Application of integrated multi-omics analysis in identifying biomarkers for early diagnosis of neonatal necrotizing enterocolitis

Application of integrated multi-omics analysis in identifying biomarkers for early diagnosis of neonatal necrotizing enterocolitis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application of integrated multi-omics analysis in identifying biomarkers for early diagnosis of neonatal necrotizing enterocolitis Xiongfeng Li, Hailong Wang, Yu Zhou, Rui Yang, Jing Zhou, Chaoyang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6891222/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background This study aims to investigate the potential application and value of metabolomics combined with proteomics in identifying early biomarkers for neonatal necrotizing enterocolitis (NEC), to provide new perspectives and strategies for early diagnosis. Method A systematic comparison was conducted between two sample groups: a case group consisting of 8 preterm infants diagnosed with NEC and a control group of 8 healthy, age- and weight-matched neonates without NEC. To capture metabolic changes at the onset of NEC, blood samples were collected within a 12-hour window after disease manifestation in NEC patients. High-performance liquid chromatography coupled with Q-TOF mass spectrometry (HPLC-QTOF-MS/MS) and 4D label-free quantitative proteomics were employed to detect differentially expressed proteins and small-molecule metabolites in plasma. Results Results revealed imbalances in amino acid metabolism pathways related to inflammatory processes during NEC progression, including γ-aminobutyric acid (GABA), arginine metabolism, and butyrate metabolism, as well as alterations in protein pathways such as glycolysis/gluconeogenesis, NOD-like receptor signaling, and Rap1 signaling. Conclusions Through integrated analysis of metabolomics and proteomics, this study suggests that butyrate metabolism may influence the pathogenesis of NEC via a non-canonical NOD-like receptor signaling pathway. This provides a highly promising approach for elucidating the pathogenesis of NEC in preterm infants, which offers new insights and evidence to advance disease understanding and intervention strategies. Necrotizing enterocolitis in newborns metabolomics proteomics biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background NEC, a severe gastrointestinal emergency particularly prevalent among preterm infants, exhibits a significantly elevated incidence rate of 5%–10% in very low and extremely low birth weight infants [1, 2] . This condition has emerged as a critical factor contributing to the high mortality rate in this vulnerable population. More alarmingly, even among treated survivors, persistent long-term adverse effects such as neurological impairments and growth retardation often occur, severely compromising the quality of life for both affected infants and their families. Literature reports indicate that approximately 45% of NEC survivors continue to face significant neurological complications during recovery [3] .Given these global health challenges, deeper exploration of NEC's pathogenesis - particularly the identification of specific early-stage biomarkers - has become an urgent priority . Recent advances in post-genomic technologies have enabled widespread use of multi-omics integration for investigating complex disease mechanisms [4] . Proteomics, which examines protein composition and dynamics in cells, tissues, and organisms [5] , focuses on these fundamental biological units to identify disease-related protein variations. This approach facilitates the discovery of specific biomarkers for early and accurate diagnosis. The 4D label-free quantitative proteomics technology has gained prominence in proteomic analysis due to its high sensitivity and bioinformatics advantages [6] .Concurrently, metabolomics has emerged as a powerful field that comprehensively analyzes biological processes through extensive metabolite profiling [7] . This approach offers unique insights, particularly when processing large-scale datasets from complex biological samples [8] , by revealing not just metabolite types and concentrations but also their interactions and temporal dynamics [9] . Therefore, integrating proteomics and metabolomics presents a promising strategy for identifying early disease biomarkers. This study focuses on NEC, a severe gastrointestinal disease that poses significant threats to preterm infant health. By employing 4D - Label - free proteomics technology integrated with comprehensive targeted metabolomics, it aims to identify early protein and metabolic biomarkers for NEC and elucidate its early pathogenesis mechanisms. This approach provides a more objective and scientific foundation for the early diagnosis and clinical management of NEC. Methods 1. Ethics This study was conducted in compliance with the Declaration of Helsinki and received approval from the Scientific Ethics Committee of the Affiliated Hospital of Inner Mongolia Medical University, China (DW2025018). Written informed consent was obtained from all participants or their legal guardians. 2. Study Subjects Preterm infants diagnosed with NEC and admitted to the Neonatal Intensive Care Unit of the Affiliated Hospital of Inner Mongolia Medical University between January 1, 2023, and December 31, 2023, were selected as the NEC group. The inclusion criteria [10] were as follows: ① Gestational age < 37 weeks at birth and postnatal age < 28 days at enrollment; ② Meeting the diagnostic criteria for NEC based on the modified Bell-NEC staging criteria [11] ;No congenital gastrointestinal malformations, severe malformations of other organs, or inherited metabolic diseases. Preterm infants hospitalized during the same period, who were fed normally without NEC, were selected as the control group. The inclusion criteria for the control group were: ① Comparable gestational age and postnatal age to the NEC preterm infants; ② Normal feeding with no symptoms of NEC; ③ No congenital gastrointestinal malformations, severe malformations of other organs, or inherited metabolic diseases. This study was approved by the Hospital's Ethics Committee. 3. Collection and Preservation of Serum Samples For the preterm NEC group, blood samples were collected within 12 hours after the onset of characteristic NEC symptoms such as abdominal distension, bloody stools, and/or vomiting. For the control group, blood samples were collected at corresponding gestational and postnatal ages relative to the NEC group. Approximately 1.0 - 1.5 mL of arterial blood was collected and placed in sodium heparin anticoagulant tubes (manufactured by BD, USA), stored at 4°C, and centrifuged at 4°C and 3000 rpm for 15 minutes within 12 hours. Subsequently, 500 μL of plasma was aliquoted and stored at -80°C for future analysis. 4. Proteomics In this study, serum samples from six participants (three NEC patients and three healthy controls) were analyzed using 4D label-free quantitative proteomics analysis. Total proteins were extracted from the samples, with a portion allocated for protein concentration determination and SDS-PAGE analysis. Following enzymatic desalting, sample peptides were identified using LC-MS/MS. First, raw mass spectrometry data were acquired through the traditional data-dependent acquisition (DDA) method using prepared mobile phases: A (100% water, 0.1% formic acid) and B (80% acetonitrile, 0.1% formic acid). The lyophilized powder was reconstituted in 10 µL of mobile phase A, centrifuged at 14,000 × g for 20 min at 4°C, and 400 ng of supernatant was injected for LC-MS analysis.The timsTOF_HT mass spectrometer was equipped with a protein analytical column (QL-HPLC-100*15) and Captive Spray ion source, operating in DDA mode with an m/z scan range of 100–1700. Primary MS resolution was set to 60,000 (at 1222 m/z) with a TIMS tunnel accumulation time of 100 ms. Instrument parameters included a capillary voltage of 1.6 kV and a mobility range of 0.6-1.6 cm²/(V·s). The total cycle time was 1.1 s with 10 PASEF cycles per acquisition.Database searching was performed using FragPipe software against the Homo sapiens SP database (UniProt; 20,434 proteins; downloaded March 7, 2024). Quality control criteria required identified peptides to be predominantly 7-40 amino acids in length, with each protein identified by at least one unique peptide. For bioinformatics analysis, raw data were median-normalized to eliminate experimental variability. Rows containing zero values were removed to generate a complete dataset. Data reproducibility and group differences were assessed through hierarchical clustering heatmaps, Pearson correlation analysis, principal component analysis (PCA), and PLS-DA. Differential protein analysis employed a T-test (p 1.5). Functional annotation utilized the COG database (http://www.ncbi.nlm.nih.gov/COG/) [12] . GO annotation was performed using EggNog-Mapper (V2.0), categorizing proteins by cellular component, molecular function, and biological process. Pathway analysis was conducted using KEGG (http://www.kegg.jp/) and Reactome annotations. Protein family classification and domain identification were performed using the Pfam database, while subcellular localization was predicted with WOLF PSORT. A protein-protein interaction (PPI) network was constructed using the STRING database (displaying interactions with a combined score >0.7). The top 20 proteins with the highest interaction degrees were selected to reconstruct a hub protein interaction network. 5. Metabolomics In this study, serum samples from 16 subjects (8 NEC patients and 8 controls) were analyzed using high-performance liquid chromatography coupled with Q-Exactive mass spectrometry. Chromatographic conditions: The analysis was performed using a Thermo Vanquish UHPLC system (Thermo Fisher Scientific, USA) equipped with an ACQUITY UPLC® HSS T3 column (2.1 × 100 mm, 1.8 µm; Waters, Milford, MA, USA). The flow rate was maintained at 0.3 mL/min with a column temperature of 40°C and an injection volume of 2 µL. For positive ion mode, the mobile phases consisted of 0.1% formic acid in acetonitrile (B2) and 0.1% formic acid in water (A2). The gradient elution program was: 0–1 min, 8% B2; 1–8 min, 8%–98% B2; 8–10 min, 98% B2; 10–10.1 min, 98%–8% B2; 10.1–12 min, 8% B2. For negative ion mode, the mobile phases were acetonitrile (B3) and 5 mM ammonium formate in water (A3). The gradient elution program was: 0–1 min, 8% B3; 1–8 min, 8%–98% B3; 8–10 min, 98% B3; 10–10.1 min, 98%–8% B3; 10.1–12 min, 8% B3 [13] . Mass spectrometry conditions: Analysis was conducted using a Thermo Q Exactive mass spectrometer (Thermo Fisher Scientific, USA) with an electrospray ionization (ESI) source in both positive and negative ion modes. The positive ion spray voltage was set at 3.50 kV and the negative ion spray voltage at -2.50 kV. Sheath gas and auxiliary gas flow rates were 40 arb and 10 arb, respectively. The capillary temperature was maintained at 325°C. Full scan mass spectra were acquired at a resolution of 70,000 (m/z range 100–1000) for primary mass spectrometry. For secondary fragmentation, HCD was employed with a collision energy of 30 eV and a resolution of 17,500. The top 10 most intense ions were selected for fragmentation, with dynamic exclusion applied to eliminate redundant MS/MS information [14] . The raw mass spectrometry files were converted to mzXML format using the MSConvert tool in the ProteoWizard software package (v3.0.8789) [15] . Peak detection, filtering, and alignment were performed using the R package XCMS [16] , with the following parameters: bw=2, ppm=15, peakwidth=c(5,30), mzwid=0.015, mzdiff=0.01, and method="centWave," generating a quantitative metabolite profile. Data normalization was performed using total peak area normalization to correct for systematic errors. Multivariate statistical analysis was conducted using