Microbiome Signature Linked to the Development and Management of Intestinal Acute Graft-versus-Host Disease in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation

Microbiome Signature Linked to the Development and Management of Intestinal Acute Graft-versus-Host Disease in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Microbiome Signature Linked to the Development and Management of Intestinal Acute Graft-versus-Host Disease in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation Hua Jiang, Yuhua Qu, Chen Xinxin, Zhou Haifei, Wenjiao Ding, Xiaojing Wang, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6997868/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Allogeneic stem cell transplantation (allo-HSCT) cures pediatric blood disorders, but graft-versus-host disease (GVHD) remains a major complication. This study investigated the link between gut microbiota and GVHD in 118 pediatric allo-HSCT patients (2020-2023). Fecal samples were collected pre-transplant and up to 90 days post-transplant.Patients developing GVHD (GVHD+, n=49) showed significantly reduced gut microbial diversity (α-diversity), especially at day 14, compared to non-GVHD patients (GVHD-, n=69). While initial community structure (β-diversity) was similar, GVHD+ patients had increased pro-inflammatory Proteobacteria and Actinobacteria, whereas GVHD- patients had more protective Stenotrophomonas. Machine learning identified predictive microbial features.Longitudinally, responders showed recovery of beneficial short-chain fatty acid (SCFA)-producing bacteria. Non-responders had persistent enrichment of Firmicutes and opportunistic pathogens. Functional analysis linked dysbiosis to impaired SCFA synthesis and carbohydrate metabolism.The findings highlight the gut microbiota's dual role as both a biomarker and modulator of acute GVHD. Strategies preserving microbial diversity, restoring SCFA producers, and using predictive microbial models could improve clinical outcomes, underscoring the potential for microbiota-targeted interventions to reduce GVHD and enhance transplant success. Health sciences/Diseases/Gastrointestinal diseases/Intestinal diseases Health sciences/Medical research/Stem-cell research Gut Microbiome Acute Graft-versus-Host Disease Allogeneic Hematopoietic Stem Cell Transplantation Alpha Diversity Short-Chain Fatty Acids (SCFA) Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Allogeneic Hematopoietic Stem Cell Transplantation (allo-HSCT) offers a potentially curative approach for various hematological conditions, yet graft-versus-host disease (GVHD) remains a significant complication. GVHD arises when donor immune cells attack host tissues, potentially causing organ damage and reducing post-transplant survival rates and quality of life, with incidence rates of 30–70% and up to 30% of cases being fatal. Acute GVHD (aGVHD) commonly affects the gastrointestinal tract, skin, and liver. Recent studies have highlighted the role of gut microbiota in aGVHD pathogenesis and treatment outcomes 1 . Patients undergoing allo-HCT often exhibit reduced fecal diversity and an increase in potentially pathogenic bacteria 2 – 4 . Loss of diversity and increased abundance of certain bacterial groups have been linked to adverse outcomes, including higher mortality and increased GVHD risk. The genus Enterococcus has been associated with GVHD after allo-HCT 4 . Higher gut microbiota diversity during the peri-engraftment period correlates with lower transplant-related mortality risk 3 , 5 . An increased abundance of the Blautia genus has been linked to reduced aGVHD lethality and improved survival, while the presence of Lachnospiraceae shows a negative correlation with severe aGVHD development. Lower concentrations of short-chain fatty acids (SCFAs), particularly butyrate, have been observed in GVHD patients 6 – 10 . Although many studies have explored the link between microbiota composition and aGVHD post-allo-HSCT, the specific changes in gut microbial structure and diversity are not fully understood. Moreover, the microbiota associated with aGVHD varies among transplantation centers. Most studies have focused on adult patients, with changes in gut microbiota in pediatric HSCT patients remaining underreported. Pediatric patients may be more susceptible to antibiotic-induced gut microbiota changes due to their immature gut microbiome with fewer beneficial commensal anaerobes 11 . Post-conditioning regimen and allo-HSCT, patients often experience reduced oral nutrient intake due to side effects like nausea, anorexia, and mucositis. Parenteral nutrition (PN), traditionally the first nutritional approach, is associated with complications such as infections and gut microbiota dysbiosis, making enteral nutrition (EN) a preferable alternative. Pediatric patients receiving PN showed reduced SCFA levels after HSCT 12 , while those treated with EN had rapid gut microbiome recovery and higher SCFA levels. Similar findings were observed in adult HSCT cohorts 13 . In this study, we explored the impact of gut microbiota composition on clinical outcomes in pediatric patients undergoing allo-HSCT by prospectively collecting fecal samples and sequencing the V3 to V4 regions of the 16S ribosomal (rRNA) gene. In parallel, from hospital admission, all patients who developed GI aGVHD were offered deeply hydrolyzed milk powder as an adjuvant therapy for first-line medication. We identified specific bacterial signatures associated with aGVHD at pre-transplant, at day 0, and peri-onset. MATERIAL AND METHODS Patients Our prospective study enrolled patients undergoing allo-HSCT from August 2020 to December 2023, with approval from the Ethics Committee and informed consent obtained in line with the Declaration of Helsinki. Patients were prepared for transplantation using modified myeloablative or reduced intensity conditioning regimens and GVHD prophylaxis based on Beijing or PT-Cy protocols. GVHD treatment was at the physician's discretion, with topical corticosteroids for mild to moderate skin GVHD, oral budesonide for mild gastrointestinal GVHD, and systemic steroids for other manifestations. Steroid-refractory aGVHD was defined as progression at day 3, unstable disease at day 7, or no remission at 2 weeks. Patients refractory to systemic steroids received second-line therapy. Nonsteroid immunosuppressants, usually calcineurin inhibitors, were used to taper systemic steroids. Patients were categorized into GVHD+ (developed gastrointestinal aGVHD) and GVHD- (no aGVHD signs) groups. aGVHD incidence was determined by time to initial diagnosis, with severity graded using modified Glucksberg criteria. GVHD + patients were classified as mild (grade I) or severe (grades II-IV). Stool samples were collected pre-transplant, at Day 0, Day 14, Day 30, and Day 90 post-transplant. For GVHD + patients, samples were grouped into pre-transplant, Day 0, pre-onset, onset, and post-treatment. For GVHD- patients, samples were grouped into the five time points. Fecal samples were stored at -80°C until sequencing. Clinical data were collected up to 100 days post-transplant, including age, diagnosis, HLA matching, conditioning regimen, GVHD status, and graft characteristics. In the GVHD + group, GVHD onset date, involved organs, stage, grade, and steroid response were recorded. Death dates and causes were also noted. The clinical response rate at 28 days post-treatment was used to divide GVHD + patients into Responder (CR/PR) and Non-Responder subgroups. Total Bacterial DNA Extraction, 16S rRNA Gene Amplification, Library Preparation, and Sequencing DNA extraction and sequencing: MagPure Stool DNA KF kit B (Magen) extracted microbial community DNA per the manufacturer's instructions. The V3-V4 regions of bacterial 16S rRNA genes were amplified using degenerate PCR primers (341F and 806R) with a barcode. PCR reactions used 15 µL Phusion® High-Fidelity PCR Master Mix (New England Biolabs), 0.2 µM primers, and ~ 10 ng template DNA. Thermal cycling included initial denaturation at 98℃ for 1 min, followed by 30 cycles of denaturation at 98℃ for 10 s, annealing at 50℃ for 30 s, and elongation at 72℃ for 30 s, with a final extension at 72℃ for 5 min. DNA quantification was done via Qubit fluorometer with a Qubit® dsDNA BR Assay kit (Invitrogen), and quality was checked on a 1% agarose gel. The PCR products were purified with AmpureXP beads and libraries qualified by Agilent 2100 bioanalyzer (Agilent). Libraries were pooled and sequenced on Illumina NovaSeq6000 (Novogene, Beijing, China) to generate 2×250bp paired-end reads. Bioinformatics and Statistics Bioinformatics analysis was conducted on paired-end raw sequences using FLASH (V1.2.11) 15 , fastp (V0.23.1) 16 , and VSEARCH 17 to obtain high-quality tags. DADA2 in QIIME2 18 software was used for denoising to obtain initial ASVs, annotated via a pre-trained Naive Bayes classifier with the Silva 138.1 database. Sample clustering was performed using UPGMA in QIIME2. ASV absolute abundance was normalized to the median sequence number. ASVs with 0.1% relative abundance in at least one sample. α-diversity metrics including Observed OTUs, Chao1, Shannon, Simpson, and Pielou’s Evenness were calculated in QIIME2. PCoA analysis was conducted using the vegan R package (v2.6-8) 19 with a Bray-Curtis dissimilarity matrix. PERMANOVA testing for β-diversity used the adonis2 function in vegan with 999 permutations. Feature selection was performed using the Boruta algorithm (R package v8.0.0), a random forest-based method to identify ASVs associated with GVHD onset. The Wilcoxon rank sum test was used to identify differentially abundant taxa between groups. Classification models were built using PLSDA and random forest algorithms via the mixOmics (v6.28.0) 20 , 21 and randomForest (v4.7-1.2) 22 R packages. Both models used five-fold cross-validation repeated five times. Model performance was evaluated using AUC-ROC curves with the pROC package (v1.18.5). LDA Effect Size analysis was performed using the microeco v1.9.1 package 23 , 24 . The Kruskal-Wallis rank-sum test was used in LEfSe to detect differences between pre-onset and post-treatment patients. Predicted metabolic functions were derived from 16S rRNA sequences using PICRUSt 3 , 25 – 27 .. Statistical analyses were performed using R v4.4.1 and GraphPad v9.0. Continuous variables were compared using the Wilcoxon rank-sum test, and categorical variables using Chi-square or Fisher’s exact tests. A two-sided P-value < 0.05 was considered statistically significant. RESULTS Baseline Characteristics and Alpha Diversity Changes Over Time We analyzed 537 fecal samples from 118 pediatric patients across five time points: pre-HSCT, HSCT day, biweekly in the first month post-HSCT, and three months post-HSCT. This yielded an average of 4.5 samples per patient. The cohort included more males (76) than females (42), with a median age of 6.0 years (range 0–14 years). Patients had either malignant or nonmalignant diseases. 