal growth factor receptor 0.01725 0.32755 39 200,464
   HRAS v-Ha-ras Harvey rat sarcoma viral oncogene homolog 0.01322 0.32256
   39 176,010
   HSP90AB1 Heat shock protein 90 alpha family class B member 1 0.01459
   0.32644 44 173,866
   PIK3CB Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit
   beta 0.00815 0.30382 56 173,100
   PIK3CD Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit
   delta 0.00776 0.30371 55 168,668
   GRB2 Growth factor receptor bound protein 2 0.01752 0.30170 45 158,654
   AKT3 AKT serine/threonine kinase 3 0.00697 0.32184 39 143,760
   KDR Kinase insert domain receptor 0.00510 0.29118 23 51,634
   ERBB2 Erb-b2 receptor tyrosine kinase 2 0.00153 0.30737 22 42,926
   IGF1R Insulin-like growth factor 1 receptor 0.00271 0.30212 23 41,246
   PTPN11 Protein tyrosine phosphatase non-receptor type 11 0.00222
   0.29394 34 38,826
   PTPN6 Protein tyrosine phosphatase, non-receptor type 6 0.00117 0.28619
   23 22,498
   JAK1 Janus kinase 1 0.00114 0.27982 24 16,474
   JAK3 Janus kinase 3 0.00079 0.27838 18 12,208
   PDGFRB Platelet-derived growth factor receptor beta 0.00013 0.28118 16
   3404
   PDGFRA Platelet-derived growth factor receptor alpha 0.00012 0.28100 15
   3182
   [73]Open in a new tab

   The skyline analysis (Fig. [74]4) identified TP53, SRC, AKT1, MAPK1,
   and ESR1 as the top five key proteins, consistent with rankings
   obtained from MNC and MCC scores. Among them, TP53 exhibited the
   highest centrality metrics, including a betweenness centrality of
   0.16580, closeness centrality of 0.36032, degree of 97, and stress
   centrality of 1,575,862, underscoring its pivotal role in network
   organization. Furthermore, 49 compounds were predicted to interact with
   these key proteins. Among them, five—kaempferitrin, alpha-tocopherol,
   kaempferol, raphanin, and beta-ionone—were specifically selected due to
   their confirmed presence in red radish.

Fig. 4.

   [75]Fig. 4
   [76]Open in a new tab

   Skyline results of the top 32 genes based on centrality metrics,
   highlighting five key genes. The legend indicates the color
   corresponding to specific genes, while point size represents degree
   values. Small black dots denote the remaining 27 genes not ranked among
   the top five.

   The enrichment analysis revealed key biological processes, molecular
   functions, and pathways associated with the target genes. Gene Ontology
   (GO) annotations (Fig. [77]5A) identified mammary gland epithelium
   development as the most significantly enriched biological process,
   while 14-3-3 protein binding was the most enriched molecular function.
   Additional enriched processes included kinase-related molecular
   functions, oxidative stress response, cellular stress mechanisms, and
   telomerase organization, highlighting the potential roles of these
   targets in cancer, inflammation, and oxidative stress pathways.

Fig. 5.

   Fig. 5
   [78]Open in a new tab

   (A) Bar plot of Gene Ontology (GO) annotations for the top five genes,
   illustrating classifications for biological processes and molecular
   functions. No cellular component annotations were identified for these
   genes; (B) KEGG pathway enrichment analysis of target genes,
   identifying endocrine resistance as the most significantly enriched
   pathway. More than half of the enriched pathways are cancer-related,
   including breast and prostate cancer, alongside key regulatory pathways
   such as apoptosis and cellular senescence. Dot size represents gene
   count, while color intensity corresponds to the false discovery rate
   (FDR), with red indicating the highest significance.

   The KEGG pathway enrichment analysis (Fig. [79]5B) ranks pathways based
   on statistical significance, represented by –log10(p) values and false
   discovery rates (FDR). Endocrine resistance emerged as the most
   enriched pathway, exhibiting the lowest FDR and highest –log10(p)
   value. Other significantly enriched pathways include thyroid hormone
   signaling, proteoglycans in cancer, and prolactin signaling. Notably,
   11 out of the 20 enriched pathways are cancer-related, encompassing
   specific malignancies such as breast, bladder, prostate, and colorectal
   cancer. Additionally, key regulatory pathways, including apoptosis and
   cellular senescence, highlight the involvement of these targets in
   cancer progression, inflammation, and oxidative stress.

In Silico profiling: drug-likeness, pharmacokinetics, toxicity, and
bioactivity predictions

   The drug-likeness of the selected compounds was evaluated using
   Lipinski’s Rule of Five, with their physicochemical properties compared
   against the established criteria (Table [80]2). Four of the five
   compounds met the criteria, each presenting fewer than two violations,
   while kaempferitrin exhibited multiple violations. Specifically,
   kaempferitrin exceeded the recommended thresholds for molecular weight
   (> 500 g/mol), hydrogen bond acceptors (> 10), hydrogen bond donors
   (> 5), and topological polar surface area (TPSA > 140 Å). However,
   deviations from Lipinski’s Rule do not preclude kaempferitrin’s
   potential therapeutic applications.

Table 2.

   Drug-likeness characteristics of the compounds evaluated based on
   Lipinski’s rule of five (Ro5) properties, with additional predictions
   derived from admetlab and ProTox analyses.
   Compound name Molecular weight (≤ 500 g/mol) Rotatable bonds (< 10)
   Hydrogen bond acceptors (≤ 10) Hydrogen bond donors (≤ 5) Topological
   polar surface area (≤ 140Å) Violation (< 2) (AdmetLAB) Lipinski’s rule
   of five
   Kaempferitrin 578.16 5 13 8 228.97 4 Rejected
   Alpha-Tocopherol 430.38 12 2 1 29.46 1 Accepted
   Kaempferol 286.05 1 5 4 111.13 0 Accepted
   Raphanin 175.01 4 3 0 35.42 0 Accepted
   Beta-Ionone 192.15 2 1 0 17.07 0 Accepted
   [81]Open in a new tab

   The pharmacokinetic assessment (Table [82]3) predicts that all selected
   compounds exhibit intestinal absorption in humans. Kaempferitrin is
   unlikely to cross the blood-brain barrier (BBB), likely due to its high
   molecular weight. Subcellular localization analysis suggests that
   kaempferitrin, alpha-tocopherol, and kaempferol predominantly localize
   to the mitochondria, whereas raphanin and beta-ionone are associated
   with the lysosome. Additionally, none of the compounds are predicted to
   undergo metabolism via CYP450 2C9 or 2D6, as they are not identified as
   substrates for these enzymes.

Table 3.

   Predicted absorption, distribution, metabolism, and excretion (ADME)
   properties of the five principal compounds.
   Compound name Human intestinal absorption BBB permeability Subcellular
   localization CYP450 2C9 substrate CYP450 2D6 substrate
   Kaempferitrin + − Mitochondria No No
   Alpha-Tocopherol + + Mitochondria No No
   Kaempferol + + Mitochondria No No
   Raphanin + + Lysosome No No
   Beta-Ionone + + Lysosome No No
   [83]Open in a new tab

   A “+” indicates a positive likelihood, while a “−” denotes a negative
   likelihood.

   Toxicity classification was assessed using the Globally Harmonized
   System of Classification and Labelling of Chemicals (GHS), which
   categorizes compounds into six classes (I–VI) based on predicted LD50
   values. Table [84]4 summarizes the toxicity predictions, identifying
   two distinct classes among the five compounds. Notably, raphanin is
   classified as Class III, with a predicted LD50 of 112 mg/kg, suggesting
   a moderate level of toxicity that warrants further investigation into
   its safety profile.

Table 4.

   Toxicity predictions for the five principal compounds, including
   predicted toxicity class, LD50 values, and hepatotoxicity assessments.
   Compound name ProTox 3 toxicity predictions
   Predicted toxicity class Predicted LD[50] Hepatoxicity
   Kaempferitrin V 5000 mg/kg No
   Alpha-Tocopherol V 5000 mg/kg No
   Kaempferol V 3919 mg/kg No
   Raphanin III 112 mg/kg No
   Beta-Ionone V 4590 mg/kg No
   [85]Open in a new tab

   Biological activity predictions, as illustrated in Fig. [86]6,
   highlight the potential roles of the five key compounds in cancer,
   inflammation, and oxidative stress regulation. Despite violating
   Lipinski’s rule of five, kaempferitrin remains a promising therapeutic
   candidate, exhibiting a high probability of antioxidant activity
   (Pa = 0.899), alongside kaempferol (Pa = 0.856). Alpha-tocopherol
   demonstrates the strongest antioxidant potential with a Pa value of
   0.967. Among the compounds, raphanin is associated with the fewest
   predicted activities, participating in only three. Overall, the five
   compounds are most likely to exert antioxidant, free radical
   scavenging, and anti-inflammatory effects, underscoring their potential
   in mitigating oxidative stress and inflammation.

