Diet-Associated Gut Microbiome Signatures in Pediatric Phenylketonuria: A Shotgun Metagenomic Study from Ecuador

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Aguirre, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9010202/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Phenylketonuria (PKU) requires lifelong phenylalanine (Phe) restriction to prevent neurotoxicity. Dietary management may remodel the gut microbiome, with potential clinical implications. We characterized taxonomic and functional features of the gut microbiome in pediatric PKU patients managed with Phe-restricted versus non-restricted diets in Ecuador. Methods We performed a cross-sectional exploratory shotgun metagenomic analysis of 15 PKU patients (non-restricted diet, n = 9; Phe-restricted diet, n = 6). Taxonomic profiles were resolved to the species level, and functional potential was assessed using KEGG orthologs and CAZy enzyme families. Community structure was evaluated using Bray–Curtis-based ordination analyses. Results Both groups were dominated by Firmicutes and Bacteroidetes, with lower representation of Actinobacteria, Proteobacteria, and Verrucomicrobia. At the genus and species levels, non-restricted samples (G1) were characterized by multiple short-chain fatty acid–associated Firmicutes, including Ruminococcus , Oscillibacter , Clostridium spp., Faecalibacterium sp. CAG74, and Subdoligranulum . In contrast, Phe-restricted samples (G2) showed recurrent enrichment of Bacteroides uniformis , Bacteroides vulgatus , Eggerthella lenta , and Bilophila wadsworthia . Ordination analyses demonstrated diet-associated stratification, with tighter clustering in G1 and greater dispersion in G2. Functionally, G2 exhibited higher relative abundance of carbohydrate-active enzymes—particularly glycoside hydrolases and glycosyltransferases—and increased KEGG orthologs related to membrane transport and signal transduction. Conclusions In this Ecuadorian pediatric cohort, dietary management in PKU is associated with distinct taxonomic and functional gut microbiome profiles. These hypothesis-generating findings support integrating microbiome analyses into PKU care and motivate larger longitudinal studies in under-represented Latin American settings. Health sciences/Diseases Health sciences/Gastroenterology Biological sciences/Microbiology Phenylketonuria metagenomic analysis gut microbiome nutritional therapy shotgun metagenomics pediatric metabolic disease Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Synopsis Dietary phenylalanine restriction in pediatric phenylketonuria is associated with distinct species-level and functional gut microbiome signatures, supporting microbiome-aware strategies to optimize dietary management in under-represented Latin American settings. Introduction The human gastrointestinal tract harbors a dense and diverse microbial community that supports host metabolism, intestinal barrier integrity, and immune regulation through fermentation of dietary substrates, colonization resistance, and signaling to epithelial and immune cells [ 1 , 2 ]. The gut microbiome—comprising microbial genomes and their metabolic outputs—has broad implications for metabolic, immune, and neurological health and is typically dominated by Firmicutes and Bacteroidetes, with relevant contributions from Actinobacteria, Proteobacteria, Fusobacteria, and Verrucomicrobia [ 2 , 3 ]. In children, microbiome structure is especially sensitive to dietary patterns, environment, and comorbidities [ 1 – 3 , 18 – 21 ]. Functionally, microbial fermentation yields short-chain fatty acids (SCFAs)—notably acetate, propionate, and butyrate—that fuel colonocytes, fortify barrier integrity, and modulate systemic inflammation [ 39 , 40 , 42 ]. Through immune–neuroendocrine signaling, the microbiome also engages the gut–brain axis, impacting neurodevelopment and behavior [ 37 , 38 , 41 ]. Phenylketonuria (PKU) is a rare autosomal recessive disorder caused by phenylalanine hydroxylase (PAH) deficiency, leading to phenylalanine (Phe) accumulation and neurotoxicity if untreated [ 4 , 5 , 36 ]. Lifelong Phe-restricted dietary therapy is the cornerstone of management, yet it can be difficult to maintain and may reshape the gut microbiota through changes in macronutrient composition and substrate availability [ 14 , 15 , 32 – 34 ]. Studies in Europe and North America describe altered diversity and taxa shifts in PKU—often involving SCFA-producing genera—and report associations with diet composition and specialized formulas or adjunct therapies [ 6 – 12 , 22 ]. Comparable dysbiosis patterns have also been reported in adult PKU cohorts, including differences relative to healthy controls and related inborn errors of metabolism such as urea cycle disorders [ 13 ]. Diet–microbiome links are reinforced by evidence that habitual intake patterns and fiber availability influence SCFA profiles and taxa such as Akkermansia muciniphila, a mucin-degrading bacterium linked to barrier function [ 14 , 30 , 31 , 32 , 36 , 40 , 41 , 43 ]. Despite this progress, data from Latin America are lacking, even though regional differences in diet, healthcare access, and availability of low-Phe products may influence both adherence and microbiome composition. To address this gap, we conducted an exploratory cross-sectional metagenomic study in 15 pediatric PKU patients from Ecuador, comparing those on a Phe-restricted versus non-restricted diet. We profiled taxonomic composition and functional potential (including carbohydrate-active enzymes and metabolic pathways) to evaluate whether dietary management is associated with distinct community structures and functions in this under-represented setting [ 8 – 12 , 21 , 39 , 40 ]. Given the rarity of PKU and contextual barriers to specialized nutrition, this work is positioned as pilot, hypothesis-generating evidence to inform larger, multicenter studies in the region. Methods Study design and participants. We conducted a cross-sectional observational pilot study of pediatric PKU patients recruited at Universidad San Francisco de Quito (USFQ). The study protocol was reviewed and approved by the Universidad San Francisco de Quito Bioethics Committee (approval code 2023-010IN). All methods were performed in accordance with the relevant guidelines and regulations and in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from the parents or legal guardians of all participants prior to enrollment, and assent was obtained from participants when appropriate. Inclusion criteria were confirmed PKU diagnosis, age 10 days–18 years, and no systemic antibiotic use within 30 days prior to sampling. Patients were classified into two a priori groups based on clinical records and caregiver report: Phe-restricted diet (medical low-Phe formula and/or low-protein food substitutes under dietetic supervision) and non-restricted diet (no structured Phe restriction). Stool collection and DNA extraction. Stool was collected at home/clinic using sterile tubes containing nucleic-acid preservative, transported under cold chain, and stored at − 80°C until processing. DNA was extracted with QIAGEN stool DNA kits per manufacturer instructions, including a bead-beating step. Extraction blanks were included in each batch. DNA integrity was verified by gel electrophoresis and quantity by fluorometry. Sequencing and Bioinformatic processing. Libraries were prepared with Illumina Nextera XT and sequenced on an Illumina MiSeq v3 platform (paired-end 2×300 bp). Raw reads were pre-processed with readfq (v8) to remove low-quality segments (default quality 40 bp), reads with ≥ 10 bp N, and adapter-contaminated reads with ≥ 15 bp overlap; potential host reads were removed by aligning to GRCh38 with Bowtie2 v2.2.4 Clean, de-hosted reads were assembled with MEGAHIT v1.0.4-beta. Taxonomic profiles were generated by aligning predicted proteins to NCBI NR (2018-01-02) using DIAMOND v0.9.9 ( blastp , e ≤ 1e-5) and assigning taxa via a lowest-common-ancestor approach (MEGAN); interactive summaries were visualized in Krona. Functional profiling targeted KEGG pathways and CAZy enzyme classes using DIAMOND ( blastp , e ≤ 1e-5) against the respective databases; relative abundances were calculated by summing features annotated to each pathway/class. Diversity and statistical analysis. Analyses were conducted in QIIME2 and R/phyloseq. Alpha diversity (Shannon, Simpson) and beta diversity (Bray–Curtis) were computed on normalized feature tables; group differences in beta diversity were tested by PERMANOVA (999 permutations). For taxa and pathway relative abundances, between-group differences were assessed by Wilcoxon rank-sum tests with Benjamini–Hochberg FDR correction; q < 0.05 was considered significant. Where indicated, results are reported with effect sizes and FDR-adjusted q -values. Cohort and groups. We analyzed fecal metagenomes from 15 pediatric PKU patients: G1 (no structured Phe restriction, n = 9) and G2 (Phe-restricted diet, n = 6). Ages ranged from 10 days to 18 years; PKU genotypes are summarized in Table 1 . Diet group assignment was independent of genotype. Table 1 PAH gene variants identified in the PKU cohort. Sample allele 1 allele 2 heter/homo P_1 c.441 + 5G > T (Intronic) c.441 + 5G > T (Intronic) homo P_10 c.441 + 5G > T (Intronic) p.Arg252Trp hetero P_11 p.Pro275Arg p.Arg252Trp hetero P_12 p.Arg252Trp p.Arg252Trp homo P_13 p.Ser349Pro p.Ser349Pro homo P_14 p.Ser349Pro p.Ser349Pro homo P_16 c.441 + 5G > T (Intronic) c.441 + 5G > T (Intronic) homo P_2 no variant no variant normal P_3 c.441 + 5G > T (Intronic) c.441 + 5G > T (Intronic) homo P_4 c.441 + 5G > T (Intronic) c.441 + 5G > T (Intronic) homo P_5 c.441 + 5G > T (Intronic) c.441 + 5G > T (Intronic) homo P_6 c.441 + 5G > T (Intronic) c.441 + 5G > T (Intronic) homo P_7 c.441 + 5G > T (Intronic) c.441 + 5G > T (Intronic) homo P_8 p.Arg252Trp p.Ala403Val hetero P_9 p.Pro275Arg c.60 + 5G > T hetero Artificial intelligence–assisted content Artificial intelligence–based tools (ChatGPT) was