92 patients received PBSCs and 22 received UCB. aGVHD primarily affected the gastrointestinal tract, skin (20 cases), and liver (2 cases). The median day of aGVHD onset was day 22 posttransplant. During follow-up, 4 patients died.The characteristics of the total cohort, GVHD + and GVHD- groups are summarized in Table 1 . Table 1 Patient, disease and transplant characteristics. Total (n = 118) aGVHD+ (n = 49) aGVHD- (n = 69) Gender Male 76 32 (65.3%) 44 (63.8%) Female 42 17 (34.7%) 25 (36.2%) Age at transplant : median (range, year) 6.0 (0–14) 6.61 6.29 Disease type Thalassemia 23 (46.9%) 24 (34.8%) ALL 6 (12.2%) 6 (8.7%) AA 4 (8.2%) 5 (7.2%) AML 5 (10.2%) 14 (20.3%) MPS 5 (10.2%) 12 (17.4%) Leukodystrophy 1 (2.0%) 4 (5.8%) Others 5 (10.2%) 4 (5.8%) Donor type Related-matched 8 (16.3%) 13 (18.8%) Related-mismatched 12 (24.5%) 23 (33.3%) Unrelated-matched 9 (18.4%) 11 (15.9%) Unrelated-mismatched 20 (40.8%) 22 (31.9%) Graft resource BM 4 1 (2.0%) 3 (4.3%) PBSC 92 43 (87.8%) 49 (71.0%) UCB 22 5 (10.2%) 17 (24.6%) ABO type Match* 21 (42.9%) 34 (49.3%) Mismatch 28 (57.1%) 35 (50.7%) Conditioning regime MAC 40 (81.6%) 54 (78.3%) MAC-TBI 6 (12.2%) 6 (8.7%) RIC 3 (6.1%) 9 (13.0%) Antibiotics post-HSCT 29 (59.2%) 41 (59.4%) aGVHD prophylaxis ATG + CNI + MMF + MTX 46 (93.9%) 60 (87.0%) PT-Cy + CNI + MMF 3 (6.1%) 9 (13.0%) aGVHD at onset Ⅱ-Ⅳ 37 (75.5%) / Ⅰ 12 (24.5%) / Median Day of Onset (days from transplantation) Organ involvement at diagnosis 22 Gastrointestinal 30 (61.2%) / Gastrointestinal + Skin 18(36.7%) / Gastrointestinal + Skin + Liver 2(4.1) / Abbreviations: ALL, acute lymphocytic leukemia; AML, Acute myelocytic leukemia; CDA, congenital dyserythropoietic anemia; SDS, Shwachman-Diamond syndrome; AA, aplastic anemia; MPS, mucopolysaccharidosis; DKC, dyskeratosis congenita; SCID, severe combined immunodeficiency; MDS, myelodysplastic syndromes. Other disease type include: CDA, congenital dyserythropoietic anemia; CML, chromic myelocytic leukemia; FA, Fanconi anemia; HLH, hemophagocytic lymphohistiocytosis; HD: Hodgkin disease; IBD, inflammatory bowel disease; WAS, Wikot-AIdrich syndrome; GI aGVHD: gastrointestinal acute graft-versus-host disease; ATG: anti-thymoglobulin; MAC, myeloablative conditioning; RIC, reduced intensity conditioning; TBI, total body irradiation. Match* in ABO type includes A to A, B to B, AB to AB and O to O. Alpha diversity assessed community diversity and richness. Baseline alpha diversity showed no significant difference in Pielou’s Evenness, species richness, observed OTUs, and Chao1, but the Shannon index was higher in GVHD- patients than GVHD + patients(Fig. 1and 2). For GVHD + patients, the Shannon index decreased from baseline to post-transplant (p < 0.05), indicating reduced gut microbiota diversity. It reached the lowest point at 14 days post-treatment and then gradually recovered. The Shannon index dynamics showed a decrease until diarrhea onset, followed by an increase after treatment, aligning with previous studies on allogeneic HSCT patients 3 , 5 . We examined microbial composition differences via PCoA analysis of beta diversity using Bray-Curtis distance. No significant beta-diversity differences were found between GVHD and non-GVHD patients (Adonis test, p = 0.423), indicating no baseline community structure difference(Fig. 2 ). Patients with severe aGVHD involving the gastrointestinal tract also showed no significant difference compared to those with mild aGVHD or non-GVHD patients. At the phylum level, the dominant microbiota across all time points were Firmicutes, Proteobacteria, Bacteroidota, and Actinobacteria. At the genus level, the co-dominant genera included Enterococcus, Escherichia-Shigella, Bacteroides, Streptococcus, Lacticaseibacillus, Parabacterioids, and Pseudomonas. GVHD-Associated Taxa Identified at Pre-Transplant by Boruta Algorithm and Model Construction Boruta feature selection identified 17 ASVs distinguishing GVHD + and GVHD- microbiomes (Fig. 2 ), with 15 showing significant abundance differences. Thirteen ASVs were overrepresented in GVHD+, including Actinobacteriota (4 taxa) and Proteobacteria (8 taxa), and Aliidiomarina sanyensis. Two ASVs were overrepresented in GVHD-: ASV9 (Proteobacteria, Stenotrophomonas) and ASV18 (Firmicutes) (Fig. 2 ). PLSDA analysis showed clear discrimination between GVHD + and GVHD- patients, with an AUC of 0.921(Fig. 3 ). The PLSDA loading plot revealed contributions of 15 taxa to component 1, with ASV9, ASV1009, and ASV231 being most relevant. The random forest model identified eight optimal ASVs for GVHD prediction, with an AUC of 0.903 (95% CI: 0.73–0.98, P = 0.0003). (Fig. 3 ). The random forest model showed weaker predictive capability (lower AUROC) than the PLSDA model. This may be because PLSDA's prediction threshold is based on specified distance, making ROC and AUC criteria less insightful in PLSDA 21 . Microbiota at Day 0 Was Associated with the Development of aGVHD PCoA of Day 0 samples showed no significant difference between GVHD + and GVHD- patients (adonis test, P = 0.213). Similarly, no separation was found between grade 2–4 and grade 1 aGVHD patients(Fig. 4 ). Boruta algorithms identified 26 ASVs, 22 of which differed in abundance. These ASVs belonged to Bacteroidota, Firmicutes, Patescibacteria, and Proteobacteria. In Proteobacteria, 14 ASVs were higher in GVHD+, while 8 were higher in GVHD-. PLSDA analysis using day 0 microbiota profiles significantly distinguished GVHD- from GVHD + patients, with an AUC of 0.954 (Fig. 4 ). The PLSDA loading plot showed that the taxa contributed to explaining the variability in the model for component 1 (50%) and component 2 (12%) (Fig. 4 ). The top contributors to component 1 were ASV82, ASV143, ASV516, ASV262, and ASV166, assigned to four genera including Halomonas and Brevundimonas. For component 2, ASV151, ASV477, ASV9, and ASV591 were the main contributors. Temporal Microbial Community Dynamics in GVHD + Patients To further assess the association between gut microbiota and the response to treatment, alterations of gut microbiota profiles were analyzed in patients who achieved complete or partial clinical remission (Responders subgroup, n = 34) versus those who did not achieve complete or partial remission (Non-Responders subgroup, n = 15). Firstly, we compared alpha diversity and revealed an overall trend towards a more consistent loss of diversity. Post-HSCT antibiotic burden was similar between R and NR subgroups (Table 1 ). At baseline, Shannon index of gut microbiota was higher in NR subgroup than in R subgroup (Median, 4.67 vs. 3.66, Mann-Whitney test, p = 0.0259). Shannon index was comparable between responders and nonresponders either at day 0 (3.00 vs. 3.38, p = 0.675) or at onset (3.18 vs. 2.68, p = 0.351). Notably, there was a significantly greater loss of alpha diversity from pre-transplant to onset day among NR patients and unexpectedly increased after treatment though the relatively small sample size prevents a more robust evaluation of this variable. Comparatively, although the diversity of the R subgroup also increased significantly after treatment, the magnitude of the change was much more stable than that of the NR group. We therefore compared longitudinal changes of microbiota between pre- and-on postset samples from grade 2–4 aGVHD who received standard first-line therapy, selecting the latest sample available in the pre-specified window when applicable. LEfSe analysis, which combines Linear Discriminant Analysis (LDA) and Wilcoxon rank-sum test, revealed that ASV96 and ASV203 were significantly enriched in responders post-treatment compared to pre-onset, with an LDA score > 2 and a p-value < 0.05. Conversely, in non-responders, all taxa enriched were assigned to the Firmicutes, one at pre-GVHD onset (ASV32) and 6 at post-treatment (ASV53, ASV 217, ASV146, ASV335, ASV661, and ASV437). Lacticaseibacillus to which ASV32 belongs is a part of fermentation microbiota with probiotic properties. The genus Faecalibacterium (ASV53) is a producer of SCFAs. The Functional Characterization of the Gut Microbiome in aGVHD Involves Understanding the Complex Interactions Between the Microbiota and the Host Immune System, Which Can Influence the Progression and Severity of aGVHD To explore the functional alterations in the gut microbiome in GVHD, we utilized PICRUSt2 to predict the functional composition profiles. We constructed the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology and the KEGG pathway/module profile. Notably, two pathways closely related to glycometabolism and SCFA metabolism exhibited statistical differences (P < 0.05) and were enriched in pre-transplant microbiomes of GVHD- patients: Transketolase and acyl carrier protein metabolism. Those findings could not be confirmed by a simultaneous analysis of plasma or cellular metabolome with no collected samples available for analysis. At Day 0, no difference of KEGG pathways was founded between two groups, even if Clostridium scindens (ASV326) was present in a lower abundance in day 0 samples of those patients who subsequently developed aGVHD. The comparison of pre-HSCT and Day 0 samples showed that the HSCT event resulted in a decrease in the relative abundance of butyrate producer genera such as Faecalibacterium , Clostridium , [Ruminococcus], Roseburia , Lachnospira , Dorea , Coprococcus , Blautia . DISCUSSION This study comprehensively analyzed the dynamic relationship between the gut microbiome and gastrointestinal acute graft-versus-host disease (aGVHD) in pediatric patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). It revealed age-specific dysbiosis patterns in children, who have unique immune and microbial vulnerabilities. The findings align with previous observations in adults and further clarify the causal mechanisms of pediatric aGVHD through multi-cohort integration. Specifically, post-transplantation, pediatric patients exhibited significantly reduced α-diversity (Shannon index), closely associated with disease severity and adverse outcomes. β-diversity analysis showed no baseline compositional differences between GVHD + and GVHD- groups, suggesting age-related microbial stability differences may influence disease susceptibility. Machine learning algorithms identified predictive microbial features, such as enriched Proteobacteria and Actinobacteria in GVHD + patients, linked to pro-inflammatory pathways, while protective genera like Stenotrophomonas may exert protective effects through competitive pathogen exclusion or local immune modulation. These insights provide a basis for GVHD risk stratification using microbial biomarkers and pave the way for age-specific microbial interventions, such as probiotic supplementation or fecal microbiota transplantation (FMT). Future research should validate these microbial signatures in independent cohorts and explore their clinical utility in guiding preventive interventions. Both pediatric and adult allo-HSCT recipients with aGVHD exhibit significantly reduced gut microbiota α-diversity (Shannon index), strongly associated with disease severity and adverse clinical outcomes, including higher mortality 28 . In pediatric patients, α-diversity loss occurs more rapidly, particularly during the post-transplantation hematopoietic reconstitution phase (reaching a nadir at day 14), likely due to their immature gut ecosystems and heightened susceptibility to antibiotic-induced dysbiosis. Despite partial recovery patterns similar to adults, reflecting shared mechanisms of pre-transplantation regimens and immune reconstitution in disrupting microbial stability, β-diversity analysis revealed no baseline structural differences between GVHD + and GVHD- groups in pediatric cohorts, contrasting with adult studies where microbial composition predicts outcomes 29 . However, machine learning models (Boruta and PLSDA) 30 identified predictive microbial features, such as enriched Proteobacteria (Aliidiomarina sanyensis) and Actinobacteria (Eggerthella) in GVHD + patients, which are linked to pro-inflammatory responses and gut barrier dysfunction in preclinical models, potentially exacerbating aGVHD progression 31 . Conversely, higher abundance of Stenotrophomonas (ASV9) in GVHD- patients suggests protective effects through competitive pathogen inhibition or immune modulation. These findings highlight age-specific microbial dynamics and support the development of early intervention strategies based on microbial biomarkers. The reduction of butyrate-producing genera (Faecalibacterium and Blautia) is a central feature of aGVHD pathogenesis. These genera enhance regulatory T cell (Treg) function, reinforce epithelial barrier integrity, and inhibit Th17-driven inflammation via short-chain fatty acids (SCFAs) 32 . In contrast, overproliferation of Enterococcus and Escherichia-Shigella in GVHD + patients exacerbates mucosal damage and neutrophil recruitment, sustaining inflammation 32 . Functional analysis using PICRUSt2 further linked dysbiosis to impaired carbohydrate metabolism and SCFA synthesis, such as reduced Clostridium scindens, which disrupts farnesoid X receptor (FXR) signaling and compromises barrier function. These findings align with metabolomic studies showing butyrate deficiency correlates with Th17 activation and aGVHD severity 33 . Our analysis identified Faecalibacterium and Blautia as protective genera, with their depletion associated with severe aGVHD and poor treatment responses. This aligns with studies 31 – 33 showing butyrate-producing bacteria (Faecalibacterium prausnitzii) mitigate gut inflammation by enhancing Treg function and epithelial integrity, while Blautia improves GVHD survival through bile acid metabolism and anti-inflammatory metabolite production. Conversely, enrichment of Enterococcus and Escherichia-Shigella in GVHD + patients corroborates prior reports linking these opportunistic pathogens to mucosal injury and neutrophil recruitment. Notably, the Gram-negative opportunist Stenotrophomonas (ASV9) was more abundant in GVHD- patients. Though typically associated with nosocomial infections, its protective role may reflect competitive exclusion of harmful Proteobacteria or modulation of local immune responses, warranting further mechanistic investigation. Longitudinal analysis revealed significant microbial dynamics differences between treatment responders and non-responders. Responders showed post-treatment recovery of SCFA-producing bacteria (Faecalibacterium, ASV96/203), while non-responders exhibited persistent enrichment of Firmicutes (Lacticaseibacillus) and opportunistic pathogens (Burkholderia), which may sustain inflammation via Toll-like receptor (TLR) signaling. EN, as a modifiable intervention, played a critical role in promoting microbial recovery 34 . EN recipients demonstrated faster reconstitution of symbiotic bacteria (Ruminococcus bromii and Faecalibacterium) and elevated SCFA levels, particularly butyrate, which maintains colonic homeostasis by fueling colonocytes, inhibiting pro-inflammatory cytokines, and promoting Treg differentiation 34 . However, reduced post-transplantation butyrate-producing bacteria (Clostridium scindens and Roseburia) highlighted their vulnerability to pre-transplant regimens and antibiotics, underscoring EN’s potential clinical value in preserving metabolic function and microbial ecology. Microbial biomarkers demonstrated high predictive efficacy (AUC: 0.921–0.954) for risk stratification and early intervention. Boruta and PLSDA algorithms enabled high-precision identification of pre-transplantation biomarkers (e.g., ASV9, ASV1009), aligning with trends integrating microbiome data into clinical decision-making. The weaker performance of random forest models (AUC: 0.903) suggested linear discriminant methods may better capture subtle microbial changes in this context. Probiotics enriched in Blautia or Faecalibacterium, FMT, and dietary adjustments promoting SCFA production represent promising intervention strategies. For instance, adult studies showed 35 Blautia-enriched FMT restored microbial diversity and reduced GVHD severity. However, pediatric-specific clinical trials are urgently needed, as developmental differences in immune-microbial interactions may require personalized approaches. Integrating multi-omics data (metagenomics, metabolomics) is crucial for establishing causality between microbial taxa and host immunity, moving beyond correlational inferences from 16S rRNA data 36 . Future research should validate these models in independent cohorts and explore their utility in guiding preventive interventions, such as microbiome-targeted therapies. Limitations and Future Directions This study acknowledges common limitations, including single-center design, moderate sample size (e.g., non-responder cohort: n = 15), and insufficient quantification of confounders like antibiotic exposure. Future research should prioritize multi-center collaboration, standardized metadata collection, and longitudinal functional validation. Mechanistic studies exploring competitive microbial interactions (e.g., Stenotrophomonas vs. Proteobacteria) and host genetic factors will refine therapeutic targets. CONCLISION Our study offers valuable insights into the relationship between gut microbiota and acute graft-versus-host disease (aGVHD) in pediatric patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). We observed significant reductions inα - diversity in GVHD + patients, underscoring the gut microbiota’s potential role in aGVHD pathogenesis. While no significant differences in microbial community structure were found between GVHD + and GVHD- groups, specific taxa like Faecalibacterium and Blautia, associated with a lower aGVHD risk and better clinical outcomes, were identified. The analysis highlights gut microbiota’s dual role as both a biomarker and modulator of aGVHD. Safeguarding microbial diversity, restoring short-chain fatty acid (SCFA)-producing bacteria, and using predictive models offer viable strategies to improve pediatric allo-HSCT outcomes. Translating these insights into clinical practice requires interdisciplinary efforts to align microbiome science with precision medicine, ultimately enhancing survival and quality of life for children post - hematopoietic stem cell transplantation. Declarations Acknowledgements We would like to express our gratitude to the patients and their families for their participation in this study. We also thank the nursing staff and physicians in the hematology department for their assistance in sample collection and clinical data recording. Additionally, we acknowledge the technical support provided by the laboratory staff at Guangzhou Women and Children’s Medical Center. The authors declare that they have not use AI-generated work in this manuscript. Author contributions YQ, XC, and HJ designed the project. HZ, WD, XW, HL, FW, and WW collected and checked clinical information. HZ and XL analyzed the data. YQ, XC and HZ interpreted the data, with assistance from CK, HH, ST, XL, FY, LL, CZ, WZ, HL, YL, RM,WG,XL,QZ, and YZ. The manuscript was written primarily by YQ and HZ, and all authors contributed substantially to revisions. All authors reviewed and approved the final manuscript. Funding This study was supported by Nestle Health Science (China) Co., LTD., with the funding number S-20200709-1.The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Ocompeting Interests The authors declare no competing interests. Availability of data and materials The raw sequence data reported in this paper have been deposited in China National Center for Bioinformation, that are publicly accessible at https://ngdc.cncb.ac.cn/gsa/s/Ea7y3q4R. ethics approval and consent to participate (1) Title of the approved project: Study Gut Microbiota Characteristics and Efficacy of Specialized Milk Powder as Adjuvant Therapy in Intestinal aGVHD Patients Following Allogeneic Hematopoietic Stem Cell Transplantation.(2) Name of the institutional approval committee or unit: the Ethics Committee of Guangzhou Women and Children's Medical Center . (3) Approval number: 2020–39400. (4) Date of approval: July 9, 2020. Informed consent Written informed consent was obtained from all candidates or their legal representatives before the screening process and initiation of any research. Consent for publication Not applicable. References Chang CC, Hayase E, Jenq RR. The role of microbiota in allogeneic hematopoietic stem cell transplantation. Expert Opin Biol Ther. 2021;21(8):1121-1131. Malard F, Holler E, Sandmaier BM, Huang H, Mohty M. Acute graft-versus-host disease. Nat Rev Dis Primers. 2023; 8;9(1):27. Peled JU, Gomes ALC, Devlin SM, Littmann ER, Taur Y, Sung AD, Weber D, Hashimoto D, Slingerland AE, Slingerland JB, et al. Microbiota as Predictor of Mortality in Allogeneic Hematopoietic-Cell Transplantation. N Engl J Med. 2020 ; 382(9): 822-834. Stein-Thoeringer CK, Nichols KB, Lazrak A, Docampo MD, Slingerland AE, Slingerland JB, Clurman AG, Armijo G, Gomes ALC, Shono Y, et al. Lactose drives Enterococcus expansion to promote graft-versus-host disease. Science. 2019; 366(6469):1143-1149. Khan N, Lindner S, Gomes ALC, Devlin SM, Shah GL, Sung AD, Sauter CS, Landau HJ, Dahi PB, Perales MA, er al. Fecal microbiota diversity disruption and clinical outcomes after auto-HCT: a multicenter observational study. Blood. 2021; 137(11):1527-1537. Burgos da Silva M, Ponce DM, Dai A, M Devlin S, Gomes ALC, Moore G, Slingerland J, Shouval R, Armijo GK, DeWolf S, et al. Preservation of the fecal microbiome is associated with reduced severity of graft-versus-host disease. Blood. 2022 Dec 1;140(22):2385-2397. Jenq RR, Taur Y, Devlin SM, Ponce DM, Goldberg JD, Ahr KF, Littmann ER, Ling L, Gobourne AC, Miller LC, et al. Intestinal Blautia Is Associated with Reduced Death from Graft-versus-Host Disease. Biol Blood Marrow Transplant. 2015; 21(8):1373-83. Golob JL, Pergam SA, Srinivasan S, Fiedler TL, Liu C, Garcia K, Mielcarek M, Ko D, Aker S, Marquis S, et al. Stool Microbiota at Neutrophil Recovery Is Predictive for Severe Acute Graft vs Host Disease After Hematopoietic Cell Transplantation. Clin Infect Dis. 2017; 65(12): 1984-1991. Payen M, Nicolis I, Robin M, Michonneau D, Delannoye J, Mayeur C, Kapel N, Berçot B, Butel MJ, Le Goff J, et al. Functional and phylogenetic alterations in gut microbiome are linked to graft-versus-host disease severity. Blood Adv. 2020; 4(9):1824-1832. Romick-Rosendale LE, Haslam DB, Lane A, Denson L, Lake K, Wilkey A, Watanabe M, Bauer S, Litts B, Luebbering N, et al. Antibiotic Exposure and Reduced Short Chain Fatty Acid Production after Hematopoietic Stem Cell Transplant. Biol Blood Marrow Transplant. 2018; 24(12):2418-2424. Simms-Waldrip TR, Sunkersett G, Coughlin LA, Savani MR, Arana C, Kim J, Kim M, Zhan X, Greenberg DE, Xie Y, et al. Antibiotic-Induced Depletion of Anti-inflammatory Clostridia Is Associated with the Development of Graft-versus-Host Disease in Pediatric Stem Cell Transplantation Patients. Biol Blood Marrow Transplant. 2017; 23(5):820-829. D'Amico F, Biagi E, Rampelli S, Fiori J, Zama D, Soverini M, Barone M, Leardini D, Muratore E, Prete A, et al. Enteral Nutrition in Pediatric Patients Undergoing Hematopoietic SCT Promotes the Recovery of Gut Microbiome Homeostasis. Nutrients. 