Forged in Iron: Molecular Insights into Iron Tolerance in Hermetia illucens

Forged in Iron: Molecular Insights into Iron Tolerance in Hermetia illucens | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Forged in Iron: Molecular Insights into Iron Tolerance in Hermetia illucens Tomer First, Hunter Walt, Valentina Ciaravolo, Simona Arena, Andrea Scaloni, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7904328/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Black soldier fly (BSF) larvae are increasingly valued as a sustainable source of proteins and essential minerals in animal feed and potentially food, yet their physiological response to substrate iron fortification is poorly defined. Here, integrated transcriptomic, proteomic, and tissue iron detection approaches were used to characterize responses of BSF larvae reared on diets containing 323 (control), 1,255, and 6,970 mg Fe/kg dry matter (DM). Larval growth at day 12 was unaffected, while prepupal emergence after 15 days showed a statistically non-significant increase at the highest iron level, suggesting only subtle developmental effects. Prussian blue staining showed a dose-depended iron accumulation in the midgut epithelium, consistent with known insect iron responsive regions of entoferritin-based sequestration. An elevated iron signal in the peritrophic matrix indicated a complementary defensive barrier. Multi-omics profiling revealed oxidative stress responses, suppression of mitochondrial and translational pathways, and activation of exoskeleton biosynthesis. Entoferritin levels rose by ~ 70% for both protein subunits despite modest transcript changes, pointing to a post-transcriptional regulation mechanism. These results suggest a gut-centered “accumulate-and-store” physiological strategy enabling BSF larvae to tolerate high dietary iron. This entoferritin-based high iron accumulation capacity highlights the potential of this insect as a sustainable source of bioavailable iron in feeds. Biological sciences/Biochemistry Biological sciences/Developmental biology Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Molecular biology Biological sciences/Physiology Biological sciences/Zoology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction There is an increasing need for climate-resilient and bioavailable sources of dietary iron for food and feed alike (Byrne & Murphy, 2022 ; Wang et al., 2024 ). Iron bioavailability in food is influenced by multiple factors, including its chemical form, whether it is bound to proteins, and the presence of absorption enhancers or inhibitors in the food matrix (Piskin et al., 2022 ). For instance, the edible insect ferritin (entoferritin) is an iron binding protein that is hypothesized to increase iron bioavailability (First et al., 2023 ). It is a spherical, hollow protein complex composed of 24 subunits that can hold thousands of iron atoms, typically assembled in a 1:1 ratio of heavy and light chains (FerHCH and FerLCH, respectively) (Pham & Winzerling, 2010 ). Several edible insect species are proposed as novel protein and fat sources as well as suppliers of essential micronutrients, including dietary iron (Mwangi et al., 2018 ). Among these, the black soldier fly (BSF, Hermetia illucens ) is the most established species in the feed industry, valued for their larvae ability to efficiently convert organic waste into high-quality biomass (Siva Raman et al., 2022 ). Notably, BSF larvae contain considerable iron levels, of around 300 mg/kg Dry Matter (DM) under typical rearing conditions, and have demonstrated the capacity to accumulate heavy metals, including iron (First et al., 2024 ; Wu et al., 2006 ; Wu et al., 2020 ). Iron is an essential nutrient for all living organisms, but its redox properties make it potentially toxic at high concentrations, as it can catalyze the formation of reactive oxygen species (ROS) via mechanisms such as the Fenton reaction (Dixon & Stockwell, 2014 ). Despite of this potential toxicity, we have recently showed that BSF larvae can tolerate and accumulate exceptionally high levels of dietary iron without adverse physiological effects. BSF larvae were reared on diets with iron concentrations ranging from 323 to 6,637 mg/kg DM, covering both typical feed iron levels and concentrations considered extreme (First et al., 2024 ). For example, five days of exposure to 10 mM ferric ammonium citrate is lethal to Drosophila melanogaster ( Bonilla-Ramirez et al., 2011 ). When reared on 6,637 mg fe/kg DM, the BSF larvae accumulated up to three times more iron and exhibited increased calcium levels, with no negative impacts on survival rate, larval weight, fat and protein content, or the molecular weight of major soluble proteins (First et al., 2024 ). How BSF larvae can accumulate and tolerate such extreme iron loads remains unclear. A role for entoferritin is suggested (First et al., 2024 ), but also specific biochemical tolerance mechanisms could be activated. The aim of this study was to investigate the mechanisms by which BSF larvae accumulate and regulate increased dietary iron content, in the context of BSF larvae used as food and feed. Specifically, transcriptomic and proteomic analyses were employed to examine molecular changes in iron-fortified larvae. In addition, imaging and relative quantification of larval intestine of the BSF were conducted. Results Stable larval growth under iron supplementation with signs of accelerated pre-pupation Twelve-day-old BSF larvae reared for 7 days on medium (12.5mM) or high (125mM) iron-enriched diets showed no difference in body weight compared to the control group, with mean values consistently ranging between 230 and 240 mg per larva (Fig. 1 A). When five-day-old larvae were reared for 12 days on iron-supplemented diets, a mild (P > 0.1), dose-dependent increase in pre-pupation rate was observed, from 30% to 37% (Fig. 1 B). Iron accumulation localized to the anterior midgut and posterior midgut as the primary storage sites Iron storage of larvae reared on the control diet was compared to that of larvae reared on medium (12.5mM) and high (125mM) iron diets. A dose-dependent increase in Prussian blue staining intensity was observed in the Posterior midgut and anterior midgut regions, as defined by Bonelli et al. ( 2020 ), indicating elevated iron accumulation (Fig. 2 ). In contrast, no iron signal was detected in whole dissected larvae, isolated fat bodies, or extracted exoskeletons, regardless the dietary treatment, suggesting no novel iron storing locations were found in iron fortified BSF larvae. Peritrophic matrix acts as a dose-responsive iron-binding barrier The peritrophic matrix (PM) was stained with Prussian blue. No iron signal was detected in the larvae reared on the control diet, while the fortified larvae presented a dose-dependent increase in iron stain intensity (Fig. 3 ). Time-dependent transcriptomic shifts between treatments separate early defense suppression from later cuticle and oxidative stress activation The transcriptomic analysis yielded 10,838 expressed genes in all the BSF larvae (Table S1 ). A total of 299 of these genes were differentially expressed on day 2, and 791 genes were differentially expressed on day 7 Between the treatments (Fig. 4 ). Principal component analysis (PCA) revealed more distinct overall expression patterns on day 7 compared to day 2, with the high (125mM) iron treatment group forming a separate cluster primarily along principal component 2 (Fig. 4 A–B). Consistent with the PCA results, the high (125mM) iron group on day 7 also clustered separately based on the differentially expressed genes using hierarchical clustering (Fig. 4 D). In contrast, hierarchical clustering of the differentially expressed genes on day 2 showed that the control group was distinct from both iron-fortified groups (Fig. 4 C). Pairwise comparisons between treatment groups further revealed that on day 2, the medium (12.5mM) iron treatment group showed the greatest transcriptomic differences from the control, accounting for 270 out of the 299 differentially expressed genes identified on that day. Fewer differentially expressed genes were detected in the comparison between the high (125mM) iron and control groups (60 genes), and only two differentially expressed genes were identified between the high (125mM) and medium (12.5mM) iron groups on day 2 (Fig. 4 E). In contrast, on day 7, the medium (12.5mM) iron group exhibited minimal differences relative to the control group, while the high (125mM) iron group displayed extensive transcriptomic changes compared to both the medium (12.5mM) iron and control groups (Fig. 4 F). Functional enrichment analysis of the differentially expressed genes revealed limited functional over-representation on day 2, but substantial enrichment on day 7 (Fig. 5 ). On day 2, Gene Ontology (GO) terms related to defense responses and protein folding were significantly enriched. Genes associated with defense responses included predicted detoxification-related cytochrome P450s (LOC119647124, LOC119651341, LOC119651369, LOC119652925, LOC119653044, and LOC119653068), small heat shock proteins (LOC119650070, LOC119650378, and LOC119650380), and lysozymes (LOC119654083, LOC119654085, LOC119654694, and LOC119654730), all of which were down-regulated. The enrichment in protein folding–related terms was primarily driven by multiple down-regulated genes encoding small heat shock proteins (Fig. 5 A). By contrast, day 7 showed a broader range of enriched biological processes. Notably, genes involved in chitin-based metabolic processes, including cuticle and trachea development, were strongly over-represented. Additionally, several genes associated with the response to toxic substances were differentially expressed, including three predicted animal heme peroxidases (LOC119651583, LOC119659891, and LOC119659737), suggesting a physiological response to iron-induced oxidative stress (Fig. 5 B). Conserved IRE-mediated regulation of ferritin expression in BSF The main known driver for ferritin regulation is the bind of iron-responsive element-binding proteins (IRP) to iron-responsive element (IRE) in the ferritin’s mRNA 5 prime UTR region (Torti & Torti, 2002 ). To investigate if this same type of regulation is affecting ferritin expression in BSF, SIREs prediction tool was used to predict which BSF Entoferritin transcripts contain IREs. Both Fer1HCH and Fer2LCH transcripts were found to contain IREs (Fig. 6 ). In the gene encoding for ferritin’s light chain (Fer2LCH), two of the three transcript variants (XM_038056904 and XM_038056903) have high-quality predicted IREs (SIRE score 7.5/8) with canonical IRE loop motifs and only one mismatch (C-A) (Fig. 6 ). The two Fer2LCH transcripts containing an IRE, accounted for the majority of Fer2LCH expression, with XM_038056903 making up for around 97% and XM_038056904 making up for about 2% overall (Table 1 ). Additionally, the only transcript encoded by BSF’s ferritin heavy chain gene (Fer1HCH) also has a high-quality predicted IRE (SIRE score 7.5/8). However, instead of a C-A mismatch, Fer1HCH has a G-U pairing, resulting in lower predicted free energy (-11.2 kcal/mol) than Fer2LCH (-4.8 kcal/mol) (Fig. 6 ). Table 1 Overall Isoform Percentages of Fer2LCH and IRE Prediction Transcript Isoform Percentage Predicted IRE XM_038056903 97% Yes XM_038056904 2% Yes XM_038056905 1% No Phylogenetic analysis confirmed the homology of BSF ribosomal L1 domain-containing 1 (RSL1D1) to other arthropod and vertebrate candidate homologs, as the ancestral branch connecting these two lineages has 100% bootstrap support (Figure S1 ). After these two lineages diverged, RSL1D1 orthologs were retained as single-copy genes, with the exception of the sandfly which has a lineage-specific duplication. In the arthropod side of the tree, closely related taxa form the expected monophyletic clades, however, the overall branching pattern is not consistent with previous phylogenomic studies in arthropods (Misof et al., 2014 ; Su et al., 2024 ). This is reflected by several lowly-supported branches, possibly due to limited sampling. Proteomic changes under high (125mM) iron treatments reveal stress defense activation, suppression of energy metabolism and translation, and modulation of chitin-associated pathways Proteomic analysis yielded a total of 2489 proteins (Table S2 ). Of these, 170 were differentially represented between samples (Fig. 7 ). The largest number of changes was observed in the high (125mM) iron vs control comparison, which included 122 differentially represented proteins. Among these, 95 proteins were unique to this comparison, 10 proteins were also differentially expressed in the medium (12.5mM) iron vs control comparison, and 22 proteins overlapped with the high (125mM) vs medium (12.5mM) iron treatments comparison. The medium (12.5mM) iron vs control comparison included 19 unique proteins, and the high (125mM) vs medium (12.5mM) iron comparison included 16 unique proteins. Two proteins were common in all three comparisons (Fig. 7 C). The heatmap revealed distinct clustering of samples based on iron fortification, indicating clear differences in protein expression profiles among the control, medium (12.5mM) and high (125mM) iron treatments (Fig. 7 A). Over representation of numerous proteins was observed in the high (125mM) iron group, while the medium (12.5mM) iron group larvae exhibited an intermediate protein representation between the control and high (125mM) iron conditions. This was supported by the PCA protein analysis of the proteomic data, where three of the four high (125mM) iron replicates clustered in the positive PC1 and PC2 quadrant, reflecting a pronounced shift in protein expression. Control replicates clustered tightly near the origin, consistent with minimal variation and a baseline expression profile. The medium (12.5mM) iron replicates were more broadly distributed (Fig. 7 B). Key over-represented proteins belonged to pathways associated with iron homeostasis (e.g., entoferritin, transferrin), oxidative stress mitigation (e.g., glutathione S-transferase GstE7, cystathionine beta-synthase Cbs), innate immune activation (e.g., croquemort crq, calmodulin Cam), and cytoskeletal remodeling (e.g., tropomyosin, coactosin-like protein CG6891, Jupiter). These modulations reflect cellular strategies aimed at sequestration of excess iron, detoxification of ROS, immune defense, and preservation of structural integrity. Over-representation of cytochrome P450 enzymes (e.g., Cyp4d2, Cyp6a14) further supports the activation of detoxification pathways, while increased expression of coatomer subunit zeta (zetaCOP) and CHK kinase-like proteins (CG10562) suggests an early activation of vesicular trafficking and stress signaling processes. Conversely, a marked under-representation of mitochondrial respiratory chain components (ND-PDSW, ND-MLRQ, ND-23, ND-B18, ND-30, COX5A) indicates suppression of oxidative phosphorylation, likely aimed at limiting ROS production. In parallel, the repression of ribosomal proteins and translation factors (RpL27A, RpL36A, RpL0, RpL4, RpL32, RpL35, RpL38, RpS12, RpL10Ab, RpL40, and the initiation/elongation factors eIF3A, eIF3B, eIF4B, eIF5, eEF1delta) suggests a broad inhibition of ribosome assembly and translational activity. Additional under-represented proteins include enzymes involved in lipid metabolism (ceramide 1-phosphate binding CG30392, hydroxysteroid dehydrogenase-like protein 2 CG5590, beta hydroxy acid dehydrogenase 1 Had1, acetyl-CoA C-acyltransferase ScpX, 3-hydroxyacyl-CoA dehydrogenase Mtpalpha), nitrogen metabolism (glutamine synthetase Gs1, glycine N-methyltransferase Gnmt), and RNA processing (nucleolar protein 56 Nop56, RNA-binding protein squid sqd), reflecting broad metabolic reprogramming under iron overload (Table S2 , Fig. 7 ). The STRING-based protein–protein interaction (PPI) network presented in Fig. 8 , built from proteins differentially represented in BSF larvae reveals several densely interconnected functional modules. The largest hub centers on translation and general protein metabolism, where numerous ribosomal subunits (RpL/RpS nodes) interlink tightly with canonical initiation and elongation factors (eIF3 subunits, eIF4B, eIF5, eEF1δ), indicating coordinated regulation of protein synthesis under augmented dietary iron supply. Additionally, pathways related to iron transport (e.g., Fer1HCH, Tsf1) and oxidoreductase activity were markedly integrated, consistent with the modulation of iron homeostasis and redox balance affected by excess iron. Notably, proteins involved in nucleobase compound metabolism (e.g., Nop56, SC35, Prp8, AdS) formed a distinct but functionally connected module associated with RNA processing, ribonucleoprotein