Fig. 6.

   [87]Fig. 6
   [88]Open in a new tab

   Predicted biological activities of the five key compounds, focusing on
   their roles in cancer, inflammation, and oxidative stress. Pa values
   indicate the probability of bioactivity, with values above 0.7
   suggesting strong potential for laboratory validation, values between
   0.5 and 0.7 indicating moderate activity with expected in vivo and in
   vitro relevance, and values below 0.5 requiring further experimental
   confirmation.

Molecular Docking and receptor-ligand interaction analysis

   The binding affinity analysis (Fig. [89]7) identifies kaempferitrin as
   the compound exhibiting the strongest interactions across all five key
   target proteins. Importantly, its high binding affinity for MAPK1 and
   SRC underscores its potential role in modulating inflammatory and
   cancer-related pathways, given the central involvement of these
   proteins in cell signaling. Furthermore, kaempferitrin’s interactions
   with AKT1 and ESR1, both implicated in cancer progression and oxidative
   stress, highlight its potential as a multi-target therapeutic agent
   with antioxidant, anti-inflammatory, and anticancer properties. The
   calculated binding-free energy (BFE) values range from − 9.2 kcal/mol
   for TP53 to − 13.8 kcal/mol for AKT1, further supporting its strong
   binding capacity to key regulators of oxidative stress, inflammation,
   and cancer.

Fig. 7.

   [90]Fig. 7
   [91]Open in a new tab

   Docking analysis of the five key proteins, demonstrating the binding
   affinities of the most prominent complexes redocked with the native
   ligand (GH4, OHT, FRZ, H8H) and the five principal compounds under
   investigation.

   Kaempferol, ranking second in binding affinity, also demonstrates
   substantial potential in modulating inflammation, oxidative stress, and
   cancer-related pathways, particularly through its interactions with
   AKT1 and SRC. Although its binding affinities are lower than those of
   kaempferitrin, its ability to engage these proteins suggests a
   potential role in exerting antioxidant and anti-inflammatory effects,
   potentially disrupting cancer cell signaling. In contrast, raphanin
   exhibited the weakest binding affinity across all target proteins, with
   significantly lower binding-free energy (BFE) values. This suggests
   that raphanin may have limited bioactivity in the context of these
   pathways, particularly when compared to kaempferitrin and kaempferol.
   Notably, TP53, a central regulator of tumor suppression and cellular
   stress responses, displayed the lowest binding affinities with all five
   compounds, indicating that their direct modulation of TP53 may be
   limited, potentially constraining their anticancer efficacy through
   this specific pathway.

   The receptor-ligand interactions of kaempferitrin with AKT1, ESR1,
   MAPK1, SRC, and TP53 were analyzed through two-dimensional interaction
   visualizations (Fig. [92]8). For AKT1 (Fig. [93]8A), kaempferitrin
   demonstrated strong binding through van der Waals forces, hydrogen
   bonds, and pi interactions, particularly with key residues involved in
   cancer, inflammation, and oxidative stress regulation. Notably,
   pi-alkyl and pi-pi stacking interactions within hydrophobic regions, as
   well as a pi-anion interaction with ASP292, contributed to the
   stability of the complex. However, an unfavorable donor-donor
   interaction with ARG200 suggests a potential structural constraint,
   highlighting an opportunity for molecular optimization to enhance
   binding affinity and minimize steric hindrance.

Fig. 8.

   [94]Fig. 8
   [95]Open in a new tab

   Two-dimensional representation of the interactions between
   kaempferitrin and key proteins: (A) AKT1-kaempferitrin interaction, (B)
   ESR1-kaempferitrin complex, (C) MAPK1-kaempferitrin interaction, (D)
   SRC-kaempferitrin complex, and (E) TP53-kaempferitrin interaction.

   Kaempferitrin’s interaction with ESR1 (Fig. [96]8B) was characterized
   by van der Waals forces with 12 residues and hydrogen bonding with
   THR347, MET343, and LEU387, along with multiple pi interactions. The
   van der Waals forces facilitated an optimal fit within ESR1’s
   hydrophobic binding site, while hydrogen bonding with polar residues
   contributed to complex stability and specificity. These interactions
   are crucial for ESR1 modulation in cancer progression, despite an
   unfavorable donor-donor interaction with ARG394.

   For MAPK1 (Fig. [97]8C), Kaempferitrin formed a stable complex through
   a combination of van der Waals forces, hydrogen bonding with MET108,
   THR110, and ASP167, and pi interactions, including pi-cation and
   pi-alkyl contacts. The pi-cation interactions stabilized the complex by
   attracting charged residues within the binding pocket, enhancing ligand
   affinity. While an unfavorable donor-donor interaction with ARG67 was
   noted, the overall binding profile suggests that kaempferitrin holds
   potential as a modulator of MAPK1 in inflammation and cancer pathways.

   In the SRC complex (Fig. [98]8D), kaempferitrin established strong
   interactions through van der Waals forces, hydrogen bonds, and pi
   interactions. While unfavorable contacts with ASP404 and MET341 suggest
   potential areas for structural refinement, the overall binding profile
   supports its role in modulating SRC-related pathways in cancer and cell
   signaling. The hydrogen bonds in this complex enhance binding
   specificity, ensuring a stable ligand-protein interaction conducive to
   SRC regulation.

   Kaempferitrin’s binding to TP53 (Fig. [99]8E) involved multiple van der
   Waals forces, hydrogen bonds, and pi-alkyl and pi-pi stacking
   interactions, highlighting its potential role in modulating this key
   tumor suppressor. The pi-alkyl interactions contributed to binding
   stability within TP53’s hydrophobic pocket, while pi-pi stacking
   further reinforced ligand affinity, suggesting a potential therapeutic
   application in cancer treatment.

   In Fig. [100]8A-D, unfavorable donor-donor interactions were noted;
   however, these are effectively counterbalanced by strong interactions,
   including conventional hydrogen bonds and pi-pi stacking. Additionally,
   the collective presence of van der Waals forces, hydrogen bonds, and pi
   interactions across all five protein complexes underscores the overall
   binding stability, thereby supporting kaempferitrin’s potential as a
   multi-target modulator in therapeutic applications for cancer,
   inflammation, and oxidative stress.

Molecular dynamics simulations analysis

   The AKT1-kaempferitrin complex was selected for molecular dynamics
   simulations due to its excellent interaction, as shown by molecular
   docking results. AKT1 is a crucial serine/threonine kinase that
   regulates cell survival, growth, and metabolism, making it an important
   target in studies of inflammation, oxidative stress, and cancer.
   Furthermore, dysregulation of AKT1 is linked to heightened inflammatory
   responses, weakened antioxidant defenses, and uncontrolled cell
   proliferation, all of which are central to these disease processes.

   Figure [101]9A shows the simulation contrasting protein and ligand RMSD
   over 100 ns. Initially, AKT1 RMSD increases steadily as the structure
   undergoes adjustments but later stabilizes around 4.0 Å after
   approximately 20 ns, indicating that the protein achieves equilibrium
   and remains structurally stable throughout the remainder of the
   simulation. In contrast, kaempferitrin RMSD exhibits significant
   fluctuations, suggesting that its binding position is dynamic and
   possibly unstable. This behavior implies that the ligand might be
   adopting multiple conformations within the binding site.

Fig. 9.

   [102]Fig. 9
   [103]Open in a new tab

   Molecular dynamics simulation of AKT1 and kaempferitrin complex. (A)
   RMSD plot over 100 ns comparing protein and ligand. The protein RMSD
   rises and stabilizes around 4.0 Å after ~ 20 ns, indicating equilibrium
   and stability, while the ligand RMSD fluctuates, suggesting dynamic
   binding or weak affinity. (B) The RMSF profile of AKT1 reveals high
   flexibility in residues 100–120 (~ 5.6 Å) adjacent to the PH domain and
   near residue 450 (~ 6.8 Å) in the C-terminal region, while the kinase
   domain (residues 150–408) exhibits low fluctuations (~ 0.8 Å)
   indicative of structural stability. Green-highlighted residues denote
   ligand interaction sites. (C) Ligand RMSF reveals low fluctuations for
   core atoms, indicating stable binding, with higher fluctuations in
   flexible regions, suggesting dynamic adjustments for optimal protein
   interactions. (D) The interaction contacts between AKT1 and
   kaempferitrin reveal stable binding via key residues: GLN-79 and TRP-80
   in the PH domain, and LYS-268, TYR-272, ASP-292, and GLY-294 in the
   kinase domain, with additional hydrophobic interactions at LYS-179,
   LEU-264, and VAL-270. These findings suggest a defined binding site at
   the interface of the PH and kinase domains. (E) The radius of gyration
   remains stable throughout the simulation, with only minor fluctuations
   indicating small conformational adjustments, confirming that the
   protein maintains its overall compactness and structural integrity. (F)
   The SASA shows a stable solvent-accessible surface area with only minor
   fluctuations, indicating that the protein-ligand complex maintains its
   native-like structure without significant conformational changes. (G)
   The GROMACS energy analysis shows that the total energy of the
   AKT1-kaempferitrin complex remains stable, fluctuating narrowly between
   − 5.26 × 10^5 and − 5.36 × 10^5 kJ/mol, indicating an equilibrated and
   energetically favorable system.