the R package ropls [17] , including Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Variable Importance in Projection (VIP) scores were calculated, and OPLS-DA dimensionality reduction methods were employed, while fold change values were used to assess the magnitude of intergroup differences. Metabolites were considered statistically significant when P-values were 1. Metabolite identification was performed by spectral matching against databases including HMDB [18] , MassBank [19] , LipidMaps [20] , mzCloud [21] , KEGG [22] , and Norminova's in-house metabolite standard database, with a mass tolerance of <30 ppm. Based on the identified differential metabolites, pathway enrichment and topological analysis were conducted using MetaboAnalyst. The enriched pathways were visualized using KEGG Mapper [23] to display both differential metabolites and pathway maps, with a significance threshold of P < 0.05. Results 1. General characteristics of the study subjects A total of 8 NEC patients were enrolled, including 5 males and 3 females, with a mean gestational age of 32.7±1.2 weeks and median onset age of 15.75 days (range: 3.75–20.50 days). According to the modified Bell-NEC staging criteria [24], the distribution was: stage I (n=4, 50%), stage IIa (n=2, 25%), stage IIb (n=1, 12.5%), and stage III (n=1, 12.5%). Blood samples were collected within 1 hour of NEC diagnosis, followed by standard management including nil per os, nasogastric decompression, antibiotic therapy, and supportive care. The control group (NC) consisted of 8 infants (3 males, 5 females) with a mean gestational age of 31.6±0.7 weeks. No statistically significant differences were observed between the two groups in terms of gestational age, sex distribution, birth weight, history of asphyxia, breastfeeding rate, or postnatal age (Table 1) . Table 1 Baseline Data Item NEC Group Control Group P-value Gestational age (x±s) weeks 32.7±1.2 31.6±0.7 0.452 Male [n(%)] 5（62.5） 3（37.5） 0.344 1 ） Birth weight <1500g [n(%)] 2（25.0） 4（50.0） 0.368 1 ） History of asphyxia [n(%)] 3（37.5） 2（25.0） 0.779 1 ） Breastfeeding [n(%)] 1（12.5） 2（25.0） 0.717 1 ） Sampling age (days) 15.75（3.75~20.50） 20.25（12.50~28.25） 0.594 Note: 1) Fisher's exact test 2. Proteomics A total of 5,727 peptides and 588 proteins were identified in this study. Comparative analysis of pre- and post-normalization data revealed that the corrected median values aligned along the baseline (y-axis ≈ 0), indicating effective normalization (Figure 1, A-B). We evaluated global clustering heatmaps, Pearson correlation analysis, principal component analysis (PCA), and PLS-DA. The results demonstrated high intra-group reproducibility and significant inter-group differences (Figure 1, C-F). Using the screening criteria of p 1.5, we identified 31 significantly differentially expressed proteins, including 11 upregulated and 20 downregulated proteins (Figure 2A) . Functional annotation of differentially expressed proteins was performed using the COG database (Figure 2B) . Gene Ontology (GO) describes gene functions, localization, and activities, encompassing three categories: cellular component, molecular function, and biological process. GO analysis of differentially expressed proteins (Figure 2C) revealed that biological processes (BP) were primarily enriched in transcriptional regulation by RNA polymerase II, glycolysis, respiratory burst regulation, chaperone-mediated autophagy, and MAP kinase activity regulation. Cellular components (CC) were mainly enriched in ribonucleoprotein complexes, nucleus, cytoplasm, secretory immunoglobulin complexes, spliceosomal complexes, and glutamatergic synapses. Molecular functions (MF) were predominantly enriched in RNA binding, ATP binding, MHC class II protein complex binding, protein homodimerization, disordered domain binding, and transcription corepressor activity. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a comprehensive database that links genomic information to higher-level systemic functions across cellular, organismal, and ecosystem levels. In this study, KEGG enrichment analysis of differentially expressed proteins (Figure 2D) revealed significant enrichment in pathways including Glycolysis/Gluconeogenesis, Salmonella infection, Amyotrophic lateral sclerosis, Rap1 signaling, NOD-like receptor signaling, RNA degradation, p53 signaling, Hippo signaling, and PI3K-Akt signaling. Reactome, derived from "reaction" and "ome," is a pathway database centered on biological reactions. The entities involved (e.g, nucleic acids, proteins, complexes, and small molecules) form interaction networks categorized into specific pathways. Based on UniProt's Reactome annotations, the identified proteins and their differentially expressed counterparts were analyzed for pathway enrichment (Figure 2E) . The enriched pathways included Glycolysis, VEGFA-VEGFR2 signaling, mRNA splicing, Gluconeogenesis, HSF1-dependent transactivation, and RHO GTPase-mediated formin activation. In protein molecules, multiple structurally distinct regions with specific functions domains. Domains the fundamental units of protein function, and the overall function of a protein is determined by the combination of its multiple domains. Studying domains helps to improve understanding of protein functions. Pfam is a protein family database that classifies proteins into different families based on multiple sequence alignments and hidden Markov models, to identify the domains contained within each protein. In this study, Pfam enrichment analysis of differentially expressed proteins (Figure 2F) revealed enrichment in domains such as Actin, VWC, ApoL, Ig-J-chain, Gp-dh-C, Gp-dh-N, and Enolase-C. Protein subcellular localization prediction plays a crucial role in bioinformatics and biological research. In this study, subcellular localization analysis of differentially expressed proteins (Figure 2G) identified compartments such as the Cytoplasm, Cytoskeleton, Endoplasmic reticulum, Nucleus, Plasma membrane, and Extracellular space. The cross-interactions between proteins within cells can reveal protein functions at the molecular level and are crucial for understanding the principles of life activities such as growth, development, differentiation, apoptosis, and biological regulatory mechanisms. This provides an important theoretical foundation for exploring the mechanisms of major diseases, therapeutic interventions, preventive strategies, and pharmaceutical innovation. In this study, a PPI interaction map was constructed to...A protein-protein interaction network was constructed for the differentially expressed proteins of interest (Figure 3) . 3. Metabolomics This study employed PCA and PLS-DA methods. The PCA results clearly showed that the NEC group and the control group samples each formed distinct clusters, achieving significant separation between the two groups (Figure 4A) . Similarly, the PLS-DA method (Figure 4B) yielded results concordant with the PCA findings, demonstrating that the metabolic patterns of the NEC and NC groups are clearly distinct. Differential metabolites were identified from the primary metabolite list using selected statistical test methods. A total of 2,436 differentially expressed metabolites were found between the NC and NEC groups, with 479 upregulated and 1,957 downregulated. A volcano plot (Figure 5A) was generated based on predefined screening criteria including FC value, P-value, and VIP, where metabolites with larger numerical changes and more significant differences were distributed at the left and right ends.. Substance identification was performed by searching and comparing spectral databases, including HMDB, MassBank, LipidMaps, mzCloud, KEGG, and an in-house metabolite standard database. Metabolites with secondary spectra in the quantitative list were matched against the fragment ion information of each secondary spectrum in the databases to achieve secondary qualitative identification. In this study, 321 metabolites were identified, including 34 upregulated and 52 downregulated differentially expressed metabolites (Table 2) . Cluster heatmap analysis provided a more intuitive representation of metabolite responses across samples (Figure 5B) , where deeper red indicates higher responses and deeper blue indicates lower responses. The volcano plot visually displayed the distribution and trends of differential metabolites between the two groups. Typically, the x-axis represents log2(FC), and the y-axis represents -log10(P-value), (Figure 5C) . Metabolites with larger quantitative changes and more significant differences were distributed at the left and right ends.The results indicated significant metabolic changes between NEC infants and normal preterm infants, primarily involving nucleotides, amino acids, and fatty acids. In the NEC group, metabolites such as γ-aminobutyric acid, L-proline, 2-methylserine, 5-aminovaleric acid, D-ribose, and 6-methylmercaptopurine were significantly higher than in the NC group, while L-phenylalanine, aspartic acid, L-asparagine, L-valine, and succinic acid were significantly lower. Table 2 Identification Results of Differential Metabolites Name Formula mz Rt log2(FC) Vip P value L-Proline C5H9NO2 116.07 52.6 1.28 1.75 0.0000 Betaine C5H11NO2 118.09 53.3 0.89 1.38 0.0008 2-Methylserine C4H9NO3 119.05 202.2 1.70 1.34 0.0043 Acetylcholine C7H16NO2 146.12 53.5 1.06 1.03 0.0290 D-Ribose C5H10O5 151.04 42.4 3.71 1.51 0.0003 6-Methylmercaptopurine C6H6N4S 166.05 80.8 2.43 1.04 0.0195 S-Adenosylmethionine C15H22N6O5S 398.24 456.3 0.65 1.34 0.0011 gamma-Aminobutyric acid C4H9NO2 102.96 54.6 0.18 1.40 0.0275 5-Aminopentanoic acid C5H11NO2 115.92 687.9 3.07 2.15 0.0000 L-Phenylalanine C9H11NO2 166.09 308.5 -2.84 1.83 0.0000 D-Galactose C6H12O6 181.01 482.2 -1.49 1.38 0.0009 Aspartame C14H18N2O5 295.134 230.6 -1.35 1.09 0.0236 L-Valine C5H11NO2 116.93 416.8 -1.28 1.42 0.0147 Succinic acid C4H6O4 117.02 42.8 -0.67 1.37 0.0288 L-Asparagine C4H8N2O3 131.05 49.1 -0.62 1.36 0.0331 Xanthine C5H4N4O2 151.03 66.6 -1.09 1.77 0.0011 Arachidonic acid C20H32O2 303.23 642.9 -0.88 1.30 0.0285 Note: Name: Identification of the substance; Formula: Molecular formula of the metabolite; mz: Mass-to-charge ratio; Rt: Retention time; log2(FC), log2 value of the fold change; VIP, Variable Importance in Projection for the first principal component in OPLS-DA; P value, statistical p-value, where a smaller value indicates greater significance of the difference. Enrichment analysis of compounds involved in metabolic pathways, as shown in Figure 6, revealed significant alterations in the following pathways in NEC group infants: alanine, aspartate, and glutamate metabolism; butanoate metabolism; lysine degradation; GABAergic synapse; pyruvate metabolism; arginine and proline metabolism; and synthesis and degradation of ketone bodies. These dysregulated metabolic pathways may be closely associated with the pathogenesis of NEC. Discussion NEC, a common and highly threatening gastrointestinal complication in neonates, particularly among very low birth weight infants, carries a mortality rate of approximately 20–30%, making it one of the leading causes of mortality in preterm infants [ 25 ] .The pathogenesis of NEC is complex, involving multiple factors including genetic susceptibility, intestinal immaturity, microvascular dysregulation, gut microbiota dysbiosis, inappropriate feeding strategies, and abnormal mucosal immune responses [ 26 ] . However, early diagnosis of NEC remains challenging due to the lack of specific biomarkers and unclear disease mechanisms.Proteomics allows for the comprehensive analysis of all proteins expressed by a cell, tissue, or organism during a specific period, typically used to investigate protein expression patterns under particular conditions or at specific time points [ 27 ] . By comparing protein expression profiles between NEC and control groups and analyzing