Deciphering Huhu (Prionoplus reticularis) grub development: Transcriptomic insights into metabolic and nutritional shifts in larval stages

Deciphering Huhu (Prionoplus reticularis) grub development: Transcriptomic insights into metabolic and nutritional shifts in larval stages | 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 Research Article Deciphering Huhu (Prionoplus reticularis) grub development: Transcriptomic insights into metabolic and nutritional shifts in larval stages Ruchita Rao Kavle*, Bennett Henzeler*, Ngoni Faya, Pascal Giehr, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6564832/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The Huhu grub ( Prionoplus reticularis ), an edible beetle larva endemic to New Zealand, has been traditionally consumed by Māori, the indigenous people of New Zealand. Despite its nutritional significance as an excellent source of proteins, little is known about the molecular mechanisms governing its developmental transitions. This study delivers the first de novo transcriptome assembly of P. reticularis and investigates differential gene expression between its small and large larval stages, aiming to uncover their metabolic capabilities and potential contributions to human dietary protein. Results Comparative transcriptomic analysis revealed distinct gene expression patterns between small and large larvae, with large larvae exhibiting enhanced protein biosynthesis, metabolic activity, and mitochondrial function. A conserved isoleucine-to-phenylalanine mutation was detected in ubiquitin-processing genes, suggesting potential impacts on protein degradation pathways. Functional annotation revealed an enrichment of membrane transport proteins, secretory peptides, and metabolic regulators in large larvae, indicating improved nutrient assimilation and physiological adaptation. Conclusion This study provides novel insights into the molecular basis of P. reticularis development, demonstrating that large larvae possess superior metabolic efficiency, enhanced protein integrity and increased biosynthetic activity. These findings corroborate previous research on the nutritional composition of the Huhu grub. They also lay the groundwork for future proteomic and metabolomic studies aimed at evaluating the nutritional potential, bioactive properties and allergenic safety of proteins derived from Huhu grubs. *Ruchita Rao Kavle and Bennett Henzeler contributed equally to this work. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Edible insect larvae or grubs, provide a sustainable and alternate protein source in various cultures worldwide[ 1 ]. Huhu grub ( Prionoplus reticularis ; Order : Coleoptera, Family : Cerambycidae and Subfamily : Prioninae) is an endemic insect species that has a long history of consumption by the indigenous Māori in New Zealand[ 2 ]. The grub undergoes five developmental stages: egg, small larvae, large larvae, pupae and adult (beetle) (Fig. 1A). Among these, the larval stages are considered the most suitable for consumption, with a preference for large larvae due to their size (Fig. 1B). Our recent study examined the total protein content across different developmental stages, which ranged from 26.2–30.5% dry weight (DW). Principal component analysis (PCA) and partial least squares regression (PLSR) further revealed significant differences in protein content and other nutritional properties between the small and large larval stages of P. reticularis [ 3 ]. The available evidence underscored the interest for further analysis of the underlying expression patterns of P. reticularis developmental stages. Figure 1 Schematic representation of the demographics of the sampling, processing and analysis. Schematic design of the workflow for sample processing, downstream experiments, and analysis. A Schematic representation of the P. reticularis lifecycle, illustrating the five stages from egg to small and large larvae, pupae, and adult beetle. B Schematic representation of the individual growth stages in their natural habitat at the time of sample collection. C Schematic representation of the sample collection point. D Schematic representation of the size distribution, anatomy, and characteristics of small and large grubs. E Schematic representation of the protocol for total RNA extraction and short read sequencing (Created in BioRender. Henzeler, B. (2025) https://BioRender.com/wxtbokl ). Figure 2 Transcriptomic characterization and comparative analysis of larval-stage Huhu. A BUSCO analysis of the assembled transcripts, indicating genome assembly quality across different domains. B Length distribution of assembled unigenes categorized by size. C PCA illustrating clustering patterns among experimental groups. D Unigene number hit based on the cluster and species highest hits. Phylogenetic-like species distribution analysis, color-coded by taxonomy. Shades of mustard indicated the Insect species clustered into distinct groups and the shades of grey indicate other species reflecting closer evolutionary relationships. The resulting tree was color-coded, with mustard representing species from Class Insecta and grey denoting species from other taxonomic classes as seen in Fig. 2D Transcriptomics plays a critical role in insect research by offering insights into gene expression patterns and regulatory mechanisms[ 4 ]. This approach facilitates the identification and characterization of transcripts involved in essential biological processes, such as development, reproduction, responses to environmental stimuli, and the molecular basis of insect traits and behaviors[ 5 , 6 ]. The scope of insect transcriptomics has expanded significantly, allowing for the investigation of a wide range of genes associated with specific tissues, insecticide targets, detoxification enzymes, and metabolic processes across various insect species[ 7 ]. Extensive transcriptomic analyses have been conducted on various Lepidoptera species , such as the diamondback moth ( Plutella xylostella )[ 8 ], corn borer ( Ostrinia nubilalis )[ 9 ] and silkworm ( Bombyx mori )[ 10 ], to explore gene expression patterns across different developmental stages. Additionally, transcriptomic studies on insects like the fruit fly ( Drosophila melanogaster )[ 11 ], oriental fruit fly ( Bactrocera dorsalis )[ 12 ], migratory locust ( Locusta migratoria manilensis )[ 13 ] and ground beetle ( Eriocheir sinensis )[ 14 ] have identified transcripts associated with insecticide resistance. These investigations have yielded valuable genetic insights into the molecular mechanisms underlying insect development and adaptation. Identifying transcripts from P. reticularis associated with human metabolic pathways is crucial for assessing the grub's edibility and nutritional suitability. These transcripts play a significant role in evaluating potential benefits and risks. This ultimately determines the extent to which the grub can be safely and effectively incorporated into human diets[ 15 ]. There is a major research gap in knowledge of P. reticularis development and its underlying genetic basis. To illustrate, P. reticularis currently lacks a complete genome sequence, and its developmental biology remains insufficiently characterized. The indigenous nature of the grub restricts the availability of its genetic sequence in public databases. This manuscript represents the first instance of employing de novo transcriptome assembly for large-scale transcript discovery in P. reticularis , achieving high coverage of protein-coding transcripts. To identify novel transcripts in P. reticularis and explore differences in expressed transcripts and associated metabolic pathways, we analyzed the evolutionary relationship between P.reticularis and its close relative (% similarity from clustalo – 84%), the Asian long-horned beetle ( Anoplophora glabripennis ). Additionally, we compared the distribution of transcripts between the the two development stages, and infer the protein biosynthesis pathways underpinning development in P. reticularis larvae and the potential effects on nutrient composition. This knowledge enhances our understanding of the molecular and genetic foundations of P. reticularis development, elucidates transcript expression profiles distinguishing these stages, and identifies potential target proteins for further investigation. Results and discussion RNA sequencing reveals P. reticularis transcript close relationship with insect lineage Total RNA was extracted from ten samples (n = 10), each consisting of three replicates of small (mean ~ 11.7cm) and large (mean ~ 5.2cm) P. reticularis larvae and sequenced on Illumina NovaSeq and MGI2000 generating 300bp paired-end reads. The reads (Table 1) were processed with bcl2fastq2, quality-checked using FastQC, trimmed with Trimmomatic, and individually assembled using Trinity. The transcriptomic dataset of the larval stages (small and large) of Huhu were characterized to provide a comprehensive overview and ensure robust assembly and completeness assessments. The assembled transcripts were run through BUSCO analysis which revealed key insights into genome assembly quality, with Eukaryota achieving the highest completeness score of 254(48 unique), compared to the significantly lower scores of 67(23 unique) for Archaea and 67(31 unique) for Bacteria (Fig. 2A). To categorize the transcripts into gene-oriented groups, the sequences were processed using UniGene. The majority of unigenes were found within the 200-500nt range (352,536 transcripts), followed by the 500-1000nt range (67,293 transcripts). The occurrence of longer unigenes decreased significantly, with 12,437 transcripts in the 1500-2000nt range, and only 2,149 transcripts exceeding 2000nt (Fig. 2B; Additional file 1: Fig. S1 ). This distribution highlights the predominance of shorter transcripts, which are often associated with highly expressed housekeeping genes, whereas longer transcripts, typically linked to low-abundance or complex genes[ 16 ], were less represented. To classify the transcripts into sample pools and distinguish differences between sample stages, cluster analysis was performed. PCA revealed distinct clustering patterns among the experimental groups, offering insights into transcriptomic variability. The small and large larvae were clearly separated at PC1 level indicating the gene expression differences and at PC2 level the PCA scatter plot demonstrated that groups such as Huhu-L3 and Huhu-S2 formed distinct clusters, while Huhu-S1 constituted a separate groups suggesting slight differences in gene expression within their respective groups (Fig. 2C). Principal Component 1 (PC1) accounted for 68.4% of the total variability, whereas Principal Component 2 (PC2) explained 31.6%, collectively capturing the majority of transcriptomic differences. A phylogenetic-like analysis of species distribution was conducted to assess genetic relationships and evolutionary proximities among various species . Several species , including A.glabripennis , Diabrotica virgifera , Leptinotarsa decemlineata , Callosobruchus maculatus and Sitophilus oryzae , clustered closely together, exhibiting similarity metrics ranging from 15.1–16.7%. Another distinct cluster comprised Tribolium castaneum , Asbolus verrucosus , Photinus pyralis and Ignelater luminosus , which were positioned at a greater evolutionary distance (0.65) and displayed slightly lower similarity percentages between 14.3–18.0%. Additional clusters included Hyalella azteca and Nymphon striatum , which exhibited similarity percentages between 14.0% and 14.5% and were positioned at an evolutionary distance of 0.52. A distinct cluster of mammalian species , including Centratherum punctatum , Mus musculus , Pan troglodytes and Homo sapiens , showed higher similarity percentages between 26.9–39.0%, reflecting their closer evolutionary relationship (Fig. 2D; Additional file 2: Table. S1-S3). Table 1 Distribution of read counts between the replicates of small and large P. reticularis larvae. Variant identification and mutation (Mut) analysis reveal an Isoleucine-to-Phenylalanine mutation across ubiquitin-associated proteins Both small and large larvae were assessed for differential SNP occurrences, providing insights into genetic within other species. Using KisSplice[ 17 ], SNPs were identified independently of a reference genome, mapped onto Trinity-assembled transcripts via BLAT, and functionally classified using KisSplice2RefTranscriptome[ 18 ] (Additional file 1: Fig. S2 A). The results underline variations in both synonymous (Syn) and non-synonymous (NSyn) mutations, evolutionary conservation and alternative splicing complexity. Figure 3A illustrates the frequency and distribution of NSyn vs. Syn mutations across species . Comparative analysis of closely related species, including H. sapiens , M. musculus , R. norvegicus and Dictyostelium discoideum , revealed a higher occurrence of NSyn mutations compared to Syn mutations (Additional file 2: Table. S6), particularly in transcripts differentially expressed in large versus small larval reads. This analysis revealed the highest number of mutations in the mammalian family, including H. sapiens [75NSyn and 36Syn], M. musculus [75NSyn and 36Syn] and R. norvegicus [75NSyn and 36Syn], with a considerable spread in D. discoideum [75NSyn and 36Syn], B. taurus [75NSyn and 36Syn] and A. thaliana [75NSyn and 36Syn]. To further examine these non-synonymous mutations, we categorized them by species and filtered mutations with a cumulative count of ≥ 5 across all transcripts within each species. High-frequency mutations included TCG (Serine) → TTG (Leucine) in IF2P [6mut], TTT (Phenylalanine)/TTG (Leucine) → TCT (Serine)/TCG (Serine) and AAG (Lysine) → AAT (Asparagine) in RHOG ( H. sapiens ) [4mut] and ATA (Isoleucine) → GTA (Valine) in SBP2 (M. musculus ) [5mut] (Fig. 3B; Additional file 2: Table. S7) The most significant SNP identified across all transcripts was ATC (Isoleucine) → TTC (Phenylalanine) [100mut], which was consistently observed in H. sapiens , A. thaliana , D. discoideum , B. taurus , M. musculus , D. melanogaster , P. pygmaeus and R. norvegicus . This variant was more frequently associated with transcripts related to ubiquitin processing, including UBQ12/11/3/4, UBIQD/I/A/G/H/F, UBB, and UBC. In H. sapiens ubiquitin-proteasome system (UPS) is crucial for regulating protein stability, cell signaling, and stress response and mutations affecting ubiquitin-related genes can lead to significant consequences for cellular function[ 19 ]. The Isoleucine-to-Phenylalanine substitution identified across multiple species likely impacts ubiquitin processing, affecting protein structure, ubiquitin binding, and degradation pathways. This mutation replaces a small, aliphatic isoleucine with a bulkier phenylalanine, which may disrupt ubiquitin-protein interactions and proteasomal targeting. Studies show that amino acid substitutions in H. sapiens can alter ubiquitin-binding efficiency, while isoleucine 44 is identified as a key residue in ubiquitin’s hydrophobic patch, crucial for degradation signaling[ 20 , 21 ].Structural studies suggest that this mutation could introduce steric hindrance, destabilizing ubiquitin interactions, while such changes may also modify degradation kinetics and disrupt protein homeostasis[ 22 , 23 ]. In plants like A. thaliana , ubiquitin regulates hormone signaling and stress adaptation, whereas in mammals, it is essential for cell cycle regulation, immune response, and neuroprotection[ 24 , 25 ]. The conservation of this mutation across species might suggest an evolutionary role in protein homeostasis and cellular signaling (Fig. 3C; Additional file 2: Table. S8). Interestingly, no SNPs were detected in A. glabripennis , the closest related insect species in our dataset. To explore potential mutations associated with transcript similarity between P. reticularis and A. glabripennis , we performed a cross-species mutation analysis. Transcripts from P. reticularis with > 90% sequence similarity to the A. glabripennis genome were identified and analyzed for SNP occurrences. Comparison of SNPs in P. reticularis against existing A. glabripennis transcripts revealed SNPs in TMEM134 (highly conserved across both vertebrates and invertebrates, including D. melanogaster and C. elegans) [ 26 , 27 ], LSM4 (mRNA degradation, RNA splicing and stress granule formation in A. thaliana )[ 28 ], ZC3H13 (mRNA methylation [m6A modification] and RNA stability in vertebrates)[ 29 ] and ZMAT2 (RNA splicing regulation via condensate formation, influencing gene expression)[ 30 ], which were not detected in the initial Kissplice analysis. While these transcripts have been well-characterized in mammals, their specific functions in insects remain unclear, though their involvement in fundamental