   Figure [104]9B illustrates the RMSF profile of AKT1, revealing distinct
   regions of flexibility and stability throughout the simulation. The
   plot indicates that residues 100–120 exhibit relatively high
   fluctuations (approximately 5.6 Å), suggesting considerable dynamic
   movement in this region, which likely corresponds to parts of the
   N-terminal region adjacent to the PH domain. In contrast, residues
   150–408, which constitute the kinase domain essential for AKT1’s
   catalytic activity, display lower fluctuations (around 0.8 Å),
   reflecting the structural stability required for enzymatic function.
   Additionally, the C-terminal region, particularly near residue 450
   where fluctuations reach about 6.8 Å, indicates increased mobility that
   may be related to regulatory processes. The green-highlighted residues,
   which engage with the ligand, further emphasize the functional
   relevance of these domains by illustrating specific interaction sites
   within these regions.

   The kaempferitrin RMSF (Fig. [105]9C) shows that its core atoms have
   minimal fluctuations, indicating a stable conformation within the
   protein’s active site. In contrast, some regions—likely the flexible
   terminal groups or side chains—exhibit greater fluctuations, suggesting
   subtle dynamic adjustments for optimal binding. Overall, while the
   ligand maintains a stable binding mode, localized flexibility is
   essential for fine-tuning its interactions with the protein.

   Figure [106]9D shows that the interaction contacts between AKT1 and
   kaempferitrin reveal stable engagement of key residues, with high
   interaction fractions observed for GLN-79 and TRP-80 in the PH domain
   and LYS-268, TYR-272, ASP-292, and GLY-294 in the kinase domain.
   Additionally, significant hydrophobic interactions are noted at
   residues LYS-179, LEU-264, and VAL-270. The binding is mediated by a
   combination of hydrogen bonds, hydrophobic interactions, water-mediated
   interactions, and some ionic interactions, with hydrogen bonds being
   especially prominent at GLN-79, ASP-292, and GLY-294 and hydrophobic
   interactions notably observed at TRP-80, LYS-179, LEU-264, VAL-270, and
   TYR-272. The clustering of these interacting residues suggests a
   defined binding site at the interface of the PH and kinase domains,
   which enhances the overall stability of the complex.

   The AKT1’s radius of gyration remains relatively constant throughout
   the simulation, indicating that its overall compactness is maintained
   (Fig. [107]9E). Although minor fluctuations are evident, these only
   suggest small conformational adjustments without any indication of
   unfolding or major structural rearrangements. This stability in the
   radius of gyration confirms that the protein structure is
   well-maintained under the simulated conditions. The SASA (solvent
   accessible surface area) analysis reveals a relatively stable profile
   throughout the simulation period (Fig. [108]9F). The plot shows minor
   fluctuations around a consistent mean value, indicating that the
   protein maintains its overall surface exposure to solvent without
   undergoing significant conformational changes that would alter its
   interaction with the surrounding environment. This stability in SASA
   values suggests that the protein-ligand complex preserves its
   structural integrity, with no major unfolding events or substantial
   rearrangements of surface-exposed regions. The consistent SASA profile
   further supports that the binding of the ligand does not induce
   dramatic alterations in the protein’s surface topology, suggesting a
   well-accommodated binding mode that maintains the native-like
   characteristics of the protein structure.

   The GROMACS energy shows that the total energy remains stable over the
   simulation (Fig. [109]9G). The potential energy is computed using a
   designated force field that accounts for van der Waals, electrostatic,
   and bonded interactions (bond, angle, and dihedral energies). The
   energy plot for the AKT1-kaempferitrin interaction, reveals significant
   fluctuations that remain confined to a narrow range of approximately
   − 5.26 × 10^5 and − 5.36 × 10^5 kJ/mol. The absence of any discernible
   trend indicates that the system has reached equilibrium and is not
   undergoing major structural changes. The random oscillations around a
   consistent average and the high negative energy values further confirm
   that the interaction between AKT1 and kaempferitrin is energetically
   favorable and stable.

   These MD simulations reveal that the AKT1-kaempferitrin complex
   exhibits a dynamic binding mode, with the ligand undergoing significant
   fluctuations, yet the protein–ligand interface remains stable due to
   persistent hydrogen bonds, hydrophobic interactions, and water-mediated
   bridges. The stable protein structure—evidenced by consistent radius of
   gyration, SASA, and energy profiles—demonstrates that its overall
   conformation is maintained throughout the simulation. These insights
   are critical for drug design, as they identify potential binding
   hotspots for developing potent and selective AKT1 inhibitors and deepen
   our understanding of AKT1’s role in essential cellular processes and
   diseases.

In vivo evaluation of RRM extract: toxicity, survival, immune modulation, and
oxidative stress regulation in Dm PGRP-LB^∆ mutants

   A toxicity assay was conducted to assess the effects of RRM extract on
   the survival and developmental progression of Drosophila melanogaster
   (Dm) PGRP-LB^∆ mutants (Fig. [110]10A). The proportion of larvae
   reaching the pupal stage was evaluated under untreated control
   conditions, ethanol (70%) treatment, and RRM extract exposure at
   concentrations of 1.25%, 0.625%, and 0.312%. No statistically
   significant differences were observed among the treatment groups,
   suggesting that RRM extract did not adversely impact larval-to-pupal
   development. Similarly, the pupal-to-adult transition rates
   (Fig. [111]10B) remained unaffected across treatments, with no
   significant reduction in adult emergence even at the lowest
   concentration (0.312%), despite a slight decrease in the average
   conversion rate in this group. These findings indicate that RRM extract
   does not exert overt toxicity on Dm PGRP-LB^∆ at the tested
   concentrations.

Fig. 10.

   [112]Fig. 10
   [113]Open in a new tab

   RRM extract is non-toxic at concentrations of 1.25%, 0.625%, and 0.312%
   in Dm PGPR-LB^∆. (A) Survival rate from larva to pupa, (B) Survival
   rate from pupa to adult. NS indicates no statistically significant
   difference.  Symbols: ‘−’ = treatment not applied; ‘+’ = treatment
   applied.

   The results presented in Fig. [114]11 demonstrate that RRM extract
   significantly enhances the survival of PGRP-LB^Δ larvae in a
   dose-dependent manner. The untreated control group maintained a
   near-100% survival rate, whereas larvae exposed to heat-killed E. coli
   (HKE) exhibited a substantial decline in survival, indicating the
   detrimental effect of HKE. Treatment with 70% ethanol showed no
   significant difference in survival compared to the HKE-exposed group.
   Larvae treated with 2.5% RRM extract showed lower survival rates,
   comparable to or even below those receiving HKE alone. In contrast,
   lower RRM concentrations (1.25%, 0.625%, and 0.312%) significantly
   restored survival to near-control levels, with statistical significance
   at p < 0.001 and p < 0.0001, indicating a beneficial effect at these
   doses. These findings highlight an inverse dose-response relationship
   where lower extract concentrations provide better protective effects in
   mitigating HKE-induced lethality in PGRP-LB^Δ larvae.

Fig. 11.

   [115]Fig. 11
   [116]Open in a new tab

   Dm PGRP-LB^Δ larvae showed reduced survival when treated with a 2.5%
   RRM extract, whereas treatments at 1.25%, 0.625%, and 0.312% enhanced
   survival. NS indicates non-significant differences; ***p < 0.001; and
   ****p < 0.0001. Each treatment was compared to heat-killed E. coli as
   the control.  Symbols: ‘−’ = treatment not applied; ‘+’ = treatment
   applied.

   Figure [117]12. Exposure to HKE induces the expression of immune system
   genes in Dm PGRP-LB^Δ larvae. Administration of RRM extract at
   concentrations of 1.25%, 0.625%, and 0.312% resulted in a decrease in
   the expression of the dptA (A) and totA (B) genes at all
   concentrations. NS indicates non-significant differences; ***p < 0.001;
   and ****p < 0.0001.  Symbols: ‘−’ = treatment not applied; ‘+’ =
   treatment applied.

Fig. 12.