complex assembly, and mRNA splicing. Their close association with translation-related proteins suggests a tightly regulated continuum from RNA maturation to active translation. Peripheral but connected clusters involve innate immune effectors, lipid metabolism enzymes, and chitin-based cuticle development proteins, indicating broader immunometabolic and developmental consequences. Integrative omics confirms coordinated molecular changes, with cuticle and oxidative stress pathways driving treatment separation Integrative analysis of the transcriptomic data and the proteomic data using DIABLO revealed that the two datasets were highly correlated, with an overall correlation coefficient of 0.92 (Fig. 9 A). When plotting the overlayed sparse partial least squares-discriminant analysis (sPLS-DA) results in two dimensions, the transcriptomic data and proteomic data form three clusters by treatment group and corresponding replicates fall closely together on the coordinate plane (Fig. 9 A). In the first sPLS-DA component, we observed a high correlation (correlation coefficient = 0.99) between the transcriptomic and proteomic datasets along with clear separation between the high (125mM) iron treatment and the control treatment (Fig. 9 B). Next, which variables (i.e. genes and proteins) are most important to cause this separation were investigated. Consistent with the differential expression analysis results, many of these genes were involved in cuticle formation, while oxidation related proteins such as cytochrome c oxidase and ribosomal protein L3 where amongst the main drivers for treatment separation (Fig. 9 C). The two omics datasets were also highly correlated in the second s-PLS-DA component (correlation coefficient = 0.98). However, component 2 mainly separated the medium (12.5mM) iron treatment from the other groups (Fig. 9 D). Similar to the first component, the genes and proteins driving this separation were not the same between the transcriptomic and the proteomic data (Fig. 9 E). Entoferritin, ferritin, transferrin, IRP1, and RSL1D1 reflect iron homeostasis adjustments under increasing iron conditions In addition to the broader omics analyses, the focus shifted to key genes and proteins involved in iron homeostasis (Fig. 10 ). Larvae reared on the high (125mM) iron treatment showed a significant increase in Entoferritin protein levels, with Fer1HCH and Fer2LCH subunits increasing by ~ 75% and ~ 70%, respectively. No significant change in Entoferritin subunit mRNA levels was observed at day 2, while mild (P > 0.1), dose-dependent increases appeared by day 7. IRP1 expression was lower in high (125mM) iron larvae at day 7, accompanied by a mild (P > 0.1) decrease in IRP1 protein abundance for both iron treatments. RSL1D1 expression at day 2 was significantly elevated in both iron treatments compared to the control, with increases of ~ 320% for medium (12.5mM) iron group and ~ 350% for the high (125mM) iron group. Transferrin mRNA showed no significant difference at day 2, but at day 7 it was significantly higher in high (125mM) iron group than in the control (~ 210%) and medium (12.5mM) iron group (~ 120%). Protein abundance of transferrin in the high (125mM) iron group at day 7 followed the same pattern, with increases of ~ 140% compared to control and ~ 110% compared to the medium (12.5mM) iron. Malvolio (Mlv), the insect homolog of Divalent Metal Transporter 1 (DMT1), was detected at all time points, with no significant treatment-related differences, though temporal increases occurred across treatments (Table S1 ). However, Mlv was not detected in the proteomic analysis. Discussion Consistent with the findings of First et al. ( 2024 ), BSF larvae showed no significant differences in body weight across treatments (Fig. 1 ), maintaining stable growth under dietary iron concentrations considerably higher than those typically applied in excess iron exposure experiments on other insect species (Bonilla-Ramirez et al., 2011 ; First et al., 2024 ; Wagers et al., 2024 ). Furthermore, larvae exposed to medium (12.5mM) iron exhibited mRNA expression profiles indistinguishable from controls by the final harvest day, suggesting effective transcriptional recovery at moderate iron levels. This contrasts with other insect species, where substantially lower iron concentrations have been shown to disrupt development or reduce survival (Bonilla-Ramirez et al., 2011 ; First et al., 2024 ; Wagers et al., 2024 ), and highlights the BSF larvae unique physiological adaptations for tolerating high dietary iron. Prussian blue staining revealed a dose-dependent increase in iron deposits, localized to the anterior and posterior midgut across all dietary treatments (Fig. 2 ). No stain was detected in fat bodies, whole larvae, or exoskeletons, indicating that storage is restricted to the gut even under high (125mM) iron exposure. This localization aligns with previously reported anterior and posterior iron regions in the black soldier fly larval midgut (Bonelli et al., 2020 ). The fact that iron accumulation was confined to this established storage site suggests that BSF larvae rely on the same pathway under increasing iron, most likely entoferritin-based sequestration, rather than recruiting novel storage mechanisms (Gorman, 2022 ). Previous work has shown that BSF larvae reared on standard chicken feed (~ 320 mg/kg iron) accumulate ~ 370 mg/kg in their bodies (First et al., 2024 ), far surpassing the 71–131 mg/kg typically observed in Drosophila species larvae (Massie et al., 1985 ; Sadraie & Missirlis, 2011 ). In line with this greater accumulation capacity, BSF larvae in this study displayed much stronger iron staining in the midgut compared to the weaker staining reported for Drosophila species. Moreover, the iron region of Drosophila is usually confined to the anterior midgut and does not include the posterior midgut (Bettedi et al., 2011 ; Sadraie & Missirlis, 2011 ; N. Wu et al., 2020 ). These differences highlight the enhanced ability of BSF larvae to accumulate iron and suggest a potential evolutionary adaptation to a metal-rich environments. In addition to storage within the midgut, iron aggregates were also detected in the peritrophic matrix of larvae reared on iron-fortified diets, but not in controls (Fig. 3 ). PM is a chitinous, semi-permeable layer that lines the insect gut lumen and acts as a protective barrier between ingested food and the gut epithelium (Lin et al., 2021 ; Orozimbo et al., 2025 ). In blood-feeding insects, free heme and iron can bind to the PM, reducing their pro-oxidant activity and limiting ROS formation (Orozimbo et al., 2025 ). A proteomic analysis of BSF larva has identified both ferritin and transferrin in its PM (Lin et al., 2021 ). Taken together, these findings suggest that, in addition to its barrier function, the BSF PM may contribute to iron handling and oxidative stress mitigation under high-iron conditions through the activation of mechanisms analogous to those described in blood-feeding insects. While iron accumulation was localized to the midgut and somatic growth remained unaffected, both transcriptomic and proteomic data indicate that BSF larvae undergo substantial physiological adjustments under iron fortification. These adjustments extend beyond iron storage itself and involve pathways and proteins linked to oxidative stress regulation, mitochondrial respiration, ribosomal structure and function, translation factors, and exoskeletal development (Figs. 5 and 8 ). Integration of the transcriptomic and proteomic data demonstrated strong agreement, with replicates clustering by treatment in both datasets (Fig. 9 ). This clustering indicates that the responses observed are not limited to isolated regulatory processes but instead reflect coordinated, system-wide remodeling under dietary iron stress. Dimension 1, the strongest driver in the DIABLO analysis, clearly separated the high-iron diet group from both the control and the medium (12.5mM) iron groups (Fig. 9 A), highlighting that the most pronounced physiological reprogramming occurs under high (125mM) iron exposure. The observed oxidative stress responses likely result from excess dietary iron increasing the pool of free iron available to larvae, which in turn promotes the generation of ROS via the Fenton reaction. These responses may represent cellular protective mechanisms aimed at mitigating ROS-induced damage. Iron exposure can impair mitochondrial function primarily through ROS-mediated pathways, which is in line with the observed reduction in abundance of ribosomal proteins under iron-fortified conditions (Zheng et al., 2018 ). This agrees with the multi-omics results, where ribosomal and cytochrome proteins were among the strongest contributors to treatment separation (Fig. 9 B), reflecting the central role of energy metabolism and translational machinery in the high-iron response. The increased iron has decreased larval ribosomal protein levels (Fig. 8 ), potentially through direct binding of free iron to ribosomal proteins, leading to their destabilization and degradation (Smethurst et al., 2020 ). Beyond oxidative stress, multi-omics analyses revealed that chitin- and cuticle-associated transcripts were major drivers of treatment clustering (Fig. 10 A). Consistently, DEG patterns of exoskeleton development pathways, together with the over expression of chitin-related proteins, indicated disruption of normal cuticle formation (Figs. 5 and 8 ). In line with these findings, First et al, ( 2024 ) reported that BSF larvae reared on a high-iron diet (6,637 mg/kg DM Fe) accumulated up to 25% more calcium compared to controls (323 mg/kg DM Fe) and developed limited black lesions on their exoskeletons, suggesting impaired cuticle development under iron stress. Calcium is a critical component of BSF cuticle development, where it is biomineralized primarily as calcium carbonate (CaCO₃) across life-stage specific structures (Rebora et al., 2023 ). Altogether, these results suggest that excessive dietary iron disrupts cuticle homeostasis through oxidative stress, leading to structural remodeling and increased calcium deposition. Calcium dynamics are not restricted to cuticle integrity but also indicate developmental progression, as levels increase across successive instars to support sclerotization prior to pupation (Liu et al., 2017 ). A mild (P > 0.1), dose-dependent increase in pre-pupate emergence was observed under iron fortification (Fig. 2 ), suggesting a potential increase in development timing. Transcriptomic data supports this interpretation, with hexamerin being the most strongly up-regulated transcript under high (125mM) iron on day 7 (Table S1 ). Hexamerin is an amino acid storage protein typically stockpiled just before metamorphosis in holometabolous insects (Burmester & Scheller, 1999 ). Hexamerin protein abundance was numerically ~ 50% higher in the high (125mM) group as opposed to the control, although the difference was not statistically significant (P > 0.1) due to variability among biological replicates (Table S2 ). Moreover, intracellular iron trafficking via transferrin and entoferritin to the prothoracic gland is essential for insect hormone production and developmental progression. A lack of transferrin or entoferritin has been shown to hinder iron signaling in the prothoracic gland (Soltani et al., 2024 ), and whether the increased abundance of these proteins observed here (Fig. 10 ) may advance the onset of iron signaling remains to be confirmed. Collectively, these observations point to accelerated developmental processes in BSF larvae exposed to high iron. However, neither the present data (Fig. 1 ) or nor a previous one (First et al., 2024 ) showed an effect of dietary iron on larval fresh weight at harvest, suggesting that such a developmental shift is subtle. Larger-scale studies spanning the full life cycle are needed to determine whether iron supplementation can enhance developmental rates in BSF production systems. The patterns observed in Entoferritin, IRP1, RSL1D1, transferrin and Malvolio transcripts and protein abundance changes, reveal that BSF larvae rely on multiple regulatory mechanisms to cope with high dietary iron (Fig. 10 ). Entoferritin, the predominant intracellular iron storage protein in insects (First et al., 2023 ; Missirlis et al., 2006 ), increased significantly at the protein level under high (125mM) iron conditions, while mRNA levels showed mild (P > 0.1), statistically insignificant changes. This divergence between transcript and protein abundance indicates that ferritin synthesis is regulated primarily at the post-transcriptional stage. The reduction of IRP1 abundance detected under both iron conditions likely diminishes IRP–IRE binding, leaving ferritin mRNAs accessible for ribosomal translation (Fig. 10 ). In mammals, IRPs binding to IREs in ferritin mRNAs prevents ribosomal assembly and translation. Under iron-replete conditions, cytosolic free iron binds to IRP1, promoting the formation of an iron–sulfur cluster that converts the protein into a cytosolic aconitase enzyme and abolishes its RNA-binding capacity. This conformational change, followed by degradation or repurposing of IRPs, frees the IRE and allows ferritin translation to proceed (Georgieva et al., 2002 ; Meyron-Holtz et al., 2004 ; Nichol & Winzerling, 2002 ). Similar post-transcriptional control has been described in Manduca sexta , whereas the IRP/IRE interaction has been demonstrated to affect entoferritin heavy chain unit translation (Zhang et al., 2001 ). IRE motif analysis supports this regulatory framework. Both FerHCH and FerLCH transcripts contained predicted IRE stem-loop structures (Fig. 6 ). Messenger RNAs of ferritin light chain containing IREs, as well as various loop mutations, are well characterized and conserved in mammals (Luscieti et al., 2013 ). This is a novel finding for a dipteran as IREs in the light chain were only reported in lepidopterans (Gorman, 2022 ; Missirlis et al., 2007 ; Pham et al., 1996 ). A light chain isoform lacking an IRE was also detected under high-iron conditions, which could bypass IRP-mediated repression and allow translation even when IRP1 levels are high (Lind et al., 1998 ). However, given the predominance of the IRE-containing isoform (Table 1 ), its overall contribution to ferritin output is likely small. Together, these features suggest that BSF entoferritin regulation integrates canonical IRP/IRE control with isoform variation to adjust storage capacity in response to dietary iron. Another potential indirect regulator of iron metabolism is RSL1D1, an mRNA-binding protein involved in various cellular processes including apoptosis, proliferation, and senescence. In mammals, RSL1D1 binds to ferritin heavy chain mRNA, stabilizing the transcript and protecting it from degradation. Knockdown of RSL1D1 in human colorectal cancer cells reduced ferritin heavy chain mRNA levels, while its presence extended mRNA stability over time (Jin et al., 2023 ). In the current study, RSL1D1 expression rose sharply early after iron exposure before returning to baseline (Fig. 10 ), which might increase the stability of heavy chain transcripts across the rearing period and in turn contribute to higher ferritin abundance. Phylogenetic analysis indicates that RSL1D1 is a conserved, single copy ortholog across vertebrates and arthropods, supporting a potentially shared role in ferritin regulation among these groups (Fig s1 ). However, a functional confirmation of the occurrence of RSL1D1 in insects is needed to confirm this hypothesis. Transferrin, an extracellular iron transporter in insects (Geiser & Winzerling, 2012 ), showed a delayed but significant increase at both the mRNA and protein levels by day 7 in the high-iron treatment (Fig. 10 ). It is established that transferrin gene expression in insects is regulated primarily at the transcriptional level (Geiser & Winzerling, 2012 ). Consistent with this, in BSF we observed a close correspondence between transcript and protein abundance, in contrast to entoferritin (Fig. 10 ). The observed concomitant up-regulation of transferrin gene expression and over-representation of the protein may reflect an increased requirement for iron absorption and transport under prolonged exposure. In mosquitoes, transferrin expression is influenced by the chemical form of dietary iron, with inorganic sources often leading to suppression (Harizanova et al., 2005 ). The opposite pattern in BSF may be linked to adaptation to a non-blood detritus-type diet in which inorganic iron is a consistent dietary component. However, given its relatively low abundance compared to entoferritin, transferrin is unlikely to make a major contribution to BSF iron bioavailability. Malvolio (Mvl), the insect homolog of DMT1, functions as a divalent metal transporter that facilitates the uptake of iron and other transition metals across cellular membranes (Bettedi et al., 2011 ). In contrast to transferrin, Mvl, showed no significant iron-fortification related changes in expression, indicating that BSF larvae do not appear to reduce dietary iron uptake under increased iron conditions (Table S1 ). In mammals, DMT1 downregulation under iron overload is mediated in part through interaction with Nedd4 family-interacting protein 1, which promotes its degradation (Howitt et al., 2009 ). In insects, however, regulatory pathways for Mvl are uncharacterized, and no expression patterns indicative of such regulation were detected. Instead, BSF larvae seem to maintain uptake capacity and rely on entoferritin storage as their primary protective strategy. Overall, these findings suggest an Entoferritin based “accumulate and store” approach to dietary iron through a mild (P > 0.1) entoferritin induction mRNA transcription increase, IRP1 repression, potential RSL1D1-mediated transcript stabilization, and increased transferrin expression to meet iron transport needs. The previously reported ~ 250% increase in total iron content (First et al., 2024 ), compared to only ~ 70% increases in entoferritin subunits in this study implies a higher iron-to-entoferritin ratio under high-iron conditions. Such disproportionate loading could affect ferritin stability (Irimia-Dominguez et al., 2020 ; Ruiyang Ji Mingyang Sun, 2023 ; Srivastava et al., 2023 ), potentially reducing iron bioavailability, though this requires further investigation. In conclusion, black soldier fly larvae tolerate a substantial level dietary iron, with larval mass unaffected and only a mild (P > 0.1), dose-dependent increase in pre-pupate emergence, suggesting that developmental shifts are subtle. Multi-omics integration revealed coordinated remodeling under iron fortification, including oxidative stress signatures, repression of mitochondrial and translational activity, and strong activation of chitin- and cuticle-associated pathways. Iron localized to the gut epithelium and peritrophic matrix, but not the fat body or exoskeleton, pointing to an “accumulate-and-store” strategy in which entoferritin serves as the primary sequestration site. Entoferritin subunits increased markedly at the protein level despite modest transcript changes, indicating post-transcriptional regulation. Decreased IRP1 abundance, predicted IREs, early RSL1D1 induction, and delayed transferrin upregulation further suggest fine-tuned control of uptake, storage, and transport. This mechanism enables larvae to withstand high iron exposure without impairing growth. From a food and feed perspective, the entoferritin-based ‘accumulate-and-store’ strategy observed here suggests that iron-fortified larvae could provide a source of highly bioavailable iron. The soluble and stable nature of ferritins, particularly entoferritin, supports their potential as effective vehicles for dietary iron fortification, a strategy already demonstrated in ferritin-fortified plant models (Lönnerdal, 2007 ; Masuda et al., 2012 ). Insects may offer additional advantages, as entoferritin is larger and potentially more water-soluble due to its secreted nature (First et al., 2023 ), and the magnitude of total iron accumulation in fortified BSF larvae is substantially higher than that typically achieved in plant systems (First et al., 2024 ; Masuda et al., 2012 ). However, comparison with previously reported whole-larval iron accumulation suggests a high iron-to-entoferritin ratio, with possible consequences for protein stability and nutrient bioavailability. Future research should quantify iron speciation, resolve regulatory nodes such as RSL1D1, and evaluate whether controlled fortification can enhance larval development, improve production efficiency. This information will definitively strengthen the role of BSF as a sustainable source of bioavailable iron. Methods Larvae rearing and collection Black soldier fly larvae were reared under controlled conditions at 27°C and 70% relative humidity, in the dark. Rearing procedures were based on First et al. ( 2024 ), with minor modifications to feed moisture content, as well as rearing and sampling timelines, depending on the analysis. Dry chicken feed (Kuikenopfokmeel no. 1, Kasper FaunaFood, the Netherlands) was sieved using a 1.0 mm to homogenize particle size of the dry substrate. Ferric ammonium citrate (FAC; CAS 1185-57-5; Merck, Darmstadt, Germany) was dissolved in water to prepare solutions of 12.5, and 125mM, corresponding to final substrate concentrations of 323 (Control, i.e. no fortification), 1,255, and 6,970 mg Fe/kg DM, respectively. Diets were prepared by mixing 200 g of dry feed with 320 mL of the respective FAC solutions (1:1.6 feed-to-water ratio), yielding 520 g of wet feed per container. A total of 0.2 g of BSF eggs (Protix, Bergen op Zoom, the Netherlands) were introduced to 520 g of wet, FAC-free feed. Larvae were reared on this control diet for five days. Then, larvae were separated from the substrate by sieving, counted, and assigned to treatments. For each treatment, 200 five-day-old larvae were transferred to new rearing containers (15.5 × 10.5 × 6 cm) containing 520 g of wet feed prepared with the corresponding FAC concentration. Each treatment was conducted in quadruplicate. Two days after exposure, four larvae per replicate were collected, flash frozen in liquid nitrogen, and stored at − 80°C for mRNA extraction. On day 7 since the start of the exposure, all remaining larvae were harvested, flash frozen in liquid nitrogen, and stored at − 80°C for protein and mRNA analyses. To assess pre-pupation, an additional cohort of five-day-old larvae was reared on iron-supplemented diets for twelve consecutive days. For each treatment (Control, 12.5, and 125 mM FAC), 200 larvae were placed in rearing containers with 520 g of wet feed with the corresponded FAC fortification. All treatments were reared in triplicate. At the end of the period, larvae were harvested, sieved, and separated into larvae and pre-pupae based on external morphology, as darkened and hardened lighter larvae were counted as pre-pupas. All individuals were counted, and their biomass was measured to validate developmental stage separation, as pre-pupae are significantly lighter than earlier instar larvae (Georgescu et al., 2020 ). For iron cluster localization, larvae were reared using the same procedure but were switched to FAC-free feed on day 7 to avoid excess iron in the larval gut. These larvae were collected on day 8, sieved and freshly dissected following a sacrifice using CO2 exposure. Tissue Prussian Blue (iron) Staining Larvae reared on control diet for 24 h after the end of iron treatments were harvested. Dissections were performed in phosphate-buffered saline (PBS, pH 7.2), at room temperature. Whole-body staining was initially performed using Prussian blue solution (2% w/v potassium ferrocyanide [K₄Fe(CN)₆] and 2% hydrochloric acid mixed in 1:1 ratio) for 10 min in dark conditions to localize iron deposits. Samples were subsequently washed in PBS to remove excess staining reagent. Following whole-body staining, gut tissues were dissected and stained separately under the same conditions. This targeted approach was guided by the initial whole-body results to refine the localization of iron deposits. Tissue imaging was performed using an Olympus SZX12 stereomicroscope equipped with a Euromex sCMEX-20 digital camera. Images were acquired and processed using ImageFocus software. mRNA and Protein extraction mRNA extraction was conducted by Novogene Europe (Cambridge, UK). Total RNA was isolated from frozen BSF larvae using TRIzol reagent (Invitrogen, USA), following the manufacturer’s instructions with slight modifications. Two to three frozen whole larvae were ground in liquid nitrogen and 50 mg of the larvae were homogenized in 1 mL of TRIzol. After chloroform extraction and phase separation, the aqueous layer was collected, and RNA was precipitated using isopropanol. The pellet was then washed with 75% ethanol and dissolved in RNase-free water. RNA concentration and quality were evaluated with an Agilent 5400 system. All steps were carried out under RNase-free conditions. For protein extraction, each frozen BSF larvae was subjected to a preliminary wash with 70% ethanol to remove potential surface contaminants, followed by three consecutive washes with PBS (137 mM NaCl, 2.7 mM KCl, 8.1 mM Na₂HPO₄·2H₂O, 1.76 mM KH₂PO₄) at a 1:10 weight-to-volume ratio, supplemented with EDTA-free protease inhibitor tablets. A RIPA buffer, composed of 25mM Tris-HCl (pH 7.6), 150 mM NaCl, 1% NP-40, and 1% sodium deoxycholate was used for protein extraction. The freeze dried larvae were homogenized on ice using an Ultraturrax at 30,000 rpm for 20 seconds, followed by a 30-second pause, for five repititions. The homogenized samples were then left on ice for 1 hour following sonication for 5 minutes with 2-minute pauses on ice, which was repeated three times. The resulting crude extracts were centrifuged at 13,000 × g for 20 minutes at 4°C. Protein concentration in the supernatants was determined using Pierce BCA Protein Assay™ kit (Thermo Scientific, Rockford, IL, USA). An aliquot containing 70 µg of protein from each sample was subjected to electrophoresis on a 12% SDS-PAGE gel under reducing conditions, and the gels were stained with Coomassie Brilliant Blue G-250 to verify the extraction protocol. Aliquots of 150 µg was reduced, alkylated in the dark, and precipitated by adding six volumes of cold acetone to remove interfering substances for shotgun analysis. After precipitation, the samples were centrifuged at 8,000 × g for 10 minutes at 4°C, and the resulting protein pellets were air-dried. Each sample was digested with freshly prepared trypsin (1:50 enzyme/protein ratio) in 50 mM TEAB buffer at 37°C overnight. The resulting peptides from each protein sample were quantified using the Pierce™ BCA Peptide Assay Kit (Thermo Scientific, Rockford, IL, USA). Subsequently, 30 µg of peptides per sample were labeled using the TMTsixteenplex™ Isobaric Label Reagent Set (Thermo Fisher Scientific, USA). After 1h of reaction, 8 µL of 5% w/v hydroxylamine was added to each tube and mixed for 15 min to stop the derivatization reaction. For a series of comparative experiments, the labeled peptide mixtures were mixed in equal molar ratios and dried in vacuom under rotation. Then, TMT-labeled peptide mixtures were suspended in 0.1% trifluoroacetic acid and fractionated using the Pierce™ High pH Reversed Phase Peptide Fractionation Kit (Thermo-Fisher Scientific) to remove unbound TMT reagents and reduce sample complexity, according to the manufacturer's instructions. After fractionation, eight TMT-labeled peptide fractions were collected, dried under vacuum, and finally reconstituted in 0.1% formic acid for subsequent mass spectrometric analysis. All mRNA extraction and peptide extraction were conducted in biological quadruplicates. Peptide Mass Spectrometry Peptide mixtures were analyzed in technical triplicate by means of a nanoLC-ESI-Q-Orbitrap-MS/MS platform consisting of an Vanquish- Neo nano-chromatography system (Thermo Fisher Scientific) interfaced to a Exploris 480 mass spectrometer through an easy-spray ion source (Thermo Fisher Scientific). Peptides were loaded on an EASY-Spray C18 column (150 mm × 75 µm ID, 2 µm particles, 100 Å pore size) (Thermo Fisher Scientific), and eluted with a gradient of solvent B (19.92/80/0.08 v/v/v water/acetonitrile/formic acid) in solvent A (99.9/0.1 v/v water/formic acid), at a flow rate of 250 nL/min. The gradient of solvent B started at 6%, increased to 31% over 120 min, increased to 50% over 5 min, increased to 95% over 5 min and remained at 95% for 4 min, and finally returned to 6% for equilibrating step. The mass spectrometer operated in data-dependent mode, using a full scan range (m/z 400–1600, resolution of 60,000 @200 m/z), followed by MS/MS scans of the 20 most abundant ions. MS/MS spectra were acquired in a dynamic scan m/z range, using a normalized collision energy of 38%, a Normalized AGC Target (%) of 200, a maximum injection target of 105 ms, and a resolution of 45000 @200 m/z. The dynamic exclusion value was set to 25 s. Transcriptomics Universal Illumina adapter sequences were trimmed from the reads using Cutadapt v3.5 (Martin, 2008 ). Next, low-quality bases were removed using Trimmomatic v0.39 with the options SLIDINGWINDOW:4:15 MINLEN:30 (Bolger et al., 2014 ). Read quality after processing was checked using fastqc v.0.11.9 (Andrews, 2010 ). Transcript abundances were quantified using Salmon v.0.14.1 (Patro et al., 2017 ). First, a salmon index was made using the BSF reference transcripts and the BSF genome (GCF_905115235.1) as “decoy” sequences. Next, transcript abundance was quantified for each sample using salmon in “quant” mode, allowing the library orientation to be determined automatically and the –validateMappings flag implemented. Transcript abundances were imported to R using tximport v1.26.1 for differential expression analysis using DESeq2 v1.38.3 (Love et al., 2014 ; Soneson et al., 2015 ). Overall gene expression patterns were visualized using principal component analysis via the plotPCA function in DESeq2 on gene expression counts that had undergone variance-stabilizing transformation. Differential expression analysis was conducted at the gene level. Differentially expressed genes between each treatment within each time point were found using the Wald test with the Benjamini-Hochberg method to correct for multiple comparisons (as implemented in DESeq2). Genes were considered differentially expressed if they had an adjusted p-value less than 0.05. Differentially expressed genes across the samples were visualized using ComplexHeatmap v2.22.0 with hierarchical clustering for both rows and columns(Gu et al., 2016 ). Venn diagrams were generated using ggVennDiagram v1.5.2 in R (Gao et al., 2024 ). To determine the over-represented functions in the differentially expressed genes, the BSF reference proteome associated with GCF_905115235.1 was functionally annotated using eggNOG mapper v2.1.9 in diamond mode (Cantalapiedra et al., 2021 ). Gene ontology (GO) term enrichment analysis was conducted with ClusterProfiler v4.14.4 using the enricher function incorporating the ”BH” method to adjust p-values for multiple comparisons. GO terms were considered ”enriched” under a p-value cutoff of 0.05. Redundant enriched GO terms were clustered and assigned to parent terms using rrvgo based on the “rel” algorithm (Sayols, 2023 ). Significant p-values were negative log10 transformed and the term with the highest score was used as the representative ”parent” term after clustering by semantic similarity (similarity threshold = 0.75). The D. melanogaster orgdb annotation (org.Dm.eg.db) was used as the GO database for annotations. To locate entoferritin subunit iron response elements (IREs), ferritin subunit gene (LOC119652653 and LOC119652654) mRNA transcripts were downloaded from NCBI. IREs in ferritin transcripts were predicted using the SIREs v3.0 web server with default settings (Suárez-Quintana et al., 2025 ). To determine the homology of BSF ribosomal L1 domain-containing protein 1 (RSL1D1) to vertebrate RSL1D1 proteins, a phylogenetic analysis was conducted. A selection of vertebrate, arthropod, and cnidarian (outgroup) protein gene models were downloaded from NCBI using biomartr v.1.0.7 (Drost & Paszkowski, 2017 ) and made into a protein BLAST database. Next BLASTp was used to align BSF, human, and D. melanogaster RSL1D1 amino acid sequences to the custom metazoan database with a minimum evalue of 1e-5. The longest isoforms from the resulting protein sequences were retrieved from NCBI using datasets v.17.3.0 (O’Leary et al., 2024 ). All protein sequences were aligned using MAFFT v.7.490 incorporating the L-INS-i algorithm and the resulting alignment was trimmed with trimal v.1.2 using the gappyout option (Capella-Gutiérrez et al., 2009 ; Katoh & Standley, 2013 ). A phylogeny was inferred from the trimmed alignment using IQ-TREE v.2.0.7 employing automatic model selection through ModelFinder (Kalyaanamoorthy et al., 2017 ; Minh et al., 2020 ). Support values are shown as ultrafast bootstrap with 1000 replicates (Hoang et al., 2018 ). Proteomics All MS and MS/MS raw data files per sample were merged for protein identification using Proteome Discoverer v.3.1 software (Thermo Fisher Scientific), allowing database searching using Mascot v. 2.4.2 algorithm (Matrix Science) according to a shotgun proteomic approach. Database searching was performed with the following