2019;11(12):2958. Andersen S, Staudacher H, Weber N, Kennedy G, Varelias A, Banks M, Bauer J. Pilot study investigating the effect of enteral and parenteral nutrition on the gastrointestinal microbiome post-allogeneic transplantation. Br J Haematol. 2020;188(4):570-581. Przepiorka D, Weisdorf D, Martin P, Klingemann HG, Beatty P, Hows J, Thomas ED. 1994 Consensus Conference on Acute GVHD Grading. Bone Marrow Transplant. 1995;15(6):825-8. Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27(21):2957-63. Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, Mills DA, Caporaso JG. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods. 2013; 10(1):57-9. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27(16):2194-200. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852-857. Dixon, P. VEGAN, a package of R functions for community ecology. Journal of Vegetation Science. 2003; 14(6):927-930. Miron B. Kursa, Witold R. Rudnicki. Feature Selection with the Boruta Package. Journal of Statistical Software. 2010; 36(11): 1-13. Rohart F, Gautier B, Singh A, Lê Cao KA. mixOmics: An R package for 'omics feature selection and multiple data integration. PLoS Comput Biol. 2017; 13(11):e1005752. teffens M, Lamina C, Illig T, Bettecken T, Vogler R, Entz P, Suk EK, Toliat MR, Klopp N, Caliebe A, et al. SNP-based analysis of genetic substructure in the German population. Hum Hered. 2006;62(1):20-9. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011; 12:77. Liu C, Cui Y, Li X, Yao M. microeco: an R package for data mining in microbial community ecology. FEMS Microbiol Ecol. 2021; 97(2):fiaa255. Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013; 31(9):814-21. Haak BW, Littmann ER, Chaubard JL, Pickard AJ, Fontana E, Adhi F, Gyaltshen Y, Ling L, Morjaria SM, Peled JU, et al. Impact of gut colonization with butyrate-producing microbiota on respiratory viral infection following allo-HCT. Blood. 2018 ;131(26):2978-2986. Han L, Zhang H, Chen S, Zhou L, Li Y, Zhao K, Huang F, Fan Z, Xuan L, Zhang X, et al. Intestinal Microbiota Can Predict Acute Graft-versus-Host Disease Following Allogeneic Hematopoietic Stem Cell Transplantation. Biol Blood Marrow Transplant. 2019;25(10):1944-1955. Gavriilaki E, Christoforidi M, Ouranos K, Minti F, Mallouri D, Varelas C, Lazaridou A, Baldoumi E, Panteliadou A, Bousiou Z, et al. Alteration of Gut Microbiota Composition and Diversity in Acute and/or Chronic Graft-versus-Host Disease Following Hematopoietic Stem Cell Transplantation: A Prospective Cohort Study. Int J Mol Sci. 2024 ;25(11):5789. Gray AN, Tobin NH, Moore TB, Li F, Aldrovandi GM. Longitudinal relationship between the gut microbiota variation and diversity and gut graft-versus-host disease (GVHD) following pediatric allogeneic hematopoietic cell transplantation (HCT) - Case series. Int J Med Microbiol. 2023 ;313(3):151580. Asnicar F, Thomas AM, Passerini A, Waldron L, Segata N. Machine learning for microbiologists. Nat Rev Microbiol. 2024;22(4):191-205. Alexander M, Ang QY, Nayak RR, Bustion AE, Sandy M, Zhang B, Upadhyay V, Pollard KS, Lynch SV, Turnbaugh PJ. Human gut bacterial metabolism drives Th17 activation and colitis. Cell Host Microbe. 2022;30(1):17-30.e9. Reddi S, Senyshyn L, Ebadi M, Podlesny D, Minot SS, Gooley T, Kabage AJ, Hill GR, Lee SJ, Khoruts A, et al. Fecal microbiota transplantation to prevent acute graft-versus-host disease: pre-planned interim analysis of donor effect. Nat Commun. 2025 ;16(1):1034. Tang XW, Wu DP. How I treat gastrointestinal tract acute graft versus host disease with fecal microbiota transplantation. Zhonghua Xue Ye Xue Za Zhi. 2022 May 14;43(5):365-369. Li Q, Wang J. The Application and Mechanism Analysis of Enteral Nutrition in Clinical Management of Chronic Diseases. Nutrients. 2025;17(3):450. Zheng YY, Yang XT, Lin GQ, Bian MR, Si YJ, Zhang XX, Zhang YM, Wu DP. Clinical study of 19 cases of steroid-refractory gastrointestinal acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation with fecal microbiota transplantation. Zhonghua Xue Ye Xue Za Zhi. 2023; 44(5):401-407. Taur Y, Jenq RR, Perales MA, Littmann ER, Morjaria S, Ling L, No D, Gobourne A, Viale A, Dahi PB, et al. The effects of intestinal tract bacterial diversity on mortality following allogeneic hematopoietic stem cell transplantation. Blood. 2014;124(7):1174-82. Additional Declarations The authors have declared there is NO conflict of interest to disclose. 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center","correspondingAuthor":false,"prefix":"","firstName":"Zhan","middleName":"","lastName":"Qianqian","suffix":""},{"id":482221349,"identity":"3f0fd759-87df-4c36-a498-72dc9ca28b1b","order_by":23,"name":"Zeng Yuqi","email":"","orcid":"","institution":"Guangzhou Women and children medical center","correspondingAuthor":false,"prefix":"","firstName":"Zeng","middleName":"","lastName":"Yuqi","suffix":""},{"id":482221350,"identity":"ed29775b-4396-4c23-9c4a-183bf2411b88","order_by":24,"name":"Xiangjun Liu","email":"","orcid":"","institution":"BFR Diagnostics","correspondingAuthor":false,"prefix":"","firstName":"Xiangjun","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-06-28 12:45:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6997868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6997868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86667484,"identity":"4fd763eb-af14-450c-9e48-a3a7f7bd62d2","added_by":"auto","created_at":"2025-07-14 11:15:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":205360,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal changes in α-diversity and pre-transplant PCoA analysis based on Bray-Curtis distance\u003cstrong\u003e\u003cbr\u003e\n \u003c/strong\u003e(A) Dynamic changes in Shannon index show a gradual decline in α-diversity of gut microbiota in GVHD+ patients post-transplant, reaching the lowest point at day 14. (B) PCoA analysis based on Bray-Curtis distance indicates no significant structural differences between GVHD+ and GVHD- groups at baseline (PERMANOVA test, p=0.523). (C) Temporal trends of α-diversity across different time points. (D) No significant separation in PCoA between GVHD+ and GVHD- groups. (E) PCoA comparison among patients with varying aGVHD severities.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6997868/v1/1dbc628fc4ad9dc7432022dc.png"},{"id":86667513,"identity":"17509e18-6705-4155-b009-388649c6ac6a","added_by":"auto","created_at":"2025-07-14 11:15:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":153236,"visible":true,"origin":"","legend":"\u003cp\u003eBoruta feature selection identifies gut microbial species associated with GVHD development\u003cstrong\u003e\u003cbr\u003e\n \u003c/strong\u003e(A) Green, blue, and purple boxplots represent the minimum, average, and maximum Z-scores of shadow attributes, respectively; yellow boxplots indicate Z-scores of confirmed attributes. (B) Heatmap displays CLR-transformed abundance of confirmed ASVs; the right histogram illustrates the contribution of each taxon to the first three components of the PLSDA biplot.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6997868/v1/ffe346b9a296fad213b02b10.png"},{"id":86667475,"identity":"aaa32777-105d-4e7a-910f-1c0ce14f499b","added_by":"auto","created_at":"2025-07-14 11:15:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePLS discriminant analysis (PLSDA) model construction and biplot for GVHD+ vs. GVHD-\u003c/strong\u003e\u003cbr\u003e\n(A) PLSDA biplot shows clear separation between GVHD+ and GVHD- groups in pre-transplant microbial composition (B) Prediction background map delineates classification boundaries. (C) ROC curve indicates a predictive AUC of 0.921 for the PLSDA model. (D) PLSDA biplot validates model performance in GVHD+ and GVHD- groups. (E) PLSDA biplot validates model performance in In different groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6997868/v1/f6c8d7c4109da9ec240df24f.png"},{"id":86667477,"identity":"5d1b7b57-f291-4cc2-9cb4-279560acbbdb","added_by":"auto","created_at":"2025-07-14 11:15:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":199466,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoruta feature selection and GVHD prediction model based on day 0 samples\u003c/strong\u003e\u003cbr\u003e\n(A) Boruta algorithm identifies 26 ASVs associated with GVHD development. (B) Differentially abundant ASVs between GVHD+ and GVHD- groups. (C) PLSDA biplot demonstrates predictive capability of day 0 microbiota (AUC=0.954). (D) Validation of model prediction performance.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6997868/v1/2fdbd30e85ce55e3fe6fc886.png"},{"id":88317569,"identity":"834d3877-de8b-45e6-8e7f-9db76be5e9ee","added_by":"auto","created_at":"2025-08-05 08:19:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1610898,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6997868/v1/7e59926c-40f0-4c82-96cd-9aeaf606eb5f.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Microbiome Signature Linked to the Development and Management of Intestinal Acute Graft-versus-Host Disease in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAllogeneic Hematopoietic Stem Cell Transplantation (allo-HSCT) offers a potentially curative approach for various hematological conditions, yet graft-versus-host disease (GVHD) remains a significant complication. GVHD arises when donor immune cells attack host tissues, potentially causing organ damage and reducing post-transplant survival rates and quality of life, with incidence rates of 30\u0026ndash;70% and up to 30% of cases being fatal. Acute GVHD (aGVHD) commonly affects the gastrointestinal tract, skin, and liver. Recent studies have highlighted the role of gut microbiota in aGVHD pathogenesis and treatment outcomes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Patients undergoing allo-HCT often exhibit reduced fecal diversity and an increase in potentially pathogenic bacteria\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Loss of diversity and increased abundance of certain bacterial groups have been linked to adverse outcomes, including higher mortality and increased GVHD risk. The genus Enterococcus has been associated with GVHD after allo-HCT\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Higher gut microbiota diversity during the peri-engraftment period correlates with lower transplant-related mortality risk \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. An increased abundance of the Blautia genus has been linked to reduced aGVHD lethality and improved survival, while the presence of Lachnospiraceae shows a negative correlation with severe aGVHD development. Lower concentrations of short-chain fatty acids (SCFAs), particularly butyrate, have been observed in GVHD patients\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAlthough many studies have explored the link between microbiota composition and aGVHD post-allo-HSCT, the specific changes in gut microbial structure and diversity are not fully understood. Moreover, the microbiota associated with aGVHD varies among transplantation centers. Most studies have focused on adult patients, with changes in gut microbiota in pediatric HSCT patients remaining underreported. Pediatric patients may be more susceptible to antibiotic-induced gut microbiota changes due to their immature gut microbiome with fewer beneficial commensal anaerobes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePost-conditioning regimen and allo-HSCT, patients often experience reduced oral nutrient intake due to side effects like nausea, anorexia, and mucositis. Parenteral nutrition (PN), traditionally the first nutritional approach, is associated with complications such as infections and gut microbiota dysbiosis, making enteral nutrition (EN) a preferable alternative. Pediatric patients receiving PN showed reduced SCFA levels after HSCT\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, while those treated with EN had rapid gut microbiome recovery and higher SCFA levels. Similar findings were observed in adult HSCT cohorts\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we explored the impact of gut microbiota composition on clinical outcomes in pediatric patients undergoing allo-HSCT by prospectively collecting fecal samples and sequencing the V3 to V4 regions of the 16S ribosomal (rRNA) gene. In parallel, from hospital admission, all patients who developed GI aGVHD were offered deeply hydrolyzed milk powder as an adjuvant therapy for first-line medication. We identified specific bacterial signatures associated with aGVHD at pre-transplant, at day 0, and peri-onset.