   [118]Fig. 12
   [119]Open in a new tab

   demonstrates that exposure to HKE markedly upregulates the expression
   of the dptA and totA genes. In contrast, treatment with RRM extract at
   concentrations of 1.25%, 0.625%, and 0.312% significantly suppresses
   the expression of both genes. These findings suggest that in Dm
   PGRP-LB^∆, RRM extract modulates immune responses, potentially
   mitigating the detrimental effects of excessive immune activation.

   HKE treatment significantly increased reactive oxygen species (ROS)
   levels in larvae, indicating oxidative stress as part of the immune
   response. Administration of RRM extract at a concentration of 1.25%
   further elevated ROS levels, whereas treatment with 0.625% and 0.312%
   led to a significant reduction (Fig. [120]13). The increase observed at
   1.25% was likely due to the presence of bioactive compounds in the
   extract that enhanced oxygen reactivity, thereby promoting oxidative
   stress. In contrast, at 0.625% and 0.312%, the extract appeared to
   provide sufficient antioxidant capacity to neutralize ROS, mitigating
   oxidative stress and lowering ROS levels.

Fig. 13.

   [121]Fig. 13
   [122]Open in a new tab

   RRM extract modulates ROS and antioxidant activity. Increased ROS (A):
   Treatment with HKE elevated ROS levels in Dm PGRP-LB^Δ larvae, while
   administration of extracts at concentrations of 0.625% and 0.312%
   resulted in a reduction in ROS levels. Increased sod1 gene expression
   (B): At a concentration of 1.25%, RRM extract led to increased
   expression of the sod1 gene, accompanied by decreased expression of the
   sod2 (C) and cat (D) genes. NS indicates non-significant differences;
   ***p < 0.001; **p < 0.01; *p < 0.05.  Symbols: ‘−’ = treatment not
   applied; ‘+’ = treatment applied.

   The increased oxidative stress at 1.25% RRM extract treatment was
   further reflected in the upregulation of sod1 gene expression
   (Fig. [123]13A and B). This suggests that cells responded to oxidative
   stress by upregulating superoxide dismutase 1 (SOD1), a key enzyme that
   catalyzes the conversion of superoxide radicals into hydrogen peroxide
   (H₂O₂), which is subsequently broken down by catalase. Notably, the
   enhanced sod1 expression correlated with a reduction in totA
   expression, which has been linked to ROS-driven cellular proliferation.
   In contrast, the expression levels of sod2 and cat genes, which encode
   SOD2 and catalase, respectively, were significantly downregulated
   following RRM extract treatment (Fig. [124]13C and D). This
   downregulation, combined with the increased ROS levels observed at
   1.25% extract concentration, suggests a potential reduction in internal
   antioxidant capacity. The impaired expression of these enzymes may have
   limited the conversion of H₂O₂ into water and oxygen, thereby
   exacerbating oxidative stress. These findings indicate that while RRM
   extract at lower concentrations (0.625% and 0.312%) exerted an
   antioxidant effect by reducing ROS levels, a higher concentration
   (1.25%) paradoxically induced oxidative stress, possibly through
   bioactive compounds that modulate redox homeostasis.

Discussion

   Species in the Brassicaceae family are rich in bioactive
   phytochemicals, including glucosinolates, isothiocyanates, phenolic
   compounds, carotenoids, tocopherols, and ascorbic acid^[125]23,[126]24.
   These compounds have been extensively studied for their roles in
   modulating inflammation, oxidative stress, and cancer-related pathways.
   Isothiocyanates (ITCs), with their reactive − N = C = S group, exhibit
   potent anticancer, anti-inflammatory, and antioxidant
   properties^[127]25,[128]26. Understanding the molecular mechanisms
   underlying these effects requires a network-based approach to identify
   key regulatory proteins.

   Metabolic profiling of RRM extract confirmed a diverse composition of
   bioactive molecules, suggesting therapeutic potential. Phenolic
   compounds, including flavonoids and glucosinolates, act as free radical
   scavengers, reducing oxidative stress and inflammation.
   Sulfur-containing heterocyclic compounds, characteristic of cruciferous
   vegetables, support detoxification and may contribute to anticancer
   activity. Additionally, lipids and fatty acyls, upon metabolism,
   generate bioactive lipid mediators that regulate immune function and
   oxidative stress. Amino acids and their derivatives contribute to
   metabolic homeostasis and enhance bioavailability of other bioactive
   compounds. The presence of vitamins, terpenes, and alkaloids further
   strengthens the extract’s antioxidant and anti-inflammatory profile.

   Network pharmacology facilitates the identification of these regulatory
   proteins through centrality measures and degree distribution.
   Centrality metrics play a crucial role in determining key nodes within
   a network: degree centrality identifies hubs based on their
   connectivity, betweenness centrality highlights bridging nodes that
   mediate communication between clusters, closeness centrality measures
   connectivity efficiency, and stress centrality quantifies the burden
   placed on nodes by the shortest paths. In Cytoscape, stress centrality
   serves as a topological metric to assess a node’s influence on network
   flow, increasing proportionally with the number of shortest paths
   passing through it^[129]27,[130]28. Collectively, these metrics provide
   critical insights into the functional significance of proteins in
   complex biological systems^[131]29–[132]31.

   The integrated network and centrality rankings identify TP53 as the
   most central node, reinforcing its crucial role in cancer and oxidative
   stress pathways. Consistent with this, previous research has
   established TP53 as a key mediator of cellular stress responses and a
   major contributor to cancer development and progression^[133]32. Other
   highly ranked proteins, including SRC, MAPK1, ESR1, and AKT1,
   demonstrated significant contributions across centrality measures,
   emphasizing their importance in signaling pathways associated with
   inflammation and cancer^[134]33–[135]36.

   Among the 32 RRM-targeted proteins associated with cancer,
   inflammation, and oxidative stress, STAT3, JAK2, and PIK3CA emerged as
   critical regulators of cell proliferation and immune responses. The
   JAK2/STAT3 signaling pathway plays a fundamental role in cytogenesis
   and is closely linked to osteoarthritis (OA) progression^[136]37.
   Additionally, PIK3CA contributes to tumor progression and immune
   infiltration by mediating interactions between CD8⁺ T cells and
   macrophages^[137]38.

   The presence of MAPK1, MAPK3, and JUN among the RRM-targeted proteins
   underscores the significance of MAPK signaling in oxidative stress and
   inflammation. The MAPK family, including extracellular signal-regulated
   kinases (ERKs), c-Jun N-terminal kinases (JNKs), and p38 MAPKs,
   mediates extracellular signal transmission to the nucleus, regulating
   gene expression. Among these, ERK1/2, JNKs, and p38 MAPKs play pivotal
   roles in modulating inflammatory responses^[138]39,[139]40.

   Additionally, the identification of heat shock proteins HSP90AA1 and
   HSP90AB1 highlights the importance of stress response mechanisms and
   protein folding in maintaining cellular stability during oxidative
   stress^[140]41. Growth factor-related proteins such as EGFR, ERBB2, and
   PTPN11, along with kinases including PIK3R1, PIK3CB, and PIK3CD,
   further emphasize the role of the PI3K/AKT and receptor tyrosine kinase
   pathways in cancer progression and cellular adaptation under oxidative
   and inflammatory stress conditions^[141]42–[142]44.

   While lower-ranked proteins such as JAK1, JAK3, PDGFRB, and PDGFRA
   remain relevant in specific regulatory pathways^[143]45,[144]46. the
   diversity of these 32 proteins highlights the extensive impact of RRM
   compounds on interconnected pathways involved in cancer, inflammation,
   and oxidative stress. Key targets include transcription factors (TP53,
   JUN, STAT3), kinases (SRC, AKT1, MAPK1), and stress-response proteins
   (HSP90AA1), which collectively contribute to the molecular mechanisms
   influenced by RRM compounds.

   Given the complex interplay among signaling networks, the
   pharmacodynamic interactions of RRM compounds warrant further
   investigation. Certain compounds may act synergistically by engaging
   complementary nodes in pathways such as PI3K/AKT and JAK/STAT, which
   are critical for regulating cellular transitions and intracellular
   signal transduction^[145]47,[146]48. Conversely, antagonistic effects
   may occur if compounds exert opposing influences on shared signaling
   molecules, potentially diminishing their overall impact^[147]49.
   Previous studies suggest that multi-targeted therapies can either
   enhance or impair treatment responses depending on the molecular
   context and pathway crosstalk^[148]50.

   Specifically, modulation of the JAK/STAT pathway by RRM extract may
   occur via multiple mechanisms. One possibility is direct receptor
   inhibition, whereby bioactive compounds interfere with cytokine binding
   to their receptors, preventing subsequent activation of Janus kinases
   (JAKs) and phosphorylation of STAT proteins^[149]51. Alternatively, the
   extract might influence the JAK/STAT pathway indirectly through its
   antioxidant properties. By neutralizing ROS, which function as
   secondary messengers and enhance inflammatory signaling, the extract
   can lower oxidative stress and prevent excessive activation of the
   pathway^[150]52. Moreover, the extract may also affect the expression
   of inhibitory regulators such as suppressor of cytokine signaling
   (SOCS) proteins, thereby further contributing to the downregulation of
   JAK/STAT signaling^[151]53.