used exclusively for language refinement, grammar, and clarity of expression. The AI tool did not contribute to study design, data analysis, data interpretation, or generation of scientific content. All content generated with AI assistance was carefully reviewed, edited, and validated by the authors, who take full responsibility for the accuracy and integrity of the manuscript. Results Taxonomic composition . At the phylum level, both groups were dominated by Firmicutes and Bacteroidetes, with lower contributions from Actinobacteria, Proteobacteria, and Verrucomicrobia. Hierarchical clustering of phylum profiles grouped most G1 samples together, whereas G2 showed greater between-subject dispersion (Fig. 1 ). At the genus level, G1 displayed higher relative abundance of Faecalibacterium and Prevotella, while G2 showed frequent enrichment of Bacteroides and Bifidobacterium (Fig. 2 ). Akkermansia and Roseburia were generally low across both groups. At the species level G2 (Phe-restricted) showed recurrent enrichment of Bacteroides uniformis and Bacteroides vulgatus, and higher representation of Eggerthella lenta and Bilophila wadsworthia in individual subjects. G1 (non-restricted) was characterized by multiple Firmicutes species, including Ruminococcus spp., Oscillibacter spp. (e.g., Oscillibacter sp. ER4/57_20), Clostridium spp. (several CAG designations), Faecalibacterium (e.g., Faecalibacterium sp. CAG74), and Butyricicoccus/ Subdoligranulum representatives—consistent with SCFA-producing lineages (Fig. 3 ). Community structure. Ordinations based on Bray–Curtis distances demonstrated diet-associated stratification. NMDS achieved low stress (~ 0.06) with G1 samples forming a tighter cluster and G2 samples more dispersed; PCA showed a similar separation (Fig. 4 ). Functional analysis. Functional profiling based on KEGG and CAZy annotations revealed a consistent enrichment of carbohydrate-active enzymes (CAZymes), particularly glycoside hydrolases (GH) and glycosyltransferases (GT), across all samples. These enzymes were more abundant in G2 (Phe-restricted) patients, suggesting a microbial shift toward enhanced carbohydrate degradation and glycan modification potential (Fig. 5 a). At the pathway level (KEGG), genes related to metabolism dominated the functional repertoire, followed by genetic information processing and environmental information processing pathways. Among metabolic pathways, carbohydrate, amino acid, and energy metabolism were the most represented categories across both groups. Subcategories within these pathways included glycolysis/gluconeogenesis, lipid metabolism, and biosynthesis of amino acids (Fig. 5 b). Analysis of key functional orthologs (Kos) revealed group-specific differences. G2 samples displayed higher abundances of genes associated with membrane transport (K02003, K02004), two-component regulatory systems (K05229, K07496), and signal transduction. Conversely, G1 samples were enriched in genes related to ribosomal structure (K03091), energy conversion (K08133), and posttranslational modification (K06180, K04759) (Fig. 6 ). Discussion Studies of healthy pediatric gut microbiomes highlight the dominance of Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria [ 17 , 18 , 19 ]. In PKU patients, however, reduced microbial diversity is observed, particularly at the phylum and genus levels. Our study found enrichment in Firmicutes and Bacteroidetes (Fig. 1 ) across all patients, consistent with healthy populations, but with a depletion of Proteobacteria, Verrucomicrobia, and Chlamydiae [ 18 , 20 , 21 , 22 ]. These observations align with prior reports [ 9 ], which noted significant changes in the relative abundance of Bacteroides and Prevotella in PKU patients compared to healthy controls. However, our findings further identified species-level patterns: patients on the Phe-restricted diet frequently showed higher Bacteroides uniformis and Bacteroides vulgatus , along with Eggerthella lenta and Bilophila wadsworthia , whereas the non-restricted group was characterized by multiple Firmicutes species including Ruminococcus , Oscillibacter , Clostridium spp., Faecalibacterium [e.g., CAG74], and Subdoligranulum (Fig. 3 ). These taxa mirror functional tendencies toward SCFA production in the non-restricted group and increased Bacteroides-associated carbohydrate processing and stress/host-interaction functions in the restricted group. Group 1 (non-restrictive diet) showed typical phylum diversity, with increased Firmicutes and Bacteroidetes, but exhibited significant reductions in Actinobacteria, Proteobacteria, and Verrucomicrobia (Fig. 1 ). The depletion of Actinobacteria, particularly genera like Bifidobacterium, could reflect a diet lower in prebiotic fibers, which are essential substrates for their growth [ 9 ]. Similarly, Proteobacteria and Verrucomicrobia, including Akkermansia muciniphila, are associated with mucin degradation, immune modulation, and a healthy gut barrier, which may be compromised in PKU patients consuming higher protein, lower fiber diets [ 31 , 32 ]. Given the small, heterogeneous cohort, we interpret these phylum-level shifts as consistent with altered ecology under differing dietary exposures rather than as deterministic markers of dysbiosis, and we note substantial inter-individual variability even within groups. Importantly, while some of these bacteria are generally considered commensal or saprophytic, they can exhibit opportunistic pathogenic behavior in immunocompromised individuals. For instance, Proteobacteria include potential pathogens like Escherichia coli, which can cause intestinal inflammation, and a reduction in Akkermansia muciniphila has been linked to gut barrier dysfunction and metabolic disorders [ 31 , 36 ]. In addition, several intestinal Clostridium species have been shown to induce cytokine responses in human mononuclear cells, underscoring the context-dependent immunological effects of this genus [ 16 ]. These shifts indicate an altered gut environment, where certain taxa thrive while others are depleted, reflecting dysbiosis and reduced microbial diversity linked to untreated PKU. Group 2 (Phe-restricted diet) exhibited greater variation, with some patients experiencing enrichment of Actinobacteria (Fig. 1 ), likely influenced by individual prebiotic intake [ 10 , 11 ]. This highlights the role of dietary modifications in shaping microbial composition, as specific prebiotics can promote the growth of beneficial bacteria like Bifidobacterium. However, a subset of patients also showed reductions in Firmicutes, key SCFA producers, which could impair gut function [ 10 ]. At the genus level, Faecalibacterium was enriched in all patients, consistent with its role in gut homeostasis (Fig. 2 ). Compared to findings from McWhorter et al. [ 11 ], which focused on microbial taxonomic shifts, our study uniquely revealed species-level and functional shifts—particularly the appearance of B. uniformis , B. vulgatus , and E. lenta in restricted patients—that help contextualize metabolic adaptation in the setting of dietary Phe restriction. Community structure analysis supported these compositional trends: Bray–Curtis ordinations (Fig. 4 ) showed group-wise stratification, with G1 forming a tighter cluster and G2 more dispersed, indicating higher inter-individual variability under dietary restriction. This pattern is consistent with differential exposure to medical foods, fiber sources, and adherence heterogeneity within the restricted group. Group 1 had higher levels of Prevotella, a genus linked to fiber-rich diets, consistent with their unrestricted eating patterns. In contrast, Group 2 displayed lower Prevotella abundance, aligning with the protein-focused and Phe-restricted diets often lacking fiber (Fig. 2 ). Additionally, our findings highlight functional adaptations within the microbiome: carbohydrate-active enzymes—especially glycoside hydrolases (GH) and glycosyltransferases (GT)—were more abundant in restricted patients (Fig. 5 ), and KEGG orthologs linked to membrane transport (e.g., K02003, K02004) and signal transduction/two-component systems (e.g., K05229, K07496, K05349) were elevated in G2 (Fig. 6 ). In contrast, G1 showed higher abundance of orthologs related to translation/ribosomal structure (e.g., K03091), energy production and conversion, and post-translational processes (e.g., K06180, K04759), suggesting a relatively more biosynthetically active community. These KO-level differences align with the CAZy signal and reinforce a shift toward transport and sensing functions under dietary restriction. Such findings expand upon previous studies like Verduci et al. [ 8 ] and Pinheiro de Oliveira et al. [ 7 ], which primarily focused on taxonomic differences without exploring functional implications. The observed reduction in SCFA-producing bacteria, particularly Faecalibacterium and Roseburia , in Group 2 highlights a critical link between dietary management and PKU pathophysiology. SCFAs, especially butyrate, are essential for maintaining gut barrier integrity, modulating systemic inflammation, and providing energy substrates for colonocytes [ 39 , 40 ]. A decline in SCFA producers may exacerbate intestinal inflammation and impair the epithelial barrier, potentially contributing to nutrient malabsorption and systemic metabolic dysregulation in PKU patients. Moreover, the loss of these beneficial taxa could influence neurological health, as SCFAs have been implicated in gut-brain axis regulation [ 41 ], an area of concern given the neurodevelopmental impact of PKU. These findings underscore the importance of restoring SCFA-producing populations through targeted interventions. Clinically, the implications of these findings are significant. Personalized approaches, such as prebiotic supplementation to boost Faecalibacterium populations or probiotic formulations targeting SCFA production, could help mitigate the adverse effects of dysbiosis in PKU patients [ 42 , 43 ]. These strategies may not only enhance