criteria: Hermetia protein sequence database (downloaded from UniProtKB and including 17615 entries); carbamidomethylation at Cys and TMTpro 16plex modification at Lys and peptide N-terminus as fixed modifications; oxidation at Met, pyroglutamate formation at N-terminal Gln, phosphorylation at Ser/Thr/Tyr, and deamidation at Asn/Gln as variable modifications. The mass tolerance of the parent peptide was set to ± 10 ppm and ± 0.05 Da for MS/MS fragments. Trypsin was set as the proteolytic enzyme and the maximum number of missed cleavages was limited to 2. Hierarchical clustering and PCA analysis were used to assess DRPs. Both the heatmap and PCA were generated using Proteome Discoverer 3.1 and based on normalized abundance data. The heatmap was created using the Euclidean distance function, complete linkage method, and scaling after clustering. In both analyses, biological replicates are shown, and the mass spectrometry analysis was performed with three technical replicates. Functional annotation of DRPs and network inference analysis were performed using STRING software with confidence score cutoff value of 0.4. Due to limited functional annotation in the BSF protein database, homologous polypeptide sequences from Insecta were used as a reference database. Integrative omics analysis Transcriptome-proteome integration analysis was conducted using DIABLO within the mixOmics v.6.30.0 R package. DIABLO is a supervised N-integration method that uses multiblock partial least squares-discriminant analysis (PLS-DA) to assess the correlation between two omics datasets (Singh et al., 2019 ). Transformed counts using the variance stabilizing method implemented in DESeq2 were used as input for the transcriptomic data, while protein abundance per replicate was used as input for the proteomic data. A weight of 0.92 was used in the experimental design and was chosen by conducting a PLS regression analysis between the transcriptomic and proteomic data. Two components were chosen for the final sparse PLS-DA (sPLS-DA) model. The optimal number of variables used for the final sPLS-DA model was found using the tune.block.splsda function with 4 folds and 10 repeats. This returned 20 and 10 variables for the transcriptome dataset, and 25 and 5 variables as optimal for the proteomic dataset. Similarity between the datasets in two-dimensional overlapping space was visualized using the plotArrows function. Correlations between the components were found using the plotDiablo function, and loadings (i.e. which variables contribute the most separation between groups) were found using the plotLoadings function. Declarations Author Contributions Statement T.F., M.M., J.V.L., D.O., and V.F. conceptualized and designed the study and contributed to the methodology. T.F. wrote the manuscript, reared the larvae, analyzed insect parameters, and prepared Figs. 1 and 10. T.F. and F.M. (Fanis Missirlis) performed the insect staining and dissections. T.F. and J.V.L. prepared Figs. 2 and 3. W.H., F.H., and F.M. (Florencia Meyer) conducted the transcriptomic analyses and prepared Figs. 4, 5, and 6. W.H. performed the integrated analysis and prepared Fig. 9. A.S., S.A., and V.C. performed the peptide extraction and proteomic analyses and prepared Figs. 7 and 8. All authors reviewed and edited the manuscript. All individuals designated as authors meet the criteria for authorship, and all who qualify for authorship are listed. Competing interests All authors declare no financial or non-financial competing interests. Funding statement The project is funded by TKI Graduate School Green Top sectors, 6153031040 Author Contribution T.F., M.M., J.V.L., D.O., and V.F. conceptualized and designed the study and contributed to the methodology. T.F. wrote the manuscript, reared the larvae, analyzed insect parameters, and prepared Figures 1 and 10. T.F. and F.M. (Fanis Missirlis) performed the insect staining and dissections. T.F. and J.V.L. prepared Figures 2 and 3. W.H., F.H., and F.M. (Florencia Meyer) conducted the transcriptomic analyses and prepared Figures 4, 5, and 6. W.H. performed the integrated analysis and prepared Figure 9. A.S., S.A., and V.C. performed the peptide extraction and proteomic analyses and prepared Figures 7 and 8. All authors reviewed and edited the manuscript. All individuals designated as authors meet the criteria for authorship, and all who qualify for authorship are listed. Acknowledgement The authors thank Protix (Bergen op Zoom, the Netherlands) for providing the insect eggs essential for this study. Special thanks are extended to Kim, Yifan Zhang, Rutger Brouwer, Caspar van Arkel, Lucas Bozzo, Andrés Mateo, and Xuan Yang for their valuable assistance with larval counting. 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07:14:26","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187231,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/cc6835822a411bf256c7bea5.png"},{"id":96710822,"identity":"3c731b82-1abb-4191-b8e8-391210732ebb","added_by":"auto","created_at":"2025-11-25 10:11:13","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":200988,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/18ff3b498d4914796999530b.png"},{"id":96693857,"identity":"31b7e06f-8a9a-4b91-86d7-24a776760463","added_by":"auto","created_at":"2025-11-25 07:14:26","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":146001,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/b36ca7b3a029082e619f330f.png"},{"id":96693858,"identity":"c4d14d0b-1e32-4545-98f0-066b744637e0","added_by":"auto","created_at":"2025-11-25 07:14:26","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":350285,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/2c5becd8f43ee2a225674049.png"},{"id":96711128,"identity":"9c9d8735-d0eb-4701-8fb6-0aca64a90470","added_by":"auto","created_at":"2025-11-25 10:11:40","extension":"xml","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":204849,"visible":true,"origin":"","legend":"","description":"","filename":"823cd627527b4b8c984245fbbbc4e4cf1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/143cb734adaf637f35fc5534.xml"},{"id":96711009,"identity":"bb92faf8-dacc-41d5-9404-15b26fea817d","added_by":"auto","created_at":"2025-11-25 10:11:30","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":219128,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/751561efb32d5d85cab79fcc.html"},{"id":96711461,"identity":"467510b2-2e82-464e-9f29-292eb115b4dc","added_by":"auto","created_at":"2025-11-25 10:12:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":392306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) Mean of BSF larvae weight (mg) after 7 days of exposure to different iron treatments. (B) Pre-pupation rate (%) of BSF larvae 12 days after exposure to the different iron treatments. \u003c/strong\u003eNo statistically significant differences were observed between the groups.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/95ae7b33eab9a63dfcafa54c.png"},{"id":96710651,"identity":"e4a81d8c-b040-4a91-8085-069654798182","added_by":"auto","created_at":"2025-11-25 10:11:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1429233,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrussian blue staining of the black soldier fly larva midgut under different dietary iron conditions.\u003c/strong\u003e On the left, schematic drawing of the larval gut showing the subdivision of the entire gut, including the subdivision inside the midgut into anterior (light blue), middle (white), and posterior (blue) regions as defined by Bonelli et al. (2019). Oesophagus and cardia constitute the foregut. In the center, images of anterior midgut, starting right after the cardia (C). On the right, images of the posterior midgut segment, showing the part until the start of the hindgut (H). Iron accumulation is visualized by Prussian blue staining in larvae reared for six days on control, medium (12.5mM), or high (125mM) iron diets, followed by 24 h on a control diet prior to dissection.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/3e6e845731e334e5f72cc435.png"},{"id":96693835,"identity":"71e41cf7-0e14-40e1-a091-9b6e53a7e7db","added_by":"auto","created_at":"2025-11-25 07:14:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11285492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePeritrophic matrix of black soldier fly larvae stained with Prussian blue (A) reared on control, (B) medium (12.5mM) and (C) high (125mM) iron diets. (D and E) Zoomed in images of the Peritrophic membrane of larvae reared on the high (125mM) iron diet after staining with Prussian blue.\u003c/strong\u003e Larvae were reared on the iron-enriched substrate for six days following a switch to control diet 24 hours prior to dissection.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/25d3c6861f04591c305de5d6.png"},{"id":96711257,"identity":"b43f4f23-8cb3-493e-934e-bb6d93035089","added_by":"auto","created_at":"2025-11-25 10:11:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1199544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential transcriptomic analysis of iron-fortified black soldier fly larvae after two days (A, C, E) and seven days (B, D, F) of exposure to control, medium (12.5mM), and high (125mM) iron treatments.\u003c/strong\u003e(A,B) Principal Component Analysis (PCA) of mRNA abundance data of the control (CTL), medium (12.5mM) and high (125mM) iron treatments. (C,D) Hierarchical heatmap of protein expression profiles in larvae fed a control (CTL) diet (grey), medium (12.5mM) iron (light blue), and high (125mM) iron (blue). The analysis was performed on normalized protein abundance data. Columns represent biological replicates; rows represent individual protein groups. Color scale indicates relative expression levels (blue = downregulation; red = upregulation). (E,F) Venn diagrams showing the overlap of significantly differentially expressed protein groups among treatments. Each circle represents one iron treatment: Control (CTL), Medium (12.5mM) and high (125mM) iron. The numbers indicate proteins uniquely or jointly differentially expressed in response to the respective treatments.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/fcca854e46c30b6b1fff7ae0.png"},{"id":96693833,"identity":"0114fe50-ea94-4228-baf7-ae5cfee0717b","added_by":"auto","created_at":"2025-11-25 07:14:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":201088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment of differentially expressed genes. (A) Day 2 and (B) Day 7.\u003c/strong\u003e Enriched GO terms (p-adjusted \u0026lt; 0.05) were found using ClusterProfiler with BH corrections for multiple testing. GO terms were assigned to representative “parent terms\" using rrvgo. The size of the boxes corresponds to the \"score” of the parent term which is equal to the –log10 of its adjusted p-value.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/3167c607723b882b730e9add.png"},{"id":96693832,"identity":"7d0c19e3-45f9-4875-ad07-78e8372eff0a","added_by":"auto","created_at":"2025-11-25 07:14:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":777180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicted iron-responsive element structures found in the mRNA of ferritin subunits in black soldier fly larvae. (A) The heavy chain subunit IRE predicted mRNA and (B) the light chain subunit IRE predicted mRNA.\u003c/strong\u003e Both IREs were predicted with high confidence using the SIREs v3.0 web server with default settings. Created in SIERs (Version 3.0).\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/cd3f10e29bccb28ba8b7277e.png"},{"id":96693840,"identity":"599f1ef2-5db2-4a51-b987-6901abaca206","added_by":"auto","created_at":"2025-11-25 07:14:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":719265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential proteomic analysis of iron fortified black soldier larvae at day 7. \u003c/strong\u003e(A) Hierarchical heatmap of protein abundance profiles in larvae fed a control diet (grey), medium (12.5mM) iron (light blue), and high (125mM) iron (blue). The analysis was performed on normalized protein abundance data. Columns represent biological replicates; rows represent individual protein groups. Color scale indicates relative expression levels (blue = downregulation; red = upregulation). (B) Principal Component Analysis (PCA) of protein abundance data of the control, medium (12.5mM) and high (125mM) iron treatments. (C) Venn diagram showing the overlap of significantly differentially expressed protein groups among treatments. Each circle represents one iron treatment: Control (CTL), Medium (12.5mM) and high (125mM) iron. The numbers indicate proteins uniquely or jointly differentially expressed in response to the respective treatments.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/b18105778bb46a379284e68b.png"},{"id":96710875,"identity":"63a54a85-7f13-4f26-bea5-d2aa629b076d","added_by":"auto","created_at":"2025-11-25 10:11:18","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":417389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCytoscape-based protein–protein interaction network of differentially expressed proteins in BSF larvae exposed to high (125mM) iron treatment. \u003c/strong\u003eThe network was constructed using STRING data and visualized with Cytoscape. Nodes represent proteins that were significantly over- or under-represented in the high (125mM) iron group compared to control (adjusted p-value \u0026lt; 0.05), and edges represent predicted functional or physical associations. This network reveals highly interconnected modules reflecting key cellular responses to iron overload, including iron sequestration, oxidative stress management, translational repression, immune activation, cytoskeletal stabilization, and metabolic adaptation.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/f4f1cfb732d3b0fe6ab8b797.jpeg"},{"id":96693839,"identity":"3db05b09-5a91-48c3-ab6d-0c5ef71bc63a","added_by":"auto","created_at":"2025-11-25 07:14:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1335940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrative analysis of transcriptomic and proteomic data using DIABLO. \u003c/strong\u003e(A)\u003cstrong\u003e \u003c/strong\u003eBlock\u003cstrong\u003e \u003c/strong\u003esPLS-DA plotted on two dimensions shows high degree of similarity between the transcriptomic and proteomic datasets.\u003cstrong\u003e \u003c/strong\u003e(B)\u003cstrong\u003e \u003c/strong\u003eCorrelation of transcriptomic and proteomic datasets in sPLS-DA component 1. (C) Loading plots of the highest contributing genes and proteins to separation between groups. Component 1 primarily separated the control group from the high (125mM) iron group. (D) Correlation of transcriptomic and proteomic datasets in sPLS-DA component 2. (E)\u003cstrong\u003e \u003c/strong\u003eLoading plots of the highest contributing genes and proteins to separation between groups. Component 1 primarily separated the control group from the medium (12.5mM) iron group.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/242503dec2a8d1915dbe17cc.png"},{"id":96693848,"identity":"7262aa58-be70-4cfb-8ccc-444249610ca1","added_by":"auto","created_at":"2025-11-25 07:14:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":460492,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene expression and protein abundancy of proteins directly involved in iron homeostasis in iron fortified BSF larvae.\u003c/strong\u003e Mrna expression is shown for day 0 (left), day 2 (center), and day 7 (right), while protein representation are shown in the rightmost panels (day 7). Y-axis scales are adjusted individually per gene to reflect differences in expression range. Asterisks indicate statistically significant differences between treatment groups based on DESeq2 analysis (\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.1 = *, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05 = **, \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.005 = ***); absence of asterisks indicates no significant difference.