   A deeper understanding of these combinatorial effects will provide
   valuable insights for optimizing RRM compounds as co-therapeutic
   agents, as well as for identifying potential counteractive interactions
   that may limit efficacy. These findings lay a foundation for further
   experimental validation to fully elucidate the therapeutic potential of
   RRM compounds in modulating these critical pathways.

   The key compounds in RRM that target the aforementioned proteins
   include kaempferitrin, alpha-tocopherol, kaempferol, raphanin, and
   beta-ionone, all of which have been previously reported in
   radish^[152]54–[153]58. Notably, radish microgreens exhibit
   significantly higher concentrations of nutrients than their mature
   counterparts, containing 2–4 times more ascorbic acid, 4.5 times more
   carotenoids, 4–5 times more isothiocyanates, and an astounding 976
   times more α-tocopherol^[154]59. To further elucidate protein
   interactions, skyline queries—used to select a subset of optimal data
   points based on multiple criteria—were conducted using the network
   topology and centrality measures of the top 32 proteins. This approach,
   with a particular emphasis on stress centrality, identified the five
   most essential proteins within the network^[155]60.

   Pharmacokinetic and drug-likeness evaluations provided insights into
   the potential efficacy and metabolic processing of the five principal
   compounds^[156]61,[157]62. Although kaempferitrin does not fully comply
   with Lipinski’s Rule of Five—a guideline primarily applicable to small
   synthetic molecules—it remains a promising therapeutic candidate, as
   many natural compounds, peptides, and larger bioactive molecules (such
   as flavonoids and antibiotics) are effective despite such
   violations^[158]63. Toxicity assessments classified four of the
   compounds as Class V (with predicted LD50 values of 2000–5000 mg/kg),
   while raphanin is the only compound in toxicity Class III (LD50 of
   112 mg/kg), underscoring the need for further safety
   evaluation^[159]64,[160]65.

   In terms of bioavailability, these compounds differ due to variations
   in structure, solubility, and metabolic processing. For example,
   kaempferitrin, a flavonoid glycoside, must be hydrolyzed to release its
   aglycone, kaempferol, which is then rapidly conjugated in the liver,
   contributing to its limited bioavailability^[161]66. In contrast,
   alpha-tocopherol—a lipophilic form of vitamin E—is efficiently absorbed
   in the presence of dietary fats and transported via chylomicrons to
   tissues^[162]67. Similarly, raphanin, produced from glucosinolates
   through myrosinase action, is rapidly absorbed as an isothiocyanate but
   is also quickly metabolized^[163]68, while beta-ionone, despite being
   absorbed via passive diffusion, faces constraints from first-pass
   metabolism and low aqueous solubility^[164]69.

   A higher Pa value indicates a greater likelihood that a compound will
   exhibit activity in the respective biological
   processes^[165]70,[166]71. Kaempferol is involved in most of these
   activities, except for its antineoplastic effects in endocrine and
   thyroid cancers.

   Endocrine resistance was identified as the most enriched pathway in our
   analysis, with the lowest FDR and highest –log10(p) value, strongly
   suggesting that RRM compounds interact with mechanisms driving
   endocrine treatment failure. This resistance is often associated with
   the activation of alternative signaling pathways (such as PI3K/AKT,
   MAPK, and mTOR) that bypass estrogen receptor signaling, along with
   alterations in growth factor receptors, co-regulators, and downstream
   transcription factors that promote continued cellular proliferation and
   survival. These findings indicate that RRM compounds may modulate these
   critical networks, warranting further investigation in relevant cancer
   models.

   In addition, kaempferol demonstrates significant anticancer and
   antioxidant potential by modulating the cell cycle, apoptosis,
   proliferation, and angiogenesis, primarily via MAPK/ERK1/2,
   PI3K/Akt/mTOR, and VEGF pathways^[167]72,[168]73. Similarly,
   kaempferitrin has shown considerable anticancer activity, with numerous
   studies underscoring its effectiveness across various cancer
   types^[169]74,[170]75. Preclinical studies have also shown that
   tocopherol modulates inflammation, oxidative stress, the cell cycle,
   and apoptosis through pathways such as NF-κB, MAPK, and PI3K/Akt/mTOR.
   Although clinical outcomes vary, tocopherol has been reported to
   improve redox and inflammatory status in metabolic conditions, while
   its effects on cancer remain inconsistent, demonstrating both pro- and
   anti-malignant outcomes^[171]76.

   Higher affinity corresponds to more stable drug-target interactions,
   thereby enhancing therapeutic potential^[172]77. Kaempferitrin’s strong
   affinity for MAPK1 and SRC underscores its potential to modulate
   inflammation and cancer pathways, while its interactions with AKT1 and
   ESR1 further support its role as a multi-target agent with antioxidant,
   anti-inflammatory, and anticancer properties. Kaempferitrin may
   interact with AKT1 by modulating its phosphorylation status, thereby
   affecting oxidative stress resistance, inflammatory responses, and
   cancer cell survival. While further research is needed to fully
   elucidate its effects on AKT1 and its downstream targets, existing
   evidence suggests that kaempferitrin holds promise as a therapeutic
   agent for oxidative stress-related diseases, inflammation, and cancer.

   Binding-free energy values further emphasize its ability to engage key
   proteins in these pathways. Specifically, kaempferitrin exhibited
   robust binding to AKT1 through van der Waals forces, hydrogen bonds,
   and pi interactions—stabilizing the complex via pi-alkyl, pi-pi
   stacking, and pi-anion interactions with critical residues. Van der
   Waals forces promote close packing and shape complementarity between
   the ligand and receptor, minimizing unfavorable interactions and
   enhancing specificity^[173]78. Additionally, pi interactions,
   particularly pi-alkyl and pi-pi stacking, contribute electronic
   stabilization and strengthen hydrophobic interactions within the
   complex^[174]79.

   The molecular docking results are strongly reinforced by the MD
   simulations, which confirm that AKT1 is robustly bound by
   kaempferitrin. Although some dynamic fluctuations are exhibited by the
   ligand, the protein–ligand interface is maintained in a consistently
   stable state by persistent hydrogen bonds, hydrophobic interactions,
   and water-mediated bridges. Furthermore, the overall structural
   integrity of the protein is supported by steady values in the radius of
   gyration, SASA, and energy profiles throughout the simulation. These
   findings validate the docking predictions and underscore the potential
   of kaempferitrin as a potent and selective AKT1 inhibitor, with
   promising implications for targeted drug design in the treatment of
   inflammation, oxidative stress, and cancer.

   Drosophila melanogaster possesses an effective immune system that
   combats infections through two primary mechanisms: humoral and cellular
   responses^[175]80. The humoral response involves the production of
   antimicrobial peptides (AMPs) that disrupt microbial membranes to
   eliminate pathogens^[176]81. A critical aspect of this defense is the
   recognition of bacterial signals by peptidoglycan recognition proteins
   (PGRPs), which are essential for insect immunity^[177]82. In
   particular, PGRP-LB modulates immune responses by degrading
   peptidoglycan from Gram-negative bacteria, thereby balancing the immune
   deficiency (Imd) pathway and preventing excessive inflammation and
   tissue damage while maintaining effective bacterial
   resistance^[178]81–[179]83.

   Dysregulation of PGRP-LB in Drosophila models such as Dm PGRP-LB∆ leads
   to elevated proinflammatory cytokines, indicative of chronic immune
   activation^[180]82. For example, overexpression of Diptericin A,
   encoded by the dptA gene, serves as a marker of Imd pathway
   overactivation, while elevated Turandot A levels, encoded by the totA
   gene and regulated by the JAK-STAT pathway, underscore the broader role
   of immune signaling in homeostasis. Moreover, Diptericin overexpression
   driven by NF-κB (Relish) overactivation has been linked to chronic
   inflammation, neurodegeneration, and reduced lifespan in flies^[181]84,
   highlighting the delicate balance required within the immune system to
   protect against pathogens while minimizing long-term damage.

   The lack of toxicity observed in the larvae may be attributed to
   beneficial compounds in the RRM extract, such as antioxidants and
   polyphenols, which support cellular health. These findings suggest that
   RRM extract is safe at the tested lower concentrations and does not
   adversely affect Dm PGPR-LB^∆ development or viability^[182]85. One
   potential explanation for the reduced protective effects at higher
   doses is hormesis, where low concentrations produce beneficial outcomes
   while higher concentrations become toxic^[183]86. At elevated doses,
   the adverse effects of toxic constituents in the extract may surpass
   its protective benefits, leading to pathway saturation and off-target
   interactions that disrupt normal cellular functions^[184]87.
   Additionally, the synergistic effects observed at low doses may shift
   to antagonistic interactions at higher doses, further diminishing the
   overall efficacy^[185]49. These observations underscore the importance
   of careful dose optimization to fully leverage the protective potential
   of the plant extract.