gut health but also improve systemic metabolic and neurological outcomes. Additionally, dietary adjustments within Phe-restriction guidelines that incorporate SCFA-promoting fibers could offer a dual benefit of supporting microbial diversity and improving clinical management of PKU [ 39 , 40 ]. It is well established that phenylalanine and tyrosine levels play critical roles in PKU pathophysiology. Patients on a non-Phe-restricted diet typically exhibit elevated phenylalanine levels due to the inability to metabolize Phe, while tyrosine remains deficient [ 32 , 33 ]. In contrast, patients adhering to Phe-restricted diets have lower phenylalanine levels, although tyrosine levels can still remain suboptimal. This imbalance can influence microbial metabolism since amino acids like phenylalanine are substrates for specific gut bacterial processes [ 34 ]. In clinical PKU cohorts, lower abundance of genus Bacteroides has been reported to be negatively correlated with blood phenylalanine levels [ 23 ]. Elevated phenylalanine levels in PKU patients can disrupt energy homeostasis, shifting reliance toward carbohydrates, particularly in untreated individuals, while Phe-restricted diets often compensate with higher carbohydrate intake [ 33 , 34 ]. This dietary shift may alter gut microbial composition, promoting taxa like Prevotella , associated with carbohydrate fermentation, and impacting SCFA production, which influences gut health [ 35 ]. While our study did not measure phenylalanine and tyrosine levels, their impact on microbial composition cannot be overlooked and warrants further investigation. Functionally, Glycoside Hydrolases (GH), essential for carbohydrate breakdown, were prevalent across all patients (Fig. 5 ), while Carbohydrate-Binding Modules (CBM) and Glycosyl Transferases (GT) were reduced in Group 2 due to limited fiber intake [ 8 , 9 ] (Fig. 3 ). Metabolic pathway analysis revealed consistent carbohydrate and amino acid metabolism across both groups, despite dietary differences. This suggests a core microbiome functionality adapted to PKU. However, patients on restricted diets showed a shift toward carbohydrate fermentation over protein metabolism, consistent with previous studies [ 9 , 10 ]. KEGG analysis revealed decreased secondary metabolite biosynthesis, indicating dietary limitations, but no significant differences between the groups, suggesting a conserved core microbiome [ 8 , 21 ]. Our findings suggest that gut microbiome profiling could serve as a complementary tool to monitor PKU patients, providing insights into microbiome disruptions associated with dietary adherence. Clinically, this data could guide personalized interventions, such as targeted prebiotic or probiotic supplementation, to restore microbial balance and improve gut health. For research, longitudinal studies could further explore the relationship between dietary interventions, microbiome functionality, and metabolic outcomes in PKU patients to optimize clinical management strategies. Importantly, this work represents, to our knowledge, the first metagenomic characterization of pediatric PKU patients from Ecuador and contributes data from an under-represented Latin American setting, where access to low-Phe products and dietetic support can differ from previously studied regions. Microbiome alterations have also been described in other metabolic conditions and therapeutic contexts, including type 1 diabetes and dietary/pharmacologic exposures [ 24 , 25 ], as well as lysosomal and copper metabolism disorders such as Gaucher disease and Wilson disease [ 26 – 29 ]. While these conditions differ mechanistically from PKU, they support the broader concept that metabolic status and treatment exposures can reshape gut microbial ecology. Limitations This pilot included a small pediatric cohort, limiting statistical power and generalizability and raising the risk of selection bias. The absence of clinical controls and variability in dietary adherence may have introduced confounding, affecting observed taxonomic and functional patterns. Its cross-sectional design prevents causal inference between diet and microbiota. Most prior work involves animals, adults, or mixed ages; our pediatric focus may not fully translate to adults, whose microbiomes and dietary responses differ with age. Future work should include larger, multicenter cohorts with appropriate controls (e.g., healthy peers or non-PKU siblings), longitudinal sampling with detailed diet and clinical metadata, and age-stratified analyses that include adults. Integrating multi-omics (e.g., metabolomics, transcriptomics) will help link microbial shifts to metabolic function and identify potential therapeutic targets. Conclusions In this exploratory, cross-sectional study of 15 pediatric PKU patients from Ecuador, we observed diet-associated shifts in gut microbiome composition and function. Phe-restricted patients (G2) showed enrichment of Bacteroides uniformis , B. vulgatus , and Eggerthella lenta , alongside higher representation of carbohydrate-active enzymes (notably GH and GT) and KEGG orthologs linked to membrane transport and signal transduction. Non-restricted patients (G1) were characterized by multiple SCFA-associated Firmicutes (e.g., Ruminococcus , Oscillibacter , Clostridium spp., Faecalibacterium sp. CAG74), and KOs related to translation/ribosomal structure and energy production. Community-level analyses (Bray–Curtis PCA/NMDS) indicated group stratification, with tighter clustering in G1 and greater inter-individual variability in G2. Taken together, these findings suggest that dietary management in PKU is associated with both taxonomic and functional reconfiguration of the gut microbiome, with restricted diets favoring transport/sensing functions and non-restricted diets retaining SCFA-linked lineages. Given the small, heterogeneous cohort and cross-sectional design, results are hypothesis-generating. Larger, age-stratified, multicenter studies in Latin America that pair metagenomics with dietary intake, Phe/Tyr and SCFA measurements, and longitudinal follow-up are needed to validate these signals and to inform targeted, microbiome-aware dietary strategies within PKU care. Abbreviations PKU phenylketonuria Phe phenylalanine SCFA short-chain fatty acid KEGG Kyoto Encyclopedia of Genes and Genomes KO KEGG ortholog CAZy Carbohydrate-Active enZymes database GH glycoside hydrolase GT glycosyltransferase NMDS non-metric multidimensional scaling PCA principal component analysis PERMANOVA permutational multivariate analysis of variance SRA Sequence Read Archive. Declarations Ethics approval and consent to participate The study protocol was reviewed and approved by the Universidad San Francisco de Quito Bioethics Committee on April 19, 2023 (approval code 2023-010IN). Written informed consent was obtained from the parents or legal guardians of all participants prior to enrollment, and assent was obtained from participants when appropriate. Following consent, relevant clinical and demographic information was collected for research purposes. Consent for publication Written informed consent for publication of anonymized data was obtained from the parents or legal guardians of all participants. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding The authors received no specific funding for this work. The authors confirm independence from the sponsors, and the content of the article was not influenced by any external funding sources. Author Contribution Paul Leon-Gomez [PL] contributed to data curation, formal analysis, investigation, methodology, validation, visualization, and writing of the original draft, as well as review and editing of the manuscript. Vanessa I. Romero [VR] contributed to conceptualization, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, visualization, and writing of the original draft, as well as review and editing of the manuscript. Ivonne Salinas [IS] contributed to conceptualization, data curation, formal analysis, investigation, methodology, visualization, and manuscript review and editing. Ariel Vargas [AV] contributed to conceptualization, formal analysis, investigation, methodology, visualization, and manuscript review and editing. Alex S. Aguirre [AA] contributed to formal analysis, investigation, methodology, and manuscript review and editing. Diego I. Montenegro [DM] contributed to formal analysis, investigation, methodology, and manuscript review and editing.All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Acknowledgement We would like to express our gratitude to the principal investigator, Dr. Vanessa Romero, for her invaluable mentorship throughout this project, as well as to the group of medical students from the Universidad San Francisco de Quito who contributed significantly to its initiation and development. We also thank our colleagues, the Instituto de Microbiología, and the Universidad San Francisco de Quito for their support and resources. Data Availability The shotgun metagenomic sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1406308. The data have been released and are publicly accessible through NCBI. 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Translational Pediatr. 10[10] , 2521–2532. https://doi.org/10.21037/tp-21-426 (2021). De Groot, P. F. et al. Distinct fecal and oral microbiota composition in human type 1 diabetes, an effect of gluten-free diet. Gut microbiome . https://doi.org/10.1371/journal.pone.0188475 (2017). 2[1]:16. Marietta, E. V., Horwath, I., Balakrishnan, B. & Sairam, V. Role of gut microbiota in patients with type 1 diabetes and differential regulation of bacterial genera by an antidiabetic drug. Diabetes Res. Clin. Pract. 135 , 151–162. https://doi.org/10.1016/j.diabres.2018.10.015 (2018). Marques, A. R., Oosterhof, A. & Ambarus, C. A. Gaucher disease and the impact of therapeutic interventions on the gut microbiome. Lysosomal Dis. 4[2] , 104–112. https://doi.org/10.1007/s10545-017-0058-y (2017). Jiang, H. et al. Copper metabolism and gut microbiome in Wilson disease. Liver Int. 37[1] , 18–25. https://doi.org/10.1111/liv.13253 (2017). Weiss, K. H. & van der Post, S. The interaction between Wilson’s disease and the gut microbiome. World J. Hepatol. 