cellular processes suggests a high degree of conservation across species (Fig. 3D; Additional file 2: Table. S9). In addition, we examined SNPs associated with the small and large larval stages and identified shifts in stop codon positioning, which may suggest premature termination of certain transcripts during development (Additional file 1: Fig. S2 B-E and Additional file 2: Table. S4 and S5). Differential expression (DE) between the larval stages identifies transcripts linked to development, metabolism, and environmental adaptation To investigate transcript dynamics during larval development, we aimed to identify the transition and disappearance of transcripts as grubs develop from small to large larval stages-the latter being the recommended stage for consumption. Transcripts from small and large larvae were categorized independently, and differential expression (DE) analysis was performed using DESeq2, edgeR and Limma using the baseline conditions (Fig. 4A; Additional file 2: Table. S10-S12). To minimize errors in transcript distribution, we compared the 8,688 common transcripts that were consistently identified as differentially expressed across all three analysis packages with a threshold of Log₂ fold change (log₂FC) > ± 2 and an adjusted p-value (Padj) < 0.05. This common set of transcripts was considered the true DE transcripts, and the expression values from DESeq2 were used as the reference for downstream analysis. The FPKM distribution analysis revealed distinct expression patterns across the small and large samples. The expression levels ranged from 0–17 FPKM in the lowest category to 500 + FPKM in the highest. Intermediate expression levels were recorded in ranges of 1-306, 8–15 and 15–60 FPKM. The highest proportion of transcripts fell within the 15–60 FPKM range, while fewer genes exhibited very high or very low expression (Fig. 4B; Additional file 2: Table. S13). These findings indicate variability in transcript abundance across the tested conditions. From the 8,688 true DE transcripts, we identified the top 8 most transcript associated with both upregulated (UPreg) and downregulated (DWreg) transcripts. The downregulated transcripts were distributed across the fraction, whereas the upregulated transcripts exhibited a clustered, fixed wave pattern. Among the most significant downregulated genes, we identified ERPa, ARYa, Hx, B4GA1, SgAbd2, SMOX ( A. glabripennis ), and SOD ( Colaphellus bowringi ). In contrast, the most significant upregulated genes included SGE1, l(2)efl, CALR, TNNC, ATPaseB ( T. castaneum ), CPA ( Aethina tumida ; Tenebrio molitor ), and EEF2 ( S. oryzae ) (Fig. 4C; Additional file 2: Table. S14). Key upregulated genes, such as SGE1, CALR, and EEF2, and downregulated genes like ERPa, SOD, and Hx, are linked to metabolism and developmental shifts, consistent with Blattella germanica transcriptomic analyses[ 31 ]. These findings reinforce the role of DE genes in species-specific adaptation and developmental regulation. Looking into the species spread of these 16 DE transcript (both upregulated and downregulated), we identified that they all were from insecta class. Next we grouped the true DE transcript as species and we identified that the Insecta class exhibited the highest number, with 4125-UPreg and 733-DWreg genes (101- species ). Followed by mamalia with 129-UPreg and 260-DWreg (83- species ), Teleostei with 22-UPreg and 73-DWreg (23- species ), Anthozoa with 2-UPreg and 105-DWreg (7- species ) and Malacostraca with 6-UPreg and 405-DWreg (5- species ) (Fig. 4D; Additional file 2: Table. S15). Within the insect class, A. glabripennis showed the strongest DE, with 4682-UPreg and 4024-DWreg; Ratio-0.86 transcripts. Other notable species included T. castaneum [212-UPreg and 3895-DWreg; Ratio-18.37], D. virgifera virgifera [364-UPreg and 458-DWreg; Ratio-1.26] and L. decemlineata [615-UPreg and 699-DWreg; Ratio-1.14], each displaying distinct expression patterns. Interestingly, genes from mammalian species , including H. sapiens [3190-UPreg and 2286-DWreg; Ratio-0.72] and M. musculus [1943-UPreg and 1292-DWreg; Ratio-0.66], were also differentially expressed, suggesting either transcript conservation across species or possible transcript misalignment[ 32 ]. A. glabripennis remained the dominant species with the highest DE count. ACP1, APER1 and Arylphorin A exhibited the highest expression levels, while ACSL5 and AGMO showed average DE (≤ 0.10) across all conditions. Endochitinase and KRT1 were DE, with Endochitinase falling from 0.17 to 0.00. Transcripts, such as PER1, B4GAT1, exhibited near-zero/minimum expression across all conditions, suggesting limited or no activation. The significant number of differentially expressed genes in insects suggests strong adaptive transcriptomic responses, potentially linked to development, metabolism, and environmental adaptation (Fig. 4E; Additional file 2: Table. S16 and S17). The analysis revealed that the Insecta class exhibited the highest number of DE transcripts, highlighting a strong adaptive transcriptomic response in larval development. A. glabripennis showed the most significant DE, suggesting species-specific regulatory mechanisms similar to findings in other insect studies[ 33 , 34 ]. The distribution of upregulated and downregulated transcripts across taxa, particularly in Mammalia and Teleostei, may indicate conserved genetic pathways or misalignment issues[ 35 , 36 ]. The dataset was also analyzed across biological replicates for both the small and large grubs, and the resulting differential expression patterns were consistent with our overall analytical strategy. One of the three replicates appeared as an outlier, which may suggest that the corresponding grubs (n = 10) was collected either at the beginning or end of the targeted developmental stage-potentially influencing its expression profile[ 37 ] (Additional file 1: Fig. S3 ). Protein profiling and domain characterization highlight developmental differences and enrichment in large P. reticularis larvae To investigate the functional relevance of the identified transcripts, the sequences were translated into protein sequences (with isoforms) and profiled for their characteristics and domain composition. The upregulated transcripts from both large and small larval stages were analyzed for their Open Reading Frame (ORF) types, which define the completeness of the coding sequence (CDS) within the transcript. The data presented focuses solely on upregulated transcripts, which have greater impact in transcripts derived protein annotation for our analysis. The distribution of ORF types revealed distinct proportions of complete and partial transcripts, with an increase in complete proteins from 18.1–25.9% during the small-to-large larval transition. Conversely, 3′-partial transcripts (lacking a stop codon) decreased from 10.7–9.8%, while 5′-partial transcripts (lacking a proper start codon) decreased from 23.2–22.6%. Notably, there was a decrease in internal ORFs from 48–41.7%, representing transcript fragments that lack both start and stop codons (Fig. 5A; Additional file 2: Table. S18 and S19). The increase in complete proteins during the small-to-large larval transition suggests enhanced transcript integrity and maturation. Concurrent decreases in partial and internal ORFs indicate improved mRNA processing and stability, potentially optimizing protein synthesis[ 38 ]. The translated transcripts also underwent signal peptide prediction analysis, revealing 974 common proteins across both small and large datasets. Additionally, 7,106 proteins were uniquely identified in the large dataset and 4,814 in the small dataset, with localization to the cytoplasm or nucleus. Further classification of secretory signal peptides (Sec pathway) identified 381 proteins in the large dataset and 319 in the small dataset, with an overlap of 10 proteins containing Signal Peptidase I (SP) sites. Similarly, transmembrane proteins included 46 unique proteins in the large dataset and 10 in the small dataset, with Signal Peptidase II (LIPO) sites. For the Tat pathway (Twin-arginine translocation), 15 proteins were detected in the large dataset and 1 in the small dataset, with minimal representation in the latter (Fig. 5B; Additional file 2: Table. S18 and S19). The signal peptide prediction analysis highlights a dynamic shift in protein localization and secretion between developmental stages. The larger dataset exhibits a higher diversity of secretory and transmembrane proteins, suggesting increased cellular complexity and specialized functions[ 39 ]. Notably, the Sec and Tat pathways show greater representation in the large dataset, indicating enhanced protein transport activity[ 40 ]. The overlap of proteins with Signal Peptidase I and II sites suggests conserved secretion mechanisms despite developmental differences[ 41 ]. To identify transcripts with a high likelihood of mitochondrial localization, we utilized TargetP-2.0, annotating proteins with a mitochondrial targeting peptide (mTP) probability of ≥ 0.75 for both small and large larval stages. This analysis revealed a subset of translated transcripts with compelling mitochondrial targeting probabilities. In the large larval stage, the top-ranked mitochondrial-targeted proteins included RNA-directed DNA polymerase (mTP: 0.93), FLYWCH-type zinc finger-containing protein 1 (mTP: 0.96), and NADH-quinone oxidoreductase subunit C (mTP: 0.96). Similarly, in the small larval stage, proteins such as Intersectin-2 (mTP: 0.99), Cation efflux system protein CzcA (mTP: 0.99), Nuclear factor NF-kappa-B p105 subunit (mTP: 0.99), and putative replication protein A (mTP: 0.99) exhibited high mitochondrial targeting probabilities. In addition to mitochondrial targeting, we assessed the presence of signal peptides (SP), indicative of potential roles in secretion or membrane targeting. Proteins such as Golgin subfamily A member 4 (SP: 0.21) and Blue-light-activated protein (SP: 0.24) displayed SP probabilities exceeding 0.20, suggesting potential involvement in extracellular transport or membrane-associated functions in both larval stages. To further characterize protein processing, cleavage site (CS) predictions were conducted, identifying conserved cleavage motifs at positions 27–28, 29–30, and 50–51. Notably, unique cleavage patterns were observed in key genes, including RNA-directed DNA polymerase (51–52, HHY-ST), FLYWCH-type zinc finger-containing protein 1 (29–30, IPW-TA), and Putative replication protein A (30–31, RAC-SK)[ 42 – 48 ]. Mitochondrial-targeted proteins play key roles in insect larval development, influencing metabolism, immune responses, and cellular regulation. RNA-directed DNA polymerase supports transcriptome regulation in migratory insects[ 49 ], while NADH-quinone oxidoreductase subunit C contributes to energy metabolism in Spodoptera frugiperda larvae[ 50 ]. FLYWCH-type zinc finger proteins likely regulate developmental genes, though their role in larvae remains underexplored. Intersectin-2 is linked to cell proliferation in developmental tissue morphogenesis[ 51 ]. CzcA aids larval stress responses via metal ion regulation[ 52 ], while NF-kappa-B p105 is crucial for immune function during development[ 53 ]. Lastly, Replication Protein A plays a role in DNA maintenance and cell cycle regulation[ 54 ]. These proteins collectively support critical developmental transitions in insect larvae, warranting further investigation. Comparative analysis between small and large larval stages indicated that large larvae exhibited slightly higher mitochondrial targeting probabilities, clustering around 0.85–0.95. In contrast, small larvae proteins demonstrated greater variability in signal peptide presence, with several proteins displaying SP values exceeding 0.20 (Fig. 5C; Additional file 2: Table. S20 and S21). The mitochondrial targeting analysis highlights stage-specific differences in protein localization, with large larvae showing higher mitochondrial targeting probabilities, while small larvae exhibit more variability in signal peptide presence. These findings suggest developmental regulation of mitochondrial function and protein trafficking. Cleavage site variations further indicate differential protein processing mechanisms between larval stages[ 55 ]. Functional annotation reveals metabolic and protein biosynthesis pathways underpinning development in large P. reticularis larvae To elucidate potential biological roles of the identified transcripts, a comprehensive functional annotation (Ann) analysis was conducted. Figure 6A (Additional file 2: Table. S22 and S23). illustrates the overlap of transcript annotations across non-redundant (NR) (181,108Ann) (Additional file 2: Table. S24), SwissProt (112,506Ann) (Additional file 2: Table. S25), Kyoto Encyclopedia of Genes and Genomes (KEGG) (34,804Ann) (Additional file 2: Table. S26 and Additional File 1: Fig. S6) and Clusters of Orthologous Genes (COG) (84,111Ann) (Additional file 2: Table. S27 and Additional File 1: Fig. S4) databases. The analysis was conducted using only the upregulated transcript list to maintain the biological functional repertoire. A large number of transcripts were annotated in multiple databases, indicating high functional consistency, with 20,658 common annotations identified for further downstream analysis. To enhance functional characterization, InterPro (61,988Ann) (Additional file 2: Table. S28) and Gene Ontology (GO) (109,936Ann) (Additional file 2: Table. S29 and Additional File 1: Fig. S5) annotations were analyzed to identify common terms. Figure 6B highlights the distribution of annotations, with InterPro classifications further divided into PRINTS (27,498Ann), ProSitePatterns (27,196Ann), ProSiteProfiles (6,906Ann), and MobiDBlite (1,327Ann). Among these, MobiDBlite exhibited a low spread of counts, indicating a weak representation of intrinsically disordered proteins[ 56 ]. The distribution of clusters in Fig. 6C highlights key biological functions with varying levels of representation. The clustering of GO terms in this dataset reveals key functional trends, emphasizing membrane transport, structural components, and signal transduction, while metabolic and enzymatic activities appear underrepresented. The dominance of membrane transport (Cluster 1, n = 34), with highly represented terms such as translation (GO:0006412, Transcript = 129) and proteolysis (GO:0006508, Transcript = 116), highlights the importance of protein synthesis and degradation in cellular transport systems, aligning with findings that membrane transport proteins are crucial for maintaining cellular homeostasis and intercellular interactions[ 57 ]. Additionally, sarcomere organization (GO:0045214, Transcript = 82) within this cluster suggests a strong relationship between muscle function and transport mechanisms, particularly in excitable tissues like neurons and muscle fibers[ 58 ]. In contrast, structural components (Cluster 0, n = 11), represented by carbohydrate metabolic processes (GO:0005975, Transcript = 141, Cluster 7, n = 4), indicate that carbohydrate metabolism plays a role not just in energy production but also in cell wall integrity and structural homeostasis[ 59 ]. Likewise, protein folding (GO:0006457, Transcript = 79, Cluster 2, n = 1) underscores the significance of chaperone-mediated protein assembly in maintaining protein stability under stress conditions[ 60 , 61 ]. Additionally, nucleotide binding (GO:0005525, Transcript = 203, Cluster 4) supports the regulatory function of ATP/GTP-binding proteins in signaling (Cluster 7) and energy production/conversion (Cluster 5, n = 2) exhibiting low representation, suggesting that these processes may be more developmentally or tissue-specific rather than universally expressed[ 62 ] (Fig. 6C; Additional file 2: Table. S30). To functionally characterize the upregulated transcripts, GO term enrichment analysis was conducted, highlighting key biological processes, cellular components, and molecular functions. The analysis revealed notable representation of translation (GO:0006412; Transcript = 49), protein folding (GO:0006457; Transcript = 10), and mRNA splicing via the spliceosome (GO:0000398; Transcript = 26). These findings align with those observed in Hermetia illucens [ 15 ] and Rhodnius prolixus [ 63 ] where protein biosynthesis and post-transcriptional regulation have been identified as essential for rapid growth and developmental processes in insect species. The glycolytic process (GO:0006096; Transcript = 29) was also enriched, reflecting high metabolic activity in larval and pupal stages, as observed in B.mori and Spodoptera litura [ 64 , 65 ]. Among cellular components, the cytoplasm (GO:0005737; Transcript = 395) was the most abundant, underscoring its role as a site for biochemical and