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/79a3e3aef46cbcaf9c01f786.png"},{"id":96922181,"identity":"1be70a72-3f9f-4a2d-9252-b93e24d6d272","added_by":"auto","created_at":"2025-11-27 14:18:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24099866,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/a65e034a-2951-46ac-b411-0bd140a3648b.pdf"},{"id":96693855,"identity":"9ccd9254-3920-40be-8e3b-2d58cb637ad8","added_by":"auto","created_at":"2025-11-25 07:14:25","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":14124348,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/e0373ea81a2872b4b6bbdb2a.xlsx"},{"id":96693865,"identity":"c0df8e0f-3763-40b1-857b-1909684c3dcf","added_by":"auto","created_at":"2025-11-25 07:14:26","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7774672,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/3d65d9693724b4ef951c6d55.xlsx"},{"id":96693830,"identity":"61655df1-772f-48af-89e4-119462c7d794","added_by":"auto","created_at":"2025-11-25 07:14:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":106854,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7904328/v1/5e227c0826e96312427362bf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Forged in Iron: Molecular Insights into Iron Tolerance in Hermetia illucens","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThere is an increasing need for climate-resilient and bioavailable sources of dietary iron for food and feed alike (Byrne \u0026amp; Murphy, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Iron bioavailability in food is influenced by multiple factors, including its chemical form, whether it is bound to proteins, and the presence of absorption enhancers or inhibitors in the food matrix (Piskin et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, the edible insect ferritin (entoferritin) is an iron binding protein that is hypothesized to increase iron bioavailability (First et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is a spherical, hollow protein complex composed of 24 subunits that can hold thousands of iron atoms, typically assembled in a 1:1 ratio of heavy and light chains (FerHCH and FerLCH, respectively) (Pham \u0026amp; Winzerling, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral edible insect species are proposed as novel protein and fat sources as well as suppliers of essential micronutrients, including dietary iron (Mwangi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Among these, the black soldier fly (BSF, \u003cem\u003eHermetia illucens\u003c/em\u003e) is the most established species in the feed industry, valued for their larvae ability to efficiently convert organic waste into high-quality biomass (Siva Raman et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, BSF larvae contain considerable iron levels, of around 300 mg/kg Dry Matter (DM) under typical rearing conditions, and have demonstrated the capacity to accumulate heavy metals, including iron (First et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIron is an essential nutrient for all living organisms, but its redox properties make it potentially toxic at high concentrations, as it can catalyze the formation of reactive oxygen species (ROS) via mechanisms such as the Fenton reaction (Dixon \u0026amp; Stockwell, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite of this potential toxicity, we have recently showed that BSF larvae can tolerate and accumulate exceptionally high levels of dietary iron without adverse physiological effects. BSF larvae were reared on diets with iron concentrations ranging from 323 to 6,637 mg/kg DM, covering both typical feed iron levels and concentrations considered extreme (First et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For example, five days of exposure to 10 mM ferric ammonium citrate is lethal to \u003cem\u003eDrosophila melanogaster (\u003c/em\u003eBonilla-Ramirez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). When reared on 6,637 mg fe/kg DM, the BSF larvae accumulated up to three times more iron and exhibited increased calcium levels, with no negative impacts on survival rate, larval weight, fat and protein content, or the molecular weight of major soluble proteins (First et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). How BSF larvae can accumulate and tolerate such extreme iron loads remains unclear. A role for entoferritin is suggested (First et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but also specific biochemical tolerance mechanisms could be activated.\u003c/p\u003e\u003cp\u003eThe aim of this study was to investigate the mechanisms by which BSF larvae accumulate and regulate increased dietary iron content, in the context of BSF larvae used as food and feed. Specifically, transcriptomic and proteomic analyses were employed to examine molecular changes in iron-fortified larvae. In addition, imaging and relative quantification of larval intestine of the BSF were conducted.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStable larval growth under iron supplementation with signs of accelerated pre-pupation\u003c/h2\u003e\u003cp\u003eTwelve-day-old BSF larvae reared for 7 days on medium (12.5mM) or high (125mM) iron-enriched diets showed no difference in body weight compared to the control group, with mean values consistently ranging between 230 and 240 mg per larva (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). When five-day-old larvae were reared for 12 days on iron-supplemented diets, a mild (P\u0026thinsp;\u0026gt;\u0026thinsp;0.1), dose-dependent increase in pre-pupation rate was observed, from 30% to 37% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIron accumulation localized to the anterior midgut and posterior midgut as the primary storage sites\u003c/h3\u003e\n\u003cp\u003eIron storage of larvae reared on the control diet was compared to that of larvae reared on medium (12.5mM) and high (125mM) iron diets. A dose-dependent increase in Prussian blue staining intensity was observed in the Posterior midgut and anterior midgut regions, as defined by Bonelli et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), indicating elevated iron accumulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, no iron signal was detected in whole dissected larvae, isolated fat bodies, or extracted exoskeletons, regardless the dietary treatment, suggesting no novel iron storing locations were found in iron fortified BSF larvae.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003ePeritrophic matrix acts as a dose-responsive iron-binding barrier\u003c/h3\u003e\n\u003cp\u003eThe peritrophic matrix (PM) was stained with Prussian blue. No iron signal was detected in the larvae reared on the control diet, while the fortified larvae presented a dose-dependent increase in iron stain intensity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTime-dependent transcriptomic shifts between treatments separate early defense suppression from later cuticle and oxidative stress activation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe transcriptomic analysis yielded 10,838 expressed genes in all the BSF larvae (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A total of 299 of these genes were differentially expressed on day 2, and 791 genes were differentially expressed on day 7 Between the treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Principal component analysis (PCA) revealed more distinct overall expression patterns on day 7 compared to day 2, with the high (125mM) iron treatment group forming a separate cluster primarily along principal component 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;B). Consistent with the PCA results, the high (125mM) iron group on day 7 also clustered separately based on the differentially expressed genes using hierarchical clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In contrast, hierarchical clustering of the differentially expressed genes on day 2 showed that the control group was distinct from both iron-fortified groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Pairwise comparisons between treatment groups further revealed that on day 2, the medium (12.5mM) iron treatment group showed the greatest transcriptomic differences from the control, accounting for 270 out of the 299 differentially expressed genes identified on that day. Fewer differentially expressed genes were detected in the comparison between the high (125mM) iron and control groups (60 genes), and only two differentially expressed genes were identified between the high (125mM) and medium (12.5mM) iron groups on day 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). In contrast, on day 7, the medium (12.5mM) iron group exhibited minimal differences relative to the control group, while the high (125mM) iron group displayed extensive transcriptomic changes compared to both the medium (12.5mM) iron and control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFunctional enrichment analysis of the differentially expressed genes revealed limited functional over-representation on day 2, but substantial enrichment on day 7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). On day 2, Gene Ontology (GO) terms related to defense responses and protein folding were significantly enriched. Genes associated with defense responses included predicted detoxification-related cytochrome P450s (LOC119647124, LOC119651341, LOC119651369, LOC119652925, LOC119653044, and LOC119653068), small heat shock proteins (LOC119650070, LOC119650378, and LOC119650380), and lysozymes (LOC119654083, LOC119654085, LOC119654694, and LOC119654730), all of which were down-regulated. The enrichment in protein folding\u0026ndash;related terms was primarily driven by multiple down-regulated genes encoding small heat shock proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). By contrast, day 7 showed a broader range of enriched biological processes. Notably, genes involved in chitin-based metabolic processes, including cuticle and trachea development, were strongly over-represented. Additionally, several genes associated with the response to toxic substances were differentially expressed, including three predicted animal heme peroxidases (LOC119651583, LOC119659891, and LOC119659737), suggesting a physiological response to iron-induced oxidative stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eConserved IRE-mediated regulation of ferritin expression in BSF\u003c/h3\u003e\n\u003cp\u003eThe main known driver for ferritin regulation is the bind of iron-responsive element-binding proteins (IRP) to iron-responsive element (IRE) in the ferritin\u0026rsquo;s mRNA 5 prime UTR region (Torti \u0026amp; Torti, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). To investigate if this same type of regulation is affecting ferritin expression in BSF, SIREs prediction tool was used to predict which BSF Entoferritin transcripts contain IREs. Both Fer1HCH and Fer2LCH transcripts were found to contain IREs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In the gene encoding for ferritin\u0026rsquo;s light chain (Fer2LCH), two of the three transcript variants (XM_038056904 and XM_038056903) have high-quality predicted IREs (SIRE score 7.5/8) with canonical IRE loop motifs and only one mismatch (C-A) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The two Fer2LCH transcripts containing an IRE, accounted for the majority of Fer2LCH expression, with XM_038056903 making up for around 97% and XM_038056904 making up for about 2% overall (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, the only transcript encoded by BSF\u0026rsquo;s ferritin heavy chain gene (Fer1HCH) also has a high-quality predicted IRE (SIRE score 7.5/8). However, instead of a C-A mismatch, Fer1HCH has a G-U pairing, resulting in lower predicted free energy (-11.2 kcal/mol) than Fer2LCH (-4.8 kcal/mol) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003c/div\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverall Isoform Percentages of Fer2LCH and IRE Prediction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTranscript\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIsoform Percentage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePredicted IRE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXM_038056903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXM_038056904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXM_038056905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePhylogenetic analysis confirmed the homology of BSF ribosomal L1 domain-containing 1 (RSL1D1) to other arthropod and vertebrate candidate homologs, as the ancestral branch connecting these two lineages has 100% bootstrap support (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). After these two lineages diverged, RSL1D1 orthologs were retained as single-copy genes, with the exception of the sandfly which has a lineage-specific duplication. In the arthropod side of the tree, closely related taxa form the expected monophyletic clades, however, the overall branching pattern is not consistent with previous phylogenomic studies in arthropods (Misof et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is reflected by several lowly-supported branches, possibly due to limited sampling.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProteomic changes under high (125mM) iron treatments reveal stress defense activation, suppression of energy metabolism and translation, and modulation of chitin-associated pathways\u003c/b\u003e\u003c/p\u003e\u003cp\u003eProteomic analysis yielded a total of 2489 proteins (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Of these, 170 were differentially represented between samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The largest number of changes was observed in the high (125mM) iron vs control comparison, which included 122 differentially represented proteins. Among these, 95 proteins were unique to this comparison, 10 proteins were also differentially expressed in the medium (12.5mM) iron vs control comparison, and 22 proteins overlapped with the high (125mM) vs medium (12.5mM) iron treatments comparison. The medium (12.5mM) iron vs control comparison included 19 unique proteins, and the high (125mM) vs medium (12.5mM) iron comparison included 16 unique proteins. Two proteins were common in all three comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The heatmap revealed distinct clustering of samples based on iron fortification, indicating clear differences in protein expression profiles among the control, medium (12.5mM) and high (125mM) iron treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Over representation of numerous proteins was observed in the high (125mM) iron group, while the medium (12.5mM) iron group larvae exhibited an intermediate protein representation between the control and high (125mM) iron conditions. This was supported by the PCA protein analysis of the proteomic data, where three of the four high (125mM) iron replicates clustered in the positive PC1 and PC2 quadrant, reflecting a pronounced shift in protein expression. Control replicates clustered tightly near the origin, consistent with minimal variation and a baseline expression profile. The medium (12.5mM) iron replicates were more broadly distributed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eKey over-represented proteins belonged to pathways associated with iron homeostasis (e.g., entoferritin, transferrin), oxidative stress mitigation (e.g., glutathione S-transferase GstE7, cystathionine beta-synthase Cbs), innate immune activation (e.g., croquemort crq, calmodulin Cam), and cytoskeletal remodeling (e.g., tropomyosin, coactosin-like protein CG6891, Jupiter). These modulations reflect cellular strategies aimed at sequestration of excess iron, detoxification of ROS, immune defense, and preservation of structural integrity. Over-representation of cytochrome P450 enzymes (e.g., Cyp4d2, Cyp6a14) further supports the activation of detoxification pathways, while increased expression of coatomer subunit zeta (zetaCOP) and CHK kinase-like proteins (CG10562) suggests an early activation of vesicular trafficking and stress signaling processes. Conversely, a marked under-representation of mitochondrial respiratory chain components (ND-PDSW, ND-MLRQ, ND-23, ND-B18, ND-30, COX5A) indicates suppression of oxidative phosphorylation, likely aimed at limiting ROS production. In parallel, the repression of ribosomal proteins and translation factors (RpL27A, RpL36A, RpL0, RpL4, RpL32, RpL35, RpL38, RpS12, RpL10Ab, RpL40, and the initiation/elongation factors eIF3A, eIF3B, eIF4B, eIF5, eEF1delta) suggests a broad inhibition of ribosome assembly and translational activity. Additional under-represented proteins include enzymes involved in lipid metabolism (ceramide 1-phosphate binding CG30392, hydroxysteroid dehydrogenase-like protein 2 CG5590, beta hydroxy acid dehydrogenase 1 Had1, acetyl-CoA C-acyltransferase ScpX, 3-hydroxyacyl-CoA dehydrogenase Mtpalpha), nitrogen metabolism (glutamine synthetase Gs1, glycine N-methyltransferase Gnmt), and RNA processing (nucleolar protein 56 Nop56, RNA-binding protein squid sqd), reflecting broad metabolic reprogramming under iron overload (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe STRING-based protein\u0026ndash;protein interaction (PPI) network presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, built from proteins differentially represented in BSF larvae reveals several densely interconnected functional modules. The largest hub centers on translation and general protein metabolism, where numerous ribosomal subunits (RpL/RpS nodes) interlink tightly with canonical initiation and elongation factors (eIF3 subunits, eIF4B, eIF5, eEF1δ), indicating coordinated regulation of protein synthesis under augmented dietary iron supply. Additionally, pathways related to iron transport (e.g., Fer1HCH, Tsf1) and oxidoreductase activity were markedly integrated, consistent with the modulation of iron homeostasis and redox balance affected by excess iron. Notably, proteins involved in nucleobase compound metabolism (e.g., Nop56, SC35, Prp8, AdS) formed a distinct but functionally connected module associated with RNA processing, ribonucleoprotein complex assembly, and mRNA splicing. Their close association with translation-related proteins suggests a tightly regulated continuum from RNA maturation to active translation. Peripheral but connected clusters involve innate immune effectors, lipid metabolism enzymes, and chitin-based cuticle development proteins, indicating broader immunometabolic and developmental consequences.