13[1] , 1–9. https://doi.org/10.4254/wjh.v13.i1.1 (2021). Singh, R., Kumar, R. & Choi, B. Influence of copper on the gut microbiome in liver disease models. J. Microbiome Res. 5[2] , 88–95. https://doi.org/10.1186/s40168-022-01234-x (2022). Derrien, M., Vaughan, E. E., Plugge, C. M. & de Vos, W. M. Akkermansia muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int. J. Syst. Evol. MicroBiol. 54[5] , 1469–1476. https://doi.org/10.1099/ijs.0.02873-0 (2004). Kim, H. N. et al. Correlation between gut microbiota composition and dietary habits with fecal short-chain fatty acids. BMC Microbiol. 20[1] , 1–12. https://doi.org/10.1186/s12866-020-01943-2 (2020). van Wegberg, A. M. J. et al. The complete European guidelines on phenylketonuria: Diagnosis and treatment. Orphanet J. Rare Dis. https://doi.org/10.1186/s13023-017-0685-2 (2017). 12[1]:162. Rocha, J. C., van Rijn, M. & van Spronsen, F. J. Optimizing treatment in phenylketonuria: The importance of dietary and non-dietary factors. J. Inherit. Metab. Dis. 36[6] , 849–858. https://doi.org/10.1007/s10545-012-9574-7 (2013). van Spronsen, F. J., van Wegberg, A. M. J., Ahring, K. & Phenylketonuria Diagnosis, treatment, and future perspectives. Nat. Reviews Endocrinol. 5[9] , 509–519. https://doi.org/10.1038/nrendo.2009.124 (2009). Pereira, D. I. & Gibson, G. R. Effects of prebiotics and the gut microbiota on human health. Curr. Opin. Clin. Nutr. Metabolic Care . 20[6] , 500–507. https://doi.org/10.1097/MCO.0000000000000417 (2017). Blau, N., van Spronsen, F. J., Levy, H. L. & Phenylketonuria Lancet ; 3769750 :1417–1427. https://doi.org/10.1016/S0140-6736[10]60961-0 (2010). Sharon, G., Sampson, T. R., Geschwind, D. H. & Mazmanian, S. K. The central nervous system and the gut microbiome. Cell 167[4] , 915–932. https://doi.org/10.1016/j.cell.2016.10.027 (2016). Rutsch, A., Kantsjö, J. B. & Ronchi, F. The gut–brain axis: How microbiota and host inflammasome influence brain physiology and pathology. Front. Immunol. 11 , 604179. https://doi.org/10.3389/fimmu.2020.604179 (2020). Morrison, D. J. & Preston, T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes . 7[3] , 189–200. https://doi.org/10.1080/19490976.2015.1134082 (2016). Canani, R. B. et al. Potential beneficial effects of butyrate in intestinal and extraintestinal diseases. World J. Gastroenterol. 17[12] , 1519–1528. https://doi.org/10.3748/wjg.v17.i12.1519 (2011). Cryan, J. F. et al. The microbiota-gut-brain axis. Physiol. Rev. 99[4] , 1877–2013. https://doi.org/10.1152/physrev.00018.2018 (2019). Ríos-Covián, D. et al. Intestinal short chain fatty acids and their link with diet and human health. Front. Microbiol. 7 , 185. https://doi.org/10.3389/fmicb.2016.00185 (2016). Vandenplas, Y., Huys, G. & Daube, G. Probiotics: An update. J. Pediatr. 96[6] , 839–846. https://doi.org/10.1016/j.jped.2020.04.002 (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 11 Mar, 2026 Editor assigned by journal 11 Mar, 2026 Editor invited by journal 09 Mar, 2026 Submission checks completed at journal 06 Mar, 2026 First submitted to journal 06 Mar, 2026 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9010202","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":604523256,"identity":"7f7597b8-5da9-49f0-bbd5-c9e12208dccd","order_by":0,"name":"Paul Leon-Gomez","email":"","orcid":"","institution":"Universidad San Francisco de Quito","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Leon-Gomez","suffix":""},{"id":604523259,"identity":"2e60f080-18e2-4165-880d-dc3b1b3ab46d","order_by":1,"name":"Ivonne Salinas","email":"","orcid":"","institution":"Universidad San Francisco de Quito","correspondingAuthor":false,"prefix":"","firstName":"Ivonne","middleName":"","lastName":"Salinas","suffix":""},{"id":604523263,"identity":"028a7ee8-e01a-4323-a454-2cf7436fcb5e","order_by":2,"name":"Ariel Vargas","email":"","orcid":"","institution":"Universidad San Francisco de Quito","correspondingAuthor":false,"prefix":"","firstName":"Ariel","middleName":"","lastName":"Vargas","suffix":""},{"id":604523265,"identity":"ead8093a-7846-464b-888e-311a99340c04","order_by":3,"name":"Alex S. Aguirre","email":"","orcid":"","institution":"Universidad San Francisco de Quito","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"S.","lastName":"Aguirre","suffix":""},{"id":604523272,"identity":"196bfdfa-4fea-4b10-8057-d4b215f27b73","order_by":4,"name":"Diego I. Montenegro","email":"","orcid":"","institution":"Universidad San Francisco de Quito","correspondingAuthor":false,"prefix":"","firstName":"Diego","middleName":"I.","lastName":"Montenegro","suffix":""},{"id":604523273,"identity":"cec877b1-d44b-4ab4-82bf-8eaa7b47499e","order_by":5,"name":"Vanessa I. Romero","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBADOQYGxgdAmpl4LcZA1QakaUlsIFqLwe3DRzd83GOTvuH8YcYbDBXWiQ3szQ/wazmXlnZzxrO03A03kpktGM6kJzbwHDPAq0Wyh8fsNs+Bw0At/MckGNsOJzZI5OB3GFjLnwOH0w3OH2aTYPwH1CL/Br8Wfh6gFoYDhxMMDiQDtTSAbOEhpIUt7WbPgTTDmSC/JBxLN27jScPvFzYe5mM3fhywkecDhdiHGmvZfvbDD/BbgwwkEkCGEK8epIUk1aNgFIyCUTBiAAAZ3UVYNwtB+wAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad San Francisco de Quito","correspondingAuthor":true,"prefix":"","firstName":"Vanessa","middleName":"I.","lastName":"Romero","suffix":""}],"badges":[],"createdAt":"2026-03-02 12:38:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9010202/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9010202/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104783358,"identity":"4e1fc2cd-4e5f-45d5-be58-67967d6678ed","added_by":"auto","created_at":"2026-03-17 07:58:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73144,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTaxonomic composition and sample clustering according to dietary management in PKU. \u003c/strong\u003eLeft panel: hierarchical clustering dendrogram based on Bray–Curtis dissimilarities of taxonomic profiles across samples. Samples are color-coded by dietary group: G1 (non-phenylalanine-restricted diet, red) and G2 (phenylalanine-restricted diet, blue). Right panel: relative abundance of bacterial phyla across individual samples. Sample IDs follow the original dataset labeling; P2 was not available, therefore 15 samples are shown (P1, P3–P16). Colors indicate major phyla, including Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Verrucomicrobia, and other low-abundance taxa. Each bar represents the proportional phylum-level composition per sample. Differences in overall community composition between G1 and G2 were assessed using PERMANOVA (p = 0.01).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9010202/v1/daf784c8709b738bfd1ec6b5.png"},{"id":104782053,"identity":"d54d047f-7cc0-42a3-b70e-d1d275929834","added_by":"auto","created_at":"2026-03-17 07:56:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":325373,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenus-level taxonomic profiles across PKU samples. \u003c/strong\u003eStacked bar plots show the relative abundance of dominant bacterial genera across individual samples (P1–P16). Colors represent major genera, including Faecalibacterium, Bifidobacterium, Bacteroides, Alistipes, Collinsella, Prevotella, Clostridium, Akkermansia, Azospirillum, Roseburia, and grouped low-abundance taxa (“Others”). The figure illustrates inter-individual variability and differences in genus-level composition between patients managed with non-restricted (G1) and phenylalanine-restricted (G2) diets.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9010202/v1/773e98379bfba16d42bf69be.png"},{"id":104689743,"identity":"32a4a73b-af71-41c4-a887-09a423068f1b","added_by":"auto","created_at":"2026-03-16 06:02:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":668670,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecies-level differences and ordination of microbial communities in PKU patients. \u003c/strong\u003e[a] Heatmap of the 30 most abundant bacterial species across all samples. Rows correspond to species and columns to samples; values are row-scaled (z-scores). Red indicates higher relative abundance and blue lower relative abundance within each species. Sample group is indicated by the top annotation bar: G1 (non-restricted diet) and G2 (phenylalanine-restricted diet). [b] Principal component analysis (PCA) based on Bray–Curtis dissimilarities showing partial separation between dietary groups. The percentage of variance explained by each axis is indicated. Black squares represent G1 samples and red circles represent G2 samples.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9010202/v1/3b800590687e3478b3e35e86.png"},{"id":104783075,"identity":"e3189924-b1de-48e7-af2d-3c72c5392275","added_by":"auto","created_at":"2026-03-17 07:58:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBeta-diversity analysis reveals differences in microbial community structure between dietary groups. \u003c/strong\u003e[a] Principal component analysis (PCA) of taxonomic profiles based on Bray–Curtis distances. The first two components explain 30.3% and 17.9% of the total variance, respectively. [b] Non-metric multidimensional scaling (NMDS) ordination based on Bray–Curtis distances (stress = 0.063). In both ordinations, G1 samples (black squares) form a more compact cluster, whereas G2 samples (red circles) display greater dispersion, indicating increased inter-individual variability under phenylalanine-restricted dietary management.