enzymatic reactions in insects such as Rhynchophorus ferrugineus [ 7 ]. The plasma membrane (GO:0005886; Transcript = 192) and mitochondrion (GO:0005739; n = 88) were also highly represented, aligning with their roles in nutrient transport and ATP generation, which are essential for the energy-intensive development of H. illucens [ 15 ]. The identification of the proteasome complex (GO:0000502; Transcript = 12) and ribosome (GO:0005840; Transcript = 33) further supports an environment of high protein turnover, crucial for molting and metamorphosis in species like Epicauta chinensis [ 66 ] and Hippodamia convergens [ 67 ]. Within molecular functions, the enrichment of ATP binding (GO:0005524; Transcript = 266) and metal ion binding (GO:0046872; Transcript = 148) suggests extensive reliance on ATP-driven enzymatic activities and metal cofactor-mediated biochemical reactions, which are essential for nutrient assimilation and metabolism in edible insect species[ 68 ]. The presence of GTPase/ATPase activity (GO:0003924, GO:0042626; Transcript = 60) and oxidoreductase activity (GO:0016705; Transcript = 7) highlights the importance of mitochondrial-dependent energy metabolism and redox homeostasis, particularly in fast-growing insect larvae such as T. molitor [ 69 ]. Additionally, ion binding (GO:0008270; Transcript = 96) was enriched, indicating its role in enzyme stability and metabolic regulation across multiple edible insect species[ 65 ] (Fig. 6D; Additional file 2: Table. S31). Interestingly, general transport processes (GO:0006811, GO:0046961, GO:0015078; Transcript = 16) were moderately represented, suggesting selective nutrient uptake and distribution rather than bulk transport, a feature observed in insects such as Dendroctonus valens during developmental transitions[ 70 ]. Developmental processes (GO:0007517, GO:0007424; Transcript = 45) were selectively enriched, reinforcing their importance in growth, metamorphosis, and reproductive cycles, particularly in species like B. mori and R. ferrugineus [ 7 , 64 ]. Overall, the dataset reflects a functional landscape optimized for protein biosynthesis, metabolic regulation, and energy production, with additional contributions from transport systems, enzymatic activity and developmental processes. GO term enrichment analysis across biological replicates revealed minimal or no significant differences in the enriched terms, indicating high consistency between replicates (Additional File 1: Fig. S4) for both upregulated and downregulated transcripts, as detailed in the supplementary data. The results suggest a efficient metabolic framework in the large P. reticularis larvae, supporting their suitability as alternative protein sources. Furthermore, SSR (Simple Sequence Repeats, also known as microsatellites) analysis identified numerous microsatellite loci within the transcriptome, including pure, compound, and interrupted repeats (Additional file 2: Table. S32 and S33). These SSRs are part of genes being actively expressed, so they're potentially linked to important functional traits, especially in non-model organisms where genome data may be limited[ 71 ]. Using Primer3, specific primers were designed to flank these SSRs, enabling their amplification. These EST-SSR (Expressed Sequence Tag-SSRs) markers, derived from expressed genes, offer valuable tools for population genetics and trait association studies. The integration of SSR and primer analysis enhances the utility of transcriptome data for downstream functional and evolutionary investigations[ 72 ]. Conclusion This study provides the first comprehensive transcriptomic characterization of P. reticularis , revealing significant genetic and molecular differences between small and large larval stages. Recent studies have highlighted the nutritional and functional potential of P. reticularis larvae and pupae, supporting its viability as a nutritious alternative protein source[ 73 ]. For example, Huhu large larvae exhibited high protein content and a nutritionally favourable profiles of amino acid, fatty acid, and minerals, making them a rich nutritional resource. The findings in the current study, align with the transcriptomic data insights indicating enhanced protein biosynthesis and metabolic efficiency in Huhu large larvae, reinforcing their superior suitability for human consumption[ 3 ]. Functional annotation and protein domain analysis indicate that large P. reticularis larvae have higher metabolic activity, enhanced protein biosynthesis, and structural integrity. GO analysis revealed increased protein biosynthesis (GO:0006412), ribosome biogenesis (GO:0005840), and ATP-binding activity (GO:0005524) in large P. reticularis larvae, suggesting greater protein turnover and nutrient assimilation efficiency[ 74 ]. Additionally, oxidoreductase activity (GO:0016705) and mitochondrial electron transport chain components were enriched, indicating improved energy metabolism-critical for biomass accumulation and growth[ 75 ]. Similar trends have been observed in edible insects like H. illucens and T. molitor , where larger developmental stages exhibit higher protein content and metabolic efficiency[ 76 ]. Protein domain characterization further supports the superiority of large larvae. The proportion of complete ORFs increased from 18.1–25.9%, suggesting greater transcript stability and enhanced protein integrity. Additionally, secretory and transmembrane proteins were significantly enriched, supporting improved nutrient transport and structural composition. These findings align with research on insect larvae, where later developmental stages accumulate more essential amino acids, unsaturated fatty acids, and bioactive compounds[ 77 , 78 ]. The study also identified a key Isoleucine-to-Phenylalanine mutation in ubiquitin-associated proteins, which may impact protein degradation pathways and metabolic regulation. Given the absence of this mutation in A. glabripennis , the closest relative, it suggests a potential evolutionary advantage in P. reticularis [ 79 ]. Additionally, the enrichment of chitin metabolism genes (GO:0005975) and structural protein domains suggests that large P. reticularis larvae has a more developed exoskeleton and improved biochemical defense mechanisms[ 80 ]. Overall, the transcriptomic data indicate that large larvae have optimized metabolic pathways, enhanced protein synthesis, and greater structural stability. Future research should validate these findings through proteomic and metabolomic studies to further explore their nutritional potential and bio-functional properties. As interest in alternative proteins grows, these insights will be crucial to the sustainable utilization of edible insects as source of alternative proteins. Methods Sample sourcing and collection P. reticularis larvae (both small and large) used in this study were obtained from colonies established using field populations collected from decomposing pine ( Pinus radiata ) logs. Grubs were sourced from Flagstaff (Three Mile Hill), near Dunedin in the Otago Region, New Zealand (latitude 45.8656, longitude 170.3785) to form the small and large larval groups, respectively (Fig. 1C). At the sample collection site, at least three grubs were sampled per log to ensure representative population coverage. P. reticularis larvae were identified based on distinct physical characteristics: small larvae possess two-chambered spiracles and shorter terminal triangular spines and setae on abdominal segments[ 3 , 81 ], while larger larvae display single spiracles and longer terminal triangular spines and setae (Fig. 1D). To collect the larvae, decomposing logs were broken open, and grubs were placed in sterile containers. The grubs were rinsed with 1X PBS to remove residual dirt, followed by one rinse with 1% RNase Away and three rinses with DEPC-treated water. Samples (n = 10), each containing three replicates of small and large larvae, were preserved in TRIzol® reagent (Invitrogen, USA) and stored at − 80°C until further analysis (Fig. 1E). RNA extraction Total RNA was extracted from small and large huhu samples in triplicate using the NucleoSpin RNA Isolation Kit (Macherey-Nagel) following the manufacturer’s instructions. RNA concentration (ng/µL) and purity were determined using a NanoDrop spectrophotometer (Thermo Fisher Scientific) and the Qubit RNA HS Assay Kit (Thermo Fisher Scientific) for each sample set. Optimal purity was defined by 260/280 and 260/230 ratios of approximately 2.0, with an ideal large-to-small RNA ratio of 7:3. RNA integrity was assessed using an Agilent Bioanalyzer (Agilent), and only samples with a RIN score greater than 8.5 were selected for downstream analysis. Library preparation and quality control Library preparation for Illumina sequencing was initiated with 1µg of total RNA per sample. Following the manufacturer's protocol, poly(A) mRNA was isolated using either the poly(A) mRNA magnetic isolation module or the rRNA removal kit. mRNA fragmentation and priming were performed using first-strand synthesis reaction buffer and random primers. First-strand cDNA synthesis employed ProtoScript II reverse transcriptase, followed by second-strand synthesis with a second-strand synthesis enzyme mix. Purified double-stranded cDNA underwent end repair and dA-tailing in a single reaction using an end-prep enzyme mix, followed by T-A ligation to attach adaptors at both ends. Size selection of adaptor-ligated fragments (~ 400 bp, with ~ 300 bp insert size) was performed using magnetic beads. PCR amplification was carried out with P5 and P7 primers, which included flow cell annealing sequences and multiplexing indices. PCR products were purified with beads, validated on a Qsep100 instrument (Bioptic, Taiwan), and quantified with a Qubit 3.0 Fluorometer (Invitrogen, USA) (Fig. 1E). Indexed libraries were pooled and sequenced on the Illumina NovaSeq (Illumina, USA) and MGI2000 (MGI, China) platforms, following the respective manufacturers' protocols. Illumina sequencing generated 300bp paired-end reads from reverse-stranded RNA enriched in poly(A). Raw sequencing data were processed using Illumina's bcl2fastq2 pipeline v2.20.0. Quality assessment was performed using FastQC v0.11.9[ 82 ], and reads passing the baseline quality cutoff were retained. Low-quality bases and adapter sequences were trimmed using Trimmomatic v0.40[ 83 ]. All bioinformatic analyses were conducted with data and literature available as of February 18th, 2025. The scripts utilized in this study are provided as supplementary materials (Additional File 3) to ensure reproducibility. Assembly, ORF prediction and redundancy-similarity search To generate a reference assembly for differential expression and functional annotation, RNA-seq data from small and large P. reticularis larvae were combined and assembled using Trinity v2.15.1[ 84 , 85 ]. Each dataset (small and large larvae) was also assembled individually for comparative analysis. Paired-end reads were processed separately, with left and right reads combined prior to assembly. To identify and remove redundant sequences from the assembled transcriptome, CD-HIT v4.8.1[ 86 ] was used. The sequences were then clustered based on a specified similarity threshold, allowing for the reduction of redundancy and the retention of unique sequences. The quality of the assembled transcriptome was evaluated using Transrate v1.0.3[ 87 ], BUSCO v5.7.0[ 88 ], and rnaQUAST v2.3[ 89 ] to identify low-quality or misassembled sequences[ 90 , 91 ]. The assembled transcripts were connected via sequence clustering into long non-redundant unigene sequences using the Unigene database[ 90 , 91 ]. Principal Component Analysis (PCA) was conducted on transcriptomes of small and large samples to evaluate clustering and variability using R v4.4.2[ 92 ] with the 'corrr' function. Open reading frames (ORFs) in the Trinity-assembled transcripts were identified with TransDecoder v5.0.2[ 93 ]. RSEM v1.3.3[ 94 ] (RNA-Seq by Expectation-Maximization) was utilized for quantifying gene and isoform abundances. Alignment, splicing and species curation The assembled transcripts were investigated by comparing them to the NCBI NR[ 95 ], SwissProt[ 96 ], GO[ 97 ], KEGG[ 98 ] and COG[ 99 ] database using BLASTX v2.6.0+[ 100 ] with an e-value cutoff of 1e-20 and used to determine the species corresponding to each transcript ID. The number of transcripts identified for each species was compared against the unigene count. The significant species relationships were analyzed using OrthoFinder[ 101 – 103 ], and an evolutionary tree was visualized with Dendroscope[ 104 ]. Alternative splicing events and single nucleotide polymorphisms (SNPs) were identified using KisSplice v2.6.0[ 17 ], which performs local assembly of RNA-seq reads and detects variants independently of a reference genome. The predicted SNPs were positioned along the Trinity-assembled transcripts using BLAT v37.1[ 105 ]. SNPs that did not map to the Trinity transcripts (orphan SNPs) were separated. The functional impact of SNPs was assessed using KisSplice2RefTranscriptome v1.3.3[ 18 ]. Differences in allele composition were recorded as “inconsistent” and evaluated for potential sequencing errors or artifacts from assembly. Differential expression and clustering Expression levels were estimated by aligning RNA-Seq reads to the transcriptome providing quantification of gene expression by using Bowtie2 v2.5.4[ 106 ]. Differential expression analysis was performed using Corset v1.0.9[ 107 ]. The counts were clustered based on their mapping coordinates, and expression levels for each cluster were calculated, enabling the identification of differentially expressed genes between the experimental conditions. The count values from corset were analyzed using three differential expression R packages: DESeq2[ 40 ], edgeR[ 41 ], and limma[ 42 ]. Transcripts were clustered based on those commonly identified across the three packages, with expression values assigned from the DESeq2 package. Differentially expressed genes were identified using a log2FC threshold of ± 2 and an adjusted p-value (Padj) cutoff of < 0.01. The transcripts were categorized based on NR annotation at different taxonomic levels, including class, species, and specifically down to A. glabripennis . Annotation, domain characterics and functional relevance GOSeq v1.34.1[ 108 ] was used to identify Gene Ontology (GO) terms from a list of enriched genes with a significant padj < 0.01. The annotated proteins were further validated in InterProScan v5.72-103.0[ 56 ] to functionally annotate the assembled transcripts, using the -goterms and -pathways plugins and the UniProtKB database[ 109 ] to screen for potential allergens, disease associations, drug targets, virus-host interactions, and toxicological implications. Transmembrane helices in the annotated protein sequences were predicted using DeeptmHMM v1.0.24[ 110 ], while SignalP v6.0[ 111 ] was utilized for the identification of signal peptides. To assess the presence of N-terminal presequences, the TargetP-2.0[ 112 ] was utilized, classifying sequences into signal peptides (SP), mitochondrial transit peptides (mTP), chloroplast transit peptides (cTP), or thylakoid luminal transit peptides (lTP). SSRs were identified from the assembled unigenes using MISA[ 113 ], TRF[ 114 ], and mreps[ 115 ], with only consensus results retained. Primer3[ 116 , 117 ] was then used to design primers flanking SSR regions for downstream validation and marker development for further studies. Quantification and statistical analysis The data and visualization were prepared, plotted, or designed using Prism 9.0 (GraphPad), 'ggplot2' package[ 118 ] from R v4.4.2[ 92 ] or Gephi. The graphs were exported as TIFF or JPEG files. Statistical analyses were conducted using (GraphPad) Prism 9 or R. p-value was calculated from the individual databases/packages/software’s with no corrections in datasets. Corrections were introduced during analysis of each dataset. Significant results were determined by a p-value of < or ≤ 0.01. Declarations Supplementary Information The online version contains supplementary material available at Additional file 1. Supplementary figures. Additional file 2. Supplementary tables containing results of analyses performed in manuscript. Additional file 3. Supplementary file with the code used to generate the results and figures. Ethics approval and consent to participate Not applicable. Consent for publication The research project was discussed with the Ngāi Tahu Research Consultation Committee in relation to research on indigenous Huhu grubs and agreed upon. Availability of data and materials The article/Additional file includes the original contributions to the study. The codes used to generate the results and figures (under University of Otago/Ludwig Maximilian University of Munich license), as well as generated data/results are presented in Additional file 3. No publicly available samples were used for this study. The raw sequencing data from this study is available in Aotearoa Genomic Data Repository and will be provided upon request ( Alligning with the principles of Māori Data Sovereignty ). For further information, please contact the corresponding authors. Competing interests The authors declare that they have no competing interests. Author details 1 Department of Food Science, University of Otago, 9054 Dunedin, New Zealand. 