differentially expressed proteins, researchers can gain insights into the pathogenesis of NEC, facilitating the discovery of diagnostic biomarkers that could significantly improve disease management. Meanwhile, metabolomics, as an emerging analytical approach, focuses on dynamic metabolic changes during biochemical processes, offering novel perspectives for early NEC detection [ 28 ] . This study integrates proteomic and metabolomic analyses to examine plasma proteins and metabolites in NEC and control groups, systematically identifying molecular changes associated with NEC progression. These subtle yet critical molecular signatures, as potential disease biomarkers, not only enhance early NEC identification but also provide valuable insights for precision medicine approaches. Metabolomic findings indicate that significant alterations in butanoate metabolism occur during the early stages of NEC. This change is not isolated, as existing literature reports that excessive butyrate, a short-chain fatty acid, contributes to necrotizing colitis [ 29 ] . Studies using intestinal injury models in young rats have shown that butyrate can induce developmental-dependent NEC-like epithelial damage through necroptosis, mirroring the developmental stage-specific intestinal injury observed in NEC [ 30 ] . Furthermore, research demonstrates that the severity of colonic mucosal damage caused by short-chain fatty acids, including butyrate, varies with age, likely due to immature mucosal defense mechanisms in neonatal rats that increase susceptibility to luminal short-chain fatty acid-induced injury [ 31 ] . These fatty acids may promote necrotizing colitis by disrupting gut microbiota balance and compromising intestinal barrier function [ 31 – 33 ] . Consequently, monitoring fecal butyrate and other short-chain fatty acid levels could serve as a non-invasive method to predict functional gastrointestinal alterations prior to the onset of NEC's clinical symptoms [ 34 ] , potentially emerging as a valuable diagnostic biomarker. KEGG pathway enrichment analysis of proteomic data revealed significant enrichment of the NOD-like receptor signaling pathway in differentially expressed proteins between NEC and control groups. NOD-like receptors (NLRs), intracellular proteins central to innate and adaptive immunity [ 35 ] , recognize specific pathogen-associated molecular patterns, activating multiple signaling pathways and cytokine secretion [ 35 ] . Growing evidence underscores the pivotal role of NLRs in gastrointestinal inflammatory diseases and cancer. For instance, Bacteroides fragilis has been shown to alleviate NEC intestinal damage by restoring bile acid metabolic balance through bile salt hydrolase and inhibiting the FXR-NLRP3 signaling pathway [ 8 ] . Additionally, the gut microbiota influences viral tolerance in NEC by modulating the STAT1-NLRC5 axis [ 36 ] , while macrophage α7nAChR mitigates intestinal injury via the mTOR/NLRP3/IL-1β pathway [ 37 ] . Animal model studies further confirm that NLRP3, a key molecule in the NOD-like receptor signaling pathway, functions as an inflammasome in NEC pathogenesis and progression [ 38 – 40 ] . In our proteomics study, significant differences were observed in the molecules TXN (Thioredoxin) and DEFA1 (Defensin Alpha 1) from the NOD-like receptor signaling pathway between the disease group and the control group. The Thioredoxin system, comprising thioredoxin reductase (TrxR), and NADPH, plays a pivotal role in redox regulation and antioxidant defense [ 41 ] .This system serves as the primary redox control mechanism, essential for scavenging reactive oxygen species and protecting cells from oxidative damage [ 42 , 43 ] . The DEFA1/DEFA3 genes encode human neutrophil peptides 1–3 (HNP1-3), which are functionally associated with innate immunity and infection [ 44 ] . HNPs represent the most abundant proteins in neutrophil granules, though their concentration varies due to extensive gene copy number polymorphism. Genetic studies have demonstrated that increased DEFA1/DEFA3 copy numbers elevate the risk of organ dysfunction during sepsis [ 45 ] . Moreover, these copy number variations correlate with susceptibility to nosocomial infections in critically ill patients [ 45 ] , intestinal damage in Behçet's disease [ 46 ] , sepsis susceptibility [ 45 , 47 ] , and tumor progression [ 48 ] . However, the roles of TXN and DEFA1 in NEC remain unexplored in the literature. Emerging evidence suggests that butyrate metabolism may influence disease progression by modulating the NOD-like receptor signaling pathway. In ulcerative diabetic foot patients, butyrate metabolism appears linked to TXN expression [ 49 ] . Additionally, Codonopsis pilosula polysaccharides can suppress NLRP3 activation by binding short-chain fatty acids (SCFAs) to GPR proteins, thereby alleviating intestinal inflammation [ 50 ] . Sodium butyrate has been shown to mitigate inflammatory responses in colitis and autoimmune prostatitis by inhibiting oxidative stress and NLRP3 inflammasome activation [ 50 , 51 ] . These findings imply that in NEC, butyrate metabolism may regulate TXN and DEFA1 expression within the NOD-like receptor pathway—a mechanism distinct from classical NLRP3 inflammasome-mediated inflammation. Targeting this pathway could unveil novel therapeutic strategies for NEC. This study employed a multi-omics analysis approach integrating metabolomics and proteomics, identifying several biological processes closely associated with the early stages of NEC. The findings suggest that butyrate metabolism may play a role in NEC by influencing non-canonical pathways of the NOD-like receptor signaling pathway. Although this study identified multiple potential early NEC biomarkers with diagnostic value, the limited sample size prevented the establishment of any specific biomarker as a gold standard for early NEC diagnosis. To detect NEC onset signals earlier, future research should focus on comprehensive investigation and clinical validation of inflammation-related biomarkers. By precisely identifying and validating these early inflammatory markers, we aim to revolutionize NEC diagnostic strategies and pioneer novel approaches for early diagnosis. Achieving this goal will not only facilitate timely implementation of effective interventions to alleviate patient suffering but also significantly improve infant outcomes, safeguarding their health and well-being. Conclusions In this study, using metabolomics techniques, we detected an imbalance in amino acid metabolic pathways related to inflammatory pathways in the serum of infants in the NEC group. These pathways include GABA, arginine metabolism, and butyrate metabolism. Existing literature has reported that excessive butyrate, a short - chain fatty acid, contributes to the development of necrotizing colitis, and our findings are consistent with current research results. By using proteomics technology, we found that there were significant differences in the molecules TXN and DEFA1 within the NOD - like receptor signaling pathway when comparing the disease group with the control group. The role of the NOD-like receptor signaling pathway in innate immunity and infection has been documented in the literature, with studies suggesting that butyrate metabolism can influence the classical pathway of this signaling pathway. While previous studies focused on the classical pathway, our research delved into the non - canonical pathway. However, our study found that the non - canonical pathway of the NOD - like receptor signaling pathway exhibited altered expression in the serum of NEC group infants, indicating that butyrate metabolism may affect disease progression by regulating TXN and DEFA1 proteins in the non - canonical pathway of the NOD - like receptor signaling pathway in NEC. Through integrated analysis of metabolomics and proteomics, this study suggests that butyrate metabolism may influence the pathogenesis of NEC via a non-canonical NOD-like receptor signaling pathway.This provides a highly promising approach for elucidating the pathogenesis of NEC in preterm infants, which offers new insights and evidence to advance disease understanding and intervention strategies. Abbreviations NEC neonatal necrotizing enterocolitis HPLC-QTOF-MS/MS High-performance liquid chromatography coupled with Q-TOF mass spectrometry GABA γ-aminobutyric acid DDA data-dependent acquisition PPI protein-protein interaction PCA Principal Component Analysis PLS-DA Partial Least Squares-Discriminant Analysis OPLS-DA Orthogonal Partial Least Squares Discriminant Analysis VIP Variable Importance in Projection GO Gene Ontology BP biological processes CC Cellular components MF Molecular functions KEGG Kyoto Encyclopedia of Genes and Genomes NLRs NOD-like receptors TXN Thioredoxin DEFA1 Defensin Alpha 1 TrxR thioredoxin reductase HNP1-3 human neutrophil peptides 1-3 SCFAs short-chain fatty acids Declarations Ethics statement This study was conducted in compliance with the Declaration of Helsinki and received approval from the Scientific Ethics Committee of the Affiliated Hospital of Inner Mongolia Medical University, China (DW2025018). Written informed consent was obtained from all participants or their legal guardians. Availability of data and materials The data that support the findings of this study are available on request from the corresponding author, Chaoyang Wang，upon reasonable request. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the Higher Education Scientific Research Project Fund of Inner Mongolia Autonomous Region (NJZY21617), Inner Mongolia Medical University Joint Funding Project (YKD2023LH053), and Inner Mongolia Autonomous Region Natural Science Foundation (2023LHMS08004). Authors' contributions Xiongfeng Li: Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Visualization. Hailong Wang, Yu Zhou and Rui Yang: Data curation, Formal analysis, Investigation, Methodology. Jing Zhou and Chaoyang Wang: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Supervision, Writing – review & editing. Acknowledgements Not Applicable. References Mihi B, Gong Q, Nolan LS, et al. Interleukin-22 signaling attenuates necrotizing enterocolitis by promoting epithelial cell regeneration. Cell Rep Med. 2021. 2(6): 100320. Murphy K, Ross RP, Ryan CA, Dempsey EM, Stanton C. Probiotics, Prebiotics, and Synbiotics for the Prevention of Necrotizing Enterocolitis. Front Nutr. 2021. 8: 667188. Coles V, Kortsalioudaki C, Eaton S, et al. Standardising the elusive diagnosis of NEC in the premature infant - A practical score. Early Hum Dev. 2022. 175: 105692. Manzoni C, Kia DA, Vandrovcova J, et al. Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences. Brief Bioinform. 2018. 19(2): 286-302. Wilkins MR, Sanchez JC, Gooley AA, et al. Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol Genet Eng Rev. 1996. 13: 19-50. Meier F, Brunner AD, Koch S, et al. Online Parallel Accumulation-Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer. Mol Cell Proteomics. 2018. 17(12): 2534-2545. Clish CB. Metabolomics: an emerging but powerful tool for precision medicine. Cold Spring Harb Mol Case Stud. 2015. 1(1): a000588. Chen Z, Chen H, Huang W, et al. Bacteroides fragilis alleviates necrotizing enterocolitis through restoring bile acid metabolism balance using bile salt hydrolase and inhibiting FXR-NLRP3 signaling pathway. Gut Microbes. 2024. 16(1): 2379566. 王圣雯, 高晶, 杨欣, 陈莹, 张佩滢, 王玉美. 代谢组学在胎儿和新生儿领域的临床应用研究进展. 中国医学创新. 2023. 20(07): 172-177. 陈佳楠, 王礼周, 吴谨准. 基于~1H-NMR法的早产儿坏死性小肠结肠炎代谢组学研究. 临床儿科杂志. 2018. 36(09): 653-657. 张志波. 新生儿坏死性小肠结肠炎：从Bell分期解读到手术指征的把握. 临床小儿外科杂志. 2022. 21(04): 306-309. Galperin MY, Makarova KS, Wolf YI, Koonin EV. 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Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. J Proteome Res. 2015. 14(8): 3322-35. Wishart DS, Tzur D, Knox C, et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007. 