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9010202/v1/84522b0549f608210e0bf8d7.png"},{"id":104689742,"identity":"6d9139f7-8bcd-49c7-b7f6-2fac5c1f4428","added_by":"auto","created_at":"2026-03-16 06:02:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1069468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional potential of the gut microbiome in PKU patients inferred from CAZy and KEGG annotations. \u003c/strong\u003e[a] Relative abundance of carbohydrate-active enzyme (CAZy) classes across individual samples. Glycoside hydrolases (GH) and glycosyltransferases (GT) are the most abundant enzyme classes, followed by carbohydrate-binding modules (CBM), carbohydrate esterases (CE), polysaccharide lyases (PL), and auxiliary activity enzymes (AA). [b] KEGG pathway annotation showing the number of genes assigned to major functional categories. Metabolic pathways predominate across samples, particularly carbohydrate, amino acid, and energy metabolism, followed by genetic and environmental information processing pathways. Gene counts reflect the aggregated functional potential of the gut microbiome in PKU patients.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9010202/v1/0196d06afce66a7c97e9d35a.png"},{"id":104689728,"identity":"32c3552b-a7cf-4528-805a-66437dcde172","added_by":"auto","created_at":"2026-03-16 06:02:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":131266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially abundant KEGG orthologs between PKU dietary groups. \u003c/strong\u003eBoxplots depict the relative abundances of the 12 KEGG orthologs (Kos) showing the strongest differences between groups. G1 (non-restricted diet) is shown in blue and G2 (phenylalanine-restricted diet) in red. Orthologs associated with membrane transport, signal transduction, and two-component systems (e.g., K02003, K02004, K05229, K07496, K05349) are enriched in G2, whereas orthologs linked to translation, ribosomal structure, energy metabolism, and protein folding (e.g., K03091, K08133, K06180, K04759) are more abundant in G1. Statistical significance was assessed using Wilcoxon rank-sum tests with Benjamini–Hochberg false discovery rate correction (q \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9010202/v1/447daebe7eaaec913ba087d6.png"},{"id":104785212,"identity":"86bfadaa-09ad-4131-9636-f0912eecf13e","added_by":"auto","created_at":"2026-03-17 08:09:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3459998,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9010202/v1/bd35425c-63ba-49a6-9660-4e34282fd9c0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diet-Associated Gut Microbiome Signatures in Pediatric Phenylketonuria: A Shotgun Metagenomic Study from Ecuador","fulltext":[{"header":"Synopsis","content":"\u003cp\u003eDietary phenylalanine restriction in pediatric phenylketonuria is associated with distinct species-level and functional gut microbiome signatures, supporting microbiome-aware strategies to optimize dietary management in under-represented Latin American settings.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe human gastrointestinal tract harbors a dense and diverse microbial community that supports host metabolism, intestinal barrier integrity, and immune regulation through fermentation of dietary substrates, colonization resistance, and signaling to epithelial and immune cells [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The gut microbiome\u0026mdash;comprising microbial genomes and their metabolic outputs\u0026mdash;has broad implications for metabolic, immune, and neurological health and is typically dominated by Firmicutes and Bacteroidetes, with relevant contributions from Actinobacteria, Proteobacteria, Fusobacteria, and Verrucomicrobia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In children, microbiome structure is especially sensitive to dietary patterns, environment, and comorbidities [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Functionally, microbial fermentation yields short-chain fatty acids (SCFAs)\u0026mdash;notably acetate, propionate, and butyrate\u0026mdash;that fuel colonocytes, fortify barrier integrity, and modulate systemic inflammation [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Through immune\u0026ndash;neuroendocrine signaling, the microbiome also engages the gut\u0026ndash;brain axis, impacting neurodevelopment and behavior [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhenylketonuria (PKU) is a rare autosomal recessive disorder caused by phenylalanine hydroxylase (PAH) deficiency, leading to phenylalanine (Phe) accumulation and neurotoxicity if untreated [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Lifelong Phe-restricted dietary therapy is the cornerstone of management, yet it can be difficult to maintain and may reshape the gut microbiota through changes in macronutrient composition and substrate availability [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Studies in Europe and North America describe altered diversity and taxa shifts in PKU\u0026mdash;often involving SCFA-producing genera\u0026mdash;and report associations with diet composition and specialized formulas or adjunct therapies [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Comparable dysbiosis patterns have also been reported in adult PKU cohorts, including differences relative to healthy controls and related inborn errors of metabolism such as urea cycle disorders [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Diet\u0026ndash;microbiome links are reinforced by evidence that habitual intake patterns and fiber availability influence SCFA profiles and taxa such as Akkermansia muciniphila, a mucin-degrading bacterium linked to barrier function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite this progress, data from Latin America are lacking, even though regional differences in diet, healthcare access, and availability of low-Phe products may influence both adherence and microbiome composition. To address this gap, we conducted an exploratory cross-sectional metagenomic study in 15 pediatric PKU patients from Ecuador, comparing those on a Phe-restricted versus non-restricted diet. We profiled taxonomic composition and functional potential (including carbohydrate-active enzymes and metabolic pathways) to evaluate whether dietary management is associated with distinct community structures and functions in this under-represented setting [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Given the rarity of PKU and contextual barriers to specialized nutrition, this work is positioned as pilot, hypothesis-generating evidence to inform larger, multicenter studies in the region.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy design and participants.\u003c/b\u003e We conducted a cross-sectional observational pilot study of pediatric PKU patients recruited at Universidad San Francisco de Quito (USFQ). The study protocol was reviewed and approved by the Universidad San Francisco de Quito Bioethics Committee (approval code 2023-010IN). All methods were performed in accordance with the relevant guidelines and regulations and in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from the parents or legal guardians of all participants prior to enrollment, and assent was obtained from participants when appropriate. Inclusion criteria were confirmed PKU diagnosis, age 10 days\u0026ndash;18 years, and no systemic antibiotic use within 30 days prior to sampling. Patients were classified into two a priori groups based on clinical records and caregiver report: Phe-restricted diet (medical low-Phe formula and/or low-protein food substitutes under dietetic supervision) and non-restricted diet (no structured Phe restriction).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStool collection and DNA extraction.\u003c/b\u003e Stool was collected at home/clinic using sterile tubes containing nucleic-acid preservative, transported under cold chain, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until processing. DNA was extracted with QIAGEN stool DNA kits per manufacturer instructions, including a bead-beating step. Extraction blanks were included in each batch. DNA integrity was verified by gel electrophoresis and quantity by fluorometry.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSequencing and Bioinformatic processing.\u003c/b\u003e Libraries were prepared with Illumina Nextera XT and sequenced on an Illumina MiSeq v3 platform (paired-end 2\u0026times;300 bp). Raw reads were pre-processed with readfq (v8) to remove low-quality segments (default quality\u0026thinsp;\u0026lt;\u0026thinsp;38 for \u0026gt;\u0026thinsp;40 bp), reads with \u0026ge;\u0026thinsp;10 bp N, and adapter-contaminated reads with \u0026ge;\u0026thinsp;15 bp overlap; potential host reads were removed by aligning to GRCh38 with Bowtie2 v2.2.4 Clean, de-hosted reads were assembled with MEGAHIT v1.0.4-beta. Taxonomic profiles were generated by aligning predicted proteins to NCBI NR (2018-01-02) using DIAMOND v0.9.9 (\u003cem\u003eblastp\u003c/em\u003e, \u003cem\u003ee\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;1e-5) and assigning taxa via a lowest-common-ancestor approach (MEGAN); interactive summaries were visualized in Krona. Functional profiling targeted KEGG pathways and CAZy enzyme classes using DIAMOND (\u003cem\u003eblastp\u003c/em\u003e, \u003cem\u003ee\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;1e-5) against the respective databases; relative abundances were calculated by summing features annotated to each pathway/class.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiversity and statistical analysis.\u003c/b\u003e Analyses were conducted in QIIME2 and R/phyloseq.\u0026nbsp;Alpha diversity (Shannon, Simpson) and beta diversity (Bray\u0026ndash;Curtis) were computed on normalized feature tables; group differences in beta diversity were tested by PERMANOVA (999 permutations). For taxa and pathway relative abundances, between-group differences were assessed by Wilcoxon rank-sum tests with Benjamini\u0026ndash;Hochberg FDR correction; \u003cem\u003eq\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Where indicated, results are reported with effect sizes and FDR-adjusted \u003cem\u003eq\u003c/em\u003e-values.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCohort and groups.