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eIntegrative omics confirms coordinated molecular changes, with cuticle and oxidative stress pathways driving treatment separation\u003c/h2\u003e\u003cp\u003eIntegrative analysis of the transcriptomic data and the proteomic data using DIABLO revealed that the two datasets were highly correlated, with an overall correlation coefficient of 0.92 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). When plotting the overlayed sparse partial least squares-discriminant analysis (sPLS-DA) results in two dimensions, the transcriptomic data and proteomic data form three clusters by treatment group and corresponding replicates fall closely together on the coordinate plane (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). In the first sPLS-DA component, we observed a high correlation (correlation coefficient\u0026thinsp;=\u0026thinsp;0.99) between the transcriptomic and proteomic datasets along with clear separation between the high (125mM) iron treatment and the control treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Next, which variables (i.e. genes and proteins) are most important to cause this separation were investigated. Consistent with the differential expression analysis results, many of these genes were involved in cuticle formation, while oxidation related proteins such as cytochrome c oxidase and ribosomal protein L3 where amongst the main drivers for treatment separation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). The two omics datasets were also highly correlated in the second s-PLS-DA component (correlation coefficient\u0026thinsp;=\u0026thinsp;0.98). However, component 2 mainly separated the medium (12.5mM) iron treatment from the other groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). Similar to the first component, the genes and proteins driving this separation were not the same between the transcriptomic and the proteomic data (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEntoferritin, ferritin, transferrin, IRP1, and RSL1D1 reflect iron homeostasis adjustments under increasing iron conditions\u003c/h3\u003e\n\u003cp\u003eIn addition to the broader omics analyses, the focus shifted to key genes and proteins involved in iron homeostasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Larvae reared on the high (125mM) iron treatment showed a significant increase in Entoferritin protein levels, with Fer1HCH and Fer2LCH subunits increasing by ~\u0026thinsp;75% and ~\u0026thinsp;70%, respectively. No significant change in Entoferritin subunit mRNA levels was observed at day 2, while mild (P\u0026thinsp;\u0026gt;\u0026thinsp;0.1), dose-dependent increases appeared by day 7. IRP1 expression was lower in high (125mM) iron larvae at day 7, accompanied by a mild (P\u0026thinsp;\u0026gt;\u0026thinsp;0.1) decrease in IRP1 protein abundance for both iron treatments. RSL1D1 expression at day 2 was significantly elevated in both iron treatments compared to the control, with increases of ~\u0026thinsp;320% for medium (12.5mM) iron group and ~\u0026thinsp;350% for the high (125mM) iron group. Transferrin mRNA showed no significant difference at day 2, but at day 7 it was significantly higher in high (125mM) iron group than in the control (~\u0026thinsp;210%) and medium (12.5mM) iron group (~\u0026thinsp;120%). Protein abundance of transferrin in the high (125mM) iron group at day 7 followed the same pattern, with increases of ~\u0026thinsp;140% compared to control and ~\u0026thinsp;110% compared to the medium (12.5mM) iron. Malvolio (Mlv), the insect homolog of Divalent Metal Transporter 1 (DMT1), was detected at all time points, with no significant treatment-related differences, though temporal increases occurred across treatments (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). However, Mlv was not detected in the proteomic analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eConsistent with the findings of First et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), BSF larvae showed no significant differences in body weight across treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), maintaining stable growth under dietary iron concentrations considerably higher than those typically applied in excess iron exposure experiments on other insect species (Bonilla-Ramirez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; First et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wagers et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, larvae exposed to medium (12.5mM) iron exhibited mRNA expression profiles indistinguishable from controls by the final harvest day, suggesting effective transcriptional recovery at moderate iron levels. This contrasts with other insect species, where substantially lower iron concentrations have been shown to disrupt development or reduce survival (Bonilla-Ramirez et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; First et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wagers et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and highlights the BSF larvae unique physiological adaptations for tolerating high dietary iron.\u003c/p\u003e\u003cp\u003ePrussian blue staining revealed a dose-dependent increase in iron deposits, localized to the anterior and posterior midgut across all dietary treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). No stain was detected in fat bodies, whole larvae, or exoskeletons, indicating that storage is restricted to the gut even under high (125mM) iron exposure. This localization aligns with previously reported anterior and posterior iron regions in the black soldier fly larval midgut (Bonelli et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The fact that iron accumulation was confined to this established storage site suggests that BSF larvae rely on the same pathway under increasing iron, most likely entoferritin-based sequestration, rather than recruiting novel storage mechanisms (Gorman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Previous work has shown that BSF larvae reared on standard chicken feed (~ 320 mg/kg iron) accumulate ~ 370 mg/kg in their bodies (First et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), far surpassing the 71–131 mg/kg typically observed in \u003cem\u003eDrosophila\u003c/em\u003e species larvae (Massie et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Sadraie \u0026amp; Missirlis, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In line with this greater accumulation capacity, BSF larvae in this study displayed much stronger iron staining in the midgut compared to the weaker staining reported for \u003cem\u003eDrosophila\u003c/em\u003e species. Moreover, the iron region of \u003cem\u003eDrosophila\u003c/em\u003e is usually confined to the anterior midgut and does not include the posterior midgut (Bettedi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sadraie \u0026amp; Missirlis, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; N. Wu et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These differences highlight the enhanced ability of BSF larvae to accumulate iron and suggest a potential evolutionary adaptation to a metal-rich environments.\u003c/p\u003e\u003cp\u003eIn addition to storage within the midgut, iron aggregates were also detected in the peritrophic matrix of larvae reared on iron-fortified diets, but not in controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). PM is a chitinous, semi-permeable layer that lines the insect gut lumen and acts as a protective barrier between ingested food and the gut epithelium (Lin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Orozimbo et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In blood-feeding insects, free heme and iron can bind to the PM, reducing their pro-oxidant activity and limiting ROS formation (Orozimbo et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A proteomic analysis of BSF larva has identified both ferritin and transferrin in its PM (Lin et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Taken together, these findings suggest that, in addition to its barrier function, the BSF PM may contribute to iron handling and oxidative stress mitigation under high-iron conditions through the activation of mechanisms analogous to those described in blood-feeding insects.\u003c/p\u003e\u003cp\u003eWhile iron accumulation was localized to the midgut and somatic growth remained unaffected, both transcriptomic and proteomic data indicate that BSF larvae undergo substantial physiological adjustments under iron fortification. These adjustments extend beyond iron storage itself and involve pathways and proteins linked to oxidative stress regulation, mitochondrial respiration, ribosomal structure and function, translation factors, and exoskeletal development (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Integration of the transcriptomic and proteomic data demonstrated strong agreement, with replicates clustering by treatment in both datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). This clustering indicates that the responses observed are not limited to isolated regulatory processes but instead reflect coordinated, system-wide remodeling under dietary iron stress. Dimension 1, the strongest driver in the DIABLO analysis, clearly separated the high-iron diet group from both the control and the medium (12.5mM) iron groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA), highlighting that the most pronounced physiological reprogramming occurs under high (125mM) iron exposure.\u003c/p\u003e\u003cp\u003eThe observed oxidative stress responses likely result from excess dietary iron increasing the pool of free iron available to larvae, which in turn promotes the generation of ROS via the Fenton reaction. These responses may represent cellular protective mechanisms aimed at mitigating ROS-induced damage. Iron exposure can impair mitochondrial function primarily through ROS-mediated pathways, which is in line with the observed reduction in abundance of ribosomal proteins under iron-fortified conditions (Zheng et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This agrees with the multi-omics results, where ribosomal and cytochrome proteins were among the strongest contributors to treatment separation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), reflecting the central role of energy metabolism and translational machinery in the high-iron response. The increased iron has decreased larval ribosomal protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), potentially through direct binding of free iron to ribosomal proteins, leading to their destabilization and degradation (Smethurst et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond oxidative stress, multi-omics analyses revealed that chitin- and cuticle-associated transcripts were major drivers of treatment clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Consistently, DEG patterns of exoskeleton development pathways, together with the over expression of chitin-related proteins, indicated disruption of normal cuticle formation (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In line with these findings, First et al, (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that BSF larvae reared on a high-iron diet (6,637 mg/kg DM Fe) accumulated up to 25% more calcium compared to controls (323 mg/kg DM Fe) and developed limited black lesions on their exoskeletons, suggesting impaired cuticle development under iron stress. Calcium is a critical component of BSF cuticle development, where it is biomineralized primarily as calcium carbonate (CaCO₃) across life-stage specific structures (Rebora et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Altogether, these results suggest that excessive dietary iron disrupts cuticle homeostasis through oxidative stress, leading to structural remodeling and increased calcium deposition.\u003c/p\u003e\u003cp\u003eCalcium dynamics are not restricted to cuticle integrity but also indicate developmental progression, as levels increase across successive instars to support sclerotization prior to pupation (Liu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A mild (P \u0026gt; 0.1), dose-dependent increase in pre-pupate emergence was observed under iron fortification (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), suggesting a potential increase in development timing. Transcriptomic data supports this interpretation, with hexamerin being the most strongly up-regulated transcript under high (125mM) iron on day 7 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Hexamerin is an amino acid storage protein typically stockpiled just before metamorphosis in holometabolous insects (Burmester \u0026amp; Scheller, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Hexamerin protein abundance was numerically ~ 50% higher in the high (125mM) group as opposed to the control, although the difference was not statistically significant (P \u0026gt; 0.1) due to variability among biological replicates (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Moreover, intracellular iron trafficking via transferrin and entoferritin to the prothoracic gland is essential for insect hormone production and developmental progression. A lack of transferrin or entoferritin has been shown to hinder iron signaling in the prothoracic gland (Soltani et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and whether the increased abundance of these proteins observed here (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) may advance the onset of iron signaling remains to be confirmed. Collectively, these observations point to accelerated developmental processes in BSF larvae exposed to high iron. However, neither the present data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) or nor a previous one (First et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed an effect of dietary iron on larval fresh weight at harvest, suggesting that such a developmental shift is subtle. Larger-scale studies spanning the full life cycle are needed to determine whether iron supplementation can enhance developmental rates in BSF production systems.\u003c/p\u003e\u003cp\u003eThe patterns observed in Entoferritin, IRP1, RSL1D1, transferrin and Malvolio transcripts and protein abundance changes, reveal that BSF larvae rely on multiple regulatory mechanisms to cope with high dietary iron (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Entoferritin, the predominant intracellular iron storage protein in insects (First et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Missirlis et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), increased significantly at the protein level under high (125mM) iron conditions, while mRNA levels showed mild (P \u0026gt; 0.1), statistically insignificant changes. This divergence between transcript and protein abundance indicates that ferritin synthesis is regulated primarily at the post-transcriptional stage.