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003e\u003cb\u003ePatients\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Our prospective study enrolled patients undergoing allo-HSCT from August 2020 to December 2023, with approval from the Ethics Committee and informed consent obtained in line with the Declaration of Helsinki. Patients were prepared for transplantation using modified myeloablative or reduced intensity conditioning regimens and GVHD prophylaxis based on Beijing or PT-Cy protocols. GVHD treatment was at the physician's discretion, with topical corticosteroids for mild to moderate skin GVHD, oral budesonide for mild gastrointestinal GVHD, and systemic steroids for other manifestations. Steroid-refractory aGVHD was defined as progression at day 3, unstable disease at day 7, or no remission at 2 weeks. Patients refractory to systemic steroids received second-line therapy. Nonsteroid immunosuppressants, usually calcineurin inhibitors, were used to taper systemic steroids.\u003c/p\u003e\u003cp\u003ePatients were categorized into GVHD+ (developed gastrointestinal aGVHD) and GVHD- (no aGVHD signs) groups. aGVHD incidence was determined by time to initial diagnosis, with severity graded using modified Glucksberg criteria. GVHD\u0026thinsp;+\u0026thinsp;patients were classified as mild (grade I) or severe (grades II-IV).\u003c/p\u003e\u003cp\u003eStool samples were collected pre-transplant, at Day 0, Day 14, Day 30, and Day 90 post-transplant. For GVHD\u0026thinsp;+\u0026thinsp;patients, samples were grouped into pre-transplant, Day 0, pre-onset, onset, and post-treatment. For GVHD- patients, samples were grouped into the five time points. Fecal samples were stored at -80\u0026deg;C until sequencing.\u003c/p\u003e\u003cp\u003eClinical data were collected up to 100 days post-transplant, including age, diagnosis, HLA matching, conditioning regimen, GVHD status, and graft characteristics. In the GVHD\u0026thinsp;+\u0026thinsp;group, GVHD onset date, involved organs, stage, grade, and steroid response were recorded. Death dates and causes were also noted. The clinical response rate at 28 days post-treatment was used to divide GVHD\u0026thinsp;+\u0026thinsp;patients into Responder (CR/PR) and Non-Responder subgroups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTotal Bacterial DNA Extraction, 16S rRNA Gene Amplification, Library Preparation, and Sequencing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDNA extraction and sequencing: MagPure Stool DNA KF kit B (Magen) extracted microbial community DNA per the manufacturer's instructions. The V3-V4 regions of bacterial 16S rRNA genes were amplified using degenerate PCR primers (341F and 806R) with a barcode. PCR reactions used 15 \u0026micro;L Phusion\u0026reg; High-Fidelity PCR Master Mix (New England Biolabs), 0.2 \u0026micro;M primers, and ~\u0026thinsp;10 ng template DNA. Thermal cycling included initial denaturation at 98℃ for 1 min, followed by 30 cycles of denaturation at 98℃ for 10 s, annealing at 50℃ for 30 s, and elongation at 72℃ for 30 s, with a final extension at 72℃ for 5 min. DNA quantification was done via Qubit fluorometer with a Qubit\u0026reg; dsDNA BR Assay kit (Invitrogen), and quality was checked on a 1% agarose gel.\u003c/p\u003e\u003cp\u003eThe PCR products were purified with AmpureXP beads and libraries qualified by Agilent 2100 bioanalyzer (Agilent). Libraries were pooled and sequenced on Illumina NovaSeq6000 (Novogene, Beijing, China) to generate 2\u0026times;250bp paired-end reads.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBioinformatics and Statistics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBioinformatics analysis was conducted on paired-end raw sequences using FLASH (V1.2.11) \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, fastp (V0.23.1) \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and VSEARCH\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e to obtain high-quality tags. DADA2 in QIIME2\u003csup\u003e18\u003c/sup\u003e software was used for denoising to obtain initial ASVs, annotated via a pre-trained Naive Bayes classifier with the Silva 138.1 database. Sample clustering was performed using UPGMA in QIIME2.\u003c/p\u003e\u003cp\u003eASV absolute abundance was normalized to the median sequence number. ASVs with \u0026lt;\u0026thinsp;1% prevalence were excluded to reduce false positives, and datasets were filtered for genera with \u0026gt;\u0026thinsp;0.1% relative abundance in at least one sample. α-diversity metrics including Observed OTUs, Chao1, Shannon, Simpson, and Pielou\u0026rsquo;s Evenness were calculated in QIIME2. PCoA analysis was conducted using the vegan R package (v2.6-8) \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e with a Bray-Curtis dissimilarity matrix. PERMANOVA testing for β-diversity used the adonis2 function in vegan with 999 permutations.\u003c/p\u003e\u003cp\u003eFeature selection was performed using the Boruta algorithm (R package v8.0.0), a random forest-based method to identify ASVs associated with GVHD onset. The Wilcoxon rank sum test was used to identify differentially abundant taxa between groups.\u003c/p\u003e\u003cp\u003eClassification models were built using PLSDA and random forest algorithms via the mixOmics (v6.28.0)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and randomForest (v4.7-1.2)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e R packages. Both models used five-fold cross-validation repeated five times. Model performance was evaluated using AUC-ROC curves with the pROC package (v1.18.5).\u003c/p\u003e\u003cp\u003eLDA Effect Size analysis was performed using the microeco v1.9.1 package\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The Kruskal-Wallis rank-sum test was used in LEfSe to detect differences between pre-onset and post-treatment patients. Predicted metabolic functions were derived from 16S rRNA sequences using PICRUSt\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e..\u003c/p\u003e\u003cp\u003eStatistical analyses were performed using R v4.4.1 and GraphPad v9.0. Continuous variables were compared using the Wilcoxon rank-sum test, and categorical variables using Chi-square or Fisher\u0026rsquo;s exact tests. A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eBaseline Characteristics and Alpha Diversity Changes Over Time\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analyzed 537 fecal samples from 118 pediatric patients across five time points: pre-HSCT, HSCT day, biweekly in the first month post-HSCT, and three months post-HSCT. This yielded an average of 4.5 samples per patient. The cohort included more males (76) than females (42), with a median age of 6.0 years (range 0\u0026ndash;14 years). Patients had either malignant or nonmalignant diseases. 92 patients received PBSCs and 22 received UCB. aGVHD primarily affected the gastrointestinal tract, skin (20 cases), and liver (2 cases). The median day of aGVHD onset was day 22 posttransplant. During follow-up, 4 patients died.The characteristics of the total cohort, GVHD\u0026thinsp;+\u0026thinsp;and GVHD- groups are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003ePatient, disease and transplant characteristics.\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;118)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eaGVHD+ (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eaGVHD- (n\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (65.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (63.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (34.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at transplant\u003c/b\u003e:\u003c/p\u003e\u003cp\u003emedian (range, year)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.0 (0\u0026ndash;14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDisease type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThalassemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (46.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (34.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (7.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAML\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (20.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (17.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukodystrophy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDonor type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelated-matched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelated-mismatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnrelated-matched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnrelated-mismatched\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (40.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (31.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGraft resource\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (2.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePBSC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (87.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (71.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUCB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (10.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eABO type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMatch*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (42.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (49.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMismatch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (50.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConditioning regime\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (81.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e54 (78.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAC-TBI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (13.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAntibiotics post-HSCT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (59.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41 (59.