2 Department of Microbiology and Immunology, University of Otago, 9054 Dunedin, New Zealand. 3 Department of Biochemistry, University of Otago, 9054 Dunedin, New Zealand. 4 Genomics Aotearoa, Department of Biochemistry, University of Otago, New Zealand. 5 Ludwig-Maximilians University, Department of Chemistry, Institute of Chemical Epigenetics - Munich (ICEM), 81377 Munich, Germany. 6 Present address: Department of Wine, Food and Molecular Biosciences, Lincoln University, New Zealand. 7 Present address: Ludwig-Maximilians University, Department of Chemistry, Institute of Chemical Epigenetics - Munich (ICEM), 81377 Munich, Germany. 8 Present address: Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States. 9 Present address: School of Chemistry, Monash University, Clayton, 3800, Victoria, Australia. Funding Bennett Henzeler (Master’s and PhD) and Ruchita Rao Kavle (PhD) were funded by the University of Otago with scholarships. Bennett Henzeler, Sabine Schneider and Pascal Giehr are funded by CRC 1309 (ID: 325871075) with Institute of Chemical Epigenetics - Munich (ICEM) server and the BMBF Cluster4Future program (Cluster for Nucleic Acid Therapeutics Munich, CNATM, ID: 03ZU1201AA). Authors' contributions R.R.K, B.H, N.F, and D.A conceived the study and designed the experiments. R.R.K and B.H conducted the experiments. R.R.K, B.H, N.F, P.G, and C.K analyzed the data. B.H wrote and revised the manuscript. D.A and N.F supervised the study. N.F, A.E.A.B, A.C, S.S and P.G provided guidance throughout the analysis. All authors read and approved the final manuscript. Acknowledgements Ruchita Rao Kavle is grateful to the City Forest, Dunedin, for allowing access to decaying logs to enable harvesting of Huhu grubs. 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Supplementary Files Table1.xlsx Table 1 Distribution of read counts between the replicates of small and large P. reticularis larvae. Additionalfile1.docx Additional file 1. Supplementary figures. Additionalfile2.xlsx Additional file 2. Supplementary tables containing results of analyses performed in manuscript. Additionalfile3.docx Additional file 3. Supplementary file with the code used to generate the results and figures. HuhuFigureS1.tiff HuhuFigureS2.tiff HuhuFigureS3.tiff HuhuFigureS4.tiff HuhuFigureS5.tiff HuhuFigureS6.tiff Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6564832","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450629049,"identity":"aa05a9a0-97a4-4ab3-b8da-5d7c81dac582","order_by":0,"name":"Ruchita Rao Kavle*","email":"","orcid":"","institution":"Department of Food Science, University of Otago, 9054 Dunedin, New Zealand; Department of Wine, Food and Molecular Biosciences, Lincoln University, New Zealand","correspondingAuthor":false,"prefix":"","firstName":"Ruchita","middleName":"Rao","lastName":"Kavle*","suffix":""},{"id":450629050,"identity":"7ee191cf-c191-4b3e-ba70-405768760a71","order_by":1,"name":"Bennett Henzeler*","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie2RsWrDMBRF1SVeBFkdTPMN1whkDKHf4mBwF0G6NaOh0C4uXf0lngOGZhGdDR7iKVMGdfNgaGWTpk0RabOVojOIK6HD1UOEWCx/EByHG0LGH4GQ6IQSYR/0OkkP579VsPpBCZzH5+a17fpQKoXZgq0lbwiKeerEK5MSZi/Xfq5bdIj8HElYSBHo0nqe0q2xBpXgHu0fVgnEFCV4Rbk7KK6AUdnsuNf1ymaHssMbWH5QFsrcQrk3jF9R/07PDrifLcbxw0zwSZYwCinYRYYYrkxuEaFm93RrfFjgSO62s+kUa8lUu7zC+KEsGrWsL5+cuDHW7KFfN6PhR0an7n/nrMsWi8Xy/3kHpztbsr9PTWAAAAAASUVORK5CYII=","orcid":"","institution":"Department of Microbiology and Immunology, University of Otago, 9054 Dunedin, New Zealand; 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Schematic design of the workflow for sample processing, downstream experiments, and analysis. \u003cstrong\u003eA\u003c/strong\u003e Schematic representation of the P. reticularis lifecycle, illustrating the five stages from egg to small and large larvae, pupae, and adult beetle. \u003cstrong\u003eB\u003c/strong\u003e Schematic representation of the individual growth stages in their natural habitat at the time of sample collection. \u003cstrong\u003eC\u003c/strong\u003e Schematic representation of the sample collection point. \u003cstrong\u003eD\u003c/strong\u003e Schematic representation of the size distribution, anatomy, and characteristics of small and large grubs. \u003cstrong\u003eE\u003c/strong\u003e Schematic representation of the protocol for total RNA extraction and short read sequencing (Created in BioRender. Henzeler, B. (2025) https://BioRender.com/wxtbokl).\u003c/p\u003e","description":"","filename":"HuhuFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/3ffdb3e95589b0078e251edf.png"},{"id":81807334,"identity":"49c60b7d-cde9-4517-bfb6-809c384c58bf","added_by":"auto","created_at":"2025-05-02 07:40:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3941113,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic characterization and comparative analysis of larval-stage Huhu. \u003cstrong\u003eA\u003c/strong\u003e BUSCO analysis of the assembled transcripts, indicating genome assembly quality across different domains. \u003cstrong\u003eB\u003c/strong\u003e Length distribution of assembled unigenes categorized by size. \u003cstrong\u003eC\u003c/strong\u003e PCA illustrating clustering patterns among experimental groups. \u003cstrong\u003eD\u003c/strong\u003eUnigene number hit based on the cluster and \u003cem\u003especies\u003c/em\u003e highest hits. Phylogenetic-like \u003cem\u003especies\u003c/em\u003e distribution analysis, color-coded by taxonomy. Shades of mustard indicated the Insect species clustered into distinct groups and the shades of grey indicate other species reflecting closer evolutionary relationships. The resulting tree was color-coded, with mustard representing species from Class Insecta and grey denoting species from other taxonomic classes as seen in Fig. 2D\u003c/p\u003e","description":"","filename":"HuhuFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/d6553e80d73080f84dfb8f82.png"},{"id":81806866,"identity":"4b717cca-1c6a-4fe3-a695-2f7f9410d068","added_by":"auto","created_at":"2025-05-02 07:32:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5040362,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of SNPs in alternative splicing-associated transcript across \u003cem\u003especies\u003c/em\u003e. \u003cstrong\u003eA\u003c/strong\u003e Categorization of \u003cem\u003especies\u003c/em\u003e based on Syn and NSyn mutation counts with the highest distribution in the pooled mutation and the closest phylogenetic relationship. \u003cstrong\u003eB\u003c/strong\u003e Distribution and frequency of NSyn mutations identified through KisSplice. The x-axis represents specific amino acid substitutions caused by SNPs, while the y-axis lists the corresponding genes categorized by \u003cem\u003especies\u003c/em\u003e. \u003cstrong\u003eC\u003c/strong\u003e Distribution and frequency of Isoleucine to Phenylalanine mutations identified. The x-axis represents \u003cem\u003especies \u003c/em\u003ewhile the y-axis lists the corresponding genes\u003cem\u003e. \u003c/em\u003e\u003cstrong\u003eD\u003c/strong\u003eDistribution and frequency of mutations associated with transcript similarity between \u003cem\u003eP. reticularis\u003c/em\u003e and \u003cem\u003eA. glabripennis\u003c/em\u003e where the x-axis represents \u003cem\u003especies \u003c/em\u003ewhile the y-axis lists the corresponding genes. Color intensity represents the frequency of SNP occurrence, with white indicating lower counts and yellow indicating higher counts.\u003c/p\u003e","description":"","filename":"HuhuFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/d82b61d94632a6ee6ef93040.png"},{"id":81806867,"identity":"53f2d4e9-497d-4ba3-ab8b-f4b645f69605","added_by":"auto","created_at":"2025-05-02 07:32:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6064195,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression and clustering. \u003cstrong\u003eA\u003c/strong\u003e Venn diagram of DETs identified by DESeq2, edgeR, and limma. Overlapping regions indicate transcripts consistently identified across multiple differential expression pipelines, while unique regions highlight genes detected exclusively by each method. \u003cstrong\u003eB\u003c/strong\u003e Volcano plot displaying DESeq2-derived DETs, filtered by log2 fold change (±2) and p-value \u0026lt; 0.05. Green dots represent upregulated transcripts from insect species, whereas red dots indicate downregulated transcripts. \u003cstrong\u003eC\u003c/strong\u003e FPKM (Fragments Per Kilobase of transcript per Million mapped reads) distribution across experimental conditions (Huhu.S1, Huhu.S2, Huhu.S3, Huhu.L2, and Huhu.L3). \u003cstrong\u003eD\u003c/strong\u003e DE classification by taxonomic class, with species counts annotated within each category. \u003cstrong\u003eE \u003c/strong\u003eDETs clustering based on significant species with distinct expression patterns. The clustering group highlights \u003cem\u003eA. glabripennis\u003c/em\u003e due to its closest phylogenetic relationship to\u003cem\u003e P. reticularis\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"HuhuFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/aa274a6c27618746fddfe0fe.png"},{"id":81806500,"identity":"3aca2413-1446-4f89-a5ff-2f109c28516a","added_by":"auto","created_at":"2025-05-02 07:24:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2791302,"visible":true,"origin":"","legend":"\u003cp\u003eProtein profiling, structural analysis, and functional annotation across larval stages. \u003cstrong\u003eA\u003c/strong\u003e Distribution of Open Reading Frame (ORF) types in small and large larval stages was assessed using TransDecoder and DeepTMHMM.The analysis highlights an increase in complete transcripts in the large larval stage, while partial and internal ORFs decrease. \u003cstrong\u003eB\u003c/strong\u003e SignalP analysis revealing the signal peptides and their spread across small and large. \u003cstrong\u003eC\u003c/strong\u003e Mitochondrial targeting and signal peptide predictions in small and large larval stages. Proteins with a mitochondrial targeting probability (mTP ≥ 0.75) are shown, with the x-axis representing the mTP score. The bubble size represents the SP probability, illustrating variations in protein localization potential between stages.\u003c/p\u003e","description":"","filename":"HuhuFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/d4569f0e06f204b31c9132af.png"},{"id":81806505,"identity":"8e396c7a-d9c2-4aab-a1fb-23c97e2b268c","added_by":"auto","created_at":"2025-05-02 07:24:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6287351,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive functional annotation and GO enrichment analysis of differentially expressed transcripts (upregulated transcript). \u003cstrong\u003eA \u003c/strong\u003eVenn diagram illustrating the overlap of transcript annotations across multiple functional databases, including NR, SwissProt, KEGG, and COG. \u003cstrong\u003eB\u003c/strong\u003e Distribution of additional annotations from InterPro and Gene ontology. \u003cstrong\u003eC\u003c/strong\u003e Cluster distribution of key biological functions, showing varying levels of representation, suggesting their selective roles. \u003cstrong\u003eD\u003c/strong\u003eGO term enrichment analysis of upregulated transcript, highlighting key biological processes, cellular components, and molecular functions.\u003c/p\u003e","description":"","filename":"HuhuFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/2918fcbddd4d00c2a6b38b9f.png"},{"id":82086675,"identity":"cc412f7f-feac-4148-9bac-cc0b1ab83443","added_by":"auto","created_at":"2025-05-06 15:16:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":38908145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/8f76c8f9-a0f3-406d-9505-8cb77d232f9f.pdf"},{"id":81806489,"identity":"dda049a3-848f-473d-9e6e-0402bcf60ba6","added_by":"auto","created_at":"2025-05-02 07:24:10","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDistribution of read counts between the replicates of small and large \u003cem\u003eP. reticularis\u003c/em\u003e larvae.\u003c/p\u003e","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/4e5238e5de4c0723784caa8f.xlsx"},{"id":81806491,"identity":"c528f89c-2613-4ec9-8726-9bc212a46d3b","added_by":"auto","created_at":"2025-05-02 07:24:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":41097,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1. Supplementary figures.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/390d0f17d4161b5bdb8c4c77.docx"},{"id":81806550,"identity":"cdc536db-b1e4-4a60-860d-afbbc7e11e5d","added_by":"auto","created_at":"2025-05-02 07:24:20","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":157659809,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2. Supplementary tables containing results of analyses performed in manuscript.\u003c/p\u003e","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/1b887f11ac2ac83bd98b5fde.xlsx"},{"id":81806499,"identity":"b3f3d0cf-7ed7-49db-8a62-a19b32a88f97","added_by":"auto","created_at":"2025-05-02 07:24:10","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":132281,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3. Supplementary file with the code used to generate the results and figures.\u003c/p\u003e","description":"","filename":"Additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/1b345bc42cf013612cdcc915.docx"},{"id":81806540,"identity":"36aa986f-7f25-4ce9-b32b-894a7a4af068","added_by":"auto","created_at":"2025-05-02 07:24:12","extension":"tiff","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2405980,"visible":true,"origin":"","legend":"","description":"","filename":"HuhuFigureS1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/a7778500c8d88caf56ab8733.tiff"},{"id":81806515,"identity":"fbc472f1-23e5-429d-8c5f-db33577d2e29","added_by":"auto","created_at":"2025-05-02 07:24:11","extension":"tiff","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2143356,"visible":true,"origin":"","legend":"","description":"","filename":"HuhuFigureS2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/28db90e2c02154f80b6cd2ce.tiff"},{"id":81806507,"identity":"18db38d4-5137-473e-a625-9105a624bade","added_by":"auto","created_at":"2025-05-02 07:24:11","extension":"tiff","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1759720,"visible":true,"origin":"","legend":"","description":"","filename":"HuhuFigureS3.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/23c22cf19fa78bfbd4b3e5db.tiff"},{"id":81806870,"identity":"c0fb1081-4407-442e-beb5-e906308e1f69","added_by":"auto","created_at":"2025-05-02 07:32:11","extension":"tiff","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1732410,"visible":true,"origin":"","legend":"","description":"","filename":"HuhuFigureS4.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/c8ab818b4f7e1a4430e7053f.tiff"},{"id":81806878,"identity":"18b5a7b4-c277-4dc5-91b5-817a48d71145","added_by":"auto","created_at":"2025-05-02 07:32:12","extension":"tiff","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1561376,"visible":true,"origin":"","legend":"","description":"","filename":"HuhuFigureS5.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/7957dbebf2fb182a05d1268d.tiff"},{"id":81806548,"identity":"f6dfa1b5-3f41-443f-8fc5-f4bc33165724","added_by":"auto","created_at":"2025-05-02 07:24:13","extension":"tiff","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":2566636,"visible":true,"origin":"","legend":"","description":"","filename":"HuhuFigureS6.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6564832/v1/bb7730a90c277771ddde21e8.tiff"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deciphering Huhu (Prionoplus reticularis) grub development: Transcriptomic insights into metabolic and nutritional shifts in larval stages","fulltext":[{"header":"Background","content":"\u003cp\u003eEdible insect larvae or grubs, provide a sustainable and alternate protein source in various cultures worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Huhu grub (\u003cem\u003ePrionoplus reticularis\u003c/em\u003e; \u003cb\u003eOrder\u003c/b\u003e: Coleoptera, \u003cb\u003eFamily\u003c/b\u003e: Cerambycidae and \u003cb\u003eSubfamily\u003c/b\u003e: Prioninae) is an endemic insect species that has a long history of consumption by the indigenous Māori in New Zealand[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The grub undergoes five developmental stages: egg, small larvae, large larvae, pupae and adult (beetle) (Fig.