35(Database issue): D521-6. Horai H, Arita M, Kanaya S, et al. MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom. 2010. 45(7): 703-14. Sud M, Fahy E, Cotter D, et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 2007. 35(Database issue): D527-32. Abdelrazig S, Safo L, Rance GA, et al. Metabolic characterisation of Magnetospirillum gryphiswaldense MSR-1 using LC-MS-based metabolite profiling. RSC Adv. 2020. 10(54): 32548-32560. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000. 28(1): 27-30. Xia J, Wishart DS. Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst. Nat Protoc. 2011. 6(6): 743-60. Zhu Z, Yuan L, Wang J, et al. Mortality and Morbidity of Infants Born Extremely Preterm at Tertiary Medical Centers in China From 2010 to 2019. JAMA Netw Open. 2021. 4(5): e219382. Cai X, Liebe HL, Golubkova A, Leiva T, Hunter CJ. A Review of the Diagnosis and Treatment of Necrotizing Enterocolitis. Curr Pediatr Rev. 2023. 19(3): 285-295. 彭征, 戴盛明. 蛋白质组学在肝细胞癌诊断研究中的进展与展望. 分子诊断与治疗杂志. 2015. 7(03): 145-150. Stokes V, Rajai A, Mukherjee D, Mukherjee A. Transfusion-associated necrotizing enterocolitis (NEC) in extremely preterm infants: experience of a tertiary neonatal center in UK. J Matern Fetal Neonatal Med. 2022. 35(25): 5054-5059. Lin J. Too much short chain fatty acids cause neonatal necrotizing enterocolitis. Med Hypotheses. 2004. 62(2): 291-3. Wang K, Tao GZ, Salimi-Jazi F, et al. Butyrate induces development-dependent necrotizing enterocolitis-like intestinal epithelial injury via necroptosis. Pediatr Res. 2023. 93(4): 801-809. Nafday SM, Chen W, Peng L, Babyatsky MW, Holzman IR, Lin J. Short-chain fatty acids induce colonic mucosal injury in rats with various postnatal ages. Pediatr Res. 2005. 57(2): 201-4. Owens J, Qiu H, Knoblich C, et al. Feeding intolerance after pediatric cardiac surgery is associated with dysbiosis, barrier dysfunction, and reduced short-chain fatty acids. Am J Physiol Gastrointest Liver Physiol. 2024. 327(5): G685-G696. Waligora-Dupriet AJ, Dugay A, Auzeil N, Huerre M, Butel MJ. Evidence for clostridial implication in necrotizing enterocolitis through bacterial fermentation in a gnotobiotic quail model. Pediatr Res. 2005. 58(4): 629-35. Szylit O, Maurage C, Gasqui P, et al. Fecal short-chain fatty acids predict digestive disorders in premature infants. JPEN J Parenter Enteral Nutr. 1998. 22(3): 136-41. Zhou Y, Yu S, Zhang W. NOD-like Receptor Signaling Pathway in Gastrointestinal Inflammatory Diseases and Cancers. Int J Mol Sci. 2023. 24(19): 14511. Subramanian S, Geng H, Wu L, et al. Microbiota regulates neonatal disease tolerance to virus-evoked necrotizing enterocolitis by shaping the STAT1-NLRC5 axis in the intestinal epithelium. Cell Host Microbe. 2024. 32(10): 1805-1821.e10. Shen L, Zhong X, Ji H, et al. Macrophage α7nAChR alleviates the inflammation of neonatal necrotizing enterocolitis through mTOR/NLRP3/IL-1β pathway. Int Immunopharmacol. 2024. 139: 112590. Filler R, Yeganeh M, Li B, et al. Bovine milk-derived exosomes attenuate NLRP3 inflammasome and NF-κB signaling in the lung during neonatal necrotizing enterocolitis. Pediatr Surg Int. 2023. 39(1): 211. Chen Z, Zhang Y, Lin R, et al. Cronobacter sakazakii induces necrotizing enterocolitis by regulating NLRP3 inflammasome expression via TLR4. J Med Microbiol. 2020. 69(5): 748-758. Yin Y, Wang J, Zhao X, et al. Overexpressed FOXO3 improves inflammatory status in mice by affecting NLRP3-mediated cell coronation in necrotizing colitis mice. Biomed Pharmacother. 2020. 125: 109867. Gencheva R, Arnér E. Thioredoxin Reductase Inhibition for Cancer Therapy. Annu Rev Pharmacol Toxicol. 2022. 62: 177-196. Nordberg J, Arnér ES. Reactive oxygen species, antioxidants, and the mammalian thioredoxin system. Free Radic Biol Med. 2001. 31(11): 1287-312. Karlenius TC, Tonissen KF. Thioredoxin and Cancer: A Role for Thioredoxin in all States of Tumor Oxygenation. Cancers (Basel). 2010. 2(2): 209-32. Zhao J, Gu Q, Wang L, et al. Low-Copy Number Polymorphism in DEFA1/DEFA3 Is Associated with Susceptibility to Hospital-Acquired Infections in Critically Ill Patients. Mediators Inflamm. 2018. 2018: 2152650. Chen Q, Yang Y, Hou J, et al. Increased gene copy number of DEFA1/DEFA3 worsens sepsis by inducing endothelial pyroptosis. Proc Natl Acad Sci U S A. 2019. 116(8): 3161-3170. Ahn JK, Cha HS, Lee J, Jeon CH, Koh EM. Correlation of DEFA1 gene copy number variation with intestinal involvement in Behcet's disease. J Korean Med Sci. 2012. 27(1): 107-9. Chen Q, Hakimi M, Wu S, et al. Increased genomic copy number of DEFA1/DEFA3 is associated with susceptibility to severe sepsis in Chinese Han population. Anesthesiology. 2010. 112(6): 1428-34. Jeong H, Park SW, Hwang YS. DEFA1, Primarily Expressed at the Invasive Tumor Front, Promotes OSCC Cell Invasion and Tumor Growth. Cancer Genomics Proteomics. 2025. 22(2): 326-345. Xu F, Rui SL, Luo PQ, Chen Y, Ma Y, Deng WQ. [Bioinformatics Analysis of Hub Genes of Diabetic Foot Ulcer and Their Biofunctions]. Sichuan Da Xue Xue Bao Yi Xue Ban. 2022. 53(6): 961-968. Zhou J, Yang Q, Wei W, Huo J, Wang W. Codonopsis pilosula polysaccharide alleviates ulcerative colitis by modulating gut microbiota and SCFA/GPR/NLRP3 pathway. J Ethnopharmacol. 2025. 337(Pt 2): 118928. Bian Z, Zhang Q, Qin Y, et al. Sodium Butyrate Inhibits Oxidative Stress and NF-κB/NLRP3 Activation in Dextran Sulfate Sodium Salt-Induced Colitis in Mice with Involvement of the Nrf2 Signaling Pathway and Mitophagy. Dig Dis Sci. 2023. 68(7): 2981-2996. Hua X, Zhang J, Chen J, et al. Sodium butyrate alleviates experimental autoimmune prostatitis by inhibiting oxidative stress and NLRP3 inflammasome activation via the Nrf2/HO-1 pathway. Prostate. 2024. 84(7): 666-681. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 26 Jan, 2026 Reviewers invited by journal 15 Jul, 2025 Editor assigned by journal 13 Jul, 2025 First submitted to journal 09 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6891222","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485849640,"identity":"6b5430ec-77c0-43a8-bb25-7724c1163cbe","order_by":0,"name":"Xiongfeng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3RMQuCQBjG8RPhXI7mi+ikDxBcHURC2Fc5EZqcw9EQbrK9wW8ROBuCLaetjro0tbS11VqT1xZ0//0H78MLgE73g0Fw6Voersj6HOdqZGBIk7Zyw4AsuRohpoTDThRe1ARU8TBYldgTprdL5L25AZdMoz6Can/Ja8hia390UuCzRd5HcD5r+BaNBaqyEQK5l/USu6WYQ2wkOLgqEiDnmAs6OeAAqpLSp1xyRlHJnJQqbLGj4tQ9wiehVtw1t9AlveQjjBRf806+FTqdTvcXvQBK50cK8wdRFAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3123-0031","institution":"Inner Mongolia Medical College Affiliated Hospital: The Affiliated Hospital of Inner Mongolia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiongfeng","middleName":"","lastName":"Li","suffix":""},{"id":485849641,"identity":"c2f8da1c-52ea-4646-a655-1adfe79a802b","order_by":1,"name":"Hailong Wang","email":"","orcid":"","institution":"Inner Mongolia Medical College Affiliated Hospital: The Affiliated Hospital of Inner Mongolia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hailong","middleName":"","lastName":"Wang","suffix":""},{"id":485849642,"identity":"40778009-8af6-4fe2-ba2b-2f7bd14704da","order_by":2,"name":"Yu Zhou","email":"","orcid":"","institution":"Inner Mongolia Medical College Affiliated Hospital: The Affiliated Hospital of Inner Mongolia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhou","suffix":""},{"id":485849643,"identity":"550d0301-2719-40ad-bea5-751ab4f6a270","order_by":3,"name":"Rui Yang","email":"","orcid":"","institution":"Inner Mongolia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Yang","suffix":""},{"id":485849644,"identity":"0aff913c-3e3b-4b59-97ca-5f72adc1fec9","order_by":4,"name":"Jing Zhou","email":"","orcid":"","institution":"Inner Mongolia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhou","suffix":""},{"id":485849645,"identity":"2922b304-1330-49df-911d-2d505d1a2fdb","order_by":5,"name":"Chaoyang Wang","email":"","orcid":"https://orcid.org/0009-0007-3220-623X","institution":"Inner Mongolia Medical College Affiliated Hospital: The Affiliated Hospital of Inner Mongolia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chaoyang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-06-14 01:48:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6891222/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6891222/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87050469,"identity":"431b0570-4517-43d3-bbdf-cbf2e93b41d8","added_by":"auto","created_at":"2025-07-18 14:56:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":620811,"visible":true,"origin":"","legend":"\u003cp\u003eData Quality Control. A: Box plot of quantitative value distribution before normalization; B: Box plot of quantitative value distribution after normalization; C: Global analysis clustering heatmap; D: Sample correlation matrix plot; E: PCA principal component analysis plot; F: PLS-DA analysis plot.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-6891222/v1/69a751f787a9fc8bbc465eab.png"},{"id":87050474,"identity":"c97614e6-5621-457d-b78e-5e5733e4cbbf","added_by":"auto","created_at":"2025-07-18 14:56:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":692462,"visible":true,"origin":"","legend":"\u003cp\u003eBioinformatics analysis. A: Volcano plot of differentially expressed proteins; B: COG functional annotation; C: GO enrichment analysis; D: KEGG enrichment analysis; E: Reactome enrichment analysis; F: Pfam enrichment analysis; G: Subcellular localization enrichment analysis.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-6891222/v1/c3530854760489034fffaad7.png"},{"id":87051565,"identity":"d8f95faf-e423-4ee2-9d36-fd3cfa5e3315","added_by":"auto","created_at":"2025-07-18 15:04:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66651,"visible":true,"origin":"","legend":"\u003cp\u003ePPI interaction network.\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-6891222/v1/74777561e0ab2a7bb6c588e9.png"},{"id":87053449,"identity":"602cead3-28c8-4ab4-b4e4-85ae632063ea","added_by":"auto","created_at":"2025-07-18 15:12:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":188697,"visible":true,"origin":"","legend":"\u003cp\u003eData Quality Control. A: PCA Principal Component Analysis Plot; B: PLS-DA Analysis Plot.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-6891222/v1/42cf75d076e4030abac0b9a0.png"},{"id":87051567,"identity":"cfc2a3fe-53ef-49f4-83a2-4ed40bfb1d80","added_by":"auto","created_at":"2025-07-18 15:04:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":551837,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential metabolite analysis. A: Volcano plot of differential metabolite analysis for primary metabolites. B: Heatmap clustering of secondary identified metabolites. C: Volcano plot of secondary identified metabolites.\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-6891222/v1/5a1e7f3e9e1a82eb4518e300.png"},{"id":87051571,"identity":"519afa03-c353-425b-8d67-611b895033d0","added_by":"auto","created_at":"2025-07-18 15:04:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2166437,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway enrichment analysis of differential metabolites. A: Enrichment analysis network diagram; B: Enrichment analysis bar chart.