   The extract’s protective properties likely derive from its bioactive
   compounds, which counteract the negative effects of HKE exposure and
   enhance larval resilience under stress. The observed dose-dependent
   response highlights the potential of RRM as a functional ingredient for
   boosting host defenses through immune modulation and stress mitigation.
   Supporting this, previous reports indicate that GABA in radish
   microgreens may contribute to improved immunity via broader
   immunomodulatory effects of the GABAergic system^[186]88.

   At the molecular level, HKE exposure triggered the expression of dptA
   and totA genes, whereas treatment with RRM extract significantly
   suppressed their expression. These results indicate that Dm PGRP-LB^Δ
   modulates immune responses, potentially mitigating the adverse effects
   of excessive activation^[187]89. Notably, totA expression, which is
   regulated by the JAK-STAT pathway and typically elevated by oxidative
   stress such as that induced by HKE, was reduced by RRM extract—likely
   due to its antioxidant and anti-inflammatory properties—thereby
   enhancing cell survival and proliferation by reducing oxidative stress
   and apoptosis^[188]90,[189]91.

   HKE treatment also increased reactive oxygen species (ROS) levels in
   larvae, further indicating oxidative stress^[190]92. SOD1 (Superoxide
   Dismutase 1) catalyzes the conversion of superoxide radicals into
   hydrogen peroxide, aiding in ROS neutralization^[191]93. In response to
   oxidative stress, cells upregulate SOD1 as an adaptive mechanism to
   protect against oxidative damage^[192]94. This upregulation of SOD1 is
   linked to reduced expression of the totA gene, which is associated with
   cell proliferation under ROS exposure^[193]95,[194]96. The upregulation
   of sod1 gene expression in response to RRM extract suggests its role in
   mitigating oxidative stress, as SOD1 converts superoxide radicals into
   H₂O₂, which is subsequently decomposed by enzymes such as catalase
   (CAT)^[195]97. Previous studies have demonstrated that overexpression
   of human SOD1 in Drosophila motor neurons can extend lifespan by up to
   40%, restoring the shortened lifespan of Sod-null mutants—an effect
   attributed to improved reactive oxygen metabolism and increased
   resistance to oxidative stress^[196]96.

   Conversely, the downregulation of SOD2 and CAT, which are critical for
   mitochondrial ROS detoxification, suggests impaired conversion of H₂O₂
   into water and oxygen, thereby reducing overall antioxidant capacity.
   SOD2 converts superoxide radicals to H₂O₂ in mitochondria^[197]98,
   while CAT decomposes H₂O₂ into water and oxygen to protect against
   oxidative damage^[198]99,[199]100. The observed decrease in sod2 and
   cat expression in Dm PGRP-LB^Δ larvae treated with RRM extract likely
   reflects mild oxidative stress and altered mitochondrial function.
   These findings underscore the complex relationship between antioxidant
   responses and phytochemical activity, with the reduction in sod2
   expression under elevated ROS conditions^[200]101, indicating a
   regulatory mechanism worth further investigation.

   At elevated doses, the extract may induce oxidative stress by
   overwhelming cellular antioxidant defenses and inhibiting the
   transcription or translation of related genes. This phenomenon may be
   linked to pro-oxidant phytochemicals, such as acylated pelargonidin
   derivatives found in red radish, which can act as either antioxidants
   or pro-oxidants depending on the prevailing conditions^[201]102.
   Moreover, the altered immune and oxidative stress responses in Dm
   PGRP-LBΔ larvae may increase sensitivity to the extract, potentially
   disrupting signaling pathways—such as NF-κB, JNK, or Nrf2—that regulate
   antioxidant gene expression. At lower concentrations, the antioxidant
   compounds in the extract may effectively scavenge ROS, reducing
   reliance on endogenous antioxidants like SOD2 and CAT, whereas higher
   concentrations may suppress these genes by interfering with regulatory
   pathways. These concentration-dependent effects appear to reflect a
   hormetic response, whereby low doses elicit protective antioxidant
   mechanisms and high doses trigger a pro-oxidant effect^[202]103.
   Specifically, a 1.25% concentration of the extract increases sod1
   expression, enhancing cytoplasmic defense against superoxide radicals,
   while concurrently reducing sod2 and cat expressions, which are
   involved in mitochondrial and peroxisomal detoxification of H₂O₂. These
   findings demonstrate a concentration-dependent regulation of
   antioxidant genes and suggest selective effects on oxidative stress
   pathways.

   Despite the reduced expression of sod2 and cat genes, 1.25% RRM extract
   does not induce overt toxicity in Dm PGRP-LB^Δ larvae. The larvae
   appear to tolerate the induced oxidative stress, as evidenced by the
   absence of significant mortality or other signs of cellular damage. The
   lower expression of these genes may represent a mild oxidative stress
   response or an adaptive regulation of antioxidant defenses rather than
   an indication of toxicity. The relatively low ROS levels observed at
   this concentration suggest that the larvae’s defense mechanisms are not
   overwhelmed, thereby preventing harmful effects. Overall, these results
   indicate that while the extract modulates oxidative stress pathways, it
   does not cause detrimental effects at the concentrations tested.

   Both in silico and in vivo analyses provide robust evidence supporting
   the therapeutic potential of RRM extract. In silico studies reveal
   strong interactions between RRM extract and key human proteins (AKT1,
   ESR1, MAPK1, SRC, and TP53), which are critical regulators of cancer,
   inflammation, and oxidative stress. This suggests that RRM extract,
   particularly its bioactive component kaempferitrin, can influence
   apoptosis, immune signaling, and oxidative stress. In vivo experiments
   in Dm PGRP-LB^Δ larvae corroborated these effects by demonstrating
   suppression of immune-related genes (dptA and totA) via NF-κB and
   JAK-STAT pathways, reduced ROS levels, and selective modulation of
   antioxidant-related genes (sod1, sod2, and cat). The alignment between
   in silico and in vivo findings reinforces the potential of RRM extract
   as a multifunctional agent with anti-inflammatory, antioxidant, and
   anticancer properties, thereby supporting its application in functional
   food development.

Methods

Preparation of red radish microgreens extract

   The cultivation method for RRM followed our previous study^[203]104.
   The seeds (sourced from Known-You Seed, Thailand) were soaked in
   deionized water for 6 h, then sown on seedling trays and incubated in
   the dark for 3 days. After germination, the sprouts were grown into
   microgreens under LED lighting (7000 lx, 52–60% humidity, 26–26.5 °C)
   and harvested 7 days after seeding.

   After harvesting, the RRM was microwave-dried until it reached a crisp
   texture. Extraction was performed using a modified microwave oven
   (Modena MV3002, 1440 W). Approximately 35.56 ± 0.22 g (n = 6) of dried
   microgreen was placed into a 500 ml round-bottom flask and wetted with
   about 50 ml of 96% ethanol. An additional 350–400 ml of 96% ethanol was
   added to the flask as the extraction solvent. The flask, containing the
   plant material and solvent, was attached to a condenser connected to
   the microwave oven. The extraction time was set for 5 min at 30% power
   (equivalent to 432 watts). The liquid extract was filtered using filter
   paper, yielding approximately 300 ml of extract. The extract was then
   concentrated using a rotary evaporator at a pressure of < 180 mbar and
   a temperature of 60 °C.

Metabolic profiling

   The metabolic profiling of RRM extract was conducted using LC-HRMS and
   GC-MS. For the LC-HRMS analysis, liquid chromatography was performed
   with a Thermo Scientific™ Vanquish™ UHPLC Binary Pump, utilizing a
   ThermoScientific™ Accucore™ Phenyl-Hexyl column (100 mm × 2.1 mm ID ×
   2.6 μm). The mobile phase consisted of MS-grade water with 0.1% formic
   acid (Eluent A) and MS-grade methanol with 0.1% formic acid (Eluent B).
   The flow rate was 0.3 mL/min with a gradient from 95% Eluent A/5%
   Eluent B to 10% Eluent A/90% Eluent B over 25 min. The injection volume
   was 3 µL, and the column was maintained at 40 °C. Mass spectrometry was
   performed using the Thermo Scientific™ Q Exactive™ Hybrid
   Quadrupole-Orbitrap™ in ESI mode (positive/negative) with N2 as sheath,
   auxiliary, and sweep gases. The spray voltage was 3.30 kV, with a
   capillary temperature of 320 °C and an auxiliary gas heater at 30 °C.
   The scan range was 66.7–1000 m/z, with resolutions of 70,000 for full
   MS and 17,600 for dd-MS2.