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eaGVHD prophylaxis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATG\u0026thinsp;+\u0026thinsp;CNI\u0026thinsp;+\u0026thinsp;MMF\u0026thinsp;+\u0026thinsp;MTX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (93.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (87.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePT-Cy\u0026thinsp;+\u0026thinsp;CNI\u0026thinsp;+\u0026thinsp;MMF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (13.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eaGVHD at onset\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅡ-Ⅳ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e37 (75.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅠ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (24.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian Day of Onset (days from transplantation)\u003c/p\u003e\u003cp\u003e\u003cb\u003eOrgan involvement at diagnosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (61.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal\u0026thinsp;+\u0026thinsp;Skin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(36.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGastrointestinal\u0026thinsp;+\u0026thinsp;Skin\u0026thinsp;+\u0026thinsp;Liver\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2(4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: ALL, acute lymphocytic leukemia; AML, Acute myelocytic leukemia; CDA, congenital dyserythropoietic anemia; SDS, Shwachman-Diamond syndrome; AA, aplastic anemia; MPS, mucopolysaccharidosis; DKC, dyskeratosis congenita; SCID, severe combined immunodeficiency; MDS, myelodysplastic syndromes. Other disease type include: CDA, congenital dyserythropoietic anemia; CML, chromic myelocytic leukemia; FA, Fanconi anemia; HLH, hemophagocytic lymphohistiocytosis; HD: Hodgkin disease; IBD, inflammatory bowel disease; WAS, Wikot-AIdrich syndrome; GI aGVHD: gastrointestinal acute graft-versus-host disease; ATG: anti-thymoglobulin; MAC, myeloablative conditioning; RIC, reduced intensity conditioning; TBI, total body irradiation.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eMatch* in ABO type includes A to A, B to B, AB to AB and O to O.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAlpha diversity assessed community diversity and richness. Baseline alpha diversity showed no significant difference in Pielou\u0026rsquo;s Evenness, species richness, observed OTUs, and Chao1, but the Shannon index was higher in GVHD- patients than GVHD\u0026thinsp;+\u0026thinsp;patients(Fig.\u0026nbsp;1and 2). For GVHD\u0026thinsp;+\u0026thinsp;patients, the Shannon index decreased from baseline to post-transplant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating reduced gut microbiota diversity. It reached the lowest point at 14 days post-treatment and then gradually recovered. The Shannon index dynamics showed a decrease until diarrhea onset, followed by an increase after treatment, aligning with previous studies on allogeneic HSCT patients\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe examined microbial composition differences via PCoA analysis of beta diversity using Bray-Curtis distance. No significant beta-diversity differences were found between GVHD and non-GVHD patients (Adonis test, p\u0026thinsp;=\u0026thinsp;0.423), indicating no baseline community structure difference(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients with severe aGVHD involving the gastrointestinal tract also showed no significant difference compared to those with mild aGVHD or non-GVHD patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the phylum level, the dominant microbiota across all time points were Firmicutes, Proteobacteria, Bacteroidota, and Actinobacteria. At the genus level, the co-dominant genera included Enterococcus, Escherichia-Shigella, Bacteroides, Streptococcus, Lacticaseibacillus, Parabacterioids, and Pseudomonas.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGVHD-Associated Taxa Identified at Pre-Transplant by Boruta Algorithm and Model Construction\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBoruta feature selection identified 17 ASVs distinguishing GVHD\u0026thinsp;+\u0026thinsp;and GVHD- microbiomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with 15 showing significant abundance differences. Thirteen ASVs were overrepresented in GVHD+, including Actinobacteriota (4 taxa) and Proteobacteria (8 taxa), and Aliidiomarina sanyensis. Two ASVs were overrepresented in GVHD-: ASV9 (Proteobacteria, Stenotrophomonas) and ASV18 (Firmicutes) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePLSDA analysis showed clear discrimination between GVHD\u0026thinsp;+\u0026thinsp;and GVHD- patients, with an AUC of 0.921(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The PLSDA loading plot revealed contributions of 15 taxa to component 1, with ASV9, ASV1009, and ASV231 being most relevant. The random forest model identified eight optimal ASVs for GVHD prediction, with an AUC of 0.903 (95% CI: 0.73\u0026ndash;0.98, P\u0026thinsp;=\u0026thinsp;0.0003). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The random forest model showed weaker predictive capability (lower AUROC) than the PLSDA model. This may be because PLSDA's prediction threshold is based on specified distance, making ROC and AUC criteria less insightful in PLSDA\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMicrobiota at Day 0 Was Associated with the Development of aGVHD\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePCoA of Day 0 samples showed no significant difference between GVHD\u0026thinsp;+\u0026thinsp;and GVHD- patients (adonis test, P\u0026thinsp;=\u0026thinsp;0.213). Similarly, no separation was found between grade 2\u0026ndash;4 and grade 1 aGVHD patients(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Boruta algorithms identified 26 ASVs, 22 of which differed in abundance. These ASVs belonged to Bacteroidota, Firmicutes, Patescibacteria, and Proteobacteria. In Proteobacteria, 14 ASVs were higher in GVHD+, while 8 were higher in GVHD-.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePLSDA analysis using day 0 microbiota profiles significantly distinguished GVHD- from GVHD\u0026thinsp;+\u0026thinsp;patients, with an AUC of 0.954 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The PLSDA loading plot showed that the taxa contributed to explaining the variability in the model for component 1 (50%) and component 2 (12%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The top contributors to component 1 were ASV82, ASV143, ASV516, ASV262, and ASV166, assigned to four genera including Halomonas and Brevundimonas. For component 2, ASV151, ASV477, ASV9, and ASV591 were the main contributors.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTemporal Microbial Community Dynamics in GVHD\u0026thinsp;+\u0026thinsp;Patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo further assess the association between gut microbiota and the response to treatment, alterations of gut microbiota profiles were analyzed in patients who achieved complete or partial clinical remission (Responders subgroup, n\u0026thinsp;=\u0026thinsp;34) versus those who did not achieve complete or partial remission (Non-Responders subgroup, n\u0026thinsp;=\u0026thinsp;15).\u003c/p\u003e\u003cp\u003eFirstly, we compared alpha diversity and revealed an overall trend towards a more consistent loss of diversity. Post-HSCT antibiotic burden was similar between R and NR subgroups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At baseline, Shannon index of gut microbiota was higher in NR subgroup than in R subgroup (Median, 4.67 vs. 3.66, Mann-Whitney test, p\u0026thinsp;=\u0026thinsp;0.0259). Shannon index was comparable between responders and nonresponders either at day 0 (3.00 vs. 3.38, p\u0026thinsp;=\u0026thinsp;0.675) or at onset (3.18 vs. 2.68, p\u0026thinsp;=\u0026thinsp;0.351). Notably, there was a significantly greater loss of alpha diversity from pre-transplant to onset day among NR patients and unexpectedly increased after treatment though the relatively small sample size prevents a more robust evaluation of this variable. Comparatively, although the diversity of the R subgroup also increased significantly after treatment, the magnitude of the change was much more stable than that of the NR group.\u003c/p\u003e\u003cp\u003eWe therefore compared longitudinal changes of microbiota between pre- and-on postset samples from grade 2\u0026ndash;4 aGVHD who received standard first-line therapy, selecting the latest sample available in the pre-specified window when applicable. LEfSe analysis, which combines Linear Discriminant Analysis (LDA) and Wilcoxon rank-sum test, revealed that ASV96 and ASV203 were significantly enriched in responders post-treatment compared to pre-onset, with an LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2 and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Conversely, in non-responders, all taxa enriched were assigned to the Firmicutes, one at pre-GVHD onset (ASV32) and 6 at post-treatment (ASV53, ASV 217, ASV146, ASV335, ASV661, and ASV437). \u003cem\u003eLacticaseibacillus\u003c/em\u003e to which ASV32 belongs is a part of fermentation microbiota with probiotic properties. The genus \u003cem\u003eFaecalibacterium\u003c/em\u003e (ASV53) is a producer of SCFAs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Functional Characterization of the Gut Microbiome in aGVHD Involves Understanding the Complex Interactions Between the Microbiota and the Host Immune System, Which Can Influence the Progression and Severity of aGVHD\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo explore the functional alterations in the gut microbiome in GVHD, we utilized PICRUSt2 to predict the functional composition profiles. We constructed the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology and the KEGG pathway/module profile. Notably, two pathways closely related to glycometabolism and SCFA metabolism exhibited statistical differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and were enriched in pre-transplant microbiomes of GVHD- patients: Transketolase and acyl carrier protein metabolism. Those findings could not be confirmed by a simultaneous analysis of plasma or cellular metabolome with no collected samples available for analysis.