\u003c/b\u003e We analyzed fecal metagenomes from 15 pediatric PKU patients: G1 (no structured Phe restriction, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9) and G2 (Phe-restricted diet, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6). Ages ranged from 10 days to 18 years; PKU genotypes are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Diet group assignment was independent of genotype.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePAH gene variants identified in the PKU cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eallele 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eallele 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eheter/homo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehomo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep.Arg252Trp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehetero\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep.Pro275Arg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep.Arg252Trp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehetero\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep.Arg252Trp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep.Arg252Trp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehomo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep.Ser349Pro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep.Ser349Pro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehomo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep.Ser349Pro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep.Ser349Pro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehomo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehomo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eno variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eno variant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003enormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehomo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehomo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehomo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehomo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.441\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T (Intronic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehomo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep.Arg252Trp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep.Ala403Val\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehetero\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep.Pro275Arg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ec.60\u0026thinsp;+\u0026thinsp;5G\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehetero\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eArtificial intelligence\u0026ndash;assisted content\u003c/h2\u003e \u003cp\u003eArtificial intelligence\u0026ndash;based tools (ChatGPT) was used exclusively for language refinement, grammar, and clarity of expression. The AI tool did not contribute to study design, data analysis, data interpretation, or generation of scientific content. All content generated with AI assistance was carefully reviewed, edited, and validated by the authors, who take full responsibility for the accuracy and integrity of the manuscript.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eTaxonomic composition\u003c/b\u003e. At the phylum level, both groups were dominated by Firmicutes and Bacteroidetes, with lower contributions from Actinobacteria, Proteobacteria, and Verrucomicrobia. Hierarchical clustering of phylum profiles grouped most G1 samples together, whereas G2 showed greater between-subject dispersion (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At the genus level, G1 displayed higher relative abundance of Faecalibacterium and Prevotella, while G2 showed frequent enrichment of Bacteroides and Bifidobacterium (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Akkermansia and Roseburia were generally low across both groups. At the species level G2 (Phe-restricted) showed recurrent enrichment of Bacteroides uniformis and Bacteroides vulgatus, and higher representation of Eggerthella lenta and Bilophila wadsworthia in individual subjects. G1 (non-restricted) was characterized by multiple Firmicutes species, including Ruminococcus spp., Oscillibacter spp. (e.g., \u003cem\u003eOscillibacter\u003c/em\u003e sp. ER4/57_20), Clostridium spp. (several \u003cem\u003eCAG\u003c/em\u003e designations), Faecalibacterium (e.g., \u003cem\u003eFaecalibacterium\u003c/em\u003e sp. CAG74), and Butyricicoccus/\u003cem\u003eSubdoligranulum\u003c/em\u003e representatives\u0026mdash;consistent with SCFA-producing lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCommunity structure.\u003c/b\u003e Ordinations based on Bray\u0026ndash;Curtis distances demonstrated diet-associated stratification. NMDS achieved low stress (~\u0026thinsp;0.06) with G1 samples forming a tighter cluster and G2 samples more dispersed; PCA showed a similar separation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional analysis.\u003c/b\u003e Functional profiling based on KEGG and CAZy annotations revealed a consistent enrichment of carbohydrate-active enzymes (CAZymes), particularly glycoside hydrolases (GH) and glycosyltransferases (GT), across all samples. These enzymes were more abundant in G2 (Phe-restricted) patients, suggesting a microbial shift toward enhanced carbohydrate degradation and glycan modification potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the pathway level (KEGG), genes related to metabolism dominated the functional repertoire, followed by genetic information processing and environmental information processing pathways. Among metabolic pathways, carbohydrate, amino acid, and energy metabolism were the most represented categories across both groups. Subcategories within these pathways included glycolysis/gluconeogenesis, lipid metabolism, and biosynthesis of amino acids (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eAnalysis of key functional orthologs (Kos) revealed group-specific differences. G2 samples displayed higher abundances of genes associated with membrane transport (K02003, K02004), two-component regulatory systems (K05229, K07496), and signal transduction. Conversely, G1 samples were enriched in genes related to ribosomal structure (K03091), energy conversion (K08133), and posttranslational modification (K06180, K04759) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudies of healthy pediatric gut microbiomes highlight the dominance of Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In PKU patients, however, reduced microbial diversity is observed, particularly at the phylum and genus levels. Our study found enrichment in Firmicutes and Bacteroidetes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) across all patients, consistent with healthy populations, but with a depletion of Proteobacteria, Verrucomicrobia, and Chlamydiae [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These observations align with prior reports [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which noted significant changes in the relative abundance of Bacteroides and Prevotella in PKU patients compared to healthy controls. However, our findings further identified species-level patterns: patients on the Phe-restricted diet frequently showed higher \u003cem\u003eBacteroides uniformis\u003c/em\u003e and \u003cem\u003eBacteroides vulgatus\u003c/em\u003e, along with \u003cem\u003eEggerthella lenta\u003c/em\u003e and \u003cem\u003eBilophila wadsworthia\u003c/em\u003e, whereas the non-restricted group was characterized by multiple Firmicutes species including \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e spp., \u003cem\u003eFaecalibacterium\u003c/em\u003e [e.g., CAG74], and \u003cem\u003eSubdoligranulum\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These taxa mirror functional tendencies toward SCFA production in the non-restricted group and increased Bacteroides-associated carbohydrate processing and stress/host-interaction functions in the restricted group.\u003c/p\u003e \u003cp\u003eGroup 1 (non-restrictive diet) showed typical phylum diversity, with increased Firmicutes and Bacteroidetes, but exhibited significant reductions in Actinobacteria, Proteobacteria, and Verrucomicrobia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The depletion of Actinobacteria, particularly genera like Bifidobacterium, could reflect a diet lower in prebiotic fibers, which are essential substrates for their growth [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, Proteobacteria and Verrucomicrobia, including Akkermansia muciniphila, are associated with mucin degradation, immune modulation, and a healthy gut barrier, which may be compromised in PKU patients consuming higher protein, lower fiber diets [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Given the small, heterogeneous cohort, we interpret these phylum-level shifts as consistent with altered ecology under differing dietary exposures rather than as deterministic markers of dysbiosis, and we note substantial inter-individual variability even within groups. Importantly, while some of these bacteria are generally considered commensal or saprophytic, they can exhibit opportunistic pathogenic behavior in immunocompromised individuals. For instance, Proteobacteria include potential pathogens like Escherichia coli, which can cause intestinal inflammation, and a reduction in Akkermansia muciniphila has been linked to gut barrier dysfunction and metabolic disorders [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In addition, several intestinal \u003cem\u003eClostridium\u003c/em\u003e species have been shown to induce cytokine responses in human mononuclear cells, underscoring the context-dependent immunological effects of this genus [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These shifts indicate an altered gut environment, where certain taxa thrive while others are depleted, reflecting dysbiosis and reduced microbial diversity linked to untreated PKU.