\u0026nbsp;1A). Among these, the larval stages are considered the most suitable for consumption, with a preference for large larvae due to their size (Fig.\u0026nbsp;1B). Our recent study examined the total protein content across different developmental stages, which ranged from 26.2\u0026ndash;30.5% dry weight (DW). Principal component analysis (PCA) and partial least squares regression (PLSR) further revealed significant differences in protein content and other nutritional properties between the small and large larval stages of \u003cem\u003eP. reticularis\u003c/em\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The available evidence underscored the interest for further analysis of the underlying expression patterns of \u003cem\u003eP. reticularis\u003c/em\u003e developmental stages.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSchematic representation of the demographics of the sampling, processing and analysis. Schematic design of the workflow for sample processing, downstream experiments, and analysis. \u003cb\u003eA\u003c/b\u003e Schematic representation of the P. reticularis lifecycle, illustrating the five stages from egg to small and large larvae, pupae, and adult beetle. \u003cb\u003eB\u003c/b\u003e Schematic representation of the individual growth stages in their natural habitat at the time of sample collection. \u003cb\u003eC\u003c/b\u003e Schematic representation of the sample collection point. \u003cb\u003eD\u003c/b\u003e Schematic representation of the size distribution, anatomy, and characteristics of small and large grubs. \u003cb\u003eE\u003c/b\u003e Schematic representation of the protocol for total RNA extraction and short read sequencing (Created in BioRender. Henzeler, B. (2025) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://BioRender.com/wxtbokl\u003c/span\u003e\u003cspan address=\"https://BioRender.com/wxtbokl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;2\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTranscriptomic characterization and comparative analysis of larval-stage Huhu. \u003cb\u003eA\u003c/b\u003e BUSCO analysis of the assembled transcripts, indicating genome assembly quality across different domains. \u003cb\u003eB\u003c/b\u003e Length distribution of assembled unigenes categorized by size. \u003cb\u003eC\u003c/b\u003e PCA illustrating clustering patterns among experimental groups. \u003cb\u003eD\u003c/b\u003e Unigene number hit based on the cluster and \u003cem\u003especies\u003c/em\u003e highest hits. Phylogenetic-like \u003cem\u003especies\u003c/em\u003e distribution analysis, color-coded by taxonomy. Shades of mustard indicated the Insect species clustered into distinct groups and the shades of grey indicate other species reflecting closer evolutionary relationships. The resulting tree was color-coded, with mustard representing species from Class Insecta and grey denoting species from other taxonomic classes as seen in Fig.\u0026nbsp;2D\u003c/p\u003e \u003cp\u003eTranscriptomics plays a critical role in insect research by offering insights into gene expression patterns and regulatory mechanisms[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This approach facilitates the identification and characterization of transcripts involved in essential biological processes, such as development, reproduction, responses to environmental stimuli, and the molecular basis of insect traits and behaviors[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The scope of insect transcriptomics has expanded significantly, allowing for the investigation of a wide range of genes associated with specific tissues, insecticide targets, detoxification enzymes, and metabolic processes across various insect species[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Extensive transcriptomic analyses have been conducted on various Lepidoptera \u003cem\u003especies\u003c/em\u003e, such as the diamondback moth (\u003cem\u003ePlutella xylostella\u003c/em\u003e)[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], corn borer (\u003cem\u003eOstrinia nubilalis\u003c/em\u003e)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and silkworm (\u003cem\u003eBombyx mori\u003c/em\u003e)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], to explore gene expression patterns across different developmental stages. Additionally, transcriptomic studies on insects like the fruit fly (\u003cem\u003eDrosophila melanogaster\u003c/em\u003e)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], oriental fruit fly (\u003cem\u003eBactrocera dorsalis\u003c/em\u003e)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], migratory locust (\u003cem\u003eLocusta migratoria manilensis\u003c/em\u003e)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and ground beetle (\u003cem\u003eEriocheir sinensis\u003c/em\u003e)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] have identified transcripts associated with insecticide resistance. These investigations have yielded valuable genetic insights into the molecular mechanisms underlying insect development and adaptation.\u003c/p\u003e \u003cp\u003eIdentifying transcripts from \u003cem\u003eP. reticularis\u003c/em\u003e associated with human metabolic pathways is crucial for assessing the grub's edibility and nutritional suitability. These transcripts play a significant role in evaluating potential benefits and risks. This ultimately determines the extent to which the grub can be safely and effectively incorporated into human diets[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. There is a major research gap in knowledge of \u003cem\u003eP. reticularis\u003c/em\u003e development and its underlying genetic basis. To illustrate, \u003cem\u003eP. reticularis\u003c/em\u003e currently lacks a complete genome sequence, and its developmental biology remains insufficiently characterized. The indigenous nature of the grub restricts the availability of its genetic sequence in public databases. This manuscript represents the first instance of employing \u003cem\u003ede novo\u003c/em\u003e transcriptome assembly for large-scale transcript discovery in \u003cem\u003eP. reticularis\u003c/em\u003e, achieving high coverage of protein-coding transcripts.\u003c/p\u003e \u003cp\u003eTo identify novel transcripts in P. reticularis and explore differences in expressed transcripts and associated metabolic pathways, we analyzed the evolutionary relationship between P.reticularis and its close relative (% similarity from clustalo \u0026ndash; 84%), the Asian long-horned beetle (\u003cem\u003eAnoplophora glabripennis\u003c/em\u003e). Additionally, we compared the distribution of transcripts between the the two development stages, and infer the protein biosynthesis pathways underpinning development in P. reticularis larvae and the potential effects on nutrient composition. This knowledge enhances our understanding of the molecular and genetic foundations of \u003cem\u003eP. reticularis\u003c/em\u003e development, elucidates transcript expression profiles distinguishing these stages, and identifies potential target proteins for further investigation.\u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eRNA sequencing reveals P. reticularis transcript close relationship with insect lineage\u003c/h2\u003e\n \u003cp\u003eTotal RNA was extracted from ten samples (n\u0026thinsp;=\u0026thinsp;10), each consisting of three replicates of small (mean\u0026thinsp;~\u0026thinsp;11.7cm) and large (mean\u0026thinsp;~\u0026thinsp;5.2cm) \u003cem\u003eP. reticularis\u003c/em\u003e larvae and sequenced on Illumina NovaSeq and MGI2000 generating 300bp paired-end reads. The reads (Table 1) were processed with bcl2fastq2, quality-checked using FastQC, trimmed with Trimmomatic, and individually assembled using Trinity. The transcriptomic dataset of the larval stages (small and large) of Huhu were characterized to provide a comprehensive overview and ensure robust assembly and completeness assessments. The assembled transcripts were run through BUSCO analysis which revealed key insights into genome assembly quality, with Eukaryota achieving the highest completeness score of 254(48 unique), compared to the significantly lower scores of 67(23 unique) for Archaea and 67(31 unique) for Bacteria (Fig. 2A). To categorize the transcripts into gene-oriented groups, the sequences were processed using UniGene. The majority of unigenes were found within the 200-500nt range (352,536 transcripts), followed by the 500-1000nt range (67,293 transcripts). The occurrence of longer unigenes decreased significantly, with 12,437 transcripts in the 1500-2000nt range, and only 2,149 transcripts exceeding 2000nt (Fig. 2B; Additional file 1: Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). This distribution highlights the predominance of shorter transcripts, which are often associated with highly expressed housekeeping genes, whereas longer transcripts, typically linked to low-abundance or complex genes[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e], were less represented. To classify the transcripts into sample pools and distinguish differences between sample stages, cluster analysis was performed. PCA revealed distinct clustering patterns among the experimental groups, offering insights into transcriptomic variability. The small and large larvae were clearly separated at PC1 level indicating the gene expression differences and at PC2 level the PCA scatter plot demonstrated that groups such as Huhu-L3 and Huhu-S2 formed distinct clusters, while Huhu-S1 constituted a separate groups suggesting slight differences in gene expression within their respective groups (Fig.\u0026nbsp;2C). Principal Component 1 (PC1) accounted for 68.4% of the total variability, whereas Principal Component 2 (PC2) explained 31.6%, collectively capturing the majority of transcriptomic differences.\u003c/p\u003e\n \u003cp\u003eA phylogenetic-like analysis of \u003cem\u003especies\u003c/em\u003e distribution was conducted to assess genetic relationships and evolutionary proximities among various \u003cem\u003especies\u003c/em\u003e. Several \u003cem\u003especies\u003c/em\u003e, including \u003cem\u003eA.glabripennis\u003c/em\u003e, \u003cem\u003eDiabrotica virgifera\u003c/em\u003e, \u003cem\u003eLeptinotarsa decemlineata\u003c/em\u003e, \u003cem\u003eCallosobruchus maculatus\u003c/em\u003e and \u003cem\u003eSitophilus oryzae\u003c/em\u003e, clustered closely together, exhibiting similarity metrics ranging from 15.1\u0026ndash;16.7%. Another distinct cluster comprised \u003cem\u003eTribolium castaneum\u003c/em\u003e, \u003cem\u003eAsbolus verrucosus\u003c/em\u003e, \u003cem\u003ePhotinus pyralis\u003c/em\u003e and \u003cem\u003eIgnelater luminosus\u003c/em\u003e, which were positioned at a greater evolutionary distance (0.65) and displayed slightly lower similarity percentages between 14.3\u0026ndash;18.0%. Additional clusters included \u003cem\u003eHyalella azteca\u003c/em\u003e and \u003cem\u003eNymphon striatum\u003c/em\u003e, which exhibited similarity percentages between 14.0% and 14.5% and were positioned at an evolutionary distance of 0.52. A distinct cluster of mammalian \u003cem\u003especies\u003c/em\u003e, including \u003cem\u003eCentratherum punctatum\u003c/em\u003e, \u003cem\u003eMus musculus\u003c/em\u003e, \u003cem\u003ePan troglodytes\u003c/em\u003e and \u003cem\u003eHomo sapiens\u003c/em\u003e, showed higher similarity percentages between 26.9\u0026ndash;39.0%, reflecting their closer evolutionary relationship (Fig.\u0026nbsp;2D; Additional file 2: Table. S1-S3).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDistribution of read counts between the replicates of small and large \u003cem\u003eP. reticularis\u003c/em\u003e larvae.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eVariant identification and mutation (Mut) analysis reveal an Isoleucine-to-Phenylalanine mutation across ubiquitin-associated proteins\u003c/h3\u003e\n\u003cp\u003eBoth small and large larvae were assessed for differential SNP occurrences, providing insights into genetic within other species. Using KisSplice[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e], SNPs were identified independently of a reference genome, mapped onto Trinity-assembled transcripts via BLAT, and functionally classified using KisSplice2RefTranscriptome[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] (Additional file 1: Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eA). The results underline variations in both synonymous (Syn) and non-synonymous (NSyn) mutations, evolutionary conservation and alternative splicing complexity. Figure 3A illustrates the frequency and distribution of NSyn vs. Syn mutations across \u003cem\u003especies\u003c/em\u003e. Comparative analysis of closely related species, including \u003cem\u003eH. sapiens\u003c/em\u003e, \u003cem\u003eM. musculus\u003c/em\u003e, \u003cem\u003eR. norvegicus\u003c/em\u003e and \u003cem\u003eDictyostelium discoideum\u003c/em\u003e, revealed a higher occurrence of NSyn mutations compared to Syn mutations (Additional file 2: Table. S6), particularly in transcripts differentially expressed in large versus small larval reads. This analysis revealed the highest number of mutations in the mammalian family, including \u003cem\u003eH. sapiens\u003c/em\u003e [75NSyn and 36Syn], \u003cem\u003eM. musculus\u003c/em\u003e [75NSyn and 36Syn] and \u003cem\u003eR. norvegicus\u003c/em\u003e [75NSyn and 36Syn], with a considerable spread in \u003cem\u003eD. discoideum\u003c/em\u003e [75NSyn and 36Syn], \u003cem\u003eB. taurus\u003c/em\u003e [75NSyn and 36Syn] and \u003cem\u003eA. thaliana\u003c/em\u003e [75NSyn and 36Syn]. To further examine these non-synonymous mutations, we categorized them by species and filtered mutations with a cumulative count of \u0026ge;\u0026thinsp;5 across all transcripts within each species. High-frequency mutations included TCG (Serine) \u0026rarr; TTG (Leucine) in IF2P [6mut], TTT (Phenylalanine)/TTG (Leucine) \u0026rarr; TCT (Serine)/TCG (Serine) and AAG (Lysine) \u0026rarr; AAT (Asparagine) in RHOG (\u003cem\u003eH. sapiens\u003c/em\u003e) [4mut] and ATA (Isoleucine) \u0026rarr; GTA (Valine) in SBP2 \u003cem\u003e(M. musculus\u003c/em\u003e) [5mut] (Fig.