\u003c/p\u003e\u003cp\u003eAt Day 0, no difference of KEGG pathways was founded between two groups, even if \u003cem\u003eClostridium scindens\u003c/em\u003e (ASV326) was present in a lower abundance in day 0 samples of those patients who subsequently developed aGVHD. The comparison of pre-HSCT and Day 0 samples showed that the HSCT event resulted in a decrease in the relative abundance of butyrate producer genera such as \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eClostridium\u003c/em\u003e, [Ruminococcus], \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eLachnospira\u003c/em\u003e, \u003cem\u003eDorea\u003c/em\u003e, \u003cem\u003eCoprococcus\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study comprehensively analyzed the dynamic relationship between the gut microbiome and gastrointestinal acute graft-versus-host disease (aGVHD) in pediatric patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). It revealed age-specific dysbiosis patterns in children, who have unique immune and microbial vulnerabilities. The findings align with previous observations in adults and further clarify the causal mechanisms of pediatric aGVHD through multi-cohort integration. Specifically, post-transplantation, pediatric patients exhibited significantly reduced α-diversity (Shannon index), closely associated with disease severity and adverse outcomes. β-diversity analysis showed no baseline compositional differences between GVHD\u0026thinsp;+\u0026thinsp;and GVHD- groups, suggesting age-related microbial stability differences may influence disease susceptibility. Machine learning algorithms identified predictive microbial features, such as enriched Proteobacteria and Actinobacteria in GVHD\u0026thinsp;+\u0026thinsp;patients, linked to pro-inflammatory pathways, while protective genera like Stenotrophomonas may exert protective effects through competitive pathogen exclusion or local immune modulation. These insights provide a basis for GVHD risk stratification using microbial biomarkers and pave the way for age-specific microbial interventions, such as probiotic supplementation or fecal microbiota transplantation (FMT). Future research should validate these microbial signatures in independent cohorts and explore their clinical utility in guiding preventive interventions.\u003c/p\u003e\u003cp\u003eBoth pediatric and adult allo-HSCT recipients with aGVHD exhibit significantly reduced gut microbiota α-diversity (Shannon index), strongly associated with disease severity and adverse clinical outcomes, including higher mortality\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In pediatric patients, α-diversity loss occurs more rapidly, particularly during the post-transplantation hematopoietic reconstitution phase (reaching a nadir at day 14), likely due to their immature gut ecosystems and heightened susceptibility to antibiotic-induced dysbiosis. Despite partial recovery patterns similar to adults, reflecting shared mechanisms of pre-transplantation regimens and immune reconstitution in disrupting microbial stability, β-diversity analysis revealed no baseline structural differences between GVHD\u0026thinsp;+\u0026thinsp;and GVHD- groups in pediatric cohorts, contrasting with adult studies where microbial composition predicts outcomes\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, machine learning models (Boruta and PLSDA)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e identified predictive microbial features, such as enriched Proteobacteria (Aliidiomarina sanyensis) and Actinobacteria (Eggerthella) in GVHD\u0026thinsp;+\u0026thinsp;patients, which are linked to pro-inflammatory responses and gut barrier dysfunction in preclinical models, potentially exacerbating aGVHD progression\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Conversely, higher abundance of Stenotrophomonas (ASV9) in GVHD- patients suggests protective effects through competitive pathogen inhibition or immune modulation. These findings highlight age-specific microbial dynamics and support the development of early intervention strategies based on microbial biomarkers.\u003c/p\u003e\u003cp\u003eThe reduction of butyrate-producing genera (Faecalibacterium and Blautia) is a central feature of aGVHD pathogenesis. These genera enhance regulatory T cell (Treg) function, reinforce epithelial barrier integrity, and inhibit Th17-driven inflammation via short-chain fatty acids (SCFAs)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In contrast, overproliferation of Enterococcus and Escherichia-Shigella in GVHD\u0026thinsp;+\u0026thinsp;patients exacerbates mucosal damage and neutrophil recruitment, sustaining inflammation\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Functional analysis using PICRUSt2 further linked dysbiosis to impaired carbohydrate metabolism and SCFA synthesis, such as reduced Clostridium scindens, which disrupts farnesoid X receptor (FXR) signaling and compromises barrier function. These findings align with metabolomic studies showing butyrate deficiency correlates with Th17 activation and aGVHD severity\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Our analysis identified Faecalibacterium and Blautia as protective genera, with their depletion associated with severe aGVHD and poor treatment responses. This aligns with studies\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e showing butyrate-producing bacteria (Faecalibacterium prausnitzii) mitigate gut inflammation by enhancing Treg function and epithelial integrity, while Blautia improves GVHD survival through bile acid metabolism and anti-inflammatory metabolite production. Conversely, enrichment of Enterococcus and Escherichia-Shigella in GVHD\u0026thinsp;+\u0026thinsp;patients corroborates prior reports linking these opportunistic pathogens to mucosal injury and neutrophil recruitment. Notably, the Gram-negative opportunist Stenotrophomonas (ASV9) was more abundant in GVHD- patients. Though typically associated with nosocomial infections, its protective role may reflect competitive exclusion of harmful Proteobacteria or modulation of local immune responses, warranting further mechanistic investigation.\u003c/p\u003e\u003cp\u003eLongitudinal analysis revealed significant microbial dynamics differences between treatment responders and non-responders. Responders showed post-treatment recovery of SCFA-producing bacteria (Faecalibacterium, ASV96/203), while non-responders exhibited persistent enrichment of Firmicutes (Lacticaseibacillus) and opportunistic pathogens (Burkholderia), which may sustain inflammation via Toll-like receptor (TLR) signaling. EN, as a modifiable intervention, played a critical role in promoting microbial recovery\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. EN recipients demonstrated faster reconstitution of symbiotic bacteria (Ruminococcus bromii and Faecalibacterium) and elevated SCFA levels, particularly butyrate, which maintains colonic homeostasis by fueling colonocytes, inhibiting pro-inflammatory cytokines, and promoting Treg differentiation\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. However, reduced post-transplantation butyrate-producing bacteria (Clostridium scindens and Roseburia) highlighted their vulnerability to pre-transplant regimens and antibiotics, underscoring EN\u0026rsquo;s potential clinical value in preserving metabolic function and microbial ecology.\u003c/p\u003e\u003cp\u003eMicrobial biomarkers demonstrated high predictive efficacy (AUC: 0.921\u0026ndash;0.954) for risk stratification and early intervention. Boruta and PLSDA algorithms enabled high-precision identification of pre-transplantation biomarkers (e.g., ASV9, ASV1009), aligning with trends integrating microbiome data into clinical decision-making. The weaker performance of random forest models (AUC: 0.903) suggested linear discriminant methods may better capture subtle microbial changes in this context. Probiotics enriched in Blautia or Faecalibacterium, FMT, and dietary adjustments promoting SCFA production represent promising intervention strategies. For instance, adult studies showed\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Blautia-enriched FMT restored microbial diversity and reduced GVHD severity. However, pediatric-specific clinical trials are urgently needed, as developmental differences in immune-microbial interactions may require personalized approaches. Integrating multi-omics data (metagenomics, metabolomics) is crucial for establishing causality between microbial taxa and host immunity, moving beyond correlational inferences from 16S rRNA data\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Future research should validate these models in independent cohorts and explore their utility in guiding preventive interventions, such as microbiome-targeted therapies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and Future Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study acknowledges common limitations, including single-center design, moderate sample size (e.g., non-responder cohort: n\u0026thinsp;=\u0026thinsp;15), and insufficient quantification of confounders like antibiotic exposure. Future research should prioritize multi-center collaboration, standardized metadata collection, and longitudinal functional validation. Mechanistic studies exploring competitive microbial interactions (e.g., Stenotrophomonas vs. Proteobacteria) and host genetic factors will refine therapeutic targets.\u003c/p\u003e"},{"header":"CONCLISION","content":"\u003cp\u003eOur study offers valuable insights into the relationship between gut microbiota and acute graft-versus-host disease (aGVHD) in pediatric patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT). We observed significant reductions inα - diversity in GVHD\u0026thinsp;+\u0026thinsp;patients, underscoring the gut microbiota\u0026rsquo;s potential role in aGVHD pathogenesis. While no significant differences in microbial community structure were found between GVHD\u0026thinsp;+\u0026thinsp;and GVHD- groups, specific taxa like Faecalibacterium and Blautia, associated with a lower aGVHD risk and better clinical outcomes, were identified. The analysis highlights gut microbiota\u0026rsquo;s dual role as both a biomarker and modulator of aGVHD. Safeguarding microbial diversity, restoring short-chain fatty acid (SCFA)-producing bacteria, and using predictive models offer viable strategies to improve pediatric allo-HSCT outcomes. Translating these insights into clinical practice requires interdisciplinary efforts to align microbiome science with precision medicine, ultimately enhancing survival and quality of life for children post - hematopoietic stem cell transplantation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eWe would like to express our gratitude to the patients and their families for their participation in this study. We also thank the nursing staff and physicians in the hematology department for their assistance in sample collection and clinical data recording. Additionally, we acknowledge the technical support provided by the laboratory staff at Guangzhou Women and Children\u0026rsquo;s Medical Center. The authors declare that they have not use AI-generated work in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u0026nbsp;YQ, XC, and HJ designed the project. HZ, WD, XW, HL, FW, and WW collected and checked clinical information. HZ and XL analyzed the data. YQ, XC and HZ interpreted the data, with assistance from CK, HH, ST, XL, FY, LL, CZ, WZ, HL, YL, RM,WG,XL,QZ, and YZ. The manuscript was written primarily by YQ and HZ, and all authors contributed substantially to revisions. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eThis study was supported by Nestle Health Science (China) Co., LTD., with the funding number S-20200709-1.The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOcompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequence data reported in this paper have been deposited in China National Center for Bioinformation, that are publicly accessible at https://ngdc.cncb.ac.cn/gsa/s/Ea7y3q4R.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) Title of the approved project: Study Gut Microbiota Characteristics and Efficacy of Specialized Milk Powder as Adjuvant Therapy in Intestinal aGVHD Patients Following Allogeneic Hematopoietic Stem Cell Transplantation.(2) Name of the institutional approval committee or unit: the Ethics Committee of Guangzhou Women and Children\u0026apos;s Medical Center . (3) Approval number: 2020\u0026ndash;39400. (4) Date of approval: July 9, 2020.