\u003c/p\u003e \u003cp\u003eGroup 2 (Phe-restricted diet) exhibited greater variation, with some patients experiencing enrichment of Actinobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), likely influenced by individual prebiotic intake [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This highlights the role of dietary modifications in shaping microbial composition, as specific prebiotics can promote the growth of beneficial bacteria like Bifidobacterium. However, a subset of patients also showed reductions in Firmicutes, key SCFA producers, which could impair gut function [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. At the genus level, Faecalibacterium was enriched in all patients, consistent with its role in gut homeostasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Compared to findings from McWhorter et al. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], which focused on microbial taxonomic shifts, our study uniquely revealed species-level and functional shifts\u0026mdash;particularly the appearance of \u003cem\u003eB. uniformis\u003c/em\u003e, \u003cem\u003eB. vulgatus\u003c/em\u003e, and \u003cem\u003eE. lenta\u003c/em\u003e in restricted patients\u0026mdash;that help contextualize metabolic adaptation in the setting of dietary Phe restriction.\u003c/p\u003e \u003cp\u003eCommunity structure analysis supported these compositional trends: Bray\u0026ndash;Curtis ordinations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed group-wise stratification, with G1 forming a tighter cluster and G2 more dispersed, indicating higher inter-individual variability under dietary restriction. This pattern is consistent with differential exposure to medical foods, fiber sources, and adherence heterogeneity within the restricted group.\u003c/p\u003e \u003cp\u003eGroup 1 had higher levels of Prevotella, a genus linked to fiber-rich diets, consistent with their unrestricted eating patterns. In contrast, Group 2 displayed lower Prevotella abundance, aligning with the protein-focused and Phe-restricted diets often lacking fiber (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, our findings highlight functional adaptations within the microbiome: carbohydrate-active enzymes\u0026mdash;especially glycoside hydrolases (GH) and glycosyltransferases (GT)\u0026mdash;were more abundant in restricted patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and KEGG orthologs linked to membrane transport (e.g., K02003, K02004) and signal transduction/two-component systems (e.g., K05229, K07496, K05349) were elevated in G2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In contrast, G1 showed higher abundance of orthologs related to translation/ribosomal structure (e.g., K03091), energy production and conversion, and post-translational processes (e.g., K06180, K04759), suggesting a relatively more biosynthetically active community. These KO-level differences align with the CAZy signal and reinforce a shift toward transport and sensing functions under dietary restriction. Such findings expand upon previous studies like Verduci et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and Pinheiro de Oliveira et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], which primarily focused on taxonomic differences without exploring functional implications.\u003c/p\u003e \u003cp\u003eThe observed reduction in SCFA-producing bacteria, particularly \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eRoseburia\u003c/em\u003e, in Group 2 highlights a critical link between dietary management and PKU pathophysiology. SCFAs, especially butyrate, are essential for maintaining gut barrier integrity, modulating systemic inflammation, and providing energy substrates for colonocytes [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A decline in SCFA producers may exacerbate intestinal inflammation and impair the epithelial barrier, potentially contributing to nutrient malabsorption and systemic metabolic dysregulation in PKU patients. Moreover, the loss of these beneficial taxa could influence neurological health, as SCFAs have been implicated in gut-brain axis regulation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], an area of concern given the neurodevelopmental impact of PKU. These findings underscore the importance of restoring SCFA-producing populations through targeted interventions.\u003c/p\u003e \u003cp\u003eClinically, the implications of these findings are significant. Personalized approaches, such as prebiotic supplementation to boost \u003cem\u003eFaecalibacterium\u003c/em\u003e populations or probiotic formulations targeting SCFA production, could help mitigate the adverse effects of dysbiosis in PKU patients [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These strategies may not only enhance gut health but also improve systemic metabolic and neurological outcomes. Additionally, dietary adjustments within Phe-restriction guidelines that incorporate SCFA-promoting fibers could offer a dual benefit of supporting microbial diversity and improving clinical management of PKU [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is well established that phenylalanine and tyrosine levels play critical roles in PKU pathophysiology. Patients on a non-Phe-restricted diet typically exhibit elevated phenylalanine levels due to the inability to metabolize Phe, while tyrosine remains deficient [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In contrast, patients adhering to Phe-restricted diets have lower phenylalanine levels, although tyrosine levels can still remain suboptimal. This imbalance can influence microbial metabolism since amino acids like phenylalanine are substrates for specific gut bacterial processes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In clinical PKU cohorts, lower abundance of genus \u003cem\u003eBacteroides\u003c/em\u003e has been reported to be negatively correlated with blood phenylalanine levels [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Elevated phenylalanine levels in PKU patients can disrupt energy homeostasis, shifting reliance toward carbohydrates, particularly in untreated individuals, while Phe-restricted diets often compensate with higher carbohydrate intake [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This dietary shift may alter gut microbial composition, promoting taxa like \u003cem\u003ePrevotella\u003c/em\u003e, associated with carbohydrate fermentation, and impacting SCFA production, which influences gut health [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. While our study did not measure phenylalanine and tyrosine levels, their impact on microbial composition cannot be overlooked and warrants further investigation.\u003c/p\u003e \u003cp\u003eFunctionally, Glycoside Hydrolases (GH), essential for carbohydrate breakdown, were prevalent across all patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), while Carbohydrate-Binding Modules (CBM) and Glycosyl Transferases (GT) were reduced in Group 2 due to limited fiber intake [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Metabolic pathway analysis revealed consistent carbohydrate and amino acid metabolism across both groups, despite dietary differences. This suggests a core microbiome functionality adapted to PKU. However, patients on restricted diets showed a shift toward carbohydrate fermentation over protein metabolism, consistent with previous studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. KEGG analysis revealed decreased secondary metabolite biosynthesis, indicating dietary limitations, but no significant differences between the groups, suggesting a conserved core microbiome [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings suggest that gut microbiome profiling could serve as a complementary tool to monitor PKU patients, providing insights into microbiome disruptions associated with dietary adherence. Clinically, this data could guide personalized interventions, such as targeted prebiotic or probiotic supplementation, to restore microbial balance and improve gut health. For research, longitudinal studies could further explore the relationship between dietary interventions, microbiome functionality, and metabolic outcomes in PKU patients to optimize clinical management strategies. Importantly, this work represents, to our knowledge, the first metagenomic characterization of pediatric PKU patients from Ecuador and contributes data from an under-represented Latin American setting, where access to low-Phe products and dietetic support can differ from previously studied regions.\u003c/p\u003e \u003cp\u003eMicrobiome alterations have also been described in other metabolic conditions and therapeutic contexts, including type 1 diabetes and dietary/pharmacologic exposures [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], as well as lysosomal and copper metabolism disorders such as Gaucher disease and Wilson disease [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. While these conditions differ mechanistically from PKU, they support the broader concept that metabolic status and treatment exposures can reshape gut microbial ecology.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis pilot included a small pediatric cohort, limiting statistical power and generalizability and raising the risk of selection bias. The absence of clinical controls and variability in dietary adherence may have introduced confounding, affecting observed taxonomic and functional patterns. Its cross-sectional design prevents causal inference between diet and microbiota. Most prior work involves animals, adults, or mixed ages; our pediatric focus may not fully translate to adults, whose microbiomes and dietary responses differ with age.\u003c/p\u003e \u003cp\u003eFuture work should include larger, multicenter cohorts with appropriate controls (e.g., healthy peers or non-PKU siblings), longitudinal sampling with detailed diet and clinical metadata, and age-stratified analyses that include adults. Integrating multi-omics (e.g., metabolomics, transcriptomics) will help link microbial shifts to metabolic function and identify potential therapeutic targets.