\u0026nbsp;3B; Additional file 2: Table. S7)\u003c/p\u003e\n\u003cp\u003eThe most significant SNP identified across all transcripts was ATC (Isoleucine) \u0026rarr; TTC (Phenylalanine) [100mut], which was consistently observed in \u003cem\u003eH. sapiens\u003c/em\u003e, \u003cem\u003eA. thaliana\u003c/em\u003e, \u003cem\u003eD. discoideum\u003c/em\u003e, \u003cem\u003eB. taurus\u003c/em\u003e, \u003cem\u003eM. musculus\u003c/em\u003e, \u003cem\u003eD. melanogaster\u003c/em\u003e, \u003cem\u003eP. pygmaeus\u003c/em\u003e and \u003cem\u003eR. norvegicus\u003c/em\u003e. This variant was more frequently associated with transcripts related to ubiquitin processing, including UBQ12/11/3/4, UBIQD/I/A/G/H/F, UBB, and UBC. In \u003cem\u003eH. sapiens\u003c/em\u003e ubiquitin-proteasome system (UPS) is crucial for regulating protein stability, cell signaling, and stress response and mutations affecting ubiquitin-related genes can lead to significant consequences for cellular function[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. The Isoleucine-to-Phenylalanine substitution identified across multiple species likely impacts ubiquitin processing, affecting protein structure, ubiquitin binding, and degradation pathways. This mutation replaces a small, aliphatic isoleucine with a bulkier phenylalanine, which may disrupt ubiquitin-protein interactions and proteasomal targeting. Studies show that amino acid substitutions in \u003cem\u003eH. sapiens\u003c/em\u003e can alter ubiquitin-binding efficiency, while isoleucine 44 is identified as a key residue in ubiquitin\u0026rsquo;s hydrophobic patch, crucial for degradation signaling[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].Structural studies suggest that this mutation could introduce steric hindrance, destabilizing ubiquitin interactions, while such changes may also modify degradation kinetics and disrupt protein homeostasis[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. In plants like \u003cem\u003eA. thaliana\u003c/em\u003e, ubiquitin regulates hormone signaling and stress adaptation, whereas in mammals, it is essential for cell cycle regulation, immune response, and neuroprotection[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The conservation of this mutation across species might suggest an evolutionary role in protein homeostasis and cellular signaling (Fig. 3C; Additional file 2: Table. S8). Interestingly, no SNPs were detected in \u003cem\u003eA. glabripennis\u003c/em\u003e, the closest related insect species in our dataset. To explore potential mutations associated with transcript similarity between \u003cem\u003eP. reticularis\u003c/em\u003e and \u003cem\u003eA. glabripennis\u003c/em\u003e, we performed a cross-species mutation analysis. Transcripts from \u003cem\u003eP. reticularis\u003c/em\u003e with \u0026gt;\u0026thinsp;90% sequence similarity to the \u003cem\u003eA. glabripennis\u003c/em\u003e genome were identified and analyzed for SNP occurrences. Comparison of SNPs in \u003cem\u003eP. reticularis\u003c/em\u003e against existing \u003cem\u003eA. glabripennis\u003c/em\u003e transcripts revealed SNPs in TMEM134 (highly conserved across both vertebrates and invertebrates, including \u003cem\u003eD. melanogaster\u003c/em\u003e and \u003cem\u003eC. elegans)\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e], LSM4 (mRNA degradation, RNA splicing and stress granule formation in \u003cem\u003eA. thaliana\u003c/em\u003e)[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], ZC3H13 (mRNA methylation [m6A modification] and RNA stability in vertebrates)[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] and ZMAT2 (RNA splicing regulation via condensate formation, influencing gene expression)[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e], which were not detected in the initial Kissplice analysis. While these transcripts have been well-characterized in mammals, their specific functions in insects remain unclear, though their involvement in fundamental cellular processes suggests a high degree of conservation across species (Fig. 3D; Additional file 2: Table. S9). In addition, we examined SNPs associated with the small and large larval stages and identified shifts in stop codon positioning, which may suggest premature termination of certain transcripts during development (Additional file 1: Fig. \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003eB-E and Additional file 2: Table. S4 and S5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential expression (DE) between the larval stages identifies transcripts linked to development, metabolism, and environmental adaptation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate transcript dynamics during larval development, we aimed to identify the transition and disappearance of transcripts as grubs develop from small to large larval stages-the latter being the recommended stage for consumption. Transcripts from small and large larvae were categorized independently, and differential expression (DE) analysis was performed using DESeq2, edgeR and Limma using the baseline conditions (Fig.\u0026nbsp;4A; Additional file 2: Table. S10-S12). To minimize errors in transcript distribution, we compared the 8,688 common transcripts that were consistently identified as differentially expressed across all three analysis packages with a threshold of Log₂ fold change (log₂FC)\u0026thinsp;\u0026gt;\u0026thinsp;\u0026plusmn;\u0026thinsp;2 and an adjusted p-value (Padj)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This common set of transcripts was considered the true DE transcripts, and the expression values from DESeq2 were used as the reference for downstream analysis. The FPKM distribution analysis revealed distinct expression patterns across the small and large samples. The expression levels ranged from 0\u0026ndash;17 FPKM in the lowest category to 500\u0026thinsp;+\u0026thinsp;FPKM in the highest. Intermediate expression levels were recorded in ranges of 1-306, 8\u0026ndash;15 and 15\u0026ndash;60 FPKM. The highest proportion of transcripts fell within the 15\u0026ndash;60 FPKM range, while fewer genes exhibited very high or very low expression (Fig.\u0026nbsp;4B; Additional file 2: Table. S13). These findings indicate variability in transcript abundance across the tested conditions. From the 8,688 true DE transcripts, we identified the top 8 most transcript associated with both upregulated (UPreg) and downregulated (DWreg) transcripts. The downregulated transcripts were distributed across the fraction, whereas the upregulated transcripts exhibited a clustered, fixed wave pattern. Among the most significant downregulated genes, we identified ERPa, ARYa, Hx, B4GA1, SgAbd2, SMOX (\u003cem\u003eA. glabripennis\u003c/em\u003e), and SOD (\u003cem\u003eColaphellus bowringi\u003c/em\u003e). In contrast, the most significant upregulated genes included SGE1, l(2)efl, CALR, TNNC, ATPaseB (\u003cem\u003eT. castaneum\u003c/em\u003e), CPA (\u003cem\u003eAethina tumida\u003c/em\u003e; \u003cem\u003eTenebrio molitor\u003c/em\u003e), and EEF2 (\u003cem\u003eS. oryzae\u003c/em\u003e) (Fig. 4C; Additional file 2: Table. S14). Key upregulated genes, such as SGE1, CALR, and EEF2, and downregulated genes like ERPa, SOD, and Hx, are linked to metabolism and developmental shifts, consistent with \u003cem\u003eBlattella germanica\u003c/em\u003e transcriptomic analyses[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings reinforce the role of DE genes in species-specific adaptation and developmental regulation.\u003c/p\u003e\n\u003cp\u003eLooking into the \u003cem\u003especies\u003c/em\u003e spread of these 16 DE transcript (both upregulated and downregulated), we identified that they all were from insecta class. Next we grouped the true DE transcript as \u003cem\u003especies\u003c/em\u003e and we identified that the Insecta class exhibited the highest number, with 4125-UPreg and 733-DWreg genes (101-\u003cem\u003especies\u003c/em\u003e). Followed by mamalia with 129-UPreg and 260-DWreg (83-\u003cem\u003especies\u003c/em\u003e), Teleostei with 22-UPreg and 73-DWreg (23-\u003cem\u003especies\u003c/em\u003e), Anthozoa with 2-UPreg and 105-DWreg (7-\u003cem\u003especies\u003c/em\u003e) and Malacostraca with 6-UPreg and 405-DWreg (5-\u003cem\u003especies\u003c/em\u003e) (Fig. 4D; Additional file 2: Table. S15). Within the insect class, \u003cem\u003eA. glabripennis\u003c/em\u003e showed the strongest DE, with 4682-UPreg and 4024-DWreg; Ratio-0.86 transcripts. Other notable \u003cem\u003especies\u003c/em\u003e included \u003cem\u003eT. castaneum\u003c/em\u003e [212-UPreg and 3895-DWreg; Ratio-18.37], \u003cem\u003eD. virgifera virgifera\u003c/em\u003e [364-UPreg and 458-DWreg; Ratio-1.26] and \u003cem\u003eL. decemlineata\u003c/em\u003e [615-UPreg and 699-DWreg; Ratio-1.14], each displaying distinct expression patterns. Interestingly, genes from mammalian \u003cem\u003especies\u003c/em\u003e, including \u003cem\u003eH. sapiens\u003c/em\u003e [3190-UPreg and 2286-DWreg; Ratio-0.72] and \u003cem\u003eM. musculus\u003c/em\u003e [1943-UPreg and 1292-DWreg; Ratio-0.66], were also differentially expressed, suggesting either transcript conservation across \u003cem\u003especies\u003c/em\u003e or possible transcript misalignment[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. \u003cem\u003eA. glabripennis\u003c/em\u003e remained the dominant species with the highest DE count. ACP1, APER1 and Arylphorin A exhibited the highest expression levels, while ACSL5 and AGMO showed average DE (\u0026le;\u0026thinsp;0.10) across all conditions. Endochitinase and KRT1 were DE, with Endochitinase falling from 0.17 to 0.00. Transcripts, such as PER1, B4GAT1, exhibited near-zero/minimum expression across all conditions, suggesting limited or no activation. The significant number of differentially expressed genes in insects suggests strong adaptive transcriptomic responses, potentially linked to development, metabolism, and environmental adaptation (Fig. 4E; Additional file 2: Table. S16 and S17). The analysis revealed that the Insecta class exhibited the highest number of DE transcripts, highlighting a strong adaptive transcriptomic response in larval development. \u003cem\u003eA. glabripennis\u003c/em\u003e showed the most significant DE, suggesting species-specific regulatory mechanisms similar to findings in other insect studies[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. The distribution of upregulated and downregulated transcripts across taxa, particularly in Mammalia and Teleostei, may indicate conserved genetic pathways or misalignment issues[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. The dataset was also analyzed across biological replicates for both the small and large grubs, and the resulting differential expression patterns were consistent with our overall analytical strategy. One of the three replicates appeared as an outlier, which may suggest that the corresponding grubs (n\u0026thinsp;=\u0026thinsp;10) was collected either at the beginning or end of the targeted developmental stage-potentially influencing its expression profile[\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] (Additional file 1: Fig. \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein profiling and domain characterization highlight developmental differences and enrichment in large\u003c/strong\u003e \u003cstrong\u003eP. reticularis\u003c/strong\u003e \u003cstrong\u003elarvae\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the functional relevance of the identified transcripts, the sequences were translated into protein sequences (with isoforms) and profiled for their characteristics and domain composition. The upregulated transcripts from both large and small larval stages were analyzed for their Open Reading Frame (ORF) types, which define the completeness of the coding sequence (CDS) within the transcript. The data presented focuses solely on upregulated transcripts, which have greater impact in transcripts derived protein annotation for our analysis. The distribution of ORF types revealed distinct proportions of complete and partial transcripts, with an increase in complete proteins from 18.1\u0026ndash;25.9% during the small-to-large larval transition. Conversely, 3\u0026prime;-partial transcripts (lacking a stop codon) decreased from 10.7\u0026ndash;9.8%, while 5\u0026prime;-partial transcripts (lacking a proper start codon) decreased from 23.2\u0026ndash;22.6%. Notably, there was a decrease in internal ORFs from 48\u0026ndash;41.7%, representing transcript fragments that lack both start and stop codons (Fig.\u0026nbsp;5A; Additional file 2: Table. S18 and S19). The increase in complete proteins during the small-to-large larval transition suggests enhanced transcript integrity and maturation. Concurrent decreases in partial and internal ORFs indicate improved mRNA processing and stability, potentially optimizing protein synthesis[\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. The translated transcripts also underwent signal peptide prediction analysis, revealing 974 common proteins across both small and large datasets. Additionally, 7,106 proteins were uniquely identified in the large dataset and 4,814 in the small dataset, with localization to the cytoplasm or nucleus. Further classification of secretory signal peptides (Sec pathway) identified 381 proteins in the large dataset and 319 in the small dataset, with an overlap of 10 proteins containing Signal Peptidase I (SP) sites. Similarly, transmembrane proteins included 46 unique proteins in the large dataset and 10 in the small dataset, with Signal Peptidase II (LIPO) sites. For the Tat pathway (Twin-arginine translocation), 15 proteins were detected in the large dataset and 1 in the small dataset, with minimal representation in the latter (Fig.\u0026nbsp;5B; Additional file 2: Table. S18 and S19). The signal peptide prediction analysis highlights a dynamic shift in protein localization and secretion between developmental stages. The larger dataset exhibits a higher diversity of secretory and transmembrane proteins, suggesting increased cellular complexity and specialized functions[\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. Notably, the Sec and Tat pathways show greater representation in the large dataset, indicating enhanced protein transport activity[\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. The overlap of proteins with Signal Peptidase I and II sites suggests conserved secretion mechanisms despite developmental differences[\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eTo identify transcripts with a high likelihood of mitochondrial localization, we utilized TargetP-2.0, annotating proteins with a mitochondrial targeting peptide (mTP) probability of \u0026ge;\u0026thinsp;0.75 for both small and large larval stages. This analysis revealed a subset of translated transcripts with compelling mitochondrial targeting probabilities. In the large larval stage, the top-ranked mitochondrial-targeted proteins included RNA-directed DNA polymerase (mTP: 0.93), FLYWCH-type zinc finger-containing protein 1 (mTP: 0.96), and NADH-quinone oxidoreductase subunit C (mTP: 0.96). Similarly, in the small larval stage, proteins such as Intersectin-2 (mTP: 0.99), Cation efflux system protein CzcA (mTP: 0.99), Nuclear factor NF-kappa-B p105 subunit (mTP: 0.99), and putative replication protein A (mTP: 0.99) exhibited high mitochondrial targeting probabilities. In addition to mitochondrial targeting, we assessed the presence of signal peptides (SP), indicative of potential roles in secretion or membrane targeting. Proteins such as Golgin subfamily A member 4 (SP: 0.21) and Blue-light-activated protein (SP: 0.24) displayed SP probabilities exceeding 0.20, suggesting potential involvement in extracellular transport or membrane-associated functions in both larval stages. To further characterize protein processing, cleavage site (CS) predictions were conducted, identifying conserved cleavage motifs at positions 27\u0026ndash;28, 29\u0026ndash;30, and 50\u0026ndash;51. Notably, unique cleavage patterns were observed in key genes, including RNA-directed DNA polymerase (51\u0026ndash;52, HHY-ST), FLYWCH-type zinc finger-containing protein 1 (29\u0026ndash;30, IPW-TA), and Putative replication protein A (30\u0026ndash;31, RAC-SK)[\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. Mitochondrial-targeted proteins play key roles in insect larval development, influencing metabolism, immune responses, and cellular regulation. RNA-directed DNA polymerase supports transcriptome regulation in migratory insects[\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e], while NADH-quinone oxidoreductase subunit C contributes to energy metabolism in \u003cem\u003eSpodoptera frugiperda\u003c/em\u003e larvae[\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e]. FLYWCH-type zinc finger proteins likely regulate developmental genes, though their role in larvae remains underexplored. Intersectin-2 is linked to cell proliferation in developmental tissue morphogenesis[\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. CzcA aids larval stress responses via metal ion regulation[\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e], while NF-kappa-B p105 is crucial for immune function during development[\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. Lastly, Replication Protein A plays a role in DNA maintenance and cell cycle regulation[\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. These proteins collectively support critical developmental transitions in insect larvae, warranting further investigation. Comparative analysis between small and large larval stages indicated that large larvae exhibited slightly higher mitochondrial targeting probabilities, clustering around 0.85\u0026ndash;0.95. In contrast, small larvae proteins demonstrated greater variability in signal peptide presence, with several proteins displaying SP values exceeding 0.20 (Fig.\u0026nbsp;5C; Additional file 2: Table. S20 and S21). The mitochondrial targeting analysis highlights stage-specific differences in protein localization, with large larvae showing higher mitochondrial targeting probabilities, while small larvae exhibit more variability in signal peptide presence. These findings suggest developmental regulation of mitochondrial function and protein trafficking. Cleavage site variations further indicate differential protein processing mechanisms between larval stages[\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional annotation reveals metabolic and protein biosynthesis pathways underpinning development in large\u003c/strong\u003e \u003cstrong\u003eP. reticularis\u003c/strong\u003e \u003cstrong\u003elarvae\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate potential biological roles of the identified transcripts, a comprehensive functional annotation (Ann) analysis was conducted. Figure\u0026nbsp;6A (Additional file 2: Table. S22 and S23). illustrates the overlap of transcript annotations across non-redundant (NR) (181,108Ann) (Additional file 2: Table. S24), SwissProt (112,506Ann) (Additional file 2: Table. S25), Kyoto Encyclopedia of Genes and Genomes (KEGG) (34,804Ann) (Additional file 2: Table. S26 and Additional File 1: Fig. S6) and Clusters of Orthologous Genes (COG) (84,111Ann) (Additional file 2: Table. S27 and Additional File 1: Fig. S4) databases. The analysis was conducted using only the upregulated transcript list to maintain the biological functional repertoire. A large number of transcripts were annotated in multiple databases, indicating high functional consistency, with 20,658 common annotations identified for further downstream analysis. To enhance functional characterization, InterPro (61,988Ann) (Additional file 2: Table. S28) and Gene Ontology (GO) (109,936Ann) (Additional file 2: Table. S29 and Additional File 1: Fig. S5) annotations were analyzed to identify common terms. Figure\u0026nbsp;6B highlights the distribution of annotations, with InterPro classifications further divided into PRINTS (27,498Ann), ProSitePatterns (27,196Ann), ProSiteProfiles (6,906Ann), and MobiDBlite (1,327Ann). Among these, MobiDBlite exhibited a low spread of counts, indicating a weak representation of intrinsically disordered proteins[\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]. The distribution of clusters in Fig.