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all candidates or their legal representatives before the screening process and initiation of any research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChang CC, Hayase E, Jenq RR. The role of microbiota in allogeneic hematopoietic stem cell transplantation. Expert Opin Biol Ther. 2021;21(8):1121-1131. \u003c/li\u003e\n\u003cli\u003eMalard F, Holler E, Sandmaier BM, Huang H, Mohty M. Acute graft-versus-host disease. Nat Rev Dis Primers. 2023; 8;9(1):27. \u003c/li\u003e\n\u003cli\u003ePeled JU, Gomes ALC, Devlin SM, Littmann ER, Taur Y, Sung AD, Weber D, Hashimoto D, Slingerland AE, Slingerland JB, et al. Microbiota as Predictor of Mortality in Allogeneic Hematopoietic-Cell Transplantation. N Engl J Med. 2020 ; 382(9): 822-834. \u003c/li\u003e\n\u003cli\u003eStein-Thoeringer CK, Nichols KB, Lazrak A, Docampo MD, Slingerland AE, Slingerland JB, Clurman AG, Armijo G, Gomes ALC, Shono Y, et al. Lactose drives Enterococcus expansion to promote graft-versus-host disease. Science. 2019; 366(6469):1143-1149. \u003c/li\u003e\n\u003cli\u003eKhan N, Lindner S, Gomes ALC, Devlin SM, Shah GL, Sung AD, Sauter CS, Landau HJ, Dahi PB, Perales MA, er al. Fecal microbiota diversity disruption and clinical outcomes after auto-HCT: a multicenter observational study. Blood. 2021; 137(11):1527-1537. \u003c/li\u003e\n\u003cli\u003eBurgos da Silva M, Ponce DM, Dai A, M Devlin S, Gomes ALC, Moore G, Slingerland J, Shouval R, Armijo GK, DeWolf S, et al. Preservation of the fecal microbiome is associated with reduced severity of graft-versus-host disease. Blood. 2022 Dec 1;140(22):2385-2397. \u003c/li\u003e\n\u003cli\u003eJenq RR, Taur Y, Devlin SM, Ponce DM, Goldberg JD, Ahr KF, Littmann ER, Ling L, Gobourne AC, Miller LC, et al. Intestinal Blautia Is Associated with Reduced Death from Graft-versus-Host Disease. Biol Blood Marrow Transplant. 2015; 21(8):1373-83. \u003c/li\u003e\n\u003cli\u003eGolob JL, Pergam SA, Srinivasan S, Fiedler TL, Liu C, Garcia K, Mielcarek M, Ko D, Aker S, Marquis S, et al. Stool Microbiota at Neutrophil Recovery Is Predictive for Severe Acute Graft vs Host Disease After Hematopoietic Cell Transplantation. Clin Infect Dis. 2017; 65(12): 1984-1991.\u003c/li\u003e\n\u003cli\u003ePayen M, Nicolis I, Robin M, Michonneau D, Delannoye J, Mayeur C, Kapel N, Ber\u0026ccedil;ot B, Butel MJ, Le Goff J, et al. Functional and phylogenetic alterations in gut microbiome are linked to graft-versus-host disease severity. Blood Adv. 2020; 4(9):1824-1832. \u003c/li\u003e\n\u003cli\u003eRomick-Rosendale LE, Haslam DB, Lane A, Denson L, Lake K, Wilkey A, Watanabe M, Bauer S, Litts B, Luebbering N, et al. Antibiotic Exposure and Reduced Short Chain Fatty Acid Production after Hematopoietic Stem Cell Transplant. Biol Blood Marrow Transplant. 2018; 24(12):2418-2424.\u003c/li\u003e\n\u003cli\u003eSimms-Waldrip TR, Sunkersett G, Coughlin LA, Savani MR, Arana C, Kim J, Kim M, Zhan X, Greenberg DE, Xie Y, et al. Antibiotic-Induced Depletion of Anti-inflammatory Clostridia Is Associated with the Development of Graft-versus-Host Disease in Pediatric Stem Cell Transplantation Patients. Biol Blood Marrow Transplant. 2017; 23(5):820-829.\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Amico F, Biagi E, Rampelli S, Fiori J, Zama D, Soverini M, Barone M, Leardini D, Muratore E, Prete A, et al. Enteral Nutrition in Pediatric Patients Undergoing Hematopoietic SCT Promotes the Recovery of Gut Microbiome Homeostasis. Nutrients. 2019;11(12):2958. \u003c/li\u003e\n\u003cli\u003eAndersen S, Staudacher H, Weber N, Kennedy G, Varelias A, Banks M, Bauer J. Pilot study investigating the effect of enteral and parenteral nutrition on the gastrointestinal microbiome post-allogeneic transplantation. Br J Haematol. 2020;188(4):570-581. \u003c/li\u003e\n\u003cli\u003ePrzepiorka D, Weisdorf D, Martin P, Klingemann HG, Beatty P, Hows J, Thomas ED. 1994 Consensus Conference on Acute GVHD Grading. Bone Marrow Transplant. 1995;15(6):825-8.\u003c/li\u003e\n\u003cli\u003eMagoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27(21):2957-63.\u003c/li\u003e\n\u003cli\u003eBokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, Mills DA, Caporaso JG. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods. 2013; 10(1):57-9.\u003c/li\u003e\n\u003cli\u003eEdgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27(16):2194-200.\u003c/li\u003e\n\u003cli\u003eBolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852-857.\u003c/li\u003e\n\u003cli\u003eDixon, P. VEGAN, a package of R functions for community ecology. Journal of Vegetation Science. 2003; 14(6):927-930.\u003c/li\u003e\n\u003cli\u003eMiron B. Kursa, Witold R. Rudnicki. Feature Selection with the Boruta Package. Journal of Statistical Software. 2010; 36(11): 1-13.\u003c/li\u003e\n\u003cli\u003eRohart F, Gautier B, Singh A, L\u0026ecirc; Cao KA. mixOmics: An R package for \u0026apos;omics feature selection and multiple data integration. PLoS Comput Biol. 2017; 13(11):e1005752. \u003c/li\u003e\n\u003cli\u003eteffens M, Lamina C, Illig T, Bettecken T, Vogler R, Entz P, Suk EK, Toliat MR, Klopp N, Caliebe A, et al. SNP-based analysis of genetic substructure in the German population. Hum Hered. 2006;62(1):20-9.\u003c/li\u003e\n\u003cli\u003eRobin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, M\u0026uuml;ller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011; 12:77.\u003c/li\u003e\n\u003cli\u003eLiu C, Cui Y, Li X, Yao M. microeco: an R package for data mining in microbial community ecology. FEMS Microbiol Ecol. 2021; 97(2):fiaa255.\u003c/li\u003e\n\u003cli\u003eLangille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013; 31(9):814-21.\u003c/li\u003e\n\u003cli\u003eHaak BW, Littmann ER, Chaubard JL, Pickard AJ, Fontana E, Adhi F, Gyaltshen Y, Ling L, Morjaria SM, Peled JU, et al. Impact of gut colonization with butyrate-producing microbiota on respiratory viral infection following allo-HCT. Blood. 2018 ;131(26):2978-2986.\u003c/li\u003e\n\u003cli\u003eHan L, Zhang H, Chen S, Zhou L, Li Y, Zhao K, Huang F, Fan Z, Xuan L, Zhang X, et al. Intestinal Microbiota Can Predict Acute Graft-versus-Host Disease Following Allogeneic Hematopoietic Stem Cell Transplantation. Biol Blood Marrow Transplant. 2019;25(10):1944-1955.\u003c/li\u003e\n\u003cli\u003eGavriilaki E, Christoforidi M, Ouranos K, Minti F, Mallouri D, Varelas C, Lazaridou A, Baldoumi E, Panteliadou A, Bousiou Z, et al. Alteration of Gut Microbiota Composition and Diversity in Acute and/or Chronic Graft-versus-Host Disease Following Hematopoietic Stem Cell Transplantation: A Prospective Cohort Study. Int J Mol Sci. 2024 ;25(11):5789. \u003c/li\u003e\n\u003cli\u003eGray AN, Tobin NH, Moore TB, Li F, Aldrovandi GM. Longitudinal relationship between the gut microbiota variation and diversity and gut graft-versus-host disease (GVHD) following pediatric allogeneic hematopoietic cell transplantation (HCT) - Case series. Int J Med Microbiol. 2023 ;313(3):151580.\u003c/li\u003e\n\u003cli\u003eAsnicar F, Thomas AM, Passerini A, Waldron L, Segata N. Machine learning for microbiologists. Nat Rev Microbiol. 2024;22(4):191-205. \u003c/li\u003e\n\u003cli\u003eAlexander M, Ang QY, Nayak RR, Bustion AE, Sandy M, Zhang B, Upadhyay V, Pollard KS, Lynch SV, Turnbaugh PJ. Human gut bacterial metabolism drives Th17 activation and colitis. Cell Host Microbe. 2022;30(1):17-30.e9.\u003c/li\u003e\n\u003cli\u003eReddi S, Senyshyn L, Ebadi M, Podlesny D, Minot SS, Gooley T, Kabage AJ, Hill GR, Lee SJ, Khoruts A, et al. Fecal microbiota transplantation to prevent acute graft-versus-host disease: pre-planned interim analysis of donor effect. Nat Commun. 2025 ;16(1):1034.\u003c/li\u003e\n\u003cli\u003eTang XW, Wu DP. How I treat gastrointestinal tract acute graft versus host disease with fecal microbiota transplantation. Zhonghua Xue Ye Xue Za Zhi. 2022 May 14;43(5):365-369.\u003c/li\u003e\n\u003cli\u003eLi Q, Wang J. The Application and Mechanism Analysis of Enteral Nutrition in Clinical Management of Chronic Diseases. Nutrients. 2025;17(3):450.\u003c/li\u003e\n\u003cli\u003eZheng YY, Yang XT, Lin GQ, Bian MR, Si YJ, Zhang XX, Zhang YM, Wu DP. Clinical study of 19 cases of steroid-refractory gastrointestinal acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation with fecal microbiota transplantation. Zhonghua Xue Ye Xue Za Zhi. 2023; 44(5):401-407.\u003c/li\u003e\n\u003cli\u003eTaur Y, Jenq RR, Perales MA, Littmann ER, Morjaria S, Ling L, No D, Gobourne A, Viale A, Dahi PB, et al. The effects of intestinal tract bacterial diversity on mortality following allogeneic hematopoietic stem cell transplantation. Blood. 2014;124(7):1174-82.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gut Microbiome, Acute Graft-versus-Host Disease, Allogeneic Hematopoietic Stem Cell Transplantation, Alpha Diversity, Short-Chain Fatty Acids (SCFA)","lastPublishedDoi":"10.21203/rs.3.rs-6997868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6997868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAllogeneic stem cell transplantation (allo-HSCT) cures pediatric blood disorders, but graft-versus-host disease (GVHD) remains a major complication. This study investigated the link between gut microbiota and GVHD in 118 pediatric allo-HSCT patients (2020-2023). Fecal samples were collected pre-transplant and up to 90 days post-transplant.Patients developing GVHD (GVHD+, n=49) showed significantly reduced gut microbial diversity (α-diversity), especially at day 14, compared to non-GVHD patients (GVHD-, n=69). While initial community structure (β-diversity) was similar, GVHD+ patients had increased pro-inflammatory Proteobacteria and Actinobacteria, whereas GVHD- patients had more protective Stenotrophomonas. Machine learning identified predictive microbial features.Longitudinally, responders showed recovery of beneficial short-chain fatty acid (SCFA)-producing bacteria. Non-responders had persistent enrichment of Firmicutes and opportunistic pathogens. Functional analysis linked dysbiosis to impaired SCFA synthesis and carbohydrate metabolism.The findings highlight the gut microbiota's dual role as both a biomarker and modulator of acute GVHD. Strategies preserving microbial diversity, restoring SCFA producers, and using predictive microbial models could improve clinical outcomes, underscoring the potential for microbiota-targeted interventions to reduce GVHD and enhance transplant success.\u003c/p\u003e","manuscriptTitle":"Microbiome Signature Linked to the Development and Management of Intestinal Acute Graft-versus-Host Disease in Pediatric Patients Undergoing Hematopoietic Stem Cell Transplantation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 11:15:39","doi":"10.21203/rs.3.rs-6997868/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05dc8b38-ee8d-4623-84b4-2fb358178325","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51200808,"name":"Health sciences/Diseases/Gastrointestinal diseases/Intestinal diseases"},{"id":51200809,"name":"Health sciences/Medical research/Stem-cell research"}],"tags":[],"updatedAt":"2025-08-05T08:11:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 11:15:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6997868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6997868","identity":"rs-6997868","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}