\u003c/p\u003e\u003cp\u003eThe reduction of IRP1 abundance detected under both iron conditions likely diminishes IRP–IRE binding, leaving ferritin mRNAs accessible for ribosomal translation (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). In mammals, IRPs binding to IREs in ferritin mRNAs prevents ribosomal assembly and translation. Under iron-replete conditions, cytosolic free iron binds to IRP1, promoting the formation of an iron–sulfur cluster that converts the protein into a cytosolic aconitase enzyme and abolishes its RNA-binding capacity. This conformational change, followed by degradation or repurposing of IRPs, frees the IRE and allows ferritin translation to proceed (Georgieva et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Meyron-Holtz et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Nichol \u0026amp; Winzerling, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Similar post-transcriptional control has been described in \u003cem\u003eManduca sexta\u003c/em\u003e, whereas the IRP/IRE interaction has been demonstrated to affect entoferritin heavy chain unit translation (Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). IRE motif analysis supports this regulatory framework. Both FerHCH and FerLCH transcripts contained predicted IRE stem-loop structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Messenger RNAs of ferritin light chain containing IREs, as well as various loop mutations, are well characterized and conserved in mammals (Luscieti et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This is a novel finding for a dipteran as IREs in the light chain were only reported in lepidopterans (Gorman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Missirlis et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Pham et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). A light chain isoform lacking an IRE was also detected under high-iron conditions, which could bypass IRP-mediated repression and allow translation even when IRP1 levels are high (Lind et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). However, given the predominance of the IRE-containing isoform (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), its overall contribution to ferritin output is likely small. Together, these features suggest that BSF entoferritin regulation integrates canonical IRP/IRE control with isoform variation to adjust storage capacity in response to dietary iron.\u003c/p\u003e\u003cp\u003eAnother potential indirect regulator of iron metabolism is RSL1D1, an mRNA-binding protein involved in various cellular processes including apoptosis, proliferation, and senescence. In mammals, RSL1D1 binds to ferritin heavy chain mRNA, stabilizing the transcript and protecting it from degradation. Knockdown of RSL1D1 in human colorectal cancer cells reduced ferritin heavy chain mRNA levels, while its presence extended mRNA stability over time (Jin et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the current study, RSL1D1 expression rose sharply early after iron exposure before returning to baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), which might increase the stability of heavy chain transcripts across the rearing period and in turn contribute to higher ferritin abundance. Phylogenetic analysis indicates that RSL1D1 is a conserved, single copy ortholog across vertebrates and arthropods, supporting a potentially shared role in ferritin regulation among these groups (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003es1\u003c/span\u003e). However, a functional confirmation of the occurrence of RSL1D1 in insects is needed to confirm this hypothesis.\u003c/p\u003e\u003cp\u003eTransferrin, an extracellular iron transporter in insects (Geiser \u0026amp; Winzerling, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), showed a delayed but significant increase at both the mRNA and protein levels by day 7 in the high-iron treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). It is established that transferrin gene expression in insects is regulated primarily at the transcriptional level (Geiser \u0026amp; Winzerling, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Consistent with this, in BSF we observed a close correspondence between transcript and protein abundance, in contrast to entoferritin (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The observed concomitant up-regulation of transferrin gene expression and over-representation of the protein may reflect an increased requirement for iron absorption and transport under prolonged exposure. In mosquitoes, transferrin expression is influenced by the chemical form of dietary iron, with inorganic sources often leading to suppression (Harizanova et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The opposite pattern in BSF may be linked to adaptation to a non-blood detritus-type diet in which inorganic iron is a consistent dietary component. However, given its relatively low abundance compared to entoferritin, transferrin is unlikely to make a major contribution to BSF iron bioavailability.\u003c/p\u003e\u003cp\u003eMalvolio (Mvl), the insect homolog of DMT1, functions as a divalent metal transporter that facilitates the uptake of iron and other transition metals across cellular membranes (Bettedi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In contrast to transferrin, Mvl, showed no significant iron-fortification related changes in expression, indicating that BSF larvae do not appear to reduce dietary iron uptake under increased iron conditions (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In mammals, DMT1 downregulation under iron overload is mediated in part through interaction with Nedd4 family-interacting protein 1, which promotes its degradation (Howitt et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In insects, however, regulatory pathways for Mvl are uncharacterized, and no expression patterns indicative of such regulation were detected. Instead, BSF larvae seem to maintain uptake capacity and rely on entoferritin storage as their primary protective strategy.\u003c/p\u003e\u003cp\u003eOverall, these findings suggest an Entoferritin based “accumulate and store” approach to dietary iron through a mild (P \u0026gt; 0.1) entoferritin induction mRNA transcription increase, IRP1 repression, potential RSL1D1-mediated transcript stabilization, and increased transferrin expression to meet iron transport needs. The previously reported ~ 250% increase in total iron content (First et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), compared to only ~ 70% increases in entoferritin subunits in this study implies a higher iron-to-entoferritin ratio under high-iron conditions. Such disproportionate loading could affect ferritin stability (Irimia-Dominguez et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ruiyang Ji Mingyang Sun, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Srivastava et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), potentially reducing iron bioavailability, though this requires further investigation.\u003c/p\u003e\u003cp\u003eIn conclusion, black soldier fly larvae tolerate a substantial level dietary iron, with larval mass unaffected and only a mild (P \u0026gt; 0.1), dose-dependent increase in pre-pupate emergence, suggesting that developmental shifts are subtle. Multi-omics integration revealed coordinated remodeling under iron fortification, including oxidative stress signatures, repression of mitochondrial and translational activity, and strong activation of chitin- and cuticle-associated pathways. Iron localized to the gut epithelium and peritrophic matrix, but not the fat body or exoskeleton, pointing to an “accumulate-and-store” strategy in which entoferritin serves as the primary sequestration site. Entoferritin subunits increased markedly at the protein level despite modest transcript changes, indicating post-transcriptional regulation. Decreased IRP1 abundance, predicted IREs, early RSL1D1 induction, and delayed transferrin upregulation further suggest fine-tuned control of uptake, storage, and transport. This mechanism enables larvae to withstand high iron exposure without impairing growth. From a food and feed perspective, the entoferritin-based ‘accumulate-and-store’ strategy observed here suggests that iron-fortified larvae could provide a source of highly bioavailable iron. The soluble and stable nature of ferritins, particularly entoferritin, supports their potential as effective vehicles for dietary iron fortification, a strategy already demonstrated in ferritin-fortified plant models (Lönnerdal, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Masuda et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Insects may offer additional advantages, as entoferritin is larger and potentially more water-soluble due to its secreted nature (First et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and the magnitude of total iron accumulation in fortified BSF larvae is substantially higher than that typically achieved in plant systems (First et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Masuda et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, comparison with previously reported whole-larval iron accumulation suggests a high iron-to-entoferritin ratio, with possible consequences for protein stability and nutrient bioavailability. Future research should quantify iron speciation, resolve regulatory nodes such as RSL1D1, and evaluate whether controlled fortification can enhance larval development, improve production efficiency. This information will definitively strengthen the role of BSF as a sustainable source of bioavailable iron.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003ch2\u003eLarvae rearing and collection\u003c/h2\u003e\u003cp\u003eBlack soldier fly larvae were reared under controlled conditions at 27°C and 70% relative humidity, in the dark. Rearing procedures were based on First et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with minor modifications to feed moisture content, as well as rearing and sampling timelines, depending on the analysis.\u003c/p\u003e\u003cp\u003eDry chicken feed (Kuikenopfokmeel no. 1, Kasper FaunaFood, the Netherlands) was sieved using a 1.0 mm to homogenize particle size of the dry substrate. Ferric ammonium citrate (FAC; CAS 1185-57-5; Merck, Darmstadt, Germany) was dissolved in water to prepare solutions of 12.5, and 125mM, corresponding to final substrate concentrations of 323 (Control, i.e. no fortification), 1,255, and 6,970 mg Fe/kg DM, respectively. Diets were prepared by mixing 200 g of dry feed with 320 mL of the respective FAC solutions (1:1.6 feed-to-water ratio), yielding 520 g of wet feed per container.\u003c/p\u003e\u003cp\u003eA total of 0.2 g of BSF eggs (Protix, Bergen op Zoom, the Netherlands) were introduced to 520 g of wet, FAC-free feed. Larvae were reared on this control diet for five days. Then, larvae were separated from the substrate by sieving, counted, and assigned to treatments.\u003c/p\u003e\u003cp\u003eFor each treatment, 200 five-day-old larvae were transferred to new rearing containers (15.5 × 10.5 × 6 cm) containing 520 g of wet feed prepared with the corresponding FAC concentration. Each treatment was conducted in quadruplicate. Two days after exposure, four larvae per replicate were collected, flash frozen in liquid nitrogen, and stored at − 80°C for mRNA extraction. On day 7 since the start of the exposure, all remaining larvae were harvested, flash frozen in liquid nitrogen, and stored at − 80°C for protein and mRNA analyses.\u003c/p\u003e\u003cp\u003eTo assess pre-pupation, an additional cohort of five-day-old larvae was reared on iron-supplemented diets for twelve consecutive days. For each treatment (Control, 12.5, and 125 mM FAC), 200 larvae were placed in rearing containers with 520 g of wet feed with the corresponded FAC fortification. All treatments were reared in triplicate. At the end of the period, larvae were harvested, sieved, and separated into larvae and pre-pupae based on external morphology, as darkened and hardened lighter larvae were counted as pre-pupas. All individuals were counted, and their biomass was measured to validate developmental stage separation, as pre-pupae are significantly lighter than earlier instar larvae (Georgescu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor iron cluster localization, larvae were reared using the same procedure but were switched to FAC-free feed on day 7 to avoid excess iron in the larval gut. These larvae were collected on day 8, sieved and freshly dissected following a sacrifice using CO2 exposure.\u003c/p\u003e\u003ch2\u003eTissue Prussian Blue (iron) Staining\u003c/h2\u003e\u003cp\u003eLarvae reared on control diet for 24 h after the end of iron treatments were harvested. Dissections were performed in phosphate-buffered saline (PBS, pH 7.2), at room temperature. Whole-body staining was initially performed using Prussian blue solution (2% w/v potassium ferrocyanide [K₄Fe(CN)₆] and 2% hydrochloric acid mixed in 1:1 ratio) for 10 min in dark conditions to localize iron deposits. Samples were subsequently washed in PBS to remove excess staining reagent.\u003c/p\u003e\u003cp\u003eFollowing whole-body staining, gut tissues were dissected and stained separately under the same conditions. This targeted approach was guided by the initial whole-body results to refine the localization of iron deposits.\u003c/p\u003e\u003cp\u003eTissue imaging was performed using an Olympus SZX12 stereomicroscope equipped with a Euromex sCMEX-20 digital camera. Images were acquired and processed using ImageFocus software.\u003c/p\u003e\u003ch2\u003emRNA and Protein extraction\u003c/h2\u003e\u003cp\u003emRNA extraction was conducted by Novogene Europe (Cambridge, UK). Total RNA was isolated from frozen BSF larvae using TRIzol reagent (Invitrogen, USA), following the manufacturer’s instructions with slight modifications. Two to three frozen whole larvae were ground in liquid nitrogen and 50 mg of the larvae were homogenized in 1 mL of TRIzol. After chloroform extraction and phase separation, the aqueous layer was collected, and RNA was precipitated using isopropanol. The pellet was then washed with 75% ethanol and dissolved in RNase-free water. RNA concentration and quality were evaluated with an Agilent 5400 system. All steps were carried out under RNase-free conditions.\u003c/p\u003e\u003cp\u003eFor protein extraction, each frozen BSF larvae was subjected to a preliminary wash with 70% ethanol to remove potential surface contaminants, followed by three consecutive washes with PBS (137 mM NaCl, 2.7 mM KCl, 8.1 mM Na₂HPO₄·2H₂O, 1.76 mM KH₂PO₄) at a 1:10 weight-to-volume ratio, supplemented with EDTA-free protease inhibitor tablets. A RIPA buffer, composed of 25mM Tris-HCl (pH 7.6), 150 mM NaCl, 1% NP-40, and 1% sodium deoxycholate was used for protein extraction. The freeze dried larvae were homogenized on ice using an Ultraturrax at 30,000 rpm for 20 seconds, followed by a 30-second pause, for five repititions. The homogenized samples were then left on ice for 1 hour following sonication for 5 minutes with 2-minute pauses on ice, which was repeated three times. The resulting crude extracts were centrifuged at 13,000 × g for 20 minutes at 4°C. Protein concentration in the supernatants was determined using Pierce BCA Protein Assay™ kit (Thermo Scientific, Rockford, IL, USA). An aliquot containing 70 µg of protein from each sample was subjected to electrophoresis on a 12% SDS-PAGE gel under reducing conditions, and the gels were stained with Coomassie Brilliant Blue G-250 to verify the extraction protocol.\u003c/p\u003e\u003cp\u003eAliquots of 150 µg was reduced, alkylated in the dark, and precipitated by adding six volumes of cold acetone to remove interfering substances for shotgun analysis. After precipitation, the samples were centrifuged at 8,000 × g for 10 minutes at 4°C, and the resulting protein pellets were air-dried. Each sample was digested with freshly prepared trypsin (1:50 enzyme/protein ratio) in 50 mM TEAB buffer at 37°C overnight. The resulting peptides from each protein sample were quantified using the Pierce™ BCA Peptide Assay Kit (Thermo Scientific, Rockford, IL, USA). Subsequently, 30 µg of peptides per sample were labeled using the TMTsixteenplex™ Isobaric Label Reagent Set (Thermo Fisher Scientific, USA). After 1h of reaction, 8 µL of 5% w/v hydroxylamine was added to each tube and mixed for 15 min to stop the derivatization reaction. For a series of comparative experiments, the labeled peptide mixtures were mixed in equal molar ratios and dried in vacuom under rotation. Then, TMT-labeled peptide mixtures were suspended in 0.1% trifluoroacetic acid and fractionated using the Pierce™ High pH Reversed Phase Peptide Fractionation Kit (Thermo-Fisher Scientific) to remove unbound TMT reagents and reduce sample complexity, according to the manufacturer's instructions. After fractionation, eight TMT-labeled peptide fractions were collected, dried under vacuum, and finally reconstituted in 0.1% formic acid for subsequent mass spectrometric analysis.\u003c/p\u003e\u003cp\u003eAll mRNA extraction and peptide extraction were conducted in biological quadruplicates.