\u0026nbsp;6C highlights key biological functions with varying levels of representation. The clustering of GO terms in this dataset reveals key functional trends, emphasizing membrane transport, structural components, and signal transduction, while metabolic and enzymatic activities appear underrepresented. The dominance of membrane transport (Cluster 1, n\u0026thinsp;=\u0026thinsp;34), with highly represented terms such as translation (GO:0006412, Transcript\u0026thinsp;=\u0026thinsp;129) and proteolysis (GO:0006508, Transcript\u0026thinsp;=\u0026thinsp;116), highlights the importance of protein synthesis and degradation in cellular transport systems, aligning with findings that membrane transport proteins are crucial for maintaining cellular homeostasis and intercellular interactions[\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. Additionally, sarcomere organization (GO:0045214, Transcript\u0026thinsp;=\u0026thinsp;82) within this cluster suggests a strong relationship between muscle function and transport mechanisms, particularly in excitable tissues like neurons and muscle fibers[\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. In contrast, structural components (Cluster 0, n\u0026thinsp;=\u0026thinsp;11), represented by carbohydrate metabolic processes (GO:0005975, Transcript\u0026thinsp;=\u0026thinsp;141, Cluster 7, n\u0026thinsp;=\u0026thinsp;4), indicate that carbohydrate metabolism plays a role not just in energy production but also in cell wall integrity and structural homeostasis[\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]. Likewise, protein folding (GO:0006457, Transcript\u0026thinsp;=\u0026thinsp;79, Cluster 2, n\u0026thinsp;=\u0026thinsp;1) underscores the significance of chaperone-mediated protein assembly in maintaining protein stability under stress conditions[\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]. Additionally, nucleotide binding (GO:0005525, Transcript\u0026thinsp;=\u0026thinsp;203, Cluster 4) supports the regulatory function of ATP/GTP-binding proteins in signaling (Cluster 7) and energy production/conversion (Cluster 5, n\u0026thinsp;=\u0026thinsp;2) exhibiting low representation, suggesting that these processes may be more developmentally or tissue-specific rather than universally expressed[\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e] (Fig.\u0026nbsp;6C; Additional file 2: Table. S30).\u003c/p\u003e\n\u003cp\u003eTo functionally characterize the upregulated transcripts, GO term enrichment analysis was conducted, highlighting key biological processes, cellular components, and molecular functions. The analysis revealed notable representation of translation (GO:0006412; Transcript\u0026thinsp;=\u0026thinsp;49), protein folding (GO:0006457; Transcript\u0026thinsp;=\u0026thinsp;10), and mRNA splicing via the spliceosome (GO:0000398; Transcript\u0026thinsp;=\u0026thinsp;26). These findings align with those observed in \u003cem\u003eHermetia illucens\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e] and \u003cem\u003eRhodnius prolixus\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e] where protein biosynthesis and post-transcriptional regulation have been identified as essential for rapid growth and developmental processes in insect species. The glycolytic process (GO:0006096; Transcript\u0026thinsp;=\u0026thinsp;29) was also enriched, reflecting high metabolic activity in larval and pupal stages, as observed in \u003cem\u003eB.mori\u003c/em\u003e and \u003cem\u003eSpodoptera litura\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]. Among cellular components, the cytoplasm (GO:0005737; Transcript\u0026thinsp;=\u0026thinsp;395) was the most abundant, underscoring its role as a site for biochemical and enzymatic reactions in insects such as \u003cem\u003eRhynchophorus ferrugineus\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]. The plasma membrane (GO:0005886; Transcript\u0026thinsp;=\u0026thinsp;192) and mitochondrion (GO:0005739; n\u0026thinsp;=\u0026thinsp;88) were also highly represented, aligning with their roles in nutrient transport and ATP generation, which are essential for the energy-intensive development of \u003cem\u003eH. illucens\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. The identification of the proteasome complex (GO:0000502; Transcript\u0026thinsp;=\u0026thinsp;12) and ribosome (GO:0005840; Transcript\u0026thinsp;=\u0026thinsp;33) further supports an environment of high protein turnover, crucial for molting and metamorphosis in species like \u003cem\u003eEpicauta chinensis\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e] and \u003cem\u003eHippodamia convergens\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e]. Within molecular functions, the enrichment of ATP binding (GO:0005524; Transcript\u0026thinsp;=\u0026thinsp;266) and metal ion binding (GO:0046872; Transcript\u0026thinsp;=\u0026thinsp;148) suggests extensive reliance on ATP-driven enzymatic activities and metal cofactor-mediated biochemical reactions, which are essential for nutrient assimilation and metabolism in edible insect species[\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. The presence of GTPase/ATPase activity (GO:0003924, GO:0042626; Transcript\u0026thinsp;=\u0026thinsp;60) and oxidoreductase activity (GO:0016705; Transcript\u0026thinsp;=\u0026thinsp;7) highlights the importance of mitochondrial-dependent energy metabolism and redox homeostasis, particularly in fast-growing insect larvae such as \u003cem\u003eT. molitor\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. Additionally, ion binding (GO:0008270; Transcript\u0026thinsp;=\u0026thinsp;96) was enriched, indicating its role in enzyme stability and metabolic regulation across multiple edible insect species[\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e] (Fig. 6D; Additional file 2: Table. S31). Interestingly, general transport processes (GO:0006811, GO:0046961, GO:0015078; Transcript\u0026thinsp;=\u0026thinsp;16) were moderately represented, suggesting selective nutrient uptake and distribution rather than bulk transport, a feature observed in insects such as \u003cem\u003eDendroctonus valens\u003c/em\u003e during developmental transitions[\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e]. Developmental processes (GO:0007517, GO:0007424; Transcript\u0026thinsp;=\u0026thinsp;45) were selectively enriched, reinforcing their importance in growth, metamorphosis, and reproductive cycles, particularly in species like \u003cem\u003eB. mori\u003c/em\u003e and \u003cem\u003eR. ferrugineus\u003c/em\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e]. Overall, the dataset reflects a functional landscape optimized for protein biosynthesis, metabolic regulation, and energy production, with additional contributions from transport systems, enzymatic activity and developmental processes. GO term enrichment analysis across biological replicates revealed minimal or no significant differences in the enriched terms, indicating high consistency between replicates (Additional File 1: Fig. S4) for both upregulated and downregulated transcripts, as detailed in the supplementary data. The results suggest a efficient metabolic framework in the large \u003cem\u003eP. reticularis\u003c/em\u003e larvae, supporting their suitability as alternative protein sources.\u003c/p\u003e\n\u003cp\u003eFurthermore, SSR (Simple Sequence Repeats, also known as microsatellites) analysis identified numerous microsatellite loci within the transcriptome, including pure, compound, and interrupted repeats (Additional file 2: Table. S32 and S33). These SSRs are part of genes being actively expressed, so they\u0026apos;re potentially linked to important functional traits, especially in non-model organisms where genome data may be limited[\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e]. Using Primer3, specific primers were designed to flank these SSRs, enabling their amplification. These EST-SSR (Expressed Sequence Tag-SSRs) markers, derived from expressed genes, offer valuable tools for population genetics and trait association studies. The integration of SSR and primer analysis enhances the utility of transcriptome data for downstream functional and evolutionary investigations[\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first comprehensive transcriptomic characterization of \u003cem\u003eP. reticularis\u003c/em\u003e, revealing significant genetic and molecular differences between small and large larval stages. Recent studies have highlighted the nutritional and functional potential of \u003cem\u003eP. reticularis\u003c/em\u003e larvae and pupae, supporting its viability as a nutritious alternative protein source[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. For example, Huhu large larvae exhibited high protein content and a nutritionally favourable profiles of amino acid, fatty acid, and minerals, making them a rich nutritional resource. The findings in the current study, align with the transcriptomic data insights indicating enhanced protein biosynthesis and metabolic efficiency in Huhu large larvae, reinforcing their superior suitability for human consumption[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Functional annotation and protein domain analysis indicate that large \u003cem\u003eP. reticularis\u003c/em\u003e larvae have higher metabolic activity, enhanced protein biosynthesis, and structural integrity. GO analysis revealed increased protein biosynthesis (GO:0006412), ribosome biogenesis (GO:0005840), and ATP-binding activity (GO:0005524) in large \u003cem\u003eP. reticularis\u003c/em\u003e larvae, suggesting greater protein turnover and nutrient assimilation efficiency[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Additionally, oxidoreductase activity (GO:0016705) and mitochondrial electron transport chain components were enriched, indicating improved energy metabolism-critical for biomass accumulation and growth[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Similar trends have been observed in edible insects like \u003cem\u003eH. illucens\u003c/em\u003e and \u003cem\u003eT. molitor\u003c/em\u003e, where larger developmental stages exhibit higher protein content and metabolic efficiency[\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Protein domain characterization further supports the superiority of large larvae. The proportion of complete ORFs increased from 18.1\u0026ndash;25.9%, suggesting greater transcript stability and enhanced protein integrity. Additionally, secretory and transmembrane proteins were significantly enriched, supporting improved nutrient transport and structural composition. These findings align with research on insect larvae, where later developmental stages accumulate more essential amino acids, unsaturated fatty acids, and bioactive compounds[\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. The study also identified a key Isoleucine-to-Phenylalanine mutation in ubiquitin-associated proteins, which may impact protein degradation pathways and metabolic regulation. Given the absence of this mutation in \u003cem\u003eA. glabripennis\u003c/em\u003e, the closest relative, it suggests a potential evolutionary advantage in \u003cem\u003eP. reticularis\u003c/em\u003e[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Additionally, the enrichment of chitin metabolism genes (GO:0005975) and structural protein domains suggests that large \u003cem\u003eP. reticularis\u003c/em\u003e larvae has a more developed exoskeleton and improved biochemical defense mechanisms[\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Overall, the transcriptomic data indicate that large larvae have optimized metabolic pathways, enhanced protein synthesis, and greater structural stability. Future research should validate these findings through proteomic and metabolomic studies to further explore their nutritional potential and bio-functional properties. As interest in alternative proteins grows, these insights will be crucial to the sustainable utilization of edible insects as source of alternative proteins.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSample sourcing and collection\u003c/h2\u003e \u003cp\u003e \u003cem\u003eP. reticularis\u003c/em\u003e larvae (both small and large) used in this study were obtained from colonies established using field populations collected from decomposing pine (\u003cem\u003ePinus radiata\u003c/em\u003e) logs. Grubs were sourced from Flagstaff (Three Mile Hill), near Dunedin in the Otago Region, New Zealand (latitude 45.8656, longitude 170.3785) to form the small and large larval groups, respectively (Fig.\u0026nbsp;1C). At the sample collection site, at least three grubs were sampled per log to ensure representative population coverage. \u003cem\u003eP. reticularis\u003c/em\u003e larvae were identified based on distinct physical characteristics: small larvae possess two-chambered spiracles and shorter terminal triangular spines and setae on abdominal segments[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], while larger larvae display single spiracles and longer terminal triangular spines and setae (Fig.\u0026nbsp;1D). To collect the larvae, decomposing logs were broken open, and grubs were placed in sterile containers. The grubs were rinsed with 1X PBS to remove residual dirt, followed by one rinse with 1% RNase Away and three rinses with DEPC-treated water. Samples (n\u0026thinsp;=\u0026thinsp;10), each containing three replicates of small and large larvae, were preserved in TRIzol\u0026reg; reagent (Invitrogen, USA) and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further analysis (Fig.\u0026nbsp;1E).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from small and large huhu samples in triplicate using the NucleoSpin RNA Isolation Kit (Macherey-Nagel) following the manufacturer\u0026rsquo;s instructions. RNA concentration (ng/\u0026micro;L) and purity were determined using a NanoDrop spectrophotometer (Thermo Fisher Scientific) and the Qubit RNA HS Assay Kit (Thermo Fisher Scientific) for each sample set. Optimal purity was defined by 260/280 and 260/230 ratios of approximately 2.0, with an ideal large-to-small RNA ratio of 7:3. RNA integrity was assessed using an Agilent Bioanalyzer (Agilent), and only samples with a RIN score greater than 8.5 were selected for downstream analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLibrary preparation and quality control\u003c/h3\u003e\n\u003cp\u003eLibrary preparation for Illumina sequencing was initiated with 1\u0026micro;g of total RNA per sample. Following the manufacturer's protocol, poly(A) mRNA was isolated using either the poly(A) mRNA magnetic isolation module or the rRNA removal kit. mRNA fragmentation and priming were performed using first-strand synthesis reaction buffer and random primers. First-strand cDNA synthesis employed ProtoScript II reverse transcriptase, followed by second-strand synthesis with a second-strand synthesis enzyme mix. Purified double-stranded cDNA underwent end repair and dA-tailing in a single reaction using an end-prep enzyme mix, followed by T-A ligation to attach adaptors at both ends. Size selection of adaptor-ligated fragments (~\u0026thinsp;400 bp, with ~\u0026thinsp;300 bp insert size) was performed using magnetic beads. PCR amplification was carried out with P5 and P7 primers, which included flow cell annealing sequences and multiplexing indices. PCR products were purified with beads, validated on a Qsep100 instrument (Bioptic, Taiwan), and quantified with a Qubit 3.0 Fluorometer (Invitrogen, USA) (Fig.