\u003c/p\u003e","description":"","filename":"Picture6.png","url":"https://assets-eu.researchsquare.com/files/rs-6891222/v1/8c6f9223d71e9785308859e4.png"},{"id":87054795,"identity":"4f777d36-6921-462a-8b97-6ccbd498feb3","added_by":"auto","created_at":"2025-07-18 15:28:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5068617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6891222/v1/c2d08fe4-e84c-4a68-83ca-e95b85fa1754.pdf"}],"financialInterests":"","formattedTitle":"Application of integrated multi-omics analysis in identifying biomarkers for early diagnosis of neonatal necrotizing enterocolitis","fulltext":[{"header":"Background","content":"\u003cp\u003eNEC, a severe gastrointestinal emergency particularly prevalent among preterm infants, exhibits a significantly elevated incidence rate of 5%–10% in very low and extremely low birth weight infants\u003csup\u003e[1, 2]\u003c/sup\u003e. This condition has emerged as a critical factor contributing to the high mortality rate in this vulnerable population. More alarmingly, even among treated survivors, persistent long-term adverse effects such as neurological impairments and growth retardation often occur, severely compromising the quality of life for both affected infants and their families. Literature reports indicate that approximately 45% of NEC survivors continue to face significant neurological complications during recovery\u0026nbsp;\u003csup\u003e[3]\u003c/sup\u003e.Given these global health challenges, deeper exploration of NEC's pathogenesis - particularly the identification of specific early-stage biomarkers - has become an urgent priority .\u003c/p\u003e\n\u003cp\u003eRecent advances in post-genomic technologies have enabled widespread use of multi-omics integration for investigating complex disease mechanisms\u003csup\u003e[4]\u003c/sup\u003e. Proteomics, which examines protein composition and dynamics in cells, tissues, and organisms\u003csup\u003e[5]\u003c/sup\u003e, focuses on these fundamental biological units to identify disease-related protein variations. This approach facilitates the discovery of specific biomarkers for early and accurate diagnosis. The 4D label-free quantitative proteomics technology has gained prominence in proteomic analysis due to its high sensitivity and bioinformatics advantages\u003csup\u003e[6]\u003c/sup\u003e.Concurrently, metabolomics has emerged as a powerful field that comprehensively analyzes biological processes through extensive metabolite profiling\u003csup\u003e[7]\u003c/sup\u003e. This approach offers unique insights, particularly when processing large-scale datasets from complex biological samples\u003csup\u003e[8]\u003c/sup\u003e, by revealing not just metabolite types and concentrations but also their interactions and temporal dynamics\u003csup\u003e[9]\u003c/sup\u003e. Therefore, integrating proteomics and metabolomics presents a promising strategy for identifying early disease biomarkers.\u003c/p\u003e\n\u003cp\u003eThis study focuses on NEC, a severe gastrointestinal disease that poses significant threats to preterm infant health. By employing 4D - Label - free proteomics technology integrated with comprehensive targeted metabolomics, it aims to identify early protein and metabolic biomarkers for NEC and elucidate its early pathogenesis mechanisms. This approach provides a more objective and scientific foundation for the early diagnosis and clinical management of NEC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e1. Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in compliance with the Declaration of Helsinki and received approval from the Scientific Ethics Committee of the Affiliated Hospital of Inner Mongolia Medical University, China (DW2025018). Written informed consent was obtained from all participants or their legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Study Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreterm infants diagnosed with NEC and admitted to the Neonatal Intensive Care Unit of the Affiliated Hospital of Inner Mongolia Medical University between January 1, 2023, and December 31, 2023, were selected as the NEC group. The inclusion criteria\u003csup\u003e[10]\u003c/sup\u003ewere as follows: ① Gestational age \u0026lt; 37 weeks at birth and postnatal age \u0026lt; 28 days at enrollment; ② Meeting the diagnostic criteria for NEC based on the modified Bell-NEC staging criteria\u003csup\u003e[11]\u003c/sup\u003e;No congenital gastrointestinal malformations, severe malformations of other organs, or inherited metabolic diseases. Preterm infants hospitalized during the same period, who were fed normally without NEC, were selected as the control group. The inclusion criteria for the control group were: ① Comparable gestational age and postnatal age to the NEC preterm infants; ② Normal feeding with no symptoms of NEC; ③ No congenital gastrointestinal malformations, severe malformations of other organs, or inherited metabolic diseases. This study was approved by the Hospital\u0026apos;s Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Collection and Preservation of Serum Samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the preterm NEC group, blood samples were collected within 12 hours after the onset of characteristic NEC symptoms such as abdominal distension, bloody stools, and/or vomiting. For the control group, blood samples were collected at corresponding gestational and postnatal ages relative to the NEC group. Approximately 1.0 - 1.5 mL of arterial blood was collected and placed in sodium heparin anticoagulant tubes (manufactured by BD, USA), stored at 4\u0026deg;C, and centrifuged at 4\u0026deg;C and 3000 rpm for 15 minutes within 12 hours. Subsequently, 500 \u0026mu;L of plasma was aliquoted and stored at -80\u0026deg;C for future analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Proteomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, serum samples from six participants (three NEC patients and three healthy controls) were analyzed using 4D label-free quantitative proteomics analysis.\u003c/p\u003e\n\u003cp\u003eTotal proteins were extracted from the samples, with a portion allocated for protein concentration determination and SDS-PAGE analysis. Following enzymatic desalting, sample peptides were identified using LC-MS/MS. First, raw mass spectrometry data were acquired through the traditional data-dependent acquisition (DDA) method using prepared mobile phases: A (100% water, 0.1% formic acid) and B (80% acetonitrile, 0.1% formic acid). The lyophilized powder was reconstituted in 10 \u0026micro;L of mobile phase A, centrifuged at 14,000 \u0026times; g for 20 min at 4\u0026deg;C, and 400 ng of supernatant was injected for LC-MS analysis.The timsTOF_HT mass spectrometer was equipped with a protein analytical column (QL-HPLC-100*15) and Captive Spray ion source, operating in DDA mode with an m/z scan range of 100\u0026ndash;1700. Primary MS resolution was set to 60,000 (at 1222 m/z) with a TIMS tunnel accumulation time of 100 ms. Instrument parameters included a capillary voltage of 1.6 kV and a mobility range of 0.6-1.6 cm\u0026sup2;/(V\u0026middot;s). The total cycle time was 1.1 s with 10 PASEF cycles per acquisition.Database searching was performed using FragPipe software against the Homo sapiens SP database (UniProt; 20,434 proteins; downloaded March 7, 2024). Quality control criteria required identified peptides to be predominantly 7-40 amino acids in length, with each protein identified by at least one unique peptide.\u003c/p\u003e\n\u003cp\u003eFor bioinformatics analysis, raw data were median-normalized to eliminate experimental variability. Rows containing zero values were removed to generate a complete dataset. Data reproducibility and group differences were assessed through hierarchical clustering heatmaps, Pearson correlation analysis, principal component analysis (PCA), and PLS-DA. Differential protein analysis employed a T-test (p \u0026lt; 0.05, absolute fold change \u0026gt;1.5). Functional annotation utilized the COG database (http://www.ncbi.nlm.nih.gov/COG/)\u003csup\u003e[12]\u003c/sup\u003e. GO annotation was performed using EggNog-Mapper (V2.0), categorizing proteins by cellular component, molecular function, and biological process. Pathway analysis was conducted using KEGG (http://www.kegg.jp/) and Reactome annotations. Protein family classification and domain identification were performed using the Pfam database, while subcellular localization was predicted with WOLF PSORT. A protein-protein interaction (PPI) network was constructed using the STRING database (displaying interactions with a combined score \u0026gt;0.7). The top 20 proteins with the highest interaction degrees were selected to reconstruct a hub protein interaction network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, serum samples from 16 subjects (8 NEC patients and 8 controls) were analyzed using high-performance liquid chromatography coupled with Q-Exactive mass spectrometry.\u003c/p\u003e\n\u003cp\u003eChromatographic conditions: The analysis was performed using a Thermo Vanquish UHPLC system (Thermo Fisher Scientific, USA) equipped with an ACQUITY UPLC\u0026reg; HSS T3 column (2.1 \u0026times; 100 mm, 1.8 \u0026micro;m; Waters, Milford, MA, USA). The flow rate was maintained at 0.3 mL/min with a column temperature of 40\u0026deg;C and an injection volume of 2 \u0026micro;L. For positive ion mode, the mobile phases consisted of 0.1% formic acid in acetonitrile (B2) and 0.1% formic acid in water (A2). The gradient elution program was: 0\u0026ndash;1 min, 8% B2; 1\u0026ndash;8 min, 8%\u0026ndash;98% B2; 8\u0026ndash;10 min, 98% B2; 10\u0026ndash;10.1 min, 98%\u0026ndash;8% B2; 10.1\u0026ndash;12 min, 8% B2. For negative ion mode, the mobile phases were acetonitrile (B3) and 5 mM ammonium formate in water (A3). The gradient elution program was: 0\u0026ndash;1 min, 8% B3; 1\u0026ndash;8 min, 8%\u0026ndash;98% B3; 8\u0026ndash;10 min, 98% B3; 10\u0026ndash;10.1 min, 98%\u0026ndash;8% B3; 10.1\u0026ndash;12 min, 8% B3\u003csup\u003e[13]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMass spectrometry conditions: Analysis was conducted using a Thermo Q Exactive mass spectrometer (Thermo Fisher Scientific, USA) with an electrospray ionization (ESI) source in both positive and negative ion modes. The positive ion spray voltage was set at 3.50 kV and the negative ion spray voltage at -2.50 kV. Sheath gas and auxiliary gas flow rates were 40 arb and 10 arb, respectively. The capillary temperature was maintained at 325\u0026deg;C. Full scan mass spectra were acquired at a resolution of 70,000 (m/z range 100\u0026ndash;1000) for primary mass spectrometry. For secondary fragmentation, HCD was employed with a collision energy of 30 eV and a resolution of 17,500. The top 10 most intense ions were selected for fragmentation, with dynamic exclusion applied to eliminate redundant MS/MS information\u003csup\u003e[14]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe raw mass spectrometry files were converted to mzXML format using the MSConvert tool in the ProteoWizard software package (v3.0.8789)\u0026nbsp;\u003csup\u003e[15]\u003c/sup\u003e. Peak detection, filtering, and alignment were performed using the R package XCMS\u003csup\u003e[16]\u003c/sup\u003e, with the following parameters: bw=2, ppm=15, peakwidth=c(5,30), mzwid=0.015, mzdiff=0.01, and method=\u0026quot;centWave,\u0026quot; generating a quantitative metabolite profile. \u0026nbsp;Data normalization was performed using total peak area normalization to correct for systematic errors. Multivariate statistical analysis was conducted using the R package ropls\u003csup\u003e[17]\u003c/sup\u003e, including Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). Variable Importance in Projection (VIP) scores were calculated, and OPLS-DA dimensionality reduction methods were employed, while fold change values were used to assess the magnitude of intergroup differences. Metabolites were considered statistically significant when P-values were \u0026lt;0.05 and VIP values were \u0026gt;1.