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this exploratory, cross-sectional study of 15 pediatric PKU patients from Ecuador, we observed diet-associated shifts in gut microbiome composition and function. Phe-restricted patients (G2) showed enrichment of \u003cem\u003eBacteroides uniformis\u003c/em\u003e, \u003cem\u003eB. vulgatus\u003c/em\u003e, and \u003cem\u003eEggerthella lenta\u003c/em\u003e, alongside higher representation of carbohydrate-active enzymes (notably GH and GT) and KEGG orthologs linked to membrane transport and signal transduction. Non-restricted patients (G1) were characterized by multiple SCFA-associated Firmicutes (e.g., \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e spp., \u003cem\u003eFaecalibacterium\u003c/em\u003e sp. CAG74), and KOs related to translation/ribosomal structure and energy production. Community-level analyses (Bray\u0026ndash;Curtis PCA/NMDS) indicated group stratification, with tighter clustering in G1 and greater inter-individual variability in G2.\u003c/p\u003e \u003cp\u003eTaken together, these findings suggest that dietary management in PKU is associated with both taxonomic and functional reconfiguration of the gut microbiome, with restricted diets favoring transport/sensing functions and non-restricted diets retaining SCFA-linked lineages. Given the small, heterogeneous cohort and cross-sectional design, results are hypothesis-generating. Larger, age-stratified, multicenter studies in Latin America that pair metagenomics with dietary intake, Phe/Tyr and SCFA measurements, and longitudinal follow-up are needed to validate these signals and to inform targeted, microbiome-aware dietary strategies within PKU care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePKU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ephenylketonuria\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePhe\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ephenylalanine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eshort-chain fatty acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKEGG ortholog\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAZy\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCarbohydrate-Active enZymes database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglycoside hydrolase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eglycosyltransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-metric multidimensional scaling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprincipal component analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePERMANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epermutational multivariate analysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSequence Read Archive.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The study protocol was reviewed and approved by the Universidad San Francisco de Quito Bioethics Committee on April 19, 2023 (approval code 2023-010IN). Written informed consent was obtained from the parents or legal guardians of all participants prior to enrollment, and assent was obtained from participants when appropriate. Following consent, relevant clinical and demographic information was collected for research purposes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003e Written informed consent for publication of anonymized data was obtained from the parents or legal guardians of all participants.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors received no specific funding for this work. The authors confirm independence from the sponsors, and the content of the article was not influenced by any external funding sources.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003ePaul Leon-Gomez [PL] contributed to data curation, formal analysis, investigation, methodology, validation, visualization, and writing of the original draft, as well as review and editing of the manuscript. Vanessa I. Romero [VR] contributed to conceptualization, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, visualization, and writing of the original draft, as well as review and editing of the manuscript. Ivonne Salinas [IS] contributed to conceptualization, data curation, formal analysis, investigation, methodology, visualization, and manuscript review and editing. Ariel Vargas [AV] contributed to conceptualization, formal analysis, investigation, methodology, visualization, and manuscript review and editing. Alex S. Aguirre [AA] contributed to formal analysis, investigation, methodology, and manuscript review and editing. Diego I. Montenegro [DM] contributed to formal analysis, investigation, methodology, and manuscript review and editing.All authors reviewed and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to express our gratitude to the principal investigator, Dr. Vanessa Romero, for her invaluable mentorship throughout this project, as well as to the group of medical students from the Universidad San Francisco de Quito who contributed significantly to its initiation and development. We also thank our colleagues, the Instituto de Microbiolog\u0026iacute;a, and the Universidad San Francisco de Quito for their support and resources.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe shotgun metagenomic sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession PRJNA1406308. The data have been released and are publicly accessible through NCBI. All other data supporting the findings of this study are included within the article and its supplementary material.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfzaal, M. et al. Human gut microbiota in health and disease: Unveiling the relationship. \u003cem\u003eFront. 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Probiotics: An update. \u003cem\u003eJ. Pediatr.\u003c/em\u003e \u003cb\u003e96[6]\u003c/b\u003e, 839\u0026ndash;846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jped.2020.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jped.2020.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Phenylketonuria, metagenomic analysis, gut microbiome, nutritional therapy, shotgun metagenomics, pediatric metabolic disease","lastPublishedDoi":"10.21203/rs.3.rs-9010202/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9010202/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePhenylketonuria (PKU) requires lifelong phenylalanine (Phe) restriction to prevent neurotoxicity. Dietary management may remodel the gut microbiome, with potential clinical implications. We characterized taxonomic and functional features of the gut microbiome in pediatric PKU patients managed with Phe-restricted versus non-restricted diets in Ecuador.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe performed a cross-sectional exploratory shotgun metagenomic analysis of 15 PKU patients (non-restricted diet, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9; Phe-restricted diet, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6). Taxonomic profiles were resolved to the species level, and functional potential was assessed using KEGG orthologs and CAZy enzyme families. Community structure was evaluated using Bray\u0026ndash;Curtis-based ordination analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBoth groups were dominated by Firmicutes and Bacteroidetes, with lower representation of Actinobacteria, Proteobacteria, and Verrucomicrobia. At the genus and species levels, non-restricted samples (G1) were characterized by multiple short-chain fatty acid\u0026ndash;associated Firmicutes, including \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eOscillibacter\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e spp., \u003cem\u003eFaecalibacterium\u003c/em\u003e sp. CAG74, and \u003cem\u003eSubdoligranulum\u003c/em\u003e. In contrast, Phe-restricted samples (G2) showed recurrent enrichment of \u003cem\u003eBacteroides uniformis\u003c/em\u003e, \u003cem\u003eBacteroides vulgatus\u003c/em\u003e, \u003cem\u003eEggerthella lenta\u003c/em\u003e, and \u003cem\u003eBilophila wadsworthia\u003c/em\u003e. Ordination analyses demonstrated diet-associated stratification, with tighter clustering in G1 and greater dispersion in G2. Functionally, G2 exhibited higher relative abundance of carbohydrate-active enzymes\u0026mdash;particularly glycoside hydrolases and glycosyltransferases\u0026mdash;and increased KEGG orthologs related to membrane transport and signal transduction.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn this Ecuadorian pediatric cohort, dietary management in PKU is associated with distinct taxonomic and functional gut microbiome profiles. These hypothesis-generating findings support integrating microbiome analyses into PKU care and motivate larger longitudinal studies in under-represented Latin American settings.\u003c/p\u003e","manuscriptTitle":"Diet-Associated Gut Microbiome Signatures in Pediatric Phenylketonuria: A Shotgun Metagenomic Study from Ecuador","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 06:01:56","doi":"10.21203/rs.3.rs-9010202/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-11T10:22:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-11T10:21:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T16:30:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-06T18:19:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-06T17:19:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"34c3e4ca-7813-4b6d-85a5-346a9a7ecf6f","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64333275,"name":"Health sciences/Diseases"},{"id":64333276,"name":"Health sciences/Gastroenterology"},{"id":64333277,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-03-16T06:01:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 06:01:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9010202","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9010202","identity":"rs-9010202","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}