\u0026nbsp;1E). Indexed libraries were pooled and sequenced on the Illumina NovaSeq (Illumina, USA) and MGI2000 (MGI, China) platforms, following the respective manufacturers' protocols. Illumina sequencing generated 300bp paired-end reads from reverse-stranded RNA enriched in poly(A). Raw sequencing data were processed using Illumina's bcl2fastq2 pipeline v2.20.0. Quality assessment was performed using FastQC v0.11.9[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], and reads passing the baseline quality cutoff were retained. Low-quality bases and adapter sequences were trimmed using Trimmomatic v0.40[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. All bioinformatic analyses were conducted with data and literature available as of February 18th, 2025. The scripts utilized in this study are provided as supplementary materials (Additional File 3) to ensure reproducibility.\u003c/p\u003e\n\u003ch3\u003eAssembly, ORF prediction and redundancy-similarity search\u003c/h3\u003e\n\u003cp\u003eTo generate a reference assembly for differential expression and functional annotation, RNA-seq data from small and large \u003cem\u003eP. reticularis\u003c/em\u003e larvae were combined and assembled using Trinity v2.15.1[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. Each dataset (small and large larvae) was also assembled individually for comparative analysis. Paired-end reads were processed separately, with left and right reads combined prior to assembly. To identify and remove redundant sequences from the assembled transcriptome, CD-HIT v4.8.1[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e] was used. The sequences were then clustered based on a specified similarity threshold, allowing for the reduction of redundancy and the retention of unique sequences. The quality of the assembled transcriptome was evaluated using Transrate v1.0.3[\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e], BUSCO v5.7.0[\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], and rnaQUAST v2.3[\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e] to identify low-quality or misassembled sequences[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. The assembled transcripts were connected via sequence clustering into long non-redundant unigene sequences using the Unigene database[\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. Principal Component Analysis (PCA) was conducted on transcriptomes of small and large samples to evaluate clustering and variability using R v4.4.2[\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] with the 'corrr' function. Open reading frames (ORFs) in the Trinity-assembled transcripts were identified with TransDecoder v5.0.2[\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. RSEM v1.3.3[\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e] (RNA-Seq by Expectation-Maximization) was utilized for quantifying gene and isoform abundances.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAlignment, splicing and species curation\u003c/h2\u003e \u003cp\u003eThe assembled transcripts were investigated by comparing them to the NCBI NR[\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], SwissProt[\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], GO[\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e], KEGG[\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e] and COG[\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e] database using BLASTX v2.6.0+[\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e] with an e-value cutoff of 1e-20 and used to determine the species corresponding to each transcript ID. The number of transcripts identified for each species was compared against the unigene count. The significant species relationships were analyzed using OrthoFinder[\u003cspan additionalcitationids=\"CR102\" citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], and an evolutionary tree was visualized with Dendroscope[\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. Alternative splicing events and single nucleotide polymorphisms (SNPs) were identified using KisSplice v2.6.0[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which performs local assembly of RNA-seq reads and detects variants independently of a reference genome. The predicted SNPs were positioned along the Trinity-assembled transcripts using BLAT v37.1[\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. SNPs that did not map to the Trinity transcripts (orphan SNPs) were separated. The functional impact of SNPs was assessed using KisSplice2RefTranscriptome v1.3.3[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Differences in allele composition were recorded as \u0026ldquo;inconsistent\u0026rdquo; and evaluated for potential sequencing errors or artifacts from assembly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferential expression and clustering\u003c/h2\u003e \u003cp\u003eExpression levels were estimated by aligning RNA-Seq reads to the transcriptome providing quantification of gene expression by using Bowtie2 v2.5.4[\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. Differential expression analysis was performed using Corset v1.0.9[\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. The counts were clustered based on their mapping coordinates, and expression levels for each cluster were calculated, enabling the identification of differentially expressed genes between the experimental conditions. The count values from corset were analyzed using three differential expression R packages: DESeq2[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], edgeR[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and limma[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Transcripts were clustered based on those commonly identified across the three packages, with expression values assigned from the DESeq2 package. Differentially expressed genes were identified using a log2FC threshold of \u0026plusmn;\u0026thinsp;2 and an adjusted p-value (Padj) cutoff of \u0026lt;\u0026thinsp;0.01. The transcripts were categorized based on NR annotation at different taxonomic levels, including class, species, and specifically down to \u003cem\u003eA. glabripennis\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnnotation, domain characterics and functional relevance\u003c/h2\u003e \u003cp\u003eGOSeq v1.34.1[\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e] was used to identify Gene Ontology (GO) terms from a list of enriched genes with a significant padj\u0026thinsp;\u0026lt;\u0026thinsp;0.01. The annotated proteins were further validated in InterProScan v5.72-103.0[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] to functionally annotate the assembled transcripts, using the \u003cem\u003e-goterms\u003c/em\u003e and \u003cem\u003e-pathways\u003c/em\u003e plugins and the UniProtKB database[\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e] to screen for potential allergens, disease associations, drug targets, virus-host interactions, and toxicological implications. Transmembrane helices in the annotated protein sequences were predicted using DeeptmHMM v1.0.24[\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e], while SignalP v6.0[\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e] was utilized for the identification of signal peptides. To assess the presence of N-terminal presequences, the TargetP-2.0[\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e] was utilized, classifying sequences into signal peptides (SP), mitochondrial transit peptides (mTP), chloroplast transit peptides (cTP), or thylakoid luminal transit peptides (lTP). SSRs were identified from the assembled unigenes using MISA[\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e], TRF[\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e], and mreps[\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e], with only consensus results retained. Primer3[\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e] was then used to design primers flanking SSR regions for downstream validation and marker development for further studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQuantification and statistical analysis\u003c/h2\u003e \u003cp\u003eThe data and visualization were prepared, plotted, or designed using Prism 9.0 (GraphPad), 'ggplot2' package[\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e] from R v4.4.2[\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e] or Gephi. The graphs were exported as TIFF or JPEG files. Statistical analyses were conducted using (GraphPad) Prism 9 or R. p-value was calculated from the individual databases/packages/software\u0026rsquo;s with no corrections in datasets. Corrections were introduced during analysis of each dataset. Significant results were determined by a p-value of \u0026lt;\u0026thinsp;or \u0026le;\u0026thinsp;0.01.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material available at\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditional file 1. Supplementary figures.\u003c/p\u003e\n\u003cp\u003eAdditional file 2. Supplementary tables containing results of analyses performed in manuscript.\u003c/p\u003e\n\u003cp\u003eAdditional file 3. Supplementary file with the code used to generate the results and figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research project was discussed with the Ngāi Tahu Research Consultation Committee in relation to research on indigenous Huhu grubs and agreed upon.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe article/Additional file includes the original contributions to the study. The codes used to generate the results and figures (under University of Otago/Ludwig Maximilian University of Munich license), as well as generated data/results are presented in Additional file 3. No publicly available samples were used for this study. The raw sequencing data from this study is available in \u0026nbsp;Aotearoa Genomic Data Repository and will be provided upon request (\u003cem\u003eAlligning\u003c/em\u003e\u003cem\u003e\u0026nbsp;with the principles of Māori Data Sovereignty\u003c/em\u003e). For further information, please contact the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Food Science, University of Otago, 9054 Dunedin, New Zealand. \u003csup\u003e2\u003c/sup\u003eDepartment of Microbiology and Immunology, University of Otago, 9054 Dunedin, New Zealand. \u003csup\u003e3\u003c/sup\u003eDepartment of Biochemistry, University of Otago, 9054 Dunedin, New Zealand. \u003csup\u003e4\u003c/sup\u003eGenomics Aotearoa, Department of Biochemistry, University of Otago, New Zealand. \u003csup\u003e5\u003c/sup\u003eLudwig-Maximilians University, Department of Chemistry, Institute of Chemical Epigenetics - Munich (ICEM), 81377 Munich, Germany. \u003csup\u003e6\u003c/sup\u003ePresent address: Department of Wine, Food and Molecular Biosciences, Lincoln University, New Zealand. \u003csup\u003e7\u003c/sup\u003ePresent address: Ludwig-Maximilians University, Department of Chemistry, Institute of Chemical Epigenetics - Munich (ICEM), 81377 Munich, Germany. \u003csup\u003e8\u003c/sup\u003ePresent address: Cincinnati Children\u0026apos;s Hospital Medical Center, Cincinnati, Ohio, United States. \u003csup\u003e9\u003c/sup\u003ePresent address: School of Chemistry, Monash University, Clayton, 3800, Victoria, Australia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBennett Henzeler (Master\u0026rsquo;s and PhD) and Ruchita Rao Kavle (PhD) were funded by the University of Otago with scholarships. Bennett Henzeler, Sabine Schneider and Pascal Giehr are funded by CRC 1309 (ID: 325871075) with Institute of Chemical Epigenetics - Munich (ICEM) server and the BMBF Cluster4Future program (Cluster for Nucleic Acid Therapeutics Munich, CNATM, ID: 03ZU1201AA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.R.K, B.H, N.F, and D.A conceived the study and designed the experiments. R.R.K and B.H conducted the experiments. R.R.K, B.H, N.F, P.G, and C.K analyzed the data. B.H wrote and revised the manuscript. D.A and N.F supervised the study. N.F, A.E.A.B, A.C, S.S and P.G provided guidance throughout the analysis. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRuchita Rao Kavle is grateful to the City Forest, Dunedin, for allowing access to decaying logs to enable harvesting of Huhu grubs. The authors acknowledge Ngāi Tahu Research Consultation Committee for their help throughout this project. We also appreciate the support from New Zealand eScience Infrastructure (NeSI): Dinindu Senanayake, Matt Bixley and ICEM: Sabine Schneider, Pascal Giehr and Markus M\u0026uuml;ller - for providing access to servers for our analysis. We thank Annabel Whibley for helping with technical troubleshooting in the early stages of the project. We also thank Louisa Sophie Dunser, Raheleh Salehi and Henning Nissan for their help with brainstorming and troubleshooting the analysis.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKim TK, Yong HI, Kim YB, Kim HW, Choi YS: \u003cstrong\u003eEdible Insects as a Protein Source: A Review of Public Perception, Processing Technology, and Research Trends.\u003c/strong\u003e \u003cem\u003eFood Sci Anim Resour \u003c/em\u003e2019, \u003cstrong\u003e39:\u003c/strong\u003e521-540.\u003c/li\u003e\n\u003cli\u003eKavle RR, Carne A, Bekhit AE-DA, Kebede B, Agyei D: \u003cstrong\u003eMacronutrients and mineral composition of wild harvested Prionoplus reticularis edible insect at various development stages: nutritional and mineral safety implications.\u003c/strong\u003e \u003cem\u003eInternational Journal of Food Science \u0026amp; Technology \u003c/em\u003e2022, \u003cstrong\u003e57:\u003c/strong\u003e6270-6278.\u003c/li\u003e\n\u003cli\u003eKavle RR, 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\u003cstrong\u003e40:\u003c/strong\u003ee115.\u003c/li\u003e\n\u003cli\u003eWickham H: \u003cem\u003eggplot2: Elegant Graphics for Data Analysis.\u003c/em\u003e New York: Springer-Verlag; 2016.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6564832/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6564832/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Huhu grub (\u003cem\u003ePrionoplus reticularis\u003c/em\u003e), an edible beetle larva endemic to New Zealand, has been traditionally consumed by Māori, the indigenous people of New Zealand. Despite its nutritional significance as an excellent source of proteins, little is known about the molecular mechanisms governing its developmental transitions. This study delivers the first \u003cem\u003ede novo\u003c/em\u003e transcriptome assembly of \u003cem\u003eP. reticularis\u003c/em\u003e and investigates differential gene expression between its small and large larval stages, aiming to uncover their metabolic capabilities and potential contributions to human dietary protein.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparative transcriptomic analysis revealed distinct gene expression patterns between small and large larvae, with large larvae exhibiting enhanced protein biosynthesis, metabolic activity, and mitochondrial function. A conserved isoleucine-to-phenylalanine mutation was detected in ubiquitin-processing genes, suggesting potential impacts on protein degradation pathways. Functional annotation revealed an enrichment of membrane transport proteins, secretory peptides, and metabolic regulators in large larvae, indicating improved nutrient assimilation and physiological adaptation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides novel insights into the molecular basis of \u003cem\u003eP. reticularis\u003c/em\u003e development, demonstrating that large larvae possess superior metabolic efficiency, enhanced protein integrity and increased biosynthetic activity. These findings corroborate previous research on the nutritional composition of the Huhu grub. They also lay the groundwork for future proteomic and metabolomic studies aimed at evaluating the nutritional potential, bioactive properties and allergenic safety of proteins derived from Huhu grubs.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Ruchita Rao Kavle and Bennett Henzeler contributed equally to this work.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Deciphering Huhu (Prionoplus reticularis) grub development: Transcriptomic insights into metabolic and nutritional shifts in larval stages","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-02 07:24:04","doi":"10.21203/rs.3.rs-6564832/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b00c199b-ca2e-47e5-86e2-ab4602e87a0b","owner":[],"postedDate":"May 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-12T08:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-02 07:24:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6564832","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6564832","identity":"rs-6564832","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}