\u003c/p\u003e\n\u003cp\u003eMetabolite identification was performed by spectral matching against databases including HMDB\u003csup\u003e[18]\u003c/sup\u003e, MassBank\u003csup\u003e[19]\u003c/sup\u003e, LipidMaps\u003csup\u003e[20]\u003c/sup\u003e, mzCloud\u003csup\u003e[21]\u003c/sup\u003e, KEGG\u003csup\u003e[22]\u003c/sup\u003e, and Norminova\u0026apos;s in-house metabolite standard database, with a mass tolerance of \u0026lt;30 ppm. Based on the identified differential metabolites, pathway enrichment and topological analysis were conducted using MetaboAnalyst. The enriched pathways were visualized using KEGG Mapper\u003csup\u003e[23]\u003c/sup\u003e to display both differential metabolites and pathway maps, with a significance threshold of P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. General characteristics of the study subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 8 NEC patients were enrolled, including 5 males and 3 females, with a mean gestational age of 32.7\u0026plusmn;1.2 weeks and median onset age of 15.75 days (range: 3.75\u0026ndash;20.50 days). According to the modified Bell-NEC staging criteria [24], the distribution was: stage I (n=4, 50%), stage IIa (n=2, 25%), stage IIb (n=1, 12.5%), and stage III (n=1, 12.5%). Blood samples were collected within 1 hour of NEC diagnosis, followed by standard management including nil per os, nasogastric decompression, antibiotic therapy, and supportive care. The control group (NC) consisted of 8 infants (3 males, 5 females) with a mean gestational age of 31.6\u0026plusmn;0.7 weeks. No statistically significant differences were observed between the two groups in terms of gestational age, sex distribution, birth weight, history of asphyxia, breastfeeding rate, or postnatal age \u003cstrong\u003e(Table 1)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Baseline Data\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2746%;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5282%;\"\u003e\n \u003cp\u003eNEC Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2958%;\"\u003e\n \u003cp\u003eControl Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9014%;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2746%;\"\u003e\n \u003cp\u003eGestational age (x\u0026plusmn;s) weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5282%;\"\u003e\n \u003cp\u003e32.7\u0026plusmn;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2958%;\"\u003e\n \u003cp\u003e31.6\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9014%;\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2746%;\"\u003e\n \u003cp\u003eMale [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5282%;\"\u003e\n \u003cp\u003e5（62.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2958%;\"\u003e\n \u003cp\u003e3（37.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9014%;\"\u003e\n \u003cp\u003e0.344\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e）\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2746%;\"\u003e\n \u003cp\u003eBirth weight \u0026lt;1500g [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5282%;\"\u003e\n \u003cp\u003e2（25.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2958%;\"\u003e\n \u003cp\u003e4（50.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9014%;\"\u003e\n \u003cp\u003e0.368\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e）\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2746%;\"\u003e\n \u003cp\u003eHistory of asphyxia [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5282%;\"\u003e\n \u003cp\u003e3（37.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2958%;\"\u003e\n \u003cp\u003e2（25.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9014%;\"\u003e\n \u003cp\u003e0.779\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e）\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2746%;\"\u003e\n \u003cp\u003eBreastfeeding [n(%)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5282%;\"\u003e\n \u003cp\u003e1（12.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2958%;\"\u003e\n \u003cp\u003e2（25.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9014%;\"\u003e\n \u003cp\u003e0.717\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e）\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2746%;\"\u003e\n \u003cp\u003eSampling age (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25.5282%;\"\u003e\n \u003cp\u003e15.75（3.75~20.50）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.2958%;\"\u003e\n \u003cp\u003e20.25（12.50~28.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.9014%;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: 1) Fisher\u0026apos;s exact test\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Proteomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 5,727 peptides and 588 proteins were identified in this study. Comparative analysis of pre- and post-normalization data revealed that the corrected median values aligned along the baseline (y-axis \u0026asymp; 0), indicating effective normalization (Figure 1, A-B). We evaluated global clustering heatmaps, Pearson correlation analysis, principal component analysis (PCA), and PLS-DA. The results demonstrated high intra-group reproducibility and significant inter-group differences (Figure 1, C-F). Using the screening criteria of p \u0026lt; 0.05 and absolute fold change \u0026gt;1.5, we identified 31 significantly differentially expressed proteins, including 11 upregulated and 20 downregulated proteins \u003cstrong\u003e(Figure 2A)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFunctional annotation of differentially expressed proteins was performed using the COG database \u003cstrong\u003e(Figure 2B)\u003c/strong\u003e. Gene Ontology (GO) describes gene functions, localization, and activities, encompassing three categories: cellular component, molecular function, and biological process. GO analysis of differentially expressed proteins \u003cstrong\u003e(Figure 2C)\u003c/strong\u003e revealed that biological processes (BP) were primarily enriched in transcriptional regulation by RNA polymerase II, glycolysis, respiratory burst regulation, chaperone-mediated autophagy, and MAP kinase activity regulation. Cellular components (CC) were mainly enriched in ribonucleoprotein complexes, nucleus, cytoplasm, secretory immunoglobulin complexes, spliceosomal complexes, and glutamatergic synapses. Molecular functions (MF) were predominantly enriched in RNA binding, ATP binding, MHC class II protein complex binding, protein homodimerization, disordered domain binding, and transcription corepressor activity.\u003c/p\u003e\n\u003cp\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) is a comprehensive database that links genomic information to higher-level systemic functions across cellular, organismal, and ecosystem levels. In this study, KEGG enrichment analysis of differentially expressed proteins \u003cstrong\u003e(Figure 2D)\u003c/strong\u003e revealed significant enrichment in pathways including Glycolysis/Gluconeogenesis, Salmonella infection, Amyotrophic lateral sclerosis, Rap1 signaling, NOD-like receptor signaling, RNA degradation, p53 signaling, Hippo signaling, and PI3K-Akt signaling.\u003c/p\u003e\n\u003cp\u003eReactome, derived from \u0026quot;reaction\u0026quot; and \u0026quot;ome,\u0026quot; is a pathway database centered on biological reactions. The entities involved (e.g, nucleic acids, proteins, complexes, and small molecules) form interaction networks categorized into specific pathways. Based on UniProt\u0026apos;s Reactome annotations, the identified proteins and their differentially expressed counterparts were analyzed for pathway enrichment \u003cstrong\u003e(Figure 2E)\u003c/strong\u003e. The enriched pathways included Glycolysis, VEGFA-VEGFR2 signaling, mRNA splicing, Gluconeogenesis, HSF1-dependent transactivation, and RHO GTPase-mediated formin activation.\u003c/p\u003e\n\u003cp\u003eIn protein molecules, multiple structurally distinct regions with specific functions domains. Domains the fundamental units of protein function, and the overall function of a protein is determined by the combination of its multiple domains. Studying domains helps to improve understanding of protein functions. Pfam is a protein family database that classifies proteins into different families based on multiple sequence alignments and hidden Markov models, to identify the domains contained within each protein. In this study, Pfam enrichment analysis of differentially expressed proteins \u003cstrong\u003e(Figure 2F)\u003c/strong\u003e revealed enrichment in domains such as Actin, VWC, ApoL, Ig-J-chain, Gp-dh-C, Gp-dh-N, and Enolase-C.\u003c/p\u003e\n\u003cp\u003eProtein subcellular localization prediction plays a crucial role in bioinformatics and biological research. In this study, subcellular localization analysis of differentially expressed proteins \u003cstrong\u003e(Figure 2G)\u003c/strong\u003e identified compartments such as the Cytoplasm, Cytoskeleton, Endoplasmic reticulum, Nucleus, Plasma membrane, and Extracellular space.\u003c/p\u003e\n\u003cp\u003eThe cross-interactions between proteins within cells can reveal protein functions at the molecular level and are crucial for understanding the principles of life activities such as growth, development, differentiation, apoptosis, and biological regulatory mechanisms. This provides an important theoretical foundation for exploring the mechanisms of major diseases, therapeutic interventions, preventive strategies, and pharmaceutical innovation. In this study, a PPI interaction map was constructed to...A protein-protein interaction network was constructed for the differentially expressed proteins of interest\u003cstrong\u003e\u0026nbsp;(Figure 3)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed PCA and PLS-DA methods. The PCA results clearly showed that the NEC group and the control group samples each formed distinct clusters, achieving significant separation between the two groups \u003cstrong\u003e(Figure 4A)\u003c/strong\u003e. Similarly, the PLS-DA method \u003cstrong\u003e(Figure 4B)\u003c/strong\u003e yielded results concordant with the PCA findings, demonstrating that the metabolic patterns of the NEC and NC groups are clearly distinct.\u003c/p\u003e\n\u003cp\u003eDifferential metabolites were identified from the primary metabolite list using selected statistical test methods. A total of 2,436 differentially expressed metabolites were found between the NC and NEC groups, with 479 upregulated and 1,957 downregulated. A volcano plot \u003cstrong\u003e(Figure 5A)\u003c/strong\u003e was generated based on predefined screening criteria including FC value, P-value, and VIP, where metabolites with larger numerical changes and more significant differences were distributed at the left and right ends..\u003c/p\u003e\n\u003cp\u003eSubstance identification was performed by searching and comparing spectral databases, including HMDB, MassBank, LipidMaps, mzCloud, KEGG, and an in-house metabolite standard database. Metabolites with secondary spectra in the quantitative list were matched against the fragment ion information of each secondary spectrum in the databases to achieve secondary qualitative identification. In this study, 321 metabolites were identified, including 34 upregulated and 52 downregulated differentially expressed metabolites \u003cstrong\u003e(Table 2)\u003c/strong\u003e. Cluster heatmap analysis provided a more intuitive representation of metabolite responses across samples\u003cstrong\u003e\u0026nbsp;(Figure 5B)\u003c/strong\u003e, where deeper red indicates higher responses and deeper blue indicates lower responses. The volcano plot visually displayed the distribution and trends of differential metabolites between the two groups. Typically, the x-axis represents log2(FC), and the y-axis represents -log10(P-value), \u003cstrong\u003e(Figure 5C)\u003c/strong\u003e. Metabolites with larger quantitative changes and more significant differences were distributed at the left and right ends.The results indicated significant metabolic changes between NEC infants and normal preterm infants, primarily involving nucleotides, amino acids, and fatty acids. In the NEC group, metabolites such as \u0026gamma;-aminobutyric acid, L-proline, 2-methylserine, 5-aminovaleric acid, D-ribose, and 6-methylmercaptopurine were significantly higher than in the NC group, while L-phenylalanine, aspartic acid, L-asparagine, L-valine, and succinic acid were significantly lower.