   The analysis of RRM extracts using Gas Chromatography-Mass Spectrometry
   (GC-MS) was performed with a Perkin Elmer Clarus 500 gas chromatograph
   paired with a Perkin Elmer Clarus SQ 8 S mass spectrometer. A Perkin
   Elmer Elite-5ms capillary column (30 m length, 0.25 mm internal
   diameter, and 0.25 μm film thickness) was used. Electron ionization at
   70 eV facilitated the mass spectrometer, with a scan range of 40 to 450
   Da. Helium served as the carrier gas in split mode with a 10:1 ratio.
   The injector was set at 250 °C, with a 1 µL injection volume and a
   2-minute solvent delay. The oven temperature program started at 110 °C
   (held for 2 min), followed by a ramp of 10 °C/min to 200 °C, and then
   5 °C/min to 280 °C, with a 9-minute hold at the final temperature.

Metabolites data preprocessing

   The LC-MS/MS data were processed following established protocols using
   Compound Discoverer 3.3 software (Thermo Fisher Scientific,
   USA)^[204]104. This workflow included peak extraction, alignment, and
   compound identification to ensure data accuracy and reliability. Raw
   data were imported and processed through multiple steps: peak
   extraction, retention time correction, adduct ion combination, missing
   value imputation, and background peak labeling. Metabolite
   identification was conducted using the BGI Library and mzCloud
   database, referencing molecular weight, retention time, peak area, and
   fragmentation patterns to enhance identification confidence.

Metabolites filtering

   To identify unique compounds, duplicates from the LC-MS and GC-MS
   analyses were removed. The compounds were then filtered to
   differentiate between synthetic and naturally occurring substances.
   This process involved searching for compound names in several
   databases, such as PubChem^[205]105, LOTUS: Natural Products Online,
   SuperNatural 3.0, and Dr. Duke’s Phytochemical and Ethnobotanical
   Databases.

Network Pharmacology analysis

Identification of gene target

   The process began with identifying the targets of the natural
   compounds. SMILES notations for the selected natural products were
   obtained from PubChem. These SMILES were then used in the
   SwissTargetPrediction server^[206]106 to identify the associated
   protein targets. To ensure data accuracy and consistency, duplicate
   entries were eliminated when multiple compounds were linked to the same
   targets. Subsequently, genes associated with cancer, inflammation, and
   oxidative stress were identified using the GeneCards database^[207]107,
   further refining the list of potential targets.

Protein–protein interaction and skyline query analyses

   A Venn analysis was conducted in Microsoft Excel to identify genes
   shared between the target genes of RRM compounds and those linked to
   cancer, inflammation, and oxidative stress. The overlapping genes were
   visualized in a Venn diagram created in the same software. These shared
   genes were further analyzed to construct a protein-protein interaction
   (PPI) network using the STRING 11.5 database
   ([208]https://www.string-db.org/) with a minimum interaction score
   threshold of 0.9, ensuring the highest level of confidence.

   The network was analyzed using Cytoscape 3.10.2 ^[209]28 to evaluate
   its topology and compute centrality metrics for each gene. The MNC
   (Maximum Neighborhood Component) and MCC (Maximal Clique Centrality)
   algorithms, available through the cytoHubba plugin^[210]108, were
   applied to identify the top 20 genes based on MNC and the top 20 genes
   based on MCC. The resulting networks were then integrated into a single
   network to remove redundancies.

   Skyline query analysis was conducted to determine the five most
   dominant genes in comparison to the other top genes identified through
   MNC and MCC algorithms. The analysis utilized closeness, betweenness,
   degree, and stress centralities as parameters for skyline evaluation.
   Once identified, the compounds targeting these five key genes were
   cross-referenced with the existing dataset. Subsequently, the five
   principal compounds were identified based on their natural occurrence
   in Raphanus sativus and the number of genes they targeted among the
   five key genes.

Genes enrichment analysis

   Gene enrichment analysis was performed to explore the biological
   processes associated with the five key genes. This analysis utilized
   the StringApp plugin for Cytoscape^[211]109 and the Metascape web
   server^[212]110. Gene Ontology (GO) annotations for Biological
   Processes, Molecular Functions, and Cellular Components^[213]111, along
   with KEGG pathway annotations^[214]112 were emphasized. Visual
   representations of the GO and KEGG annotations were generated using the
   SRplot web server^[215]113.

Prediction of pharmacokinetics, drug-likeness, and biological activities

   Pharmacokinetics and drug-likeness assessments were conducted for the
   five principal compounds, focusing on ADMET (Absorption, Distribution,
   Metabolism, Excretion, and Toxicity) properties and Lipinski’s Rule of
   Five^[216]114. These evaluations utilized the web servers admetSAR 1.0
   ([217]https://lmmd.ecust.edu.cn/admetsar1/)^[218]115, ADMETlab 3.0
   ([219]https://admetlab3.scbdd.com/)^[220]116, and ProTox III
   ([221]https://tox.charite.de/protox3/)^[222]117. Additionally, the
   biological activity of the compounds was analyzed using the Way2Drug
   PASS online tool ([223]https://www.way2drug.com/passonline/)^[224]118.
   Visualization of the biological activity data and their corresponding
   Pa (probability of activity) values was performed using the SRplot web
   server^[225]113.

Molecular Docking

   The molecular docking analysis was conducted to explore interactions
   between the protein structures of five key proteins (receptors) and
   five main chemical compounds (ligands), resulting in a total of 20
   docking simulations. Additionally, native ligands of the receptors were
   redocked with their respective receptors to ensure the reliability of
   the docking procedure. The five proteins studied were AKT1, ESR1
   (encoding the ER-alpha protein, PDB ID: 3ERT), MAPK1 (encoding the ERK2
   protein, PDB ID: 1TVO), SRC (PDB ID: 2H8H), and TP53 (encoding the P53
   protein, PDB ID: 8F2H). The corresponding protein structures and their
   PDB identifiers were obtained from the Protein Data Bank (PDB). For
   AKT1, homology modeling was performed in silico using the SWISS-MODEL
   platform^[226]119, with the protein sequence retrieved from NCBI
   (Accession Number: [227]NP_001369359.1). The modeling utilized the
   template of crystal structure of AKT1 in complex with
   covalent-allosteric AKT inhibitor 24b (PDB ID: 6HHJ), which featured
   three mutations and exhibited missing residues in certain non-terminal
   regions^[228]120. The structural validation of each receptor was
   performed using the MolProbity web server^[229]121 by conducting a
   Ramachandran analysis.

   The docking analysis of five key proteins was performed using five
   principal compounds: kaempferitrin (CID: 5486199), D-alpha-tocopherol
   (commonly known as alpha-tocopherol, CID: 14985), kaempferol (CID:
   5280863), raphanin (CID: 6433206), and EN0350000 (also known as
   beta-Ionone, CID: 638014). These compounds were retrieved from PubChem
   in 3D .sdf format. The docking simulations were carried out using
   Gnina, a deep-learning molecular docking tool derived from Smina and
   AutoDock Vina^[230]122. Gnina is available on GitHub
   ([231]https://github.com/gnina/gnina) and was executed on Google Colab
   with GPU runtime support.

   Initial redocking using the co-crystallized ligands was carried out to
   confirm the reliability of the docking procedure, with the process
   deemed valid if the root-mean-square deviation (RMSD) of the best
   redocked complex was 2Å or less^[232]61. In each docking method, the
   grid box was configured using the “--autobox_ligand” parameter, which
   automatically adjusted the grid box to the binding site of the native
   ligand. Since the P53 protein lacks a native ligand, redocking was not
   performed. Instead, its active site was identified using the CASTp
   online server^[233]123, and the grid box dimensions were determined
   using AutoDockTools v.1.5.7 ^[234]124. The grid box dimensions were
   manually adjusted in the command using the “--center” and “--size”
   parameters rather than the “--autobox” option, ensuring precise ligand
   positioning.

   Molecular docking data were selected and prioritized based on binding
   affinity, with the lowest binding-free energy (BFE) indicating the most
   favorable docked complexes. The interactions within these favorable
   complexes were visualized using Biovia Discovery Studio software.

Molecular dynamics (MD) simulations

   Desmond from Schrödinger LLC was employed to run 100 ns MD simulations
   based on a previous procedure^[235]125. The selected proteins and
   ligands were optimized and energy-minimized using Maestro’s Protein
   Preparation Wizard to remove steric clashes, poor contacts, and
   distorted geometries. The system was built using the System Builder
   tool with an orthorhombic TIP3P solvent model and the OPLS_2005 force
   field^[236]126. Counter ions were added to neutralize the models, and
   0.15 M sodium chloride was introduced to simulate physiological
   conditions, maintained at 310 K and 1 atm. Trajectories were recorded
   every 100 ps, and protein–ligand stability was confirmed via
   root-meansquare deviation (RMSD) analysis over time. The measurements
   of the radius of gyration (Rg), solvent-accessible surface area (SASA),
   and hydrogen bonds for both the protein alone and its interactions with
   the ligand were performed using the GROMACS simulation package
   following standard procedures^[237]127–[238]129. Additionally,
   binding-free energy was estimated using the MM-PBSA method by
   extracting frames at regular intervals to assess the stability of the
   ligand–protein interactions.