\u003c/p\u003e\u003ch2\u003ePeptide Mass Spectrometry\u003c/h2\u003e\u003cp\u003ePeptide mixtures were analyzed in technical triplicate by means of a nanoLC-ESI-Q-Orbitrap-MS/MS platform consisting of an Vanquish- Neo nano-chromatography system (Thermo Fisher Scientific) interfaced to a Exploris 480 mass spectrometer through an easy-spray ion source (Thermo Fisher Scientific). Peptides were loaded on an EASY-Spray C18 column (150 mm × 75 µm ID, 2 µm particles, 100 Å pore size) (Thermo Fisher Scientific), and eluted with a gradient of solvent B (19.92/80/0.08 v/v/v water/acetonitrile/formic acid) in solvent A (99.9/0.1 v/v water/formic acid), at a flow rate of 250 nL/min. The gradient of solvent B started at 6%, increased to 31% over 120 min, increased to 50% over 5 min, increased to 95% over 5 min and remained at 95% for 4 min, and finally returned to 6% for equilibrating step. The mass spectrometer operated in data-dependent mode, using a full scan range (m/z 400–1600, resolution of 60,000 @200 m/z), followed by MS/MS scans of the 20 most abundant ions. MS/MS spectra were acquired in a dynamic scan m/z range, using a normalized collision energy of 38%, a Normalized AGC Target (%) of 200, a maximum injection target of 105 ms, and a resolution of 45000 @200 m/z. The dynamic exclusion value was set to 25 s.\u003c/p\u003e\u003ch2\u003eTranscriptomics\u003c/h2\u003e\u003cp\u003eUniversal Illumina adapter sequences were trimmed from the reads using Cutadapt v3.5 (Martin, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Next, low-quality bases were removed using Trimmomatic v0.39 with the options SLIDINGWINDOW:4:15 MINLEN:30 (Bolger et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Read quality after processing was checked using fastqc v.0.11.9 (Andrews, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Transcript abundances were quantified using Salmon v.0.14.1 (Patro et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). First, a salmon index was made using the BSF reference transcripts and the BSF genome (GCF_905115235.1) as “decoy” sequences. Next, transcript abundance was quantified for each sample using salmon in “quant” mode, allowing the library orientation to be determined automatically and the –validateMappings flag implemented. Transcript abundances were imported to R using tximport v1.26.1 for differential expression analysis using DESeq2 v1.38.3 (Love et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Soneson et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Overall gene expression patterns were visualized using principal component analysis via the plotPCA function in DESeq2 on gene expression counts that had undergone variance-stabilizing transformation. Differential expression analysis was conducted at the gene level. Differentially expressed genes between each treatment within each time point were found using the Wald test with the Benjamini-Hochberg method to correct for multiple comparisons (as implemented in DESeq2). Genes were considered differentially expressed if they had an adjusted p-value less than 0.05. Differentially expressed genes across the samples were visualized using ComplexHeatmap v2.22.0 with hierarchical clustering for both rows and columns(Gu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Venn diagrams were generated using ggVennDiagram v1.5.2 in R (Gao et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo determine the over-represented functions in the differentially expressed genes, the BSF reference proteome associated with GCF_905115235.1 was functionally annotated using eggNOG mapper v2.1.9 in diamond mode (Cantalapiedra et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Gene ontology (GO) term enrichment analysis was conducted with ClusterProfiler v4.14.4 using the enricher function incorporating the ”BH” method to adjust p-values for multiple comparisons. GO terms were considered ”enriched” under a p-value cutoff of 0.05. Redundant enriched GO terms were clustered and assigned to parent terms using rrvgo based on the “rel” algorithm (Sayols, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Significant p-values were negative log10 transformed and the term with the highest score was used as the representative ”parent” term after clustering by semantic similarity (similarity threshold = 0.75). The \u003cem\u003eD. melanogaster\u003c/em\u003e orgdb annotation (org.Dm.eg.db) was used as the GO database for annotations.\u003c/p\u003e\u003cp\u003eTo locate entoferritin subunit iron response elements (IREs), ferritin subunit gene (LOC119652653 and LOC119652654) mRNA transcripts were downloaded from NCBI. IREs in ferritin transcripts were predicted using the SIREs v3.0 web server with default settings (Suárez-Quintana et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo determine the homology of BSF ribosomal L1 domain-containing protein 1 (RSL1D1) to vertebrate RSL1D1 proteins, a phylogenetic analysis was conducted. A selection of vertebrate, arthropod, and cnidarian (outgroup) protein gene models were downloaded from NCBI using biomartr v.1.0.7 (Drost \u0026amp; Paszkowski, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and made into a protein BLAST database. Next BLASTp was used to align BSF, human, and \u003cem\u003eD. melanogaster\u003c/em\u003e RSL1D1 amino acid sequences to the custom metazoan database with a minimum evalue of 1e-5. The longest isoforms from the resulting protein sequences were retrieved from NCBI using datasets v.17.3.0 (O’Leary et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). All protein sequences were aligned using MAFFT v.7.490 incorporating the L-INS-i algorithm and the resulting alignment was trimmed with trimal v.1.2 using the gappyout option (Capella-Gutiérrez et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Katoh \u0026amp; Standley, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A phylogeny was inferred from the trimmed alignment using IQ-TREE v.2.0.7 employing automatic model selection through ModelFinder (Kalyaanamoorthy et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Minh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Support values are shown as ultrafast bootstrap with 1000 replicates (Hoang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eProteomics\u003c/h2\u003e\u003cp\u003eAll MS and MS/MS raw data files per sample were merged for protein identification using Proteome Discoverer v.3.1 software (Thermo Fisher Scientific), allowing database searching using Mascot v. 2.4.2 algorithm (Matrix Science) according to a shotgun proteomic approach. Database searching was performed with the following criteria: \u003cem\u003eHermetia\u003c/em\u003e protein sequence database (downloaded from UniProtKB and including 17615 entries); carbamidomethylation at Cys and TMTpro 16plex modification at Lys and peptide N-terminus as fixed modifications; oxidation at Met, pyroglutamate formation at N-terminal Gln, phosphorylation at Ser/Thr/Tyr, and deamidation at Asn/Gln as variable modifications. The mass tolerance of the parent peptide was set to ± 10 ppm and ± 0.05 Da for MS/MS fragments. Trypsin was set as the proteolytic enzyme and the maximum number of missed cleavages was limited to 2.\u003c/p\u003e\u003cp\u003eHierarchical clustering and PCA analysis were used to assess DRPs. Both the heatmap and PCA were generated using Proteome Discoverer 3.1 and based on normalized abundance data. The heatmap was created using the Euclidean distance function, complete linkage method, and scaling after clustering. In both analyses, biological replicates are shown, and the mass spectrometry analysis was performed with three technical replicates. Functional annotation of DRPs and network inference analysis were performed using STRING software with confidence score cutoff value of 0.4. Due to limited functional annotation in the BSF protein database, homologous polypeptide sequences from \u003cem\u003eInsecta\u003c/em\u003e were used as a reference database.\u003c/p\u003e\u003ch2\u003eIntegrative omics analysis\u003c/h2\u003e\u003cp\u003eTranscriptome-proteome integration analysis was conducted using DIABLO within the mixOmics v.6.30.0 R package. DIABLO is a supervised N-integration method that uses multiblock partial least squares-discriminant analysis (PLS-DA) to assess the correlation between two omics datasets (Singh et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Transformed counts using the variance stabilizing method implemented in DESeq2 were used as input for the transcriptomic data, while protein abundance per replicate was used as input for the proteomic data. A weight of 0.92 was used in the experimental design and was chosen by conducting a PLS regression analysis between the transcriptomic and proteomic data. Two components were chosen for the final sparse PLS-DA (sPLS-DA) model. The optimal number of variables used for the final sPLS-DA model was found using the tune.block.splsda function with 4 folds and 10 repeats. This returned 20 and 10 variables for the transcriptome dataset, and 25 and 5 variables as optimal for the proteomic dataset. Similarity between the datasets in two-dimensional overlapping space was visualized using the plotArrows function. Correlations between the components were found using the plotDiablo function, and loadings (i.e. which variables contribute the most separation between groups) were found using the plotLoadings function.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eAuthor Contributions Statement\u003c/h2\u003e\u003cp\u003eT.F., M.M., J.V.L., D.O., and V.F. conceptualized and designed the study and contributed to the methodology. T.F. wrote the manuscript, reared the larvae, analyzed insect parameters, and prepared Figs.\u0026nbsp;1 and 10. T.F. and F.M. (Fanis Missirlis) performed the insect staining and dissections. T.F. and J.V.L. prepared Figs.\u0026nbsp;2 and 3. W.H., F.H., and F.M. (Florencia Meyer) conducted the transcriptomic analyses and prepared Figs.\u0026nbsp;4, 5, and 6. W.H. performed the integrated analysis and prepared Fig.\u0026nbsp;9. A.S., S.A., and V.C. performed the peptide extraction and proteomic analyses and prepared Figs.\u0026nbsp;7 and 8. All authors reviewed and edited the manuscript. All individuals designated as authors meet the criteria for authorship, and all who qualify for authorship are listed.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eAll authors declare no financial or non-financial competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e\u003cp\u003eThe project is funded by TKI Graduate School Green Top sectors, 6153031040\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.F., M.M., J.V.L., D.O., and V.F. conceptualized and designed the study and contributed to the methodology. T.F. wrote the manuscript, reared the larvae, analyzed insect parameters, and prepared Figures 1 and 10. T.F. and F.M. (Fanis Missirlis) performed the insect staining and dissections. T.F. and J.V.L. prepared Figures 2 and 3. W.H., F.H., and F.M. (Florencia Meyer) conducted the transcriptomic analyses and prepared Figures 4, 5, and 6. W.H. performed the integrated analysis and prepared Figure 9. A.S., S.A., and V.C. performed the peptide extraction and proteomic analyses and prepared Figures 7 and 8. All authors reviewed and edited the manuscript. All individuals designated as authors meet the criteria for authorship, and all who qualify for authorship are listed.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Protix (Bergen op Zoom, the Netherlands) for providing the insect eggs essential for this study. Special thanks are extended to Kim, Yifan Zhang, Rutger Brouwer, Caspar van Arkel, Lucas Bozzo, Andr\u0026eacute;s Mateo, and Xuan Yang for their valuable assistance with larval counting.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analyzed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndrews, S. (2010). \u003cem\u003eFastQC: a quality control tool for high throughput sequence data.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cir.nii.ac.jp/crid/1370584340724053142.bib?lang=en\u003c/span\u003e\u003cspan address=\"https://cir.nii.ac.jp/crid/1370584340724053142.bib?lang=en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBettedi, L., Aslam, M. 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Iron overload promotes mitochondrial fragmentation in mesenchymal stromal cells from myelodysplastic syndrome patients through activation of the AMPK/MFF/Drp1 pathway. \u003cem\u003eCell Death \u0026amp; Disease\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(5), 515. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41419-018-0552-7\u003c/span\u003e\u003cspan address=\"10.1038/s41419-018-0552-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-science-of-food","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjscifood","sideBox":"Learn more about [npj Science of Food](http://www.nature.com/npjscifood/)","snPcode":"41538","submissionUrl":"https://submission.springernature.com/new-submission/41538/3","title":"npj Science of Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7904328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7904328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBlack soldier fly (BSF) larvae are increasingly valued as a sustainable source of proteins and essential minerals in animal feed and potentially food, yet their physiological response to substrate iron fortification is poorly defined. Here, integrated transcriptomic, proteomic, and tissue iron detection approaches were used to characterize responses of BSF larvae reared on diets containing 323 (control), 1,255, and 6,970 mg Fe/kg dry matter (DM). Larval growth at day 12 was unaffected, while prepupal emergence after 15 days showed a statistically non-significant increase at the highest iron level, suggesting only subtle developmental effects. Prussian blue staining showed a dose-depended iron accumulation in the midgut epithelium, consistent with known insect iron responsive regions of entoferritin-based sequestration. An elevated iron signal in the peritrophic matrix indicated a complementary defensive barrier. Multi-omics profiling revealed oxidative stress responses, suppression of mitochondrial and translational pathways, and activation of exoskeleton biosynthesis. Entoferritin levels rose by ~\u0026thinsp;70% for both protein subunits despite modest transcript changes, pointing to a post-transcriptional regulation mechanism. These results suggest a gut-centered \u0026ldquo;accumulate-and-store\u0026rdquo; physiological strategy enabling BSF larvae to tolerate high dietary iron. This entoferritin-based high iron accumulation capacity highlights the potential of this insect as a sustainable source of bioavailable iron in feeds.\u003c/p\u003e","manuscriptTitle":"Forged in Iron: Molecular Insights into Iron Tolerance in Hermetia illucens","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 07:14:20","doi":"10.21203/rs.3.rs-7904328/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-18T06:04:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T03:46:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104915806875062051849245066833200506460","date":"2026-03-27T11:30:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-05T13:25:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179195358248967647234479963769709971255","date":"2025-11-14T08:03:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-14T06:30:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-14T06:29:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-03T12:46:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Science of Food","date":"2025-10-20T09:32:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-science-of-food","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjscifood","sideBox":"Learn more about [npj Science of Food](http://www.nature.com/npjscifood/)","snPcode":"41538","submissionUrl":"https://submission.springernature.com/new-submission/41538/3","title":"npj Science of Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"280bf860-d6d9-489f-9a2d-dd568912c398","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58480584,"name":"Biological sciences/Biochemistry"},{"id":58480585,"name":"Biological sciences/Developmental biology"},{"id":58480586,"name":"Biological sciences/Ecology"},{"id":58480587,"name":"Earth and environmental sciences/Ecology"},{"id":58480588,"name":"Biological sciences/Molecular biology"},{"id":58480589,"name":"Biological sciences/Physiology"},{"id":58480590,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-05-17T06:08:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 07:14:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7904328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7904328","identity":"rs-7904328","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}