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Identification Results of Differential Metabolites\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eFormula\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003emz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003eRt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003elog2(FC)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003eVip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eL-Proline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC5H9NO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e116.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e52.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eBetaine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC5H11NO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e118.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003e2-Methylserine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC4H9NO3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e119.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e202.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eAcetylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC7H16NO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e146.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e53.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0290\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eD-Ribose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC5H10O5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e151.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e42.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003e6-Methylmercaptopurine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC6H6N4S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e166.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e80.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eS-Adenosylmethionine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC15H22N6O5S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e398.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e456.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003egamma-Aminobutyric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC4H9NO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e102.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0275\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003e5-Aminopentanoic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC5H11NO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e115.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e687.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eL-Phenylalanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC9H11NO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e166.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e-2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eD-Galactose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC6H12O6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e181.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e482.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e-1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eAspartame\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC14H18N2O5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e295.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e230.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e-1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eL-Valine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC5H11NO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e116.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e416.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e-1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eSuccinic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC4H6O4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e117.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e42.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eL-Asparagine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC4H8N2O3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e131.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e49.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e-0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eXanthine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC5H4N4O2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e151.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e66.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.3144%;\"\u003e\n \u003cp\u003eArachidonic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.2706%;\"\u003e\n \u003cp\u003eC20H32O2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7002%;\"\u003e\n \u003cp\u003e303.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 8.77514%;\"\u003e\n \u003cp\u003e642.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.3455%;\"\u003e\n \u003cp\u003e-0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.883%;\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.7112%;\"\u003e\n \u003cp\u003e0.0285\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eName: Identification of the substance; Formula: Molecular formula of the metabolite; mz: Mass-to-charge ratio; Rt: Retention time; log2(FC), log2 value of the fold change; VIP, Variable Importance in Projection for the first principal component in OPLS-DA; P value, statistical p-value, where a smaller value indicates greater significance of the difference.\u003c/p\u003e\n\u003cp\u003eEnrichment analysis of compounds involved in metabolic pathways, as shown in Figure 6, revealed significant alterations in the following pathways in NEC group infants: alanine, aspartate, and glutamate metabolism; butanoate metabolism; lysine degradation; GABAergic synapse; pyruvate metabolism; arginine and proline metabolism; and synthesis and degradation of ketone bodies. These dysregulated metabolic pathways may be closely associated with the pathogenesis of NEC.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNEC, a common and highly threatening gastrointestinal complication in neonates, particularly among very low birth weight infants, carries a mortality rate of approximately 20\u0026ndash;30%, making it one of the leading causes of mortality in preterm infants\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.The pathogenesis of NEC is complex, involving multiple factors including genetic susceptibility, intestinal immaturity, microvascular dysregulation, gut microbiota dysbiosis, inappropriate feeding strategies, and abnormal mucosal immune responses\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. However, early diagnosis of NEC remains challenging due to the lack of specific biomarkers and unclear disease mechanisms.Proteomics allows for the comprehensive analysis of all proteins expressed by a cell, tissue, or organism during a specific period, typically used to investigate protein expression patterns under particular conditions or at specific time points\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. By comparing protein expression profiles between NEC and control groups and analyzing differentially expressed proteins, researchers can gain insights into the pathogenesis of NEC, facilitating the discovery of diagnostic biomarkers that could significantly improve disease management. Meanwhile, metabolomics, as an emerging analytical approach, focuses on dynamic metabolic changes during biochemical processes, offering novel perspectives for early NEC detection\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. This study integrates proteomic and metabolomic analyses to examine plasma proteins and metabolites in NEC and control groups, systematically identifying molecular changes associated with NEC progression. These subtle yet critical molecular signatures, as potential disease biomarkers, not only enhance early NEC identification but also provide valuable insights for precision medicine approaches.\u003c/p\u003e\u003cp\u003eMetabolomic findings indicate that significant alterations in butanoate metabolism occur during the early stages of NEC. This change is not isolated, as existing literature reports that excessive butyrate, a short-chain fatty acid, contributes to necrotizing colitis \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Studies using intestinal injury models in young rats have shown that butyrate can induce developmental-dependent NEC-like epithelial damage through necroptosis, mirroring the developmental stage-specific intestinal injury observed in NEC\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Furthermore, research demonstrates that the severity of colonic mucosal damage caused by short-chain fatty acids, including butyrate, varies with age, likely due to immature mucosal defense mechanisms in neonatal rats that increase susceptibility to luminal short-chain fatty acid-induced injury\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. These fatty acids may promote necrotizing colitis by disrupting gut microbiota balance and compromising intestinal barrier function\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. Consequently, monitoring fecal butyrate and other short-chain fatty acid levels could serve as a non-invasive method to predict functional gastrointestinal alterations prior to the onset of NEC's clinical symptoms\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, potentially emerging as a valuable diagnostic biomarker.\u003c/p\u003e\u003cp\u003eKEGG pathway enrichment analysis of proteomic data revealed significant enrichment of the NOD-like receptor signaling pathway in differentially expressed proteins between NEC and control groups. NOD-like receptors (NLRs), intracellular proteins central to innate and adaptive immunity\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, recognize specific pathogen-associated molecular patterns, activating multiple signaling pathways and cytokine secretion\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Growing evidence underscores the pivotal role of NLRs in gastrointestinal inflammatory diseases and cancer. For instance, Bacteroides fragilis has been shown to alleviate NEC intestinal damage by restoring bile acid metabolic balance through bile salt hydrolase and inhibiting the FXR-NLRP3 signaling pathway\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Additionally, the gut microbiota influences viral tolerance in NEC by modulating the STAT1-NLRC5 axis\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, while macrophage α7nAChR mitigates intestinal injury via the mTOR/NLRP3/IL-1β pathway\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Animal model studies further confirm that NLRP3, a key molecule in the NOD-like receptor signaling pathway, functions as an inflammasome in NEC pathogenesis and progression\u003csup\u003e[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn our proteomics study, significant differences were observed in the molecules TXN (Thioredoxin) and DEFA1 (Defensin Alpha 1) from the NOD-like receptor signaling pathway between the disease group and the control group. The Thioredoxin system, comprising thioredoxin reductase (TrxR), and NADPH, plays a pivotal role in redox regulation and antioxidant defense\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.This system serves as the primary redox control mechanism, essential for scavenging reactive oxygen species and protecting cells from oxidative damage\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. The DEFA1/DEFA3 genes encode human neutrophil peptides 1\u0026ndash;3 (HNP1-3), which are functionally associated with innate immunity and infection\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. HNPs represent the most abundant proteins in neutrophil granules, though their concentration varies due to extensive gene copy number polymorphism. Genetic studies have demonstrated that increased DEFA1/DEFA3 copy numbers elevate the risk of organ dysfunction during sepsis\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Moreover, these copy number variations correlate with susceptibility to nosocomial infections in critically ill patients\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e, intestinal damage in Beh\u0026ccedil;et's disease\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e, sepsis susceptibility\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e, and tumor progression\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. However, the roles of TXN and DEFA1 in NEC remain unexplored in the literature.