Preclinical validation using the Drosophila melanogaster PGPR-LB^∆

   Drosophila melanogaster PGPR-LB^∆ (Dm PGPR-LB^∆) strain was obtained
   from the Host and Defense Laboratory at Kanazawa University in Japan.
   Drosophila melanogaster deficient in PGRP-LB demonstrate an elevated
   production of proinflammatory cytokines^[239]82. A 50% extract solution
   of RRM was prepared by dissolving 1.25 g of the extract in 2.5 mL of
   70% ethanol, followed by dilution to achieve concentrations of 1.25%,
   0.625%, and 0.312%. The insects were provided with a diet consisting of
   RRM, corn flour, granulated sugar, agar, and Miller Luria-Bertani broth
   (LBB) (HIMEDIA Lab Chemicals & Biochemicals, India).

Preparation of heat-killed Escherichia coli

   E. coli FNCC 0091 was cultured in nutrient agar (NA) medium for 24 h.
   Following this incubation period, 2 mL of sterile water was added to
   the culture and incubated for an additional 2 h. The resulting sterile
   water containing E. coli was then transferred to LBB medium and
   incubated at 37 °C for 24 h while being agitated on a shaker. After
   incubation, the E. coli culture in LBB medium was subjected to
   autoclaving at 121 °C and 2 atm pressure for 30 min to deactivate the
   bacteria.

Preparation of Dm PGPR-LB^∆ feed containing heat-killed E. coli

   The insect feed was formulated with a mixture of corn flour, sugar, and
   agar, and a heat-killed E. coli (HKE) solution. The feed was heated to
   100 °C while being stirred until it reached a consistent and thickened
   texture. It was then placed into feeding tubes designated for the
   insects.

Toxicity test

   A toxicity test was conducted to evaluate the viability of Dm PGPR-LB^∆
   following treatment with the various concentration of RMM’s extract.
   Second-instar larvae of Dm PGPR-LB^∆ were used across multiple
   treatment groups. The groups included: (1) a control group without any
   treatment, (2) a group treated with 70% ethanol, and (3) groups exposed
   to varying concentrations of RRM extract with three biological
   replications. Larval development was observed throughout the
   experimental period, specifically monitoring the progression from the
   larval to pupal stage and from pupal to adult fly.

Survival analysis

   The survival test was designed to assess the growth of Dm PGPR-LB^∆
   through its larval, pupal, and adult stages after administering HKE
   alone and in combination with various RRM extract. Second-instar larvae
   were monitored until they reached the adult stage, with each treatment
   condition replicated three times. Daily counts were recorded to track
   the number of larvae that progressed to pupae and subsequently to adult
   flies, with observations continuing until all adult insects had died.

Isolation of RNA for gene expression analysis

   RNA isolation followed the spin column method (PureLink RNA Mini Kit,
   Invitrogen, Thermo Fisher Scientific Inc., Carlsbad), based on the
   manufacturer’s protocol. Ten Dm PGPR-LB^∆ larvae per group were
   homogenized in 175 µl RNA lysis buffer. After adding 350 µl RNA
   dilution buffer, the mixture was inverted, incubated at 70 °C for
   3 min, and centrifuged at 14,000 G for 10 min at 4 °C. The supernatant
   was transferred to a fresh microcentrifuge tube using a micropipette.
   Two hundred microliters of 95% ethanol were added and homogenized using
   a micropipette approximately 3–4 times. The mixture was then
   transferred to the spin column apparatus. Centrifugation was performed
   at 14,000 G for 1 min. The spin basket was removed from the spin column
   assembly, and the liquid in the collection tube was discarded.
   Subsequently, 600 µl of RNA wash solution was added to the spin column
   assembly and centrifuged at 14,000 G for 1 min.

   The collection tube was emptied, and a DNase mixture was prepared by
   combining 40 µl of yellow core buffer, 5 µl of 0.09 M MnCl2, and 5 µl
   of DNase I enzyme, with the reaction volume increased by approximately
   10%. This mixture was homogenized using a micropipette. Fifty
   microliters of the mixture were transferred to the spin basket. The
   assembly was incubated for 15 min at a temperature of 20–25 °C.
   Following incubation, 200 µl of DNase stop solution were added to the
   spin basket and centrifuged at 14,000 G for 1 min. The collection tube
   was not emptied for the subsequent step.

   Next, 600 µl of RNA wash solution were added and centrifuged at 14,000
   G for 1 min. The collection tube was emptied, and 250 µl of RNA wash
   solution were added, followed by centrifugation at 14,000 G for 2 min.
   The spin basket was then transferred from the collection tube to the
   elution tube (microtube), and 100 µl of nuclease-free water were added
   to the elution tube. The spin basket assembly was centrifuged at 14,000
   G for 1 min. Finally, the spin basket was removed, and the elution tube
   containing the isolated RNA was stored at approximately − 80 °C.

Real-time reverse transcriptase PCR (RT-PCR)

   Gene expression levels in Dm PGPR-LB^∆ were measured using RT-qPCR with
   the Universal One-Step RT-qPCR Kit (Luna, New England Biolabs, Inc.,
   U.S.) based on the manufacturer’s protocol, with slight
   modifications^[240]130. The RT-qPCR reactions were performed in a final
   volume of 10 μL. The cycling conditions included reverse transcription
   at 50°C for 10 minutes, followed by an initial enzyme activation step
   at 95°C for 2 minutes. Amplification was carried out for 40 cycles,
   with denaturation at 95°C for 10 seconds, annealing at 60°C for 30
   seconds, and extension at 72°C for 30 seconds. A melt curve analysis
   was conducted to confirm the specificity of the amplified products,
   spanning a temperature range of 60°C to 95°C. The primer sequences used
   in this assay are provided in Table [241]5. Additionally, RNA levels of
   the target gene were assessed using rp49 primers as an internal
   reference for normalization. Gene expression analysis was performed
   using three biological replicates.

Table 5.

   Primers used in RT-qPCR analysis.
   Genes Forward primers               Reverse primers
   rp49  5ʹ-GACGCTTCAAGGGACAGTATCTG-3ʹ 5ʹ-AAACGCGGTTCTGCATGAG-3ʹ
   dptA  5ʹ-AGGTGTGGACCAGCGACAA-3ʹ     5ʹ-TGCTGTCCATATCCTCCATTCA-3ʹ
   totA  5ʹ-GAATAGCCCATGCATAGAGGAC-3ʹ  5ʹ-CCAAAATGAATTCTTCAACTGCT-3ʹ
   sod1  5ʹ-AGGTCAACATCACCGACTCC-3ʹ    5ʹ-GTTGACTTGCTCAGCTCGTG-3ʹ
   sod2  5ʹ-TGGCCACATCAACCACAC-3ʹ      5ʹ-TTCCACTGCGACTCGATG-3ʹ
   cat   5ʹ-TTCCTGGATGAGATGTCGCACT-3ʹ  5ʹ-TTCTGGGTGTGAATGAAGGTGG-3ʹ
   [242]Open in a new tab

Nitroblue tetrazolium (NBT) assay

   To quantify ROS in larval hemolymph, a nitroblue tetrazolium (NBT)
   reduction assay was performed as previously described^[243]89,[244]131.
   In this assay, ROS reduce the yellow NBT dye into insoluble formazan
   particles that absorb at 595 nm. Fifty larvae were rinsed with
   phosphate-buffered saline (PBS) to eliminate food debris, and hemolymph
   was extracted on ice to prevent melanization. A 300 µL mixture was
   prepared by combining 100 µL hemolymph with 200 µL 1× PBS, then adding
   an equal volume of NBT solution. The mixture was incubated in the dark
   at room temperature for 1 h, after which 300 µL of 100% glacial acetic
   acid was added to stop the reaction. Following centrifugation at
   maximum speed for 1 min, absorbance at 595 nm was measured after the
   addition of 50% acetic acid.

Data analysis

   All data were analyzed using GraphPad Prism 9. Each experiment,
   including survival assays and gene expression analyses, was performed
   with three replicates. Bar graphs were used to present the data, and
   one-way analysis of variance (ANOVA) was applied for statistical
   comparisons. Variability was assessed by calculating the standard
   deviation and 95% confidence intervals, and statistical significance
   was defined as a p-value of less than 0.05.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [245]Supplementary Material 1^ (1.5MB, docx)

Acknowledgements