\u003c/p\u003e\u003cp\u003eEmerging evidence suggests that butyrate metabolism may influence disease progression by modulating the NOD-like receptor signaling pathway. In ulcerative diabetic foot patients, butyrate metabolism appears linked to TXN expression\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Additionally, Codonopsis pilosula polysaccharides can suppress NLRP3 activation by binding short-chain fatty acids (SCFAs) to GPR proteins, thereby alleviating intestinal inflammation\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. Sodium butyrate has been shown to mitigate inflammatory responses in colitis and autoimmune prostatitis by inhibiting oxidative stress and NLRP3 inflammasome activation\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. These findings imply that in NEC, butyrate metabolism may regulate TXN and DEFA1 expression within the NOD-like receptor pathway\u0026mdash;a mechanism distinct from classical NLRP3 inflammasome-mediated inflammation. Targeting this pathway could unveil novel therapeutic strategies for NEC.\u003c/p\u003e\u003cp\u003eThis study employed a multi-omics analysis approach integrating metabolomics and proteomics, identifying several biological processes closely associated with the early stages of NEC. The findings suggest that butyrate metabolism may play a role in NEC by influencing non-canonical pathways of the NOD-like receptor signaling pathway. Although this study identified multiple potential early NEC biomarkers with diagnostic value, the limited sample size prevented the establishment of any specific biomarker as a gold standard for early NEC diagnosis. To detect NEC onset signals earlier, future research should focus on comprehensive investigation and clinical validation of inflammation-related biomarkers. By precisely identifying and validating these early inflammatory markers, we aim to revolutionize NEC diagnostic strategies and pioneer novel approaches for early diagnosis. Achieving this goal will not only facilitate timely implementation of effective interventions to alleviate patient suffering but also significantly improve infant outcomes, safeguarding their health and well-being.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, using metabolomics techniques, we detected an imbalance in amino acid metabolic pathways related to inflammatory pathways in the serum of infants in the NEC group. These pathways include GABA, arginine metabolism, and butyrate metabolism. Existing literature has reported that excessive butyrate, a short - chain fatty acid, contributes to the development of necrotizing colitis, and our findings are consistent with current research results. By using proteomics technology, we found that there were significant differences in the molecules TXN and DEFA1 within the NOD - like receptor signaling pathway when comparing the disease group with the control group. The role of the NOD-like receptor signaling pathway in innate immunity and infection has been documented in the literature, with studies suggesting that butyrate metabolism can influence the classical pathway of this signaling pathway. While previous studies focused on the classical pathway, our research delved into the non - canonical pathway. However, our study found that the non - canonical pathway of the NOD - like receptor signaling pathway exhibited altered expression in the serum of NEC group infants, indicating that butyrate metabolism may affect disease progression by regulating TXN and DEFA1 proteins in the non - canonical pathway of the NOD - like receptor signaling pathway in NEC.\u003c/p\u003e\u003cp\u003eThrough integrated analysis of metabolomics and proteomics, this study suggests that butyrate metabolism may influence the pathogenesis of NEC via a non-canonical NOD-like receptor signaling pathway.This provides a highly promising approach for elucidating the pathogenesis of NEC in preterm infants, which offers new insights and evidence to advance disease understanding and intervention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eNEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eneonatal necrotizing enterocolitis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eHPLC-QTOF-MS/MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eHigh-performance liquid chromatography coupled with Q-TOF mass spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eGABA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003e\u0026gamma;-aminobutyric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eDDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003edata-dependent acquisition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eprotein-protein interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003ePLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003ePartial Least Squares-Discriminant Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eOPLS-DA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eOrthogonal Partial Least Squares Discriminant Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eVIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eVariable Importance in Projection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003ebiological processes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eCellular components\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eMolecular functions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eNLRs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eNOD-like receptors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eTXN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eThioredoxin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eDEFA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eDefensin Alpha 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eTrxR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003ethioredoxin reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eHNP1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003ehuman neutrophil peptides 1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003eSCFAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003eshort-chain fatty acids\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 33.5677%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66.4323%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in compliance with the Declaration of Helsinki and received approval from the Scientific Ethics Committee of the Affiliated Hospital of Inner Mongolia Medical University, China (DW2025018). Written informed consent was obtained from all participants or their legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author, Chaoyang Wang，upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Higher Education Scientific Research Project Fund of Inner Mongolia Autonomous Region (NJZY21617), Inner Mongolia Medical University Joint Funding Project (YKD2023LH053), and Inner Mongolia Autonomous Region Natural Science Foundation (2023LHMS08004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiongfeng Li:\u003c/strong\u003e Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Visualization.\u0026nbsp;\u003cstrong\u003eHailong Wang, Yu Zhou and Rui Yang:\u003c/strong\u003e Data curation, Formal analysis, Investigation, Methodology.\u0026nbsp;\u003cstrong\u003eJing Zhou\u003c/strong\u003e and\u0026nbsp;\u003cstrong\u003eChaoyang Wang:\u003c/strong\u003e Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Supervision, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMihi B, Gong Q, Nolan LS, et al. 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Prostate. 2024. 84(7): 666-681.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"italian-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itjp","sideBox":"Learn more about [Italian Journal of Pediatrics](http://ijponline.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ITJP/default.aspx","title":"Italian Journal of Pediatrics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Necrotizing enterocolitis in newborns, metabolomics, proteomics, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-6891222/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6891222/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThis study aims to investigate the potential application and value of metabolomics combined with proteomics in identifying early biomarkers for neonatal necrotizing enterocolitis (NEC), to provide new perspectives and strategies for early diagnosis.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eA systematic comparison was conducted between two sample groups: a case group consisting of 8 preterm infants diagnosed with NEC and a control group of 8 healthy, age- and weight-matched neonates without NEC. To capture metabolic changes at the onset of NEC, blood samples were collected within a 12-hour window after disease manifestation in NEC patients. High-performance liquid chromatography coupled with Q-TOF mass spectrometry (HPLC-QTOF-MS/MS) and 4D label-free quantitative proteomics were employed to detect differentially expressed proteins and small-molecule metabolites in plasma.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eResults revealed imbalances in amino acid metabolism pathways related to inflammatory processes during NEC progression, including γ-aminobutyric acid (GABA), arginine metabolism, and butyrate metabolism, as well as alterations in protein pathways such as glycolysis/gluconeogenesis, NOD-like receptor signaling, and Rap1 signaling.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThrough integrated analysis of metabolomics and proteomics, this study suggests that butyrate metabolism may influence the pathogenesis of NEC via a non-canonical NOD-like receptor signaling pathway. This provides a highly promising approach for elucidating the pathogenesis of NEC in preterm infants, which offers new insights and evidence to advance disease understanding and intervention strategies.\u003c/p\u003e","manuscriptTitle":"Application of integrated multi-omics analysis in identifying biomarkers for early diagnosis of neonatal necrotizing enterocolitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 14:56:48","doi":"10.21203/rs.3.rs-6891222/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-01-26T07:07:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T14:57:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-13T23:55:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Italian Journal of Pediatrics","date":"2025-07-09T05:06:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"italian-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"itjp","sideBox":"Learn more about [Italian Journal of Pediatrics](http://ijponline.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ITJP/default.aspx","title":"Italian Journal of Pediatrics","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd28403c-4cf4-4b98-8b3f-c2ca0c2a8d24","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-18T14:56:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 14:56:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6891222","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6891222","identity":"rs-6891222","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}